Continuous Calibration Improvement in Solar Reflective Bands: Landsat 5 Through Landsat 8

Science.gov (United States)

Mishra, Nischal; Helder, Dennis; Barsi, Julia; Markham, Brian

2016-01-01

Launched in February 2013, the Operational Land Imager (OLI) on-board Landsat 8 continues to perform exceedingly well and provides high science quality data globally. Several design enhancements have been made in the OLI instrument relative to prior Landsat instruments: pushbroom imaging which provides substantially improved Signal-to-Noise Ratio (SNR), spectral bandpasses refinement to avoid atmospheric absorption features, 12 bit data resolution to provide a larger dynamic range that limits the saturation level, a set of well-designed onboard calibrators to monitor the stability of the sensor. Some of these changes such as refinements in spectral bandpasses compared to earlier Landsats and well-designed on-board calibrator have a direct impact on the improved radiometric calibration performance of the instrument from both the stability of the response and the ability to track the changes. The on-board calibrator lamps and diffusers indicate that the instrument drift is generally less than 0.1% per year across the bands. The refined bandpasses of the OLI indicate that temporal uncertainty of better than 0.5% is possible when the instrument is trended over vicarious targets such as Pseudo Invariant Calibration Sites (PICS), a level of precision that was never achieved with the earlier Landsat instruments. The stability measurements indicated by on-board calibrators and PICS agree much better compared to the earlier Landsats, which is very encouraging and bodes well for the future Landsat missions too.

Landsat Science Team meeting: Winter 2015

Science.gov (United States)

Schroeder, Todd A.; Loveland, Thomas; Wulder, Michael A.; Irons, James R.

2015-01-01

The summer meeting of the joint U.S. Geological Survey (USGS)–NASA Landsat Science Team (LST) was held at the USGS’s Earth Resources Observation and Science (EROS) Center July 7-9, 2015, in Sioux Falls, SD. The LST co-chairs, Tom Loveland [EROS—Senior Scientist] and Jim Irons [NASA’s Goddard Space Flight Center (GSFC)—Landsat 8 Project Scientist], opened the three-day meeting on an upbeat note following the recent successful launch of the European Space Agency’s Sentinel-2 mission on June 23, 2015 (see image on page 14), and the news that work on Landsat 9 has begun, with a projected launch date of 2023.With over 60 participants in attendance, this was the largest LST meeting ever held. Meeting topics on the first day included Sustainable Land Imaging and Landsat 9 development, Landsat 7 and 8 operations and data archiving, the Landsat 8 Thermal Infrared Sensor (TIRS) stray-light issue, and the successful Sentinel-2 launch. In addition, on days two and three the LST members presented updates on their Landsat science and applications research. All presentations are available at landsat.usgs.gov/science_LST_Team_ Meetings.php.

Comparison of Landsat-8 and Sentinel-2A reflectance and normalized difference vegetation index

Science.gov (United States)

Zhang, H.; Roy, D. P.; Yan, L.; Li, Z.; Huang, H.

2017-12-01

The moderate spatial resolution satellite data from the polar-orbiting Landsat-8 (launched 2013) and Sentinel-2A (launched 2015) sensors provide 10 m to 30 m multi-spectral global coverage with a better than 5-day revisit. Although a national laboratory traceable cross-calibration comparison of the Landsat-8 Operational Land Imager (OLI) and the Sentinel-2A MultiSpectral Instrument (MSI) was undertaken pre-launch, there are a number of other sensor differences, notably due to spectral, spatial and angular differences. To examine these in a comprehensive way, Landsat-8 and Sentinel-2A data for approximately 20° × 10° of southern Africa acquired in the summer (January to March) and winter (July to September) of 2016 were compared. Only Landsat-8 and Sentinel-2A observations acquired within one-day apart were considered. The sensor data were registered and then each orbit projected into 30 m fixed global Web Enabled Landsat Data (GWELD) tiles defined in the MODIS sinusoidal equal area projection. Only corresponding sensor observations of each 30 m tile pixel that were flagged as cloud and snow-free, unsaturated, and that had no significant change in their one day separation, were compared. Both the Landsat-8 and Sentinel-2A data were atmospherically corrected using the Landsat Surface Reflectance Code (LaSRC) and were also corrected to nadir BRDF adjusted reflectance (NBAR). Top of atmosphere and surface reflectance for the spectrally corresponding visible, near infrared and shortwave infrared OLI and MSI bands, and derived normalized difference vegetation index (NDVI), were compared and their differences quantified using regression analyses. The resulting statistical transformations may be used to improve the consistency between the Landsat-8 OLI and Sentinel-2A MSI data. The importance and sensitivity of the results to correct filtering, atmospheric correction and adjustment to NBAR is demonstrated.

Improving the mapping of crop types in the Midwestern U.S. by fusing Landsat and MODIS satellite data

Science.gov (United States)

Zhu, Likai; Radeloff, Volker C.; Ives, Anthony R.

2017-06-01

Mapping crop types is of great importance for assessing agricultural production, land-use patterns, and the environmental effects of agriculture. Indeed, both radiometric and spatial resolution of Landsat's sensors images are optimized for cropland monitoring. However, accurate mapping of crop types requires frequent cloud-free images during the growing season, which are often not available, and this raises the question of whether Landsat data can be combined with data from other satellites. Here, our goal is to evaluate to what degree fusing Landsat with MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) data can improve crop-type classification. Choosing either one or two images from all cloud-free Landsat observations available for the Arlington Agricultural Research Station area in Wisconsin from 2010 to 2014, we generated 87 combinations of images, and used each combination as input into the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to predict Landsat-like images at the nominal dates of each 8-day MODIS NBAR product. Both the original Landsat and STARFM-predicted images were then classified with a support vector machine (SVM), and we compared the classification errors of three scenarios: 1) classifying the one or two original Landsat images of each combination only, 2) classifying the one or two original Landsat images plus all STARFM-predicted images, and 3) classifying the one or two original Landsat images together with STARFM-predicted images for key dates. Our results indicated that using two Landsat images as the input of STARFM did not significantly improve the STARFM predictions compared to using only one, and predictions using Landsat images between July and August as input were most accurate. Including all STARFM-predicted images together with the Landsat images significantly increased average classification error by 4% points (from 21% to 25%) compared to using only Landsat

A regional land use survey based on remote sensing and other data: A report on a LANDSAT and computer mapping project, volume 2

Science.gov (United States)

Nez, G. (Principal Investigator); Mutter, D.

1977-01-01

The author has identified the following significant results. The project mapped land use/cover classifications from LANDSAT computer compatible tape data and combined those results with other multisource data via computer mapping/compositing techniques to analyze various land use planning/natural resource management problems. Data were analyzed on 1:24,000 scale maps at 1.1 acre resolution. LANDSAT analysis software and linkages with other computer mapping software were developed. Significant results were also achieved in training, communication, and identification of needs for developing the LANDSAT/computer mapping technologies into operational tools for use by decision makers.

Landsat-8: Science and product vision for terrestrial global change research

Science.gov (United States)

Roy, David P.; Wulder, M.A.; Loveland, Thomas R.; Woodcock, C.E.; Allen, R. G.; Anderson, M. C.; Helder, D.; Irons, J.R.; Johnson, D.M.; Kennedy, R.; Scambos, T.A.; Schaaf, Crystal B.; Schott, J.R.; Sheng, Y.; Vermote, E. F.; Belward, A.S.; Bindschadler, R.; Cohen, W.B.; Gao, F.; Hipple, J. D.; Hostert, Patrick; Huntington, J.; Justice, C.O.; Kilic, A.; Kovalskyy, Valeriy; Lee, Z. P.; Lymburner, Leo; Masek, J.G.; McCorkel, J.; Shuai, Y.; Trezza, R.; Vogelmann, James; Wynne, R.H.; Zhu, Z.

2014-01-01

Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the remarkable 40 year Landsat record and has enhanced capabilities including new spectral bands in the blue and cirrus cloud-detection portion of the spectrum, two thermal bands, improved sensor signal-to-noise performance and associated improvements in radiometric resolution, and an improved duty cycle that allows collection of a significantly greater number of images per day. This paper introduces the current (2012–2017) Landsat Science Team's efforts to establish an initial understanding of Landsat 8 capabilities and the steps ahead in support of priorities identified by the team. Preliminary evaluation of Landsat 8 capabilities and identification of new science and applications opportunities are described with respect to calibration and radiometric characterization; surface reflectance; surface albedo; surface temperature, evapotranspiration and drought; agriculture; land cover, condition, disturbance and change; fresh and coastal water; and snow and ice. Insights into the development of derived ‘higher-level’ Landsat products are provided in recognition of the growing need for consistently processed, moderate spatial resolution, large area, long-term terrestrial data records for resource management and for climate and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land imaging constellations by combining Landsat data with data collected from other international sensing systems, and consideration of successor Landsat mission requirements.

Landsat eyes help guard the world's forests

Science.gov (United States)

Campbell, Jon

2017-03-03

SummaryThe Landsat program is a joint effort between the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), but the partner agencies have distinct roles. NASA develops remote-sensing instruments and spacecraft, launches satellites, and validates their performance in orbit. The USGS owns and operates Landsat satellites in space and manages their data transmissions, including ground reception, archiving, product generation, and public distribution. In 2008, with support from the U.S. Department of the Interior, the USGS made its Landsat data free to anyone in the world.The current satellites in the Landsat program, Landsat 7 (launched in 1999) and Landsat 8 (launched in 2013), provide complete coverage of the Earth every eight days. A Landsat 9 satellite is scheduled for launch in late 2020.

Application of LANDSAT to the surveillance and control of lake eutrophication in the Great Lakes Basin

Science.gov (United States)

Rogers, R. H. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Preliminary results in Saginaw Bay show that processed LANDSAT data provides a synoptic view of turbidity and circulation patterns that no degree of ground monitoring can provide. Processed imagery was produced to show nine discrete categories of turbidity, as indicated by nine Secchi depths between 0.3 and 3.3 meters. Analysis of lakes near Madison, Wisconsin show that inland lake water can be categorized by LANDSAT as clear, tannin, algal, and red clay. LANDSAT's capability to inventory watershed land use was throughly demonstrated in the Ohio-Kentucky-Indiana regional planning area. Computer tabulations providing area covered by each of 16 land use categories were rapidly and economically produced for each of the 225 watersheds and nine counties.

Landsat-8 Sensor Characterization and Calibration

Directory of Open Access Journals (Sweden)

Brian Markham

2015-02-01

Full Text Available Landsat-8 was launched on 11 February 2013 with two new Earth Imaging sensors to provide a continued data record with the previous Landsats. For Landsat-8, pushbroom technology was adopted, and the reflective bands and thermal bands were split into two instruments. The Operational Land Imager (OLI is the reflective band sensor and the Thermal Infrared Sensor (TIRS, the thermal. In addition to these fundamental changes, bands were added, spectral bandpasses were refined, dynamic range and data quantization were improved, and numerous other enhancements were implemented. As in previous Landsat missions, the National Aeronautics and Space Administration (NASA and United States Geological Survey (USGS cooperated in the development, launch and operation of the Landsat-8 mission. One key aspect of this cooperation was in the characterization and calibration of the instruments and their data. This Special Issue documents the efforts of the joint USGS and NASA calibration team and affiliates to characterize the new sensors and their data for the benefit of the scientific and application users of the Landsat archive. A key scientific use of Landsat data is to assess changes in the land-use and land cover of the Earth’s surface over the now 43-year record. [...

Automated Agricultural Field Extraction from Multi-temporal Web Enabled Landsat Data

Science.gov (United States)

Yan, L.; Roy, D. P.

2012-12-01

Agriculture has caused significant anthropogenic surface change. In many regions agricultural field sizes may be increasing to maximize yields and reduce costs resulting in decreased landscape spatial complexity and increased homogenization of land uses with potential for significant biogeochemical and ecological effects. To date, studies of the incidence, drivers and impacts of changing field sizes have not been undertaken over large areas because of computational constraints and because consistently processed appropriate resolution data have not been available or affordable. The Landsat series of satellites provides near-global coverage, long term, and appropriate spatial resolution (30m) satellite data to document changing field sizes. The recent free availability of all the Landsat data in the U.S. Landsat archive now provides the opportunity to study field size changes in a global and consistent way. Commercial software can be used to extract fields from Landsat data but are inappropriate for large area application because they require considerable human interaction. This paper presents research to develop and validate an automated computational Geographic Object Based Image Analysis methodology to extract agricultural fields and derive field sizes from Web Enabled Landsat Data (WELD) (http://weld.cr.usgs.gov/). WELD weekly products (30m reflectance and brightness temperature) are classified into Satellite Image Automatic Mapper™ (SIAM™) spectral categories and an edge intensity map and a map of the probability of each pixel being agricultural are derived from five years of 52 weeks of WELD and corresponding SIAM™ data. These data are fused to derive candidate agriculture field segments using a variational region-based geometric active contour model. Geometry-based algorithms are used to decompose connected segments belonging to multiple fields into coherent isolated field objects with a divide and conquer strategy to detect and merge partial circle

Radiometric characterization of Landsat Collection 1 products

Science.gov (United States)

Micijevic, Esad; Haque, Md. Obaidul; Mishra, Nischal

2017-09-01

Landsat data in the U.S. Geological Survey (USGS) archive are being reprocessed to generate a tiered collection of consistently geolocated and radiometrically calibrated products that are suitable for time series analyses. With the implementation of the collection management, no major updates will be made to calibration of the Landsat sensors within a collection. Only calibration parameters needed to maintain the established calibration trends without an effect on derived environmental records will be regularly updated, while all other changes will be deferred to a new collection. This first collection, Collection 1, incorporates various radiometric calibration updates to all Landsat sensors including absolute and relative gains for Landsat 8 Operational Land Imager (OLI), stray light correction for Landsat 8 Thermal Infrared Sensor (TIRS), absolute gains for Landsat 4 and 5 Thematic Mappers (TM), recalibration of Landsat 1-5 Multispectral Scanners (MSS) to ensure radiometric consistency among different formats of archived MSS data, and a transfer of Landsat 8 OLI reflectance based calibration to all previous Landsat sensors. While all OLI/TIRS, ETM+ and majority of TM data have already been reprocessed to Collection 1, a completion of MSS and remaining TM data reprocessing is expected by the end of this year. It is important to note that, although still available for download from the USGS web pages, the products generated using the Pre-Collection processing do not benefit from the latest radiometric calibration updates. In this paper, we are assessing radiometry of solar reflective bands in Landsat Collection 1 products through analysis of trends in on-board calibrator and pseudo invariant site (PICS) responses.

LANDSAT menhaden and thread herring resources investigation. [Gulf of Mexico

Science.gov (United States)

Kemmerer, A. J. (Principal Investigator); Brucks, J. T.; Butler, J. A.; Faller, K. H.; Holley, H. J.; Leming, T. D.; Savastano, K. J.; Vanselous, T. M.

1977-01-01

The author has identified the following significant results. The relationship between the distribution of menhaden and selected oceanographic parameters (water color, turbidity, and possibly chlorophyll concentrations) was established. Similar relationships for thread herring were not established nor were relationships relating to the abundance of either species. Use of aircraft and LANDSAT remote sensing instruments to measure or infer a set of basic oceanographic parameters was evaluated. Parameters which could be accurately inferred included surface water temperature, salinity, and color. Water turbidity (Secchi disk) was evaluated as marginally inferrable from the LANDSAT MSS data and chlorophyll-a concentrations as less than marginal. These evaluations considered the parameters only as experienced in the two test areas using available sensors and statistical techniques.

The use of Landsat-4 MSS digital data in temporal data sets and the evaluation of scene-to-scene registration accuracy

Science.gov (United States)

Anderson, J. E.

1985-01-01

The MSS sensor on Landsat 4 is, in certain performance aspects, diferent from those of Landsats 1 through 3. These differences created some concern in the NASA research community as to whether individual data sets can be registered accurately enough to produce acceptable data sets for multitemporal data analysis. The use of Landsat 4 MSS digital data in temporal data sets is examined and a method is presented for estimating temporal registration accuracy based on the use of an X-Y digitizer and grey tone electrostatic plots. Results indicate that the RMS temporal registration errors are not significantly different from the temporal data sets generated using Landsat 4 and Landsat 2 data (33.35 meters) and the temporal data set constructed from two Landsat 2 data sets (33.61 meters). A derivation of the model used to evaluate the temporal registration is included.

Monitoring water quality from LANDSAT. [satellite observation of Virginia

Science.gov (United States)

Barker, J. L.

1975-01-01

Water quality monitoring possibilities from LANDSAT were demonstrated both for direct readings of reflectances from the water and indirect monitoring of changes in use of land surrounding Swift Creek Reservoir in a joint project with the Virginia State Water Control Board and NASA. Film products were shown to have insufficient resolution and all work was done by digitally processing computer compatible tapes. Land cover maps of the 18,000 hectare Swift Creek Reservoir watershed, prepared for two dates in 1974, are shown. A significant decrease in the pine cover was observed in a 740 hectare construction site within the watershed. A measure of the accuracy of classification was obtained by comparing the LANDSAT results with visual classification at five sites on a U-2 photograph. Such changes in land cover can alert personnel to watch for potential changes in water quality.

Computer mapping of turbidity and circulation patterns in Saginaw Bay, Michigan from LANDSAT data

Science.gov (United States)

Rogers, R. H. (Principal Investigator); Reed, L. E.; Smith, V. E.

1975-01-01

The author has identified the following significant results. LANDSAT was used as a basis for producing geometrically-corrected, color-coded imagery of turbidity and circulation patterns in Saginaw Bay, Michigan (Lake Huron). This imagery shows nine discrete categories of turbidity, as indicated by nine Secchi depths between 0.3 and 3.3 meters. The categorized imagery provided an economical basis for extrapolating water quality parameters from point samples to unsample areas. LANDSAT furnished a synoptic view of water mass boundaries that no amount of ground sampling or monitoring could provide.

Land use change detection with LANDSAT-2 data for monitoring and predicting regional water quality degradation. [Arkansas

Science.gov (United States)

Macdonald, H.; Steele, K. (Principal Investigator); Waite, W.; Rice, R.; Shinn, M.; Dillard, T.; Petersen, C.

1977-01-01

The author has identified the following significant results. Comparison between LANDSAT 1 and 2 imagery of Arkansas provided evidence of significant land use changes during the 1972-75 time period. Analysis of Arkansas historical water quality information has shown conclusively that whereas point source pollution generally can be detected by use of water quality data collected by state and federal agencies, sampling methodologies for nonpoint source contamination attributable to surface runoff are totally inadequate. The expensive undertaking of monitoring all nonpoint sources for numerous watersheds can be lessened by implementing LANDSAT change detection analyses.

Estimating the leaf area index (LAI) of black wattle from Landsat ...

African Journals Online (AJOL)

Regression analysis revealed that actual LAI had significant relationships with PAI and NDVI. The results indicate the potential of the Landsat ETM+ satellite imageries to estimate values of important canopy attributes of A. mearnsii that are related to stand productivity that may be used as inputs into process-based models ...

Landsat change detection can aid in water quality monitoring

Science.gov (United States)

Macdonald, H. C.; Steele, K. F.; Waite, W. P.; Shinn, M. R.

1977-01-01

Comparison between Landsat-1 and -2 imagery of Arkansas provided evidence of significant land use changes during the 1972-75 time period. Analysis of Arkansas historical water quality information has shown conclusively that whereas point source pollution generally can be detected by use of water quality data collected by state and federal agencies, sampling methodologies for nonpoint source contamination attributable to surface runoff are totally inadequate. The expensive undertaking of monitoring all nonpoint sources for numerous watersheds can be lessened by implementing Landsat change detection analyses.

Improving automated disturbance maps using snow-covered landsat time series stacks

Science.gov (United States)

Kirk M. Stueve; Ian W. Housman; Patrick L. Zimmerman; Mark D. Nelson; Jeremy Webb; Charles H. Perry; Robert A. Chastain; Dale D. Gormanson; Chengquan Huang; Sean P. Healey; Warren B. Cohen

2012-01-01

Snow-covered winter Landsat time series stacks are used to develop a nonforest mask to enhance automated disturbance maps produced by the Vegetation Change Tracker (VCT). This method exploits the enhanced spectral separability between forested and nonforested areas that occurs with sufficient snow cover. This method resulted in significant improvements in Vegetation...

A one year Landsat 8 conterminous United States study of spatial and temporal patterns of cirrus and non-cirrus clouds and implications for the long term Landsat archive.

Science.gov (United States)

Kovalskyy, V.; Roy, D. P.

2014-12-01

The successful February 2013 launch of the Landsat 8 satellite is continuing the 40+ year legacy of the Landsat mission. The payload includes the Operational Land Imager (OLI) that has a new 1370 mm band designed to monitor cirrus clouds and the Thermal Infrared Sensor (TIRS) that together provide 30m low, medium and high confidence cloud detections and 30m low and high confidence cirrus cloud detections. A year of Landsat 8 data over the Conterminous United States (CONUS), composed of 11,296 acquisitions, was analyzed comparing the spatial and temporal incidence of these cloud and cirrus states. This revealed (i) 36.5% of observations were detected with high confidence cloud with spatio-temporal patterns similar to those observed by previous Landsat 7 cloud analyses, (ii) 29.2% were high confidence cirrus, (iii) 20.9% were both high confidence cloud and high confidence cirrus, (iv) 8.3% were detected as high confidence cirrus but not as high confidence cloud. The results illustrate the value of the cirrus band for improved Landsat 8 terrestrial monitoring but imply that the historical CONUS Landsat archive has a similar 8% of undetected cirrus contaminated pixels. The implications for long term Landsat time series records, including the global Web Enabled Landsat Data (WELD) product record, are discussed.

Integrating Landsat-derived disturbance maps with FIA inventory data: Applications for state-Level forest resource assessments

Science.gov (United States)

Sonja Oswalt; Chengquan Huang; Hua Shi; James Vogelmann; Zhiliang Zhu; Samuel N. Goward; John Coulston

2009-01-01

Landsat images have been widely used for assessing forest characteristics and dynamics. Recently, significant progress has been made towards indepth exploration of the rich Landsat archive kept by the U.S. Geological Survey to improve our under standing of forest disturbance and recovery processes. In this study, we used Landsat images to map forest disturbances at...

NASA 3D Models: Landsat 7

Data.gov (United States)

National Aeronautics and Space Administration — The Landsat Program is a series of Earth-observing satellite missions jointly managed by NASA and the U.S. Geological Survey. Since 1972, Landsat satellites have...

Analysis and correction of Landsat 4 and 5 Thematic Mapper Sensor Data

Science.gov (United States)

Bernstein, R.; Hanson, W. A.

1985-01-01

Procedures for the correction and registration and registration of Landsat TM image data are examined. The registration of Landsat-4 TM images of San Francisco to Landsat-5 TM images of the San Francisco using the interactive geometric correction program and the cross-correlation technique is described. The geometric correction program and cross-correlation results are presented. The corrections of the TM data to a map reference and to a cartographic database are discussed; geometric and cartographic analyses are applied to the registration results.

Landsat Data Continuity Mission

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,

2012-01-01

The Landsat Data Continuity Mission (LDCM) is a partnership formed between the National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS) to place the next Landsat satellite in orbit in January 2013. The Landsat era that began in 1972 will become a nearly 41-year global land record with the successful launch and operation of the LDCM. The LDCM will continue the acquisition, archiving, and distribution of multispectral imagery affording global, synoptic, and repetitive coverage of the Earth's land surfaces at a scale where natural and human-induced changes can be detected, differentiated, characterized, and monitored over time. The mission objectives of the LDCM are to (1) collect and archive medium resolution (30-meter spatial resolution) multispectral image data affording seasonal coverage of the global landmasses for a period of no less than 5 years; (2) ensure that LDCM data are sufficiently consistent with data from the earlier Landsat missions in terms of acquisition geometry, calibration, coverage characteristics, spectral characteristics, output product quality, and data availability to permit studies of landcover and land-use change over time; and (3) distribute LDCM data products to the general public on a nondiscriminatory basis at no cost to the user.

River morphodynamics from space: the Landsat frontier

Science.gov (United States)

Schwenk, Jon; Khandelwal, Ankush; Fratkin, Mulu; Kumar, Vipin; Foufoula-Georgiou, Efi

2017-04-01

NASA's Landsat family of satellites have been observing the entire globe since 1984, providing over 30 years of snapshots with an 18 day frequency and 30 meter resolution. These publicly-available Landsat data are particularly exciting to researchers interested in river morphodynamics, who are often limited to use of historical maps, aerial photography, and field surveys with poor and irregular time resolutions and limited spatial extents. Landsat archives show potential for overcoming these limitations, but techniques and tools for accurately and efficiently mining the vault of scenes must first be developed. In this PICO presentation, we detail the problems we encountered while mapping and quantifying planform dynamics of over 1,300 km of the actively-migrating, meandering Ucayali River in Peru from Landsat imagery. We also present methods to overcome these obstacles and introduce the Matlab-based RivMAP (River Morphodynamics from Analysis of Planforms) toolbox that we developed to extract banklines and centerlines, compute widths, curvatures, and angles, identify cutoffs, and quantify planform changes via centerline migration and erosion/accretion over large spatial domains with high temporal resolution. Measurement uncertainties were estimated by analyzing immobile, abandoned oxbow lakes. Our results identify hotspots of planform changes, and combined with limited precipitation, stage, and topography data, we parse three simultaneous controls on river migration: climate, sediment, and meander cutoff. Overall, this study demonstrates the vast potential locked within Landsat archives to identify multi-scale controls on river migration, observe the co-evolution of width, curvature, discharge, and migration, and discover and develop new geomorphic insights.

ANALISIS DINAMIKA SEBARAN SPASIAL SEDIMENTASI MUARA SUNGAI CANTUNG MENGGUNAKAN CITRA LANDSAT MULTITEMPORAL

Directory of Open Access Journals (Sweden)

Zulaiha Zulaiha

2017-01-01

Full Text Available ABSTRACT: Given the pivotal role played by the Cantung River for the supervision and management of the public good becomes important. Incoming sediment load can damage the uncontrolled flow conditions of the Cantung river and estuary. Observations of suspended sediment can take advantage of multitemporal Landsat imagery. This study uses Landsat satellite image data corrected 5TM March 5, 1992 data acquisition path/row 117/62, Landsat data acquisition 5TM 22 May 1997 path/row 117/62, Landsat data acquisition 5TM March 27, 2000 the path/row 117/62. Several stages in processing the image, that is the conversion of DN to reflectance values, cropping, water-not water secession, and the class divide sediment concentration by density slicing technique. Spatial distribution of suspended sediment in the estuary of the Cantung River Landsat image processing results 5TM March 5, 1992, Landsat 5TM May 22, 1997, and March 27, 2000 Landsat 5TM show distribution patterns of suspended sediment from the River Cantung the same direction, that is northeast. Sediment concentrations were detected in the Landsat image processing 5TM March 5, 1992 the largest-value 27,564096 mg/l and the smallest 14,886048 mg/l. Sediment concentrations were detected in the Landsat image processing 5TM May 22, 1997 the largest-value 121,476776 mg/l and the smallest 12,647415 mg/l. Sediment concentrations were detected in the Landsat image processing 5TM March 27, 2000 most valuable 159,256704 mg/l and the smallest 10,584161 mg/l. Getting away from the effect Cantung River estuary sediment concentration of river flow Cantung tends to get smaller. Changes in the distribution area of the sediments of March 5, 1992 until March 27, 2000 amounted to 450 m2/year.   Keywords: Remote Sensing, Sedimentation, Landsat, Cantung River

Landsat Science Team: 2017 Winter Meeting Summary

Science.gov (United States)

Schroeder, Todd A.; Loveland, Thomas; Wulder, Michael A.; Irons, James R.

2017-01-01

The summer meeting of the joint U.S. Geological Survey (USGS)-NASA Landsat Science Team (LST) was held July 26-28, 2016, at South Dakota State University (SDSU) in Brookings, SD. LST co-chair Tom Loveland [USGS’s Earth Resources Observation and Science Center (EROS)] and Kevin Kephart [SDSU] welcomed more than 80 participants to the three-day meeting. That attendance at such meetings continues to increase—likely due to the development of new data products and sensor systems—further highlights the growing interest in the Landsat program. The main objectives of this meeting were to provide a status update on Landsat 7 and 8, review team member research activities, and to begin identifying priorities for future Landsat missions.

Landsat: A global land-imaging mission

Science.gov (United States)

,

2012-01-01

Across four decades since 1972, Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. NASA develops remote-sensing instruments and spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and distribution. The result of this program is a long-term record of natural and human induced changes on the global landscape.

Multispectral Landsat images of Antartica

Energy Technology Data Exchange (ETDEWEB)

Lucchitta, B.K.; Bowell, J.A.; Edwards, K.L.; Eliason, E.M.; Fergurson, H.M.

1988-01-01

The U.S. Geological Survey has a program to map Antarctica by using colored, digitally enhanced Landsat multispectral scanner images to increase existing map coverage and to improve upon previously published Landsat maps. This report is a compilation of images and image mosaic that covers four complete and two partial 1:250,000-scale quadrangles of the McMurdo Sound region.

Comparison of LANDSAT-2 and field spectrometer reflectance signatures of south Texas rangeland plant communities

Science.gov (United States)

Richardson, A. J.; Escobar, D. E.; Gausman, H. W.; Everitt, J. H. (Principal Investigator)

1982-01-01

The accuracy was assessed for an atmospheric correction method that depends on clear water bodies to infer solar and atmospheric parameters for radiative transfer equations by measuring the reflectance signature of four prominent south Texas rangeland plants with the LANDSAT satellite multispectral scanner (MSS) and a ground based spectroradiometer. The rangeland plant reflectances produced by the two sensors were correlated with no significant deviation of the slope from unity or of the intercept from zero. These results indicated that the atmospheric correction produced LANDSAT MSS estimates of rangeland plant reflectances that are as accurate as the ground based spectroradiometer.

LANDSAT menhaden and thread herring resources investigation. [Northern Gulf of Mexico

Science.gov (United States)

Kemmerer, A. J. (Principal Investigator)

1975-01-01

The author has identified the following significant results. The most significant achievement realized thus far has been the successful completion of the data acquisition phase. This success must be attributed to the interest, support, and competency of the participants. The apparent consistency of water color and turbidity condition over time and between test sites at sites of menhaden capture is significant especially since color is readily measured with satellite and aircraft sensors and a LANDSAT MSS based computer model for inferring tubidity has been developed.

A time-series analysis of flood disaster around Lena river using Landsat TM/ETM+

Science.gov (United States)

Sakai, Toru; Hatta, Shigemi; Okumura, Makoto; Takeuchi, Wataru; Hiyama, Tetsuya; Inoue, Gen

2010-05-01

Tabaga (61.83°N, 129.60°E) was frozen hard until early May 2007. River-ice breakup began in patches on 13 May 2007. Then, the area of Lena river rapidly increased due to overhead flooding on 14 May 2007, and reached the peak on 15 May 2007. In the brief period of one or two days, the area of Lena river was more than twice. After this, the area of Lena river exponentially decreased over three months, and it was quite stable in late August 2007. A time-series of Landsat TM/ETM+ images could detect these large temporal variations. In addition, the temporal variations in the area of Lena river synchronized with water stage measured in the field. These results indicate that a time-series of Landsat TM/ETM+ images enables to monitor natural disturbances caused at short-term intervals, although significantly limited to local scales. The requirement of spatial and temporal resolution is often application specific in the context of the desired measurement goals. This type of research and resultant information is critical for the utilization of remote sensing data to the fullest extent.

Improved land use classification from Landsat and Seasat satellite imagery registered to a common map base

Science.gov (United States)

Clark, J.

1981-01-01

In the case of Landsat Multispectral Scanner System (MSS) data, ambiguities in spectral signature can arise in urban areas. A study was initiated in the belief that Seasat digital SAR could help provide the spectral separability needed for a more accurate urban land use classification. A description is presented of the results of land use classifications performed on Landsat and preprocessed Seasat imagery that were registered to a common map base. The process of registering imagery and training site boundary coordinates to a common map has been reported by Clark (1980). It is found that preprocessed Seasat imagery provides signatures for urban land uses which are spectrally separable from Landsat signatures. This development appears to significantly improve land use classifications in an urban setting for class 12 (Commercial and Services), class 13 (Industrial), and class 14 (Transportation, Communications, and Utilities).

Landsat 1-5 Multispectral Scanner V1

Data.gov (United States)

National Aeronautics and Space Administration — Abstract: The Landsat Multispectral Scanner (MSS) was a sensor onboard Landsats 1 through 5 and acquired images of the Earth nearly continuously from July 1972 to...

Anaglyph, Landsat Overlay: Wellington, New Zealand

Science.gov (United States)

2000-01-01

Wellington, the capital city of New Zealand, is located on the shores of Port Nicholson, a natural harbor at the south end of North Island. The city was founded in 1840 by British emigrants and now has a regional population of more than 400,000 residents. As seen here, the natural terrain imposes strong control over the urban growth pattern. Rugged hills generally rising to 300 meters (1,000 feet) help protect the city and harbor from strong winter winds.New Zealand is seismically active and faults are readily seen in the topography. The Wellington Fault forms the straight northwestern (upper left) shoreline of the harbor. Toward the southwest (lower left) the fault crosses through the city, then forms linear canyons in the hills before continuing offshore. Toward the northeast (upper right) the fault forms the sharp mountain front along the northern edge of the heavily populated Hutt Valley.This anaglyph was generated by first draping a Landsat Thematic Mapper image over a topographic map from the Shuttle Radar Topography Mission, then using the topographic data to create two differing perspectives, one for each eye. When viewed through special glasses, the result is a vertically exaggerated view of the Earth's surface in its full three dimensions. Anaglyph glasses cover the left eye with a red filter and cover the right eye with a blue filter.Landsat satellites have provided visible light and infrared images of the Earth continuously since 1972. SRTM topographic data match the 30 meter (99 foot) spatial resolution of most Landsat images and will provide a valuable complement for studying the historic and growing Landsat data archive. The Landsat 7 Thematic Mapper image used here was provided to the SRTM project by the United States Geological Survey, Earth Resources Observation Systems (EROS) data Center, Sioux Falls, South Dakota.Elevation data used in this image was acquired by the Shuttle Radar Topography Mission (SRTM) aboard the Space Shuttle Endeavour

Remote sensing of species diversity using Landsat 8 spectral variables

Science.gov (United States)

Madonsela, Sabelo; Cho, Moses Azong; Ramoelo, Abel; Mutanga, Onisimo

2017-11-01

The application of remote sensing in biodiversity estimation has largely relied on the Normalized Difference Vegetation Index (NDVI). The NDVI exploits spectral information from red and near infrared bands of Landsat images and it does not consider canopy background conditions hence it is affected by soil brightness which lowers its sensitivity to vegetation. As such NDVI may be insufficient in explaining tree species diversity. Meanwhile, the Landsat program also collects essential spectral information in the shortwave infrared (SWIR) region which is related to plant properties. The study was intended to: (i) explore the utility of spectral information across Landsat-8 spectrum using the Principal Component Analysis (PCA) and estimate alpha diversity (α-diversity) in the savannah woodland in southern Africa, and (ii) define the species diversity index (Shannon (H‧), Simpson (D2) and species richness (S) - defined as number of species in a community) that best relates to spectral variability on the Landsat-8 Operational Land Imager dataset. We designed 90 m × 90 m field plots (n = 71) and identified all trees with a diameter at breast height (DbH) above 10 cm. H‧, D2 and S were used to quantify tree species diversity within each plot and the corresponding spectral information on all Landsat-8 bands were extracted from each field plot. A stepwise linear regression was applied to determine the relationship between species diversity indices (H‧, D2 and S) and Principal Components (PCs), vegetation indices and Gray Level Co-occurrence Matrix (GLCM) texture layers with calibration (n = 46) and test (n = 23) datasets. The results of regression analysis showed that the Simple Ratio Index derivative had a higher relationship with H‧, D2 and S (r2= 0.36; r2= 0.41; r2= 0.24 respectively) compared to NDVI, EVI, SAVI or their derivatives. Moreover the Landsat-8 derived PCs also had a higher relationship with H‧ and D2 (r2 of 0.36 and 0.35 respectively) than the

Landsat science team meeting: Summer 2015

Science.gov (United States)

Schroeder, Todd; Loveland, Thomas; Wulder, Michael A.; Irons, James R.

2015-01-01

The summer meeting of the joint U.S. Geological Survey (USGS)–NASA Landsat Science Team (LST) was held at the USGS’s Earth Resources Observation and Science (EROS) Center July 7-9, 2015, in Sioux Falls, SD. The LST co-chairs, Tom Loveland [EROS—Senior Scientist] and Jim Irons [NASA’s Goddard Space Flight Center (GSFC)—Landsat 8 Project Scientist], opened the three-day meeting on an upbeat note following the recent successful launch of the European Space Agency’s Sentinel-2 mission on June 23, 2015 (see image on page 14), and the news that work on Landsat 9 has begun, with a projected launch date of 2023.

Using ecological zones to increase the detail of Landsat classifications

Science.gov (United States)

Fox, L., III; Mayer, K. E.

1981-01-01

Changes in classification detail of forest species descriptions were made for Landsat data on 2.2 million acres in northwestern California. Because basic forest canopy structures may exhibit very similar E-M energy reflectance patterns in different environmental regions, classification labels based on Landsat spectral signatures alone become very generalized when mapping large heterogeneous ecological regions. By adding a seven ecological zone stratification, a 167% improvement in classification detail was made over the results achieved without it. The seven zone stratification is a less costly alternative to the inclusion of complex collateral information, such as terrain data and soil type, into the Landsat data base when making inventories of areas greater than 500,000 acres.

Incorporating Spatio-temporal Phenological Variation in Detecting Exotic Saltcedar Using Landsat Time Series

Science.gov (United States)

Diao, C.; Wang, L.

2017-12-01

The invasion of exotic species compromises ecosystem functions and causes substantial economic losses at the global scale. Over the past century, non-native saltcedar has expanded into most riparian zones in southwestern United States and posed significant threats to the native biotic communities. Repeated monitoring of saltcedar distribution is essential for conservation agencies to locate highly susceptible areas and develop corresponding control strategies. Throughout the phenological cycle, the leaf senescence stage has been found to be the most crucial in spectrally detecting saltcedar. However, due to climate variability and anthropogenic forcing, the timing of saltcedar leaf senescence may vary over space and time. This spatial and inter-annual variation need to be accommodated to pinpoint the appropriate remotely sensed imagery for saltcedar mapping. The objective of this study was to develop a Landsat-based Multiyear Spectral Angle Clustering (MSAC) model to monitor the inter-annual leaf senescence of exotic saltcedar. At the Landsat scale, the time series analysis of vegetation phenology is usually limited by the temporal resolution of images. The MSAC model can overcome this limit and take advantage of the Landsat images from multiple years to compensate the lack of images in a single year. Results indicated the MSAC model provided a Landsat-based solution to capture the inter-annual leaf senescence of saltcedar. Compared to traditional NDVI-based phenological approaches, the proposed model achieved a more accurate classification results of saltcedar across years. The MSAC model provides unique opportunities to guide the selection of appropriate remotely sensed image for repetitive saltcedar mapping.

Landsat 8 Data Modeled as DGGS Data Cubes

Science.gov (United States)

Sherlock, M. J.; Tripathi, G.; Samavati, F.

2016-12-01

In the context of tracking recent global changes in the Earth's landscape, Landsat 8 provides high-resolution multi-wavelength data with a temporal resolution of sixteen days. Such a live dataset can benefit novel applications in environmental monitoring. However, a temporal analysis of this dataset in its native format is a challenging task mostly due to the huge volume of geospatial images and imperfect overlay of different day Landsat 8 images. We propose the creation of data cubes derived from Landsat 8 data, through the use of a Discrete Global Grid System (DGGS). DGGS referencing of Landsat 8 data provides a cell-based representation of the pixel values for a fixed area on earth, indexed by keys. Having the calibrated cell-based Landsat 8 images can speed up temporal analysis and facilitate parallel processing using distributed systems. In our method, the Landsat 8 dataset hosted on Amazon Web Services (AWS) is downloaded using a web crawler and stored on a filesystem. We apply the cell-based DGGS referencing (using Pyxis SDK) to Landsat 8 images which provide a rhombus based tessellation of equal area cells for our use-case. After this step, the cell-images which overlay perfectly on different days, are stacked in the temporal dimension and stored into data cube units. The depth of the cube represents the number of temporal images of the same cell and can be updated when new images are received each day. Harnessing the regular spatio-temporal structure of data cubes, we want to compress, query, transmit and visualize big Landsat 8 data in an efficient way for temporal analysis.

Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images

Science.gov (United States)

Chen, Xuexia; Vogelmann, James E.; Chander, Gyanesh; Ji, Lei; Tolk, Brian; Huang, Chengquan; Rollins, Matthew

2013-01-01

what were generated using the LTSS data set, and their kappa coefficients were higher than 0.97. These results indicate that AWiFS can be used instead of Landsat data to detect multitemporal disturbance in the event of Landsat data discontinuity.

Exploiting Data Intensive Applications on High Performance Computers to Unlock Australia's Landsat Archive

Science.gov (United States)

Purss, Matthew; Lewis, Adam; Edberg, Roger; Ip, Alex; Sixsmith, Joshua; Frankish, Glenn; Chan, Tai; Evans, Ben; Hurst, Lachlan

2013-04-01

Australia's Earth Observation Program has downlinked and archived satellite data acquired under the NASA Landsat mission for the Australian Government since the establishment of the Australian Landsat Station in 1979. Geoscience Australia maintains this archive and produces image products to aid the delivery of government policy objectives. Due to the labor intensive nature of processing of this data there have been few national-scale datasets created to date. To compile any Earth Observation product the historical approach has been to select the required subset of data and process "scene by scene" on an as-needed basis. As data volumes have increased over time, and the demand for the processed data has also grown, it has become increasingly difficult to rapidly produce these products and achieve satisfactory policy outcomes using these historic processing methods. The result is that we have been "drowning in a sea of uncalibrated data" and scientists, policy makers and the public have not been able to realize the full potential of the Australian Landsat Archive and its value is therefore significantly diminished. To overcome this critical issue, the Australian Space Research Program has funded the "Unlocking the Landsat Archive" (ULA) Project from April 2011 to June 2013 to improve the access and utilization of Australia's archive of Landsat data. The ULA Project is a public-private consortium led by Lockheed Martin Australia (LMA) and involving Geoscience Australia (GA), the Victorian Partnership for Advanced Computing (VPAC), the National Computational Infrastructure (NCI) at the Australian National University (ANU) and the Cooperative Research Centre for Spatial Information (CRC-SI). The outputs from the ULA project will become a fundamental component of Australia's eResearch infrastructure, with the Australian Landsat Archive hosted on the NCI and made openly available under a creative commons license. NCI provides access to researchers through significant HPC

Kerr Reservoir LANDSAT experiment analysis for March 1981

Science.gov (United States)

Lecroy, S. R. (Principal Investigator)

1982-01-01

LANDSAT radiance data were used in an experiment conducted on the waters of Kerr Reservoir to determine if reliable algorithms could be developed that relate water quality parameters to remotely sensed data. A mix of different types of algorithms using the LANDSAT bands was generated to provide a thorough understanding of the relationships among the data involved. Except for secchi depth, the study demonstrated that for the ranges measured, the algorithms that satisfactorily represented the data encompass a mix of linear and nonlinear forms using only one LANDSAT band. Ratioing techniques did not improve the results since the initial design of the experiment minimized the errors against which this procedure is effective. Good correlations were found for total suspended solids, iron, turbidity, and secchi depth. Marginal correlations were discovered for nitrate and tannin + lignin. Quantification maps of Kerr Reservoir are presented for many of the water quality parameters using the developed algorithms.

Diazo processing of LANDSAT imagery: A low-cost instructional technique

Science.gov (United States)

Lusch, D. P.

1981-01-01

Diazo processing of LANDSAT imagery is a relatively simple and cost effective method of producing enhanced renditions of the visual LANDSAT products. This technique is capable of producing a variety of image enhancements which have value in a teaching laboratory environment. Additionally, with the appropriate equipment, applications research which relys on accurate and repeatable results is possible. Exposure and development equipment options, diazo materials, and enhancement routines are discussed.

Small forest cuttings mapped with Landsat digital data

Science.gov (United States)

Bryant, E.; Dodge, A. G.; Eger, M. J. E.

1979-01-01

The Cooperative Landsat Applications Research Group used computer classification of Landsat digital data to map forest cuttings (clearcuts) in northern New Hampshire. Cuttings as small as 3 hectares were identified. Several ages or conditions of clearcuts could be distinguished. Progress in two methods of duplicating classification categories from one Landsat pass to another are discussed. One method was used in making maps of areas in 1973, 1975, and 1978.

Quality Assessment of Landsat Surface Reflectance Products Using MODIS Data

Science.gov (United States)

Feng, Min; Huang, Chengquan; Channan, Saurabh; Vermote, Eric; Masek, Jeffrey G.; Townshend, John R.

2012-01-01

Surface reflectance adjusted for atmospheric effects is a primary input for land cover change detection and for developing many higher level surface geophysical parameters. With the development of automated atmospheric correction algorithms, it is now feasible to produce large quantities of surface reflectance products using Landsat images. Validation of these products requires in situ measurements, which either do not exist or are difficult to obtain for most Landsat images. The surface reflectance products derived using data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS), however, have been validated more comprehensively. Because the MODIS on the Terra platform and the Landsat 7 are only half an hour apart following the same orbit, and each of the 6 Landsat spectral bands overlaps with a MODIS band, good agreements between MODIS and Landsat surface reflectance values can be considered indicators of the reliability of the Landsat products, while disagreements may suggest potential quality problems that need to be further investigated. Here we develop a system called Landsat-MODIS Consistency Checking System (LMCCS). This system automatically matches Landsat data with MODIS observations acquired on the same date over the same locations and uses them to calculate a set of agreement metrics. To maximize its portability, Java and open-source libraries were used in developing this system, and object-oriented programming (OOP) principles were followed to make it more flexible for future expansion. As a highly automated system designed to run as a stand-alone package or as a component of other Landsat data processing systems, this system can be used to assess the quality of essentially every Landsat surface reflectance image where spatially and temporally matching MODIS data are available. The effectiveness of this system was demonstrated using it to assess preliminary surface reflectance products derived using the Global Land Survey (GLS) Landsat

Multitemporal Snow Cover Mapping in Mountainous Terrain for Landsat Climate Data Record Development

Science.gov (United States)

Crawford, Christopher J.; Manson, Steven M.; Bauer, Marvin E.; Hall, Dorothy K.

2013-01-01

A multitemporal method to map snow cover in mountainous terrain is proposed to guide Landsat climate data record (CDR) development. The Landsat image archive including MSS, TM, and ETM+ imagery was used to construct a prototype Landsat snow cover CDR for the interior northwestern United States. Landsat snow cover CDRs are designed to capture snow-covered area (SCA) variability at discrete bi-monthly intervals that correspond to ground-based snow telemetry (SNOTEL) snow-water-equivalent (SWE) measurements. The June 1 bi-monthly interval was selected for initial CDR development, and was based on peak snowmelt timing for this mountainous region. Fifty-four Landsat images from 1975 to 2011 were preprocessed that included image registration, top-of-the-atmosphere (TOA) reflectance conversion, cloud and shadow masking, and topographic normalization. Snow covered pixels were retrieved using the normalized difference snow index (NDSI) and unsupervised classification, and pixels having greater (less) than 50% snow cover were classified presence (absence). A normalized SCA equation was derived to independently estimate SCA given missing image coverage and cloud-shadow contamination. Relative frequency maps of missing pixels were assembled to assess whether systematic biases were embedded within this Landsat CDR. Our results suggest that it is possible to confidently estimate historical bi-monthly SCA from partially cloudy Landsat images. This multitemporal method is intended to guide Landsat CDR development for freshwaterscarce regions of the western US to monitor climate-driven changes in mountain snowpack extent.

LEAST SQUARE APPROACH FOR ESTIMATING OF LAND SURFACE TEMPERATURE FROM LANDSAT-8 SATELLITE DATA USING RADIATIVE TRANSFER EQUATION

Directory of Open Access Journals (Sweden)

Y. Jouybari-Moghaddam

2017-09-01

Full Text Available Land Surface Temperature (LST is one of the significant variables measured by remotely sensed data, and it is applied in many environmental and Geoscience studies. The main aim of this study is to develop an algorithm to retrieve the LST from Landsat-8 satellite data using Radiative Transfer Equation (RTE. However, LST can be retrieved from RTE, but, since the RTE has two unknown parameters including LST and surface emissivity, estimating LST from RTE is an under the determined problem. In this study, in order to solve this problem, an approach is proposed an equation set includes two RTE based on Landsat-8 thermal bands (i.e.: band 10 and 11 and two additional equations based on the relation between the Normalized Difference Vegetation Index (NDVI and emissivity of Landsat-8 thermal bands by using simulated data for Landsat-8 bands. The iterative least square approach was used for solving the equation set. The LST derived from proposed algorithm is evaluated by the simulated dataset, built up by MODTRAN. The result shows the Root Mean Squared Error (RMSE is less than 1.18°K. Therefore; the proposed algorithm can be a suitable and robust method to retrieve the LST from Landsat-8 satellite data.

Least Square Approach for Estimating of Land Surface Temperature from LANDSAT-8 Satellite Data Using Radiative Transfer Equation

Science.gov (United States)

Jouybari-Moghaddam, Y.; Saradjian, M. R.; Forati, A. M.

2017-09-01

Land Surface Temperature (LST) is one of the significant variables measured by remotely sensed data, and it is applied in many environmental and Geoscience studies. The main aim of this study is to develop an algorithm to retrieve the LST from Landsat-8 satellite data using Radiative Transfer Equation (RTE). However, LST can be retrieved from RTE, but, since the RTE has two unknown parameters including LST and surface emissivity, estimating LST from RTE is an under the determined problem. In this study, in order to solve this problem, an approach is proposed an equation set includes two RTE based on Landsat-8 thermal bands (i.e.: band 10 and 11) and two additional equations based on the relation between the Normalized Difference Vegetation Index (NDVI) and emissivity of Landsat-8 thermal bands by using simulated data for Landsat-8 bands. The iterative least square approach was used for solving the equation set. The LST derived from proposed algorithm is evaluated by the simulated dataset, built up by MODTRAN. The result shows the Root Mean Squared Error (RMSE) is less than 1.18°K. Therefore; the proposed algorithm can be a suitable and robust method to retrieve the LST from Landsat-8 satellite data.

Landsat Science: 40 Years of Innovation and Opportunity

Science.gov (United States)

Cook, Bruce D.; Irons, James R.; Masek, Jeffrey G.; Loveland, Thomas R.

2012-01-01

Landsat satellites have provided unparalleled Earth-observing data for nearly 40 years, allowing scientists to describe, monitor and model the global environment during a period of time that has seen dramatic changes in population growth, land use, and climate. The success of the Landsat program can be attributed to well-designed instrument specifications, astute engineering, comprehensive global acquisition and calibration strategies, and innovative scientists who have developed analytical techniques and applications to address a wide range of needs at local to global scales (e.g., crop production, water resource management, human health and environmental quality, urbanization, deforestation and biodiversity). Early Landsat contributions included inventories of natural resources and land cover classification maps, which were initially prepared by a visual interpretation of Landsat imagery. Over time, advances in computer technology facilitated the development of sophisticated image processing algorithms and complex ecosystem modeling, enabling scientists to create accurate, reproducible, and more realistic simulations of biogeochemical processes (e.g., plant production and ecosystem dynamics). Today, the Landsat data archive is freely available for download through the USGS, creating new opportunities for scientists to generate global image datasets, develop new change detection algorithms, and provide products in support of operational programs such as Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD). In particular, the use of dense (approximately annual) time series to characterize both rapid and progressive landscape change has yielded new insights into how the land environment is responding to anthropogenic and natural pressures. The launch of the Landsat Data Continuity Mission (LDCM) satellite in 2012 will continue to propel innovative Landsat science.

Applications of LANDSAT data to the integrated economic development of Mindoro, Phillipines

Science.gov (United States)

Wagner, T. W.; Fernandez, J. C.

1977-01-01

LANDSAT data is seen as providing essential up-to-date resource information for the planning process. LANDSAT data of Mindoro Island in the Philippines was processed to provide thematic maps showing patterns of agriculture, forest cover, terrain, wetlands and water turbidity. A hybrid approach using both supervised and unsupervised classification techniques resulted in 30 different scene classes which were subsequently color-coded and mapped at a scale of 1:250,000. In addition, intensive image analysis is being carried out in evaluating the images. The images, maps, and aerial statistics are being used to provide data to seven technical departments in planning the economic development of Mindoro. Multispectral aircraft imagery was collected to compliment the application of LANDSAT data and validate the classification results.

SPOT: How good for geology? A comparison with LANDSAT MSS

Science.gov (United States)

Sesoeren, A.

1986-12-01

Geological interpretation possibilities of SPOT MSS and LANDSAT MSS positive prints enlarged to the same scale were compared, using as a test area part of the Jebel Amour (Algeria). The SPOT imagery offers many advantages, filling the gap between remote sensing from space and aerial photography. The best results by visual interpretation are obtained in combining SPOT for the required details with LANDSAT for the synoptic veiw. Further improvements are expected from the use of SPOT stereo-pairs.

Spectral Unmixing Analysis of Time Series Landsat 8 Images

Science.gov (United States)

Zhuo, R.; Xu, L.; Peng, J.; Chen, Y.

2018-05-01

Temporal analysis of Landsat 8 images opens up new opportunities in the unmixing procedure. Although spectral analysis of time series Landsat imagery has its own advantage, it has rarely been studied. Nevertheless, using the temporal information can provide improved unmixing performance when compared to independent image analyses. Moreover, different land cover types may demonstrate different temporal patterns, which can aid the discrimination of different natures. Therefore, this letter presents time series K-P-Means, a new solution to the problem of unmixing time series Landsat imagery. The proposed approach is to obtain the "purified" pixels in order to achieve optimal unmixing performance. The vertex component analysis (VCA) is used to extract endmembers for endmember initialization. First, nonnegative least square (NNLS) is used to estimate abundance maps by using the endmember. Then, the estimated endmember is the mean value of "purified" pixels, which is the residual of the mixed pixel after excluding the contribution of all nondominant endmembers. Assembling two main steps (abundance estimation and endmember update) into the iterative optimization framework generates the complete algorithm. Experiments using both simulated and real Landsat 8 images show that the proposed "joint unmixing" approach provides more accurate endmember and abundance estimation results compared with "separate unmixing" approach.

Validating gap-filling of Landsat ETM+ satellite images in the Golestan Province, Iran

NARCIS (Netherlands)

Mohammdy, M.; Moradi, H.R.; Zeinivand, H.; Temme, A.J.A.M.; Pourghasemi, H.R.; Alizadeh, H.

2014-01-01

The Landsat series of satellites provides a valuable data source for land surface mapping and monitoring. Unfortunately, the scan line corrector (SLC) of the Landsat7 Enhanced Thematic Mapper plus (ETM+) sensor failed on May 13, 2003. This problem resulted in about 22 % of the pixels per scene not

Bridging the Divide: Translating Landsat Research Into Usable Science

Science.gov (United States)

Rocchio, L. E.; Davis, A. L.

2006-12-01

Science has long served humankind. Breakthroughs in medicine have increased longevity and advances in technology have made modern-day conveniences possible. Yet, social benefits begotten by the environmental sciences, although critical for the survival of humanity, have not always been as widely recognized or used. To benefit today's rapidly growing population, the divides between environmental research, applied environmental science, and use of this information by decision makers must be bridged. Lessons about the translation from research to usable science can be learned from the four decades of Landsat history, and these lessons can serve as useful models for bridging the gaps between new technology, scientific research, and the use of that research and technology in real-world problem solving. In 1965, William Pecora, then-director of the U.S. Geological Survey, proposed the idea of a remote sensing satellite program to gather facts about natural resources of Earth. For the next seven years, an intense campaign showing the depth and diversity of satellite imagery applications was waged. This led to the 1972 launch of the first civilian land-observing satellite, Landsat 1. By 1975, successful application research based on Landsat 1 imagery prompted then-NASA Administrator Dr. James Fletcher to proclaim that if one space age development would save the world, it would be Landsat and its successor satellites. Thirty-four years of continual Landsat imaging and related-research has lead to the implementation of many socially beneficial applications, such as improved water management techniques, crop insurance fraud reduction, illicit crop inventories, natural disaster relief planning, continent-scale carbon estimates, and extensive cartographic advances. Despite these successes, the challenge of translating Landsat research into realized social benefits remains. Even in this geospatially-savvy era, the utility of Landsat largely escapes policymakers. Here, in an

Filling Landsat ETM+ SLC-off gaps using a segmentation model approach

Science.gov (United States)

Maxwell, Susan

2004-01-01

The purpose of this article is to present a methodology for filling Landsat Scan Line Corrector (SLC)-off gaps with same-scene spectral data guided by a segmentation model. Failure of the SLC on the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) instrument resulted in a loss of approximately 25 percent of the spectral data. The missing data span across most of the image with scan gaps varying in size from two pixels near the center of the image to 14 pixels along the east and west edges. Even with the scan gaps, the radiometric and geometric qualities of the remaining portions of the image still meet design specifications and therefore contain useful information (see http:// landsat7.usgs.gov for additional information). The U.S. Geological Survey EROS Data Center (EDC) is evaluating several techniques to fill the gaps in SLC-off data to enhance the usability of the imagery (Howard and Lacasse 2004) (PE&RS, August 2004). The method presented here uses a segmentation model approach that allows for same-scene spectral data to be used to fill the gaps. The segment model is generated from a complete satellite image with no missing spectral data (e.g., Landsat 5, Landsat 7 SLCon, SPOT). The model is overlaid on the Landsat SLC-off image, and the missing data within the gaps are then estimated using SLC-off spectral data that intersect the segment boundary. A major advantage of this approach is that the gaps are filled using spectral data derived from the same SLC-off satellite image.

A Landsat study of water quality in Lake Okeechobee

Science.gov (United States)

Gervin, J. C.; Marshall, M. L.

1976-01-01

This paper uses multiple regression techniques to investigate the relationship between Landsat radiance values and water quality measurements. For a period of over one year, the Central and Southern Florida Flood Control District sampled the water of Lake Okeechobee for chlorophyll, carotenoids, turbidity, and various nutrients at the time of Landsat overpasses. Using an overlay map of the sampling stations, Landsat radiance values were measured from computer compatible tapes using a GE image 100 and averaging over a 22-acre area at each station. These radiance values in four bands were used to form a number of functions (powers, logarithms, exponentials, and ratios), which were then compared with the ground measurements using multiple linear regression techniques. Several dates were used to provide generality and to study possible seasonal variations. Individual correlations were presented for the various water quality parameters and best fit equations were examined for chlorophyll and turbidity. The results and their relationship to past hydrological research were discussed.

Statistical rice yield modeling using blended MODIS-Landsat based crop phenology metrics in Taiwan

Science.gov (United States)

Chen, C. R.; Chen, C. F.; Nguyen, S. T.; Lau, K. V.

2015-12-01

Taiwan is a populated island with a majority of residents settled in the western plains where soils are suitable for rice cultivation. Rice is not only the most important commodity, but also plays a critical role for agricultural and food marketing. Information of rice production is thus important for policymakers to devise timely plans for ensuring sustainably socioeconomic development. Because rice fields in Taiwan are generally small and yet crop monitoring requires information of crop phenology associating with the spatiotemporal resolution of satellite data, this study used Landsat-MODIS fusion data for rice yield modeling in Taiwan. We processed the data for the first crop (Feb-Mar to Jun-Jul) and the second (Aug-Sep to Nov-Dec) in 2014 through five main steps: (1) data pre-processing to account for geometric and radiometric errors of Landsat data, (2) Landsat-MODIS data fusion using using the spatial-temporal adaptive reflectance fusion model, (3) construction of the smooth time-series enhanced vegetation index 2 (EVI2), (4) rice yield modeling using EVI2-based crop phenology metrics, and (5) error verification. The fusion results by a comparison bewteen EVI2 derived from the fusion image and that from the reference Landsat image indicated close agreement between the two datasets (R2 > 0.8). We analysed smooth EVI2 curves to extract phenology metrics or phenological variables for establishment of rice yield models. The results indicated that the established yield models significantly explained more than 70% variability in the data (p-value 0.8), in both cases. The root mean square error (RMSE) and mean absolute error (MAE) used to measure the model accuracy revealed the consistency between the estimated yields and the government's yield statistics. This study demonstrates advantages of using EVI2-based phenology metrics (derived from Landsat-MODIS fusion data) for rice yield estimation in Taiwan prior to the harvest period.

a Comparative Analysis of Spatiotemporal Data Fusion Models for Landsat and Modis Data

Science.gov (United States)

Hazaymeh, K.; Almagbile, A.

2018-04-01

In this study, three documented spatiotemporal data fusion models were applied to Landsat-7 and MODIS surface reflectance, and NDVI. The algorithms included the spatial and temporal adaptive reflectance fusion model (STARFM), sparse representation based on a spatiotemporal reflectance fusion model (SPSTFM), and spatiotemporal image-fusion model (STI-FM). The objectives of this study were to (i) compare the performance of these three fusion models using a one Landsat-MODIS spectral reflectance image pairs using time-series datasets from the Coleambally irrigation area in Australia, and (ii) quantitatively evaluate the accuracy of the synthetic images generated from each fusion model using statistical measurements. Results showed that the three fusion models predicted the synthetic Landsat-7 image with adequate agreements. The STI-FM produced more accurate reconstructions of both Landsat-7 spectral bands and NDVI. Furthermore, it produced surface reflectance images having the highest correlation with the actual Landsat-7 images. This study indicated that STI-FM would be more suitable for spatiotemporal data fusion applications such as vegetation monitoring, drought monitoring, and evapotranspiration.

An automated approach to mapping corn from Landsat imagery

Science.gov (United States)

Maxwell, S.K.; Nuckols, J.R.; Ward, M.H.; Hoffer, R.M.

2004-01-01

Most land cover maps generated from Landsat imagery involve classification of a wide variety of land cover types, whereas some studies may only need spatial information on a single cover type. For example, we required a map of corn in order to estimate exposure to agricultural chemicals for an environmental epidemiology study. Traditional classification techniques, which require the collection and processing of costly ground reference data, were not feasible for our application because of the large number of images to be analyzed. We present a new method that has the potential to automate the classification of corn from Landsat satellite imagery, resulting in a more timely product for applications covering large geographical regions. Our approach uses readily available agricultural areal estimates to enable automation of the classification process resulting in a map identifying land cover as ‘highly likely corn,’ ‘likely corn’ or ‘unlikely corn.’ To demonstrate the feasibility of this approach, we produced a map consisting of the three corn likelihood classes using a Landsat image in south central Nebraska. Overall classification accuracy of the map was 92.2% when compared to ground reference data.

The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM)

Science.gov (United States)

Slonecker, Terry; Jones, Daniel K.; Pellerin, Brian A.

2016-01-01

Due to a combination of factors, such as a new coastal/aerosol band and improved radiometric sensitivity of the Operational Land Imager aboard Landsat 8, the atmospherically-corrected Surface Reflectance product for Landsat data, and the growing availability of corrected fDOM data from U.S. Geological Survey gaging stations, moderate-resolution remote sensing of fDOM may now be achievable. This paper explores the background of previous efforts and shows preliminary examples of the remote sensing and data relationships between corrected fDOM and Landsat 8 reflectance values. Although preliminary results before and after Hurricane Sandy are encouraging, more research is needed to explore the full potential of Landsat 8 to continuously map fDOM in a number of water profiles.

The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM).

Science.gov (United States)

Slonecker, E Terrence; Jones, Daniel K; Pellerin, Brian A

2016-06-30

Due to a combination of factors, such as a new coastal/aerosol band and improved radiometric sensitivity of the Operational Land Imager aboard Landsat 8, the atmospherically-corrected Surface Reflectance product for Landsat data, and the growing availability of corrected fDOM data from U.S. Geological Survey gaging stations, moderate-resolution remote sensing of fDOM may now be achievable. This paper explores the background of previous efforts and shows preliminary examples of the remote sensing and data relationships between corrected fDOM and Landsat 8 reflectance values. Although preliminary results before and after Hurricane Sandy are encouraging, more research is needed to explore the full potential of Landsat 8 to continuously map fDOM in a number of water profiles. Published by Elsevier Ltd.

LandSAT TM 1994

Data.gov (United States)

Kansas Data Access and Support Center — Before the Landsat commercialization contract was signed between the Department of Commerce and the Earth Observation Satellite Company (EOSAT) on September 27,...

Continental-Scale Mapping of Adelie Penguin Colonies from Landsat Imagery

Science.gov (United States)

Schwaller, Mathew R.; Southwell, Colin; Emmerson, Louise

2013-01-01

Breeding distribution of the Adlie penguin, Pygoscelis adeliae, was surveyed with Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data in an area covering approximately 330 of longitude along the coastline of Antarctica.An algorithm was designed to minimize radiometric noise and to retrieve Adlie penguin colony location and spatial extent from the ETM+data. In all, 9143 individual pixels were classified as belonging to an Adlie penguin colony class out of the entire dataset of 195 ETM+ scenes, where the dimension of each pixel is 30 m by 30 m,and each scene is approximately 180 km by 180 km. Pixel clustering identified a total of 187 individual Adlie penguin colonies, ranging in size from a single pixel (900 sq m) to a maximum of 875 pixels (0.788 sq km). Colony retrievals have a very low error of commission, on the order of 1% or less, and the error of omission was estimated to be 3% to 4% by population based on comparisons with direct observations from surveys across east Antarctica. Thus, the Landsat retrievals successfully located Adlie penguin colonies that accounted for 96 to 97% of the regional population used as ground truth. Geographic coordinates and the spatial extent of each colony retrieved from the Landsat data are available publically. Regional analysis found several areas where the Landsat retrievals suggest populations that are significantly larger than published estimates. Six Adlie penguin colonies were found that are believed to be previously unreported in the literature.

Local search for optimal global map generation using mid-decadal landsat images

Science.gov (United States)

Khatib, L.; Gasch, J.; Morris, Robert; Covington, S.

2007-01-01

NASA and the US Geological Survey (USGS) are seeking to generate a map of the entire globe using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor data from the "mid-decadal" period of 2004 through 2006. The global map is comprised of thousands of scene locations and, for each location, tens of different images of varying quality to chose from. Furthermore, it is desirable for images of adjacent scenes be close together in time of acquisition, to avoid obvious discontinuities due to seasonal changes. These characteristics make it desirable to formulate an automated solution to the problem of generating the complete map. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving it using local search. Preliminary results of running the algorithm on image data sets are summarized. The results suggest a significant improvement in map quality using constraint-based solutions. Copyright ?? 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Comparison of Sentinel-2A and Landsat-8 Nadir BRDF Adjusted Reflectance (NBAR) over Southern Africa

Science.gov (United States)

Li, J.; Roy, D. P.; Zhang, H.

2016-12-01

The Landsat satellites have been providing moderate resolution imagery of the Earth's surface for over 40 years with continuity provided by the Landsat 8 and planned Landsat 9 missions. The European Space Agency Sentinel-2 satellite was successfully launched into a polar sun-synchronous orbit in 2015 and carries the Multi Spectral Instrument (MSI) that has Landsat-like bands and acquisition coverage. These new sensors acquire images at view angles ± 7.5° (Landsat) and ± 10.3° (Sentinel-2) from nadir that result in small directional effects in the surface reflectance. When data from adjoining paths, or from long time series are used, a model of the surface anisotropy is required to adjust observations to a uniform nadir view (primarily for visual consistency, vegetation monitoring, or detection of subtle surface changes). Recently a generalized approach was published that provides consistent Landsat view angle corrections to provide nadir BRDF-adjusted reflectance (NBAR). Because the BRDF shapes of different terrestrial surfaces are sufficiently similar over the narrow 15° Landsat field of view, a fixed global set of MODIS BRDF spectral model parameters was shown to be adequate for Landsat NBAR derivation with little sensitivity to the land cover type, condition, or surface disturbance. This poster demonstrates the application of this methodology to Sentinel-2 data over a west-east transect across southern Africa. The reflectance differences between adjacent overlapping paths in the forward and backward scatter directions are quantified for both before and after BRDF correction. Sentinel-2 and Landsat-8 reflectance and NBAR inter-comparison results considering different stages of cloud and saturation filtering, and filtering to reduce surface state differences caused by acquisition time differences, demonstrate the utility of the approach. The relevance and limitations of the corrections for providing consistent moderate resolution reflectance are discussed.

A study of atmospheric diffusion from the LANDSAT imagery. [pollution transport over the ocean

Science.gov (United States)

Dejesusparada, N. (Principal Investigator); Viswanadham, Y.; Torsani, J. A.

1981-01-01

LANDSAT multispectral scanner data of the smoke plumes which originated in eastern Cabo Frio, Brazil and crossed over into the Atlantic Ocean, are analyzed to illustrate how high resolution LANDSAT imagery can aid meteorologists in evaluating specific air pollution events. The eleven LANDSAT images selected are for different months and years. The results show that diffusion is governed primarily by water and air temperature differences. With colder water, low level air is very stable and the vertical diffusion is minimal; but water warmer than the air induces vigorous diffusion. The applicability of three empirical methods for determining the horizontal eddy diffusivity coefficient in the Gaussian plume formula was evaluated with the estimated standard deviation of the crosswind distribution of material in the plume from the LANDSAT imagery. The vertical diffusion coefficient in stable conditions is estimated using Weinstock's formulation. These results form a data base for use in the development and validation of meso scale atmospheric diffusion models.

LANDSAT-8 OPERATIONAL LAND IMAGER CHANGE DETECTION ANALYSIS

Directory of Open Access Journals (Sweden)

W. Pervez

2017-05-01

Full Text Available This paper investigated the potential utility of Landsat-8 Operational Land Imager (OLI for change detection analysis and mapping application because of its superior technical design to previous Landsat series. The OLI SVM classified data was successfully classified with regard to all six test classes (i.e., bare land, built-up land, mixed trees, bushes, dam water and channel water. OLI support vector machine (SVM classified data for the four seasons (i.e., spring, autumn, winter, and summer was used to change detection results of six cases: (1 winter to spring which resulted reduction in dam water mapping and increases of bushes; (2 winter to summer which resulted reduction in dam water mapping and increase of vegetation; (3 winter to autumn which resulted increase in dam water mapping; (4 spring to summer which resulted reduction of vegetation and shallow water; (5 spring to autumn which resulted decrease of vegetation; and (6 summer to autumn which resulted increase of bushes and vegetation . OLI SVM classified data resulted higher overall accuracy and kappa coefficient and thus found suitable for change detection analysis.

Landsat and water pollution

Science.gov (United States)

Castruccio, P.; Fowler, T.; Loats, H., Jr.

1979-01-01

Report presents data derived from satellite images predicting pollution loads after rainfall. It explains method for converting Landsat images of Eastern United States into cover maps for Baltimore/five county region.

Modified Optimization Water Index (mowi) for LANDSAT-8 Oli/tirs

Science.gov (United States)

Moradi, M.; Sahebi, M.; Shokri, M.

2017-09-01

Water is one of the most important resources that essential need for human life. Due to population growth and increasing need of human to water, proper management of water resources will be one of the serious challenges of next decades. Remote sensing data is the best way to the management of water resources due time and cost effectiveness over a greater range of temporal and spatial scales. Between many kinds of satellite data, from SAR to optic or from high resolution to low resolution, Landsat imagery is more interesting data for water detection and management of earth surface water. Landsat8 OLI/TIRS is the newest version of Landsat satellite series. In this paper, we investigated the full spectral potential of Landsat8 for water detection. It is developed many kinds of methods for this purpose that index based methods have some advantages than other methods. Pervious indices just use a limited number of spectral band. In this paper, Modified Optimization Water Index (MOWI) defined by consideration of a linear combination of bands that each coefficient of bands calculated by particle swarm algorithm. The result shows that modified optimization water index (MOWI) has a proper performance on different condition like cloud, cloud shadow and mountain shadow.

Landsat 6 contract signed

Science.gov (United States)

Maggs, William Ward

A new agreement provides $220 million for development and construction of the Landsat 6 remote sensing satellite and its ground systems. The contract, signed on March 31, 1988, by the Department of Commerce (DOC) and the Earth Observation Satellite (EOSAT) Company of Lanham, Md., came just days after approval of DOC's Landsat commercialization plan by subcommittees of the House and Senate appropriations committees.The Landsat 6 spacecraft is due to be launched into orbit on a Titan II rocket in June 1991 from Vandenburg Air Force Base, Calif. The satellite will carry an Enhanced Thematic Mapper (ETM) sensor, an instrument sensitive to electromagnetic radiation in seven ranges or bands of wavelengths. The satellite's payload will also include the Sea Wide Field Sensor (Sea-WiFS), designed to provide information on sea surface temperature and ocean color. The sensor is being developed in a cooperative effort by EOSAT and the National Aeronautics and Space Administration (NASA). A less certain passenger is a proposed 5-m resolution, three-band sensor sensitive to visible light. EOSAT is trying to find both private financing for the device and potential buyers of the high-resolution imagery that it could produce. The company has been actively courting U.S. television networks, which have in the past used imagery from the European Système Probatoire d'Observation de la Terre (SPOT) satellite for news coverage.

Evaluation of directional normalization methods for Landsat TM/ETM+ over primary Amazonian lowland forests

Science.gov (United States)

Van doninck, Jasper; Tuomisto, Hanna

2017-06-01

Biodiversity mapping in extensive tropical forest areas poses a major challenge for the interpretation of Landsat images, because floristically clearly distinct forest types may show little difference in reflectance. In such cases, the effects of the bidirectional reflection distribution function (BRDF) can be sufficiently strong to cause erroneous image interpretation and classification. Since the opening of the Landsat archive in 2008, several BRDF normalization methods for Landsat have been developed. The simplest of these consist of an empirical view angle normalization, whereas more complex approaches apply the semi-empirical Ross-Li BRDF model and the MODIS MCD43-series of products to normalize directional Landsat reflectance to standard view and solar angles. Here we quantify the effect of surface anisotropy on Landsat TM/ETM+ images over old-growth Amazonian forests, and evaluate five angular normalization approaches. Even for the narrow swath of the Landsat sensors, we observed directional effects in all spectral bands. Those normalization methods that are based on removing the surface reflectance gradient as observed in each image were adequate to normalize TM/ETM+ imagery to nadir viewing, but were less suitable for multitemporal analysis when the solar vector varied strongly among images. Approaches based on the MODIS BRDF model parameters successfully reduced directional effects in the visible bands, but removed only half of the systematic errors in the infrared bands. The best results were obtained when the semi-empirical BRDF model was calibrated using pairs of Landsat observation. This method produces a single set of BRDF parameters, which can then be used to operationally normalize Landsat TM/ETM+ imagery over Amazonian forests to nadir viewing and a standard solar configuration.

Digital color analysis of color-ratio composite LANDSAT scenes. [Nevada

Science.gov (United States)

Raines, G. L.

1977-01-01

A method is presented that can be used to calculate approximate Munsell coordinates of the colors produced by making a color composite from three registered images. Applied to the LANDSAT MSS data of the Goldfield, Nevada, area, this method permits precise and quantitative definition of the limonitic areas originally observed in a LANDSAT color ratio composite. In addition, areas of transported limonite can be discriminated from the limonite in the hydrothermally altered areas of the Goldfield mining district. From the analysis, the numerical distinction between limonitic and nonlimonitic ground is generally less than 3% using the LANDSAT bands and as much as 8% in ratios of LANDSAT MSS bands.

Green leaf phenology at Landsat resolution: scaling from the plot to satellite

Science.gov (United States)

Fisher, J. I.; Mustard, J. F.; Vadeboncour, M.

2005-12-01

Despite the large number of in situ, plot-level phenological measurements and satellite-derived phenological studies, there has been little success to date in merging these records temporally or spatially. In particular, while most phenological patterns and trends derived from satellites appear realistic and coherent, they may not reflect spatial and temporal patterns at the plot level. An obvious explanation is the drastic scale difference from plot-level to most satellite observations. In this research, we bridge this scale gap through higher resolution satellite records (Landsat) and quantify the accuracy of satellite-derived metrics with direct field measurements. We compiled fifty-seven Landsat scenes from southern New England (P12 R51) from 1984 to 2002. Green vegetation areal abundance for each scene was derived from spectral mixture analysis and a single set of endmembers. The leaf area signal was fit with a logistic-growth simulating sigmoid curve to derive phenological markers (half-maximum leaf-onset and offset). Spring leaf-onset dates in homogenous stands of deciduous forests displayed significant and persistent local variability. The local variability was validated with multiple springtime ground observations (r2 = 0.91). The highest degree of verified small-scale variation occurred where contiguous forests displayed leaf-onset gradients of 10-14 days over short distances (example, our results indicate that deciduous forests in the Providence, RI metropolitan area leaf out 5-7 days earlier than comparable rural areas. In preliminary work, we validated the Landsat-derived metrics with similar analyses of MODIS and AVHRR, and demonstrate that aggregating diverse local phenologies into coarse grids may convolute interpretations. Despite these complications, the platform-independent curve-fit methodology may be extended across platforms and field data. The methodologically consistent approach, in tandem with Landsat data, allows us to effectively scale

Landsat 7 - A challenge to America

Science.gov (United States)

Colvocoresses, Alden P.

Factors in favor of Landsat 7 are discussed; they include: reasonable cost, a base on which to examine global change, and the need for comprehensive and continuous satellite coverage of the earth at moderate (5-30 m) resolution, in view of various occurrences on the earth's surface, ranging from the Chernobyl disaster to deforestation to the Persian Gulf conflict. Attention is given to proposed parameters for Landsat 7 and suggested actions that should be taken by Congress, the Administration, and the public to implement this space program.

Spatiotemporal Change Detection Using Landsat Imagery: the Case Study of Karacabey Flooded Forest, Bursa, Turkey

Science.gov (United States)

Akay, A. E.; Gencal, B.; Taş, İ.

2017-11-01

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

Operational calibration and validation of landsat data continuity mission (LDCM) sensors using the image assessment system (IAS)

Science.gov (United States)

Micijevic, Esad; Morfitt, Ron

2010-01-01

Systematic characterization and calibration of the Landsat sensors and the assessment of image data quality are performed using the Image Assessment System (IAS). The IAS was first introduced as an element of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) ground segment and recently extended to Landsat 4 (L4) and 5 (L5) Thematic Mappers (TM) and Multispectral Sensors (MSS) on-board the Landsat 1-5 satellites. In preparation for the Landsat Data Continuity Mission (LDCM), the IAS was developed for the Earth Observer 1 (EO-1) Advanced Land Imager (ALI) with a capability to assess pushbroom sensors. This paper describes the LDCM version of the IAS and how it relates to unique calibration and validation attributes of its on-board imaging sensors. The LDCM IAS system will have to handle a significantly larger number of detectors and the associated database than the previous IAS versions. An additional challenge is that the LDCM IAS must handle data from two sensors, as the LDCM products will combine the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) spectral bands.

From Landsat through SLI: Ball Aerospace Instrument Architecture for Earth Surface Monitoring

Science.gov (United States)

Wamsley, P. R.; Gilmore, A. S.; Malone, K. J.; Kampe, T. U.; Good, W. S.

2017-12-01

The Landsat legacy spans more than forty years of moderate resolution, multi-spectral imaging of the Earth's surface. Applications for Landsat data include global environmental change, disaster planning and recovery, crop and natural resource management, and glaciology. In recent years, coastal water science has been greatly enhanced by the outstanding on-orbit performance of Landsat 8. Ball Aerospace designed and built the Operational Land Imager (OLI) instrument on Landsat 8, and is in the process of building OLI 2 for Landsat 9. Both of these instruments have the same design however improved performance is expected from OLI 2 due to greater image bit depth (14 bit on OLI 2 vs 12 bit on OLI). Ball Aerospace is currently working on two novel instrument architectures applicable to Sustainable Land Imaging for Landsat 10 and beyond. With increased budget constraints probable for future missions, technological improvements must be included in future instrument architectures to enable increased capabilities at lower cost. Ball presents the instrument architectures and associated capabilities enabling new science in past, current, and future Landsat missions.

Forest management applications of Landsat data in a geographic information system

Science.gov (United States)

Maw, K. D.; Brass, J. A.

1982-01-01

The utility of land-cover data resulting from Landsat MSS classification can be greatly enhanced by use in combination with ancillary data. A demonstration forest management applications data base was constructed for Santa Cruz County, California, to demonstrate geographic information system applications of classified Landsat data. The data base contained detailed soils, digital terrain, land ownership, jurisdictional boundaries, fire events, and generalized land-use data, all registered to a UTM grid base. Applications models were developed from problems typical of fire management and reforestation planning.

Georgia resource assessment project: Institutionalizing LANDSAT and geographic data base techniques

Science.gov (United States)

Pierce, R. R.; Rado, B. Q.; Faust, N.

1981-01-01

Digital data from LANDSAT for each 1.1-acre cell in Georgia were processed and the land cover conditions were categorized. Several test cases were completed and an operational hardware and software processing capability was established at the Georgia Institute of Technology. The operational capability was developed to process the entire state (60,000 sq. miles and 14 LANDSAT scenes) in a cooperative project between eleven divisions and agencies at the regional, state, and federal levels. Products were developed for State agencies such as in both mapped and statistical formats. A computerized geographical data base was developed for management programs. To a large extent the applications of the data base evolved as users of LANDSAT information requested that other data (i.e., soils, slope, land use, etc.) be made compatible with LANDSAT for management programs. To date, geographic data bases incorporating LANDSAT and other spatial data deal with elements of the municipal solid waste management program, and reservoir management for the Corps of Engineers. LANDSAT data are also being used for applications in wetland, wildlife, and forestry management.

The users, uses, and value of Landsat and other moderate-resolution satellite imagery in the United States-Executive report

Science.gov (United States)

Miller, Holly M.; Sexton, Natalie R.; Koontz, Lynne; Loomis, John; Koontz, Stephen R.; Hermans, Caroline

2011-01-01

Moderate-resolution imagery (MRI), such as that provided by the Landsat satellites, provides unique spatial information for use by many people both within and outside of the United States (U.S.). However, exactly who these users are, how they use the imagery, and the value and benefits derived from the information are, to a large extent, unknown. To explore these issues, social scientists at the USGS Fort Collins Science Center conducted a study of U.S.-based MRI users from 2008 through 2010 in two parts: 1) a user identification and 2) a user survey. The objectives for this study were to: 1) identify and classify U.S.-based users of this imagery; 2) better understand how and why MRI, and specifically Landsat, is being used; and 3) qualitatively and quantitatively measure the value and societal benefits of MRI (focusing on Landsat specifically). The results of the survey revealed that respondents from multiple sectors use Landsat imagery in many different ways, as demonstrated by the breadth of project locations and scales, as well as application areas. The value of Landsat imagery to these users was demonstrated by the high importance placed on the imagery, the numerous benefits received from projects using Landsat imagery, the negative impacts if Landsat imagery was no longer available, and the substantial willingness to pay for replacement imagery in the event of a data gap. The survey collected information from users who are both part of and apart from the known user community. The diversity of the sample delivered results that provide a baseline of knowledge about the users, uses, and value of Landsat imagery. While the results supply a wealth of information on their own, they can also be built upon through further research to generate a more complete picture of the population of Landsat users as a whole.

Automated mapping of persistent ice and snow cover across the western U.S. with Landsat

Science.gov (United States)

Selkowitz, David J.; Forster, Richard R.

2016-01-01

We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s–1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010–2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987–1988 and 2008–2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25–50 years.

Perspective View with Landsat Overlay, Costa Rica

Science.gov (United States)

2002-01-01

This perspective view shows the Caribbean coastal plain of Costa Rica, with the Cordillera Central rising in the background and the Pacific Ocean in the distance. The prominent river in the center of the image is the Rio Sucio, which merges with the Rio Sarapiqui at the bottom of the image and eventually joins with Rio San Juan on the Nicaragua border.Like much of Central America, Costa Rica is generally cloud covered so very little satellite imagery is available. The ability of the Shuttle Radar Topography Mission (SRTM) instrument to penetrate clouds and make three-dimensional measurements will allow generation of the first complete high-resolution topographic map of the entire region. These data were used to generate the image.This three-dimensional perspective view was generated using elevation data from SRTM and an enhanced false-color Landsat 7 satellite image. Colors are from Landsat bands 5, 4, and 2 as red, green and blue, respectively. Topographic expression is exaggerated two times.Landsat has been providing visible and infrared views of the Earth since 1972. SRTM elevation data matches the 30-meter resolution of most Landsat images and will substantially help in analyses of the large and growing Landsat image archive. The Landsat 7 Thematic Mapper image used here was provided to the SRTM by the United States Geological Survey, Earth Resources Observation Systems (EROS) Data Center, Sioux Falls, S.D.Elevation data used in this image was acquired by the SRTM aboard the Space Shuttle Endeavour, launched on February 11, 2000. SRTM used the same radar instrument that comprised the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) that flew twice on the Space Shuttle Endeavour in 1994. SRTM was designed to collect three-dimensional measurements of the Earth's surface. To collect the 3-D data, engineers added a 60-meter-long (200-foot) mast, installed additional C-band and X-band antennas, and improved tracking and navigation devices

Assessment of Forest Degradation in Vietnam Using Landsat Time Series Data

Directory of Open Access Journals (Sweden)

James E. Vogelmann

2017-07-01

Full Text Available Landsat time series data were used to characterize forest degradation in Lam Dong Province, Vietnam. We conducted three types of image change analyses using Landsat time series data to characterize the land cover changes. Our analyses concentrated on the timeframe of 1973–2014, with much emphasis on the latter part of that range. We conducted a field trip through Lam Dong Province to develop a better understanding of the ground conditions of the region, during which we obtained many photographs of representative forest sites with Global Positioning System locations to assist us in our image interpretations. High-resolution Google Earth imagery and Landsat data of the region were used to validate results. In general, our analyses indicated that many land-use changes have occurred throughout Lam Dong Province, including gradual forest to non-forest transitions. Recent changes are most marked along the relatively narrow interfaces between agricultural and forest areas that occur towards the boundaries of the province. One important observation is that the most highly protected national reserves in the region have not changed much over the entire Landsat timeframe (1972–present. Spectral changes within these regions have not occurred at the same levels as those areas adjacent to the reserves.

Use of Landsat data to predict the trophic state of Minnesota lakes

Science.gov (United States)

Lillesand, T. M.; Johnson, W. L.; Deuell, R. L.; Lindstrom, O. M.; Meisner, D. E.

1983-01-01

Near-concurrent Landsat Multispectral Scanner (MSS) and ground data were obtained for 60 lakes distributed in two Landsat scene areas. The ground data included measurement of secchi disk depth, chlorophyll-a, total phosphorous, turbidity, color, and total nitrogen, as well as Carlson Trophic State Index (TSI) values derived from the first three parameters. The Landsat data best correlated with the TSI values. Prediction models were developed to classify some 100 'test' lakes appearing in the two analysis scenes on the basis of TSI estimates. Clouds, wind, poor image data, small lake size, and shallow lake depth caused some problems in lake TSI prediction. Overall, however, the Landsat-predicted TSI estimates were judged to be very reliable for the secchi-derived TSI estimation, moderately reliable for prediction of the chlorophyll-a TSI, and unreliable for the phosphorous value. Numerous Landsat data extraction procedures were compared, and the success of the Landsat TSI prediction models was a strong function of the procedure employed.

LANDSAT 8 MULTISPECTRAL AND PANSHARPENED IMAGERY PROCESSING ON THE STUDY OF CIVIL ENGINEERING ISSUES

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M. A. Lazaridou

2016-06-01

Full Text Available Scientific and professional interests of civil engineering mainly include structures, hydraulics, geotechnical engineering, environment, and transportation issues. Topics included in the context of the above may concern urban environment issues, urban planning, hydrological modelling, study of hazards and road construction. Land cover information contributes significantly on the study of the above subjects. Land cover information can be acquired effectively by visual image interpretation of satellite imagery or after applying enhancement routines and also by imagery classification. The Landsat Data Continuity Mission (LDCM – Landsat 8 is the latest satellite in Landsat series, launched in February 2013. Landsat 8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12bits, the capability of merging the high resolution panchromatic band of 15 meters with multispectral imagery of 30 meters as well as the policy of free data. In this paper, Landsat 8 multispectral and panchromatic imageries are being used, concerning surroundings of a lake in north-western Greece. Land cover information is extracted, using suitable digital image processing software. The rich spectral context of the multispectral image is combined with the high spatial resolution of the panchromatic image, applying image fusion – pansharpening, facilitating in this way visual image interpretation to delineate land cover. Further processing concerns supervised image classification. The classification of pansharpened image preceded multispectral image classification. Corresponding comparative considerations are also presented.

Landsat 8 Multispectral and Pansharpened Imagery Processing on the Study of Civil Engineering Issues

Science.gov (United States)

Lazaridou, M. A.; Karagianni, A. Ch.

2016-06-01

Scientific and professional interests of civil engineering mainly include structures, hydraulics, geotechnical engineering, environment, and transportation issues. Topics included in the context of the above may concern urban environment issues, urban planning, hydrological modelling, study of hazards and road construction. Land cover information contributes significantly on the study of the above subjects. Land cover information can be acquired effectively by visual image interpretation of satellite imagery or after applying enhancement routines and also by imagery classification. The Landsat Data Continuity Mission (LDCM - Landsat 8) is the latest satellite in Landsat series, launched in February 2013. Landsat 8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12bits, the capability of merging the high resolution panchromatic band of 15 meters with multispectral imagery of 30 meters as well as the policy of free data. In this paper, Landsat 8 multispectral and panchromatic imageries are being used, concerning surroundings of a lake in north-western Greece. Land cover information is extracted, using suitable digital image processing software. The rich spectral context of the multispectral image is combined with the high spatial resolution of the panchromatic image, applying image fusion - pansharpening, facilitating in this way visual image interpretation to delineate land cover. Further processing concerns supervised image classification. The classification of pansharpened image preceded multispectral image classification. Corresponding comparative considerations are also presented.

Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity

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Quintano, C.; Fernández-Manso, A.; Fernández-Manso, O.

2018-02-01

Nowadays Earth observation satellites, in particular Landsat, provide a valuable help to forest managers in post-fire operations; being the base of post-fire damage maps that enable to analyze fire impacts and to develop vegetation recovery plans. Sentinel-2A MultiSpectral Instrument (MSI) records data in similar spectral wavelengths that Landsat 8 Operational Land Imager (OLI), and has higher spatial and temporal resolutions. This work compares two types of satellite-based maps for evaluating fire damage in a large wildfire (around 8000 ha) located in Sierra de Gata (central-western Spain) on 6-11 August 2015. 1) burn severity maps based exclusively on Landsat data; specifically, on differenced Normalized Burn Ratio (dNBR) and on its relative versions (Relative dNBR, RdNBR, and Relativized Burn Ratio, RBR) and 2) burn severity maps based on the same indexes but combining pre-fire data from Landsat 8 OLI with post-fire data from Sentinel-2A MSI data. Combination of both Landsat and Sentinel-2 data might reduce the time elapsed since forest fire to the availability of an initial fire damage map. Interpretation of ortho-photograph Pléiades 1 B data (1:10,000) provided us the ground reference data to measure the accuracy of both burn severity maps. Results showed that Landsat based burn severity maps presented an adequate assessment of the damage grade (κ statistic = 0.80) and its spatial distribution in wildfire emergency response. Further using both Landsat and Sentinel-2 MSI data the accuracy of burn severity maps, though slightly lower (κ statistic = 0.70) showed an adequate level for be used by forest managers.

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

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Yaolin Liu

2017-10-01

Full Text Available Coastline extraction is a fundamental work for coastal resource management, coastal environmental protection and coastal sustainable development. Due to the free access and long-term record, Landsat series images have the potential to be used for coastline extraction. However, dynamic features of different types of coastlines (e.g., rocky, sandy, artificial, caused by sea level fluctuation from tidal, storm and reclamation, make it difficult to be accurately extracted with coarse spatial resolution, e.g., 30 m, of Landsat images. To access this problem, we analyze the performance of coastline extraction by integrating downscaling, pansharpening and water index approaches in increasing the accuracy of coastline extraction from the latest Landsat-8 Operational Land Imager (OLI imagery. In order to prove the availability of the proposed method, we designed three strategies: (1 Strategy 1 uses the traditional water index method to extract coastline directly from original 30 m Landsat-8 OLI multispectral (MS image; (2 Strategy 2 extracts coastlines from 15 m fused MS images generated by integrating 15 m panchromatic (PAN band and 30 m MS image with ten pansharpening algorithms; (3 Strategy 3 first downscales the PAN band to a finer spatial resolution (e.g., 7.5 m band, and then extracts coastlines from pansharpened MS images generated by integrating downscaled spatial resolution PAN band and 30 m MS image with ten pansharpening algorithms. Using the coastline extracted from ZiYuan-3 (ZY-3 5.8 m MS image as reference, accuracies of coastlines extracted from MS images in three strategies were validated visually and quantitatively. The results show that, compared with coastline extracted directly from 30 m Landsat-8 MS image (strategy 1, strategy 3 achieves the best accuracies with optimal mean net shoreline movement (MNSM of −2.54 m and optimal mean absolute difference (MAD of 11.26 m, followed by coastlines extracted in strategy 2 with optimal MNSM

Assessing the role of climate and resource management on groundwater dependent ecosystem changes in arid environments with the Landsat archive

Science.gov (United States)

Huntington, Justin; McGwire, Kenneth C.; Morton, Charles; Snyder, Keirith A.; Peterson, Sarah; Erickson, Tyler; Niswonger, Richard G.; Carroll, Rosemary W.H.; Smith, Guy; Allen, Richard

2016-01-01

along with downscaled North American Land Data Assimilation System gridded meteorological data, which are used for both atmospheric correction and correlation analysis. Results from the cross-sensor comparison indicate a benefit from the application of a consistent atmospheric correction method, and that NDVI derived from Landsat 7 and 8 are very similar within the study area. Results from continuous Landsat time series analysis clearly illustrate that there are strong correlations between changes in vegetation vigor, precipitation, evaporative demand, depth to groundwater, and riparian restoration. Trends in summer NDVI associated with riparian restoration and groundwater level changes were found to be statistically significant, and interannual summer NDVI was found to be moderately correlated to interannual water-year precipitation for baseline study sites. Results clearly highlight the complementary relationship between water-year PPT, NDVI, and evaporative demand, and are consistent with regional vegetation index and complementary relationship studies. This work is supporting land and water managers for evaluation of GDEs with respect to climate, groundwater, and resource management.

Remote Sensing of Residue Management in Farms using Landsat 8 Sensor Imagery

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M. A Rostami

2017-10-01

study were NDVI, BAI, NBR and NBRT. Classification accuracy was evaluated and expressed by confusion matrix and Kappa coefficient. Natural surfaces are rarely composed of a single uniform material. Spectral mixing occurs when materials with different spectral properties are represented by a single image pixel. The condition where scale of the mixing is large (macroscopic, mixing would occur in a linear fashion. However for microscopic situations, the mixing is generally nonlinear. The linear model ahich wasadopted in this study, assumes that there is no interaction between materials. Assumption of LSUA is that each pixel on the surface is a physical mixture of several constituents weighted by surface abundance, and the spectrum of the mixture is a linear combination of the endmember reflectance spectra. Within the context of this study, LSUA is a classification method that can determine contribution of each material (or endmember such as soil or residue for each image pixel. Results and Discussion The spectral response curve extracted from Landsat 8 image used as input into the LSUA model in ENVI software. As expected, crop burned residue (Ash spectra had lower reflectance when compared to the soil, residue and green plant spectra. The contrast between residue, green plant, soil and residue ash spectra was particularly evident in the NIR and SWIR bands. It is suggested that these bands are essential for residue discrimination. Differences of reflectance in the visible bands were minimal, providing little discrimination between residue, green plant, soil and residue ash. Burned area estimated by LSUA method from Landsat 8 image was correlated against the ground data (measured coincident to the ground data. The overall accuracy of classification with BAI index and LSUA method was 91.7 and 88.3 and Kappa coefficient was 0.89 and 0.84 respectively. Results indicated that burned field area can be located and its area can be estimated using Landsat 8 images. The Index BAI was

Updating stand-level forest inventories using airborne laser scanning and Landsat time series data

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Bolton, Douglas K.; White, Joanne C.; Wulder, Michael A.; Coops, Nicholas C.; Hermosilla, Txomin; Yuan, Xiaoping

2018-04-01

Vertical forest structure can be mapped over large areas by combining samples of airborne laser scanning (ALS) data with wall-to-wall spatial data, such as Landsat imagery. Here, we use samples of ALS data and Landsat time-series metrics to produce estimates of top height, basal area, and net stem volume for two timber supply areas near Kamloops, British Columbia, Canada, using an imputation approach. Both single-year and time series metrics were calculated from annual, gap-free Landsat reflectance composites representing 1984-2014. Metrics included long-term means of vegetation indices, as well as measures of the variance and slope of the indices through time. Terrain metrics, generated from a 30 m digital elevation model, were also included as predictors. We found that imputation models improved with the inclusion of Landsat time series metrics when compared to single-year Landsat metrics (relative RMSE decreased from 22.8% to 16.5% for top height, from 32.1% to 23.3% for basal area, and from 45.6% to 34.1% for net stem volume). Landsat metrics that characterized 30-years of stand history resulted in more accurate models (for all three structural attributes) than Landsat metrics that characterized only the most recent 10 or 20 years of stand history. To test model transferability, we compared imputed attributes against ALS-based estimates in nearby forest blocks (>150,000 ha) that were not included in model training or testing. Landsat-imputed attributes correlated strongly to ALS-based estimates in these blocks (R2 = 0.62 and relative RMSE = 13.1% for top height, R2 = 0.75 and relative RMSE = 17.8% for basal area, and R2 = 0.67 and relative RMSE = 26.5% for net stem volume), indicating model transferability. These findings suggest that in areas containing spatially-limited ALS data acquisitions, imputation models, and Landsat time series and terrain metrics can be effectively used to produce wall-to-wall estimates of key inventory attributes, providing an

Validation of the USGS Landsat Burned Area Essential Climate Variable (BAECV) across the conterminous United States

Science.gov (United States)

Vanderhoof, Melanie; Fairaux, Nicole; Beal, Yen-Ju G.; Hawbaker, Todd J.

2017-01-01

agricultural lands of the Great Plains in central CONUS (62% and 57%, respectively). The BAECV product detected most (> 65%) fire events > 10 ha across the western CONUS (Arid and Mountain West ecoregions). Our approach and results demonstrate that a thorough validation of Landsat science products can be completed with independent Landsat-derived reference data, but could be strengthened by the use of complementary sources of high-resolution data.

Assessment of The trophic state and Chlorophyll-a concentrations using Landsat OLI in Karaoun reservoir, Lebanon

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Ali Fadel

2016-12-01

Full Text Available Fadel, A., Faour G. and Slim K. 2016. Assessment of the trophic state and chlorophyll-a concentrations using Landsat OLI in Karaoun reservoir, Lebanon. Lebanese Science Journal, 17(2: 130-145. Harmful algal blooms have become a worldwide environmental problem. A regular and cost-effective monitoring of these blooms is highly needed by lakes managers. Satellite remote sensing imagery like Landsat Operational Land Imager (OLI can be used to assess and monitor chlorophyll-a in water bodies over large areas in a cost-effective way. In this study, the accuracy of Landsat OLI to estimate chlorophyll-a was examined. Four field campaigns and cloud free images of Landsat OLI with 30 m resolution (01 May 2013, 21 August 2013, 10 July 2015, and 11 August 2015 were used in this study to determine the accuracy of Landsat OLI in estimating chlorophyll-a in a 12 km2 freshwater body, Karaoun reservoir. After atmospheric correction of these images, reflectance of single and multiple band combinations were compared to field chlorophyll-a data. Results of field campaigns showed that the trophic state of Karaoun reservoir is still eutrophic to hypereutrophic with high nutrient concentration and low phytoplankton biodiversity, dominated by cyanobacteria species, Microcystis aeruginosa and Aphanizomenon ovalisporum. On single band level, the in situ chlorophyll-a measurement correlated best with band 5 (0.85 - 0.88 µm, with R=0.75 and R2=0.57. Highest correlation (R=0.84 and R2=0.72 was obtained using band combination, B2:B4 band ratio multiplied by B5. Results indicated that Landsat OLI can be used effectively to determine chlorophyll-a concentration in lakes and reservoirs. We recommend the application of Landsat OLI as a satisfactory and cost effective method for monitoring chlorophyll-a in other lakes through-out the world

Valuing geospatial information: Using the contingent valuation method to estimate the economic benefits of Landsat satellite imagery

Science.gov (United States)

Loomis, John; Koontz, Steve; Miller, Holly M.; Richardson, Leslie A.

2015-01-01

While the U.S. government does not charge for downloading Landsat images, the images have value to users. This paper demonstrates a method that can value Landsat and other imagery to users. A survey of downloaders of Landsat images found: (a) established US users have a mean value of $912 USD per scene; (b) new US users and users returning when imagery became free have a mean value of $367 USD per scene. Total US user benefits for the 2.38 million scenes downloaded is $1.8 billion USD. While these benefits indicate a high willingness-to-pay among many Landsat downloaders, it would be economically inefficient for the US government to charge for Landsat imagery. Charging a price of $100 USD a scene would result in an efficiency loss of $37.5 million a year. This economic information should be useful to policy-makers who must decide about the future of this and similar remote sensing programs.

Landsat Data Continuity Mission (LDCM) - Optimizing X-Band Usage

Science.gov (United States)

Garon, H. M.; Gal-Edd, J. S.; Dearth, K. W.; Sank, V. I.

2010-01-01

The NASA version of the low-density parity check (LDPC) 7/8-rate code, shortened to the dimensions of (8160, 7136), has been implemented as the forward error correction (FEC) schema for the Landsat Data Continuity Mission (LDCM). This is the first flight application of this code. In order to place a 440 Msps link within the 375 MHz wide X band we found it necessary to heavily bandpass filter the satellite transmitter output . Despite the significant amplitude and phase distortions that accompanied the spectral truncation, the mission required BER is maintained at LDPC code and the amplitude and phase compensation provided in the receiver. Similar results were obtained with receivers from several vendors.

Burned area detection based on Landsat time series in savannas of southern Burkina Faso

Science.gov (United States)

Liu, Jinxiu; Heiskanen, Janne; Maeda, Eduardo Eiji; Pellikka, Petri K. E.

2018-02-01

West African savannas are subject to regular fires, which have impacts on vegetation structure, biodiversity and carbon balance. An efficient and accurate mapping of burned area associated with seasonal fires can greatly benefit decision making in land management. Since coarse resolution burned area products cannot meet the accuracy needed for fire management and climate modelling at local scales, the medium resolution Landsat data is a promising alternative for local scale studies. In this study, we developed an algorithm for continuous monitoring of annual burned areas using Landsat time series. The algorithm is based on burned pixel detection using harmonic model fitting with Landsat time series and breakpoint identification in the time series data. This approach was tested in a savanna area in southern Burkina Faso using 281 images acquired between October 2000 and April 2016. An overall accuracy of 79.2% was obtained with balanced omission and commission errors. This represents a significant improvement in comparison with MODIS burned area product (67.6%), which had more omission errors than commission errors, indicating underestimation of the total burned area. By observing the spatial distribution of burned areas, we found that the Landsat based method misclassified cropland and cloud shadows as burned areas due to the similar spectral response, and MODIS burned area product omitted small and fragmented burned areas. The proposed algorithm is flexible and robust against decreased data availability caused by clouds and Landsat 7 missing lines, therefore having a high potential for being applied in other landscapes in future studies.

Fire monitoring capability of the joint Landsat and Sentinel 2 constellation

Science.gov (United States)

Murphy, S.; Wright, R.

2017-12-01

Fires are a global hazard. Landsat and Sentinel 2 can monitor the Earth's surface every 2 - 4 days. This provides an important opportunity to complement the operational (lower resolution) fire monitoring systems. Landsat-class sensors can detect small fires that would be missed by MODIS-classed sensors. All large fires start out as small fires. We analyze fire patterns in California from 1984 to 2017 and compare the performance of Landsat-type and MODIS-type sensors. Had an operational Landsat-Sentinel 2 fire detection system been in place at the time of the Soberanes fire last year (i.e. August 2016), the cost of suppressing of this fire event (US $236 million) could potentially have been reduced by an order of magnitude.

Cloud detection algorithm comparison and validation for operational Landsat data products

Science.gov (United States)

Foga, Steven Curtis; Scaramuzza, Pat; Guo, Song; Zhu, Zhe; Dilley, Ronald; Beckmann, Tim; Schmidt, Gail L.; Dwyer, John L.; Hughes, MJ; Laue, Brady

2017-01-01

Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate

Geothermal Anomaly Mapping Using Landsat ETM+ Data in Ilan Plain, Northeastern Taiwan

Science.gov (United States)

Chan, Hai-Po; Chang, Chung-Pai; Dao, Phuong D.

2018-01-01

Geothermal energy is an increasingly important component of green energy in the globe. A prerequisite for geothermal energy development is to acquire the local and regional geothermal prospects. Existing geophysical methods of estimating the geothermal potential are usually limited to the scope of prospecting because of the operation cost and site reachability in the field. Thus, explorations in a large-scale area such as the surface temperature and the thermal anomaly primarily rely on satellite thermal infrared imagery. This study aims to apply and integrate thermal infrared (TIR) remote sensing technology with existing geophysical methods for the geothermal exploration in Taiwan. Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) imagery is used to retrieve the land surface temperature (LST) in Ilan plain. Accuracy assessment of satellite-derived LST is conducted by comparing with the air temperature data from 11 permanent meteorological stations. The correlation coefficient of linear regression between air temperature and LST retrieval is 0.76. The MODIS LST product is used for the cross validation of Landsat derived LSTs. Furthermore, Landsat ETM+ multi-temporal brightness temperature imagery for the verification of the LST anomaly results were performed. LST Results indicate that thermal anomaly areas appear correlating with the development of faulted structure. Selected geothermal anomaly areas are validated in detail by field investigation of hot springs and geothermal drillings. It implies that occurrences of hot springs and geothermal drillings are in good spatial agreement with anomaly areas. In addition, the significant low-resistivity zones observed in the resistivity sections are echoed with the LST profiles when compared with in the Chingshui geothermal field. Despite limited to detecting the surficial and the shallow buried geothermal resources, this work suggests that TIR remote sensing is a valuable tool by providing an effective way of mapping

Landsat Remote Sensing Data as an Alternative Approach for ...

African Journals Online (AJOL)

Rungwe Volcanic Province (RVP) is mostly covered by extrusive rocks that overlain the Precambrian basement. The use of Landsat data in this area has revealed the need of effective use of these data in geological mapping programs in Tanzania. Landsat band ratios 5/1, 3/7, 5/7 and 5/4 as well as R: G: B composite ...

The value of earth observations: methods and findings on the value of Landsat imagery

Science.gov (United States)

Miller, Holly M.; Serbina, Larisa O.; Richardson, Leslie A.; Ryker, Sarah J.; Newman, Timothy R.

2016-01-01

Data from Earth observation systems are used extensively in managing and monitoring natural resources, natural hazards, and the impacts of climate change, but the value of such data can be difficult to estimate, particularly when it is available at no cost. Assessing the socioeconomic and scientific value of these data provides a better understanding of the existing and emerging research, science, and applications related to this information and contributes to the decision making process regarding current and future Earth observation systems. Recent USGS research on Landsat data has advanced the literature in this area by using a variety of methods to estimate value. The results of a 2012 survey of Landsat users, a 2013 requirements assessment, and 2013 case studies of applications of Landsat imagery are discussed.

Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series

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Valerie J. Pasquarella

2017-07-01

Full Text Available Introduced insects and pathogens impact millions of acres of forested land in the United States each year, and large-scale monitoring efforts are essential for tracking the spread of outbreaks and quantifying the extent of damage. However, monitoring the impacts of defoliating insects presents a significant challenge due to the ephemeral nature of defoliation events. Using the 2016 gypsy moth (Lymantria dispar outbreak in Southern New England as a case study, we present a new approach for near-real-time defoliation monitoring using synthetic images produced from Landsat time series. By comparing predicted and observed images, we assessed changes in vegetation condition multiple times over the course of an outbreak. Initial measures can be made as imagery becomes available, and season-integrated products provide a wall-to-wall assessment of potential defoliation at 30 m resolution. Qualitative and quantitative comparisons suggest our Landsat Time Series (LTS products improve identification of defoliation events relative to existing products and provide a repeatable metric of change in condition. Our synthetic-image approach is an important step toward using the full temporal potential of the Landsat archive for operational monitoring of forest health over large extents, and provides an important new tool for understanding spatial and temporal dynamics of insect defoliators.

Evaluation of solar angle variation over digital processing of LANDSAT imagery. [Brazil

Science.gov (United States)

Parada, N. D. J. (Principal Investigator); Novo, E. M. L. M.

1984-01-01

The effects of the seasonal variation of illumination over digital processing of LANDSAT images are evaluated. Original images are transformed by means of digital filtering to enhance their spatial features. The resulting images are used to obtain an unsupervised classification of relief units. After defining relief classes, which are supposed to be spectrally different, topographic variables (declivity, altitude, relief range and slope length) are used to identify the true relief units existing on the ground. The samples are also clustered by means of an unsupervised classification option. The results obtained for each LANDSAT overpass are compared. Digital processing is highly affected by illumination geometry. There is no correspondence between relief units as defined by spectral features and those resulting from topographic features.

Mapping fractional woody cover in semi-arid savannahs using multi-seasonal composites from Landsat data

Science.gov (United States)

Higginbottom, Thomas P.; Symeonakis, Elias; Meyer, Hanna; van der Linden, Sebastian

2018-05-01

Increasing attention is being directed at mapping the fractional woody cover of savannahs using Earth-observation data. In this study, we test the utility of Landsat TM/ ETM-based spectral-temporal variability metrics for mapping regional-scale woody cover in the Limpopo Province of South Africa, for 2010. We employ a machine learning framework to compare the accuracies of Random Forest models derived using metrics calculated from different seasons. We compare these results to those from fused Landsat-PALSAR data to establish if seasonal metrics can compensate for structural information from the PALSAR signal. Furthermore, we test the applicability of a statistical variable selection method, the recursive feature elimination (RFE), in the automation of the model building process in order to reduce model complexity and processing time. All of our tests were repeated at four scales (30, 60, 90, and 120 m-pixels) to investigate the role of spatial resolution on modelled accuracies. Our results show that multi-seasonal composites combining imagery from both the dry and wet seasons produced the highest accuracies (R2 = 0.77, RMSE = 9.4, at the 120 m scale). When using a single season of observations, dry season imagery performed best (R2 = 0.74, RMSE = 9.9, at the 120 m resolution). Combining Landsat and radar imagery was only marginally beneficial, offering a mean relative improvement of 1% in accuracy at the 120 m scale. However, this improvement was concentrated in areas with lower densities of woody coverage (continue to exploit the Landsat archive, but should aim to use multi-seasonal derived information. When the coarser 120 m pixel scale is adequate, integration of Landsat and SAR data should be considered, especially in areas with lower woody cover densities. The use of multiple seasonal compositing periods offers promise for large-area mapping of savannahs, even in regions with a limited historical Landsat coverage.

Temporal validation for landsat-based volume estimation model

Science.gov (United States)

Renaldo J. Arroyo; Emily B. Schultz; Thomas G. Matney; David L. Evans; Zhaofei Fan

2015-01-01

Satellite imagery can potentially reduce the costs and time associated with ground-based forest inventories; however, for satellite imagery to provide reliable forest inventory data, it must produce consistent results from one time period to the next. The objective of this study was to temporally validate a Landsat-based volume estimation model in a four county study...

A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data

KAUST Repository

Houborg, Rasmus; McCabe, Matthew

2018-01-01

using a multi-scale target sampling scheme that draws Landsat 8 reference data from a series of scenes by using MODIS-consistent surface reflectance time series to quantify relative changes in Landsat-scale reflectances over given Landsat

Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat

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National Aeronautics and Space Administration — The Global Human Built-up And Settlement Extent (HBASE) Dataset from Landsat is a global map of HBASE derived from the Global Land Survey (GLS) Landsat dataset for...

LANDSAT-1 data, its use in a soil survey program

Science.gov (United States)

Westin, F. C.; Frazee, C. J.

1975-01-01

The following applications of LANDSAT imagery were investigated: assistance in recognizing soil survey boundaries, low intensity soil surveys, and preparation of a base map for publishing thematic soils maps. The following characteristics of LANDSAT imagery were tested as they apply to the recognition of soil boundaries in South Dakota and western Minnesota: synoptic views due to the large areas covered, near-orthography and lack of distortion, flexibility of selecting the proper season, data recording in four parts of the spectrum, and the use of computer compatible tapes. A low intensity soil survey of Pennington County, South Dakota was completed in 1974. Low intensity inexpensive soil surveys can provide the data needed to evaluate agricultural land for the remaining counties until detailed soil surveys are completed. In using LANDSAT imagery as a base map for publishing thematic soil maps, the first step was to prepare a mosaic with 20 LANDSAT scenes from several late spring passes in 1973.

Assessment of the Trophic State and Chlorophyll-A Concentrations using Landsat OLI in Karaoun Reservoir Lebanon

International Nuclear Information System (INIS)

Fadel, A.; Faour, GH.; Slim, K.

2016-01-01

Harmful algal blooms have become a worldwide environmental problem. A regular and cost -effective monitoring of these blooms is highly needed by lakes managers. Satellite remote sensing imagery like Landsat Operational Land Imager (OLI) can be used to assess and monitor chlorophyll-a in water bodies over large areas in a cost-effective way. In this study, the accuracy of Landsat OLI to estimate chlorophyll-a was examined. Four field campaigns and cloud free images of Landsat OLI with 30 m resolution (01 May 2013, 21 August 2013, 10 July 2015, and 11 August 2015) were used in this study to determine the accuracy of Landsat OLI in estimating chlorophyll-a in a 12 km2 fresh water body, Karaoun reservoir. After atmospheric correction of these images, reflectance of single and multiple band combinations were compared to field chlorophyll-a data. Results of field campaigns showed that the trophic state of Karaoun reservoir is still eutrophic to hypereutrophic withhigh nutrient concentration andlow phytoplankton biodiversity, dominatedby cyanobacteria species, Microcystis aeruginosa and Aphanizomenon ovalisporum. On single band level, the n situ chlorophyll-a measurement correlated best with band 5 (0.85 -0.88 μm), with R=0.75 and R2=0.57.Highest correlation (R=0.84 and R2=0.72) was obtained using band combination, B2:B4 band ratio multiplied by B5. Results indicated that Landsat OLI can be used effectively to determine chlorophyll-a concentration in lakes and reservoirs. We recommend the application of Landsat OLI as a satisfactory and cost effective method for monitoring chlorophyll-a in other lakes through-out the world. (author)

Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches

Directory of Open Access Journals (Sweden)

Yinghai Ke

2016-03-01

Full Text Available This study presented a MODIS 8-day 1 km evapotranspiration (ET downscaling method based on Landsat 8 data (30 m and machine learning approaches. Eleven indicators including albedo, land surface temperature (LST, and vegetation indices (VIs derived from Landsat 8 data were first upscaled to 1 km resolution. Machine learning algorithms including Support Vector Regression (SVR, Cubist, and Random Forest (RF were used to model the relationship between the Landsat indicators and MODIS 8-day 1 km ET. The models were then used to predict 30 m ET based on Landsat 8 indicators. A total of thirty-two pairs of Landsat 8 images/MODIS ET data were evaluated at four study sites including two in United States and two in South Korea. Among the three models, RF produced the lowest error, with relative Root Mean Square Error (rRMSE less than 20%. Vegetation greenness related indicators such as Normalized Difference Vegetation Index (NDVI, Enhanced Vegetation Index (EVI, Soil Adjusted Vegetation Index (SAVI, and vegetation moisture related indicators such as Normalized Difference Infrared Index—Landsat 8 OLI band 7 (NDIIb7 and Normalized Difference Water Index (NDWI were the five most important features used in RF model. Temperature-based indicators were less important than vegetation greenness and moisture-related indicators because LST could have considerable variation during each 8-day period. The predicted Landsat downscaled ET had good overall agreement with MODIS ET (average rRMSE = 22% and showed a similar temporal trend as MODIS ET. Compared to the MODIS ET product, the downscaled product demonstrated more spatial details, and had better agreement with in situ ET observations (R2 = 0.56. However, we found that the accuracy of MODIS ET was the main control factor of the accuracy of the downscaled product. Improved coarse-resolution ET estimation would result in better finer-resolution estimation. This study proved the potential of using machine learning

a Landsat Time-Series Stacks Model for Detection of Cropland Change

Science.gov (United States)

Chen, J.; Chen, J.; Zhang, J.

2017-09-01

Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

2017 Landsat Science Team Summer Meeting Summary

Science.gov (United States)

Crawford, Christopher J.; Loveland, Thomas R.; Wulder, Michael A.; Irons, James R.

2018-01-01

The summer meeting of the U.S. Geological Survey (USGS)-NASA Landsat Science Team (LST) was held June 11-13, 2017, at the USGS’s Earth Resources Observation and Science (EROS) Center near Sioux Falls, SD. This was the final meeting of the Second (2012-2017) LST.1 Frank Kelly [EROS—Center Director] welcomed the attendees and expressed his thanks to the LST members for their contributions. He then introduced video-recorded messages from South Dakota’s U.S. senators, John Thune and Mike Rounds, in which they acknowledged the efforts of the team in advancing the societal impacts of the Landsat Program.

Principles of computer processing of Landsat data for geologic applications

Science.gov (United States)

Taranik, James V.

1978-01-01

The main objectives of computer processing of Landsat data for geologic applications are to improve display of image data to the analyst or to facilitate evaluation of the multispectral characteristics of the data. Interpretations of the data are made from enhanced and classified data by an analyst trained in geology. Image enhancements involve adjustments of brightness values for individual picture elements. Image classification involves determination of the brightness values of picture elements for a particular cover type. Histograms are used to display the range and frequency of occurrence of brightness values. Landsat-1 and -2 data are preprocessed at Goddard Space Flight Center (GSFC) to adjust for the detector response of the multispectral scanner (MSS). Adjustments are applied to minimize the effects of striping, adjust for bad-data lines and line segments and lost individual pixel data. Because illumination conditions and landscape characteristics vary considerably and detector response changes with time, the radiometric adjustments applied at GSFC are seldom perfect and some detector striping remain in Landsat data. Rotation of the Earth under the satellite and movements of the satellite platform introduce geometric distortions in the data that must also be compensated for if image data are to be correctly displayed to the data analyst. Adjustments to Landsat data are made to compensate for variable solar illumination and for atmospheric effects. GeoMetric registration of Landsat data involves determination of the spatial location of a pixel in. the output image and the determination of a new value for the pixel. The general objective of image enhancement is to optimize display of the data to the analyst. Contrast enhancements are employed to expand the range of brightness values in Landsat data so that the data can be efficiently recorded in a manner desired by the analyst. Spatial frequency enhancements are designed to enhance boundaries between features

DEVELOPMENT OF TIME-SERIES HUMAN SETTLEMENT MAPPING SYSTEM USING HISTORICAL LANDSAT ARCHIVE

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H. Miyazaki

2016-06-01

Full Text Available Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC, was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.

Characterization of active fires in West African savannas by analysis of satellite data: Landsat Thematic Mapper

International Nuclear Information System (INIS)

Brustet, J.M.; Vickos, J.B.; Fontan, J.; Podaire, A.; Lavenu, F.

1991-01-01

Landsat Thematic Mapper provides valuable information on biomass burning, such as the apparent temperature of a fire and its shape. However, the surface determined by remote sensing does not exactly correspond to the burning area, due to an artificial enlargement of the fire front width. This enlargement of the fire front width. This enlargement may have diverse origins. In particular, it is difficult to estimate the temperature of the areas that are behind the fire front and that have been just burned. Emissions from these areas may be detectable by Landsat channels, thus resulting in the observed enlargement of the fire front. Additional experiments including remote sensing by plane are necessary to allow a more complete understanding of these phenomena. Biomass burning is an important source of atmospheric pollution on a global scale. This study indicates that a fire is a significant source of pollution on a local scale

Landsat Imagery-Based Above Ground Biomass Estimation and Change Investigation Related to Human Activities

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Chaofan Wu

2016-02-01

Full Text Available Forest biomass is a significant indicator for substance accumulation and forest succession, and a spatiotemporal biomass map would provide valuable information for forest management and scientific planning. In this study, Landsat imagery and field data cooperated with a random forest regression approach were used to estimate spatiotemporal Above Ground Biomass (AGB in Fuyang County, Zhejiang Province of East China. As a result, the AGB retrieval showed an increasing trend for the past decade, from 74.24 ton/ha in 2004 to 99.63 ton/ha in 2013. Topography and forest management were investigated to find their relationships with the spatial distribution change of biomass. In general, the simulated AGB increases with higher elevation, especially in the range of 80–200 m, wherein AGB acquires the highest increase rate. Moreover, the forest policy of ecological forest has a positive effect on the AGB increase, particularly within the national level ecological forest. The result in this study demonstrates that human activities have a great impact on biomass distribution and change tendency. Furthermore, Landsat image-based biomass estimates would provide illuminating information for forest policy-making and sustainable development.

Transformations of Mangrove Forests in Bahia Magdalena, Baja California Sur, Mexico: Two Decade Results Based on Landsat Imageries

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Suresh Babu, S.; Abdul Rahaman, S.; Muthushankar, G.; Jonathan, M. P.

2014-12-01

Mangrove forests which thrive along the tropical and subtropical regions are the most productive ecosystems in the world with a wide range of ecological and economical services to mankind. With the rapid urbanization across the globe, these forests tend to be destroying at an alarming rate. The area of concern for this study, Bahia Magdalena is very important for the economy of the state as nearly 50% of the artisan fisheries are established in the mangrove zone. Henceforth this study is an attempt for a regional assessment and to accurately quantify the mangroves using LANDSAT imageries for over two decades in Bahia Magdalena, Baja California. Satellite imageries from the year 1986 through 2014 were analysed to assess the prolonged changes taking place in and around the mangrove reserve. Using the estimates of land use/cover for all the years, the spatio - temporal data was validated using ArcGIS software. The results revealed that the spatial extent of mangroves are decreasing until 2005 due to the developmental plans such as tourism, shrimp farming and establishment of industries in this part of the country. During the past 10 years (~ after 2005) there is no much change in the area extent of mangrove reserves due to afforestation and conservation efforts. Thus the unbiased dataset generated may be widely used for an improved understanding of the role of mangrove forests in the socio economic aspects, protection from natural disasters, identify possible areas for conservation, restoration and rehabilitation; and improve estimates of the amount of carbon stored in mangrove vegetation and the associated marine environment. Keywords: Mangroves, LANDSAT, Bahia Magdalena, México.

Geologic mapping using LANDSAT data

Science.gov (United States)

Siegal, B. S.; Abrams, M. J.

1976-01-01

The feasibility of automated classification for lithologic mapping with LANDSAT digital data was evaluated using three classification algorithms. The two supervised algorithms analyzed, a linear discriminant analysis algorithm and a hybrid algorithm which incorporated the Parallelepiped algorithm and the Bayesian maximum likelihood function, were comparable in terms of accuracy; however, classification was only 50 per cent accurate. The linear discriminant analysis algorithm was three times as efficient as the hybrid approach. The unsupervised classification technique, which incorporated the CLUS algorithm, delineated the major lithologic boundaries and, in general, correctly classified the most prominent geologic units. The unsupervised algorithm was not as efficient nor as accurate as the supervised algorithms. Analysis of spectral data for the lithologic units in the 0.4 to 2.5 microns region indicated that a greater separability of the spectral signatures could be obtained using wavelength bands outside the region sensed by LANDSAT.

Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data

Science.gov (United States)

Vogelmann, James; Gallant, Alisa L.; Shi, Hua; Zhu, Zhe

2016-01-01

There are many types of changes occurring over the Earth's landscapes that can be detected and monitored using Landsat data. Here we focus on monitoring “within-state,” gradual changes in vegetation in contrast with traditional monitoring of “abrupt” land-cover conversions. Gradual changes result from a variety of processes, such as vegetation growth and succession, damage from insects and disease, responses to shifts in climate, and other factors. Despite the prevalence of gradual changes across the landscape, they are largely ignored by the remote sensing community. Gradual changes are best characterized and monitored using time-series analysis, and with the successful launch of Landsat 8 we now have appreciable data continuity that extends the Landsat legacy across the previous 43 years. In this study, we conducted three related analyses: (1) comparison of spectral values acquired by Landsats 7 and 8, separated by eight days, to ensure compatibility for time-series evaluation; (2) tracking of multitemporal signatures for different change processes across Landsat 5, 7, and 8 sensors using anniversary-date imagery; and (3) tracking the same type of processes using all available acquisitions. In this investigation, we found that data representing natural vegetation from Landsats 5, 7, and 8 were comparable and did not indicate a need for major modification prior to use for long-term monitoring. Analyses using anniversary-date imagery can be very effective for assessing long term patterns and trends occurring across the landscape, and are especially good for providing insights regarding trends related to long-term and continuous trends of growth or decline. We found that use of all available data provided a much more comprehensive level of understanding of the trends occurring, providing information about rate, duration, and intra- and inter-annual variability that could not be readily gleaned from the anniversary date analyses. We observed that using all

LBA-ECO LC-24 Landsat TM and ETM+ Land Cover, Southern Para, Brazil: 1984-2003

Data.gov (United States)

National Aeronautics and Space Administration — This data set is a five-class land cover for Southern Para for the years 1984 (Landsat MSS), 1988 (Landsat TM), 1996, and 2003 (Landsat ETM+). The final...

The use of landsat 7 enhanced thematic mapper plus for mapping leafy spurge

Science.gov (United States)

Mladinich, C.S.; Bustos, M.R.; Stitt, S.; Root, R.; Brown, K.; Anderson, G.L.; Hager, S.

2006-01-01

Euphorbia esula L. (leafy spurge) is an invasive weed that is a major problem in much of the Upper Great Plains region, including parts of Montana, South Dakota, North Dakota, Nebraska, and Wyoming. Infestations in North Dakota alone have had a serious economic impact, estimated at $87 million annually in 1991, to the state's wildlife, tourism, and agricultural economy. Leafy spurge degrades prairie and badland ecosystems by displacing native grasses and forbs. It is a major threat to protected ecosystems in many national parks, national wild lands, and state recreational areas in the region. This study explores the use of Landsat 7 Enhanced Thematic Mapper Plus (Landsat) imagery and derived products as a management tool for mapping leafy spurge in Theodore Roosevelt National Park, in southwestern North Dakota. An unsupervised clustering approach was used to map leafy spurge classes and resulted in overall classification accuracies of approximately 63%. The uses of Landsat imagery did not provide the accuracy required for detailed mapping of small patches of the weed. However, it demonstrated the potential for mapping broad-scale (regional) leafy spurge occurrence. This paper offers recommendations on the suitability of Landsat imagery as a tool for use by resource managers to map and monitor leafy spurge populations over large areas.

LBA-ECO LC-24 Landsat TM and ETM+ Land Cover, Southern Para, Brazil: 1984-2003

Data.gov (United States)

National Aeronautics and Space Administration — ABSTRACT: This data set is a five-class land cover for Southern Para for the years 1984 (Landsat MSS), 1988 (Landsat TM), 1996, and 2003 (Landsat ETM+). The final...

Comparison of satellite imagery from LISS-III/Resourcesat-1 and TM/Landsat 5 to estimate stand-level timber volume

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Elias Fernando Berra

2017-01-01

Full Text Available After Landsat 5 activities were discontinued, sensors on board ResourceSat-1 satellite have been pointed as an option for Landsat series. The aim of this study is to estimate timber volume from a slash pine (Pinus elliottii Engelm. stand using images from both LISS-III/ResourceSat-1 and TM/Landsat 5 sensors, cross comparing their performances. Reflectance values from the four spectral bands considered equivalent for both sensors were compared regarding sensitivity to changes in timber volume. Trends were similar, with direct relationship in the near-infrared bands and inverse relationships in the visible and mid-infrared bands. Significant differences were only found in the equivalent band of green. Multiple linear regressions were used to select spectral bands that would better explain variations in timber volume. The best fit equations for each sensor were inverted to generate maps of timber volume, estimates which were compared at pixel and stand level. None of the scales showed significant differences between estimates generated from the two sensors. We concluded that LISS-III and TM have generally very similar performance for monitoring timber volume, and LISS-III could therefore be potentially used as a complement or substitute to Landsat series.

Downscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches

Science.gov (United States)

Gu, Yingxin; Wylie, Bruce K.

2015-01-01

The satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. The 250-m GSN data estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been used for terrestrial ecosystem modeling and monitoring. High temporal resolution with a wide range of wavelengths make the MODIS land surface products robust and reliable. The long-term 30-m Landsat data provide spatial detailed information for characterizing human-scale processes and have been used for land cover and land change studies. The main goal of this study is to combine 250-m MODIS GSN and 30-m Landsat observations to generate a quality-improved high spatial resolution (30-m) GSN database. A rule-based piecewise regression GSN model based on MODIS and Landsat data was developed. Results show a strong correlation between predicted GSN and actual GSN (r = 0.97, average error = 0.026). The most important Landsat variables in the GSN model are Normalized Difference Vegetation Indices (NDVIs) in May and August. The derived MODIS-Landsat-based 30-m GSN map provides biophysical information for moderate-scale ecological features. This multiple sensor study retains the detailed seasonal dynamic information captured by MODIS and leverages the high-resolution information from Landsat, which will be useful for regional ecosystem studies.

Mapping forest tree species over large areas with partially cloudy Landsat imagery

Science.gov (United States)

Turlej, K.; Radeloff, V.

2017-12-01

Forests provide numerous services to natural systems and humankind, but which services forest provide depends greatly on their tree species composition. That makes it important to track not only changes in forest extent, something that remote sensing excels in, but also to map tree species. The main goal of our work was to map tree species with Landsat imagery, and to identify how to maximize mapping accuracy by including partially cloudy imagery. Our study area covered one Landsat footprint (26/28) in Northern Wisconsin, USA, with temperate and boreal forests. We selected this area because it contains numerous tree species and variable forest composition providing an ideal study area to test the limits of Landsat data. We quantified how species-level classification accuracy was affected by a) the number of acquisitions, b) the seasonal distribution of observations, and c) the amount of cloud contamination. We classified a single year stack of Landsat-7, and -8 images data with a decision tree algorithm to generate a map of dominant tree species at the pixel- and stand-level. We obtained three important results. First, we achieved producer's accuracies in the range 70-80% and user's accuracies in range 80-90% for the most abundant tree species in our study area. Second, classification accuracy improved with more acquisitions, when observations were available from all seasons, and is the best when images with up to 40% cloud cover are included. Finally, classifications for pure stands were 10 to 30 percentage points better than those for mixed stands. We conclude that including partially cloudy Landsat imagery allows to map forest tree species with accuracies that were previously only possible for rare years with many cloud-free observations. Our approach thus provides important information for both forest management and science.

Remote sensing of geologic mineral occurrences for the Colorado mineral belt using LANDSAT data

Science.gov (United States)

Carpenter, R. H. (Principal Investigator); Trexler, D. W.

1976-01-01

The author has identified the following significant results. LANDSAT imagery was examined as a practical and productive tool for mineral exploration along the Colorado Mineral Belt. An attempt was made to identify all large, active and/or abandoned mining districts on the imagery which initially were discovered by surface manifestations. A number of strong photolinements, circular features, and color anomalies were identified. Some of these form a part of the structural and igneous volcanic framework in which mineral deposits occur. No specific mineral deposits such as veins or porphyries were identified. Promising linear and concentric features were field checked at several locations. Some proved to be fault zones and calderas; others were strictly topographic features related to stream or glacial entrenchment. The Silverton Caldera region and the Idaho Springs-Central City district were chosen and studied as case histories to evaluate the application of LANDSAT imagery to mineral exploration. Evidence of specific mineralization related to ore deposits in these two areas were observed only on low level photography.

Browning of the landscape of interior Alaska based on 1986-2009 Landsat sensor NDVI

Science.gov (United States)

Rebecca A. Baird; David Verbyla; Teresa N. Hollingsworth

2012-01-01

We used a time series of 1986-2009 Landsat sensor data to compute the Normalized Difference Vegetation Index (NDVI) for 30 m pixels within the Bonanza Creek Experimental Forest of interior Alaska. Based on simple linear regression, we found significant (p

Investigating Land Surface Temperature Changes Using Landsat Data in Konya, Turkey

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O. Orhan

2016-06-01

Full Text Available The main purpose of this paper is to investigate multi-temporal land surface temperature (LST and Normalized Difference Vegetation Index (NDVI changes of Konya in Turkey using remotely sensed data. Konya is located in the semi-arid central Anatolian region of Turkey and hosts many important wetland sites including Salt Lake. Six images taken by Landsat-5 TM and Landsat 8- OLI satellites were used as the basic data source. These raw images were taken in 1984, 2011 and 2014 intended as long-term and short-term. Firstly, those raw images was corrected radiometric and geometrically within the scope of project. Three mosaic images were obtained by using the full-frame images of Landsat-5 TM / 8- OLI which had been already transformed comparison each other. Then, Land Surface Temperature (LST, Normalized Difference Vegetation Index (NDVI maps have been produced to determine the dimension of the drought. The obtained results showed that surface temperature rates in the basin increased about 5°C between 1984 and 2014 as long periods, increased about 2-3°C between 2011and 2014 as short periods. Meteorological data supports the increase in temperature.

Landsat 7 ETM/1G satellite imagery - Hawaiian Islands cloud-free mosaics

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Cloud-free Landsat satellite imagery mosaics of the islands of the main 8 Hawaiian Islands (Hawaii, Maui, Kahoolawe, Lanai, Molokai, Oahu, Kauai and Niihau). Landsat...

LANDSAT-D ground segment operations plan, revision A

Science.gov (United States)

Evans, B.

1982-01-01

The basic concept for the utilization of LANDSAT ground processing resources is described. Only the steady state activities that support normal ground processing are addressed. This ground segment operations plan covers all processing of the multispectral scanner and the processing of thematic mapper through data acquisition and payload correction data generation for the LANDSAT 4 mission. The capabilities embedded in the hardware and software elements are presented from an operations viewpoint. The personnel assignments associated with each functional process and the mechanisms available for controlling the overall data flow are identified.

Perspective with Landsat Overlay, Mount Kilimanjaro, Tanzania

Science.gov (United States)

2002-01-01

Mount Kilimanjaro (Kilima Njaro or 'shining mountain' in Swahili), the highest point in Africa, reaches 5,895 meters (19,340 feet) above sea level, tall enough to maintain a permanent snow cap despite being just 330 kilometers (210 miles) south of the equator. It is the tallest free-standing mountain on the Earth's land surface world, rising about 4,600 meters (15,000 feet) above the surrounding plain. Kilimanjaro is a triple volcano (has three peaks) that last erupted perhaps more than 100,000 years ago but still exudes volcanic gases. It is accompanied by about 20 other nearby volcanoes, some of which are seen to the west (left) in this view, prominently including Mount Meru, which last erupted only about a century ago. The volcanic mountain slopes are commonly fertile and support thick forests, while the much drier grasslands of the plains are home to elephants, lions, and other savanna wildlife.This 3-D perspective view was generated using topographic data from the Shuttle Radar Topography Mission (SRTM), a Landsat 7 satellite image, and a false sky. Topographic expression is vertically exaggerated two times.Landsat has been providing visible and infrared views of the Earth since 1972. SRTM elevation data matches the 30-meter (98-foot) resolution of most Landsat images and will substantially help in analyzing the large and growing Landsat image archive, managed by the U.S. Geological Survey (USGS).Elevation data used in this image was acquired by the Shuttle Radar Topography Mission (SRTM) aboard the Space Shuttle Endeavour, launched on Feb. 11, 2000. SRTM used the same radar instrument that comprised the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) that flew twice on the Space Shuttle Endeavour in 1994. SRTM was designed to collect 3-D measurements of the Earth's surface. To collect the 3-D data, engineers added a 60-meter (approximately 200-foot) mast, installed additional C-band and X-band antennas, and improved tracking and

A PRELIMINARY INVESTIGATION ON COMPARISON AND TRANSFORMATION OF SENTINEL-2 MSI AND LANDSAT 8 OLI

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F. Chen

2018-05-01

Full Text Available A PRELIMINARY INVESTIGATION ON COMPARISON AND TRANSFORMATION OF SENTINEL-2 MSI AND LANDSAT 8 OLI Timely and accurate earth observation with short revisit interval is usually necessary, especially for emergency response. Currently, several new generation sensors provided with similar channel characteristics have been operated onboard different satellite platforms, including Sentinel-2 and Landsat 8. Joint use of the observations by different sensors offers an opportunity to meet the demands for emergency requirements. For example, through the combination of Landsat and Sentinel-2 data, the land can be observed every 2–3 days at medium spatial resolution. However, differences are expected in radiometric values (e.g., channel reflectance of the corresponding channels between two sensors. Spectral response function (SRF is taken as an important aspect of sensor settings. Accordingly, between-sensor differences due to SRFs variation need to be quantified and compensated. The comparison of SRFs shows difference (more or less in channel settings between Sentinel-2 Multi-Spectral Instrument (MSI and Landsat 8 Operational Land Imager (OLI. Effect of the difference in SRF on corresponding values between MSI and OLI was investigated, mainly in terms of channel reflectance and several derived spectral indices. Spectra samples from ASTER Spectral Library Version 2.0 and Hyperion data archives were used in obtaining channel reflectance simulation of MSI and OLI. Preliminary results show that MSI and OLI are well comparable in several channels with small relative discrepancy (< 5 %, including the Costal Aerosol channel, a NIR (855–875 nm channel, the SWIR channels, and the Cirrus channel. Meanwhile, for channels covering Blue, Green, Red, and NIR (785–900 nm, the between-sensor differences are significantly presented. Compared with the difference in reflectance of each individual channel, the difference in derived spectral index is more

Seasonal Variation of Colored Dissolved Organic Matter in Barataria Bay, Louisiana, Using Combined Landsat and Field Data

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Ishan Joshi

2015-09-01

Full Text Available Coastal bays, such as Barataria Bay, are important transition zones between the terrigenous and marine environments that are also optically complex due to elevated amounts of particulate and dissolved constituents. Monthly field data collected over a period of 15 months in 2010 and 2011 in Barataria Bay were used to develop an empirical band ratio algorithm for the Landsat-5 TM that showed a good correlation with the Colored Dissolved Organic Matter (CDOM absorption coefficient at 355 nm (ag355 (R2 = 0.74. Landsat-derived CDOM maps generally captured the major details of CDOM distribution and seasonal influences, suggesting the potential use of Landsat imagery to monitor biogeochemistry in coastal water environments. An investigation of the seasonal variation in ag355 conducted using Landsat-derived ag355 as well as field data suggested the strong influence of seasonality in the different regions of the bay with the marine end members (lower bay experiencing generally low but highly variable ag355 and the freshwater end members (upper bay experiencing high ag355 with low variability. Barataria Bay experienced a significant increase in ag355 during the freshwater release at the Davis Pond Freshwater Diversion (DPFD following the Deep Water Horizon oil spill in 2010 and following the Mississippi River (MR flood conditions in 2011, resulting in a weak linkage to salinity in comparison to the other seasons. Tree based statistical analysis showed the influence of high river flow conditions, high- and low-pressure systems that appeared to control ag355 by ~28%, 29% and 43% of the time duration over the study period at the marine end member just outside the bay. An analysis of CDOM variability in 2010 revealed the strong influence of the MR in controlling CDOM abundance in the lower bay during the high flow conditions, while strong winds associated with cold fronts significantly increase CDOM abundance in the upper bay, thus revealing the important

Combining Estimation of Green Vegetation Fraction in an Arid Region from Landsat 7 ETM+ Data

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Kun Jia

2017-11-01

Full Text Available Fractional vegetation cover (FVC, or green vegetation fraction, is an important parameter for characterizing conditions of the land surface vegetation, and also a key variable of models for simulating cycles of water, carbon and energy on the land surface. There are several types of FVC estimation models using remote sensing data, and evaluating their performance over a specific region is of great significance. Therefore, this study firstly evaluated three types of FVC estimation models using Landsat 7 ETM+ data in an agriculture region of Heihe River Basin, China, and then proposed a combination strategy from different individual models to improve the FVC estimation accuracy, which employed the multiple linear regression (MLR and Bayesian model average (BMA methods. The validation results indicated that the spectral mixture analysis model with three endmembers (SMA3 achieved the best FVC estimation accuracy (determination coefficient (R2 = 0.902, root mean square error (RMSE = 0.076 among the seven individual models using Landsat 7 ETM+ data. In addition, the MLR and BMA combination methods could both improve FVC estimation accuracy (R2 = 0.913, RMSE = 0.063 and R2 = 0.904, RMSE = 0.069 for MLR and BMA, respectively. Therefore, it could be concluded that both MLR and BMA combination methods integrating FVC estimates from different models using Landsat 7 ETM+ data could effectively weaken the estimation errors of individual models and improve the final FVC estimation accuracy.

Mapping afforestation and its carbon stock using time-series Landsat stacks

Science.gov (United States)

Liu, L.; Wu, Y.

2015-12-01

The Three Norths Shelter Forest Programme (TNSFP) is the largest afforestation reconstruction project in the world. Remote sensing is a crucial tool to map land cover and cover changes, but it is still challenging to accurately quantify the plantation and its carbon stock from time-series satellite images. In this paper, the Yulin district, Shaanxi province, representing a typical afforestation area in the TNSFP region, was selected as the study area, and there were twenty-nine Landsat MSS/TM/ETM+ epochs were collected from 1974 to 2012 to reconstruct the forest changes and carbon stock in last 40 years. Firstly, the Landsat ground surface reflectance (GSR) images from 1974 to 2013 were collected and processed based on 6S atmospheric transfer code and a relative reflectance normalization algorithm. Subsequently, we developed a vegetation change tracking method to reconstruct the forest change history (afforestation and deforestation) from the dense time-series Landsat GSR images based on the integrated forest z-score (IFZ) model, and the afforestation age was successfully retrieved from the Landsat time-series stacks in the last forty years and shown to be consistent with the surveyed tree ages, with a RMSE value of 4.32 years and a determination coefficient (R²) of 0.824. Then, the AGB regression models were successfully developed by integrating vegetation indices and tree age. The simple ratio vegetation index (SR) is the best candidate of the commonly used vegetation indices for estimating forest AGB, and the forest AGB model was significantly improved using the combination of SR and tree age, with R² values from 0.50 to 0.727. Finally, the forest AGB images were mapped at eight epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in six counties of Yulin District increased by 20.8 G kg, from 5.8 G kg in 1986 to 26.6 G kg in 2013, a total increase of 360%. For the forest area since 1974, the forest AGB density increased from 15.72 t

Near Real-Time Browsable Landsat-8 Imagery

Directory of Open Access Journals (Sweden)

Cheng-Chien Liu

2017-01-01

Full Text Available The successful launch and operation of Landsat-8 extends the remarkable 40-year acquisition of space-based land remote-sensing data. To respond quickly to emergency needs, real-time data are directly downlinked to 17 ground stations across the world on a routine basis. With a size of approximately 1 Gb per scene, however, the standard level-1 product provided by these stations is not able to serve the general public. Users would like to browse the most up-to-date and historical images of their regions of interest (ROI at full-resolution from all kinds of devices without the need for tedious data downloading, decompressing, and processing. This paper reports on the Landsat-8 automatic image processing system (L-8 AIPS that incorporates the function of mask developed by United States Geological Survey (USGS, the pan-sharpening technique of spectral summation intensity modulation, the adaptive contrast enhancement technique, as well as the Openlayers and Google Maps/Earth compatible superoverlay technique. Operation of L-8 AIPS enables the most up-to-date Landsat-8 images of Taiwan to be browsed with a clear contrast enhancement regardless of the cloud condition, and in only one hour’s time after receiving the raw data from the USGS Level 1 Product Generation System (LPGS. For any ROI in Taiwan, all historical Landsat-8 images can also be quickly viewed in time series at full resolution (15 m. The debris flow triggered by Typhoon Soudelor (8 August 2015, as well as the barrier lake formed and the large-scale destruction of vegetation after Typhoon Nepartak (7 July 2016, are given as three examples of successful applications to demonstrate that the gap between the user’s needs and the existing Level-1 product from LPGS can be bridged by providing browsable images in near real-time.

Harmonized Landsat/Sentinel-2 Reflectance Products for Land Monitoring

Science.gov (United States)

Masek, J. G.; Ju, J.; Claverie, M.; Vermote, E.; Dungan, J. L.; Roger, J. C.; Skakun, S.; Justice, C. O.

2017-12-01

Many land applications require more frequent observations than can be obtained from a single "Landsat class" sensor. Agricultural monitoring, inland water quality assessment, stand-scale phenology, and numerous other applications all require near-daily imagery at better than 1ha resolution. Thus the land science community has begun expressing a desire for a "30-meter MODIS" global monitoring capability. One cost-effective way to achieve this goal is via merging data from multiple, international observatories into a single virtual constellation. The Harmonized Landsat/Sentinel-2 (HLS) project has been working to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2. Harmonization in this context requires a series of radiometric and geometric transforms to create a single surface reflectance time series agnostic to sensor origin. Radiometric corrections include a common atmospheric correction using the Landsat-8 LaSRC/6S approach, a simple BRDF adjustment to constant solar and nadir view angle, and spectral bandpass adjustments to fit the Landsat-8 OLI reference. Data are then resampled to a consistent 30m UTM grid, using the Sentinel-2 global tile system. Cloud and shadow masking are also implemented. Quality assurance (QA) involves comparison of the output 30m HLS products with near-simultaneous MODIS nadir-adjusted observations. Prototoype HLS products have been processed for 7% of the global land area using the NASA Earth Exchange (NEX) compute environment at NASA Ames, and can be downloaded from the HLS web site (https://hls.gsfc.nasa.gov). A wall-to-wall North America data set is being prepared for 2018.This talk will review the objectives and status of the HLS project, and illustrate applications of high-density optical time series data for agriculture and ecology. We also discuss lessons learned from HLS in the general context of implementing virtual constellations.

REGIONAL GEOLGICAL MAPPING IN TROPICAL ENVIRONMENTS USING LANDSAT TM AND SRTM REMOTE SENSING DATA

Directory of Open Access Journals (Sweden)

A. Beiranvand Pour

2015-10-01

Full Text Available Landsat Thematic Mapper (TM and Shuttle Radar Topography Mission (SRTM data were used to produce geological maps in tropical environments. Lineament, lithology and landform maps were produced for all states in peninsular Malaysia in this study. Kedah, Perak and Terengganu states have been selected as case studies to demonstrate the results of the data and techniques used. Directional filtering technique was applied to Landsat TM bands 4, 5 and 3 for lineament mapping. The lithology map was produced using Landsat TM bands combination consist of bands 4, 3 and 2. Digital elevation model and landform map were produced using SRTM data in 3 Dimension (3D and 2 Dimension (2D perspective views, respectively. The produced geological maps and the remote sensing data and methods applied in this study are mostly appropriate for hazard risk mapping applications and mineral exploration projects in the peninsular Malaysia and tropical environments.

How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms?

Directory of Open Access Journals (Sweden)

Warren B. Cohen

2017-03-01

Full Text Available Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping algorithms. Many of these algorithms take advantage of the high temporal data volume to mine subtle signals in Landsat time series, but as those signals become subtler, they are more likely to be mixed with noise in Landsat data. This study examines the similarity among seven different algorithms in their ability to map the full range of magnitudes of forest disturbance over six different Landsat scenes distributed across the conterminous US. The maps agreed very well in terms of the amount of undisturbed forest over time; however, for the ~30% of forest mapped as disturbed in a given year by at least one algorithm, there was little agreement about which pixels were affected. Algorithms that targeted higher-magnitude disturbances exhibited higher omission errors but lower commission errors than those targeting a broader range of disturbance magnitudes. These results suggest that a user of any given forest disturbance map should understand the map’s strengths and weaknesses (in terms of omission and commission error rates, with respect to the disturbance targets of interest.

Harmonic regression of Landsat time series for modeling attributes from national forest inventory data

Science.gov (United States)

Barry T. Wilson; Joseph F. Knight; Ronald E. McRoberts

2018-01-01

Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several...

Forest Disturbance Mapping Using Dense Synthetic Landsat/MODIS Time-Series and Permutation-Based Disturbance Index Detection

Directory of Open Access Journals (Sweden)

David Frantz

2016-03-01

Full Text Available Spatio-temporal information on process-based forest loss is essential for a wide range of applications. Despite remote sensing being the only feasible means of monitoring forest change at regional or greater scales, there is no retrospectively available remote sensor that meets the demand of monitoring forests with the required spatial detail and guaranteed high temporal frequency. As an alternative, we employed the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM to produce a dense synthetic time series by fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS nadir Bidirectional Reflectance Distribution Function (BRDF adjusted reflectance. Forest loss was detected by applying a multi-temporal disturbance detection approach implementing a Disturbance Index-based detection strategy. The detection thresholds were permutated with random numbers for the normal distribution in order to generate a multi-dimensional threshold confidence area. As a result, a more robust parameterization and a spatially more coherent detection could be achieved. (i The original Landsat time series; (ii synthetic time series; and a (iii combined hybrid approach were used to identify the timing and extent of disturbances. The identified clearings in the Landsat detection were verified using an annual woodland clearing dataset from Queensland’s Statewide Landcover and Trees Study. Disturbances caused by stand-replacing events were successfully identified. The increased temporal resolution of the synthetic time series indicated promising additional information on disturbance timing. The results of the hybrid detection unified the benefits of both approaches, i.e., the spatial quality and general accuracy of the Landsat detection and the increased temporal information of synthetic time series. Results indicated that a temporal improvement in the detection of the disturbance date could be achieved relative to the irregularly spaced Landsat

LBA-ECO CD-34 Landsat Fractional Land Cover Analysis, Manaus, Brazil: 2004-2005

Data.gov (United States)

National Aeronautics and Space Administration — This data set provides the results of fractional land cover analysis for nonphotosynthetic vegetation (NPV) from two Landsat images of Manaus, Brazil, for October...

Estimating the chlorophyll content in the waters of Guanabara Bay from the LANDSAT multispectral scanning digital data

Science.gov (United States)

Dejesusparada, N. (Principal Investigator); Bentancurt, J. J. V.; Herz, B. R.; Molion, L. B.

1980-01-01

Detection of water quality in Guanabara Bay using multispectral scanning digital data taken from LANDSAT satellites was examined. To test these processes, an empirical (statistical) approach was choosen to observe the degree of relationship between LANDSAT data and the in situ data taken simultaneously. The linear and nonlinear regression analyses were taken from among those developed by INPE in 1978. Results indicate that the major regression was in the number six MSS band, atmospheric effects, which indicated a correction coefficient of 0.99 and an average error of 6.59 micrograms liter. This error was similar to that obtained in the laboratory. The chlorophyll content was between 0 and 100 micrograms/liter, as taken from the MSS of LANDSAT.

Landsat at 45: How it Changed the Way We See the Earth

Science.gov (United States)

Uri, John

2017-01-01

On October 24, 1946, more than 10 years before the launch of the first artificial satellite Sputnik, scientists at the White Sands Missile Range in New Mexico placed a camera on top of a captured German V-2 ballistic missile. As the rocket flew to an altitude of about 65 miles - just above the generally recognized border of outer space - the 35-millimeter motion picture camera snapped a frame every one and a half seconds. Minutes later, the missile came crashing back down and slammed into the ground at more than 340 mph, but the film survived and gave us our first glimpse of Earth from space. Earth Resources Technology Satellite aka Landsat It was images like those first grainy black and white pictures and later those taken by America's first astronauts in the 1960's that inspired the development of the Earth Resources Technology Satellite (ERTS). From the unique vantage point of space, we could now observe Earth using a variety of different instruments to monitor changes over time. The ERTS-1 satellite, wisely renamed Landsat-1, was launched aboard a Delta rocket on July 23, 1972, into a Sun-synchronous polar orbit at an altitude of about 560 miles. In this unique orbit, Landsat could observe the same point on the Earth every 18 days, always with the same solar illumination, allowing for precise monitoring of changes on the ground over time. Landsat-1, derived from the highly successful Nimbus weather satellites, carried two instruments that allowed it to take images not only in visible light but also in infrared, well-suited to track changes in vegetation over time. Designed to last only one year, Landsat-1 actually operated for nearly three years, by which time it had been joined in space by Landsat-2, a near identical copy of the original. Since then, ever more sophisticated instruments were flown aboard Landsat-3 through -8, with Landsat-9 planned for launch in 2020, acquiring millions of images of Earth over more than four decades. At first, images from

Multi-temporal environmental analysis of oil field activities in south-central Oklahoma using Landsat thematic mapper, aerial photography and GIS

International Nuclear Information System (INIS)

Janks, J.S.; Edwards, G.S.; Prelat, A.E.

1995-01-01

Environmental assessments of oil field activities, historical and present, were made using a combination of Landsat Thematic Mapper, aerial photographic and GIS information. Landsat data was used to assess vegetation health in and around the oil fields, and aerial photography was used to document historic changes. We found no evidence of vegetation damage from the oil field activities, even though many fields are located along anticlines and drain into major waterways. GIS technology, mapping roads, wells, rivers, ponds and environmentally-sensitive areas, was used to minimize environmental effects on the placement of shotpoints and receivers. When either shotpoints or receivers were found to interfere with sensitive areas, the points were moved to nearby roads or other open locations. The application of this technology resulted in minimal environmental damage and significant cost savings

Machine learning-based Landsat-MODIS data fusion approach for 8-day 30m evapotranspiration monitoring

Science.gov (United States)

Im, J.; Ke, Y.; Park, S.

2016-12-01

Continuous monitoring of evapotranspiration (ET) is important for understanding of hydrological cycles and energy flux dynamics. At regional and local scales, routine ET estimation is a critical for efficient water management, drought impact assessment and ecosystem health monitoring, etc. Remote sensing has long been recognized to be able to provide ET monitoring over large areas. However, no single satellite could provide temporally continuous ET at relatively high spatial resolution due to the trade-off between the spatial and temporal resolution of current satellite sensors. Landsat-series satellites provide optical and thermal imagery at 30-100m resolution, whereas the 16-day revisit cycle hinders the observation of ET dynamics; MODIS provides sources of ET estimation at daily basis, but the 500-1000m ground sampling distance is too coarse for field level applications. In this study, we present a machine learning and STARFM based method for Landsat/MODIS ET fusion. The approach first downscales MODIS 8-day 1km ET (MOD16A2) to 30m based on eleven Landsat-derived indicators such as NDVI, EVI, NDWI etc on the cloud-free Landsat-available days using Random Forest approach. For the days when Landsat data are not available, downscaled ET is synthesized by MODIS and Landsat data fusion with STARFM and STI-FM approaches. The models are evaluated using in situ flux tower measurements at US-ARM and US-Twt AmeriFlux sites the United States. Results show that the downscaled 30m ET have good agreement with MODIS ET (RMSE=0.42-3.4mm/8days, rRMSE=3.2%-26%) and the downscaled ET have higher accuracy than MODIS ET when compared to in-situ measurements.

Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand

Directory of Open Access Journals (Sweden)

Uday Pimple

2017-02-01

Full Text Available The accurate mapping and monitoring of forests is essential for the sustainable management of forest ecosystems. Advancements in the Landsat satellite series have been very useful for various forest mapping applications. However, the topographic shadows of irregular mountains are major obstacles to accurate forest classification. In this paper, we test five topographic correction methods: improved cosine correction, Minnaert, C-correction, Statistical Empirical Correction (SEC and Variable Empirical Coefficient Algorithm (VECA, with multisource digital elevation models (DEM to reduce the topographic relief effect in mountainous terrain produced by the Landsat Thematic Mapper (TM-5 and Operational Land Imager (OLI-8 sensors. The effectiveness of the topographic correction methods are assessed by visual interpretation and the reduction in standard deviation (SD, by means of the coefficient of variation (CV. Results show that the SEC performs best with the Shuttle Radar Topographic Mission (SRTM 30 m × 30 m DEM. The random forest (RF classifier is used for forest classification, and the overall accuracy of forest classification is evaluated to compare the performances of the topographic corrections. Our results show that the C-correction, SEC and VECA corrected imagery were able to improve the forest classification accuracy of Landsat TM-5 from 78.41% to 81.50%, 82.38%, and 81.50%, respectively, and OLI-8 from 81.06% to 81.50%, 82.38%, and 81.94%, respectively. The highest accuracy of forest type classification is obtained with the newly available high-resolution SRTM DEM and SEC method.

An algorithm to retrieve Land Surface Temperature using Landsat-8 ...

African Journals Online (AJOL)

Ayodeji Ogunode;Mulemwa Akombelwa

The results show temperature variation over a long period of time can be ... Remote sensing of LST using infrared radiation gives the average surface temperature of the scene ... advantage over previous Landsat series. ..... Li, F., Jackson, T. J., Kustas, W. P., Schmugge, T. J., French, A. N., Cosh, M. H. & Bindlish, R. 2004.

The Potential of Using Landsat 7 Data for the Classification of Sea Ice Surface Conditions During Summer

Science.gov (United States)

Markus, Thorsten; Cavalieri, Donald J.; Ivanoff, Alvaro; Koblinsky, Chester J. (Technical Monitor)

2001-01-01

During spring and summer, the Surface of the Arctic sea ice cover undergoes rapid changes that greatly affect the surface albedo and significantly impact the further decay of the sea ice. These changes are primarily the development of a wet snow cover and the development of melt ponds. As melt pond diameters generally do not exceed a couple of meters, the spatial resolutions of sensors like AVHRR and MODIS are too coarse for their identification. Landsat 7, on the other hand, has a spatial resolution of 30 m (15 m for the pan-chromatic band). The different wavelengths (bands) from blue to near-infrared offer the potential to distinguish among different surface conditions. Landsat 7 data for the Baffin Bay region for June 2000 have been analyzed. The analysis shows that different surface conditions, such as wet snow and meltponded areas, have different signatures in the individual Landsat bands. Consistent with in-situ albedo measurements, melt ponds show up as blueish whereas dry and wet ice have a white to gray appearance in the Landsat true-color image. These spectral differences enable the distinction of melt ponds. The melt pond fraction for the scene studied in this paper was 37%.

Application and Comparison of the MODIS-Derived Enhanced Vegetation Index to VIIRS, Landsat 5 TM and Landsat 8 OLI Platforms: A Case Study in the Arid Colorado River Delta, Mexico

Science.gov (United States)

Jarchow, Christopher J.; Didan, Kamel; Barreto-Muñoz, Armando; Glenn, Edward P.

2018-01-01

The Enhanced Vegetation Index (EVI) is a key Earth science parameter used to assess vegetation, originally developed and calibrated for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. With the impending decommissioning of the MODIS sensors by the year 2020/2022, alternative platforms will need to be used to estimate EVI. We compared Landsat 5 (2000–2011), 8 (2013–2016) and the Visible Infrared Imaging Radiometer Suite (VIIRS; 2013–2016) to MODIS EVI (2000–2016) over a 420,083-ha area of the arid lower Colorado River Delta in Mexico. Over large areas with mixed land cover or agricultural fields, we found high correspondence between Landsat and MODIS EVI (R2 = 0.93 for the entire area studied and 0.97 for agricultural fields), but the relationship was weak over bare soil (R2 = 0.27) and riparian vegetation (R2 = 0.48). The correlation between MODIS and Landsat EVI was higher over large, homogeneous areas and was generally lower in narrow riparian areas. VIIRS and MODIS EVI were highly similar (R2 = 0.99 for the entire area studied) and did not show the same decrease in performance in smaller, narrower regions as Landsat. Landsat and VIIRS provide EVI estimates of similar quality and characteristics to MODIS, but scale, seasonality and land cover type(s) should be considered before implementing Landsat EVI in a particular area. PMID:29757265

Flood mapping from Sentinel-1 and Landsat-8 data: a case study from river Evros, Greece

Science.gov (United States)

Kyriou, Aggeliki; Nikolakopoulos, Konstantinos

2015-10-01

Floods are suddenly and temporary natural events, affecting areas which are not normally covered by water. The influence of floods plays a significant role both in society and the natural environment, therefore flood mapping is crucial. Remote sensing data can be used to develop flood map in an efficient and effective way. This work is focused on expansion of water bodies overtopping natural levees of the river Evros, invading the surroundings areas and converting them in flooded. Different techniques of flood mapping were used using data from active and passive remote sensing sensors like Sentinlel-1 and Landsat-8 respectively. Space borne pairs obtained from Sentinel-1 were processed in this study. Each pair included an image during the flood, which is called "crisis image" and another one before the event, which is called "archived image". Both images covering the same area were processed producing a map, which shows the spread of the flood. Multispectral data From Landsat-8 were also processed in order to detect and map the flooded areas. Different image processing techniques were applied and the results were compared to the respective results of the radar data processing.

Post Landsat-D advanced concept evaluation /PLACE/

Science.gov (United States)

Alexander, L. D.; Alvarado, U. R.; Flatow, F. S.

1979-01-01

The aim of the Post Landsat-D Advanced Concept Evaluation (PLACE) program was to identify the key technology requirements of earth resources satellite systems for the 1985-2000 period. The program involved four efforts: (1) examination of future needs in the earth resources area, (2) creation of a space systems technology model capable of satisfying these needs, (3) identification of key technology requirements posed by this model, and (4) development of a methodology (PRISM) to assist in the priority structuring of the resulting technologies.

Landsat satellite evidence of the decline of northern California bull kelp

Science.gov (United States)

Renshaw, A.; Houskeeper, H. F.; Kudela, R. M.

2017-12-01

Bull kelp (Nereocystis luetkeana), a species of canopy-forming brown macroalga dominant in the Pacific Northwest of North America, provides critical ecological services such as habitat for a diverse array of marine species, nutrient regulation, photosynthesis, and regional marine carbon cycling. Starting around 2014, annual aerial surveys of bull kelp forests along California's northern coastline conducted by the California Department of Fish and Wildlife (CDFW) have reported a sudden 93% reduction in bull kelp canopy area. Remote sensing using satellite imagery is a robust, highly accurate tool for detecting and quantifying the abundance of the canopy-forming giant kelp, Macrocystis pyrifera; however, it has not been successfully applied to measuring northern bull kelp forests. One of the main difficulties associated with bull kelp detection via satellite is the small surface area of bull kelp canopies. As a result, bull kelp beds often only constitute part of a satellite pixel, making it difficult to obtain a kelp reflectance signal significantly different than water's reflectance signal. As part of the NASA Student Airborne Research Program (SARP), we test a novel method for assessing bull kelp canopy using a multiple endmember spectral mixing analysis (MESMA) applied to Landsat 5 and Landsat 8 imagery from 2003-2016. Water and kelp spectral endmembers are selected along the northern California coastline from Havens Neck cape to Point Arena. MESMA results are ground truthed with the CDFW aerial multispectral imagery data. This project will present a satellite-based time series of bull kelp canopy area and evaluate canopy change in a northern California kelp ecosystem.

COMPARATIVE ASSESSMENT OF FOREST COVER IN THE REPUBLICS OF MORDOVIA AND MARI EL ACCORDING TO THE RESULTS OF THE LANDSAT SATELITE IMAGES CLASSIFICATION

Directory of Open Access Journals (Sweden)

E. S. Vdovin

2015-01-01

Full Text Available Thestudy presents the results of an assessment of forest cover of the territories of the republics of Mordovia and Mari El on the color classification results of multispectral Landsat 8 in comparison with the data of the state register of forests. The study highlights the problem of transformation of the structure of land due to natural afforestation of agricultural land. Emphasized the importance of managing the recovery process "wildlife" in the regions of compact residence of the Finno-Ugric peoples using the methods of ecological planning of land for the purpose of solving the reconstruction of the ethnic environment of the Finno-Ugric peoples.

Stable and accurate methods for identification of water bodies from Landsat series imagery using meta-heuristic algorithms

Science.gov (United States)

Gamshadzaei, Mohammad Hossein; Rahimzadegan, Majid

2017-10-01

Identification of water extents in Landsat images is challenging due to surfaces with similar reflectance to water extents. The objective of this study is to provide stable and accurate methods for identifying water extents in Landsat images based on meta-heuristic algorithms. Then, seven Landsat images were selected from various environmental regions in Iran. Training of the algorithms was performed using 40 water pixels and 40 nonwater pixels in operational land imager images of Chitgar Lake (one of the study regions). Moreover, high-resolution images from Google Earth were digitized to evaluate the results. Two approaches were considered: index-based and artificial intelligence (AI) algorithms. In the first approach, nine common water spectral indices were investigated. AI algorithms were utilized to acquire coefficients of optimal band combinations to extract water extents. Among the AI algorithms, the artificial neural network algorithm and also the ant colony optimization, genetic algorithm, and particle swarm optimization (PSO) meta-heuristic algorithms were implemented. Index-based methods represented different performances in various regions. Among AI methods, PSO had the best performance with average overall accuracy and kappa coefficient of 93% and 98%, respectively. The results indicated the applicability of acquired band combinations to extract accurately and stably water extents in Landsat imagery.

TOTAL WOOD VOLUME ESTIMATION OF EUCALYPTUS SPECIES BY IMAGES OF LANDSAT SATELLITE

Directory of Open Access Journals (Sweden)

Elias Fernando Berra

2012-12-01

Full Text Available http://dx.doi.org/10.5902/198050987566Models relating spectral answers with biophysical parameters aim estimate variables, like wood volume, without the necessity of frequent field measurements. The objective was to develop models to estimate wood volume by Landsat 5 TM images, supported by regional forest inventory data. The image was geo-referenced and converted to spectral reflectance. After, the images-index NDVI (Normalized Difference Vegetation Index and SR (Simple Ratio was generated. The reflectance values of the bands (TM1, TM2, TM3 e TM4 and of the indices (NDVI and SR was related with the wood volume. The biggest correlation with volume was with the NDVI and SR indices. The variables selection was made by Stepwise method, which returned three regression models as significant to explain the variation in volume. Finally, the best fitted model was selected (volume = -830,95 + 46,05 (SR + 107,47 (TM2, which was applied on the Landsat image where the pixels had started to represent the estimated volume in m³/ha on the Eucalyptus sp. production units. This model, significant at 95% confidence level, explains 68% of the wood volume variation.

Use of LANDSAT data to define soil boundaries in Carroll County, Missouri

Science.gov (United States)

Davidson, S. E.

1981-01-01

Bands 4, 5 and 7 false color composite photographs were prepared using data from LANDSAT scenes acquired during April 1977 and April 1981 on computer compatible tapes, and these color composites were compared with band 7 black and white photographs prepared for the entire county. Delineations of soil boundaries at the soil association level were achieved using LANDSAT spectral reflectance data and slope maps for a portion of Carroll County, Missouri. Forty two spectral reflectance classes from April 1977 LANDSAT data were overlaid on digitized slope maps of nine USGS 7.5 minute series topographic quadrangle slope maps to achieve boundary delineations of the soil associations.

Mapping paddy rice distribution using multi-temporal Landsat imagery in the Sanjiang Plain, northeast China

Science.gov (United States)

XIAO, Xiangming; DONG, Jinwei; QIN, Yuanwei; WANG, Zongming

2016-01-01

Information of paddy rice distribution is essential for food production and methane emission calculation. Phenology-based algorithms have been utilized in the mapping of paddy rice fields by identifying the unique flooding and seedling transplanting phases using multi-temporal moderate resolution (500 m to 1 km) images. In this study, we developed simple algorithms to identify paddy rice at a fine resolution at the regional scale using multi-temporal Landsat imagery. Sixteen Landsat images from 2010–2012 were used to generate the 30 m paddy rice map in the Sanjiang Plain, northeast China—one of the major paddy rice cultivation regions in China. Three vegetation indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI), were used to identify rice fields during the flooding/transplanting and ripening phases. The user and producer accuracies of paddy rice on the resultant Landsat-based paddy rice map were 90% and 94%, respectively. The Landsat-based paddy rice map was an improvement over the paddy rice layer on the National Land Cover Dataset, which was generated through visual interpretation and digitalization on the fine-resolution images. The agricultural census data substantially underreported paddy rice area, raising serious concern about its use for studies on food security. PMID:27695637

Development of the Landsat Data Continuity Mission Cloud Cover Assessment Algorithms

Science.gov (United States)

Scaramuzza, Pat; Bouchard, M.A.; Dwyer, John L.

2012-01-01

The upcoming launch of the Operational Land Imager (OLI) will start the next era of the Landsat program. However, the Automated Cloud-Cover Assessment (CCA) (ACCA) algorithm used on Landsat 7 requires a thermal band and is thus not suited for OLI. There will be a thermal instrument on the Landsat Data Continuity Mission (LDCM)-the Thermal Infrared Sensor-which may not be available during all OLI collections. This illustrates a need for CCA for LDCM in the absence of thermal data. To research possibilities for full-resolution OLI cloud assessment, a global data set of 207 Landsat 7 scenes with manually generated cloud masks was created. It was used to evaluate the ACCA algorithm, showing that the algorithm correctly classified 79.9% of a standard test subset of 3.95 109 pixels. The data set was also used to develop and validate two successor algorithms for use with OLI data-one derived from an off-the-shelf machine learning package and one based on ACCA but enhanced by a simple neural network. These comprehensive CCA algorithms were shown to correctly classify pixels as cloudy or clear 88.5% and 89.7% of the time, respectively.

Fusion of MODIS and landsat-8 surface temperature images: a new approach.

Science.gov (United States)

Hazaymeh, Khaled; Hassan, Quazi K

2015-01-01

Here, our objective was to develop a spatio-temporal image fusion model (STI-FM) for enhancing temporal resolution of Landsat-8 land surface temperature (LST) images by fusing LST images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS); and implement the developed algorithm over a heterogeneous semi-arid study area in Jordan, Middle East. The STI-FM technique consisted of two major components: (i) establishing a linear relationship between two consecutive MODIS 8-day composite LST images acquired at time 1 and time 2; and (ii) utilizing the above mentioned relationship as a function of a Landsat-8 LST image acquired at time 1 in order to predict a synthetic Landsat-8 LST image at time 2. It revealed that strong linear relationships (i.e., r2, slopes, and intercepts were in the range 0.93-0.94, 0.94-0.99; and 2.97-20.07) existed between the two consecutive MODIS LST images. We evaluated the synthetic LST images qualitatively and found high visual agreements with the actual Landsat-8 LST images. In addition, we conducted quantitative evaluations of these synthetic images; and found strong agreements with the actual Landsat-8 LST images. For example, r2, root mean square error (RMSE), and absolute average difference (AAD)-values were in the ranges 084-0.90, 0.061-0.080, and 0.003-0.004, respectively.

Harmonized Landsat/Sentinel-2 Reflectance Products for Land Monitoring (Invited)

Science.gov (United States)

Masek, Jeffrey G.; Dungan, Jennifer L.; Ju, Junchang; Roger, Jean-Claude; Claverie, Martin P.; Skakun, Sergii; Vermote, Eric; Justice, Christopher Owen

2017-01-01

Many land applications require more frequent observations than can be obtained from a single 'Landsat class'� sensor. Agricultural monitoring, inland water quality assessment, stand-scale phenology, and numerous other applications all require near-daily imagery at better than 1ha resolution. Thus the land science community has begun expressing a desire for a '30-meter MODIS' global monitoring capability. One cost-effective way to achieve this goal is via merging data from multiple, international observatories into a single virtual constellation. The Harmonized Landsat/Sentinel-2 (HLS) project has been working to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2. Harmonization in this context requires a series of radiometric and geometric transforms to create a single surface reflectance time series agnostic to sensor origin. Radiometric corrections include a common atmospheric correction using the Landsat-8 LaSRC/6S approach, a simple BRDF adjustment to constant solar and nadir view angle, and spectral bandpass adjustments to fit the Landsat-8 OLI reference. Data are then resampled to a consistent 30m UTM grid, using the Sentinel-2 global tile system. Cloud and shadow masking are also implemented. Quality assurance (QA) involves comparison of the output 30m HLS products with near-simultaneous MODIS nadir-adjusted observations. Prototoype HLS products have been processed for approximately 7% of the global land area using the NASA Earth Exchange (NEX) compute environment at NASA Ames, and can be downloaded from the HLS web site (https://hls.gsfc.nasa.gov). A wall-to-wall North America data set is being prepared for 2018. This talk will review the objectives and status of the HLS project, and illustrate applications of high-density optical time series data for agriculture and ecology. We also discuss lessons learned from HLS in the general context of implementing virtual constellations.

Analysis of the Relationship between Land Surface Temperature and Wildfire Severity in a Series of Landsat Images

Directory of Open Access Journals (Sweden)

Lidia Vlassova

2014-06-01

Full Text Available The paper assesses spatio-temporal patterns of land surface temperature (LST and fire severity in the Las Hurdes wildfire of Pinus pinaster forest, which occurred in July 2009, in Extremadura (Spain, from a time series of fifteen Landsat 5 TM images corresponding to 27 post-fire months. The differenced Normalized Burn Ratio (dNBR was used to evaluate burn severity. The mono-window algorithm was applied to estimate LST from the Landsat thermal band. The burned zones underwent a significant increase in LST after fire. Statistically significant differences have been detected between the LST within regions of burn severity categories. More substantial changes in LST are observed in zones of greater fire severity, which can be explained by the lower emissivity of combustion products found in the burned area and changes in the energy balance related to vegetation removal. As time progresses over the 27 months after fire, LST differences decrease due to vegetation regeneration. The differences in LST and Normalized Difference Vegetation Index (NDVI values between burn severity categories in each image are highly correlated (r = 0.84. Spatial patterns of severity and post-fire LST obtained from Landsat time series enable an evaluation of the relationship between these variables to predict the natural dynamics of burned areas.

A contemporary decennial global Landsat sample of changing agricultural field sizes

Science.gov (United States)

White, Emma; Roy, David

2014-05-01

Agriculture has caused significant human induced Land Cover Land Use (LCLU) change, with dramatic cropland expansion in the last century and significant increases in productivity over the past few decades. Satellite data have been used for agricultural applications including cropland distribution mapping, crop condition monitoring, crop production assessment and yield prediction. Satellite based agricultural applications are less reliable when the sensor spatial resolution is small relative to the field size. However, to date, studies of agricultural field size distributions and their change have been limited, even though this information is needed to inform the design of agricultural satellite monitoring systems. Moreover, the size of agricultural fields is a fundamental description of rural landscapes and provides an insight into the drivers of rural LCLU change. In many parts of the world field sizes may have increased. Increasing field sizes cause a subsequent decrease in the number of fields and therefore decreased landscape spatial complexity with impacts on biodiversity, habitat, soil erosion, plant-pollinator interactions, and impacts on the diffusion of herbicides, pesticides, disease pathogens, and pests. The Landsat series of satellites provide the longest record of global land observations, with 30m observations available since 1982. Landsat data are used to examine contemporary field size changes in a period (1980 to 2010) when significant global agricultural changes have occurred. A multi-scale sampling approach is used to locate global hotspots of field size change by examination of a recent global agricultural yield map and literature review. Nine hotspots are selected where significant field size change is apparent and where change has been driven by technological advancements (Argentina and U.S.), abrupt societal changes (Albania and Zimbabwe), government land use and agricultural policy changes (China, Malaysia, Brazil), and/or constrained by

Perspective View with Landsat Overlay, Lakes Managua and Nicaragua

Science.gov (United States)

2002-01-01

This perspective view shows Lakes Managua and Nicaragua near the Pacific coast of Nicaragua. Lake Managua is the 65-kilometer (40-mile)-long fresh water lake in the foreground of this south-looking view, emptying via the Tipitapa River into the much larger Lake Nicaragua in the distance. The capital city of Managua, with a population of more than 500,000, is located along the southern shore of Lake Managua, the area with the highest population density in Nicaragua.The physical setting of Lake Managua is dominated by the numerous volcanic features aligned in a northwest-southeast axis. The cone-like feature in the foreground is Momotombo, a 1,280-meter (4,199-foot)-high stratovolcano located on the northwest end of the lake. Two water-filled volcanic craters (Apoyegue and Jiloa volcanoes) reside on the Chiltepe Peninsula protruding into the lake from the west. Two volcanoes can also be seen on the island of Ometepe in Lake Nicaragua: El Maderas rising to 1,394 meters (4,573 feet) and the active El Conception at 1,610 meters (5,282 feet).This three-dimensional perspective view was generated using topographic data from the Shuttle Radar Topography Mission (SRTM) and an enhanced false-color Landsat 7 satellite image. Colors are from Landsat bands 5, 4, and 2 as red, green and blue, respectively. Topographic expression is exaggerated two times.Landsat has been providing visible and infrared views of the Earth since 1972. SRTM elevation data matches the 30-meter resolution of most Landsat images and will substantially help in analyses of the large and growing Landsat image archive. The Landsat 7 Thematic Mapper image used here was provided to the SRTM by the United States Geological Survey, Earth Resources Observation Systems (EROS) Data Center, Sioux Falls, S.D.Elevation data used in this image was acquired by the SRTM aboard the Space Shuttle Endeavour, launched on February 11, 2000. SRTM used the same radar instrument that comprised the Spaceborne Imaging Radar

Using Landsat-derived disturbance history (1972-2010) to predict current forest structure

Science.gov (United States)

Dirk Pflugmacher; Warren B. Cohen; Robert E. Kennedy

2012-01-01

Lidar is currently the most accurate method for remote estimation of forest structure, but it has limited spatial and temporal coverage. Conversely, Landsat data are more widely available, but exhibit a weaker relationship with structure under medium to high leaf area conditions. One potentially valuable means of enhancing the relationship between Landsat reflectance...

Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications

Science.gov (United States)

Zhu, Zhe

2017-08-01

The free and open access to all archived Landsat images in 2008 has completely changed the way of using Landsat data. Many novel change detection algorithms based on Landsat time series have been developed We present a comprehensive review of four important aspects of change detection studies based on Landsat time series, including frequencies, preprocessing, algorithms, and applications. We observed the trend that the more recent the study, the higher the frequency of Landsat time series used. We reviewed a series of image preprocessing steps, including atmospheric correction, cloud and cloud shadow detection, and composite/fusion/metrics techniques. We divided all change detection algorithms into six categories, including thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression. Within each category, six major characteristics of different algorithms, such as frequency, change index, univariate/multivariate, online/offline, abrupt/gradual change, and sub-pixel/pixel/spatial were analyzed. Moreover, some of the widely-used change detection algorithms were also discussed. Finally, we reviewed different change detection applications by dividing these applications into two categories, change target and change agent detection.

Sun position calculator (SPC) for Landsat imagery with geodetic latitudes

Science.gov (United States)

Seong, Jeong C.

2015-12-01

Landsat imagery comes with sun position information such as azimuth and sun elevation, but they are available only at the center of a scene. To aid in the use of Landsat imagery for various solar radiation applications such as topographic correction, solar power, urban heat island, agriculture, climate and vegetation, it is necessary to calculate the sun position information at every pixel. This research developed a PC application that creates sun position data layers in ArcGIS at every pixel in a Landsat scene. The SPC program is composed of two major routines - converting universal transverse Mercator (UTM) projection coordinates to geographic longitudes and latitudes, and calculating sun position information based on the Meeus' routine. For the latter, an innovative method was also implemented to account for the Earth's flattening on an ellipsoid. The Meeus routine implemented in this research showed about 0.2‧ of mean absolute difference from the National Renewable Energy Laboratory (NREL) Solar Position Algorithm (SPA) routine when solar zenith and azimuth angles were tested with every 30 min data at four city locations (Fairbanks, Atlanta, Sydney and Rio Grande) on June 30, 2014. The Meeus routine was about ten times faster than the SPA routine. Professionals who need the Sun's position information for Landsat imagery will benefit from the SPC application.

Normalizing Landsat and ASTER Data Using MODIS Data Products for Forest Change Detection

Science.gov (United States)

Gao, Feng; Masek, Jeffrey G.; Wolfe, Robert E.; Tan, Bin

2010-01-01

Monitoring forest cover and its changes are a major application for optical remote sensing. In this paper, we present an approach to integrate Landsat, ASTER and MODIS data for forest change detection. Moderate resolution (10-100m) images (e.g. Landsat and ASTER) acquired from different seasons and times are normalized to one "standard" date using MODIS data products as reference. The normalized data are then used to compute forest disturbance index for forest change detection. Comparing to the results from original data, forest disturbance index from the normalized images is more consistent spatially and temporally. This work demonstrates an effective approach for mapping forest change over a large area from multiple moderate resolution sensors on various acquisition dates.

Space science for applications - The history of Landsat

Science.gov (United States)

Mach, P. E.

1981-01-01

The history of the Landsat project is discussed in terms of three historical phases, each characterized by a dominant problem. From 1964 to 1967, the challenge was to develop interagency cooperation and to achieve consensus on basic plans for the satellite. Between 1968 and 1971, the cooperating agencies had to persuade the Bureau of the Budget to provide funding for the project. Since 1972, the challenge to NASA has been to encourage applications of the Landsat data and plan the shift from an experimental program to an operational one. The tension between experimental and operational goals has run through all these phases, and the conflicts between agencies is detailed, as well as the interaction between technological and political systems.

Seasonally-managed wetland footprint delineation using Landsat ETM+ satellite imagery

Energy Technology Data Exchange (ETDEWEB)

Quinn, Nigel W. T. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Epshtein, Olga [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Arizona State Univ., Tempe, AZ (United States). School of Sustainable Engineering and the Built Environment

2014-01-09

One major challenge in water resource management is the estimation of evapotranspiration losses from seasonally managed wetlands. Quantifying these losses is complicated by the dynamic nature of the wetlands' areal footprint during the periods of flood-up and drawdown. In this paper, we present a data-lean solution to this problem using an example application in the San Joaquin Basin, California. Through analysis of high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) satellite imagery, we develop a metric to better capture the extent of total flooded wetland area. The procedure is validated using year-long, continuously-logged field datasets for two wetlands within the study area. The proposed classification which uses a Landsat ETM + Band 5 (mid-IR wavelength) to Band 2 (visible green wavelength) ratio improves estimates by 30–50% relative to previous wetland delineation studies. Finally, requiring modest ancillary data, the study results provide a practical and efficient option for wetland management in data-sparse regions or un-gauged watersheds.

Shape selection in Landsat time series: A tool for monitoring forest dynamics

Science.gov (United States)

Gretchen G. Moisen; Mary C. Meyer; Todd A. Schroeder; Xiyue Liao; Karen G. Schleeweis; Elizabeth A. Freeman; Chris Toney

2016-01-01

We present a new methodology for fitting nonparametric shape-restricted regression splines to time series of Landsat imagery for the purpose of modeling, mapping, and monitoring annual forest disturbance dynamics over nearly three decades. For each pixel and spectral band or index of choice in temporal Landsat data, our method delivers a smoothed rendition of...

Tabular data base construction and analysis from thematic classified Landsat imagery of Portland, Oregon

Science.gov (United States)

Bryant, N. A.; George, A. J., Jr.; Hegdahl, R.

1977-01-01

A systematic verification of Landsat data classifications of the Portland, Oregon metropolitan area has been undertaken on the basis of census tract data. The degree of systematic misclassification due to the Bayesian classifier used to process the Landsat data was noted for the various suburban, industrialized and central business districts of the metropolitan area. The Landsat determinations of residential land use were employed to estimate the number of automobile trips generated in the region and to model air pollution hazards.

Study of the Nevada Test Site using Landsat satellite imagery

International Nuclear Information System (INIS)

Zimmerman, P.D.

1993-07-01

In the period covered by the purchase order CSIS has obtained one Landsat image and determined that two images previously supplied to the principal investigator under a subcontract with George Washington University were inherently defective. We have negotiated with EOSAT over the reprocessing of those scenes and anticipate final delivery within the next few weeks. A critical early purchase during the subcontract period was of an EXABYTE tape drive, Adaptec SCSI interface, and the appropriate software with which to read Landsat images at CSIS. This gives us the capability of reading and manipulating imagery in house without reliance on outside services which have not proven satisfactory. In addition to obtaining imagery for the study, we have also performed considerable analytic work on the newly and previously purchased images. A technique developed under an earlier subcontract for identifying underground nuclear tests at Pahute Mesa has been significantly refined, and similar techniques were applied to the summit of Rainier Mesa and to the Yucca Flats area. An entirely new technique for enhancing the spectral signatures of different regions of NTS was recently developed, and appears to have great promise of success

LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code

Data.gov (United States)

National Aeronautics and Space Administration — ABSTRACT: The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) is a NASA project to map disturbance, regrowth, and permanent forest conversion...

Online Global Land Surface Temperature Estimation from Landsat

Directory of Open Access Journals (Sweden)

David Parastatidis

2017-11-01

Full Text Available This study explores the estimation of land surface temperature (LST for the globe from Landsat 5, 7 and 8 thermal infrared sensors, using different surface emissivity sources. A single channel algorithm is used for consistency among the estimated LST products, whereas the option of using emissivity from different sources provides flexibility for the algorithm’s implementation to any area of interest. The Google Earth Engine (GEE, an advanced earth science data and analysis platform, allows the estimation of LST products for the globe, covering the time period from 1984 to present. To evaluate the method, the estimated LST products were compared against two reference datasets: (a LST products derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer, as higher-level products based on the temperature-emissivity separation approach; (b Landsat LST data that have been independently produced, using different approaches. An overall RMSE (root mean square error of 1.52 °C was observed and it was confirmed that the accuracy of the LST product is dependent on the emissivity; different emissivity sources provided different LST accuracies, depending on the surface cover. The LST products, for the full Landsat 5, 7 and 8 archives, are estimated “on-the-fly” and are available on-line via a web application.

Perspective View with Landsat Overlay, Salt Lake City, Utah

Science.gov (United States)

2002-01-01

Most of the population of Utah lives just west of the Wasatch Mountains in the north central part of the state. This broad east-northeastward view shows that region with the cities of Ogden, Salt Lake City, and Provo seen from left to right. The Great Salt Lake (left) and Utah Lake (right) are quite shallow and appear greenish in this enhanced natural color view. Thousands of years ago ancient Lake Bonneville covered all of the lowlands seen here. Its former shoreline is clearly seen as a wave-cut bench and/or light colored 'bathtub ring' at several places along the base of the mountain front - evidence seen from space of our ever-changing planet.This 3-D perspective view was generated using topographic data from the Shuttle Radar Topography Mission (SRTM), a Landsat 5 satellite image mosaic, and a false sky. Topographic expression is exaggerated four times.Landsat has been providing visible and infrared views of the Earth since 1972. SRTM elevation data matches the 30-meter (98-foot) resolution of most Landsat images and will substantially help in analyzing the large and growing Landsat image archive, managed by the U.S. Geological Survey (USGS).Elevation data used in this image was acquired by the Shuttle Radar Topography Mission (SRTM) aboard the Space Shuttle Endeavour, launched on Feb. 11, 2000. SRTM used the same radar instrument that comprised the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) that flew twice on the Space Shuttle Endeavour in 1994. SRTM was designed to collect 3-D measurements of the Earth's surface. To collect the 3-D data, engineers added a 60-meter (approximately 200-foot) mast, installed additional C-band and X-band antennas, and improved tracking and navigation devices. The mission is a cooperative project between NASA, the National Imagery and Mapping Agency (NIMA) of the U.S. Department of Defense and the German and Italian space agencies. It is managed by NASA's Jet Propulsion Laboratory, Pasadena, Calif

Detecting Uniform Areas for Vicarious Calibration using Landsat TM Imagery: A Study using the Arabian and Saharan Deserts

Science.gov (United States)

Hilbert, Kent; Pagnutti, Mary; Ryan, Robert; Zanoni, Vicki

2002-01-01

This paper discusses a method for detecting spatially uniform sites need for radiometric characterization of remote sensing satellites. Such information is critical for scientific research applications of imagery having moderate to high resolutions (African Saharan and Arabian deserts contained extremely uniform sites with respect to spatial characteristics. We developed an algorithm for detecting site uniformity and applied it to orthorectified Landsat Thematic Mapper (TM) imagery over eight uniform regions of interest. The algorithm's results were assessed using both medium-resolution (30-m GSD) Landsat 7 ETM+ and fine-resolution (research shows that Landsat TM products appear highly useful for detecting potential calibration sites for system characterization. In particular, the approach detected spatially uniform regions that frequently occur at multiple scales of observation.

Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations using multi-temporal Landsat 8 NDVI anomalies

Science.gov (United States)

Chemura, Abel; Mutanga, Onisimo; Dube, Timothy

2017-05-01

The development of cost-effective, reliable and easy to implement crop condition monitoring methods is urgently required for perennial tree crops such as coffee (Coffea arabica), as they are grown over large areas and represent long term and higher levels of investment. These monitoring methods are useful in identifying farm areas that experience poor crop growth, pest infestation, diseases outbreaks and/or to monitor response to management interventions. This study compares field level coffee mean NDVI and LSWI anomalies and age-adjusted coffee mean NDVI and LSWI anomalies in identifying and mapping incongruous patches across perennial coffee plantations. To achieve this objective, we first derived deviation of coffee pixels from the global coffee mean NDVI and LSWI values of nine sequential Landsat 8 OLI image scenes. We then evaluated the influence of coffee age class (young, mature and old) on Landsat-scale NDVI and LSWI values using a one-way ANOVA and since results showed significant differences, we adjusted NDVI and LSWI anomalies for age-class. We then used the cumulative inverse distribution function (α ≤ 0.05) to identify fields and within field areas with excessive deviation of NDVI and LSWI from the global and the age-expected mean for each of the Landsat 8 OLI scene dates spanning three seasons. Results from accuracy assessment indicated that it was possible to separate incongruous and healthy patches using these anomalies and that using NDVI performed better than using LSWI for both global and age-adjusted mean anomalies. Using the age-adjusted anomalies performed better in separating incongruous and healthy patches than using the global mean for both NDVI (Overall accuracy = 80.9% and 68.1% respectively) and for LSWI (Overall accuracy = 68.1% and 48.9% respectively). When applied to other Landsat 8 OLI scenes, the results showed that the proportions of coffee fields that were modelled incongruent decreased with time for the young age category and

A Hybrid Color Mapping Approach to Fusing MODIS and Landsat Images for Forward Prediction

OpenAIRE

Chiman Kwan; Bence Budavari; Feng Gao; Xiaolin Zhu

2018-01-01

We present a new, simple, and efficient approach to fusing MODIS and Landsat images. It is well known that MODIS images have high temporal resolution and low spatial resolution, whereas Landsat images are just the opposite. Similar to earlier approaches, our goal is to fuse MODIS and Landsat images to yield high spatial and high temporal resolution images. Our approach consists of two steps. First, a mapping is established between two MODIS images, where one is at an earlier time, t1, and the...

SRTM Perspective View with Landsat Overlay: San Jose, Costa Rica

Science.gov (United States)

2001-01-01

This perspective view shows the capital city of San Jose, Costa Rica, in the right center of the image (gray area). Rising behind it are the volcanoes Irazu, 3402 meters high (11,161 feet) and Turrialba, 3330 meters high (10,925 feet.)Irazu is the highest volcano in Costa Rica and is located in the Irazu Volcano National Park, established in 1955. There have been at least 23 eruptions of Irazu since 1723, the most recent during 1963 to 1965. This activity sent tephra and secondary mudflows into cultivated areas, caused at least 40 deaths, and destroyed 400 houses and some factories.This image was generated in support of the Central American Commission for Environment and Development through an agreement with NASA. The Commission involves eight nations working to develop the Mesoamerican Biological Corridor, an effort to study and preserve some of the most biologically diverse regions of the planet.This three-dimensional perspective view was generated using topographic data from the Shuttle Radar Topography Mission (SRTM) and an enhanced false-color Landsat 7 satellite image. Colors are from Landsat bands 5, 4, and 2 as red, green and blue, respectively. Topographic expression is exaggerated 2X.Landsat has been providing visible and infrared views of the Earth since 1972. SRTM elevation data matches the 30-meter resolution of most Landsat images and will substantially help in analyses of the large and growing Landsat image archive. The Landsat 7 Thematic Mapper image used here was provided to the SRTM by the United States Geological Survey, Earth Resources Observation Systems (EROS) Data Center, Sioux Falls, South Dakota.Elevation data used in this image was acquired by the Shuttle Radar Topography Mission (SRTM) aboard the Space Shuttle Endeavour, launched on February 11,2000. SRTM used the same radar instrument that comprised the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) that flew twice on the Space Shuttle Endeavour in 1994. SRTM

An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band

Directory of Open Access Journals (Sweden)

Jordi Cristóbal

2018-03-01

Full Text Available Land surface temperature (LST is one of the sources of input data for modeling land surface processes. The Landsat satellite series is the only operational mission with more than 30 years of archived thermal infrared imagery from which we can retrieve LST. Unfortunately, stray light artifacts were observed in Landsat-8 TIRS data, mostly affecting Band 11, currently making the split-window technique impractical for retrieving surface temperature without requiring atmospheric data. In this study, a single-channel methodology to retrieve surface temperature from Landsat TM and ETM+ was improved to retrieve LST from Landsat-8 TIRS Band 10 using near-surface air temperature (Ta and integrated atmospheric column water vapor (w as input data. This improved methodology was parameterized and successfully evaluated with simulated data from a global and robust radiosonde database and validated with in situ data from four flux tower sites under different types of vegetation and snow cover in 44 Landsat-8 scenes. Evaluation results using simulated data showed that the inclusion of Ta together with w within a single-channel scheme improves LST retrieval, yielding lower errors and less bias than models based only on w. The new proposed LST retrieval model, developed with both w and Ta, yielded overall errors on the order of 1 K and a bias of −0.5 K validated against in situ data, providing a better performance than other models parameterized using w and Ta or only w models that yielded higher error and bias.

Glacial lake monitoring in the Karakoram Range using historical Landsat Thematic Mapper archive (1982 - 2014)

Science.gov (United States)

Chan, J. Y. H.; Kelly, R. E. J.; Evans, S. G.

2014-12-01

Glacierized regions are one of the most dynamic land surface environments on the planet (Evans and Delaney, In Press). They are susceptible to various types of natural hazards such as landslides, glacier avalanches, and glacial lake outburst floods (GLOF). GLOF events are increasingly common and present catastrophic flood hazards, the causes of which are sensitive to climate change in complex high mountain topography (IPCC, 2013). Inundation and debris flows from GLOF events have repeatedly caused significant infrastructure damages and loss of human lives in the high mountain regions of the world (Huggel et al, 2002). The research is designed to develop methods for the consistent detection of glacier lakes formation during the Landsat Thematic Mapper (TM) era (1982 - present), to quantify the frequency of glacier lake development and estimate lake volume using Landsat imagery and digital elevation model (DEM) data. Landsat TM scenes are used to identify glacier lakes in the Shimshal and Shaksgam valley, particularly the development of Lake Virjeab in year 2000 and Kyagar Lake in 1998. A simple thresholding technique using Landsat TM infrared bands, along with object-based segmentation approaches are used to isolate lake extent. Lake volume is extracted by intersecting the lake extent with the DEM surface. Based on previous studies and DEM characterization in the region, Shuttle Radar Topography Mission (SRTM) DEM is preferred over Advanced Spaceborne Thermal Emission and Reflection (ASTER) GDEM due to higher accuracy. Calculated errors in SRTM height estimates are 5.81 m compared with 8.34 m for ASTER. SRTM data are preferred because the DEM measurements were made over short duration making the DEM internally consistent. Lake volume derived from the Landsat TM imagery and DEM are incorporated into a simple GLOF model identified by Clague and Matthews (1973) to estimate the potential peak discharge (Qmax) of a GLOF event. We compare the simple Qmax estimates with

Preparing Landsat Image Time Series (LITS for Monitoring Changes in Vegetation Phenology in Queensland, Australia

Directory of Open Access Journals (Sweden)

Santosh Bhandari

2012-06-01

Full Text Available Time series of images are required to extract and separate information on vegetation change due to phenological cycles, inter-annual climatic variability, and long-term trends. While images from the Landsat Thematic Mapper (TM sensor have the spatial and spectral characteristics suited for mapping a range of vegetation structural and compositional properties, its 16-day revisit period combined with cloud cover problems and seasonally limited latitudinal range, limit the availability of images at intervals and durations suitable for time series analysis of vegetation in many parts of the world. Landsat Image Time Series (LITS is defined here as a sequence of Landsat TM images with observations from every 16 days for a five-year period, commencing on July 2003, for a Eucalyptus woodland area in Queensland, Australia. Synthetic Landsat TM images were created using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM algorithm for all dates when images were either unavailable or too cloudy. This was done using cloud-free scenes and a MODIS Nadir BRDF Adjusted Reflectance (NBAR product. The ability of the LITS to measure attributes of vegetation phenology was examined by: (1 assessing the accuracy of predicted image-derived Foliage Projective Cover (FPC estimates using ground-measured values; and (2 comparing the LITS-generated normalized difference vegetation index (NDVI and MODIS NDVI (MOD13Q1 time series. The predicted image-derived FPC products (value ranges from 0 to 100% had an RMSE of 5.6. Comparison between vegetation phenology parameters estimated from LITS-generated NDVI and MODIS NDVI showed no significant difference in trend and less than 16 days (equal to the composite period of the MODIS data used difference in key seasonal parameters, including start and end of season in most of the cases. In comparison to similar published work, this paper tested the STARFM algorithm in a new (broadleaf forest environment and also

Design and implementation of the next generation Landsat satellite communications system

Science.gov (United States)

Mah, Grant R.; O'Brien, Michael; Garon, Howard; Mott, Claire; Ames, Alan; Dearth, Ken

2012-01-01

The next generation Landsat satellite, Landsat 8 (L8), also known as the Landsat Data Continuity Mission (LDCM), uses a highly spectrally efficient modulation and data formatting approach to provide large amounts of downlink (D/L) bandwidth in a limited X-Band spectrum allocation. In addition to purely data throughput and bandwidth considerations, there were a number of additional constraints based on operational considerations for prevention of interference with the NASA Deep-Space Network (DSN) band just above the L8 D/L band, minimization of jitter contributions to prevent impacts to instrument performance, and the need to provide an interface to the Landsat International Cooperator (IC) community. A series of trade studies were conducted to consider either X- or Ka-Band, modulation type, and antenna coverage type, prior to the release of the request for proposal (RFP) for the spacecraft. Through use of the spectrally efficient rate-7/8 Low-Density Parity-Check error-correction coding and novel filtering, an XBand frequency plan was developed that balances all the constraints and considerations, while providing world-class link performance, fitting 384 Mbits/sec of data into the 375 MHz X-Band allocation with bit-error rates better than 10-12 using an earth-coverage antenna.

Cross calibration of the Landsat-7 ETM+ and EO-1 ALI sensor

Science.gov (United States)

Chander, G.; Meyer, D.J.; Helder, D.L.

2004-01-01

As part of the Earth Observer 1 (EO-1) Mission, the Advanced Land Imager (ALI) demonstrates a potential technological direction for Landsat Data Continuity Missions. To evaluate ALI's capabilities in this role, a cross-calibration methodology has been developed using image pairs from the Landsat-7 (L7) Enhanced Thematic Mapper Plus (ETM+) and EO-1 (ALI) to verify the radiometric calibration of ALI with respect to the well-calibrated L7 ETM+ sensor. Results have been obtained using two different approaches. The first approach involves calibration of nearly simultaneous surface observations based on image statistics from areas observed simultaneously by the two sensors. The second approach uses vicarious calibration techniques to compare the predicted top-of-atmosphere radiance derived from ground reference data collected during the overpass to the measured radiance obtained from the sensor. The results indicate that the relative sensor chip assemblies gains agree with the ETM+ visible and near-infrared bands to within 2% and the shortwave infrared bands to within 4%.

Two techniques for mapping and area estimation of small grains in California using Landsat digital data

Science.gov (United States)

Sheffner, E. J.; Hlavka, C. A.; Bauer, E. M.

1984-01-01

Two techniques have been developed for the mapping and area estimation of small grains in California from Landsat digital data. The two techniques are Band Ratio Thresholding, a semi-automated version of a manual procedure, and LCLS, a layered classification technique which can be fully automated and is based on established clustering and classification technology. Preliminary evaluation results indicate that the two techniques have potential for providing map products which can be incorporated into existing inventory procedures and automated alternatives to traditional inventory techniques and those which currently employ Landsat imagery.

Using the Landsat data archive to assess long-term regional forest dynamics assessment in Eastern Europe, 1985-2012

Science.gov (United States)

Turubanova, S.; Potapov, P.; Krylov, A.; Tyukavina, A.; McCarty, J. L.; Radeloff, V. C.; Hansen, M. C.

2015-04-01

Dramatic political and economic changes in Eastern European countries following the dissolution of the "Eastern Bloc" and the collapse of the Soviet Union greatly affected land-cover and land-use trends. In particular, changes in forest cover dynamics may be attributed to the collapse of the planned economy, agricultural land abandonment, economy liberalization, and market conditions. However, changes in forest cover are hard to quantify given inconsistent forest statistics collected by different countries over the last 30 years. The objective of our research was to consistently quantify forest cover change across Eastern Europe from 1985 until 2012 using the complete Landsat data archive. We developed an algorithm for processing imagery from different Landsat platforms and sensors (TM and ETM+), aggregating these images into a common set of multi-temporal metrics, and mapping annual gross forest cover loss and decadal gross forest cover gain. Our results show that forest cover area increased from 1985 to 2012 by 4.7% across the region. Average annual gross forest cover loss was 0.41% of total forest cover area, with a statistically significant increase from 1985 to 2012. Most forest disturbance recovered fast, with only 12% of the areas of forest loss prior to 1995 not being recovered by 2012. Timber harvesting was the main cause of forest loss. Logging area declined after the collapse of socialism in the late 1980s, increased in the early 2000s, and decreased in most countries after 2007 due to the global economic crisis. By 2012, Central and Baltic Eastern European countries showed higher logging rates compared to their Western neighbours. Comparing our results with official forest cover and change estimates showed agreement in total forest area for year 2010, but with substantial disagreement between Landsat-based and official net forest cover area change. Landsat-based logging areas exhibit strong relationship with reported roundwood production at national

Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

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Tetsuji Ota

2014-11-01

Full Text Available In this study, we test and demonstrate the utility of disturbance and recovery information derived from annual Landsat time series to predict current forest vertical structure (as compared to the more common approaches, that consider a sample of airborne Lidar and single-date Landsat derived variables. Mean Canopy Height (MCH was estimated separately using single date, time series, and the combination of single date and time series variables in multiple regression and random forest (RF models. The combination of single date and time series variables, which integrate disturbance history over the entire time series, overall provided better MCH prediction than using either of the two sets of variables separately. In general, the RF models resulted in improved performance in all estimates over those using multiple regression. The lowest validation error was obtained using Landsat time series variables in a RF model (R2 = 0.75 and RMSE = 2.81 m. Combining single date and time series data was more effective when the RF model was used (opposed to multiple regression. The RMSE for RF mean canopy height prediction was reduced by 13.5% when combining the two sets of variables as compared to the 3.6% RMSE decline presented by multiple regression. This study demonstrates the value of airborne Lidar and long term Landsat observations to generate estimates of forest canopy height using the random forest algorithm.

Assessment of Forest Damage in Croatia using Landsat-8 OLI Images

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Anita Simic Milas

2015-05-01

Full Text Available Background and Purpose: Rapid assessments of forest damage caused by natural disasters such as ice-break, wind, flooding, hurricane, or forest fires are necessary for mitigation and forest management. Forest damage directly impacts carbon uptake and biogeochemical cycles, and thus, has an impact on climate change. It intensifies erosion and flooding, and influences socio-economic well-being of population. Quantification of forest cover change represents a challenge for the scientific community as damaged areas are often in the mountainous and remote regions. Forested area in the western Croatia was considerably damaged by ice-breaking and flooding in 2014. Satellite remote sensing technology has opened up new possibilities for detecting and quantifying forest damage. Several remote sensing tools are available for rapid assessment of forest damage. These include aerial photographic interpretation, and airborne and satellite imagery. This study evaluates the capability of Landsat-8 optical data and a vegetation index for mapping forest damage in Croatia that occurred during the winter of 2014. Materials and Methods: The change detection analysis in this study was based on the Normalized Difference Vegetation Index (NDVI difference approach, where pre- and post- event Landsat-8 images were employed in the ENVI image change workflow. The validation was done by comparing the satellite-generated change detection map with the ground truth data based on field observations and spatial data of forest management units and plans. Results: The overall damage assessment from this study suggests that the total damaged area covers 45,265.32 ha of forest. It is 19.20% less than estimated by Vuletić et al. [3] who found that 56,021.86 ha of forest were affected. Most damage was observed in the mixed, broadleaf and coniferous forest. The change errors of commission and omission were calculated to be 35.73% and 31.60%, respectively. Conclusions: Landsat-8 optical

Monitoring Thermal Pollution in Rivers Downstream of Dams with Landsat ETM+ Thermal Infrared Images

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Feng Ling

2017-11-01

Full Text Available Dams play a significant role in altering the spatial pattern of temperature in rivers and contribute to thermal pollution, which greatly affects the river aquatic ecosystems. Understanding the temporal and spatial variation of thermal pollution caused by dams is important to prevent or mitigate its harmful effect. Assessments based on in-situ measurements are often limited in practice because of the inaccessibility of water temperature records and the scarcity of gauges along rivers. By contrast, thermal infrared remote sensing provides an alternative approach to monitor thermal pollution downstream of dams in large rivers, because it can cover a large area and observe the same zone repeatedly. In this study, Landsat Enhanced Thematic Mapper Plus (ETM+ thermal infrared imagery were applied to assess the thermal pollution caused by two dams, the Geheyan Dam and the Gaobazhou Dam, located on the Qingjiang River, a tributary of the Yangtze River downstream of the Three Gorges Reservoir in Central China. The spatial and temporal characteristics of thermal pollution were analyzed with water temperatures estimated from 54 cloud-free Landsat ETM+ scenes acquired in the period from 2000 to 2014. The results show that water temperatures downstream of both dams are much cooler than those upstream of both dams in summer, and the water temperature remains stable along the river in winter, showing evident characteristic of the thermal pollution caused by dams. The area affected by the Geheyan Dam reaches beyond 20 km along the downstream river, and that affected by the Gaobazhou Dam extends beyond the point where the Qingjiang River enters the Yangtze River. Considering the long time series and global coverage of Landsat ETM+ imagery, the proposed technique in the current study provides a promising method for globally monitoring the thermal pollution caused by dams in large rivers.

Data fusion of Landsat TM and IRS images in forest classification

Science.gov (United States)

Guangxing Wang; Markus Holopainen; Eero Lukkarinen

2000-01-01

Data fusion of Landsat TM images and Indian Remote Sensing satellite panchromatic image (IRS-1C PAN) was studied and compared to the use of TM or IRS image only. The aim was to combine the high spatial resolution of IRS-1C PAN to the high spectral resolution of Landsat TM images using a data fusion algorithm. The ground truth of the study was based on a sample of 1,020...

Significant results from using earth observation satellites for mineral and energy resource exploration

Science.gov (United States)

Carter, William D.

1981-01-01

A large number of Earth-observation satellites orbit our world several times each day, providing new information about the land and sea surfaces and the overlying thin layer of atmosphere that makes our planet unique. Meteorological satellites have had the longest history of experimental use and most are now considered operational. The geologic information collected by the Landsat, Polar Orbiting Geophysical Observatory (POGO), Magsat, Heat Capacity Mapping Mission (HCMM) and Seasat land and ocean observation systems is being thoroughly tested, and some of these systems are now approaching operational use.

Continuous 1985-2012 Landsat monitoring to assess fire effects on meadows in Yosemite National Park, California

Science.gov (United States)

Soulard, Christopher E.; Albano, Christine M.; Villarreal, Miguel; Walker, Jessica

2016-01-01

To assess how montane meadow vegetation recovered after a wildfire that occurred in Yosemite National Park, CA in 1996, Google Earth Engine image processing was applied to leverage the entire Landsat Thematic Mapper archive from 1985 to 2012. Vegetation greenness (normalized difference vegetation index [NDVI]) was summarized every 16 days across the 28-year Landsat time series for 26 meadows. Disturbance event detection was hindered by the subtle influence of low-severity fire on meadow vegetation. A hard break (August 1996) was identified corresponding to the Ackerson Fire, and monthly composites were used to compare NDVI values and NDVI trends within burned and unburned meadows before, immediately after, and continuously for more than a decade following the fire date. Results indicate that NDVI values were significantly lower at 95% confidence level for burned meadows following the fire date, yet not significantly lower at 95% confidence level in the unburned meadows. Burned meadows continued to exhibit lower monthly NDVI in the dormant season through 2012. Over the entire monitoring period, the negative-trending, dormant season NDVI slopes in the burned meadows were also significantly lower than unburned meadows at 90% confidence level. Lower than average NDVI values and slopes in the dormant season compared to unburned meadows, coupled with photographic evidence, strongly suggest that evergreen vegetation was removed from the periphery of some meadows after the fire. These analyses provide insight into how satellite imagery can be used to monitor low-severity fire effects on meadow vegetation.

Generating Global Leaf Area Index from Landsat: Algorithm Formulation and Demonstration

Science.gov (United States)

Ganguly, Sangram; Nemani, Ramakrishna R.; Zhang, Gong; Hashimoto, Hirofumi; Milesi, Cristina; Michaelis, Andrew; Wang, Weile; Votava, Petr; Samanta, Arindam; Melton, Forrest;

A Useful Tool for Atmospheric Correction and Surface Temperature Estimation of Landsat Infrared Thermal Data

Science.gov (United States)

Rivalland, Vincent; Tardy, Benjamin; Huc, Mireille; Hagolle, Olivier; Marcq, Sébastien; Boulet, Gilles

2016-04-01

Land Surface temperature (LST) is a critical variable for studying the energy and water budgets at the Earth surface, and is a key component of many aspects of climate research and services. The Landsat program jointly carried out by NASA and USGS has been providing thermal infrared data for 40 years, but no associated LST product has been yet routinely proposed to community. To derive LST values, radiances measured at sensor-level need to be corrected for the atmospheric absorption, the atmospheric emission and the surface emissivity effect. Until now, existing LST products have been generated with multi channel methods such as the Temperature/Emissivity Separation (TES) adapted to ASTER data or the generalized split-window algorithm adapted to MODIS multispectral data. Those approaches are ill-adapted to the Landsat mono-window data specificity. The atmospheric correction methodology usually used for Landsat data requires detailed information about the state of the atmosphere. This information may be obtained from radio-sounding or model atmospheric reanalysis and is supplied to a radiative transfer model in order to estimate atmospheric parameters for a given coordinate. In this work, we present a new automatic tool dedicated to Landsat thermal data correction which improves the common atmospheric correction methodology by introducing the spatial dimension in the process. The python tool developed during this study, named LANDARTs for LANDsat Automatic Retrieval of surface Temperature, is fully automatic and provides atmospheric corrections for a whole Landsat tile. Vertical atmospheric conditions are downloaded from the ERA Interim dataset from ECMWF meteorological organization which provides them at 0.125 degrees resolution, at a global scale and with a 6-hour-time step. The atmospheric correction parameters are estimated on the atmospheric grid using the commercial software MODTRAN, then interpolated to 30m resolution. We detail the processing steps

Spatiotemporal Built-up Land Density Mapping Using Various Spectral Indices in Landsat-7 ETM+ and Landsat-8 OLI/TIRS (Case Study: Surakarta City)

Science.gov (United States)

Risky, Yanuar S.; Aulia, Yogi H.; Widayani, Prima

2017-12-01

Spectral indices variations support for rapid and accurate extracting information such as built-up density. However, the exact determination of spectral waves for built-up density extraction is lacking. This study explains and compares the capabilities of 5 variations of spectral indices in spatiotemporal built-up density mapping using Landsat-7 ETM+ and Landsat-8 OLI/TIRS in Surakarta City on 2002 and 2015. The spectral indices variations used are 3 mid-infrared (MIR) based indices such as the Normalized Difference Built-up Index (NDBI), Urban Index (UI) and Built-up and 2 visible based indices such as VrNIR-BI (visible red) and VgNIR-BI (visible green). Linear regression statistics between ground value samples from Google Earth image in 2002 and 2015 and spectral indices for determining built-up land density. Ground value used amounted to 27 samples for model and 7 samples for accuracy test. The classification of built-up density mapping is divided into 9 classes: unclassified, 0-12.5%, 12.5-25%, 25-37.5%, 37.5-50%, 50-62.5%, 62.5-75%, 75-87.5% and 87.5-100 %. Accuracy of built-up land density mapping in 2002 and 2015 using VrNIR-BI (81,823% and 73.235%), VgNIR-BI (78.934% and 69.028%), NDBI (34.870% and 74.365%), UI (43.273% and 64.398%) and Built-up (59.755% and 72.664%). Based all spectral indices, Surakarta City on 2000-2015 has increased of built-up land density. VgNIR-BI has better capabilities for built-up land density mapping on Landsat-7 ETM + and Landsat-8 OLI/TIRS.

Landsat Data Continuity Mission (LDCM) Standard Product Generation and Characteristics

Science.gov (United States)

Micijevic, E.; Hayes, R.

2012-12-01

The LDCM's Landsat 8 (L8), planned for launch in February 2013, is the latest satellite in the 40 year history of the Landsat program. The satellite will have two imagers: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The data from both sensors will be processed and combined into the final Level 1 Terrain (L1T) standard product by the Landsat Product Generation System (LPGS) at the USGS Earth Resources Observation and Science (EROS). Landsat 8 products will nominally have 11 image bands; however, products will still be created if OLI only, or TIRS only collections are acquired. The LPGS is designed to create L1T products from Level 0 data by merging OLI and TIRS outputs and performing systematic radiometric and geometric corrections, followed by precision and terrain corrections that include Ground Control Points (GCP), and a Digital Elevation Model (DEM) for topographic accuracy. Scenes that have a quality score of 9 or greater and a percent cloud cover less than 40 will be automatically processed. In addition, any archived scene, regardless of cloud cover, can be requested for processing through USGS EROS clients, GloVis or Earth Explorer. While most data will be processed as Level L1T, some scenes will not have ground control or elevation data necessary for precision or terrain correction, respectively. In these cases, the best level of correction will be applied (Level 1G-systematic or Level 1Gt-systematic terrain). The standard Level 1T products will contain scaled Top of Atmosphere (TOA) reflectance data, only for OLI. The conversion between radiance and reflectance within radiometric processing (L1R) will be performed using the band specific coefficients that are proportional to the respective exoatmospheric solar irradiances and the Earth-Sun distance for the scene's acquisition day. The TIRS data will contain scaled at-sensor radiances and no at-sensor brightness temperature or emissivity conversions are planned. For users that

Use of landsat ETM+ SLC-off segment-based gap-filled imagery for crop type mapping

Science.gov (United States)

Maxwell, S.K.; Craig, M.E.

2008-01-01

Failure of the Scan Line Corrector (SLC) on the Landsat ETM+ sensor has had a major impact on many applications that rely on continuous medium resolution imagery to meet their objectives. The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) program uses Landsat imagery as the primary source of data to produce crop-specific maps for 20 states in the USA. A new method has been developed to fill the image gaps resulting from the SLC failure to support the needs of Landsat users who require coincident spectral data, such as for crop type mapping and monitoring. We tested the new gap-filled method for a CDL crop type mapping project in eastern Nebraska. Scan line gaps were simulated on two Landsat 5 images (spring and late summer 2003) and then gap-filled using landscape boundary models, or segment models, that were derived from 1992 and 2002 Landsat images (used in the gap-fill process). Various date combinations of original and gap-filled images were used to derive crop maps using a supervised classification process. Overall kappa values were slightly higher for crop maps derived from SLC-off gap-filled images compared to crop maps derived from the original imagery (0.3–1.3% higher). Although the age of the segment model used to derive the SLC-off gap-filled product did not negatively impact the overall agreement, differences in individual cover type agreement did increase (−0.8%–1.6% using the 2002 segment model to −5.0–5.1% using the 1992 segment model). Classification agreement also decreased for most of the classes as the size of the segment used in the gap-fill process increased.

Regional analysis of tertiary volcanic Calderas (western U.S.) using Landsat Thematic Mapper imagery

Science.gov (United States)

Spatz, David M.; Taranik, James V.

1989-01-01

The Landsat Thematic Mapper (TM) imagery of the Basin and Range province of southern Nevada was analyzed to identify and map volcanic rock assemblages at three Tertiary calderas. It was found that the longer-wavelength visible and the NIR TM Bands 3, 5, and 7 provide more effective lithologic discrimination than the shorter-wavelength bands, due partly to deeper penetration of the longer-wavelength bands, resulting in more lithologically driven radiances. Shorter-wavelength TM Bands 1 and 2 are affected more by surficial weathering products including desert varnish which may or may not provide an indirect link to lithologic identity. Guidelines for lithologic analysis of volcanic terrains using Landsat TM imagery are outlined.

Interpretasi Vulkanostratigrafi Daerah Mamuju Berdasarkan Analisis Citra Landsat-8

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Frederikus Dian Indrastomo

2015-11-01

Full Text Available Mamuju and its surrounding area are constructed mainly by volcanic rocks. Volcanoclastic sedimentary rocks and limestones are laid above the volcanic rocks. Volcanic activities create some unique morphologies such as craters, lava domes, and pyroclastic flow paths as their volcanic products. These products are identified from their circular features characters on Landsat-8 imagery. After geometric and atmospheric corrections had been done, a visual interpretation on Landsat-8 imagery was conducted to identify structure, geomorphology, and geological condition of the area. Regional geological structures show trend to southeast – northwest direction which is affects the formation of Adang volcano. Geomorphology of the area are classified into 16 geomorphology units based on their genetic aspects, i.e Sumare fault block ridge, Mamuju cuesta ridge, Adang eruption crater, Labuhan Ranau eruption crater, Sumare eruption crater, Ampalas volcanic cone, Adang lava dome, Labuhan Ranau intrusion hill, Adang pyroclastic flow ridge, Sumare pyroclastic flow ridge, Adang volcanic remnant hills, Malunda volcanic remnant hills, Talaya volcanic remnant hills, Tapalang karst hills, Mamuju alluvium plains, and Karampuang reef terrace plains. Based on the Landsat-8 imagery interpretation result and field confirmation, the geology of Mamuju area is divided into volcanic rocks and sedimentary rocks. There are two groups of volcanic rocks; Talaya complex and Mamuju complex. The Talaya complex consists of Mambi, Malunda, and Kalukku volcanic rocks with andesitic composition, while Mamuju complex consist of Botteng, Ahu, Tapalang, Adang, Ampalas, Sumare, danLabuhanRanau volcanic rocks with andesite to leucitic basalt composition. The volcanostratigraphy of Mamuju area was constructed based on its structure, geomorphology and lithology distribution analysis. Volcanostratigraphy of Mamuju area is classified into Khuluk Talaya and Khuluk Mamuju. The Khuluk Talaya consists

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

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Julio Pastor-Guzman

2015-11-01

Full Text Available There is a need to develop indicators of mangrove condition using remotely sensed data. However, remote estimation of leaf and canopy biochemical properties and vegetation condition remains challenging. In this paper, we (i tested the performance of selected hyperspectral and broad band indices to predict chlorophyll concentration (CC on mangrove leaves and (ii showed the potential of Landsat 8 for estimation of mangrove CC at the landscape level. Relative leaf CC and leaf spectral response were measured at 12 Elementary Sampling Units (ESU distributed along the northwest coast of the Yucatan Peninsula, Mexico. Linear regression models and coefficients of determination were computed to measure the association between CC and spectral response. At leaf level, the narrow band indices with the largest correlation with CC were Vogelmann indices and the MTCI (R2 > 0.5. Indices with spectral bands around the red edge (705–753 nm were more sensitive to mangrove leaf CC. At the ESU level Landsat 8 NDVI green, which uses the green band in its formulation explained most of the variation in CC (R2 > 0.8. Accuracy assessment between estimated CC and observed CC using the leave-one-out cross-validation (LOOCV method yielded a root mean squared error (RMSE = 15 mg·cm−2, and R2 = 0.703. CC maps showing the spatiotemporal variation of CC at landscape scale were created using the linear model. Our results indicate that Landsat 8 NDVI green can be employed to estimate CC in large mangrove areas where ground networks cannot be applied, and mapping techniques based on satellite data, are necessary. Furthermore, using upcoming technologies that will include two bands around the red edge such as Sentinel 2 will improve mangrove monitoring at higher spatial and temporal resolutions.

Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI

OpenAIRE

Limin Liao; Jinling Song; Jindi Wang; Zhiqiang Xiao; Jian Wang

2016-01-01

Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resol...

Application of spectrometer cropscan MSR 16R and Landsat imagery for identification the spectral characteristics of land cover

Science.gov (United States)

Tampubolon, Togi; Abdullah, Khiruddin bin; San, Lim Hwee

2013-09-01

The spectral characteristics of land cover are basic references in classifying satellite image for geophysics analysis. It can be obtained from the measurements using spectrometer and satellite image processing. The aims of this study to investigate the spectral characteristics of land cover based on the results of measurement using Spectrometer Cropscan MSR 16R and Landsat satellite imagery. The area of study in this research is in Medan, (Deli Serdang, North Sumatera) Indonesia. The scope of this study is the basic survey from the measurements of spectral land cover which is covered several type of land such as a cultivated and managed terrestrial areas, natural and semi-natural, cultivated aquatic or regularly flooded areas, natural and semi-natural aquatic, artificial surfaces and associated areas, bare areas, artificial waterbodies and natural waterbodies. The measurement and verification were conducted using a spectrometer provided their spectral characteristics and Landsat imagery, respectively. The results of the spectral characteristics of land cover shows that each type of land cover have a unique characteristic. The correlation of spectral land cover based on spectrometer Cropscan MSR 16R and Landsat satellite image are above 90 %. However, the land cover of artificial waterbodiese have a correlation under 40 %. That is because the measurement of spectrometer Cropscan MSR 16R and acquisition of Landsat satellite imagery has a time different.

Using GPS-surveyed intertidal zones to determine the validity of shorelines automatically mapped by Landsat water indices

Science.gov (United States)

Kelly, Joshua T.; Gontz, Allen M.

2018-03-01

Satellite remote sensing has been used extensively in a variety of shoreline studies and validated using aerial photography. This ground truth method only represents an instantaneous depiction of the shoreline at the time of acquisition and does not take into account the spatial and temporal variability of the dynamic shoreline boundary. Landsat 8‧s Operational Land Imager sensor's capability to accurately delineate a shoreline is assessed by comparing all known Landsat water index-derived shorelines with two GPS-surveyed intertidal zones that coincide with the satellite flyover date, one of which had near-neap tide conditions. Seven indices developed for automatically classifying water pixels were evaluated for their ability to delineate shorelines. The shoreline is described here as the area above and below maximum low and high tide, otherwise known as the intertidal zone. The high-water line, or wet/dry sediment line, was chosen as the shoreline indicator to be mapped using a handheld GPS. The proportion of the Landsat-derived shorelines that fell within this zone and their alongshore profile lengths were calculated. The most frequently used water index and the predecessor to Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI), was found to be the least accurate by a significant margin. Other indices required calibration of their threshold value to achieve accurate results, thus diminishing their replicability success for other regions. MNDWI was determined to be the best index for automated shoreline mapping, based on its superior accuracy and repeatable, stable threshold value.

Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images

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Weili Kou

2015-01-01

Full Text Available Accurate and updated finer resolution maps of rubber plantations and stand ages are needed to understand and assess the impacts of rubber plantations on regional ecosystem processes. This study presented a simple method for mapping rubber plantation areas and their stand ages by integration of PALSAR 50-m mosaic images and multi-temporal Landsat TM/ETM+ images. The L-band PALSAR 50-m mosaic images were used to map forests (including both natural forests and rubber trees and non-forests. For those PALSAR-based forest pixels, we analyzed the multi-temporal Landsat TM/ETM+ images from 2000 to 2009. We first studied phenological signatures of deciduous rubber plantations (defoliation and foliation and natural forests through analysis of surface reflectance, Normal Difference Vegetation Index (NDVI, Enhanced Vegetation Index (EVI, and Land Surface Water Index (LSWI and generated a map of rubber plantations in 2009. We then analyzed phenological signatures of rubber plantations with different stand ages and generated a map, in 2009, of rubber plantation stand ages (≤5, 6–10, >10 years-old based on multi-temporal Landsat images. The resultant maps clearly illustrated how rubber plantations have expanded into the mountains in the study area over the years. The results in this study demonstrate the potential of integrating microwave (e.g., PALSAR and optical remote sensing in the characterization of rubber plantations and their expansion over time.

Geomorphic domains and linear features on Landsat images, Circle Quadrangle, Alaska

Science.gov (United States)

Simpson, S.L.

1984-01-01

feature data, corresponds to the strike of foliations in metamorphic rocks and magnetic anomalies reflecting compositional variations suggesting that most linear features in the southern part of the quadrangle probably are related to lithologic variations brought about by folding and foliation of metamorphic rocks. A second important trend interval, N.14-35E., may be related to thrusting south of the Tintina fault zone, as high concentrations of linear features within this interval are found in areas of mapped thrusts. Low concentrations of linear features are found in areas of most igneous intrusives. High concentrations of linear features do not correspond to areas of mineralization in any consistent or significant way that would allow concentration patterns to be easily used as an aid in locating areas of mineralization. The results of this remote sensing study indicate that there are several possibly important areas where further detailed studies are warranted.

Landsat analysis of the Yangjiatan tungsten district, Hunan Province, People's Republic of China

Science.gov (United States)

Carter, W.D.; Kiilsgaard, T.H.

1983-01-01

The Yangjiatan tungsten district at latitude 27??28??? N. and longitude 111??54???E. is located about 140 km southwest of the city of Changsha and 35 km northeast of the town of Shaoyang, southeast Hunan Province, People's Republic of China. The deposits, consisting largely of scheelite in veins (Wang, 1975), are contained in highly folded and faulted sedimentary rocks of Paleozoic, Mesozoic, and Cenozoic age intruded by granitic plutons that are circular in plan view. The major faults and folds trend in a northeasterly direction; whereas, the plutons are clustered in a more easterly trending band across the Landsat image. Landsat image E-2338-02202, acquired December 26, 1975, is number 470 in the "Landsat Image Atlas of the People's Republic of China" printed by the Publishing House of Geology in 1979. A computer-compatible tape of the image was analyzed and used as a demonstration project under a United Nations technical assistance program. Supervised classification of soils, rocks, and vegetation; band ratioing to detect limonite alteration; and edge enhancement were all conducted to demonstrate the flexibility and capability of interactive computer systems. Field evaluation of the results of this work will be conducted by colleagues of the Remote Sensing Center for Geology, Ministry of Geology, in China. ?? 1983.

Landsat imagery: a unique resource

Science.gov (United States)

Miller, H.; Sexton, N.; Koontz, L.

2011-01-01

Landsat satellites provide high-quality, multi-spectral imagery of the surface of the Earth. These moderate-resolution, remotely sensed images are not just pictures, but contain many layers of data collected at different points along the visible and invisible light spectrum. These data can be manipulated to reveal what the Earth’s surface looks like, including what types of vegetation are present or how a natural disaster has impacted an area (Fig. 1).

A Harmonized Landsat-Sentinel-2 Surface Reflectance product: a resource for Agricultural Monitoring

Science.gov (United States)

Masek, J. G.; Claverie, M.; Ju, J.; Vermote, E.; Justice, C. O.

2015-12-01

The combination of Landsat and Sentinel-2 data offers a unique opportunity to observe globally the land every 2-3 days at medium (reflectance data from Landsat and Sentinel-2 missions and to deliver them to the community in a combined, seamless form. The HLS will be beneficial for global agricultural monitoring applications that require medium spatial resolution and weekly or more frequent observations. In particular, the provided opportunity to track crop phenology at the scale of individual fields will support detailed mapping of crop type and type-specific vegetation conditions. To create a compatible set of radiometric measurements, the HLS product relies on rigorous pre- and post-launch cross-calibration (Landsat-8 OLI and Sentinel-2 MSI) activities. The processing chain includes the following components: atmospheric correction, cloud/shadow masking, nadir BRDF-adjustment, spectral-adjustment, regridding, and temporal composite. The atmospheric correction and cloud masking is based on the OLI atmospheric correction developed at NASA-GSFC and has been adapted to the MSI data. The BRDF-adjustment is based on a disaggregation technique using MODIS-based BRDF coefficients. The technique has been evaluated using the multi-angular acquisition from the SPOT 4 and 5 (Take5) experiments. The spectral-adjustment relies on a linear regression that has been calibrated and evaluated using synthetic data and surface reflectance processed from a large number of hyperspectral EO-1 Hyperion scenes. Finally, significant effort is placed on product validation and evaluation. The delivered data set will include surface reflectance products at different levels: Using the native gridding, i.e. UTM, 30m for Landsat-8, and UTM, 10-20m for Sentinel-2 Using a common global gridding (Sinusoidal, 30m) Temporal composite (Sinusoidal, 30m, 5-day) During the first year of operation of Sentinel-2A, the HLS will be prototyped over a selection of 30 sites that includes some of the JECAM sites

Techniques for land use change detection using Landsat imagery

Science.gov (United States)

Angelici, G. L.; Bryant, N. A.; Friedman, S. Z.

1977-01-01

A variety of procedures were developed for the delineation of areas of land use change using Landsat Multispectral Scanner data and the generation of statistics revealing the nature of the changes involved (i.e., number of acres changed from rural to urban). Techniques of the Image Based Information System were utilized in all stages of the procedure, from logging the Landsat data and registering two frames of imagery, to extracting the changed areas and printing tabulations of land use change in acres. Two alternative methods of delineating land use change are presented while enumerating the steps of the entire process. The Houston, Texas urban area, and the Orlando, Florida urban area, are used as illustrative examples of various procedures.

Application of integrated Landsat, geochemical and geophysical data in mineral exploration

International Nuclear Information System (INIS)

Conradsen, K.; Nilsson, G.; Thyrsted, T.; Gronlands Geologiske Undersogelse, Copenhagen, Denmark)

1985-01-01

In South Greenland (20000 sq. km) a remote sensing investigation is executed in connection with uranium exploration. The investigation includes analysis of Landsat data, conversion of geological, geochemical and geophysical data to image format compatible with Landsat images, and analysis of the total set of integrated data. The available geochemical data consisted of samples from 2000 sites, analyzed for U, K, Rb, Sr, Nb, Ga, Y, and Fe. The geophysical data comprised airborne gamma-spectrometric measurements and aeromagnetic data. The interpolation routines consisted of a kriging procedure for the geochemical data and a minimum curvature routine for the geophysical data. The analysis of the integrated data set is at a preliminary stage. As example a composite image showing Landsat channel 7, magnetic values, and Fe values as respectively intensity, hue and saturation is analyzed. It reveals alkaline intrusions and basaltic layers as anomalies while other anomalies cannot be accounted for on the basis of the present geological knowledge. 12 references

Suppression of vegetation in LANDSAT ETM+ remote sensing images

Science.gov (United States)

Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

2010-05-01

value and scaling all pixels at each vegetation index level by an amount that shifts the curve to the target digital number (DN). The main drawback of their algorithm is severe distortions of the DN values of non-vegetated areas, a suggested solution is masking outliers such as cloud, water, etc. We therefore extend this algorithm by masking non-vegetated areas. Our algorithm comprises the following three steps: (1) masking of barren or sparsely vegetated areas using a threshold based on a vegetation index that is calculated after atmosphere correction (dark pixel correction and ACTOR were compared) in order to conserve their original spectral information through the subsequent processing; (2) applying Crippen and Blom's forced invariance algorithm to suppress the spectral response of vegetation only in vegetated areas; and (3) combining the processed vegetated areas with the masked barren or sparsely vegetated areas followed by histogram equalization to eliminate the differences in color-scales between these two types of areas, and enhance the integrated image. The output images of both study areas showed significant improvement over the original images in terms of suppression of vegetation reflectance and enhancement of the underlying geological information. The processed images show clear banding, probably associated with lithological variations in the underlying rock formations. The colors of non-vegetated pixels are distorted in the unmasked results but in the same location the pixels in the masked results show regions of higher contrast. We conclude that the algorithm offers an effective way to enhance geological information in LANDSAT TM/ETM+ images of terrains with significant vegetation cover. It is also suitable to other multispectral satellite data have bands in similar wavelength regions. In addition, an application of this method to hyperspectral data may be possible as long as it can provide the vegetation band ratios.

Thermal Infrared Radiometric Calibration of the Entire Landsat 4, 5, and 7 Archive (1982-2010)

Science.gov (United States)

Schott, John R.; Hook, Simon J.; Barsi, Julia A.; Markham, Brian L.; Miller, Jonathan; Padula, Francis P.; Raqueno, Nina G.

2012-01-01

Landsat's continuing record of the thermal state of the earth's surface represents the only long term (1982 to the present) global record with spatial scales appropriate for human scale studies (i.e., tens of meters). Temperature drives many of the physical and biological processes that impact the global and local environment. As our knowledge of, and interest in, the role of temperature on these processes have grown, the value of Landsat data to monitor trends and process has also grown. The value of the Landsat thermal data archive will continue to grow as we develop more effective ways to study the long term processes and trends affecting the planet. However, in order to take proper advantage of the thermal data, we need to be able to convert the data to surface temperatures. A critical step in this process is to have the entire archive completely and consistently calibrated into absolute radiance so that it can be atmospherically compensated to surface leaving radiance and then to surface radiometric temperature. This paper addresses the methods and procedures that have been used to perform the radiometric calibration of the earliest sizable thermal data set in the archive (Landsat 4 data). The completion of this effort along with the updated calibration of the earlier (1985 1999) Landsat 5 data, also reported here, concludes a comprehensive calibration of the Landsat thermal archive of data from 1982 to the present

Calibration and Validation of Landsat Tree Cover in the Taiga−Tundra Ecotone

Directory of Open Access Journals (Sweden)

Paul Mannix Montesano

2016-06-01

Full Text Available Monitoring current forest characteristics in the taiga−tundra ecotone (TTE at multiple scales is critical for understanding its vulnerability to structural changes. A 30 m spatial resolution Landsat-based tree canopy cover map has been calibrated and validated in the TTE with reference tree cover data from airborne LiDAR and high resolution spaceborne images across the full range of boreal forest tree cover. This domain-specific calibration model used estimates of forest height to determine reference forest cover that best matched Landsat estimates. The model removed the systematic under-estimation of tree canopy cover >80% and indicated that Landsat estimates of tree canopy cover more closely matched canopies at least 2 m in height rather than 5 m. The validation improved estimates of uncertainty in tree canopy cover in discontinuous TTE forests for three temporal epochs (2000, 2005, and 2010 by reducing systematic errors, leading to increases in tree canopy cover uncertainty. Average pixel-level uncertainties in tree canopy cover were 29.0%, 27.1% and 31.1% for the 2000, 2005 and 2010 epochs, respectively. Maps from these calibrated data improve the uncertainty associated with Landsat tree canopy cover estimates in the discontinuous forests of the circumpolar TTE.

Integrated Landsat Image Analysis and Hydrologic Modeling to Detect Impacts of 25-Year Land-Cover Change on Surface Runoff in a Philippine Watershed

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Enrico Paringit

2011-05-01

Full Text Available Landsat MSS and ETM+ images were analyzed to detect 25-year land-cover change (1976–2001 in the critical Taguibo Watershed in Mindanao Island, Southern Philippines. This watershed has experienced historical modifications of its land-cover due to the presence of logging industries in the 1950s, and continuous deforestation due to illegal logging and slash-and-burn agriculture in the present time. To estimate the impacts of land-cover change on watershed runoff, land-cover information derived from the Landsat images was utilized to parameterize a GIS-based hydrologic model. The model was then calibrated with field-measured discharge data and used to simulate the responses of the watershed in its year 2001 and year 1976 land-cover conditions. The availability of land-cover information on the most recent state of the watershed from the Landsat ETM+ image made it possible to locate areas for rehabilitation such as barren and logged-over areas. We then created a “rehabilitated” land-cover condition map of the watershed (re-forestation of logged-over areas and agro-forestation of barren areas and used it to parameterize the model and predict the runoff responses of the watershed. Model results showed that changes in land-cover from 1976 to 2001 were directly related to the significant increase in surface runoff. Runoff predictions showed that a full rehabilitation of the watershed, especially in barren and logged-over areas, will be likely to reduce the generation of a huge volume of runoff during rainfall events. The results of this study have demonstrated the usefulness of multi-temporal Landsat images in detecting land-cover change, in identifying areas for rehabilitation, and in evaluating rehabilitation strategies for management of tropical watersheds through its use in hydrologic modeling.

Evaluation of Two Absolute Radiometric Normalization Algorithms for Pre-processing of Landsat Imagery

Institute of Scientific and Technical Information of China (English)

Xu Hanqiu

2006-01-01

In order to evaluate radiometric normalization techniques, two image normalization algorithms for absolute radiometric correction of Landsat imagery were quantitatively compared in this paper, which are the Illumination Correction Model proposed by Markham and Irish and the Illumination and Atmospheric Correction Model developed by the Remote Sensing and GIS Laboratory of the Utah State University. Relative noise, correlation coefficient and slope value were used as the criteria for the evaluation and comparison, which were derived from pseudo-invariant features identified from multitemtween the normalized multitemporal images were significantly reduced when the seasons of multitemporal images were different. However, there was no significant difference between the normalized and unnormalized images with a similar seasonal condition. Furthermore, the correction results of two algorithms are similar when the images are relatively clear with a uniform atmospheric condition. Therefore, the radiometric normalization procedures should be carried out if the multitemporal images have a significant seasonal difference.

Landsat-based monitoring of crop water demand in the San Joaquin Valley

Science.gov (United States)

Johnson, L.; Trout, T.; Wang, D.; Melton, F. S.

2010-12-01

Fresh water resources are becoming increasingly scarce in California due to urbanization, environmental regulation, and groundwater depletion. The strain is projected to worsen under various climate change scenarios and is exacerbated by declining water delivery infrastructure. It is estimated that irrigated agriculture currently commands more than 70% of the state’s water supply, and many growers are striving to improve water use efficiency in order to help maintain the state’s rich agricultural heritage. Remote sensing technology offers the potential to monitor cropland evapotranspiration (ET) regionally, while making farm-based irrigation scheduling more practical, convenient, and possibly more accurate. Landsat5-TM imagery was used in this study to monitor basal crop evapotranspiration (ETcb), which is primarily related to plant transpiration, for several San Joaquin Valley fields throughout the 2008 growing season. A ground-based digital camera was used to measure fractional cover of 48 study fields planted to 18 different crop types (row crops, grains, orchard, and vineyard) of varying maturity over 12 dates coinciding with Landsat overpasses. Landsat L1T terrain-corrected images were atmospherically corrected to surface reflectance by an implementation of the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS), then converted to normalized difference vegetation index (NDVI) on a per-pixel basis. A strong linear relationship between NDVI and fractional cover was observed (r2=0.96), and a resulting conversion equation was used to transform all imagery to fractional cover. Conversion equations previously developed by use of weighting lysimeters were then used to transform fractional cover to basal crop coefficient (Kcb; ratio of crop transpiration plus a small diffusive soil evaporation component to reference ET). Finally, measurements of grass reference ET (ETo) from the California Irrigation Management Information System were used to

A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data

KAUST Repository

Houborg, Rasmus

2018-03-19

Satellite sensing in the visible to near-infrared (VNIR) domain has been the backbone of land surface monitoring and characterization for more than four decades. However, a limitation of conventional single-sensor satellite missions is their limited capacity to observe land surface dynamics at the very high spatial and temporal resolutions demanded by a wide range of applications. One solution to this spatio-temporal divide is an observation strategy based on the CubeSat standard, which facilitates constellations of small, inexpensive satellites. Repeatable near-daily image capture in RGB and near-infrared (NIR) bands at 3–4 m resolution has recently become available via a constellation of >130 CubeSats operated commercially by Planet. While the observing capacity afforded by this system is unprecedented, the relatively low radiometric quality and cross-sensor inconsistencies represent key challenges in the realization of their full potential as a game changer in Earth observation. To address this issue, we developed a Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) that uses a multi-scale machine-learning technique to correct for radiometric inconsistencies between CubeSat acquisitions. The CESTEM produces Landsat 8 consistent atmospherically corrected surface reflectances in blue, green, red, and NIR bands, but at the spatial scale and temporal frequency of the CubeSat observations. An application of CESTEM over an agricultural dryland system in Saudi Arabia demonstrated CubeSat-based reproduction of Landsat 8 consistent VNIR data with an overall relative mean absolute deviation of 1.6% or better, even when the Landsat 8 and CubeSat acquisitions were temporally displaced by >32 days. The consistently high retrieval accuracies were achieved using a multi-scale target sampling scheme that draws Landsat 8 reference data from a series of scenes by using MODIS-consistent surface reflectance time series to quantify relative changes in Landsat

LANDSAT-4 MSS and Thematic Mapper data quality and information content analysis

Science.gov (United States)

Anuta, P.; Bartolucci, L.; Dean, E.; Lozano, F.; Malaret, E.; Mcgillem, C. D.; Valdes, J.; Valenzuela, C.

1984-01-01

LANDSAT-4 thematic mapper (TM) and multispectral scanner (MSS) data were analyzed to obtain information on data quality and information content. Geometric evaluations were performed to test band-to-band registration accuracy. Thematic mapper overall system resolution was evaluated using scene objects which demonstrated sharp high contrast edge responses. Radiometric evaluation included detector relative calibration, effects of resampling, and coherent noise effects. Information content evaluation was carried out using clustering, principal components, transformed divergence separability measure, and supervised classifiers on test data. A detailed spectral class analysis (multispectral classification) was carried out to compare the information content of the MSS and TM for a large number of scene classes. A temperature-mapping experiment was carried out for a cooling pond to test the quality of thermal-band calibration. Overall TM data quality is very good. The MSS data are noisier than previous LANDSAT results.

Regional analysis of Landsat data concerning unconformity-vein uranium deposits, Pine Creek Geosyncline, Australia

International Nuclear Information System (INIS)

Raines, G.L.

1982-01-01

Linear features mapped from enhanced Landsat images in zones defining lineaments trending northeast and east-northeast across the uranium area of northern Australia. A model using Landsat data to select areas for uranium exploration is proposed, based on the observed spatial relation of uranium deposits and the newly defined major lineaments

Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery

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Samuel Hislop

2018-03-01

Full Text Available Satellite earth observation is being increasingly used to monitor forests across the world. Freely available Landsat data stretching back four decades, coupled with advances in computer processing capabilities, has enabled new time-series techniques for analyzing forest change. Typically, these methods track individual pixel values over time, through the use of various spectral indices. This study examines the utility of eight spectral indices for characterizing fire disturbance and recovery in sclerophyll forests, in order to determine their relative merits in the context of Landsat time-series. Although existing research into Landsat indices is comprehensive, this study presents a new approach, by comparing the distributions of pre and post-fire pixels using Glass’s delta, for evaluating indices without the need of detailed field information. Our results show that in the sclerophyll forests of southeast Australia, common indices, such as the Normalized Difference Vegetation Index (NDVI and the Normalized Burn Ratio (NBR, both accurately capture wildfire disturbance in a pixel-based time-series approach, especially if images from soon after the disturbance are available. However, for tracking forest regrowth and recovery, indices, such as NDVI, which typically capture chlorophyll concentration or canopy ‘greenness’, are not as reliable, with values returning to pre-fire levels in 3–5 years. In comparison, indices that are more sensitive to forest moisture and structure, such as NBR, indicate much longer (8–10 years recovery timeframes. This finding is consistent with studies that were conducted in other forest types. We also demonstrate that additional information regarding forest condition, particularly in relation to recovery, can be extracted from less well known indices, such as NBR2, as well as textural indices incorporating spatial variance. With Landsat time-series gaining in popularity in recent years, it is critical to

Distinguishing Bark Beetle-infested Vegetation by Tree Species Types and Stress Levels using Landsat Data

Science.gov (United States)

Sivanpillai, R.; Ewers, B. E.; Speckman, H. N.; Miller, S. N.

2015-12-01

In the Western United States, more than 3 million hectares of lodgepole pine forests have been impacted by the Mountain pine beetle outbreak, while another 166,000 hectares of spruce-fir forests have been attacked by Spruce beetle. Following the beetle attack, the trees lose their hydraulic conductivity thus altering their carbon and water fluxes. These trees go through various stages of stress until mortality, described by color changes in their needles prior to losing them. Modeling the impact of these vegetation types require thematically precise land cover data that distinguishes lodgepole pine and spruce-fir forests along with the stage of impact since the ecosystem fluxes are different for these two systems. However, the national and regional-scale land cover datasets derived from remotely sensed data do not have this required thematic precision. We evaluated the feasibility of multispectral data collected by Landsat 8 to distinguish lodgepole pine and spruce fir, and subsequently model the different stages of attack using field data collected in Medicine Bow National Forest (Wyoming, USA). Operational Land Imager, onboard Landsat 8 has more spectral bands and higher radiometric resolution (12 bit) in comparison to sensors onboard earlier Landsat missions which could improve the ability to distinguish these vegetation types and their stress conditions. In addition to these characteristics, its repeat coverage, rigorous radiometric calibration, wide swath width, and no-cost data provide unique advantages to Landsat data for mapping large geographic areas. Initial results from this study highlight the importance of SWIR bands for distinguishing different levels of stress, and the need for ancillary data for distinguishing species types. Insights gained from this study could lead to the generation of land cover maps with higher thematic precision, and improve the ability to model various ecosystem processes as a result of these infestations.

Landsat Thematic Mapper monitoring of turbid inland water quality

Science.gov (United States)

Lathrop, Richard G., Jr.

1992-01-01

This study reports on an investigation of water quality calibration algorithms under turbid inland water conditions using Landsat Thematic Mapper (TM) multispectral digital data. TM data and water quality observations (total suspended solids and Secchi disk depth) were obtained near-simultaneously and related using linear regression techniques. The relationships between reflectance and water quality for Green Bay and Lake Michigan were compared with results for Yellowstone and Jackson Lakes, Wyoming. Results show similarities in the water quality-reflectance relationships, however, the algorithms derived for Green Bay - Lake Michigan cannot be extrapolated to Yellowstone and Jackson Lake conditions.

Data-driven simulations of the Landsat Data Continuity Mission (LDCM) platform

Science.gov (United States)

Gerace, Aaron; Gartley, Mike; Schott, John; Raqueño, Nina; Raqueño, Rolando

2011-06-01

The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are two new sensors being developed by the Landsat Data Continuity Mission (LDCM) that will extend over 35 years of archived Landsat data. In a departure from the whiskbroom design used by all previous generations of Landsat, the LDCM system will employ a pushbroom technology. Although the newly adopted modular array, pushbroom architecture has several advantages over the previous whiskbroom design, registration of the multi-spectral data products is a concern. In this paper, the Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool was used to simulate an LDCM collection, which gives the team access to data that would not otherwise be available prior to launch. The DIRSIG model was used to simulate the two-instrument LDCM payload in order to study the geometric and radiometric impacts of the sensor design on the proposed processing chain. The Lake Tahoe area located in eastern California was chosen for this work because of its dramatic change in elevation, which was ideal for studying the geometric effects of the new Landsat sensor design. Multi-modal datasets were used to create the Lake Tahoe site model for use in DIRSIG. National Elevation Dataset (NED) data were used to create the digital elevation map (DEM) required by DIRSIG, QuickBird data were used to identify different material classes in the scene, and ASTER and Hyperion spectral data were used to assign radiometric properties to those classes. In order to model a realistic Landsat orbit in these simulations, orbital parameters were obtained from a Landsat 7 two-line element set and propagated with the SGP4 orbital position model. Line-of-sight vectors defining how the individual detector elements of the OLI and TIRS instruments project through the optics were measured and provided by NASA. Additionally, the relative spectral response functions for the 9 bands of OLI and the 2 bands of TIRS were measured and provided by NASA

Landsat 5 TM images and DEM in lithologic mapping of Payen Volcanic Field (Mendoza Province, Argentina)

International Nuclear Information System (INIS)

Fornaciai, A.; Bisson, M.; Mazzarini, F.; Del Carlo, P.; Pasquare, G.

2009-01-01

Satellite image such as Landsat 5 TM scene provides excellent representation of Earth and synoptic view of large geographic areas in different band combination. Landsat TM images allow automatic and semi-automatic classification of land cover, nevertheless the software frequently may some difficulties in distinguishing between similar radiometric surfaces. In this case, the use of Digital Elevation Model (DEM) can be an important tool to identify different surface covers. In this study, several False Color Composite (FCC) of Landsat 5 TM Image, DEM and the respective draped image of them, were used to delineate lithological boundaries and tectonic features of regional significance of the Paven Volcanic Field (PVF). PFV is a Quaternary fissural structure belonging to the black-arc extensional areas of the Andes in the Mendoza Province (Argentina) characterized by many composite basaltic lava flow fields. The necessity to identify different lava flows with the same composition, and then with same spectral features, allows to highlight the improvement of synergic use of TM images and shaded DEM in the visual interpretation. Information obtained from Satellite data and DEM have been compared with previous geological maps and transferred into a topographical base map. Based on these data a new lithological map at 1:100.000 scale has been presented [it

CALIBRATION/VALIDATION OF LANDSAT-DERIVED OCEAN COLOUR PRODUCTS IN BOSTON HARBOUR

Directory of Open Access Journals (Sweden)

N. Pahlevan

2016-06-01

Full Text Available The Landsat data archive provides a unique opportunity to investigate the long-term evolution of coastal ecosystems at fine spatial scales that cannot be resolved by ocean colour (OC satellite sensors. Recognizing Landsat’s limitations in applications over coastal waters, we have launched a series of field campaigns in Boston Harbor and Massachusetts Bay (MA, USA to validate OC products derived from Landsat-8. We will provide a preliminary demonstration on the calibration/validation of the existing OC algorithms (atmospheric correction and in-water optical properties to enhance monitoring efforts in Boston Harbor. To do so, Landsat optical images were first compared against ocean colour products over high-latitude regions. The in situ cruise data, including optical data (remote sensing reflectance and water samples were analyzed to obtain insights into the optical and biogeochemical properties of near-surface waters. Along with the cruise data, three buoys were deployed in three locations across the Harbor to complement our database of concentrations of chlorophyll a, total suspended solids (TSS, and absorption of colour dissolved organic matter (CDOM. The data collected during the first year of the project are used to develop and/or tune OC algorithms. The data will be combined with historic field data to map in-water constituents back to the early 1990’s. This paper presents preliminary analysis of some of the data collected under Landsat-8 overpasses.

Revealing livestock effects on bunchgrass vegetation with Landsat ETM+ data across a grazing season

Science.gov (United States)

Jansen, Vincent S.

Remote sensing provides monitoring solutions for more informed grazing management. To investigate the ability to detect the effects of cattle grazing on bunchgrass vegetation with Landsat Enhanced Thematic Mapper Plus (ETM+) data, we conducted a study on the Zumwalt Prairie in northeastern Oregon across a gradient of grazing intensities. Biophysical vegetation data was collected on vertical structure, biomass, and cover at three different time periods during the grazing season: June, August, and October 2012. To relate these measures to the remotely sensed Landsat ETM+ data, Pearson's correlations and multiple regression models were computed. Using the best models, predicted vegetation metrics were then mapped across the study area. Results indicated that models using common vegetation indices had the ability to discern different levels of grazing across the study area. Results can be distributed to land managers to help guide grassland conservation by improving monitoring of bunchgrass vegetation for sustainable livestock management.

Global Man-made Impervious Surface (GMIS) Dataset From Landsat

Data.gov (United States)

National Aeronautics and Space Administration — The Global Man-made Impervious Surface (GMIS) Dataset From Landsat consists of global estimates of fractional impervious cover derived from the Global Land Survey...

Reconstructing turbidity in a glacially influenced lake using the Landsat TM and ETM+ surface reflectance climate data record archive, Lake Clark, Alaska

Science.gov (United States)

Baughman, Carson; Jones, Benjamin M.; Bartz, Krista K.; Young, Daniel B.; Zimmerman, Christian E.

2015-01-01

Lake Clark is an important nursery lake for sockeye salmon (Oncorhynchus nerka) in the headwaters of Bristol Bay, Alaska, the most productive wild salmon fishery in the world. Reductions in water clarity within Alaska lake systems as a result of increased glacial runoff have been shown to reduce salmon production via reduced abundance of zooplankton and macroinvertebrates. In this study, we reconstruct long-term, lake-wide water clarity for Lake Clark using the Landsat TM and ETM+ surface reflectance products (1985–2014) and in situwater clarity data collected between 2009 and 2013. Analysis of a Landsat scene acquired in 2009, coincident with in situ measurements in the lake, and uncertainty analysis with four scenes acquired within two weeks of field data collection showed that Band 3 surface reflectance was the best indicator of turbidity (r2 = 0.55,RMSE turbidity for Lake Clark between 1991 and 2014. We did, however, detect interannual variation that exhibited a non-significant (r2 = 0.20) but positive correlation (r = 0.20) with regional mean summer air temperature and found the month of May exhibited a significant positive trend (r2 = 0.68, p = 0.02) in turbidity between 2000 and 2014. This study demonstrates the utility of hindcasting turbidity in a glacially influenced lake using the Landsat surface reflectance products. It may also help land and resource managers reconstruct turbidity records for lakes that lack in situ monitoring, and may be useful in predicting future water clarity conditions based on projected climate scenarios.

A prototype for automation of land-cover products from Landsat Surface Reflectance Data Records

Science.gov (United States)

Rover, J.; Goldhaber, M. B.; Steinwand, D.; Nelson, K.; Coan, M.; Wylie, B. K.; Dahal, D.; Wika, S.; Quenzer, R.

2014-12-01

Landsat data records of surface reflectance provide a three-decade history of land surface processes. Due to the vast number of these archived records, development of innovative approaches for automated data mining and information retrieval were necessary. Recently, we created a prototype utilizing open source software libraries for automatically generating annual Anderson Level 1 land cover maps and information products from data acquired by the Landsat Mission for the years 1984 to 2013. The automated prototype was applied to two target areas in northwestern and east-central North Dakota, USA. The approach required the National Land Cover Database (NLCD) and two user-input target acquisition year-days. The Landsat archive was mined for scenes acquired within a 100-day window surrounding these target dates, and then cloud-free pixels where chosen closest to the specified target acquisition dates. The selected pixels were then composited before completing an unsupervised classification using the NLCD. Pixels unchanged in pairs of the NLCD were used for training decision tree models in an iterative process refined with model confidence measures. The decision tree models were applied to the Landsat composites to generate a yearly land cover map and related information products. Results for the target areas captured changes associated with the recent expansion of oil shale production and agriculture driven by economics and policy, such as the increase in biofuel production and reduction in Conservation Reserve Program. Changes in agriculture, grasslands, and surface water reflect the local hydrological conditions that occurred during the 29-year span. Future enhancements considered for this prototype include a web-based client, ancillary spatial datasets, trends and clustering algorithms, and the forecasting of future land cover.

Use of LANDSAT 8 images for depth and water quality assessment of El Guájaro reservoir, Colombia

Science.gov (United States)

González-Márquez, Luis Carlos; Torres-Bejarano, Franklin M.; Torregroza-Espinosa, Ana Carolina; Hansen-Rodríguez, Ivette Renée; Rodríguez-Gallegos, Hugo B.

2018-03-01

The aim of this study was to evaluate the viability of using Landsat 8 spectral images to estimate water quality parameters and depth in El Guájaro Reservoir. On February and March 2015, two samplings were carried out in the reservoir, coinciding with the Landsat 8 images. Turbidity, dissolved oxygen, electrical conductivity, pH and depth were evaluated. Through multiple regression analysis between measured water quality parameters and the reflectance of the pixels corresponding to the sampling stations, statistical models with determination coefficients between 0.6249 and 0.9300 were generated. Results indicate that from a small number of measured parameters we can generate reliable models to estimate the spatial variation of turbidity, dissolved oxygen, pH and depth, as well the temporal variation of electrical conductivity, so models generated from Landsat 8 can be used as a tool to facilitate the environmental, economic and social management of the reservoir.

Enhancing a Simple MODIS Cloud Mask Algorithm for the Landsat Data Continuity Mission

Science.gov (United States)

Wilson, Michael J.; Oreopoulos, Lazarous

2011-01-01

The presence of clouds in images acquired by the Landsat series of satellites is usually an undesirable, but generally unavoidable fact. With the emphasis of the program being on land imaging, the suspended liquid/ice particles of which clouds are made of fully or partially obscure the desired observational target. Knowing the amount and location of clouds in a Landsat scene is therefore valuable information for scene selection, for making clear-sky composites from multiple scenes, and for scheduling future acquisitions. The two instruments in the upcoming Landsat Data Continuity Mission (LDCM) will include new channels that will enhance our ability to detect high clouds which are often also thin in the sense that a large fraction of solar radiation can pass through them. This work studies the potential impact of these new channels on enhancing LDCM's cloud detection capabilities compared to previous Landsat missions. We revisit a previously published scheme for cloud detection and add new tests to capture more of the thin clouds that are harder to detect with the more limited arsenal channels. Since there are no Landsat data yet that include the new LDCM channels, we resort to data from another instrument, MODIS, which has these bands, as well as the other bands of LDCM, to test the capabilities of our new algorithm. By comparing our revised scheme's performance against the performance of the official MODIS cloud detection scheme, we conclude that the new scheme performs better than the earlier scheme which was not very good at thin cloud detection.

Chesapeake Bay plume dynamics from LANDSAT

Science.gov (United States)

Munday, J. C., Jr.; Fedosh, M. S.

1981-01-01

LANDSAT images with enhancement and density slicing show that the Chesapeake Bay plume usually frequents the Virginia coast south of the Bay mouth. Southwestern (compared to northern) winds spread the plume easterly over a large area. Ebb tide images (compared to flood tide images) show a more dispersed plume. Flooding waters produce high turbidity levels over the shallow northern portion of the Bay mouth.

Landsat TM and ETM+ Kansas Satellite Image Database (KSID)

Data.gov (United States)

Kansas Data Access and Support Center — The Kansas Satellite Image Database (KSID):2000-2001 consists of terrain-corrected, precision rectified spring, summer, and fall Landsat 5 Thematic Mapper (TM) and...

Monitoring of urban growth in the state of Hidalgo using Landsat images

Directory of Open Access Journals (Sweden)

Laura Cano Salinas

2017-03-01

Given this background, this paper is focused on the generation of geographic information for regional urban planning and the overall aim is to examine urban growth rate during the period 2000-2014 in the state of Hidalgo, Mexico and identify potential areas of expansion from Landsat images. The methodology was based on techniques of remote sensing and Geographical Information System (GIS. The inputs used were six Landsat scenes: three for 2000 year and three for 2014. Image processing was performed on ERDAS Imagine® 9.1 and the spatial analysis of urban coverage statewide on ArcGIS 10.0 by ESRI®. First, the radiometric correction was made and we obtained the urban polygons of the 2000 year through of supervised classification. The 2014 urban layer was digitized manually due to the spectral incompatibility between the bands of the Landsat sensor 5 and 7, and the Landsat sensor 8. Then, we build a road density map and the spatial relationship of the urban centers with the road influence area was evaluated. For the year 2000, 103 urban polygons were mapped, whilst for 2014 were identified ten polygons more with a mapped minimum area of 24 ha. The main results indicated that in the state has increased 72.3 km2 urban area from 2000 to 2014. This represents an average growth rate of 1.8% per year. The most widespread municipalities are located in the region of Valle del Mezquital, however, Mineral de la Reforma, Tetepango, Tizayuca and Pachuca showed growth rates of 183.44%, 102% 94% and 68.5% in fourteen years, respectively. According to the road map density, these municipalities are located in areas of greatest influence of infrastructure as the Arco Norte highway in the state. The above findings, lead us to conclude that the Mezquital Valley and the Basin of Mexico are potential areas of urban spreading and it is associated with road development in the Central Mexico.

Early Spring Post-Fire Snow Albedo Dynamics in High Latitude Boreal Forests Using Landsat-8 OLI Data

Science.gov (United States)

Wang, Zhuosen; Erb, Angela M.; Schaaf, Crystal B.; Sun, Qingsong; Liu, Yan; Yang, Yun; Shuai, Yanmin; Casey, Kimberly A.; Roman, Miguel O.

2016-01-01

Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (less than 100 m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high-burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500 m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will

Comparative analysis of Worldview-2 and Landsat 8 for coastal saltmarsh mapping accuracy assessment

Science.gov (United States)

Rasel, Sikdar M. M.; Chang, Hsing-Chung; Diti, Israt Jahan; Ralph, Tim; Saintilan, Neil

2016-05-01

Coastal saltmarsh and their constituent components and processes are of an interest scientifically due to their ecological function and services. However, heterogeneity and seasonal dynamic of the coastal wetland system makes it challenging to map saltmarshes with remotely sensed data. This study selected four important saltmarsh species Pragmitis australis, Sporobolus virginicus, Ficiona nodosa and Schoeloplectus sp. as well as a Mangrove and Pine tree species, Avecinia and Casuarina sp respectively. High Spatial Resolution Worldview-2 data and Coarse Spatial resolution Landsat 8 imagery were selected in this study. Among the selected vegetation types some patches ware fragmented and close to the spatial resolution of Worldview-2 data while and some patch were larger than the 30 meter resolution of Landsat 8 data. This study aims to test the effectiveness of different classifier for the imagery with various spatial and spectral resolutions. Three different classification algorithm, Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were tested and compared with their mapping accuracy of the results derived from both satellite imagery. For Worldview-2 data SVM was giving the higher overall accuracy (92.12%, kappa =0.90) followed by ANN (90.82%, Kappa 0.89) and MLC (90.55%, kappa = 0.88). For Landsat 8 data, MLC (82.04%) showed the highest classification accuracy comparing to SVM (77.31%) and ANN (75.23%). The producer accuracy of the classification results were also presented in the paper.

Monitoring of suspended sediment variation using Landsat and MODIS in the Saemangeum coastal area of Korea.

Science.gov (United States)

Min, Jee-Eun; Ryu, Joo-Hyung; Lee, Seok; Son, Seunghyun

2012-02-01

Suspended sediment concentration (SS) is an important indicator of marine environmental changes due to natural causes such as tides, tidal currents, and river discharges, as well as human activities such as construction in coastal regions. In the Saemangeum area on the west coast of Korea, construction of a huge tidal dyke for land reclamation has strongly influenced the coastal environment. This study used remotely sensed data to analyze the SS changes in coastal waters caused by the dyke construction. Landsat and MODIS satellite images were used for the spatial analysis of finer patterns and for the detailed temporal analysis, respectively. Forty Landsat scenes and 105 monthly composite MODIS images observed during 1985-2010 were employed, and four field campaigns (from 2005 to 2006) were performed to verify the image-derived SS. The results of the satellite data analyses showed that the seawater was clear before the dyke construction, with SS values lower than 20 g/m(3). These values increased continuously as the dyke construction progressed. The maximum SS values appeared just before completion of the fourth dyke. Values decreased to below 5 g/m(3) after dyke construction. These changes indicated tidal current modification. Some eddies and plumes were observed in the images generated from Landsat data. Landsat and MODIS can reveal that coastal water turbidity was greatly reduced after completion of the construction. Copyright © 2011 Elsevier Ltd. All rights reserved.

Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China

Directory of Open Access Journals (Sweden)

Haoming Xia

2017-01-01

Full Text Available Wetland inundation is crucial to the survival and prosperity of fauna and flora communities in wetland ecosystems. Even small changes in surface inundation may result in a substantial impact on the wetland ecosystem characteristics and function. This study presented a novel method for wetland inundation mapping at a subpixel scale in a typical wetland region on the Zoige Plateau, northeast Tibetan Plateau, China, by combining use of an unmanned aerial vehicle (UAV and Landsat-8 Operational Land Imager (OLI data. A reference subpixel inundation percentage (SIP map at a Landsat-8 OLI 30 m pixel scale was first generated using high resolution UAV data (0.16 m. The reference SIP map and Landsat-8 OLI imagery were then used to develop SIP estimation models using three different retrieval methods (Linear spectral unmixing (LSU, Artificial neural networks (ANN, and Regression tree (RT. Based on observations from 2014, the estimation results indicated that the estimation model developed with RT method could provide the best fitting results for the mapping wetland SIP (R2 = 0.933, RMSE = 8.73% compared to the other two methods. The proposed model with RT method was validated with observations from 2013, and the estimated SIP was highly correlated with the reference SIP, with an R2 of 0.986 and an RMSE of 9.84%. This study highlighted the value of high resolution UAV data and globally and freely available Landsat data in combination with the developed approach for monitoring finely gradual inundation change patterns in wetland ecosystems.

Estimating Forest fAPAR from Multispectral Landsat-8 Data Using the Invertible Forest Reflectance Model INFORM

Directory of Open Access Journals (Sweden)

Huili Yuan

2015-06-01

Full Text Available The estimation of the Fraction of Absorbed Photosynthetically Active Radiation in forests (forest fAPAR from multi-spectral Landsat-8 data is investigated in this paper using a physically based radiative transfer model (Invertible Forest Reflectance Model, INFORM combined with an inversion strategy based on artificial neural nets (ANN. To derive the forest fAPAR for the Dabie mountain test site in China in 30 m spatial resolution (size approximately 3000 km2, a database of forest canopy spectral reflectances was simulated with INFORM taking into account structural variables such as leaf area index (LAI, crown coverage and stem density as well as leaf composition. To establish the relationship between forest fAPAR and the reflectance modeled by INFORM, a logarithmic relationship between LAI and fAPAR was used previously established using on-site field measurements. On this basis, predictive models between Landsat-8 reflectance and fAPAR were established using an artificial neural network. After calibrating INFORM for the test site, forty-two forest stands were used to validate the performance of the method. The results show that spectral signatures modeled by INFORM correspond reasonably well with the forest canopy reflectance spectra derived from Landsat data. Deviations increase with increasing angle between surface normal of the hilly terrain and sun incidence. The comparison of estimated and measured fAPAR (R2 = 0.47, RMSE = 0.11 demonstrates that INFORM can be inverted using neural nets to provide acceptable estimates of forest fAPAR. The accuracy of the predictions increased significantly when excluding pixels located in very steep terrain. This demonstrates that the applied topographic correction was not sufficiently accurate and should be improved for making optimum use of radiative transfer models such as INFORM.

Monitoring water quality in a hypereutrophic reservoir using Landsat ETM+ and OLI sensors: how transferable are the water quality algorithms?

Science.gov (United States)

Deutsch, Eliza S; Alameddine, Ibrahim; El-Fadel, Mutasem

2018-02-15

The launch of the Landsat 8 in February 2013 extended the life of the Landsat program to over 40 years, increasing the value of using Landsat to monitor long-term changes in the water quality of small lakes and reservoirs, particularly in poorly monitored freshwater systems. Landsat-based water quality hindcasting often incorporate several Landsat sensors in an effort to increase the temporal range of observations; yet the transferability of water quality algorithms across sensors remains poorly examined. In this study, several empirical algorithms were developed to quantify chlorophyll-a, total suspended matter (TSM), and Secchi disk depth (SDD) from surface reflectance measured by Landsat 7 ETM+ and Landsat 8 OLI sensors. Sensor-specific multiple linear regression models were developed by correlating in situ water quality measurements collected from a semi-arid eutrophic reservoir with band ratios from Landsat ETM+ and OLI sensors, along with ancillary data (water temperature and seasonality) representing ecological patterns in algae growth. Overall, ETM+-based models outperformed (adjusted R 2 chlorophyll-a = 0.70, TSM = 0.81, SDD = 0.81) their OLI counterparts (adjusted R 2 chlorophyll-a = 0.50, TSM = 0.58, SDD = 0.63). Inter-sensor differences were most apparent for algorithms utilizing the Blue spectral band. The inclusion of water temperature and seasonality improved the power of TSM and SDD models.

Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine

Science.gov (United States)

Dong, Jinwei; Xiao, Xiangming; Menarguez, Michael A.; Zhang, Geli; Qin, Yuanwei; Thau, David; Biradar, Chandrashekhar; Moore, Berrien

2016-01-01

Area and spatial distribution information of paddy rice are important for understanding of food security, water use, greenhouse gas emission, and disease transmission. Due to climatic warming and increasing food demand, paddy rice has been expanding rapidly in high latitude areas in the last decade, particularly in northeastern (NE) Asia. Current knowledge about paddy rice fields in these cold regions is limited. The phenology- and pixel-based paddy rice mapping (PPPM) algorithm, which identifies the flooding signals in the rice transplanting phase, has been effectively applied in tropical areas, but has not been tested at large scale of cold regions yet. Despite the effects from more snow/ice, paddy rice mapping in high latitude areas is assumed to be more encouraging due to less clouds, lower cropping intensity, and more observations from Landsat sidelaps. Moreover, the enhanced temporal and geographic coverage from Landsat 8 provides an opportunity to acquire phenology information and map paddy rice. This study evaluated the potential of Landsat 8 images on annual paddy rice mapping in NE Asia which was dominated by single cropping system, including Japan, North Korea, South Korea, and NE China. The cloud computing approach was used to process all the available Landsat 8 imagery in 2014 (143 path/rows, ~3290 scenes) with the Google Earth Engine (GEE) platform. The results indicated that the Landsat 8, GEE, and improved PPPM algorithm can effectively support the yearly mapping of paddy rice in NE Asia. The resultant paddy rice map has a high accuracy with the producer (user) accuracy of 73% (92%), based on the validation using very high resolution images and intensive field photos. Geographic characteristics of paddy rice distribution were analyzed from aspects of country, elevation, latitude, and climate. The resultant 30-m paddy rice map is expected to provide unprecedented details about the area, spatial distribution, and landscape pattern of paddy rice fields

Alaskan resources, current development. Traditional cultural values, and the role of LANDSAT data in current and future land use management planning

Science.gov (United States)

Laperriere, A. J.

1975-01-01

Past, present, and proposed applications of LANDSAT data for renewable resource assessments in Alaska are described. Specific projects briefly discussed include: a feasibility investigation applying LANDSAT data to caribou habitat mapping in northeast Alaska, analysis of a native corporate region in southwest Alaska, analysis of a game management unit in interior Alaska, and two proposed analyses in northwest Alaska. These analyses principally address range evaluations concerning caribou, moose, and Dall sheep, but results have application to other renewable resource themes. Application of resource assessment results to a statewide land use management plan is discussed.

Detecting of forest afforestation and deforestation in Hainan Jianfengling Forest Park (China) using yearly Landsat time-series images

Science.gov (United States)

Jiao, Quanjun; Zhang, Xiao; Sun, Qi

2018-03-01

The availability of dense time series of Landsat images pro-vides a great chance to reconstruct forest disturbance and change history with high temporal resolution, medium spatial resolution and long period. This proposal aims to apply forest change detection method in Hainan Jianfengling Forest Park using yearly Landsat time-series images. A simple detection method from the dense time series Landsat NDVI images will be used to reconstruct forest change history (afforestation and deforestation). The mapping result showed a large decrease occurred in the extent of closed forest from 1980s to 1990s. From the beginning of the 21st century, we found an increase in forest areas with the implementation of forestry measures such as the prohibition of cutting and sealing in our study area. Our findings provide an effective approach for quickly detecting forest changes in tropical original forest, especially for afforestation and deforestation, and a comprehensive analysis tool for forest resource protection.

SRTM Perspective View with Landsat Overlay: Costa Rica Coastal Plain

Science.gov (United States)

2001-01-01

This perspective view shows the northern coastal plain of Costa Rica with the Cordillera Central, composed of a number of active and dormant volcanoes, rising in the background. This view looks toward the south over the Rio San Juan, which marks the boundary between Costa Rica and Nicaragua. The smaller river joining Rio San Juan in the center of the image is Rio Sarapiqui, which is navigable upstream as far inland as Puerto Viejo (Old Port) de Sarapiqui at the mountain's base. This river was an important transportation route for those few hardy settlers who first moved into this region, although as recently as 1953 a mere three thatched-roof houses were all that comprised the village of Puerto Viejo.This coastal plain is a sedimentary basin formed about 50 million years ago composed of river alluvium and lahar (mud and ash flow) deposits from the volcanoes of the Cordillera Central. It comprises the province of Heredia (the smallest of Costa Rica's seven) and demonstrates a wide range of climatic conditions, from warm and humid lowlands to cool and damp highlands, and including the mild but seasonally wet and dry Central Valley.This image was generated in support of the Central American Commission for Environment and Development through an agreement with NASA. The Commission involves eight nations working to develop the Mesoamerican Biological Corridor, an effort to study and preserve some of the most biologically diverse regions of the planet.This three-dimensional perspective view was generated using topographic data from the Shuttle Radar Topography Mission (SRTM) and an enhanced false-color Landsat 7 satellite image. Colors are from Landsat bands 5, 4, and 2 as red, green and blue, respectively. Topographic expression is exaggerated 2X.Landsat has been providing visible and infrared views of the Earth since 1972. SRTM elevation data matches the 30-meter resolution of most Landsat images and will substantially help in analyses of the large and growing Landsat

Landsat 9 OLI 2 focal plane subsystem: design, performance, and status

Science.gov (United States)

Malone, Kevin J.; Schrein, Ronald J.; Bradley, M. Scott; Irwin, Ronda; Berdanier, Barry; Donley, Eric

2017-09-01

The Landsat 9 mission will continue the legacy of Earth remote sensing that started in 1972. The Operational Land Imager 2 (OLI 2) is one of two instruments on the Landsat 9 satellite. The OLI 2 instrument is essentially a copy of the OLI instrument flying on Landsat 8. A key element of the OLI 2 instrument is the focal plane subsystem, or FPS, which consists of the focal plane array (FPA), the focal plane electronics (FPE) box, and low-thermal conductivity cables. This paper presents design details of the OLI 2 FPS. The FPA contains 14 critically-aligned focal plane modules (FPM). Each module contains 6 visible/near-IR (VNIR) detector arrays and three short-wave infrared (SWIR) arrays. A complex multi-spectral optical filter is contained in each module. Redundant pixels for each array provide exceptional operability. Spare detector modules from OLI were recharacterized after six years of storage. Radiometric test results are presented and compared with data recorded in 2010. Thermal, optical, mechanical and structural features of the FPA will be described. Special attention is paid to the thermal design of the FPA since thermal stability is crucial to ensuring low-noise and low-drift operation of the detectors which operate at -63°C. The OLI 2 FPE provides power, timing, and control to the focal plane modules. It also digitizes the video data and formats it for the solid-state recorder. Design improvements to the FPA-FPE cables will be discussed and characterization data will be presented. The paper will conclude with the status of the flight hardware assembly and testing.

BOREAS RSS-7 Landsat TM LAI IMages of the SSA and NSA

Science.gov (United States)

Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Chen, Jing; Cihlar, Josef

2000-01-01

The BOReal Ecosystem-Atmosphere Study Remote Sensing Science (BOREAS RSS-7) team used Landsat Thematic Mapper (TM) images processed at CCRS to produce images of Leaf Area Index (LAI) for the BOREAS study areas. Two images acquired on 06-Jun and 09-Aug-1991 were used for the SSA, and one image acquired on 09-Jun-1994 was used for the NSA. The LAI images are based on ground measurements and Landsat TM Reduced Simple Ratio (RSR) images. The data are stored in binary image-format files.

landsat remote sensing data as an alternative approach

African Journals Online (AJOL)

Mgina

The use of Landsat data in this area has revealed the need of effective use of these data in ... and efficient in all aspects of cost. ... Removal of noise and cloud cover effects can well be ... approximatelly1000 km from the business city of Dar es ...

Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin

Science.gov (United States)

Senay, Gabriel; Friedrichs, MacKenzie O.; Singh, Ramesh K.; Velpuri, Naga Manohar

2016-01-01

Evapotranspiration (ET) mapping at the Landsat spatial resolution (100 m) is essential to fully understand water use and water availability at the field scale. Water use estimates in the Colorado River Basin (CRB), which has diverse ecosystems and complex hydro-climatic regions, will be helpful to water planners and managers. Availability of Landsat 8 images, starting in 2013, provides the opportunity to map ET in the CRB to assess spatial distribution and patterns of water use. The Operational Simplified Surface Energy Balance (SSEBop) model was used with 528 Landsat 8 images to create seamless monthly and annual ET estimates at the inherent 100 m thermal band resolution. Annual ET values were summarized by land use/land cover classes. Croplands were the largest consumer of “blue” water while shrublands consumed the most “green” water. Validation using eddy covariance (EC) flux towers and water balance approaches showed good accuracy levels with R2 ranging from 0.74 to 0.95 and the Nash–Sutcliffe model efficiency coefficient ranging from 0.66 to 0.91. The root mean square error (and percent bias) ranged from 0.48 mm (13%) to 0.60 mm (22%) for daily (days of satellite overpass) ET and from 7.75 mm (2%) to 13.04 mm (35%) for monthly ET. The spatial and temporal distribution of ET indicates the utility of Landsat 8 for providing important information about ET dynamics across the landscape. Annual crop water use was estimated for five selected irrigation districts in the Lower CRB where annual ET per district ranged between 681 mm to 772 mm. Annual ET by crop type over the Maricopa Stanfield irrigation district ranged from a low of 384 mm for durum wheat to a high of 990 mm for alfalfa fields. A rainfall analysis over the five districts suggested that, on average, 69% of the annual ET was met by irrigation. Although the enhanced cloud-masking capability of Landsat 8 based on the cirrus band and utilization of the Fmask algorithm improved the

Forest Tree Species Distribution Mapping Using Landsat Satellite Imagery and Topographic Variables with the Maximum Entropy Method in Mongolia

Science.gov (United States)

Hao Chiang, Shou; Valdez, Miguel; Chen, Chi-Farn

2016-06-01

Forest is a very important ecosystem and natural resource for living things. Based on forest inventories, government is able to make decisions to converse, improve and manage forests in a sustainable way. Field work for forestry investigation is difficult and time consuming, because it needs intensive physical labor and the costs are high, especially surveying in remote mountainous regions. A reliable forest inventory can give us a more accurate and timely information to develop new and efficient approaches of forest management. The remote sensing technology has been recently used for forest investigation at a large scale. To produce an informative forest inventory, forest attributes, including tree species are unavoidably required to be considered. In this study the aim is to classify forest tree species in Erdenebulgan County, Huwsgul province in Mongolia, using Maximum Entropy method. The study area is covered by a dense forest which is almost 70% of total territorial extension of Erdenebulgan County and is located in a high mountain region in northern Mongolia. For this study, Landsat satellite imagery and a Digital Elevation Model (DEM) were acquired to perform tree species mapping. The forest tree species inventory map was collected from the Forest Division of the Mongolian Ministry of Nature and Environment as training data and also used as ground truth to perform the accuracy assessment of the tree species classification. Landsat images and DEM were processed for maximum entropy modeling, and this study applied the model with two experiments. The first one is to use Landsat surface reflectance for tree species classification; and the second experiment incorporates terrain variables in addition to the Landsat surface reflectance to perform the tree species classification. All experimental results were compared with the tree species inventory to assess the classification accuracy. Results show that the second one which uses Landsat surface reflectance coupled

FOREST TREE SPECIES DISTRIBUTION MAPPING USING LANDSAT SATELLITE IMAGERY AND TOPOGRAPHIC VARIABLES WITH THE MAXIMUM ENTROPY METHOD IN MONGOLIA

Directory of Open Access Journals (Sweden)

S. H. Chiang

2016-06-01

Full Text Available Forest is a very important ecosystem and natural resource for living things. Based on forest inventories, government is able to make decisions to converse, improve and manage forests in a sustainable way. Field work for forestry investigation is difficult and time consuming, because it needs intensive physical labor and the costs are high, especially surveying in remote mountainous regions. A reliable forest inventory can give us a more accurate and timely information to develop new and efficient approaches of forest management. The remote sensing technology has been recently used for forest investigation at a large scale. To produce an informative forest inventory, forest attributes, including tree species are unavoidably required to be considered. In this study the aim is to classify forest tree species in Erdenebulgan County, Huwsgul province in Mongolia, using Maximum Entropy method. The study area is covered by a dense forest which is almost 70% of total territorial extension of Erdenebulgan County and is located in a high mountain region in northern Mongolia. For this study, Landsat satellite imagery and a Digital Elevation Model (DEM were acquired to perform tree species mapping. The forest tree species inventory map was collected from the Forest Division of the Mongolian Ministry of Nature and Environment as training data and also used as ground truth to perform the accuracy assessment of the tree species classification. Landsat images and DEM were processed for maximum entropy modeling, and this study applied the model with two experiments. The first one is to use Landsat surface reflectance for tree species classification; and the second experiment incorporates terrain variables in addition to the Landsat surface reflectance to perform the tree species classification. All experimental results were compared with the tree species inventory to assess the classification accuracy. Results show that the second one which uses Landsat surface

Mapping Annual Forest Cover in Sub-Humid and Semi-Arid Regions through Analysis of Landsat and PALSAR Imagery

Directory of Open Access Journals (Sweden)

Yuanwei Qin

2016-11-01

Full Text Available Accurately mapping the spatial distribution of forests in sub-humid to semi-arid regions over time is important for forest management but a challenging task. Relatively large uncertainties still exist in the spatial distribution of forests and forest changes in the sub-humid and semi-arid regions. Numerous publications have used either optical or synthetic aperture radar (SAR remote sensing imagery, but the resultant forest cover maps often have large errors. In this study, we propose a pixel- and rule-based algorithm to identify and map annual forests from 2007 to 2010 in Oklahoma, USA, a transitional region with various climates and landscapes, using the integration of the L-band Advanced Land Observation Satellite (ALOS PALSAR Fine Beam Dual Polarization (FBD mosaic dataset and Landsat images. The overall accuracy and Kappa coefficient of the PALSAR/Landsat forest map were about 88.2% and 0.75 in 2010, with the user and producer accuracy about 93.4% and 75.7%, based on the 3270 random ground plots collected in 2012 and 2013. Compared with the forest products from Japan Aerospace Exploration Agency (JAXA, National Land Cover Database (NLCD, Oklahoma Ecological Systems Map (OKESM and Oklahoma Forest Resource Assessment (OKFRA, the PALSAR/Landsat forest map showed great improvement. The area of the PALSAR/Landsat forest was about 40,149 km2 in 2010, which was close to the area from OKFRA (40,468 km2, but much larger than those from JAXA (32,403 km2 and NLCD (37,628 km2. We analyzed annual forest cover dynamics, and the results show extensive forest cover loss (2761 km2, 6.9% of the total forest area in 2010 and gain (3630 km2, 9.0% in southeast and central Oklahoma, and the total area of forests increased by 684 km2 from 2007 to 2010. This study clearly demonstrates the potential of data fusion between PALSAR and Landsat images for mapping annual forest cover dynamics in sub-humid to semi-arid regions, and the resultant forest maps would be

A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data

Science.gov (United States)

Sun, Lin; Liu, Xinyan; Yang, Yikun; Chen, TingTing; Wang, Quan; Zhou, Xueying

2018-04-01

Although enhanced over prior Landsat instruments, Landsat 8 OLI can obtain very high cloud detection precisions, but for the detection of cloud shadows, it still faces great challenges. Geometry-based cloud shadow detection methods are considered the most effective and are being improved constantly. The Function of Mask (Fmask) cloud shadow detection method is one of the most representative geometry-based methods that has been used for cloud shadow detection with Landsat 8 OLI. However, the Fmask method estimates cloud height employing fixed temperature rates, which are highly uncertain, and errors of large area cloud shadow detection can be caused by errors in estimations of cloud height. This article improves the geometry-based cloud shadow detection method for Landsat OLI from the following two aspects. (1) Cloud height no longer depends on the brightness temperature of the thermal infrared band but uses a possible dynamic range from 200 m to 12,000 m. In this case, cloud shadow is not a specific location but a possible range. Further analysis was carried out in the possible range based on the spectrum to determine cloud shadow location. This effectively avoids the cloud shadow leakage caused by the error in the height determination of a cloud. (2) Object-based and pixel spectral analyses are combined to detect cloud shadows, which can realize cloud shadow detection from two aspects of target scale and pixel scale. Based on the analysis of the spectral differences between the cloud shadow and typical ground objects, the best cloud shadow detection bands of Landsat 8 OLI were determined. The combined use of spectrum and shape can effectively improve the detection precision of cloud shadows produced by thin clouds. Several cloud shadow detection experiments were carried out, and the results were verified by the results of artificial recognition. The results of these experiments indicated that this method can identify cloud shadows in different regions with correct

Regional scale net radiation estimation by means of Landsat and TERRA/AQUA imagery and GIS modeling

Science.gov (United States)

Cristóbal, J.; Ninyerola, M.; Pons, X.; Llorens, P.; Poyatos, R.

2009-04-01

balance among the net shortwave radiation Rn and the net longwave radiation. In addition, two types of approaches have been carried out for its determination: the estimation of the variables implied in the calculation of Rn at daily level (Rndl); and the calculation of the Rn at the time of satellite pass (Rni) and its subsequent conversion to daily Rn by means of the Rn ratio. Net shortwave radiation has been computed by means of albedo and a solar radiation model obtained through a DEM following the methodology of Pons and Ninyerola (2008).This methodology takes into account the position of the Sun, the angles of incidence, the projected shadows and the distance from the Earth to the Sun at one hour intervals. The diffuse radiation is estimated from the direct radiaton and the exoatmospheric direct solar irradiance is estimated with the Page equation (1986) and fitted by Baldasano et al. (1994). Net longwave radiation has been calculated through land surface temperature and emissivity, atmospheric water vapour and air temperature. Air temperature has been modeled by means of multiple regression analysis and GIS interpolation using ground meteorological stations. Finally, air emissivity has been computed using air temperature models and atmospheric water vapour following the methodology developed by Dilley and O'Brien (1998). Finally, models have been validated through a set of 13 ground meteorological standard stations and an experimental station placed in a Mediterranean mountain area over a Pinus sylvestris stand. Obtained results show a mean RMSE of 20 W m-2 in the case of Landsat and a mean RMSE of 22 W m-2 in the case of TERRA/AQUA MODIS, being these results in agreement with other published results, but also offering better RMSE in some cases. Keywords: Net radiation, Landsat, TERRA/AQUA MODIS, GIS modeling, regional scale.

Landsat TM data processing for lithological discrimination in the Caraculo area (Namibe Province, SW Angola)

Science.gov (United States)

Alberti, A.; Alessandro, V.; Pieruccini, U.; Pranzini, E.

1993-10-01

Landsat TM data were used for lithological discrimination and mapping in the little-known, semiarid 900 km 2 area around Caraculo station and the middle course of the Rio Giraul (Namibe Province, SW Angola) following two main procedures. The first of these was based on visual evaluation of three-band composites, band-ratio composites and Principal Component Analysis. The second method relied on the extraction of spectral signatures, and their use to obtain automatic classifications. Satisfactory results were reached with the first procedure, thus allowing - with limited support of ground information — the draft of a lithological map, while the second method was not systematically efficient, even for confirmation of data acquired with the first procedure. Image interpretation suggests that an extensive but hithertoun differentiated metasedimentary complex consisting of a heterogeneous supracrustal sequence should be subdivided into at least two units. Field observations proved that one of these is marked by a notable frequency of marbles and the other is characterized by a widespread occurrence of amphibolitic bodies. Moreover, a belt of undetermined (thermally metamorphosed ?) metamorphic rocks is interposed between them. The distinction of so far unidentified units, though restricted to interpretation of processed Landsat TM data, has significant geological implications also in the regional context and will be helpful in guiding future work with conventional geological methods.

Obtaining land cover changes information from multitemporal analysis of Landsat-TM images: results from a case study in West African dryland

Science.gov (United States)

Nutini, F.; Boschetti, M.; Brivio, P. A.; Antoninetti, M.

2012-04-01

The Sahelian belt of West Africa is a semiarid region characterized by wide climate variations, which can in turn affect the livelihood of local populations particularly in rangeland areas, as happens during the dramatic food crisis in the 70-80s caused by rainfall scarcity. The monitoring of natural resources and rainfed agricultural activities, with the aim to provide information to support Sahelian food security action, needs the production of detailed thematic maps as emphasized by several scientific papers. In this framework, a study was conducted to develop a method to exploit time series of remote sensed satellite data to 1) provide reliable land cover (LC) map at local scale in a dry region and 2) obtain a LC change (LCC) map that contribute to identify the plausible causes of local environmental instability. Satellite images used for this work consist in a time series of Landsat Thematic Mapper (TM) (path row 195-50) acquired in the 2000 (6 scenes) and 2007 (9 scenes) from February (Dry season) to September (end of wet season). The study investigates the different contribution provided by spectra information of a single Landsat TM image and by time series of derived NDVI. Different tests have been conducted with different combination of data set (spectral and temporal)in order to identify the best approach to obtain a LC map in five classes of interest: Shrubland, Cultivated Land, Water body, Herbaceous vegetation and Bare soil. The best classification approach is exposed and applied on two years in the last decade. The comparison between this two LC results in land cover change map, that displays the changes of vegetation patterns that have been characterized the area. The discussed results show a largely stable dryland region, but locally characterized by hot-spot of decreasing in natural vegetation inside the rangelands and an increasing of cultivations along fossil valleys where human activities are slightly intense. The discussion shows that this hot

Phenological monitoring of Acadia National Park using Landsat, MODIS and VIIRS observations and fused data

Science.gov (United States)

Liu, Y.; McDonough MacKenzie, C.; Primack, R.; Zhang, X.; Schaaf, C.; Sun, Q.; Wang, Z.

2015-12-01

Monitoring phenology with remotely sensed data has become standard practice in large-plot agriculture but remains an area of research in complex terrain. Landsat data (30m) provides a more appropriate spatial resolution to describe such regions but may only capture a few cloud-free images over a growing period. Daily data from the MODerate resolution Imaging Spectroradiometer(MODIS) and Visible Infrared Imaging Radiometer Suite(VIIRS) offer better temporal acquisitions but at coarse spatial resolutions of 250m to 1km. Thus fused data sets are being employed to provide the temporal and spatial resolutions necessary to accurately monitor vegetation phenology. This study focused on Acadia National Park, Maine, attempts to compare green-up from remote sensing and ground observations over varying topography. Three north-south field transects were established in 2013 on parallel mountains. Along these transects, researchers record the leaf out and flowering phenology for thirty plant species biweekly. These in situ spring phenological observations are compared with the dates detected by Landsat 7, Landsat 8, MODIS, and VIIRS observations, both separately and as fused data, to explore the ability of remotely sensed data to capture the subtle variations due to elevation. Daily Nadir BRDF Adjusted Reflectances(NBAR) from MODIS and VIIRS are fused with Landsat imagery to simulate 30m daily data via the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model(ESTARFM) algorithm. Piecewise logistic functions are fit to the time series to establish spring leaf-out dates. Acadia National Park, a region frequently affected by coastal clouds, is a particularly useful study area as it falls in a Landsat overlap region and thus offers the possibility of acquiring as many as 4 Landsat observations in a 16 day period. With the recent launch of Sentinel 2A, the community will have routine access to such high spatial and temporal data for phenological monitoring.

Vegetation burn severity mapping using Landsat-8 and WorldView-2

Science.gov (United States)

Wu, Zhuoting; Middleton, Barry R.; Hetzler, Robert; Vogel, John M.; Dye, Dennis G.

2015-01-01

We used remotely sensed data from the Landsat-8 and WorldView-2 satellites to estimate vegetation burn severity of the Creek Fire on the San Carlos Apache Reservation, where wildfire occurrences affect the Tribe's crucial livestock and logging industries. Accurate pre- and post-fire canopy maps at high (0.5-meter) resolution were created from World- View-2 data to generate canopy loss maps, and multiple indices from pre- and post-fire Landsat-8 images were used to evaluate vegetation burn severity. Normalized difference vegetation index based vegetation burn severity map had the highest correlation coefficients with canopy loss map from WorldView-2. Two distinct approaches - canopy loss mapping from WorldView-2 and spectral index differencing from Landsat-8 - agreed well with the field-based burn severity estimates and are both effective for vegetation burn severity mapping. Canopy loss maps created with WorldView-2 imagery add to a short list of accurate vegetation burn severity mapping techniques that can help guide effective management of forest resources on the San Carlos Apache Reservation, and the broader fire-prone regions of the Southwest.

What Can We Learn About Glaciers and Ice Sheets From 30 Years of Landsat Imagery?

Science.gov (United States)

Gardner, A. S.; Scambos, T.; Fahnestock, M. A.; Moholdt, G.; Nilsson, J.

2015-12-01

Glacier and ice sheets are known to be rapidly changing and currently account for two thirds of observed sea level rise. Attributing the causes of the rapid decline in land ice requires separation of mass change processes, i.e. accumulation of precipitation, meltwater runoff, and solid ice discharge. Here we examine a 30 year record of Landsat imagery to determine trends in glacier velocity at a global scale in an attempt to identify anomalies in glacier flow that are contributing to changes in land ice mass. The Landsat archive represents a treasure trove of information with hundreds of thousands of images acquired over glaciers and ice sheets during the past 30 years. Gleaning useful and consistent surface displacement information from a multiple sensor archive that is heavily contaminated by cloud, saturated images, poorly resolved sensor geometry, and data gaps has proved challenging. Temporal stacking of displacement fields (Dehecq et al., 2015) and correcting for unresolved topography (Roseanau et al., 2012) have been shown to greatly improve derived velocities. Here we present results from a global processing of the complete Landsat archive for information on glacier surface displacements. We highlight patterns of coherent regional change as well as well as rapid basin-scale changes in glacier flow.

Distribution of Snow and Maximum Snow Water Equivalent Obtained by LANDSAT Data and Degree Day Method

Science.gov (United States)

Takeda, K.; Ochiai, H.; Takeuchi, S.

1985-01-01

Maximum snow water equivalence and snowcover distribution are estimated using several LANDSAT data taken in snowmelting season over a four year period. The test site is Okutadami-gawa Basin located in the central position of Tohoku-Kanto-Chubu District. The year to year normalization for snowmelt volume computation on the snow line is conducted by year to year correction of degree days using the snowcover percentage within the test basin obtained from LANDSAT data. The maximum snow water equivalent map in the test basin is generated based on the normalized snowmelt volume on the snow line extracted from four LANDSAT data taken in a different year. The snowcover distribution on an arbitrary day in snowmelting of 1982 is estimated from the maximum snow water equivalent map. The estimated snowcover is compared with the snowcover area extracted from NOAA-AVHRR data taken on the same day. The applicability of the snow estimation using LANDSAT data is discussed.

Opening the archive: how free data has enabled the science and monitoring promise of Landsat

Science.gov (United States)

Michael A. Wulder; Jeffrey G. Masek; Warren B. Cohen; Thomas R. Loveland; Curtis E. Woodcock

2012-01-01

Landsat occupies a unique position in the constellation of civilian earth observation satellites, with a long and rich scientific and applications heritage. With nearly 40 years of continuous observation—since launch of the first satellite in 1972—the Landsat program has benefited from insightful technical specification, robust engineering, and the necessary...

Development of remote sensing technology in New Zealand, part 1. Seismotectonic, structural, volcanologic and geomorphic study of New Zealand, part 2. Indigenous forest assessment, part 3. Mapping land use and environmental studies in New Zealand, part 4. New Zealand forest service LANDSAT projects, part 5. Vegetation map and landform map of Aupouri Peninsula, Northland, part 6. Geographical applications of LANDSAT mapping, part 7

Science.gov (United States)

Probine, M. C.; Suggate, R. P.; Mcgreevy, M. G.; Stirling, I. F. (Principal Investigator)

1977-01-01

The author has identified the following significant results. Inspection of pixels obtained from LANDSAT of New Zealand revealed that not only can ships and their wakes be detected, but that information on the size, state of motion, and direction of movement was inferred by calculating the total number of pixels occupied by the vessel and wake, the orientation of these pixels, and the sum of their radiance values above the background level. Computer enhanced images showing the Waimihia State Forest and much of Kaingaroa State Forest on 22 December 1975 were examined. Most major forest categories were distinguished on LANDSAT imagery. However, the LANDSAT imagery seemed to be most useful for updating and checking existing forest maps, rather than making new maps with many forest categories. Snow studies were performed using two basins: Six Mile Creek and Mt. Robert. The differences in radiance levels indicated that a greater areal snow cover in Six Mile Creek Basin with the effect of lower radiance values from vegetation/snow regions. A comparison of the two visible bands (MSS 4 and 5) demonstrate this difference for the two basins.

Monitoring Springs in the Mojave Desert Using Landsat Time Series Analysis

Science.gov (United States)

Potter, Christopher S.

2018-01-01

The purpose of this study, based on Landsat satellite data was to characterize variations and trends over 30 consecutive years (1985-2016) in perennial vegetation green cover at over 400 confirmed Mojave Desert spring locations. These springs were surveyed between in 2015 and 2016 on lands managed in California by the U.S. Bureau of Land Management (BLM) and on several land trusts within the Barstow, Needles, and Ridgecrest BLM Field Offices. The normalized difference vegetation index (NDVI) from July Landsat images was computed at each spring location and a trend model was first fit to the multi-year NDVI time series using least squares linear regression.Â

Implementation and testing of WELD and automatic spectral rule-based classifications for Landsat ETM+ in South Africa

CSIR Research Space (South Africa)

Wessels, Konrad J

2013-04-01

Full Text Available The Web-enabled Landsat Data (WELD) system was successfully installed in South Africa (SA) and used for pre-processing large amounts of Landsat ETM+ data to composited seasonal mosaics. In pursuit of automated land cover mapping, the overall...

Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach

Science.gov (United States)

Taufik, Afirah; Sakinah Syed Ahmad, Sharifah

2016-06-01

The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.

Centralized mission planning and scheduling system for the Landsat Data Continuity Mission

Science.gov (United States)

Kavelaars, Alicia; Barnoy, Assaf M.; Gregory, Shawna; Garcia, Gonzalo; Talon, Cesar; Greer, Gregory; Williams, Jason; Dulski, Vicki

2014-01-01

Satellites in Low Earth Orbit provide missions with closer range for studying aspects such as geography and topography, but often require efficient utilization of space and ground assets. Optimizing schedules for these satellites amounts to a complex planning puzzle since it requires operators to face issues such as discontinuous ground contacts, limited onboard memory storage, constrained downlink margin, and shared ground antenna resources. To solve this issue for the Landsat Data Continuity Mission (LDCM, Landsat 8), all the scheduling exchanges for science data request, ground/space station contact, and spacecraft maintenance and control will be coordinated through a centralized Mission Planning and Scheduling (MPS) engine, based upon GMV’s scheduling system flexplan9 . The synchronization between all operational functions must be strictly maintained to ensure efficient mission utilization of ground and spacecraft activities while working within the bounds of the space and ground resources, such as Solid State Recorder (SSR) and available antennas. This paper outlines the functionalities that the centralized planning and scheduling system has in its operational control and management of the Landsat 8 spacecraft.

Perspective view, Landsat overlay San Andreas Fault, Palmdale, California

Science.gov (United States)

2000-01-01

The prominent linear feature straight down the center of this perspective view is the San Andreas Fault. This segment of the fault lies near the city of Palmdale, California (the flat area in the right half of the image) about 60 kilometers (37 miles) north of Los Angeles. The fault is the active tectonic boundary between the North American plate on the right, and the Pacific plate on the left. Relative to each other, the Pacific plate is moving away from the viewer and the North American plate is moving toward the viewer along what geologists call a right lateral strike-slip fault. Two large mountain ranges are visible, the San Gabriel Mountains on the left and the Tehachapi Mountains in the upper right. The Lake Palmdale Reservoir, approximately 1.5 kilometers (0.9 miles) across, sits in the topographic depression created by past movement along the fault. Highway 14 is the prominent linear feature starting at the lower left edge of the image and continuing along the far side of the reservoir. The patterns of residential and agricultural development around Palmdale are seen in the Landsat imagery in the right half of the image. SRTM topographic data will be used by geologists studying fault dynamics and landforms resulting from active tectonics.This type of display adds the important dimension of elevation to the study of land use and environmental processes as observed in satellite images. The perspective view was created by draping a Landsat satellite image over an SRTM elevation model. Topography is exaggerated 1.5 times vertically. The Landsat image was provided by the United States Geological Survey's Earth Resources Observations Systems (EROS) Data Center, Sioux Falls, South Dakota.Elevation data used in this image was acquired by the Shuttle Radar Topography Mission (SRTM) aboard the Space Shuttle Endeavour, launched on February 11,2000. SRTM used the same radar instrument that comprised the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR

Perspective View with Landsat Overlay, Salt Lake City Olympics Venues, Utah

Science.gov (United States)

2002-01-01

The 2002 Winter Olympics are hosted by Salt Lake City at several venues within the city, in nearby cities, and within the adjacent Wasatch Mountains. This computer generated perspective image provides a northward looking 'view from space' that includes all of these Olympic sites. In the south, next to Utah Lake, Provo hosts the ice hockey competition. In the north, northeast of the Great Salt Lake, Ogden hosts curling, and the nearby Snow Basin ski area hosts the downhill events. In between, southeast of the Great Salt Lake, Salt Lake City hosts the Olympic Village and the various skating events. Further east, across the Wasatch Mountains, the Park City area ski resorts host the bobsled, ski jumping, and snowboarding events. The Winter Olympics are always hosted in mountainous terrain. This view shows the dramatic landscape that makes the Salt Lake City region a world-class center for winter sports.This 3-D perspective view was generated using topographic data from the Shuttle Radar Topography Mission (SRTM) and a Landsat 5 satellite image mosaic. Topographic expression is exaggerated four times.For a full-resolution, annotated version of this image, please select Figure 1, below: [figure removed for brevity, see original site] Landsat has been providing visible and infrared views of the Earth since 1972. SRTM elevation data matches the 30-meter (98-foot) resolution of most Landsat images and will substantially help in analyzing the large and growing Landsat image archive, managed by the U.S. Geological Survey (USGS).Elevation data used in this image was acquired by the Shuttle Radar Topography Mission (SRTM) aboard the Space Shuttle Endeavour, launched on Feb. 11, 2000. SRTM used the same radar instrument that comprised the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) that flew twice on the Space Shuttle Endeavour in 1994. SRTM was designed to collect 3-D measurements of the Earth's surface. To collect the 3-D data, engineers added a 60

Spatiotemporal Dynamics of Surface Water Extent from Three Decades of Seasonally Continuous Landsat Time Series at Subcontinental Scale

Science.gov (United States)

Tulbure, M. G.; Broich, M.; Stehman, Stephen V.

2016-06-01

Surface water is a critical resource in semi-arid areas. The Murray-Darling Basin (MDB) of Australia, one of the largest semi-arid basins in the world is aiming to set a worldwide example of how to balance multiple interests (i.e. environment, agriculture and urban use), but has suffered significant water shrinkages during the Millennium Drought (1999-2009), followed by extensive flooding. Baseline information and systematic quantification of surface water (SW) extent and flooding dynamics in space and time are needed for managing SW resources across the basin but are currently lacking. To synoptically quantify changes in SW extent and flooding dynamics over MDB, we used seasonally continuous Landsat TM and ETM+ data (1986 - 2011) and generic machine learning algorithms. We further mapped flooded forest at a riparian forest site that experienced severe tree dieback due to changes in flooding regime. We used a stratified sampling design to assess the accuracy of the SW product across time. Accuracy assessment yielded an overall classification accuracy of 99.94%, with producer's and user's accuracy of SW of 85.4% and 97.3%, respectively. Overall accuracy was the same for Landsat 5 and 7 data but user's and producer's accuracy of water were higher for Landsat 7 than 5 data and stable over time. Our validated results document a rapid loss in SW bodies. The number, size, and total area of SW showed high seasonal variability with highest numbers in winter and lowest numbers in summer. SW extent per season per year showed high interannual and seasonal variability, with low seasonal variability during the Millennium Drought. Examples of current uses of the new dataset will be presented and include (1) assessing ecosystem response to flooding with implications for environmental water releases, one of the largest investment in environment in Australia; (2) quantifying drivers of SW dynamics (e.g. climate, human activity); (3) quantifying changes in SW dynamics and

An Initial Analysis of LANDSAT-4 Thematic Mapper Data for the Discrimination of Agricultural, Forested Wetland, and Urban Land Covers

Science.gov (United States)

Quattrochi, D. A.

1984-01-01

An initial analysis of LANDSAT 4 Thematic Mapper (TM) data for the discrimination of agricultural, forested wetland, and urban land covers is conducted using a scene of data collected over Arkansas and Tennessee. A classification of agricultural lands derived from multitemporal LANDSAT Multispectral Scanner (MSS) data is compared with a classification of TM data for the same area. Results from this comparative analysis show that the multitemporal MSS classification produced an overall accuracy of 80.91% while the TM classification yields an overall classification accuracy of 97.06% correct.

Cádiz bay walers lurbidily varialions from Landsat TM images analysis

OpenAIRE

Gutiérrez Mas, José Manuel; Luna del Barco, A.; Parrado Román, J. M.; Sánchez, E.; Fernández Palacios, A.; Ojeda, J.

1999-01-01

Landsat TM images has been analysed to obtain extent and direction data about turbidity flumes in several hydrodinamic sinoptic situations in Cadiz bay waters. Results are beside data from water samples. Five turbidity levels has been differentiated: very high turbidity, high, middle, low and very low, and three geographic sectors: a) Inner zone, closed to coast and with shoal waters of high and very high turbidity. This sector is very affected by littoral processes (tidals, surge and contine...

Harmonic analysis of dense time series of landsat imagery for modeling change in forest conditions

Science.gov (United States)

Barry Tyler. Wilson

2015-01-01

This study examined the utility of dense time series of Landsat imagery for small area estimation and mapping of change in forest conditions over time. The study area was a region in north central Wisconsin for which Landsat 7 ETM+ imagery and field measurements from the Forest Inventory and Analysis program are available for the decade of 2003 to 2012. For the periods...

Landsat's role in ecological applications of remote sensing.

Science.gov (United States)

Warren B. Cohen; Samuel N. Goward

2004-01-01

Remote sensing, geographic information systems, and modeling have combined to produce a virtual explosion of growth in ecological investigations and applications that are explicitly spatial and temporal. Of all remotely sensed data, those acquired by landsat sensors have played the most pivotal role in spatial and temporal scaling. Modern terrestrial ecology relies on...

Development and implementation of a low cost micro computer system for LANDSAT analysis and geographic data base applications

Science.gov (United States)

Faust, N.; Jordon, L.

1981-01-01

Since the implementation of the GRID and IMGRID computer programs for multivariate spatial analysis in the early 1970's, geographic data analysis subsequently moved from large computers to minicomputers and now to microcomputers with radical reduction in the costs associated with planning analyses. Programs designed to process LANDSAT data to be used as one element in a geographic data base were used once NIMGRID (new IMGRID), a raster oriented geographic information system, was implemented on the microcomputer. Programs for training field selection, supervised and unsupervised classification, and image enhancement were added. Enhancements to the color graphics capabilities of the microsystem allow display of three channels of LANDSAT data in color infrared format. The basic microcomputer hardware needed to perform NIMGRID and most LANDSAT analyses is listed as well as the software available for LANDSAT processing.

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

Directory of Open Access Journals (Sweden)

Flavia Di Palma

2016-07-01

Full Text Available The negative impacts of land take on natural components and economic resources affect planning choices and territorial policies. The importance of land take monitoring, in Italy, has been only recently considered, but despite this awareness, in the great part of the country, effective monitoring and containment measures have not been started, yet. This research proposes a methodology to map and monitor land use changes. To this end, a time series from 1985–2010, based on the multi-temporal Landsat data Thematic Mapper (TM, has been analyzed in the Vulture Alto-Bradano area, a mountain zone of the Basilicata region (Southern Italy. Results confirm a double potentiality of using these data: on the one hand, the use of multi-temporal Landsat data allows going very back in time, producing accurate datasets that provide a phenomenon trend over time; on the other hand, these data can be considered a first experience of open data in the field of spatial information. The proposed methodology provides agencies, local authorities and practitioners with a valuable tool to implement monitoring actions. This represents the first step to pursue territorial governance methods based on sustainability, limiting the land take.

a Preliminary Investigation on Comparison and Transformation of SENTINEL-2 MSI and Landsat 8 Oli

Science.gov (United States)

Chen, F.; Lou, S.; Fan, Q.; Li, J.; Wang, C.; Claverie, M.

2018-05-01

A PRELIMINARY INVESTIGATION ON COMPARISON AND TRANSFORMATION OF SENTINEL-2 MSI AND LANDSAT 8 OLI Timely and accurate earth observation with short revisit interval is usually necessary, especially for emergency response. Currently, several new generation sensors provided with similar channel characteristics have been operated onboard different satellite platforms, including Sentinel-2 and Landsat 8. Joint use of the observations by different sensors offers an opportunity to meet the demands for emergency requirements. For example, through the combination of Landsat and Sentinel-2 data, the land can be observed every 2-3 days at medium spatial resolution. However, differences are expected in radiometric values (e.g., channel reflectance) of the corresponding channels between two sensors. Spectral response function (SRF) is taken as an important aspect of sensor settings. Accordingly, between-sensor differences due to SRFs variation need to be quantified and compensated. The comparison of SRFs shows difference (more or less) in channel settings between Sentinel-2 Multi-Spectral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI). Effect of the difference in SRF on corresponding values between MSI and OLI was investigated, mainly in terms of channel reflectance and several derived spectral indices. Spectra samples from ASTER Spectral Library Version 2.0 and Hyperion data archives were used in obtaining channel reflectance simulation of MSI and OLI. Preliminary results show that MSI and OLI are well comparable in several channels with small relative discrepancy (model is not ensured when the target belongs to another spectra collection. If an improper transformation model is selected, the between-sensor discrepancy will even largely increase. In conclusion, improvement in between-sensor consistency is possibly a challenge, through linear transformation based on model(s) generated from other spectra collections.

Human-induced geomorphology: Modeling slope failure in Dominical, Costa Rica using Landsat imagery

Science.gov (United States)

Miller, Andrew J.

Unchecked human development has ravaged the region between Dominical and Uvita, Costa Rica. Much of the development transition has been driven by tourism and further foreign direct investment in residential, service and commercial enterprises. The resulting land-use/land-cover change has removed traditional forest cover in exchange for impervious surfaces, physical structures, and bare ground which is no longer mechanically supported by woody vegetation. Combined with a tropical climate, deeply weathered soils and lithography which are prone to erosion, land cover change has led to an increase in slope failure occurrences. Given the remoteness of the Dominical-Uvita region, its rate of growth and the lack of monitoring, new techniques for monitoring land use and slope failure susceptibility are needed. Two new indices are presented here that employ a Digital Elevation Model (DEM) and widely available Landsat imagery to assist in this endeavor. The first index, or Vegetation Influenced Landslide Index (VILI), incorporates slope derived from a DEM and Lu et al.'s (2007) Surface Cover Index to quantify vegetative cover as a means of mechanical stabilization in landslide prone areas. The second index, or Slope Multiplier Index (SMI), uses individual Landsat data bands and basic Landsat band ratios as environmental proxies to replicate soil, vegetative and hydrologic properties. Both models achieve accuracy over 70% and rival results from more complicated published literature. The accuracy of the indices was assessed with the creation of a landslide inventory developed from field observations occurring in December 2007 and November 2008. The creation of these indices represents an efficient and accurate way of determining landslide susceptibility zonation in data poor areas where environmental protection practitioners may be overextended, under-trained or both.

A COMPARISON OF HAZE REMOVAL ALGORITHMS AND THEIR IMPACTS ON CLASSIFICATION ACCURACY FOR LANDSAT IMAGERY

Directory of Open Access Journals (Sweden)

Yang Xiao

Full Text Available The quality of Landsat images in humid areas is considerably degraded by haze in terms of their spectral response pattern, which limits the possibility of their application in using visible and near-infrared bands. A variety of haze removal algorithms have been proposed to correct these unsatisfactory illumination effects caused by the haze contamination. The purpose of this study was to illustrate the difference of two major algorithms (the improved homomorphic filtering (HF and the virtual cloud point (VCP for their effectiveness in solving spatially varying haze contamination, and to evaluate the impacts of haze removal on land cover classification. A case study with exploiting large quantities of Landsat TM images and climates (clear and haze in the most humid areas in China proved that these haze removal algorithms both perform well in processing Landsat images contaminated by haze. The outcome of the application of VCP appears to be more similar to the reference images compared to HF. Moreover, the Landsat image with VCP haze removal can improve the classification accuracy effectively in comparison to that without haze removal, especially in the cloudy contaminated area

Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass

Science.gov (United States)

Glenn, Nancy F.; Neuenschwander, Amy; Vierling, Lee A.; Spaete, Lucas; Li, Aihua; Shinneman, Douglas; Pilliod, David S.; Arkle, Robert; McIlroy, Susan

2016-01-01

The Landsat 8 mission provides new opportunities for quantifying the distribution of above-ground carbon at moderate spatial resolution across the globe, and in particular drylands. Furthermore, coupled with structural information from space-based and airborne laser altimetry, Landsat 8 provides powerful capabilities for large-area, long-term studies that quantify temporal and spatial changes in above-ground biomass and cover. With the planned launch of ICESat-2 in 2017 and thus the potential to couple Landsat 8 and ICESat-2 data, we have unprecedented opportunities to address key challenges in drylands, including quantifying fuel loads, habitat quality, biodiversity, carbon cycling, and desertification.

Mapping burned areas using dense time-series of Landsat data

Science.gov (United States)

Hawbaker, Todd J.; Vanderhoof, Melanie; Beal, Yen-Ju G.; Takacs, Joshua; Schmidt, Gail L.; Falgout, Jeff T.; Williams, Brad; Brunner, Nicole M.; Caldwell, Megan K.; Picotte, Joshua J.; Howard, Stephen M.; Stitt, Susan; Dwyer, John L.

2017-01-01

Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, in many areas existing fire occurrence datasets are known to be incomplete. Consequently, the need to systematically collect burned area information has been recognized by the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change, which have both called for the production of essential climate variables (ECVs), including information about burned area. In this paper, we present an algorithm that identifies burned areas in dense time-series of Landsat data to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm uses gradient boosted regression models to generate burn probability surfaces using band values and spectral indices from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference predictors. Burn classifications are generated from the burn probability surfaces using pixel-level thresholding in combination with a region growing process. The algorithm can be applied anywhere Landsat and training data are available. For this study, BAECV products were generated for the conterminous United States from 1984 through 2015. These products consist of pixel-level burn probabilities for each Landsat scene, in addition to, annual composites including: the maximum burn probability and a burn classification. We compared the BAECV burn classification products to the existing Global Fire Emissions Database (GFED; 1997–2015) and Monitoring Trends in Burn Severity (MTBS; 1984–2013) data. We found that the BAECV products mapped 36% more burned area than the GFED and 116% more burned area than MTBS. Differences between the BAECV products and the GFED were especially high in the West and East where the

Landsat Image Map Production Methods at the U. S. Geological Survey

Science.gov (United States)

Kidwell, R.D.; Binnie, D.R.; Martin, S.

1987-01-01

To maintain consistently high quality in satellite image map production, the U. S. Geological Survey (USGS) has developed standard procedures for the photographic and digital production of Landsat image mosaics, and for lithographic printing of multispectral imagery. This paper gives a brief review of the photographic, digital, and lithographic procedures currently in use for producing image maps from Landsat data. It is shown that consistency in the printing of image maps is achieved by standardizing the materials and procedures that affect the image detail and color balance of the final product. Densitometric standards are established by printing control targets using the pressplates, inks, pre-press proofs, and paper to be used for printing.

OHIO RIVER WATER QUALITY ASSESSMENT USING LANDSAT-7 DATA

Science.gov (United States)

The objectives of this project were (1) to develop a universal index for measuring Turbidity and Chlorophyll-A from remote sensing data and (2) to correlate satellite image parameters from Landsat-7 data with field measurements of water quality for five parameters: Chlorophyll-A ...

Use of LANDSAT for Managing Nonpoint Source Pollution in Coastal Ecosystems of the U. S. Virgin Islands

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The data results show for the first time Landsat-based land use maps of both the terrestrial and benthic habitats of the U. S. Virgin Islands, spanning a period of...

Evaluating the influence of spatial resolution of Landsat predictors on the accuracy of biomass models for large-area estimation across the eastern USA

Science.gov (United States)

Deo, Ram K.; Domke, Grant M.; Russell, Matthew B.; Woodall, Christopher W.; Andersen, Hans-Erik

2018-05-01

Aboveground biomass (AGB) estimates for regional-scale forest planning have become cost-effective with the free access to satellite data from sensors such as Landsat and MODIS. However, the accuracy of AGB predictions based on passive optical data depends on spatial resolution and spatial extent of target area as fine resolution (small pixels) data are associated with smaller coverage and longer repeat cycles compared to coarse resolution data. This study evaluated various spatial resolutions of Landsat-derived predictors on the accuracy of regional AGB models at three different sites in the eastern USA: Maine, Pennsylvania-New Jersey, and South Carolina. We combined national forest inventory data with Landsat-derived predictors at spatial resolutions ranging from 30–1000 m to understand the optimal spatial resolution of optical data for large-area (regional) AGB estimation. Ten generic models were developed using the data collected in 2014, 2015 and 2016, and the predictions were evaluated (i) at the county-level against the estimates of the USFS Forest Inventory and Analysis Program which relied on EVALIDator tool and national forest inventory data from the 2009–2013 cycle and (ii) within a large number of strips (~1 km wide) predicted via LiDAR metrics at 30 m spatial resolution. The county-level estimates by the EVALIDator and Landsat models were highly related (R 2 > 0.66), although the R 2 varied significantly across sites and resolution of predictors. The mean and standard deviation of county-level estimates followed increasing and decreasing trends, respectively, with models of coarser resolution. The Landsat-based total AGB estimates were larger than the LiDAR-based total estimates within the strips, however the mean of AGB predictions by LiDAR were mostly within one-standard deviations of the mean predictions obtained from the Landsat-based model at any of the resolutions. We conclude that satellite data at resolutions up to 1000 m provide

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

Directory of Open Access Journals (Sweden)

Fei Wang

2015-04-01

Full Text Available The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST. However, calibration notices issued by the United States Geological Survey (USGS indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial

Using the Direct Sampling Multiple-Point Geostatistical Method for Filling Gaps in Landsat 7 ETM+ SLC-off Imagery

KAUST Repository

Yin, Gaohong

2016-05-01

Since the failure of the Scan Line Corrector (SLC) instrument on Landsat 7, observable gaps occur in the acquired Landsat 7 imagery, impacting the spatial continuity of observed imagery. Due to the highly geometric and radiometric accuracy provided by Landsat 7, a number of approaches have been proposed to fill the gaps. However, all proposed approaches have evident constraints for universal application. The main issues in gap-filling are an inability to describe the continuity features such as meandering streams or roads, or maintaining the shape of small objects when filling gaps in heterogeneous areas. The aim of the study is to validate the feasibility of using the Direct Sampling multiple-point geostatistical method, which has been shown to reconstruct complicated geological structures satisfactorily, to fill Landsat 7 gaps. The Direct Sampling method uses a conditional stochastic resampling of known locations within a target image to fill gaps and can generate multiple reconstructions for one simulation case. The Direct Sampling method was examined across a range of land cover types including deserts, sparse rural areas, dense farmlands, urban areas, braided rivers and coastal areas to demonstrate its capacity to recover gaps accurately for various land cover types. The prediction accuracy of the Direct Sampling method was also compared with other gap-filling approaches, which have been previously demonstrated to offer satisfactory results, under both homogeneous area and heterogeneous area situations. Studies have shown that the Direct Sampling method provides sufficiently accurate prediction results for a variety of land cover types from homogeneous areas to heterogeneous land cover types. Likewise, it exhibits superior performances when used to fill gaps in heterogeneous land cover types without input image or with an input image that is temporally far from the target image in comparison with other gap-filling approaches.

Preliminary analysis of the potential of LANDSAT imagery to study desertification. [Xique-Xique, Brazil

Science.gov (United States)

Dejesusparada, N. (Principal Investigator); Lombardo, M. A.; Decarvalho, V. C.

1980-01-01

The use of LANDSAT imagery to define and delimit areas under process of desertification was investigated. Imagery for two different years (1973 and 1978) and two different seasons (dry and rainy seasons in 1976), were used to identify terrain morphology and vegetation cover. The analysis of LANDSAT interpretation, combined with geological and soil information obtained from published literature, allowed the identification of eleven ecological units which were classified corresponding to the degree of the Xique Xique region of Rio Sao Francisco.

Determination of turbidity patterns in Lake Chicot from LANDSAT MSS imagery

Science.gov (United States)

Lecroy, S. R.

1982-01-01

A historical analysis of all the applicable LANDSAT imagery was conducted on the turbidity patterns of Lake Chicot, located in the southeastern corner of Arkansas. By examining the seasonal and regional turbidity patterns, a record of sediment dynamics and possible disposition can be obtained. Sketches were generated from the suitable imagery, displaying different intensities of brightness observed in bands 5 and 7 of LANDSAT's multispectral scanner data. Differences in and between bands 5 and 7 indicate variances in the levels of surface sediment concentrations. High sediment loads are revealed when distinct patterns appear in the band 7 imagery. Additionally, the upwelled signal is exponential in nature and saturates in band 5 at low wavelengths for large concentrations of suspended solids.

Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree.

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Acharya, Tri Dev; Lee, Dong Ha; Yang, In Tae; Lee, Jae Kang

2016-07-12

Water bodies are essential to humans and other forms of life. Identification of water bodies can be useful in various ways, including estimation of water availability, demarcation of flooded regions, change detection, and so on. In past decades, Landsat satellite sensors have been used for land use classification and water body identification. Due to the introduction of a New Operational Land Imager (OLI) sensor on Landsat 8 with a high spectral resolution and improved signal-to-noise ratio, the quality of imagery sensed by Landsat 8 has improved, enabling better characterization of land cover and increased data size. Therefore, it is necessary to explore the most appropriate and practical water identification methods that take advantage of the improved image quality and use the fewest inputs based on the original OLI bands. The objective of the study is to explore the potential of a J48 decision tree (JDT) in identifying water bodies using reflectance bands from Landsat 8 OLI imagery. J48 is an open-source decision tree. The test site for the study is in the Northern Han River Basin, which is located in Gangwon province, Korea. Training data with individual bands were used to develop the JDT model and later applied to the whole study area. The performance of the model was statistically analysed using the kappa statistic and area under the curve (AUC). The results were compared with five other known water identification methods using a confusion matrix and related statistics. Almost all the methods showed high accuracy, and the JDT was successfully applied to the OLI image using only four bands, where the new additional deep blue band of OLI was found to have the third highest information gain. Thus, the JDT can be a good method for water body identification based on images with improved resolution and increased size.

Estimating urban vegetation fraction across 25 cities in pan-Pacific using Landsat time series data

Science.gov (United States)

Lu, Yuhao; Coops, Nicholas C.; Hermosilla, Txomin

2017-04-01

Urbanization globally is consistently reshaping the natural landscape to accommodate the growing human population. Urban vegetation plays a key role in moderating environmental impacts caused by urbanization and is critically important for local economic, social and cultural development. The differing patterns of human population growth, varying urban structures and development stages, results in highly varied spatial and temporal vegetation patterns particularly in the pan-Pacific region which has some of the fastest urbanization rates globally. Yet spatially-explicit temporal information on the amount and change of urban vegetation is rarely documented particularly in less developed nations. Remote sensing offers an exceptional data source and a unique perspective to map urban vegetation and change due to its consistency and ubiquitous nature. In this research, we assess the vegetation fractions of 25 cities across 12 pan-Pacific countries using annual gap-free Landsat surface reflectance products acquired from 1984 to 2012, using sub-pixel, spectral unmixing approaches. Vegetation change trends were then analyzed using Mann-Kendall statistics and Theil-Sen slope estimators. Unmixing results successfully mapped urban vegetation for pixels located in urban parks, forested mountainous regions, as well as agricultural land (correlation coefficient ranging from 0.66 to 0.77). The greatest vegetation loss from 1984 to 2012 was found in Shanghai, Tianjin, and Dalian in China. In contrast, cities including Vancouver (Canada) and Seattle (USA) showed stable vegetation trends through time. Using temporal trend analysis, our results suggest that it is possible to reduce noise and outliers caused by phenological changes particularly in cropland using dense new Landsat time series approaches. We conclude that simple yet effective approaches of unmixing Landsat time series data for assessing spatial and temporal changes of urban vegetation at regional scales can provide

Volcano stratigraphy interpretation of Mamuju area based on Landsat-8 imagery analysis

International Nuclear Information System (INIS)

Frederikus Dian Indrastomo; I Gde Sukadana; Dhatu Kamajati; Asep Saepuloh; Agus Handoyo Harsolumakso

2015-01-01

Mamuju and its surrounding area are constructed mainly by volcanic rocks. Volcanoclastic sedimentary rocks and limestones are laid above the volcanic rocks. Volcanic activities create some unique morphologies such as craters, lava domes, and pyroclastic flow paths as their volcanic products. These products are identified from their circular features characters on Landsat-8 imagery. Geometric and atmospheric corrections had been done, a visual interpretation on Landsat-8 imagery was conducted to identify structure, geomorphology, and geological condition of the area. Regional geological structures show trend to southeast – northwest direction which is affects the formation of Adang volcano. Geomorphology of the area are classified into 16 geomorphology units based on their genetic aspects, i.e Sumare fault block ridge, Mamuju cuesta ridge, Adang eruption crater, Labuhan Ranau eruption crater, Sumare eruption crater, Ampalas volcanic cone, Adang lava dome, Labuhan Ranau intrusion hill, Adang pyroclastic flow ridge, Sumare pyroclastic flow ridge, Adang volcanic remnant hills, Malunda volcanic remnant hills, Talaya volcanic remnant hills, Tapalang karst hills, Mamuju alluvium plains, and Karampuang reef terrace plains. Based on the Landsat-8 imagery interpretation result and field confirmation, the geology of Mamuju area is divided into volcanic rocks and sedimentary rocks. There are two groups of volcanic rocks; Talaya complex and Mamuju complex. The Talaya complex consists of Mambi, Malunda, and Kalukku volcanic rocks with andesitic composition, while Mamuju complex consist of Botteng, Ahu, Tapalang, Adang, Ampalas, Sumare, and Labuhan Ranau volcanic rocks with andesite to leucitic basalt composition. The volcano stratigraphy of Mamuju area was constructed based on its structure, geomorphology and lithology distribution analysis. Volcano stratigraphy of Mamuju area is classified into Khuluk Talaya and Khuluk Mamuju. The Khuluk Talaya consists of Gumuk Mambi, Gumuk

Improved Topographic Normalization for Landsat TM Images by Introducing the MODIS Surface BRDF

Directory of Open Access Journals (Sweden)

Yanli Zhang

2015-05-01

Full Text Available In rugged terrain, the accuracy of surface reflectance estimations is compromised by atmospheric and topographic effects. We propose a new method to simultaneously eliminate atmospheric and terrain effects in Landsat Thematic Mapper (TM images based on a 30 m digital elevation model (DEM and Moderate Resolution Imaging Spectroradiometer (MODIS atmospheric products. Moreover, we define a normalized factor of a Bidirectional Reflectance Distribution Function (BRDF to convert the sloping pixel reflectance into a flat pixel reflectance by using the Ross Thick-Li Sparse BRDF model (Ambrals algorithm and MODIS BRDF/albedo kernel coefficient products. Sole atmospheric correction and topographic normalization were performed for TM images in the upper stream of the Heihe River Basin. The results show that using MODIS atmospheric products can effectively remove atmospheric effects compared with the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH model and the Landsat Climate Data Record (CDR. Moreover, superior topographic effect removal can be achieved by considering the surface BRDF when compared with the surface Lambertian assumption of topographic normalization.

Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery

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Komeil Rokni

2014-05-01

Full Text Available Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010 in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000–2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI, Modified NDWI (MNDWI, Normalized Difference Moisture Index (NDMI, Water Ratio Index (WRI, Normalized Difference Vegetation Index (NDVI, and Automated Water Extraction Index (AWEI were investigated for the extraction of surface water from Landsat data. Overall, the NDWI was found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000–2013, especially between 2010 and 2013 when the lake lost about one third of its surface area compared to the year 2000. The results illustrate the effectiveness of the NDWI-PCs approach for surface water change detection, especially in detecting the changes between two and three different times, simultaneously.

Geometric pattern recognition techniques applied to land Landsat digital data for uranium exploration

International Nuclear Information System (INIS)

1979-01-01

Although the results we obtained cannot be described as providing a new tool for uranium exploration, we saw clear evidence that computed texture measures are describing some element of the Landsat data source that cannot be readily observed or analyzed by human interpreters. The success of Eigenband 3 is less surprising, since we had other evidence suggesting that such a band ought to be relevant to image textures

A combined spectral and object-based approach to transparent cloud removal in an operational setting for Landsat ETM+

Science.gov (United States)

Watmough, Gary R.; Atkinson, Peter M.; Hutton, Craig W.

2011-04-01

The automated cloud cover assessment (ACCA) algorithm has provided automated estimates of cloud cover for the Landsat ETM+ mission since 2001. However, due to the lack of a band around 1.375 μm, cloud edges and transparent clouds such as cirrus cannot be detected. Use of Landsat ETM+ imagery for terrestrial land analysis is further hampered by the relatively long revisit period due to a nadir only viewing sensor. In this study, the ACCA threshold parameters were altered to minimise omission errors in the cloud masks. Object-based analysis was used to reduce the commission errors from the extended cloud filters. The method resulted in the removal of optically thin cirrus cloud and cloud edges which are often missed by other methods in sub-tropical areas. Although not fully automated, the principles of the method developed here provide an opportunity for using otherwise sub-optimal or completely unusable Landsat ETM+ imagery for operational applications. Where specific images are required for particular research goals the method can be used to remove cloud and transparent cloud helping to reduce bias in subsequent land cover classifications.

Budapest, Hungary, Perspective View, SRTM Elevation Model with Landsat Overlay

Science.gov (United States)

2004-01-01

After draining the northern flank of the Alps Mountains in Germany and Austria, the Danube River flows east as it enters this west-looking scene (upper right) and forms the border between Slovakia and Hungary. The river then leaves the border as it enters Hungary and transects the Transdanubian Mountains, which trend southwest to northeast. Upon exiting the mountains, the river turns southward, flowing past Budapest (purplish blue area) and along the western margin of the Great Hungarian Plain.South and west of the Danube, the Transdanubian Mountains have at most only about 400 meters (about 1300 feet) of relief but they exhibit varied landforms, which include volcanic, tectonic, fluvial (river), and eolian (wind) features. A thick deposit of loess (dust deposits likely blown from ancient glacial outwash) covers much of this area, and winds from the northwest, funneled between the Alps and the Carpathian Mountains, are apparently responsible for a radial pattern of erosional streaks across the entire region.This image was generated from a Landsat satellite image draped over an elevation model produced by the Shuttle Radar Topography Mission (SRTM). The view uses a 3-times vertical exaggeration to enhance topographic expression. The false colors of the scene result from displaying Landsat bands 1, 4, and 7 in blue, green, and red, respectively. Band 1 is visible blue light, but bands 4 and 7 are reflected infrared light. This band combination maximizes color contrasts between the major land cover types, namely vegetation (green), bare ground (red), and water (blue). Shading of the elevation model was used to further highlight the topographic features.Elevation data used in this image was acquired by the Shuttle Radar Topography Mission (SRTM) aboard the Space Shuttle Endeavour, launched on February 11, 2000. SRTM used the same radar instrument that comprised the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) that flew twice on the Space

Landsat as a Political Entity: Meaningful Communication for a National Asset

Science.gov (United States)

Rocchio, L. E.

2010-12-01

Understanding the health of our planet on a global scale is essential to the world’s populace. Earth-observing satellites have long been collecting data that enable a robust comprehension of Earth’s complex and interconnected systems. Despite these important contributions, the fleet of U.S. Earth-observing satellites is aging and operational status for most onboard sensors has not materialized. These satellites are imperative objective viewers of our changing planet as we try to monitor and deal with natural disasters, carbon budgeting, water consumption, and food production. But the satellite building and launching process needed to sustain an operational observatory is extremely political. Landsat, the oldest civilian land-observing satellite, has a long and checkered political past, and it is only because of a handful of political champions that the program has endured. This begs the question: are policymakers aware of the contributions of satellites to our national wellbeing? And if not, can the science community better communicate with the general public at large and policy makers in particular? Here Landsat is examined as a political entity and the six pillars of effective science communication (context, trust, dialogue, clarity, respect, nuance) are used to develop, refine, and analyze a fact sheet and case study that explain the importance of Landsat Earth-observation to our society.

Preliminary classification of water areas within the Atchafalaya Basin Floodway System by using landsat imagery

Science.gov (United States)

Allen, Yvonne C.; Constant, Glenn C.; Couvillion, Brady R.

2008-01-01

The southern portion of the Atchafalaya Basin Floodway System (ABFS) is a large area (2,571 km2) in south central Louisiana bounded on the east and west sides by a levee system. The ABFS is a sparsely populated area that includes some of the Nation's most significant extents of bottomland hardwoods, swamps, bayous, and backwater lakes, holding a rich abundance and diversity of terrestrial and aquatic species. The seasonal flow of water through the ABFS is critical to maintaining its ecological integrity. Because of strong interdependencies among species, habitat quality, and water flow in the ABFS, there is a need to better define the paths by which water moves at various stages of the hydrocycle. Although river level gages have collected a long historical record of water level variation, very little synoptic information has been available regarding the distribution and character of water at more remote locations in the basin. Most water management plans for the ABFS strive to improve water quality by increasing water flow and circulation from the main stem of the Atchafalaya River into isolated areas. To describe the distribution of land and water on a basin-wide scale, we chose to use Landsat 5 and Landsat 7 imagery to determine the extent of water distribution from 1985 to 2006 and at a variety of river stages. Because the visual signature of river water is high turbidity, we also used Landsat imagery to describe the distribution of turbid water in the ABFS. The ability to track water flow patterns by tracking turbid waters will enhance the characterization of water movement and aid in planning.

Spatio-Temporal Assessment of Margalla Hills Forest by Using Landsat Imagery for Year 2000 and 2018

Science.gov (United States)

Batool, R.; Javaid, K.

2018-04-01

Environmental imbalance due to human activities has shown serious threat to ecosystem and produced negative impacts. The main goal of this study is to identify, monitor and classify temporal changes of forest cover, build up and open spaces in Margalla Hills National Park, Islamabad. Geographic Information Sciences (GISc) and Remote Sensing (RS) techniques has been used for the assessment of analysis. LANDSAT-7 Enhanced Thematic Mapper (ETM+) and LANDSAT-8 Operational Land Imager (OLI) were utilized for obtaining data of year 2000 and 2018. Temporal changes were evaluated after applying supervised classification and discrimination was analyzed by Per-Pixel based change detection. Results depicts forest cover decrease from 87 % to 74 % whereas build up has increased from 5 % to 7 % over the span. Consequences also justify the presence of open land in study area that has been increased from 2 % to 7 % respectively.

Application of two regression-based methods to estimate the effects harvest on forest structure using Landsat data

Science.gov (United States)

Sean P. Healey; Zhiqiang Yang; Warren B. Cohen; D. John Pierce

2006-01-01

Although partial harvests are common in many forest types globally, there has been little assessment of the potential to map the intensity of these harvests using Landsat data. We modeled basal area removal and percent cover change in a study area in central Washington (northwestern USA) using biennial Landsat imagery and reference data from historical aerial photos...

An assessment of areal evapotranspiration using Landsat TM data

Energy Technology Data Exchange (ETDEWEB)

Chae, Hyo-Sok; Park, Jae-Young [Water Resources Research Institute, Taejeon(Korea); Song, Young-Soo [Chonbuk National Univ., Chonju(Korea)

2000-08-31

Surface energy balance components were evaluated by Landsat TM data and GIS with meteorological data. Calibration and validation for the applicability of this methodology were made through the estimating of the large-scale evapotranspiration (ET). In addition, sensitivity and error analysis was conducted to see the effects of the surface energy balance components on ET and the accuracy of each components. Bochong-chon located on the upper part of Guem River basin was selected as the case study area. Spatial distribution map of ET were produced for five dates: Jan. 1, Apr. 3, May. 10, and Nov. 27, 1995. The study results showed that ET was greatly varied with the aspect and the land use type on the surface. In the case of having northeast and southeast in the aspect, ET was linearly increased depending on growing net radiation. While surface temperature has a high value, NDVI(Normalized Difference Vegetation Index) has a low value in the vegetated area. Therefore, ground heat flux was increased but ET was relatively decreased. The results of sensitivity and error analysis showed that net radiation is most sensitive and effective, ranging from 12.5% to 23.6% of sensitivity. Furthermore, the surface temperature, air temperature, and wind speed have the significant effects on ET estimation using remotely sensed data. (author). 26 refs., 4 tabs., 8 figs.

Combined Use of Landsat-8 and Sentinel-2 Data for Agricultural Monitoring

Science.gov (United States)

Skakun, S.; Roger, J. C.; Vermote, E.; Franch, B.; Becker-Reshef, I.; Justice, C. O.; Masek, J. G.

2017-12-01

Timely and accurate information on crop yield and production is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to provide temporal resolution of 3-5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (10-30 m). Here, we explore the combined use of Landsat-8 and Sentinel-2 data for winter crop mapping and winter wheat yield assessment at regional scale. For the former, we adapt a previously developed approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument at 250 m resolution that allows automatic mapping of winter crops taking into account a priori knowledge on crop calendar. For the latter, we use a generalized winter wheat yield forecasting model that is based on estimation of the peak Normalized Difference Vegetation Index (NDVI) from MODIS image time-series, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2 data improves both winter crop mapping and winter wheat yield assessment.

Mapping Impervious Surfaces Globally at 30m Resolution Using Landsat Global Land Survey Data

Science.gov (United States)

Brown de Colstoun, E.; Huang, C.; Wolfe, R. E.; Tan, B.; Tilton, J.; Smith, S.; Phillips, J.; Wang, P.; Ling, P.; Zhan, J.; Xu, X.; Taylor, M. P.

2013-12-01

Impervious surfaces, mainly artificial structures and roads, cover less than 1% of the world's land surface (1.3% over USA). Regardless of the relatively small coverage, impervious surfaces have a significant impact on the environment. They are the main source of the urban heat island effect, and affect not only the energy balance, but also hydrology and carbon cycling, and both land and aquatic ecosystem services. In the last several decades, the pace of converting natural land surface to impervious surfaces has increased. Quantitatively monitoring the growth of impervious surface expansion and associated urbanization has become a priority topic across both the physical and social sciences. The recent availability of consistent, global scale data sets at 30m resolution such as the Global Land Survey from the Landsat satellites provides an unprecedented opportunity to map global impervious cover and urbanization at this resolution for the first time, with unprecedented detail and accuracy. Moreover, the spatial resolution of Landsat is absolutely essential to accurately resolve urban targets such a buildings, roads and parking lots. With long term GLS data now available for the 1975, 1990, 2000, 2005 and 2010 time periods, the land cover/use changes due to urbanization can now be quantified at this spatial scale as well. In the Global Land Survey - Imperviousness Mapping Project (GLS-IMP), we are producing the first global 30 m spatial resolution impervious cover data set. We have processed the GLS 2010 data set to surface reflectance (8500+ TM and ETM+ scenes) and are using a supervised classification method using a regression tree to produce continental scale impervious cover data sets. A very large set of accurate training samples is the key to the supervised classifications and is being derived through the interpretation of high spatial resolution (~2 m or less) commercial satellite data (Quickbird and Worldview2) available to us through the unclassified

Combining land use data acquired from Landsat with soil map data

Science.gov (United States)

Westin, F. C.; Brandner, T. M.

1981-01-01

A method currently used to derive agrophysical units (APUs), i.e., geographical areas having definable/comparable agronomic and physical parameters which reflect a range in agricultural use and management, is discussed with reference to results obtained for South Dakota and an area in China. The method consists of combining agricultural land use data acquired from Landsat with soil map data. The resulting map units are soil associations characterized by cropland use intensity, and they can be used to identify major cropland areas and to develop a rating reflecting the relative potential of the soils in the delineated area for crop production, as well as to update small-scale soil maps.

Implementation on Landsat Data of a Simple Cloud Mask Algorithm Developed for MODIS Land Bands

Science.gov (United States)

Oreopoulos, Lazaros; Wilson, Michael J.; Varnai, Tamas

2010-01-01

This letter assesses the performance on Landsat-7 images of a modified version of a cloud masking algorithm originally developed for clear-sky compositing of Moderate Resolution Imaging Spectroradiometer (MODIS) images at northern mid-latitudes. While data from recent Landsat missions include measurements at thermal wavelengths, and such measurements are also planned for the next mission, thermal tests are not included in the suggested algorithm in its present form to maintain greater versatility and ease of use. To evaluate the masking algorithm we take advantage of the availability of manual (visual) cloud masks developed at USGS for the collection of Landsat scenes used here. As part of our evaluation we also include the Automated Cloud Cover Assesment (ACCA) algorithm that includes thermal tests and is used operationally by the Landsat-7 mission to provide scene cloud fractions, but no cloud masks. We show that the suggested algorithm can perform about as well as ACCA both in terms of scene cloud fraction and pixel-level cloud identification. Specifically, we find that the algorithm gives an error of 1.3% for the scene cloud fraction of 156 scenes, and a root mean square error of 7.2%, while it agrees with the manual mask for 93% of the pixels, figures very similar to those from ACCA (1.2%, 7.1%, 93.7%).

Geological analysis of parts of the southern Arabian Shield based on Landsat imagery

Science.gov (United States)

Qari, Mohammed Yousef Hedaytullah T.

This thesis examines the capability and applicability of Landsat multispectral remote sensing data for geological analysis in the arid southern Arabian Shield, which is the eastern segment of the Nubian-Arabian Shield surrounding the Red Sea. The major lithologies in the study area are Proterozoic metavolcanics, metasediments, gneisses and granites. Three test-sites within the study area, located within two tectonic assemblages, the Asir Terrane and the Nabitah Mobile Belt, were selected for detailed comparison of remote sensing methods and ground geological studies. Selected digital image processing techniques were applied to full-resolution Landsat TM imagery and the results are interpreted and discussed. Methods included: image contrast improvement, edge enhancement for detecting lineaments and spectral enhancement for geological mapping. The last method was based on two principles, statistical analysis of the data and the use of arithmetical operators. New and detailed lithological and structural maps were constructed and compared with previous maps of these sites. Examples of geological relations identified using TM imagery include: recognition and mapping of migmatites for the first time in the Arabian Shield; location of the contact between the Asir Terrane and the Nabitah Mobile Belt; and mapping of lithologies, some of which were not identified on previous geological maps. These and other geological features were confirmed by field checking. Methods of lineament enhancement implemented in this study revealed structural lineaments, mostly mapped for the first time, which can be related to regional tectonics. Structural analysis showed that the southern Arabian Shield has been affected by at least three successive phases of deformation. The third phase is the most dominant and widespread. A crustal evolutionary model in the vicinity of the study area is presented showing four stages, these are: arc stage, accretion stage, collision stage and post

FEASIBILITY STUDY OF LANDSAT-8 IMAGERY FOR RETRIEVING SEA SURFACE TEMPERATURE (CASE STUDY PERSIAN GULF

Directory of Open Access Journals (Sweden)

F. Bayat

2016-06-01

on board on previous Landsat sensors. Third, TIRS is one of the best space born and high spatial resolution with 30 m. in this regards, Landsat-8 can use the Split-Window (SW algorithm for retrieving SST dataset. Although several SWs have been developed to use with other sensors, some adaptations are required in order to implement them for the TIRS spectral bands. Therefore, the objective of this paper is to develop a SW, adapted for use with Landsat-8 TIRS data, along with its accuracy assessment. In this research, that has been done for modelling SST using thermal Landsat 8-imagery of the Persian Gulf. Therefore, by incorporating contemporary in situ data and SST map estimated from other sensors like MODIS, we examine our proposed method with coefficient of determination (R2 and root mean square error (RMSE on check point to model SST retrieval for Landsat-8 imagery. Extracted results for implementing different SW's clearly shows superiority of utilized method by R2 = 0.95 and RMSE = 0.24.

Progress Towards a 2012 Landsat Launch

Science.gov (United States)

Irons, Jim; Sabelhaus, Phil; Masek, Jeff; Cook, Bruce; Dabney, Phil; Loveland, Tom

2012-01-01

The Landsat Data Continuity Mission (LDCM) is on schedule for a December 2012 launch date. The mission is being managed by an interagency partnership between NASA and the U.S. Geological Survey (USGS). NASA leads the development and launch of the satellite observatory while leads ground system development. USGS will assume responsibility for operating the satellite and for collecting, archiving, and distributing the LDCM data following launch. When launched the satellite will carry two sensors into orbit. The Operational Land Imager (OLI) will collect data for nine shortwave spectral bands with a spatial resolution of 30 m (with a 15 m panchromatic band). The Thermal Infrared Sensor (TIRS) will coincidently collect data for two thermal infrared bands with a spatial resolution of 100 m. The OLI is fully assembled and tested and has been shipped by it?s manufacturer, Ball Aerospace and Technology Corporation, to the Orbital Sciences Corporation (Orbital) facility where it is being integrated onto the LDCM spacecraft. Pre-launch testing indicates that OLI will meet all performance specification with margin. TIRS is in development at the NASA Goddard Space Flight Center (GSFC) and is in final testing before shipping to the Orbital facility in January, 2012. The ground data processing system is in development at the USGS Earth Resources Observation and Science (EROS) Center. The presentation will describe the LDCM satellite system, provide the status of system development, and present prelaunch performance data for OLI and TIRS. The USGS has committed to renaming the satellite as Landsat 8 following launch.

Geospatial Method for Computing Supplemental Multi-Decadal U.S. Coastal Land-Use and Land-Cover Classification Products, Using Landsat Data and C-CAP Products

Science.gov (United States)

Spruce, J. P.; Smoot, James; Ellis, Jean; Hilbert, Kent; Swann, Roberta

2012-01-01

This paper discusses the development and implementation of a geospatial data processing method and multi-decadal Landsat time series for computing general coastal U.S. land-use and land-cover (LULC) classifications and change products consisting of seven classes (water, barren, upland herbaceous, non-woody wetland, woody upland, woody wetland, and urban). Use of this approach extends the observational period of the NOAA-generated Coastal Change and Analysis Program (C-CAP) products by almost two decades, assuming the availability of one cloud free Landsat scene from any season for each targeted year. The Mobile Bay region in Alabama was used as a study area to develop, demonstrate, and validate the method that was applied to derive LULC products for nine dates at approximate five year intervals across a 34-year time span, using single dates of data for each classification in which forests were either leaf-on, leaf-off, or mixed senescent conditions. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and C-CAP value-added products. Each classification's overall accuracy was assessed by comparing stratified random locations to available reference data, including higher spatial resolution satellite and aerial imagery, field survey data, and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall Kappa statistics ranging from 0.78 to 0.89. The accuracies are comparable to those from similar, generalized LULC products derived from C-CAP data. The Landsat MSS-based LULC product accuracies are similar to those from Landsat TM or ETM+ data. Accurate classifications were computed for all nine dates, yielding effective results regardless of season. This classification method yielded products that were used to compute LULC change products via additive GIS overlay techniques.

An Effort to Map and Monitor Baldcypress Forest Areas in Coastal Louisiana, Using Landsat, MODIS, and ASTER Satellite Data

Science.gov (United States)

Spruce, Joseph P.; Sader, Steve; Smoot, James

2012-01-01

This presentation discusses a collaborative project to develop, test, and demonstrate baldcypress forest mapping and monitoring products for aiding forest conservation and restoration in coastal Louisiana. Low lying coastal forests in the region are being negatively impacted by multiple factors, including subsidence, salt water intrusion, sea level rise, persistent flooding, hydrologic modification, annual insect-induced forest defoliation, timber harvesting, and conversion to urban land uses. Coastal baldcypress forests provide invaluable ecological services in terms of wildlife habitat, forest products, storm buffers, and water quality benefits. Before this project, current maps of baldcypress forest concentrations and change did not exist or were out of date. In response, this project was initiated to produce: 1) current maps showing the extent and location of baldcypress dominated forests; and 2) wetland forest change maps showing temporary and persistent disturbance and loss since the early 1970s. Project products are being developed collaboratively with multiple state and federal agencies. Products are being validated using available reference data from aerial, satellite, and field survey data. Results include Landsat TM- based classifications of baldcypress in terms of cover type and percent canopy cover. Landsat MSS data was employed to compute a circa 1972 classification of swamp and bottomland hardwood forest types. Landsat data for 1972-2010 was used to compute wetland forest change products. MODIS-based change products were applied to view and assess insect-induced swamp forest defoliation. MODIS, Landsat, and ASTER satellite data products were used to help assess hurricane and flood impacts to coastal wetland forests in the region.

Application of Landsat Thematic Mapper data for coastal thermal plume analysis at Diablo Canyon

Science.gov (United States)

Gibbons, D. E.; Wukelic, G. E.; Leighton, J. P.; Doyle, M. J.

1989-01-01

The possibility of using Landsat Thematic Mapper (TM) thermal data to derive absolute temperature distributions in coastal waters that receive cooling effluent from a power plant is demonstrated. Landsat TM band 6 (thermal) data acquired on June 18, 1986, for the Diablo Canyon power plant in California were compared to ground truth temperatures measured at the same time. Higher-resolution band 5 (reflectance) data were used to locate power plant discharge and intake positions and identify locations of thermal pixels containing only water, no land. Local radiosonde measurements, used in LOWTRAN 6 adjustments for atmospheric effects, produced corrected ocean surface radiances that, when converted to temperatures, gave values within approximately 0.6 C of ground truth. A contour plot was produced that compared power plant plume temperatures with those of the ocean and coastal environment. It is concluded that Landsat can provide good estimates of absolute temperatures of the coastal power plant thermal plume. Moreover, quantitative information on ambient ocean surface temperature conditions (e.g., upwelling) may enhance interpretation of numerical model prediction.

TEMPERATURA DE SUPERFÍCIE CELSIUS DO SENSOR TIRS/LANDSAT-8: METODOLOGIA E APLICAÇÕES

Directory of Open Access Journals (Sweden)

André Luiz Nascentes Coelho

2013-11-01

Full Text Available Este trabalho tem como objetivo, contribuir na difusão e operacionalização das geotecnologias, apresentando os algoritmos para obtenção de temperatura da superfície horizontal Celsius na faixa infravermelho termal do sensor TIRS/Landsat-8, banda 10. A aplicação das equações proporcionou não só identificar os maiores percentuais de temperatura de superfície, em diferentes escalas espaciais, como também, definir o perfil do campo térmico em distintas texturas. Além disso, foi possível comparar, em imagens, a melhoria da resolução espacial do canal infravermelho termal Landsat-8 em relação ao Landsat-5. Tal metodologia possibilita a aplicação em outros intervalos de datas e locais distintos, contribuindo nas pesquisas e no auxílio detomadas de decisões.

TEMPERATURA DE SUPERFÍCIE CELSIUS DO SENSOR TIRS/LANDSAT-8: METODOLOGIA E APLICAÇÕES

Directory of Open Access Journals (Sweden)

André Luiz Nascentes Coelho

2015-09-01

Full Text Available Este trabalho tem como objetivo, contribuir na difusão e operacionalização das geotecnologias, apresentando os algoritmos para obtenção de temperatura da superfície horizontal Celsius na faixa infravermelho termal do sensor TIRS/Landsat-8, banda 10. A aplicação das equações proporcionou não só identificar os maiores percentuais de temperatura de superfície, em diferentes escalas espaciais, como também, definir o perfil do campo térmico em distintas texturas. Além disso, foi possível comparar, em imagens, a melhoria da resolução espacial do canal infravermelho termal Landsat-8 em relação ao Landsat-5. Tal metodologia possibilita a aplicação em outros intervalos de datas e locais distintos, contribuindo nas pesquisas e no auxílio detomadas de decisões.

BOREAS RSS-15 SIR-C and Landsat TM Biomass and Landcover Maps of the NSA

Science.gov (United States)

Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Ranson, K. Jon

2000-01-01

As part of BOREAS, the RSS-15 team conducted an investigation using SIR-C, X-SAR, and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape. The procedure involved image classification with SAR and Landsat TM data and development of simple mapping techniques using combinations of SAR channels. For the SSA, the SIR-C data used were acquired on 06-Oct-1994, and the Landsat TM data used were acquired on 02-Sep-1995. The maps of the NSA were developed from SIR-C data acquired on 13-Apr-1994. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Landsat 8 Operational Land Imager (OLI)_Thermal Infared Sensor (TIRS) V1

Data.gov (United States)

National Aeronautics and Space Administration — Abstract:The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are instruments onboard the Landsat 8 satellite, which was launched in February of...

Quantifying BRDF Effects in Comparing Landsat-7 and AVIRIS Near-Simultaneous Acquisitions for Studies of High Plains Vegetation Cover

Science.gov (United States)

Goetz, A. F. H.; Heidebrecht, K. B.; Gutmann, E. D.; Warner, A. S.; Johnson, E. L.; Lestak, L. R.

1999-01-01

Approximately 100,000 sq. km of the High Plains of the central United States are covered by sand dunes and sand sheets deposited during the Holocene. Soil-dating evidence shows that there were at least four periods of dune reactivation during major droughts in the last 10,000 years. The dunes in this region are anchored by vegetation. We have undertaken a study of land-use change in the High Plains from 1985 to the present using Landsat 5 TM and Landsat 7 ETM+ images to map variation in vegetation cover during wet and dry years. Mapping vegetation cover of less than 20% is important in modeling potential surface reactivation since at this level the vegetation no longer sufficiently shields sandy surfaces from movement by wind. Landsat TM data have both the spatial resolution and temporal coverage to facilitate vegetation cover analysis for model development and verification. However, there is still the question of how accurate TM data are for the measurement of both growing and senescent vegetation in and and semi-arid regions. AVIRIS provides both high spectral resolution as well as high signal-to-noise ratio and can be used to test the accuracy of Landsat TM and ETM+ data. We have analyzed data from AVIRIS flown nearly concurrently with a Landsat 7 overpass. The comparison between an AVIRIS image swath of 11 km width subtending a 30 deg. angle and the same area covered by a 0.8 deg. angle from Landsat required accounting for the BRDF. A normalization technique using the ratio of the reflectances from registered AVIRIS and Landsat data proved superior to the techniques of column averaging on AVIRIS data alone published previously by Kennedy et al. This technique can be applied to aircraft data covering a wider swath angle than AVIRIS to develop BRDF responses for a wide variety of surfaces more efficiently than from ground measurements.

Exploration for porphyry copper deposits in Pakistan using digital processing of Landsat-1 data

Science.gov (United States)

Schmidt, R. G.

1976-01-01

Rock-type classification by digital-computer processing of Landsat-1 multispectral scanner data has been used to select 23 prospecting targets in the Chagai District, Pakistan, five of which have proved to be large areas of hydrothermally altered porphyry containing pyrite. Empirical maximum and minimum apparent reflectance limits were selected for each multispectral scanner band in each rock type classified, and a relatively unrefined classification table was prepared. Where the values for all four bands fitted within the limits designated for a particular class, a symbol for the presumed rock type was printed by the computer at the appropriate location. Drainage channels, areas of mineralized quartz diorite, areas of pyrite-rich rock, and the approximate limit of propylitic alteration were very well delineated on the computer-generated map of the test area. The classification method was used to evaluate 2,100 sq km in the Mashki Chah region. The results of the experiment show that outcrops of hydrothermally altered and mineralized rock can be identified from Landsat-1 data under favorable conditions.

Mapping Canopy Damage from Understory Fires in Amazon Forests Using Annual Time Series of Landsat and MODIS Data

Science.gov (United States)

Morton, Douglas C.; DeFries, Ruth S.; Nagol, Jyoteshwar; Souza, Carlos M., Jr.; Kasischke, Eric S.; Hurtt, George C.; Dubayah, Ralph

2011-01-01

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars less than 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (greater than 500 ha) burn scars that accounted for the majority of all fire-damaged forest in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 square kilometers) were an order of magnitude higher than during the 1997-1998 El Nino event (124 square kilometers and 39 square kilometers, respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by

Evaluation of Drought Impact on Evapotranspiration (ET) over a Forested Landscape in North Carolina, USA using daily Landsat-scale ET

Science.gov (United States)

Yang, Yun; Anderson, Martha; Gao, Feng; Hain, Christopher; Kustas, William; Noormets, Asko; Sun, Ge; Wynne, Randolph; Thomas, Valerie

2017-04-01

There are 14 million hectares of loblolly pine plantations in the southern US, constituting almost one-half of the area of the world's industrial forest plantation. Hence, improved understanding of the impact of drought on pine plantations is extremely important. Using Thermal Infrared (TIR) imagery acquired from satellites to investigate forest conditions and study impacts of stand management on water yield has recently started to become accepted in forest research community. As a key factor monitoring forest health and regional water use, ET can be estimated based on the TIR imagery using energy balance model. One challenge in using TIR remote sensing is the need for both high spatial and temporal resolution imagery. While Landsat TIR data can provide high spatial resolution, the long revisiting time limits the frequency of ET estimation. This limitation can be addressed by using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to fuse ET retrieval from Landsat and MODIS. In this study, we applied an energy balance based multi-sensor data fusion method to fuse ET retrieved from Landsat and MODIS to get daily Landsat-scale ET estimation over a forested landscape ( 900km2) on the humid lower coastal plains in North Carolina, USA. The simulation period was from 2006 to 2012, with 2007 and 2008 considered years having severe drought. The simulated long-term ET datacube was evaluated at two separate AmeriFlux sites dominated by a mature and a recently clearcut plantation, showing good agreement with observed fluxes. The ET datacube was mined to investigate changes in water use patterns in response to land cover type, forest stand age, and climatic forcings. Analyses show differential response to extreme drought events, with young plantations and short vegetation showing larger impacts than mature pine plantations with significantly deeper rooting systems.

ESTIMATIVA DO VOLUME TOTAL DE MADEIRA EM ESPÉCIES DE EUCALIPTO A PARTIR DE IMAGENS DE SATÉLITE LANDSAT

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Elias Fernando Berra

2012-01-01

Full Text Available Models relating spectral answers with biophysical parameters aim estimate variables, such as wood volume, without the necessity of frequent field measurements. The objective was to develop models to estimatewood volume by Landsat 5 TM images, supported by regional forest inventory data. The image was georeferenced and converted to spectral reflectance. After, the images-index NDVI (Normalized Difference Vegetation Index and SR (Simple Ratio was generated. The reflectance values of both spectral bands (TM1, TM2, TM3 e TM4 and indices (NDVI and SR was related with the wood volume. The biggest correlation with volume was with the NDVI and SR indices. The variables selection was made by Stepwise method, which returned three regression models as significant to explain the variation in volume. Finally,the best fitted model was selected (volume = -830,95 + 46,05 × (SR + 107,47 × (TM2, which was applied on the Landsat image where the pixels had started to represent the estimated volume in m³/ha on the Eucalyptus sp. production units. This model, significant at 95 % confidence level, explains 68 % of the wood volume variation.

Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method

KAUST Repository

Yin, Gaohong; Mariethoz, Gregoire; McCabe, Matthew

2016-01-01

The failure of the Scan Line Corrector (SLC) on Landsat 7 imposed systematic data gaps on retrieved imagery and removed the capacity to provide spatially continuous fields. While a number of methods have been developed to fill these gaps, most

Mapping disturbance dynamics in wet sclerophyll forests using time series Landsat

NARCIS (Netherlands)

Haywood, A.; Verbesselt, J.; Baker, P.J.

2016-01-01

In this study, we characterised the temporal-spectral patterns associated with identifying acute-severity disturbances and low-severity disturbances between 1985 and 2011 with the objective to test whether different disturbance agents within these categories can be identified with annual Landsat

Near Real-Time Browsable Landsat-8 Imagery

OpenAIRE

Cheng-Chien Liu; Ryosuke Nakamura; Ming-Hsun Ko; Tomoya Matsuo; Soushi Kato; Hsiao-Yuan Yin; Chung-Shiou Huang

2017-01-01

The successful launch and operation of Landsat-8 extends the remarkable 40-year acquisition of space-based land remote-sensing data. To respond quickly to emergency needs, real-time data are directly downlinked to 17 ground stations across the world on a routine basis. With a size of approximately 1 Gb per scene, however, the standard level-1 product provided by these stations is not able to serve the general public. Users would like to browse the most up-to-date and historical images of thei...

Regional estimates of reef carbonate dynamics and productivity Using Landsat 7 ETM+, and potential impacts from ocean acidification

Science.gov (United States)

Moses, C.S.; Andrefouet, S.; Kranenburg, C.; Muller-Karger, F. E.

2009-01-01

Using imagery at 30 m spatial resolution from the most recent Landsat satellite, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), we scale up reef metabolic productivity and calcification from local habitat-scale (10 -1 to 100 km2) measurements to regional scales (103 to 104 km2). Distribution and spatial extent of the North Florida Reef Tract (NFRT) habitats come from supervised classification of the Landsat imagery within independent Landsat-derived Millennium Coral Reef Map geomorphologic classes. This system minimizes the depth range and variability of benthic habitat characteristics found in the area of supervised classification and limits misclassification. Classification of Landsat imagery into 5 biotopes (sand, dense live cover, sparse live cover, seagrass, and sparse seagrass) by geomorphologic class is >73% accurate at regional scales. Based on recently published habitat-scale in situ metabolic measurements, gross production (P = 3.01 ?? 109 kg C yr -1), excess production (E = -5.70 ?? 108 kg C yr -1), and calcification (G = -1.68 ?? 106 kg CaCO 3 yr-1) are estimated over 2711 km2 of the NFRT. Simple models suggest sensitivity of these values to ocean acidification, which will increase local dissolution of carbonate sediments. Similar approaches could be applied over large areas with poorly constrained bathymetry or water column properties and minimal metabolic sampling. This tool has potential applications for modeling and monitoring large-scale environmental impacts on reef productivity, such as the influence of ocean acidification on coral reef environments. ?? Inter-Research 2009.

Oyster Aquaculture Site Selection Using Landsat 8-Derived Sea Surface Temperature, Turbidity, and Chlorophyll a

Directory of Open Access Journals (Sweden)

Jordan Snyder

2017-06-01

Full Text Available Remote sensing data is useful for selection of aquaculture sites because it can provide water-quality products mapped over large regions at low cost to users. However, the spatial resolution of most ocean color satellites is too coarse to provide usable data within many estuaries. The Landsat 8 satellite, launched February 11, 2013, has both the spatial resolution and the necessary signal to noise ratio to provide temperature, as well as ocean color derived products along complex coastlines. The state of Maine (USA has an abundance of estuarine indentations (~3,500 miles of tidal shoreline within 220 miles of coast, and an expanding aquaculture industry, which makes it a prime case-study for using Landsat 8 data to provide products suitable for aquaculture site selection. We collected the Landsat 8 scenes over coastal Maine, flagged clouds, atmospherically corrected the top-of-the-atmosphere radiances, and derived time varying fields (repeat time of Landsat 8 is 16 days of temperature (100 m resolution, turbidity (30 m resolution, and chlorophyll a (30 m resolution. We validated the remote-sensing-based products at several in situ locations along the Maine coast where monitoring buoys and programs are in place. Initial analysis of the validated fields revealed promising new areas for oyster aquaculture. The approach used is applicable to other coastal regions and the data collected to date show potential for other applications in marine coastal environments, including water quality monitoring and ecosystem management.

Mapping Canopy Height and Growing Stock Volume Using Airborne Lidar, ALOS PALSAR and Landsat ETM+

Directory of Open Access Journals (Sweden)

Wayne Walker

2012-10-01

Full Text Available We have investigated for forest plantations in Chile the stand-level retrieval of canopy height (CH and growing stock volume (GSV using Airborne Laser Scanner (ALS, ALOS PALSAR and Landsat. In a two-stage up-scaling approach, ensemble regression tree models (randomForest were used to relate a suite of ALS canopy structure indices to stand-level in situ measurements of CH and GSV for 319 stands. The retrieval of CH and GSV with ALS yielded high accuracies with R2s of 0.93 and 0.81, respectively. A second set of randomForest models was developed using multi-temporal ALOS PALSAR intensities and repeat-pass coherences in two polarizations as well as Landsat data as predictor and stand-level ALS based estimates of CH and GSV as response variables. At three test sites, the retrieval of CH and GSV with PALSAR/Landsat reached promising accuracies with R2s in the range of 0.7 to 0.85. We show that the combined use of multi-temporal PALSAR intensity, coherence and Landsat yields higher retrieval accuracies than the retrieval with any of the datasets alone. Potential limitations for the large-area application of the fusion approach included (1 the low sensitivity of ALS first/last return data to forest horizontal structure, affecting the retrieval of GSV in less managed types of forest, and (2 the dense ALS sampling required to achieve high retrieval accuracies at larger scale.

Use of GLOBE Observations to Derive a Landsat 8 Split Window Algorithm for Urban Heat Island

Science.gov (United States)

Fagerstrom, L.; Czajkowski, K. P.

2017-12-01

Surface temperature has been studied to investigate the warming of urban climates, also known as urban heat islands, which can impact urban planning, public health, pollution levels, and energy consumption. However, the full potential of remotely sensed images is limited when analyzing land surface temperature due to the daunting task of correcting for atmospheric effects. Landsat 8 has two thermal infrared sensors. With two bands in the infrared region, a split window algorithm (SWA), can be applied to correct for atmospheric effects. This project used in situ surface temperature measurements from NASA's ground observation program, the Global Learning and Observations to Benefit the Environment (GLOBE), to derive the correcting coefficients for use in the SWA. The GLOBE database provided land surface temperature data that coincided with Landsat 8 overpasses. The land surface temperature derived from Landsat 8 SWA can be used to analyze for urban heat island effect.

Regional crop gross primary production and yield estimation using fused Landsat-MODIS data

Science.gov (United States)

He, M.; Kimball, J. S.; Maneta, M. P.; Maxwell, B. D.; Moreno, A.

2017-12-01

Accurate crop yield assessments using satellite-based remote sensing are of interest for the design of regional policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations are generally too coarse to capture cropland heterogeneity. Merging information from sensors with reciprocal spatial and temporal resolution can improve the accuracy of these retrievals. In this study, we estimate annual crop yields for seven important crop types -alfalfa, barley, corn, durum wheat, peas, spring wheat and winter wheat over Montana, United States (U.S.) from 2008 to 2015. Yields are estimated as the product of gross primary production (GPP) and a crop-specific harvest index (HI) at 30 m spatial resolution. To calculate GPP we used a modified form of the MOD17 LUE algorithm driven by a 30 m 8-day fused NDVI dataset constructed by blending Landsat (5 or 7) and MODIS Terra reflectance data. The fused 30-m NDVI record shows good consistency with the original Landsat and MODIS data, but provides better spatiotemporal information on cropland vegetation growth. The resulting GPP estimates capture characteristic cropland patterns and seasonal variations, while the estimated annual 30 m crop yield results correspond favorably with county-level crop yield data (r=0.96, pcrop yield performance was generally lower, but still favorable in relation to field-scale crop yield surveys (r=0.42, p<0.01). Our methods and results are suitable for operational applications at regional scales.

Detecting Historical Vegetation Changes in the Dunhuang Oasis Protected Area Using Landsat Images

Directory of Open Access Journals (Sweden)

Xiuxia Zhang

2017-09-01

Full Text Available Abstract: Given its proximity to an artificial oasis, the Donghu Nature Reserve in the Dunhuang Oasis has faced environmental pressure and vegetation disturbances in recent decades. Satellite vegetation indices (VIs can be used to detect such changes in vegetation if the satellite images are calibrated to surface reflectance (SR values. The aim of this study was to select a suitable VI based on the Landsat Climate Data Record (CDR products and the absolute radiation-corrected results of Landsat L1T images to detect the spatio-temporal changes in vegetation for the Donghu Reserve during 1986–2015. The results showed that the VI difference (ΔVI images effectively reduced the changes in the source images. Compared with the other VIs, the soil-adjusted vegetation index (SAVI displayed greater robustness to atmospheric effects in the two types of SR images and was more responsive to vegetation changes caused by human factors. From 1986 to 2015, the positive changes in vegetation dominated the overall change trend, with changes in vegetation in the reserve decreasing during 1990–1995, increasing until 2005–2010, and then decreasing again. The vegetation changes were mainly distributed at the edge of the artificial oasis outside the reserve. The detected changes in vegetation in the reserve highlight the increased human pressure on the reserve.

Regional Geological Mapping in the Graham Land of Antarctic Peninsula Using LANDSAT-8 Remote Sensing Data

Science.gov (United States)

Pour, A. B.; Hashim, M.; Park, Y.

2017-10-01

Geological investigations in Antarctica confront many difficulties due to its remoteness and extreme environmental conditions. In this study, the applications of Landsat-8 data were investigated to extract geological information for lithological and alteration mineral mapping in poorly exposed lithologies in inaccessible domains such in Antarctica. The north-eastern Graham Land, Antarctic Peninsula (AP) was selected in this study to conduct a satellite-based remote sensing mapping technique. Continuum Removal (CR) spectral mapping tool and Independent Components Analysis (ICA) were applied to Landsat-8 spectral bands to map poorly exposed lithologies at regional scale. Pixels composed of distinctive absorption features of alteration mineral assemblages associated with poorly exposed lithological units were detected by applying CR mapping tool to VNIR and SWIR bands of Landsat-8.Pixels related to Si-O bond emission minima features were identified using CR mapping tool to TIR bands in poorly mapped andunmapped zones in north-eastern Graham Land at regional scale. Anomaly pixels in the ICA image maps related to spectral featuresof Al-O-H, Fe, Mg-O-H and CO3 groups and well-constrained lithological attributions from felsic to mafic rocks were detectedusing VNIR, SWIR and TIR datasets of Landsat-8. The approach used in this study performed very well for lithological andalteration mineral mapping with little available geological data or without prior information of the study region.

Detecting Forest Disturbance Events from MODIS and Landsat Time Series for the Conterminous United States

Science.gov (United States)

Zhang, G.; Ganguly, S.; Saatchi, S. S.; Hagen, S. C.; Harris, N.; Yu, Y.; Nemani, R. R.

2013-12-01

Spatial and temporal patterns of forest disturbance and regrowth processes are key for understanding aboveground terrestrial vegetation biomass and carbon stocks at regional-to-continental scales. The NASA Carbon Monitoring System (CMS) program seeks key input datasets, especially information related to impacts due to natural/man-made disturbances in forested landscapes of Conterminous U.S. (CONUS), that would reduce uncertainties in current carbon stock estimation and emission models. This study provides a end-to-end forest disturbance detection framework based on pixel time series analysis from MODIS (Moderate Resolution Imaging Spectroradiometer) and Landsat surface spectral reflectance data. We applied the BFAST (Breaks for Additive Seasonal and Trend) algorithm to the Normalized Difference Vegetation Index (NDVI) data for the time period from 2000 to 2011. A harmonic seasonal model was implemented in BFAST to decompose the time series to seasonal and interannual trend components in order to detect abrupt changes in magnitude and direction of these components. To apply the BFAST for whole CONUS, we built a parallel computing setup for processing massive time-series data using the high performance computing facility of the NASA Earth Exchange (NEX). In the implementation process, we extracted the dominant deforestation events from the magnitude of abrupt changes in both seasonal and interannual components, and estimated dates for corresponding deforestation events. We estimated the recovery rate for deforested regions through regression models developed between NDVI values and time since disturbance for all pixels. A similar implementation of the BFAST algorithm was performed over selected Landsat scenes (all Landsat cloud free data was used to generate NDVI from atmospherically corrected spectral reflectances) to demonstrate the spatial coherence in retrieval layers between MODIS and Landsat. In future, the application of this largely parallel disturbance

Ground measured evapotranspiration scaled to stand level using MODIS and Landsat sensors to study Tamarix spp.response to repeated defoliation by the Tamarix leaf beetle at two sites

Science.gov (United States)

Pearlstein, S.; Nagler, P. L.; Glenn, E. P.; Hultine, K. R.

2012-12-01

The Dolores River in Southern Utah and the Virgin River in Southern Nevada are ecosystems under pressure from increased groundwater withdrawal due to growing populations and introduced riparian species. We studied the impact of the biocontrol Tamarix leaf beetles (Dirohabda carinulata and D. elongata) on the introduced riparian species, Tamarix spp., phenology and water use over multiple cycles of annual defoliation. Heat balance sap flow measurements, leaf area index (LAI), well data, allometry and satellite imagery from Landsat Thematic Mapper 5 and EOS-1 Moderate Resolution Imaging Spectrometer (MODIS) sensors were used to assess the distribution of beetle defoliation and its effect on evapotranspiration (ET). Study objectives for the Virgin River were to measure pre-beetle arrival ET, while the Dolores River site has had defoliation since 2004 and is a site of long-term beetle effect monitoring. This study focuses on measurements conducted over two seasons, 2010 and 2011. At the Dolores River site, results from 2010 were inconclusive due to sensor malfunctions but plant ET by sap flow in 2011 averaged 1.02 mm/m^2 leaf area/day before beetle arrival, dropping to an average of 0.75 mm/m^2 leaf area/day after beetle arrival. Stand level estimations from May - December, 2010 by MODIS were about 0.63 mm/ day, results from Landsat were 0.51 mm/day in June and 0.78 in August. For January -September, 2011, MODIS values were about 0.6 mm/day, and Landsat was 0.57 mm/day in June and 0.62 mm/day in August. These values are lower than previously reported ET values for this site meaning that repeated defoliation does diminish stand level water use. The Virgin River site showed plant ET from sap flow averaged about 3.9-4 mm/m^2 leaf area/day from mid-May - September, 2010. In 2011, ET from sap flow averaged 3.83 mm/m^2 leaf area/day during June - July, but dropped to 3.73 mm/ m^2 leaf area/day after beetle arrival in August. The slight drop in plant ET is not significant

Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data

Directory of Open Access Journals (Sweden)

Belachew Gizachew

2016-06-01

Full Text Available Abstract Background A functional forest carbon measuring, reporting and verification (MRV system to support climate change mitigation policies, such as REDD+, requires estimates of forest biomass carbon, as an input to estimate emissions. A combination of field inventory and remote sensing is expected to provide those data. By linking Landsat 8 and forest inventory data, we (1 developed linear mixed effects models for total living biomass (TLB estimation as a function of spectral variables, (2 developed a 30 m resolution map of the total living carbon (TLC, and (3 estimated the total TLB stock of the study area. Inventory data consisted of tree measurements from 500 plots in 63 clusters in a 15,700 km2 study area, in miombo woodlands of Tanzania. The Landsat 8 data comprised two climate data record images covering the inventory area. Results We found a linear relationship between TLB and Landsat 8 derived spectral variables, and there was no clear evidence of spectral data saturation at higher biomass values. The root-mean-square error of the values predicted by the linear model linking the TLB and the normalized difference vegetation index (NDVI is equal to 44 t/ha (49 % of the mean value. The estimated TLB for the study area was 140 Mt, with a mean TLB density of 81 t/ha, and a 95 % confidence interval of 74–88 t/ha. We mapped the distribution of TLC of the study area using the TLB model, where TLC was estimated at 47 % of TLB. Conclusion The low biomass in the miombo woodlands, and the absence of a spectral data saturation problem suggested that Landsat 8 derived NDVI is suitable auxiliary information for carbon monitoring in the context of REDD+, for low-biomass, open-canopy woodlands.

Determining the rate of forest conversion in Mato Grosso, Brazil, using Landsat MSS and AVHRR data

Science.gov (United States)

Nelson, Ross; Horning, Ned; Stone, Thomas A.

1987-01-01

AVHRR-LAC thermal data and Landsat MSS and TM spectral data were used to estimate the rate of forest clearing in Mato Grosso, Brazil, between 1981 and 1984. The Brazilian state was stratified into forest and nonforest. A list sampling procedure was used in the forest stratum to select Landsat MSS scenes for processing based on estimates of fire activity in the scenes. Fire activity in 1984 was estimated using AVHRR-LAC thermal data. State-wide estimates of forest conversion indicate that between 1981 and 1984, 353,966 ha + or - 77,000 ha (0.4 percent of the state area) were converted per year. No evidence of reforestation was found in this digital sample. The relationship between forest clearing rate (based on MSS-TM analysis) and fire activity (estimated using AVHRR data) was noisy (R-squared = 0.41). The results suggest that AVHRR data may be put to better use as a stratification tool than as a subsidiary variable in list sampling.

United States forest disturbance trends observed with landsat time series

Science.gov (United States)

Jeffrey G. Masek; Samuel N. Goward; Robert E. Kennedy; Warren B. Cohen; Gretchen G. Moisen; Karen Schleweiss; Chengquan. Huang

2013-01-01

Disturbance events strongly affect the composition, structure, and function of forest ecosystems; however, existing US land management inventories were not designed to monitor disturbance. To begin addressing this gap, the North American Forest Dynamics (NAFD) project has examined a geographic sample of 50 Landsat satellite image time series to assess trends in forest...

Bulk processing of the Landsat MSS/TM/ETM+ archive of the European Space Agency: an insight into the level 1 MSS processing

Science.gov (United States)

Saunier, Sébastien; Northrop, Amy; Lavender, Samantha; Galli, Luca; Ferrara, Riccardo; Mica, Stefano; Biasutti, Roberto; Goryl, Philippe; Gascon, Ferran; Meloni, Marco

2017-10-01

Whilst recent years have witnessed the development and exploitation of operational Earth Observation (EO) satellite constellation data, the valorisation of historical archives has been a challenge. The European Space Agency (ESA) Landsat Multi Spectral Scanner (MSS) products cover Greenland, Iceland, Continental Europe and North Africa represent an archive of over 600,000 processed Level 1 (L1) scenes that will accompany around 1 million ESA Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) products already available. ESA began acquiring MSS data in 1975 and it is well known that this dataset can be degraded due to missing data and a loss in accuracy. For these reasons, the content of the product format has been reviewed and the ESA Landsat processing baseline significantly updated to ensure products are fit for user purposes. This paper presents the new MSS product format including the updated metadata parameters for error traceability, and the specification of the Quality Assurance Band (BQA) engineered to allow the best pixel selection and also the application of image restoration techniques. This paper also discusses major improvements applied to the radiometric and geometric processing. For the benefits of the community, ESA is now able to maximize the number of L1 MSS products that can potentially be generated from the raw Level 0 (L0) data and ensure the highest possible data quality is reached. Also, by improving product format, processing and adding a pixel based quality band, the MSS archive becomes interoperable with recently reprocessed Landsat data and that from live missions by way of assuring product quality on a pixel basis.

Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood

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Gareth Ireland

2015-03-01

Full Text Available This study explored the capability of Support Vector Machines (SVMs and regularised kernel Fisher’s discriminant analysis (rkFDA machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877. Both methods outperformed the standard Normalized Difference Water Index (NDWI thresholding (OA = 94.63, K = 0.818 by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models. This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing.

Science.gov (United States)

Midekisa, Alemayehu; Holl, Felix; Savory, David J; Andrade-Pacheco, Ricardo; Gething, Peter W; Bennett, Adam; Sturrock, Hugh J W

2017-01-01

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Impacts of dust aerosol and adjacency effects on the accuracy of Landsat 8 and RapidEye surface reflectances

KAUST Repository

Houborg, Rasmus; McCabe, Matthew

2017-01-01

The atmospheric correction of satellite data is challenging over desert agricultural systems, due to the relatively high aerosol optical thicknesses (τ550), bright soils, and a heterogeneous surface reflectance field. Indeed, the contribution of reflected radiation from adjacent pixels scattered into the field of view of a target pixel is considerable and can significantly affect the fidelity of retrieved reflectances. In this study, uncertainties and quantitative errors associated with the atmospheric correction of multi-spectral Landsat 8 and RapidEye data were characterized over a desert agricultural landscape in Saudi Arabia. Surface reflectances were retrieved using an implementation of the 6SV atmospheric correction code, and validated against field collected spectroradiometer measurements over desert, cultivated soil, and vegetated surface targets. A combination of satellite and Aerosol Robotic Network (AERONET) data were used to parameterize aerosol properties and atmospheric state parameters. With optimal specification of τ550 and aerosol optical properties and correction for adjacency effects, the relative Mean Absolute Deviation (MAD) for all bands combined was 5.4% for RapidEye and 6.8% for Landsat 8. However uncertainties associated with satellite-based τ550 retrievals were shown to introduce significant error into the reflectance estimates. With respect to deriving common vegetation indices from corrected reflectance data, the Normalized Difference Vegetation Index (NDVI) was associated with the smallest errors (3–8% MAD). Surface reflectance errors were highest for bands in the visible part of the spectrum, particularly the blue band (5–16%), while there was more consistency within the red-edge (~ 5%) and near-infrared (5–7%). Results were generally better constrained when a τ550-dependent aerosol model for desert dust particles, parameterized on the basis of nearby AERONET site data, was used in place of a generic rural or background

Impacts of dust aerosol and adjacency effects on the accuracy of Landsat 8 and RapidEye surface reflectances

KAUST Repository

Houborg, Rasmus

2017-03-29

The atmospheric correction of satellite data is challenging over desert agricultural systems, due to the relatively high aerosol optical thicknesses (τ550), bright soils, and a heterogeneous surface reflectance field. Indeed, the contribution of reflected radiation from adjacent pixels scattered into the field of view of a target pixel is considerable and can significantly affect the fidelity of retrieved reflectances. In this study, uncertainties and quantitative errors associated with the atmospheric correction of multi-spectral Landsat 8 and RapidEye data were characterized over a desert agricultural landscape in Saudi Arabia. Surface reflectances were retrieved using an implementation of the 6SV atmospheric correction code, and validated against field collected spectroradiometer measurements over desert, cultivated soil, and vegetated surface targets. A combination of satellite and Aerosol Robotic Network (AERONET) data were used to parameterize aerosol properties and atmospheric state parameters. With optimal specification of τ550 and aerosol optical properties and correction for adjacency effects, the relative Mean Absolute Deviation (MAD) for all bands combined was 5.4% for RapidEye and 6.8% for Landsat 8. However uncertainties associated with satellite-based τ550 retrievals were shown to introduce significant error into the reflectance estimates. With respect to deriving common vegetation indices from corrected reflectance data, the Normalized Difference Vegetation Index (NDVI) was associated with the smallest errors (3–8% MAD). Surface reflectance errors were highest for bands in the visible part of the spectrum, particularly the blue band (5–16%), while there was more consistency within the red-edge (~ 5%) and near-infrared (5–7%). Results were generally better constrained when a τ550-dependent aerosol model for desert dust particles, parameterized on the basis of nearby AERONET site data, was used in place of a generic rural or background

Research and development of LANDSAT-based crop inventory techniques

Science.gov (United States)

Horvath, R.; Cicone, R. C.; Malila, W. A. (Principal Investigator)

1982-01-01

A wide spectrum of technology pertaining to the inventory of crops using LANDSAT without in situ training data is addressed. Methods considered include Bayesian based through-the-season methods, estimation technology based on analytical profile fitting methods, and expert-based computer aided methods. Although the research was conducted using U.S. data, the adaptation of the technology to the Southern Hemisphere, especially Argentina was considered.

Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US

Science.gov (United States)

Schleeweis, Karen; Goward, Samuel N.; Huang, Chengquan; Dwyer, John L.; Dungan, Jennifer L.; Lindsey, Mary A.; Michaelis, Andrew; Rishmawi, Khaldoun; Masek, Jeffery G.

2016-01-01

Using the NASA Earth Exchange platform, the North American Forest Dynamics (NAFD) project mapped forest history wall-to-wall, annually for the contiguous US (1986–2010) using the Vegetation Change Tracker algorithm. As with any effort to identify real changes in remotely sensed time-series, data gaps, shifts in seasonality, misregistration, inconsistent radiometry and cloud contamination can be sources of error. We discuss the NAFD image selection and processing stream (NISPS) that was designed to minimize these sources of error. The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration, unreliability of the pre-2009 L5 cloud cover assessments algorithm, missing growing-season imagery and paucity of clear views. Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study. Over 150,000+ Landsat images were considered for the NAFD project. Optimally, one high quality cloud-free image in each year or a total of 12,152 images would be used. However, to accommodate data gaps and cloud/shadow contamination 23,338 images were needed. In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.

Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data.

Science.gov (United States)

S.P. Healey; Z. Yang; W.B. Cohen; D.J. Pierce

2006-01-01

Although partial harvests are common in many forest types globally, there has been little assessment of the potential to map the intensity of these harvests using Landsat data. We modeled basal area removal and percentage cover change in a study area in central Washington (northwestern USA) using biennial Landsat imagery and reference data from historical aerial photos...

Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms

Science.gov (United States)

Robert E. Kennedy; Zhiqiang Yang; Warren B. Cohen

2010-01-01

We introduce and test LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery), a new approach to extract spectral trajectories of land surface change from yearly Landsat time-series stacks (LTS). The method brings together two themes in time-series analysis of LTS: capture of short-duration events and smoothing of long-term trends. Our strategy is...

North American forest disturbance mapped from a decadal Landsat record

Science.gov (United States)

Jeffrey G. Masek; Chengquan Huang; Robert Wolfe; Warren Cohen; Forrest Hall; Jonathan Kutler; Peder. Nelson

2008-01-01

Forest disturbance and recovery are critical ecosystem processes, but the spatial pattern of disturbance has never been mapped across North America. The LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System) project has assembled a wall-to-wall record of stand-clearing disturbance (clearcut harvest, fire) for the United States and Canada for the period 1990-...

Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images.

Science.gov (United States)

Bustamante, Javier; Pacios, Fernando; Díaz-Delgado, Ricardo; Aragonés, David

2009-05-01

We have used Landsat-5 TM and Landsat-7 ETM+ images together with simultaneous ground-truth data at sample points in the Doñana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Landsat-5 and 5 Landsat-7 overpasses. The best model for water turbidity in the marsh explained 38% of variance in ground-truth data and included as predictors band 3 (630-690 nm), band 5 (1550-1750 nm) and the ratio between bands 1 (450-520 nm) and 4 (760-900 nm). Water turbidity is easier to predict for water bodies like the Guadalquivir River and artificial ponds that are deep and not affected by bottom soil reflectance and aquatic vegetation. For the latter, a simple model using band 3 reflectance explains 78.6% of the variance. Water depth is easier to predict than turbidity. The best model for water depth in the marsh explains 78% of the variance and includes as predictors band 1, band 5, the ratio between band 2 (520-600 nm) and band 4, and bottom soil reflectance in band 4 in September, when the marsh is dry. The water turbidity and water depth models have been developed in order to reconstruct historical changes in Doñana wetlands during the last 30 years using the Landsat satellite images time series.

Geological interpretation of Landsat TM imagery and aeromagnetic survey data, northern Precordillera region, Argentina

Science.gov (United States)

Chernicoff, C. J.; Nash, C. R.

2002-03-01

This case study demonstrates a methodology for obtaining maximum geoscientific value from reconnaissance (1000 m line spacing) aeromagnetic data through integration with high-resolution satellite imagery. In this study, lithostratigraphic interpretation of optimally processed Landsat TM data at reconnaissance mapping scale (1:100,000) has been carried out as a precursor to geophysical interpretation, providing the basic 'framework' in which to view the imaged geophysical data. The Landsat-derived framework shows the correct positions and vergences of major structures, which characterize this part of the Andean foreland thrust-and-fold belt. Within the structural framework derived from satellite imagery, the locations of major shallow-source aeromagnetic anomalies related to intermediate/mafic extrusive and subvolcanic rocks and the controlling structures of these economically important magmatic events can be correctly interpreted. Results of the study indicate a significant, coherent, and previously unrecognized post-Permian, pre-Miocene volcanic/subvolcanic center, which is probably associated with regional sinistral strike-slip along a reactivated N-S accretionary suture and a pre-existing Precambrian/Paleozoic basement structure. Subsequent west-vergent thick-skinned thrusting associated with uplift of Sierra Valle Fertil Precambrian block has developed a set of distinctive NW-oriented strike-slip faults at the site of the volcanic center. The NW structures cut and rotate late Miocene thin-skinned structures associated with the Precordillera fold-and-thrust belt. Intrusive rocks associated with the inferred Oligocene volcanic center form easily recognizable, partially remanent dipole anomalies, are associated with alteration and Au mineralization (Cerro Guachi, El Pescado, Gnrl. Belgrano mines), and are located along NW-oriented sinistral splay faults. The strike-slip related tectonic/magmatic event is currently regarded as Oligocene in age and may correlate

Mission to planet earth: A call to repeal the Landsat Act

International Nuclear Information System (INIS)

Gabrynowicz, J.I.; Wood, C.A.

1991-01-01

Mission to Planet Earth (MPE) is being planned to provide scientists with data and information about global climate change so that policymakers can make informed decisions in directing the nation's response to the change. A crucial component of MPE is the Landsat system and the continuous, multidecade database it has produced. Yet because of an ill-conceived and poorly-executed commercialization policy, MPE researchers are being deprived of these vital national assets, profoundly hampering their ability to carry out the work of MPE. Effects of the US commercialization policy have reached abroad. The French, Japanese, and Canadians, following the US lead, have also commercialized their existing and future land remote sensing systems - further inhibiting the free flow of data among these complementary systems and preventing a truly international, scientific effort in MPE. Meeting the challenges of global climate change requires reconsidering the proper role of the Landsat system and identifying appropriate and inappropriate commercial remote sensing activities

LBA-ECO LC-24 Landsat ETM+ Forest Cover Classification, Uruara, Para, Brazil: 1999

Data.gov (United States)

National Aeronautics and Space Administration — ABSTRACT: This data set contains a 1999 Landsat ETM+ mosaic image land of cover classification showing forested and deforestation areas in Uruara, Para, Brazil. This...

A procedure used for a ground truth study of a land use map of North Alabama generated from LANDSAT data

Science.gov (United States)

Downs, S. W., Jr.; Sharma, G. C.; Bagwell, C.

1977-01-01

A land use map of a five county area in North Alabama was generated from LANDSAT data using a supervised classification algorithm. There was good overall agreement between the land use designated and known conditions, but there were also obvious discrepancies. In ground checking the map, two types of errors were encountered - shift and misclassification - and a method was developed to eliminate or greatly reduce the errors. Randomly selected study areas containing 2,525 pixels were analyzed. Overall, 76.3 percent of the pixels were correctly classified. A contingency coefficient of correlation was calculated to be 0.7 which is significant at the alpha = 0.01 level. The land use maps generated by computers from LANDSAT data are useful for overall land use by regional agencies. However, care must be used when making detailed analysis of small areas. The procedure used for conducting the ground truth study together with data from representative study areas is presented.

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery.

Science.gov (United States)

Li, Guiying; Lu, Dengsheng; Moran, Emilio; Hetrick, Scott

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.

LBA-ECO LC-24 Landsat ETM+ Forest Cover Classification, Uruara, Para, Brazil: 1999

Data.gov (United States)

National Aeronautics and Space Administration — This data set contains a 1999 Landsat ETM+ mosaic image land of cover classification showing forested and deforestation areas in Uruara, Para, Brazil. This image may...

Proposal for a remote sensing trophic state index based upon Thematic Mapper/Landsat images

Directory of Open Access Journals (Sweden)

Evlyn Márcia Leão de Moraes Novo

2013-12-01

Full Text Available This work proposes a trophic state index based on the remote sensing retrieval of chlorophyll-α concentration. For that, in situ Bidirectional Reflectance Factor (BRF data acquired in the Ibitinga reservoir were resampled to match Landsat/TM spectral simulated bands (TM_sim bands and used to run linear correlation with concurrent measurements of chlorophyll-α concentration. Monte Carlo simulation was then applied to select the most suitable model relating chlorophyll-α concentration and simulated TM/Landsat reflectance. TM4_sim/TM3_sim ratio provided the best model with a R2 value of 0.78. The model was then inverted to create a look-up-table (LUT relating TM4_sim/TM3_sim ratio intervals to chlorophyll-α concentration trophic state classes covering the entire range measured in the reservoir. Atmospheric corrected Landsat TM images converted to surface reflectance were then used to generate a TM4/TM3 ratio image. The ratio image frequency distribution encompassed the range of TM4_sim/TM3_sim ratio indicating agreement between in situ and satellite data and supporting the use of satellite data to map chlorophyll- concentration trophic state distribution in the reservoir. Based on that, the LUT was applied to a Landsat/TM ratio image to map the spatial distribution of chlorophyll- trophic state classes in Ibitinga reservoir. Despite the stochastic selection of TM4_sim/TM3_sim ratio as the best input variable for modeling the chlorophyll-α concentration, it has a physical basis: high concentration of phytoplankton increases the reflectance in the near-infrared (TM4 and decreases the reflectance in the red (TM3. The band ratio, therefore, enhances the relationship between chlorophyll- concentration and remotely sensed reflectance.

Harmonic regression of Landsat time series for modeling attributes from national forest inventory data

Science.gov (United States)

Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E.

2018-03-01

Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009-2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10-20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.

An Operational Scheme for Deriving Standardised Surface Reflectance from Landsat TM/ETM+ and SPOT HRG Imagery for Eastern Australia

Directory of Open Access Journals (Sweden)

Neil Flood

2013-01-01

Full Text Available Operational monitoring of vegetation and land surface change over large areas can make good use of satellite sensors that measure radiance reflected from the Earth’s surface. Monitoring programs use multiple images for complete spatial coverage over time. Accurate retrievals of vegetation cover and vegetation change estimates can be hampered by variation, in both space and time, in the measured radiance, caused by atmospheric conditions, topography, sensor location, and sun elevation. In order to obtain estimates of cover that are comparable between images, and to retrieve accurate estimates of change, these sources of variation must be removed. In this paper we present a preprocessing scheme for minimising atmospheric, topographic and bi-directional reflectance effects on Landsat-5 TM, Landsat-7 ETM+ and SPOT-5 HRG imagery. The approach involves atmospheric correction to compute surface-leaving radiance, and bi-directional reflectance modelling to remove the effects of topography and angular variation in reflectance. The bi-directional reflectance model has been parameterised for eastern Australia, but the general approach is more widely applicable. The result is surface reflectance standardised to a fixed viewing and illumination geometry. The method can be applied to the entire record for these instruments, without intervention, which is of increasing importance with the increased availability of long term image archives. Validation shows that the corrections improve the estimation of reflectance at any given angular configuration, thus allowing the removal from the reflectance signal of much variation due to factors independent of the land surface. The method has been used to process over 45,000 Landsat-5 TM and Landsat-7 ETM+ scenes and 2,500 SPOT-5 scenes, over eastern Australia, and is now in use in operational monitoring programs.

Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods

Science.gov (United States)

Xian, George; Homer, Collin G.; Fry, Joyce

2009-01-01

The recent release of the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001, which represents the nation's land cover status based on a nominal date of 2001, is widely used as a baseline for national land cover conditions. To enable the updating of this land cover information in a consistent and continuous manner, a prototype method was developed to update land cover by an individual Landsat path and row. This method updates NLCD 2001 to a nominal date of 2006 by using both Landsat imagery and data from NLCD 2001 as the baseline. Pairs of Landsat scenes in the same season in 2001 and 2006 were acquired according to satellite paths and rows and normalized to allow calculation of change vectors between the two dates. Conservative thresholds based on Anderson Level I land cover classes were used to segregate the change vectors and determine areas of change and no-change. Once change areas had been identified, land cover classifications at the full NLCD resolution for 2006 areas of change were completed by sampling from NLCD 2001 in unchanged areas. Methods were developed and tested across five Landsat path/row study sites that contain several metropolitan areas including Seattle, Washington; San Diego, California; Sioux Falls, South Dakota; Jackson, Mississippi; and Manchester, New Hampshire. Results from the five study areas show that the vast majority of land cover change was captured and updated with overall land cover classification accuracies of 78.32%, 87.5%, 88.57%, 78.36%, and 83.33% for these areas. The method optimizes mapping efficiency and has the potential to provide users a flexible method to generate updated land cover at national and regional scales by using NLCD 2001 as the baseline.

USING LANDSAT IMAGES IN MAPPING AND MONITORING WATER BODIES IN MĂGURA BASIN

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MEREUȚĂ M.

2015-03-01

Full Text Available The work is part of a wider range of interdisciplinary studies undertaken in Măgura catchment, a right-side tributary of Bahlui River. The Măgura River flows from the massif Great Hill-Hârlău. Before the year 2000 there were 11 lakes, and today are only 4. The purpose of this project is to determine the accuracy of the simple techniques in digital image processing for mapping and monitoring lakes and wetlands. Landsat 7 ETM + and Landsat 8 OLI TIRS data sets are used. The paper highlights the bands’ thematic classification accuracy using minimum technical and digital (software resources. The water bodies’ delineated boundaries of each digital classification procedure were compared with the limits obtained by digitizing the topographical plans (1973 and aerial images (2008. The comparisons show that the Landsat data can be used to map accurately the water bodies. It is a simple method of determining the silting degree, especially for lakes with an area of at least 1 ha. Măgura basin has a high archaeological potential (prehistory up to the modern period, part of the national and international cultural heritage. Creating a GIS database, in order to analyze the human-environment relationship, began by studying the hydrological variables. This factor has an important role in the society’s development, both prehistoric and current.

An Algorithm for the Retrieval of 30-m Snow-Free Albedo from Landsat Surface Reflectance and MODIS BRDF

Science.gov (United States)

Shuai, Yanmin; Masek, Jeffrey G.; Gao, Feng; Schaaf, Crystal B.

2011-01-01

We present a new methodology to generate 30-m resolution land surface albedo using Landsat surface reflectance and anisotropy information from concurrent MODIS 500-m observations. Albedo information at fine spatial resolution is particularly useful for quantifying climate impacts associated with land use change and ecosystem disturbance. The derived white-sky and black-sky spectral albedos maybe used to estimate actual spectral albedos by taking into account the proportion of direct and diffuse solar radiation arriving at the ground. A further spectral-to-broadband conversion based on extensive radiative transfer simulations is applied to produce the broadband albedos at visible, near infrared, and shortwave regimes. The accuracy of this approach has been evaluated using 270 Landsat scenes covering six field stations supported by the SURFace RADiation Budget Network (SURFRAD) and Atmospheric Radiation Measurement Southern Great Plains (ARM/SGP) network. Comparison with field measurements shows that Landsat 30-m snow-free shortwave albedos from all seasons generally achieve an absolute accuracy of +/-0.02 - 0.05 for these validation sites during available clear days in 2003-2005,with a root mean square error less than 0.03 and a bias less than 0.02. This level of accuracy has been regarded as sufficient for driving global and regional climate models. The Landsat-based retrievals have also been compared to the operational 16-day MODIS albedo produced every 8-days from MODIS on Terra and Aqua (MCD43A). The Landsat albedo provides more detailed landscape texture, and achieves better agreement (correlation and dynamic range) with in-situ data at the validation stations, particularly when the stations include a heterogeneous mix of surface covers.

Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS

Directory of Open Access Journals (Sweden)

Said Nawar

2014-11-01

Full Text Available The monitoring of soil salinity levels is necessary for the prevention and mitigation of land degradation in arid environments. To assess the potential of remote sensing in estimating and mapping soil salinity in the El-Tina Plain, Sinai, Egypt, two predictive models were constructed based on the measured soil electrical conductivity (ECe and laboratory soil reflectance spectra resampled to Landsat sensor’s resolution. The models used were partial least squares regression (PLSR and multivariate adaptive regression splines (MARS. The results indicated that a good prediction of the soil salinity can be made based on the MARS model (R2 = 0.73, RMSE = 6.53, and ratio of performance to deviation (RPD = 1.96, which performed better than the PLSR model (R2 = 0.70, RMSE = 6.95, and RPD = 1.82. The models were subsequently applied on a pixel-by-pixel basis to the reflectance values derived from two Landsat images (2006 and 2012 to generate quantitative maps of the soil salinity. The resulting maps were validated successfully for 37 and 26 sampling points for 2006 and 2012, respectively, with R2 = 0.72 and 0.74 for 2006 and 2012, respectively, for the MARS model, and R2 = 0.71 and 0.73 for 2006 and 2012, respectively, for the PLSR model. The results indicated that MARS is a more suitable technique than PLSR for the estimation and mapping of soil salinity, especially in areas with high levels of salinity. The method developed in this paper can be used for other satellite data, like those provided by Landsat 8, and can be applied in other arid and semi-arid environments.

Identification of brome grass infestations in southwest Oklahoma using multi-temporal Landsat imagery

Science.gov (United States)

Yan, D.; de Beurs, K.

2013-12-01

The extensive infestation of brome grasses (Cheatgrass, Rye brome and Japanese brome) in southwest Oklahoma imposes negative impacts on local economy and ecosystem in terms of decreasing crop and forage production and increasing fire risk. Previously proposed methodologies on brome grass detection are found ill-suitable for southwest Oklahoma as a result of similar responses of background vegetation to inter-annual variability of rainfall. In this study, we aim to identify brome grass infestations by detecting senescent brome grasses using the 2011 Cultivated Land Cover Data Sets and the difference Normalized Difference Infrared Index (NDII) derived from multi-temporal Landsat imagery. Landsat imageries acquired on May 18th and June 10th 2013 by Operational Land Imager and Enhanced Thematic Mapper plus were used. The imagery acquisition dates correspond to the peak growth and senescent time of brome grasses, respectively. The difference NDII was calculated by subtracting the NDII image acquired in May from the June NDII image. Our hypotheses is that senescent brome grasses and crop/pasture fields harvested between the two image acquisition dates can be distinguished from background land cover classes because of their increases in NDII due to decreased water absorption by senescent vegetation in the shortwave infrared region. The Cultivated Land Cover Data Sets were used to further separate senescent brome grass patches from newly harvested crop/pasture fields. Ground truth data collected during field trips in June, July and August of 2013 were used to validate the detection results.

Landsat Time-series for the Masses: Predicting Wood Biomass Growth from Tree-rings Using Departures from Mean Phenology in Google Earth Engine

Science.gov (United States)

Foster, J. R.; D'Amato, A. W.; Itter, M.; Reinikainen, M.; Curzon, M.

2017-12-01

The terrestrial carbon cycle is perturbed when disturbances remove leaf biomass from the forest canopy during the growing season. Changes in foliar biomass arise from defoliation caused by insects, disease, drought, frost or human management. As ephemeral disturbances, these often go undetected and their significance to models that predict forest growth from climatic drivers remains unknown. Here, we seek to distinguish the roles of weather vs. canopy disturbance on forest growth by using dense Landsat time-series to quantify departures in mean phenology that in turn predict changes in leaf biomass. We estimated a foliar biomass index (FBMI) from 1984-2016, and predict plot-level wood growth over 28 years on 156 tree-ring monitoring plots in Minnesota, USA. We accessed the entire Landsat archive (sensors 4, 5 & 7) to compute FBMI using Google Earth Engine's cloud computing platform (GEE). GEE allows this pixel-level approach to be applied at any location; a feature we demonstrate with published wood-growth data from flux tower sites. Our Bayesian models predicted biomass changes from tree-ring plots as a function of Landsat FBMI and annual climate data. We expected model parameters to vary by tree functional groups defined by differences in xylem anatomy and leaf longevity, two traits with linkages to phenology, as reported in a recent review. We found that Landsat FBMI was a surprisingly strong predictor of aggregate wood-growth, explaining up to 80% of annual growth variation for some deciduous plots. Growth responses to canopy disturbance varied among tree functional groups, and the importance of some seasonal climate metrics diminished or changed sign when FBMI was included (e.g. fall and spring climatic water deficit), while others remained unchanged (current and lagged summer deficit). Insights emerging from these models can clear up sources of persistent uncertainty and open a new frontier for models of forest productivity.

Monitoring Annual Urban Changes in a Rapidly Growing Portion of Northwest Arkansas with a 20-Year Landsat Record

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Ryan Reynolds

2017-01-01

Full Text Available Northwest Arkansas has undergone a significant urban transformation in the past several decades and is considered to be one of the fastest growing regions in the United States. The urban area expansion and the associated demographic increases bring unprecedented pressure to the environment and natural resources. To better understand the consequences of urbanization, accurate and long-term depiction on urban dynamics is critical. Although urban mapping activities using remote sensing have been widely conducted, long-term urban growth mapping at an annual pace is rare and the low accuracy of change detection remains a challenge. In this study, a time series Landsat stack covering the period from 1995 to 2015 was employed to detect the urban dynamics in Northwest Arkansas via a two-stage classification approach. A set of spectral indices that have been proven to be useful in urban area extraction together with the original Landsat spectral bands were used in the maximum likelihood classifier and random forest classifier to distinguish urban from non-urban pixels for each year. A temporal trajectory polishing method, involving temporal filtering and heuristic reasoning, was then applied to the sequence of classified urban maps for further improvement. Based on a set of validation samples selected for five distinct years, the average overall accuracy of the final polished maps was 91%, which improved the preliminary classifications by over 10%. Moreover, results from this study also indicated that the temporal trajectory polishing method was most effective with initial low accuracy classifications. The resulting urban dynamic map is expected to provide unprecedented details about the area, spatial configuration, and growing trends of urban land-cover in Northwest Arkansas.

The Use of Landsat and Aerial Photography for the Assessment of ...

African Journals Online (AJOL)

Coastal erosion is a worldwide hazard, the consequences of which can only be mitigated via thorough and efficient monitoring of erosion. This study aimed to employ remote sensing techniques on aerial photographs and Landsat TM/ETM+ imagery for the detection and monitoring of coastal erosion in False Bay, South ...

Characterization of tropospheric desert aerosols at solar wavelengths by multispectral radiometry from landsat

International Nuclear Information System (INIS)

Otterman, J.; Fraser, R.S.; Bahethi, O.P.

1982-01-01

Characteristics of tropospheric desert aerosols are derived by comparing nadir spectral reflectivities computed from the radiative transfer models with reflectivities measured from Landsat. Over the ocean, reflectivites are compared, but over land the comparison is carried out by determining the ratios of the nadir reflectivity of the surface-atmpsphere system over heavy aerosol concentration to the reflectivity of the underlying surface. This remote sensing technique is found to be a sensitive approach for measuring n 2 , the imaginary part of the refractive index. The desert aerosols under study, in the Iran and Pakistan area, are essentially pure scatterers, inasmuch as an n 2 value of 0.001 +- 0.001 was determined for each of the four Landsat spectral bands, that is, for a spectral interval from 0.5 to 1.1 μm

A forest map of Southern Africa with the aid of LANDSAT imagery

CSIR Research Space (South Africa)

Van der Zel, DW

1988-01-01

Full Text Available Even after 300 years of indigenous forest protection as well as 100 years of plantation forestry, no forestry map of South Africa was available. The development and availability of LANDSAT images in the early 1970s opened possibility to use...

Retrieval and Mapping of Heavy Metal Concentration in Soil Using Time Series Landsat 8 Imagery

Science.gov (United States)

Fang, Y.; Xu, L.; Peng, J.; Wang, H.; Wong, A.; Clausi, D. A.

2018-04-01

Heavy metal pollution is a critical global environmental problem which has always been a concern. Traditional approach to obtain heavy metal concentration relying on field sampling and lab testing is expensive and time consuming. Although many related studies use spectrometers data to build relational model between heavy metal concentration and spectra information, and then use the model to perform prediction using the hyperspectral imagery, this manner can hardly quickly and accurately map soil metal concentration of an area due to the discrepancies between spectrometers data and remote sensing imagery. Taking the advantage of easy accessibility of Landsat 8 data, this study utilizes Landsat 8 imagery to retrieve soil Cu concentration and mapping its distribution in the study area. To enlarge the spectral information for more accurate retrieval and mapping, 11 single date Landsat 8 imagery from 2013-2017 are selected to form a time series imagery. Three regression methods, partial least square regression (PLSR), artificial neural network (ANN) and support vector regression (SVR) are used to model construction. By comparing these models unbiasedly, the best model are selected to mapping Cu concentration distribution. The produced distribution map shows a good spatial autocorrelation and consistency with the mining area locations.

Large Area Crop Inventory Experiment (LACIE). Detecting and monitoring agricultural vegetative water stress over large areas using LANDSAT digital data. [Great Plains

Science.gov (United States)

Thompson, D. R.; Wehmanen, O. A. (Principal Investigator)

1978-01-01

The author has identified the following significant results. The Green Number Index technique which uses LANDSAT digital data from 5X6 nautical mile sampling frames was expanded to evaluate its usefulness in detecting and monitoring vegetative water stress over the Great Plains. At known growth stages for wheat, segments were classified as drought or non drought. Good agreement was found between the 18 day remotely sensed data and a weekly ground-based crop moisture index. Operational monitoring of the 1977 U.S.S.R. and Australian wheat crops indicated drought conditions. Drought isoline maps produced by the Green Number Index technique were in good agreement with conventional sources.

An evaluation of simulated Thematic Mapper data and Landsat MSS data for discriminating suburban and regional land use and land cover

Science.gov (United States)

Toll, D. L.

1984-01-01

An airborne multispectral scanner, operating in the same spectral channels as the Landsat Thematic Mapper (TM), was used in a region east of Denver, CO, for a simulation test performed in the framework of using TM to discriminate the level I and level II classes. It is noted that at the 30-m spatial resolution of the Thematic Mapper Simulator (TMS) the overall discrimination for such classes as commercial/industrial land, rangeland, irrigated sod, irrigated alfalfa, and irrigated pasture was superior to that of the Landsat Multispectral Scanner, primarily due to four added spectral bands. For residential and other spectrally heterogeneous classes, however, the higher resolution of TMS resulted in increased variability within the class and a larger spectral overlap.

SUPPORT VECTOR MACHINE CLASSIFICATION OF OBJECT-BASED DATA FOR CROP MAPPING, USING MULTI-TEMPORAL LANDSAT IMAGERY

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R. Devadas

2012-07-01

Full Text Available Crop mapping and time series analysis of agronomic cycles are critical for monitoring land use and land management practices, and analysing the issues of agro-environmental impacts and climate change. Multi-temporal Landsat data can be used to analyse decadal changes in cropping patterns at field level, owing to its medium spatial resolution and historical availability. This study attempts to develop robust remote sensing techniques, applicable across a large geographic extent, for state-wide mapping of cropping history in Queensland, Australia. In this context, traditional pixel-based classification was analysed in comparison with image object-based classification using advanced supervised machine-learning algorithms such as Support Vector Machine (SVM. For the Darling Downs region of southern Queensland we gathered a set of Landsat TM images from the 2010–2011 cropping season. Landsat data, along with the vegetation index images, were subjected to multiresolution segmentation to obtain polygon objects. Object-based methods enabled the analysis of aggregated sets of pixels, and exploited shape-related and textural variation, as well as spectral characteristics. SVM models were chosen after examining three shape-based parameters, twenty-three textural parameters and ten spectral parameters of the objects. We found that the object-based methods were superior to the pixel-based methods for classifying 4 major landuse/land cover classes, considering the complexities of within field spectral heterogeneity and spectral mixing. Comparative analysis clearly revealed that higher overall classification accuracy (95% was observed in the object-based SVM compared with that of traditional pixel-based classification (89% using maximum likelihood classifier (MLC. Object-based classification also resulted speckle-free images. Further, object-based SVM models were used to classify different broadacre crop types for summer and winter seasons. The influence of

Identification of "ever-cropped" land (1984-2010) using Landsat annual maximum NDVI image composites: Southwestern Kansas case study.

Science.gov (United States)

Maxwell, Susan K; Sylvester, Kenneth M

2012-06-01

A time series of 230 intra- and inter-annual Landsat Thematic Mapper images was used to identify land that was ever cropped during the years 1984 through 2010 for a five county region in southwestern Kansas. Annual maximum Normalized Difference Vegetation Index (NDVI) image composites (NDVI(ann-max)) were used to evaluate the inter-annual dynamics of cropped and non-cropped land. Three feature images were derived from the 27-year NDVI(ann-max) image time series and used in the classification: 1) maximum NDVI value that occurred over the entire 27 year time span (NDVI(max)), 2) standard deviation of the annual maximum NDVI values for all years (NDVI(sd)), and 3) standard deviation of the annual maximum NDVI values for years 1984-1986 (NDVI(sd84-86)) to improve Conservation Reserve Program land discrimination.Results of the classification were compared to three reference data sets: County-level USDA Census records (1982-2007) and two digital land cover maps (Kansas 2005 and USGS Trends Program maps (1986-2000)). Area of ever-cropped land for the five counties was on average 11.8 % higher than the area estimated from Census records. Overall agreement between the ever-cropped land map and the 2005 Kansas map was 91.9% and 97.2% for the Trends maps. Converting the intra-annual Landsat data set to a single annual maximum NDVI image composite considerably reduced the data set size, eliminated clouds and cloud-shadow affects, yet maintained information important for discriminating cropped land. Our results suggest that Landsat annual maximum NDVI image composites will be useful for characterizing land use and land cover change for many applications.

Forest cover change and fragmentation using Landsat data in Maçka State Forest Enterprise in Turkey.

Science.gov (United States)

Cakir, Günay; Sivrikaya, Fatih; Keleş, Sedat

2008-02-01

Monitoring forest cover change and understanding the dynamic of forest cover is increasingly important in sustainable development and management of forest ecosystems. This paper uses remote sensing (RS) techniques to monitor forest cover change in Maçka State Forest Enterprise (MSFE) located in NE of Turkey through 1975 to 2000 and then analyses spatial and temporal changes in forest cover by Geographical Information Systems (GIS) and FRAGSTATStrade mark. Forest cover changes were detected from a time series of satellite images of Landsat MSS in 1975, Landsat TM in 1987, and Landsat ETM+ in 2000 using RS and GIS. The results showed that total forest area, productive forest area and degraded forest area increased while broadleaf forest area and non forest area decreased. Mixed forest and degraded forest increased during the first (1975-1987) period, but decreased during the second (1987-2000) period. During the whole study period, the annual forestation rate was 152 ha year(-1), equivalent to 0.27% year(-1) using the compound-interest-rate formula. The total number of patches increased from 36,204 to 48,092 (33%), and mean size of forest patch (MPS) decreased from 2.8 ha to 2.1 ha during a 25 year period. Number of smaller patches (patches in 0-100 ha size class) increased, indicating more fragmented landscape over time that might create a risk for the maintenance of biodiversity of the area. While total population increased from 1975 to 2000 (3.7%), rural population constantly decreased. The increase of forest areas may well be explained by the fact that demographic movement of rural areas concentrated into Maçka City Center. These figures also indicated that decrease in the rural population might likely lead to the release of human pressure to forest areas, probably resulting in a positive development of forest areas.

Multispectral processing of ERTS-A (LANDSAT) data for uranium exploration in the Wind River Basin, Wyoming: a visible region ratio to enhance surface alteration associated with roll-type uraium deposits. Final report, June 1974--July 1975

International Nuclear Information System (INIS)

Salmon, B.C.; Pillars, W.W.

1975-07-01

The purpose of this report is to document possible detection capabilities of the LANDSAT multispectral scanner data for use in exploration for uranium roll-type deposits. Spectral reflectivity, mineralogy, iron content, and color paramenters were measured for twenty natural surface samples collected from a semiarid region. The relationships of these properties to LANDSAT response-weighted reflectances and to reflectance ratios are discussed. It was found that the single ratio technique of multispectral processing is likely to be sensitive enough to separate hematitic stain, but not limonitic. A combination of the LANDSAT R/sub 5,4/ and R/sub 7,6/ ratios, and a processing technique sensitive to vegetative cover is recommended for detecting areas of limonitic stain. Digital level slicing of LANDSAT R/sub 5,4/ over the Wind River Basin, after geometric correction, resulted in adequate enhancement of Triassic redbeds and lighter red materials, but not for limonitic areas. No recommendations for prospects in the area were made. Information pertaining to techniques of evaluating laboratory reflectance spectra for remote sensing applications, ratio processing, and planimetric correction of LANDSAT data is presented qualitatively

Reclamation of mosquito breeding sites using Landsat-8 remote sensing data: A case study of Birnin Kebbi, Nigeria

Science.gov (United States)

Amusuk, Danboyi Joseph; Hashim, Mazlan; Beiranvand Pour, Amin

2016-06-01

It is believed by recent releases of World Health Organization (WHO) that more than half of the world's population (3.2 billion) live in areas that are at risk of malaria transmission. Although increased efforts are dramatically reducing the malaria burden in some places where the rate of new cases indicates a fall by 37% globally and 60% death rate. Unfortunately, the subSaharan Africa still shares 89% of malaria and 91% of malaria deaths. Essentially, attacking the causative vectors and reclamation of the vector breeding sites could be remarkable for the rolling back the malaria epidemic project. Consequently, it is essential to explore the possibility of using recent Landsat-8 data remote sensing data and applications of Geographic Information System (GIS) technique in contributing to the realization of this objective. This investigation used for identifying mosquito breeding habitat (Derelict Ponds) zones the application of supervised classification of the Landsat-8 image in conjunction with GIS layering which allowed identification of high risk prone regions for mosquito breeding habitat. The methodology delineated 10 spatial locations of the Derelict Ponds (DP) spread around the Birnin Kebbi urban environment. Moreover, the results combined with comparative analysis of the link between warm climatic (temperature and rainfall data) conditions and Malaria prevalence that is associated with urban poverty. This study indicates that the application of Landsat-8 data and GIS techniques can be a useful tool for planning and management of environmental health and mapping of hot spot environmental problem areas.

Analyzing Landsat time-series data across adjacent path/rows and across multiple cycles of FIA: Lessons learned in southern Missouri

Science.gov (United States)

Mark Nelson; Sean Healey; W. Keith Moser; Mark Hansen; Warren Cohen; Mark Hatfield; Nancy Thomas; Jeff Masek

2009-01-01

The North American Forest Dynamics (NAFD) Program is assessing disturbance and regrowth in the forests of the continent. These forest dynamics are interpreted from per-pixel estimates of forest biomass, which are produced for a time series of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced TM Plus images. Image data are combined with sample plot data from the...

Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm

Directory of Open Access Journals (Sweden)

Offer Rozenstein

2014-03-01

Full Text Available Land surface temperature (LST is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS. This paper presents an adjustment of the split window algorithm (SWA for TIRS that uses atmospheric transmittance and land surface emissivity (LSE as inputs. Various alternatives for estimating these SWA inputs are reviewed, and a sensitivity analysis of the SWA to misestimating the input parameters is performed. The accuracy of the current development was assessed using simulated Modtran data. The root mean square error (RMSE of the simulated LST was calculated as 0.93 °C. This SWA development is leading to progress in the determination of LST by Landsat-8 TIRS.

Landsat Evapotranspiration for Historical Field-scale Water Use (1984-2015) in the Upper Rio Grande River Basin

Science.gov (United States)

Senay, G. B.; Schauer, M.; Singh, R. K.; Friedrichs, M.

2017-12-01

Field-scale water use maps derived from evapotranspiration (ET) can characterize water use patterns and the impacts of water management decisions. This project generated historical (1984-2015) Landsat-based ET maps for the entire Upper Rio Grande basin which makes this one of the largest regions in the United States with remotely sensed historical ET at Landsat resolution. More than 10,000 Landsat images spanning 32 years were processed using the Operational Simplified Surface Energy Balance (SSEBop) model which integrates weather data and remotely sensed images to estimate monthly and annual ET. Time-series analysis focused on three water-intensive study areas within the basin: the San Luis Valley in Colorado, irrigated fields along the Rio Grande River near Albuquerque, NM, and irrigated fields near Las Cruces, NM. Preliminary analysis suggests land use changes result in declining water use in irrigated areas of the basin which corresponds with increases in land surface temperatures. Time-series analysis of water use patterns at multiple temporal and spatial scales demonstrates the impact of water management decisions on the availability of water in the basin. Comparisons with cropland data from the USDA (NASS CDL) demonstrate how water use for particular crop types changes over time in response to land use changes and shifts in water management. This study illustrates a useful application of "Big Data" earth observation science for quantifying impacts of climate and land use changes on water availability within the United States as well as applications in planning water resource allocation, managing water rights, and sustaining agricultural production in the Upper Rio Grande basin.

Surface Water Mapping from Suomi NPP-VIIRS Imagery at 30 m Resolution via Blending with Landsat Data

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Chang Huang

2016-07-01

Full Text Available Monitoring the dynamics of surface water using remotely sensed data generally requires both high spatial and high temporal resolutions. One effective and popular approach for achieving this is image fusion. This study adopts a widely accepted fusion model, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM, for blending the newly available coarse-resolution Suomi NPP-VIIRS data with Landsat data in order to derive water maps at 30 m resolution. The Pan-sharpening technique was applied to preprocessing NPP-VIIRS data to achieve a higher-resolution before blending. The modified Normalized Difference Water Index (mNDWI was employed for mapping surface water area. Two fusion alternatives, blend-then-index (BI or index-then-blend (IB, were comparatively analyzed against a Landsat derived water map. A case study of mapping Poyang Lake in China, where water distribution pattern is complex and the water body changes frequently and drastically, was conducted. It has been revealed that the IB method derives more accurate results with less computation time than the BI method. The BI method generally underestimates water distribution, especially when the water area expands radically. The study has demonstrated the feasibility of blending NPP-VIIRS with Landsat for achieving surface water mapping at both high spatial and high temporal resolutions. It suggests that IB is superior to BI for water mapping in terms of efficiency and accuracy. The finding of this study also has important reference values for other blending works, such as image blending for vegetation cover monitoring.

Arctic Sea Ice Structure and Texture over Four Decades Using Landsat Archive Data

Science.gov (United States)

Doulgeris, A. P.; Scambos, T.; Tiampo, K. F.

2017-12-01

Arctic sea ice cover is a sensitive indicator of Arctic climate change, and has shown dramatic changes in recent decades, having thinned by 70% ( 3.5 m to 1.2 m between 1980 and 2015). Age distribution of the ice has changed in a similar fashion, with over 90% of the ice older than 5 winters now lost relative to 1985. To date, most of the data have been based on the continuous passive microwave record that began in 1978, which has 25 km grid resolution, or on SAR imagery with somewhat less frequent, less continuous observations. Landsat image data exist for the Arctic sea ice region north of Alaska and the MacKenzie River Delta area in Canada, the Canadian Archipelago, and Baffin Bay, extending back over 40 years. Resolution of the earliest Landsat MSS data is 56-70 m per pixel, and after 1984 many additional images at 30 m resolution are available. This 40+ year time period is used to investigate long-term changes in sea ice properties, such as comparing image-based snapshots with the trend in seasonal extents today, as well as more novel properties like sea ice roughness, lead structure and texture. The proposed study will initially investigate Landsat image analysis techniques to extract quantitative measures of ice roughness, lead fraction and perhaps morphological measures like lead linearity (which potentially indicate strength and compression history within the ice), and to explore these measures over the 40+ year time frame.

An Analysis LANDSAT-4 Thematic Mapper Geometric Properties

Science.gov (United States)

Walker, R. E.; Zobrist, A. L.; Bryant, N. A.; Gokhman, B.; Friedman, S. Z.; Logan, T. L.

1984-01-01

LANDSAT Thematic Mapper P-data of Washington, D. C., Harrisburg, PA, and Salton Sea, CA are analyzed to determine magnitudes and causes of error in the geometric conformity of the data to known Earth surface geometry. Several tests of data geometry are performed. Intraband and interband correlation and registration are investigated, exclusive of map based ground truth. The magnitudes and statistical trends of pixel offsets between a single band's mirror scans (due to processing procedures) are computed, and the inter-band integrity of registration is analyzed. A line to line correlation analysis is included.

Demonstration and validation of automated agricultural field extraction from multi-temporal Landsat data for the majority of United States harvested cropland

Science.gov (United States)

Yan, L.; Roy, D. P.

2014-12-01

The spatial distribution of agricultural fields is a fundamental description of rural landscapes and the location and extent of fields is important to establish the area of land utilized for agricultural yield prediction, resource allocation, and for economic planning, and may be indicative of the degree of agricultural capital investment, mechanization, and labor intensity. To date, field objects have not been extracted from satellite data over large areas because of computational constraints, the complexity of the extraction task, and because consistently processed appropriate resolution data have not been available or affordable. A recently published automated methodology to extract agricultural crop fields from weekly 30 m Web Enabled Landsat data (WELD) time series was refined and applied to 14 states that cover 70% of harvested U.S. cropland (USDA 2012 Census). The methodology was applied to 2010 combined weekly Landsat 5 and 7 WELD data. The field extraction and quantitative validation results are presented for the following 14 states: Iowa, North Dakota, Illinois, Kansas, Minnesota, Nebraska, Texas, South Dakota, Missouri, Indiana, Ohio, Wisconsin, Oklahoma and Michigan (sorted by area of harvested cropland). These states include the top 11 U.S states by harvested cropland area. Implications and recommendations for systematic application to global coverage Landsat data are discussed.

Examining Land Cover and Greenness Dynamics in Hangzhou Bay in 1985–2016 Using Landsat Time-Series Data

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Dengqiu Li

2017-12-01

Full Text Available Land cover changes significantly influence vegetation greenness in different regions. Dense Landsat time series stacks provide unique opportunity to analyze land cover change and vegetation greenness trends at finer spatial scale. In the past three decades, large reclamation activities have greatly changed land cover and vegetation growth of coastal areas. However, rarely has research investigated these frequently changed coastal areas. In this study, Landsat Normalized Difference Vegetation Index time series (1984–2016 data and the Breaks For Additive Seasonal and Trend algorithm were used to detect the intensity and dates of abrupt changes in a typical coastal area—Hangzhou Bay, China. The prior and posterior land cover categories of each change were classified using phenology information through a Random Forest model. The impacts of land cover change on vegetation greenness trends of the inland and reclaimed areas were analyzed through distinguishing gradual and abrupt changes. The results showed that the intensity and date of land cover change were detected successfully with overall accuracies of 88.7% and 86.1%, respectively. The continuous land cover dynamics were retrieved accurately with an overall accuracy of 91.0% for ten land cover classifications. Coastal reclamation did not alleviate local cropland occupation, but prompted the vegetation greenness of the reclaimed area. Most of the inland area showed a browning trend. The main contributors to the greenness and browning trends were also quantified. These findings will help the natural resource management community generate better understanding of coastal reclamation and make better management decisions.

Modeling of multi-strata forest fire severity using Landsat TM data

Science.gov (United States)

Q. Meng; R.K. Meentemeyer

2011-01-01

Most of fire severity studies use field measures of composite burn index (CBI) to represent forest fire severity and fit the relationships between CBI and Landsat imagery derived differenced normalized burn ratio (dNBR) to predict and map fire severity at unsampled locations. However, less attention has been paid on the multi-strata forest fire severity, which...

Radiometric Non-Uniformity Characterization and Correction of Landsat 8 OLI Using Earth Imagery-Based Techniques

Directory of Open Access Journals (Sweden)

Frank Pesta

2014-12-01

Full Text Available Landsat 8 is the first satellite in the Landsat mission to acquire spectral imagery of the Earth using pushbroom sensor instruments. As a result, there are almost 70,000 unique detectors on the Operational Land Imager (OLI alone to monitor. Due to minute variations in manufacturing and temporal degradation, every detector will exhibit a different behavior when exposed to uniform radiance, causing a noticeable striping artifact in collected imagery. Solar collects using the OLI’s on-board solar diffuser panels are the primary method of characterizing detector level non-uniformity. This paper reports on an approach for using a side-slither maneuver to estimate relative detector gains within each individual focal plane module (FPM in the OLI. A method to characterize cirrus band detector-level non-uniformity using deep convective clouds (DCCs is also presented. These approaches are discussed, and then, correction results are compared with the diffuser-based method. Detector relative gain stability is assessed using the side-slither technique. Side-slither relative gains were found to correct streaking in test imagery with quality comparable to diffuser-based gains (within 0.005% for VNIR/PAN; 0.01% for SWIR and identified a 0.5% temporal drift over a year. The DCC technique provided relative gains that visually decreased striping over the operational calibration in many images.

Assessment of coastal wetland resources of central west coast, India, using LANDSAT data

Digital Repository Service at National Institute of Oceanography (India)

Jagtap, T.G.; Naik, S.; Nagle, V.L.

The part of central west coast (Maharashtra and Goa) of India has been classified and quantified for coastal wetlands using LANDSAT data of 1985-86. The classification accuracy of the maps and area estimates achieved was 84% at 90% confidence level...

Mapping and Evaluation of NDVI Trends from Synthetic Time Series Obtained by Blending Landsat and MODIS Data around a Coalfield on the Loess Plateau

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Kun Wang

2013-09-01

Full Text Available The increasingly intensive and extensive coal mining activities on the Loess Plateau pose a threat to the fragile local ecosystems. Quantifying the effects of coal mining activities on environmental conditions is of great interest for restoring and managing the local ecosystems and resources. This paper generates dense NDVI (Normalized Difference Vegetation Index time series between 2000 and 2011 at a spatial resolution of 30 m by blending Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer data using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM and further evaluates its capability for mapping vegetation trends around a typical coalfield on the Loss Plateau. Synthetic NDVI images were generated using (1 STARFM-generated NIR (near infrared and red band reflectance data (scheme 1 and (2 Landsat and MODIS NDVI images directly as inputs for STARFM (scheme 2. By comparing the synthetic NDVI images with the corresponding Landsat NDVI, we found that scheme 2 consistently generated better results (0.70 < R2 < 0.76 than scheme 1 (0.56 < R2 < 0.70 in this study area. Trend analysis was then performed with the synthetic dense NDVI time series and the annual maximum NDVI (NDVImax time series. The accuracy of these trends was evaluated by comparing to those from the corresponding MODIS time series, and it was concluded that both the trends from synthetic/MODIS NDVI dense time series and synthetic/MODIS NDVImax time series (2000–2011 were highly consistent. Compared to trends from MODIS time series, trends from synthetic time series are better able to capture fine scale vegetation changes. STARFM-generated synthetic NDVI time series could be used to quantify the effects of mining activities on vegetation, but the test areas should be selected with caution, as the trends derived from synthetic and MODIS time series may be significantly different in some areas.

Shifts in Forest Structure in Northwest Montana from 1972 to 2015 Using the Landsat Archive from Multispectral Scanner to Operational Land Imager

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Shannon L. Savage

2018-03-01

Full Text Available There is a pressing need to map changes in forest structure from the earliest time period possible given forest management policies and accelerated disturbances from climate change. The availability of Landsat data from over four decades helps researchers study an ecologically meaningful length of time. Forest structure is most often mapped utilizing lidar data, however these data are prohibitively expensive and cover a narrow temporal window relative to the Landsat archive. Here we describe a technique to use the entire length of the Landsat archive from Multispectral Scanner to Operational Land Imager (M2O to produce three novel outcomes: (1 we used the M2O dataset and standard change vector analysis methods to classify annual forest structure in northwestern Montana from 1972 to 2015, (2 we improved the accuracy of each yearly forest structure classification by applying temporal continuity rules to the whole time series, with final accuracies ranging from 97% to 68% respectively for two and six-category classifications, and (3 we demonstrated the importance of pre-1984 Landsat data for long-term change studies. As the Landsat program continues to acquire Earth imagery into the foreseeable future, time series analyses that aid in classifying forest structure accurately will be key to the success of any land management changes in the future.

Global Water Surface Dynamics: Toward a Near Real Time Monitoring Using Landsat and Sentinel Data

Science.gov (United States)

Pekel, J. F.; Belward, A.; Gorelick, N.

2017-12-01

Global surface water dynamics and its long-term changes have been documented at 30m spatial resolution using the entire multi-temporal orthorectified Landsat 5, 7 and 8 archive for the years 1984 to 2015. This validated dataset recorded the months and years when water was present, where occurrence changed and what form changes took (in terms of seasonality), documents inter-annual variability, and multi-annual trends. This information is freely available from the global surface water explorer https://global-surface-water.appspot.com. Here we extend this work (doi:10.1038/nature20584 ) by combining post 2015 Landsat 7 and 8 data with imagery from the Copernicus program's Sentinel 2a and b satellites. Using these data in combination improves the spatial resolution (from 30m to a nominal 10m) and temporal resolution (from 8 days to 4 days revisit time at the equator). The improved geographic and temporal completeness of the combined Landsat / Sentinel dataset also offers new opportunities for the identification and characterization of seasonally occurring waterbodies. These improvements are also being examined in the light of reporting progress against Agenda 2030's Sustainable Development Goal 6, especially the indicator used to measure 'change in the extent of water-related ecosystems over time'.

Reconstructing Historical Land Cover Type and Complexity by Synergistic Use of Landsat Multispectral Scanner and CORONA

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Amir Reza Shahtahmassebi

2017-07-01

Full Text Available Survey data describing land cover information such as type and diversity over several decades are scarce. Therefore, our capacity to reconstruct historical land cover using field data and archived remotely sensed data over large areas and long periods of time is somewhat limited. This study explores the relationship between CORONA texture—a surrogate for actual land cover type and complexity—with spectral vegetation indices and texture variables derived from Landsat MSS under the Spectral Variation Hypothesis (SVH such as to reconstruct historical continuous land cover type and complexity. Image texture of CORONA was calculated using a mean occurrence measure while image textures of Landsat MSS were calculated by occurrence and co-occurrence measures. The relationship between these variables was evaluated using correlation and regression techniques. The reconstruction procedure was undertaken through regression kriging. The results showed that, as expected, texture based on the visible bands and corresponding indices indicated larger correlation with CORONA texture, a surrogate of land cover (correlation >0.65. In terms of prediction, the combination of the first-order mean of band green, second-order measure of tasseled cap brightness, second-order mean of Normalized Visible Index (NVI and second-order entropy of NIR yielded the best model with respect to Akaike’s Information Criterion (AIC, r-square, and variance inflation factors (VIF. The regression model was then used in regression kriging to map historical continuous land cover. The resultant maps indicated the type and degree of complexity in land cover. Moreover, the proposed methodology minimized the impacts of topographic shadow in the region. The performance of this approach was compared with two conventional classification methods: hard classifiers and continuous classifiers. In contrast to conventional techniques, the technique could clearly quantify land cover complexity and

Mapping and Change Analysis in Mangrove Forest by Using Landsat Imagery

Science.gov (United States)

Dan, T. T.; Chen, C. F.; Chiang, S. H.; Ogawa, S.

2016-06-01

Mangrove is located in the tropical and subtropical regions and brings good services for native people. Mangrove in the world has been lost with a rapid rate. Therefore, monitoring a spatiotemporal distribution of mangrove is thus critical for natural resource management. This research objectives were: (i) to map the current extent of mangrove in the West and Central Africa and in the Sundarbans delta, and (ii) to identify change of mangrove using Landsat data. The data were processed through four main steps: (1) data pre-processing including atmospheric correction and image normalization, (2) image classification using supervised classification approach, (3) accuracy assessment for the classification results, and (4) change detection analysis. Validation was made by comparing the classification results with the ground reference data, which yielded satisfactory agreement with overall accuracy 84.1% and Kappa coefficient of 0.74 in the West and Central Africa and 83.0% and 0.73 in the Sundarbans, respectively. The result shows that mangrove areas have changed significantly. In the West and Central Africa, mangrove loss from 1988 to 2014 was approximately 16.9%, and only 2.5% was recovered or newly planted at the same time, while the overall change of mangrove in the Sundarbans increased approximately by 900 km2 of total mangrove area. Mangrove declined due to deforestation, natural catastrophes deforestation and mangrove rehabilitation programs. The overall efforts in this study demonstrated the effectiveness of the proposed method used for investigating spatiotemporal changes of mangrove and the results could provide planners with invaluable quantitative information for sustainable management of mangrove ecosystems in these regions.

Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors

Science.gov (United States)

Chander, Gyanesh; Markham, Brian L.; Helder, Dennis L.

2009-01-01

This paper provides a summary of the current equations and rescaling factors for converting calibrated Digital Numbers (DNs) to absolute units of at-sensor spectral radiance, Top-Of- Atmosphere (TOA) reflectance, and at-sensor brightness temperature. It tabulates the necessary constants for the Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Advanced Land Imager (ALI) sensors. These conversions provide a basis for standardized comparison of data in a single scene or between images acquired on different dates or by different sensors. This paper forms a needed guide for Landsat data users who now have access to the entire Landsat archive at no cost.

The Significance of Land Cover Delineation on Soil Erosion Assessment.

Science.gov (United States)

Efthimiou, Nikolaos; Psomiadis, Emmanouil

2018-04-25

The study aims to evaluate the significance of land cover delineation on soil erosion assessment. To that end, RUSLE (Revised Universal Soil Loss Equation) was implemented at the Upper Acheloos River catchment, Western Central Greece, annually and multi-annually for the period 1965-92. The model estimates soil erosion as the linear product of six factors (R, K, LS, C, and P) considering the catchment's climatic, pedological, topographic, land cover, and anthropogenic characteristics, respectively. The C factor was estimated using six alternative land use delineations of different resolution, namely the CORINE Land Cover (CLC) project (2000, 2012 versions) (1:100,000), a land use map conducted by the Greek National Agricultural Research Foundation (NAGREF) (1:20,000), a land use map conducted by the Greek Payment and Control Agency for Guidance and Guarantee Community Aid (PCAGGCA) (1:5,000), and the Landsat 8 16-day Normalized Difference Vegetation Index (NDVI) dataset (30 m/pixel) (two approximations) based on remote sensing data (satellite image acquired on 07/09/2016) (1:40,000). Since all other factors remain unchanged per each RUSLE application, the differences among the yielded results are attributed to the C factor (thus the land cover pattern) variations. Validation was made considering the convergence between simulated (modeled) and observed sediment yield. The latter was estimated based on field measurements conducted by the Greek PPC (Public Power Corporation). The model performed best at both time scales using the Landsat 8 (Eq. 13) dataset, characterized by a detailed resolution and a satisfactory categorization, allowing the identification of the most susceptible to erosion areas.

Automated Sargassum Detection for Landsat Imagery

Science.gov (United States)

McCarthy, S.; Gallegos, S. C.; Armstrong, D.

2016-02-01

We implemented a system to automatically detect Sargassum, a floating seaweed, in 30-meter LANDSAT-8 Operational Land Imager (OLI) imagery. Our algorithm for Sargassum detection is an extended form of Hu's approach to derive a floating algae index (FAI) [1]. Hu's algorithm was developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data, but we extended it for use with the OLI bands centered at 655, 865, and 1609 nm, which are comparable to the MODIS bands located at 645, 859, and 1640 nm. We also developed a high resolution true color product to mask cloud pixels in the OLI scene by applying a threshold to top of the atmosphere (TOA) radiances in the red (655 nm), green (561 nm), and blue (443 nm) wavelengths, as well as a method for removing false positive identifications of Sargassum in the imagery. Hu's algorithm derives a FAI for each Sargassum identified pixel. Our algorithm is currently set to only flag the presence of Sargassum in an OLI pixel by classifying any pixel with a FAI > 0.0 as Sargassum. Additionally, our system geo-locates the flagged Sargassum pixels identified in the OLI imagery into the U.S. Navy Global HYCOM model grid. One element of the model grid covers an area 0.125 degrees of latitude by 0.125 degrees of longitude. To resolve the differences in spatial coverage between Landsat and HYCOM, a scheme was developed to calculate the percentage of pixels flagged within the grid element and if above a threshold, it will be flagged as Sargassum. This work is a part of a larger system, sponsored by NASA/Applied Science and Technology Project at J.C. Stennis Space Center, to forecast when and where Sargassum will land on shore. The focus area of this work is currently the Texas coast. Plans call for extending our efforts into the Caribbean. References: [1] Hu, Chuanmin. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment 113 (2009) 2118-2129.

Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems

KAUST Repository

Rosas, Jorge; Houborg, Rasmus; McCabe, Matthew

2017-01-01

The land surface temperature (LST) represents a critical element in efforts to characterize global surface energy and water fluxes, as well as being an essential climate variable in its own right. Current satellite platforms provide a range of spatial and temporal resolution radiance data from which LST can be determined. One of the most complete records of data comes via the Landsat series of satellites, which provide a continuous sequence that extends back to 1982. However, for much of this time, Landsat thermal data were provided through a single broadband thermal channel, making surface temperature retrieval challenging. To fully exploit the valuable time-series of thermal information that is available from these satellites requires efforts to better describe and understand the accuracy of temperature retrievals. Here, we contribute to these efforts by examining the impact of atmospheric correction on the estimation of LST, using atmospheric profiles derived from a range of in-situ, reanalysis, and satellite data. Radiance data from the thermal infrared (TIR) sensor onboard Landsat 8 was converted to LST by using the MODTRAN version 5.2 radiative transfer model, allowing the production of an LST time series based upon 28 Landsat overpasses. LST retrievals were then evaluated against in-situ thermal measurements collected over an arid zone farmland comprising both bare soil and vegetated surface types. Atmospheric profiles derived from AIRS, MOD07, ECMWF, NCEP, and balloon-based radiosonde data were used to drive the MODTRAN simulations. In addition to examining the direct impact of using various profile data on LST retrievals, randomly distributed errors were introduced into a range of forcing variables to better understand retrieval uncertainty. Results indicated differences in LST of up to 1 K for perturbations in emissivity and profile measurements, with the analysis also highlighting the challenges in modeling aerosol optical depth (AOD) over arid lands and

Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems

KAUST Repository

Rosas, Jorge

2017-09-26

The land surface temperature (LST) represents a critical element in efforts to characterize global surface energy and water fluxes, as well as being an essential climate variable in its own right. Current satellite platforms provide a range of spatial and temporal resolution radiance data from which LST can be determined. One of the most complete records of data comes via the Landsat series of satellites, which provide a continuous sequence that extends back to 1982. However, for much of this time, Landsat thermal data were provided through a single broadband thermal channel, making surface temperature retrieval challenging. To fully exploit the valuable time-series of thermal information that is available from these satellites requires efforts to better describe and understand the accuracy of temperature retrievals. Here, we contribute to these efforts by examining the impact of atmospheric correction on the estimation of LST, using atmospheric profiles derived from a range of in-situ, reanalysis, and satellite data. Radiance data from the thermal infrared (TIR) sensor onboard Landsat 8 was converted to LST by using the MODTRAN version 5.2 radiative transfer model, allowing the production of an LST time series based upon 28 Landsat overpasses. LST retrievals were then evaluated against in-situ thermal measurements collected over an arid zone farmland comprising both bare soil and vegetated surface types. Atmospheric profiles derived from AIRS, MOD07, ECMWF, NCEP, and balloon-based radiosonde data were used to drive the MODTRAN simulations. In addition to examining the direct impact of using various profile data on LST retrievals, randomly distributed errors were introduced into a range of forcing variables to better understand retrieval uncertainty. Results indicated differences in LST of up to 1 K for perturbations in emissivity and profile measurements, with the analysis also highlighting the challenges in modeling aerosol optical depth (AOD) over arid lands and

Utilizing LANDSAT imagery to monitor land-use change - A case study in Ohio

Science.gov (United States)

Gordon, S. I.

1980-01-01

A study, performed in Ohio, of the nature and extent of interpretation errors in the application of Landsat imagery to land-use planning and modeling is reported. Potential errors associated with the misalignment of pixels after geometric correction and with misclassification of land cover or land use due to spectral similarities were identified on interpreted computer-compatible tapes of a portion of Franklin County for two adjacent days of 1975 and one day of 1973, and the extents of these errors were quantified by comparison with a ground-checked set of aerial-photograph interpretations. The open-space and agricultural categories are found to be the most consistently classified, while the more urban areas were classified correctly only from about 43 to 8% of the time. It is thus recommended that the direct application of Landsat data to land-use planning must await improvements in classification techniques and accuracy.

Refinamento de imagens termais do Landsat 5 - TM com base em classes de NDVI Sharpening of thermal Landsat 5 - TM imagery data based on NDVI classification

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Argemiro Lucena de Araújo

2012-12-01

Full Text Available O objetivo desse estudo foi avaliar um método simplificado, baseado em classes de NDVI para refinamento das imagens de temperatura da superfície (Ts, obtidas pelo sensor TM do Landsat 5 referentes aos anos de 2005 e 2006. Para tanto, foram propostos e comparados três modelos de refinamento baseados no método de regressão linear. Os erros percentuais e erros médios quadráticos obtidos com a utilização dos modelos avaliados foram, respectivamente, da ordem de 0,37% e 1,38 ºC, enquanto o modelo original apresentou erro médio quadrático da ordem de 1,32 ºC. Foram constatados que os erros obtidos com as calibrações realizadas não influenciaram significativamente nos valores médios das imagens termais, e que os resultados contribuíram substancialmente para a melhoria da resolução espacial das mesmas. O refinamento permitiu ainda a identificação precisa de alvos da superfície e a identificação de feições não detectáveis na resolução original. Isto evidencia que o método simplificado sugerido neste estudo, permite um refinamento preciso com uma forma de obtenção mais simples em relação ao modelo original.The objective of this study was to use a simplified method based on NDVI classes for the sharpening of the Landsat 5 - TM surface temperature images (Ts obtained during the years of 2005 and 2006. Thus, three sharpening models, based on the linear regression method, were proposed and compared. The relative and the root mean square errors obtained through the suggested models were of 0.37% and 1.38 ºC, respectively, while the original model presented root mean square error of 1.32 ºC. It was verified that the errors obtained with the accomplished calibrations did not significantly influence in the average values of the thermal images and the results contributed substantially to the improvement of their spatial resolution. The sharpening allowed the precise identification of the targets and features undetectable at

A 30 m Resolution Surface Water Mask Including Estimation of Positional and Thematic Differences Using Landsat 8, SRTM and OpenStreetMap: A Case Study in the Murray-Darling Basin, Australia

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Gennadii Donchyts

2016-05-01

Full Text Available Accurate maps of surface water are essential for many environmental applications. Surface water maps can be generated by combining measurements from multiple sources. Precise estimation of surface water using satellite imagery remains a challenging task due to the sensor limitations, complex land cover, topography, and atmospheric conditions. As a complementary dataset, in the case of hilly landscapes, a drainage network can be extracted from high-resolution digital elevation models. Additionally, Volunteered Geographic Information (VGI initiatives, such as OpenStreetMap, can also be used to produce high-resolution surface water masks. In this study, we derive a high-resolution water mask using Landsat 8 imagery and OpenStreetMap as well as (potential a drainage network using 30 m SRTM. Our approach to derive a surface water mask from Landsat 8 imagery comprises the use of a lower 15% percentile of Landsat 8 Top of Atmosphere (TOA reflectance from 2013 to 2015. We introduce a new non-parametric unsupervised method based on the Canny edge filter and Otsu thresholding to detect water in flat areas. For hilly areas, the method is extended with an additional supervised classification step used to refine the water mask. We applied the method across the Murray-Darling basin, Australia. Differences between our new Landsat-based water mask and the OpenStreetMap water mask regarding positional differences along the rivers and overall coverage were analyzed. Our results show that about 50% of the OpenStreetMap linear water features can be confirmed using the water mask extracted from Landsat 8 imagery and the drainage network derived from SRTM. We also show that the observed distances between river features derived from OpenStreetMap and Landsat 8 are mostly smaller than 60 m. The differences between the new water mask and SRTM-based linear features and hilly areas are slightly larger (110 m. The overall agreement between OpenStreetMap and Landsat 8 water

High Resolution Mapping of Drought Impacts on Small Waterbodies using Sentinel 1 SAR and Landsat Observations

Science.gov (United States)

Slinski, K.; Hogue, T. S.; McCray, J. E.

2017-12-01

Drought in semi-arid areas can have substantial impact on ephemeral and small water bodies, which provide critical ecological habitat and have important socio-economic value. This is particularly true in the pastoral areas of East Africa, where these ecosystems provide local communities with water for human and animal consumption and pasture for livestock. However, monitoring the impact of drought on ephemeral and small water bodies in East Africa is challenging because of sparse in situ observational systems. Satellite remote sensing observations have been shown to be a viable option for monitoring surface water change in data-poor regions. Landsat data is widely used to detect open water, but the use of Landsat data in small waterbody studies is limited by its 30-meter spatial resolution. New remote sensing-based tools are necessary to better understand the vulnerability of ephemeral and small waterbodies in semi-arid areas to drought and to monitor drought impacts. This study combines Landsat and Sentinel 1 SAR observations to create a series of monthly waterbody maps over the Awash River basin in Ethiopia depicting the change in surface water from October 2014 to March 2017. The study time period corresponds with a major drought event in the area. Waterbody maps were generated using a 10-meter resolution and utilized to monitor drought impacts on ephemeral and small waterbodies in the Awash River basin over the course of the drought event. Initial results show that surface waterbodies in the lower catchments of the Awash basin were more severely impacted by the drought event than the upper catchments. It is anticipated that the new information provided by this tool will inform decisions affecting the water, energy, agriculture and other sectors in East Africa reliant on water resources, enabling water authorities to better manage future drought events.

Landsat ETM+ and SRTM Data Provide Near Real-Time Monitoring of Chimpanzee (Pan troglodytes Habitats in Africa

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Samuel M. Jantz

2016-05-01

Full Text Available All four chimpanzee sub-species populations are declining due to multiple factors including human-caused habitat loss. Effective conservation efforts are therefore needed to ensure their long-term survival. Habitat suitability models serve as useful tools for conservation planning by depicting relative environmental suitability in geographic space over time. Previous studies mapping chimpanzee habitat suitability have been limited to small regions or coarse spatial and temporal resolutions. Here, we used Random Forests regression to downscale a coarse resolution habitat suitability calibration dataset to estimate habitat suitability over the entire chimpanzee range at 30-m resolution. Our model predicted habitat suitability well with an r2 of 0.82 (±0.002 based on 50-fold cross validation where 75% of the data was used for model calibration and 25% for model testing; however, there was considerable variation in the predictive capability among the four sub-species modeled individually. We tested the influence of several variables derived from Landsat Enhanced Thematic Mapper Plus (ETM+ that included metrics of forest canopy and structure for four three-year time periods between 2000 and 2012. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. Because the models were sensitive to such temporally based predictors, our results are the first to highlight the value of integrating continuously updated variables derived from satellite remote sensing into temporally dynamic habitat suitability models to support  near real-time monitoring of habitat status and decision support systems.

Landsat Thematic Mapper digital information content for agricultural environments

Science.gov (United States)

Haack, Barry; Bryant, Nevin; Adams, Steven

1986-01-01

Landsat Thematic Mapper (TM) data collected for Imperial Valley, California in December, 1982 were digitally examined to assess their utility to distinguish among agricultural and other land-covers. Statistics for thirty-seven training sites representing a variety of crops plus urban, water and desert land-covers were obtained and analyzed using transformed divergence (TD) calculations. TD values were employed to assess intraclass variability and the best bands for classification. Four subscenes were selected for clustering or unsupervised signature extraction. These areas were agriculture, urban, desert and water land-covers. The number of clusters for these subscenes were examined and the best TM bands for interclass separability were identified. The results of the clustering and training site analyses for interclass separability were compared. The TM data were useful for the digital delimitation of most crops and other cover types in this analysis. Four bands of data are adequate for classification with the best results obtained by the selection of one band from each of the available portions of the electromagnetic spectrum. Different band combinations are best for various land-cover intraclass separability.

Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks.

Science.gov (United States)

Liu, Liangyun; Peng, Dailiang; Wang, Zhihui; Hu, Yong

2014-11-01

China maintains the largest artificial forest area in the world. Studying the dynamic variation of forest biomass and carbon stock is important to the sustainable use of forest resources and understanding of the artificial forest carbon budget in China. In this study, we investigated the potential of Landsat time series stacks for aboveground biomass (AGB) estimation in Yulin District, a key region of the Three-North Shelter region of China. Firstly, the afforestation age was successfully retrieved from the Landsat time series stacks in the last 40 years (from 1974 to 2013) and shown to be consistent with the surveyed tree ages, with a root-mean-square error (RMSE) value of 4.32 years and a determination coefficient (R (2)) of 0.824. Then, the AGB regression models were successfully developed by integrating vegetation indices and tree age. The simple ratio vegetation index (SR) is the best candidate of the commonly used vegetation indices for estimating forest AGB, and the forest AGB model was significantly improved using the combination of SR and tree age, with R (2) values from 0.50 to 0.727. Finally, the forest AGB images were mapped at eight epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in seven counties of Yulin District increased by 20.8 G kg, from 5.8 G kg in 1986 to 26.6 G kg in 2013, a total increase of 360 %. For the persistent forest area since 1974, the forest AGB density increased from 15.72 t/ha in 1986 to 44.53 t/ha in 2013, with an annual rate of about 0.98 t/ha. For the artificial forest planted after 1974, the AGB density increased about 1.03 t/ha a year from 1974 to 2013. The results present a noticeable carbon increment for the planted artificial forest in Yulin District over the last four decades.

TRAJECTORY ANALYSIS OF FOREST CHANGES IN NORTHERN AREA OF CHANGBAI MOUNTAINS, CHINA FROM LANDSAT TM IMAGE

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F. Huang

2012-07-01

Full Text Available Based on the information from integrated Landsat TM/ETM images and geographic information systems (GIS, using dynamic model, landscape indices and temporal trajectory analysis, spatio-temporal changes in forest in the northern area of Changbai Mountains were investigated in the past 20 years. The results showed that the forests decreased by 141461 ha at the annual decrease rate of 0.19% from 1986 to 2006. The numbers of forest patch increased, while the patch size of forest land declined. Forestland experienced the process of substantial fragmentation. Close forest showed a net reduction of 13.3×104ha. The typical trajectories of forest changes included forestland-forestland-cropland, forestland-cropland-cropland, forestland-forestland-grassland and forestland-cropland-built-up land. The total area of human-induced change is 1.7 times than that of natural change in the study area. Population, cropland area and gross domestic product increased significantly as forests decreased.

Uma biblioteca de pontos de controle para imagens MSS Landsat

OpenAIRE

Fernando Augusto Mitsuo Ii

1985-01-01

O objetivo deste trabalho e desenvolver um sistema para criacao, manutencao e gerenciamento de uma biblioteca de pontos de controle para imagens MSS Landsat. Um ponto de controle e uma caracteristica fisicamente detectavel numa cena, cuja localizacao geodesica e precisamente conhecida. O uso destes pontos e de fundamental importancia num sistema de correcao geometrica de imagens de satelite. A biblioteca permitira que pontos de controle pertencentes a uma dada cena sejam recuperados de uma ma...

A procedure for merging land cover/use data from Landsat, aerial photography, and map sources - Compatibility, accuracy and cost

Science.gov (United States)

Enslin, W. R.; Tilmann, S. E.; Hill-Rowley, R.; Rogers, R. H.

1977-01-01

A method is developed to merge land cover/use data from Landsat, aerial photography and map sources into a grid-based geographic information system. The method basically involves computer-assisted categorization of Landsat data to provide certain user-specified land cover categories; manual interpretation of aerial photography to identify other selected land cover/use categories that cannot be obtained from Landsat data; identification of special features from aerial photography or map sources; merging of the interpreted data from all the sources into a computer compatible file under a standardized coding structure; and the production of land cover/use maps, thematic maps, and tabular data. The specific tasks accomplished in producing the merged land cover/use data file and subsequent output products are identified and discussed. It is shown that effective implementation of the merging method is critically dependent on selecting the 'best' data source for each user-specified category in terms of accuracy and time/cost tradeoffs.

Comparison of MODIS and Landsat TM5 images for mapping tempo-spatial dynamics of Secchi disk depths in Poyang Lake national nature reserve, China

NARCIS (Netherlands)

Wu, G.; Leeuw, de J.; Skidmore, A.K.; Prins, H.H.T.; Liu, Y.

2008-01-01

Landsat has successfully been applied to map Secchi disk depth of inland water bodies. Operational use for monitoring a dynamic variable like Secchi disk depth is however limited by the 16-day overpass cycle of the Landsat system and cloud cover. Low spatial resolution Moderate Resolution Imaging

Are studies reporting significant results more likely to be published?

Science.gov (United States)

Koletsi, Despina; Karagianni, Anthi; Pandis, Nikolaos; Makou, Margarita; Polychronopoulou, Argy; Eliades, Theodore

2009-11-01

Our objective was to assess the hypothesis that there are variations of the proportion of articles reporting a significant effect, with a higher percentage of those articles published in journals with impact factors. The contents of 5 orthodontic journals (American Journal of Orthodontics and Dentofacial Orthopedics, Angle Orthodontist, European Journal of Orthodontics, Journal of Orthodontics, and Orthodontics and Craniofacial Research), published between 2004 and 2008, were hand-searched. Articles with statistical analysis of data were included in the study and classified into 4 categories: behavior and psychology, biomaterials and biomechanics, diagnostic procedures and treatment, and craniofacial growth, morphology, and genetics. In total, 2622 articles were examined, with 1785 included in the analysis. Univariate and multivariate logistic regression analyses were applied with statistical significance as the dependent variable, and whether the journal had an impact factor, the subject, and the year were the independent predictors. A higher percentage of articles showed significant results relative to those without significant associations (on average, 88% vs 12%) for those journals. Overall, these journals published significantly more studies with significant results, ranging from 75% to 90% (P = 0.02). Multivariate modeling showed that journals with impact factors had a 100% increased probability of publishing a statistically significant result compared with journals with no impact factor (odds ratio [OR], 1.99; 95% CI, 1.19-3.31). Compared with articles on biomaterials and biomechanics, all other subject categories showed lower probabilities of significant results. Nonsignificant findings in behavior and psychology and diagnosis and treatment were 1.8 (OR, 1.75; 95% CI, 1.51-2.67) and 3.5 (OR, 3.50; 95% CI, 2.27-5.37) times more likely to be published, respectively. Journals seem to prefer reporting significant results; this might be because of authors

An Object-Based Approach for Fire History Reconstruction by Using Three Generations of Landsat Sensors

Directory of Open Access Journals (Sweden)

Thomas Katagis

2014-06-01

Full Text Available In this study, the capability of geographic object-based image analysis (GEOBIA in the reconstruction of the recent fire history of a typical Mediterranean area was investigated. More specifically, a semi-automated GEOBIA procedure was developed and tested on archived and newly acquired Landsat Multispectral Scanner (MSS, Thematic Mapper (TM, and Operational Land Imager (OLI images in order to accurately map burned areas in the Mediterranean island of Thasos. The developed GEOBIA ruleset was built with the use of the TM image and then applied to the other two images. This process of transferring the ruleset did not require substantial adjustments or any replacement of the initially selected features used for the classification, thus, displaying reduced complexity in processing the images. As a result, burned area maps of very high accuracy (over 94% overall were produced. In addition to the standard error matrix, the employment of additional measures of agreement between the produced maps and the reference data revealed that “spatial misplacement” was the main source of classification error. It can be concluded that the proposed approach can be potentially used for reconstructing the recent (40-year fire history in the Mediterranean, based on extended time series of Landsat or similar data.

Landsat-Based Long-Term Monitoring of Total Suspended Matter Concentration Pattern Change in the Wet Season for Dongting Lake, China

Directory of Open Access Journals (Sweden)

Zhubin Zheng

2015-10-01

Full Text Available Assessing the impacts of environmental change and anthropogenic activities on the historical and current total suspended matter (TSM pattern in Dongting Lake, China, is a large challenge. We addressed this challenge by using more than three decades of Landsat data. Based on in situ measurements, we developed an algorithm based on the near-infrared (NIR band to estimate TSM in Dongting Lake. The algorithm was applied to Landsat images to derive TSM distribution maps from 1978 to 2013 in the wet season, revealing significant inter-annual and spatial variability. The relationship of TSM to water level, precipitation, and wind speed was analyzed, and we found that: (1 sand mining areas usually coincide with regions that have high TSM levels in Dongting Lake; (2 water level and seven-day precipitation were both important to TSM variation, but no significant relationship was found between TSM and wind speed or other meteorological data; (3 the increased level of sand mining in response to rapid economic growth has deeply influenced the TSM pattern since 2000 due to the resuspension of sediment; and (4 TSM variation might be associated with policy changes regarding the management of sand mining; it might also be affected by lower water levels caused by the impoundment of the Three Gorges Dam since 2000.

Electronic spreadsheet to acquire the reflectance from the TM and ETM+ Landsat images

Directory of Open Access Journals (Sweden)

Antonio R. Formaggio

2005-08-01

Full Text Available The reflectance of agricultural cultures and other terrestrial surface "targets" is an intrinsic parameter of these targets, so in many situations, it must be used instead of the values of "gray levels" that is found in the satellite images. In order to get reflectance values, it is necessary to eliminate the atmospheric interference and to make a set of calculations that uses sensor parameters and information regarding the original image. The automation of this procedure has the advantage to speed up the process and to reduce the possibility of errors during calculations. The objective of this paper is to present an electronic spreadsheet that simplifies and automatizes the transformation of the digital numbers of the TM/Landsat-5 and ETM+/Landsat-7 images into reflectance. The method employed for atmospheric correction was the dark object subtraction (DOS. The electronic spreadsheet described here is freely available to users and can be downloaded at the following website: http://www.dsr.inpe.br/Calculo_Reflectancia.xls.

Techniques for the creation of land use maps and tabulations from Landsat imagery

Science.gov (United States)

Angelici, G. L.; Bryant, N. A.

1977-01-01

Methods for creating color thematic maps and land use tabulations, employing both Landsat imagery and computer image processing, are discussed. The system, the Multiple Input Land Use System (MILUS) has been tested in the metropolitan section of Dayton, Ohio. Training areas for land use were first digitized by coordinates and then transformed onto an image of white lines on a black background. This image was added to a Landsat image of the same area. Then multispectral classification was performed. A tape of digitized census tract boundaries was computer interfaced to yield an image of tract boundaries on a background registered to the thematic land-use map. Using a data management system, the data were then used to produce figures for the area and percent of land use in each tract. Future work is expected to convert most of the steps into interactive processing. This would greatly reduce the time needed to edit and register the data sets.

Use of Landsat data in soil and agricultural land use studies

Science.gov (United States)

Westin, F. C.; Brandner, T. M.

1980-01-01

This paper describes how the synoptic, multispectral, and temporal characteristics of Landsat can be used to locate Soil Association boundaries. Then, using these techniques we describe how a low intensity soil survey was conducted and how some interpretive maps were developed from this. Finally, we describe how soil suitability and land use interpretations were made to aid in defining Agrophysical units used in crop inventories.

Analysis of land cover/use changes using Landsat 5 TM data and indices.

Science.gov (United States)

Ettehadi Osgouei, Paria; Kaya, Sinasi

2017-04-01

Urban expansion and unprecedented rural to urban transition, along with a huge population growth, are major driving forces altering land cover/use in metropolitan areas. Many of the land cover classes such as farmlands, wetlands, forests, and bare soils have been transformed during the past years into human settlements. Identification of the city growth trends and the impact of it on the vegetation cover of an area is essential for a better understanding of the sustainability of urban development processes, both planned and unplanned. Analyzing the causes and consequences of land use dynamics helps local government, urban planners, and managers for the betterment of future plans and minimizing the negative effects.This study determined temporal changes in vegetation cover and built-up area in Istanbul (Turkey) using the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and built-up area index (BUAI). The temporal data were based on Landsat 5 Thematic Mapper (TM) images acquired in June of 1984, 2002, 2007, 2009, and 2011. The NDVI was applied to all the Landsat images, and the resulting NDVI images were overlaid to generate an NDVI layer stack image. The same procedure was repeated using the SAVI and BUAI images. The layer stack images revealed those areas that had changed in terms of the different indices over the years. To determine temporal change trends, the values of 150 randomly selected control points were extracted from the same locations in the NDVI, SAVI, and BUAI layer stack images. The results obtained from these control points showed that vegetation cover decreased considerably because of a remarkable increase in the built-up area.

Characterizing spatiotemporal dynamics in phenology of urban ecosystems based on Landsat data

Energy Technology Data Exchange (ETDEWEB)

Li, Xuecao; Zhou, Yuyu; Asrar, Ghassem R.; Meng, Lin

2017-12-01

Seasonal phenology of vegetation plays an important role in global carbon cycle and ecosystem productivity. In urban environments, vegetation phenology is also important because of its influence on public health (e.g., allergies), and energy demand (e.g. cooling effects). In this study, we studied the potential use of remotely sensed observations (i.e. Landsat data) to derive some phenology indicators for vegetation embedded within the urban core domains in four distinctly different U.S. regions (Washington, D.C., King County in Washington, Polk County in Iowa, and Baltimore City and County in Maryland) during the past three decades. We used all available Landsat observations (circa 3000 scenes) from 1982 to 2015 and a self-adjusting double logistic model to detect and quantify the annual change of vegetation phenophases, i.e. indicators of seasonal changes in vegetation. The proposed model can capture and quantify not only phenophases of dense vegetation in rural areas, but also those of mixed vegetation in urban core domains. The derived phenology indicators show a good agreement with similar indicators derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and in situ observations, suggesting that the phenology dynamic depicted by the proposed model is reliable. The vegetation phenology and its seasonal and interannual dynamics demonstrate a distinct spatial pattern in urban domains with an earlier (9–14 days) start-of season (SOS) and a later (13–20 days) end-of season (EOS), resulting in an extended (5–30 days) growing season length (GSL) when compared to the surrounding suburban and rural areas in the four study regions. There is a general long-term trend of decreasing SOS (-0.30 day per year), and increasing EOS and GSL (0.50 and 0.90 day per year, respectively) over past three decades for these study regions. The magnitude of these trends varies among the four urban systems due to their diverse local climate conditions, vegetation

Landsat surface reflectance quality assurance extraction (version 1.7)

Science.gov (United States)

Jones, J.W.; Starbuck, M.J.; Jenkerson, Calli B.

2013-01-01

The U.S. Geological Survey (USGS) Land Remote Sensing Program is developing an operational capability to produce Climate Data Records (CDRs) and Essential Climate Variables (ECVs) from the Landsat Archive to support a wide variety of science and resource management activities from regional to global scale. The USGS Earth Resources Observation and Science (EROS) Center is charged with prototyping systems and software to generate these high-level data products. Various USGS Geographic Science Centers are charged with particular ECV algorithm development and (or) selection as well as the evaluation and application demonstration of various USGS CDRs and ECVs. Because it is a foundation for many other ECVs, the first CDR in development is the Landsat Surface Reflectance Product (LSRP). The LSRP incorporates data quality information in a bit-packed structure that is not readily accessible without postprocessing services performed by the user. This document describes two general methods of LSRP quality-data extraction for use in image processing systems. Helpful hints for the installation and use of software originally developed for manipulation of Hierarchical Data Format (HDF) produced through the National Aeronautics and Space Administration (NASA) Earth Observing System are first provided for users who wish to extract quality data into separate HDF files. Next, steps follow to incorporate these extracted data into an image processing system. Finally, an alternative example is illustrated in which the data are extracted within a particular image processing system.

Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images.

Science.gov (United States)

Zhou, Yuting; Xiao, Xiangming; Qin, Yuanwei; Dong, Jinwei; Zhang, Geli; Kou, Weili; Jin, Cui; Wang, Jie; Li, Xiangping

2016-04-01

Accurate and up-to-date information on the spatial distribution of paddy rice fields is necessary for the studies of trace gas emissions, water source management, and food security. The phenology-based paddy rice mapping algorithm, which identifies the unique flooding stage of paddy rice, has been widely used. However, identification and mapping of paddy rice in rice-wetland coexistent areas is still a challenging task. In this study, we found that the flooding/transplanting periods of paddy rice and natural wetlands were different. The natural wetlands flood earlier and have a shorter duration than paddy rice in the Panjin Plain, a temperate region in China. We used this asynchronous flooding stage to extract the paddy rice planting area from the rice-wetland coexistent area. MODIS Land Surface Temperature (LST) data was used to derive the temperature-defined plant growing season. Landsat 8 OLI imagery was used to detect the flooding signal and then paddy rice was extracted using the difference in flooding stages between paddy rice and natural wetlands. The resultant paddy rice map was evaluated with in-situ ground-truth data and Google Earth images. The estimated overall accuracy and Kappa coefficient were 95% and 0.90, respectively. The spatial pattern of OLI-derived paddy rice map agrees well with the paddy rice layer from the National Land Cover Dataset from 2010 (NLCD-2010). The differences between Rice Landsat and Rice NLCD are in the range of ±20% for most 1-km grid cell. The results of this study demonstrate the potential of the phenology-based paddy rice mapping algorithm, via integrating MODIS and Landsat 8 OLI images, to map paddy rice fields in complex landscapes of paddy rice and natural wetland in the temperate region.

Using space-time features to improve detection of forest disturbances from Landsat time series

NARCIS (Netherlands)

Hamunyela, E.; Reiche, J.; Verbesselt, J.; Herold, M.

2017-01-01

Current research on forest change monitoring using medium spatial resolution Landsat satellite data aims for accurate and timely detection of forest disturbances. However, producing forest disturbance maps that have both high spatial and temporal accuracy is still challenging because of the

Change detection of bare areas in the Xolobeni region, South Africa using Landsat NDVI

CSIR Research Space (South Africa)

Singh, RG

2015-06-01

Full Text Available to provide some information on the inter-relationship between vegetated classes and bare areas. Normalised Difference Vegetation Index (NDVI) data derived from multi-temporal Landsat 5 imagery has formed the baseline information for this study. A density...

Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data.

Science.gov (United States)

Garcia, Mariano; Saatchi, Sassan; Casas, Angeles; Koltunov, Alexander; Ustin, Susan; Ramirez, Carlos; Garcia-Gutierrez, Jorge; Balzter, Heiko

2017-02-01

Quantifying biomass consumption and carbon release is critical to understanding the role of fires in the carbon cycle and air quality. We present a methodology to estimate the biomass consumed and the carbon released by the California Rim fire by integrating postfire airborne LiDAR and multitemporal Landsat Operational Land Imager (OLI) imagery. First, a support vector regression (SVR) model was trained to estimate the aboveground biomass (AGB) from LiDAR-derived metrics over the unburned area. The selected model estimated AGB with an R 2 of 0.82 and RMSE of 59.98 Mg/ha. Second, LiDAR-based biomass estimates were extrapolated to the entire area before and after the fire, using Landsat OLI reflectance bands, Normalized Difference Infrared Index, and the elevation derived from LiDAR data. The extrapolation was performed using SVR models that resulted in R 2 of 0.73 and 0.79 and RMSE of 87.18 (Mg/ha) and 75.43 (Mg/ha) for the postfire and prefire images, respectively. After removing bias from the AGB extrapolations using a linear relationship between estimated and observed values, we estimated the biomass consumption from postfire LiDAR and prefire Landsat maps to be 6.58 ± 0.03 Tg (10 12  g), which translate into 12.06 ± 0.06 Tg CO2 e released to the atmosphere, equivalent to the annual emissions of 2.57 million cars.

Evaluation of LANDSAT-2 (ERTS) images applied to geologic structures and mineral resources of South America. [Salar de Coposa, Chile and Salar of Uyuni, Bolivia

Science.gov (United States)

Carter, W. D. (Principal Investigator); Kowalik, W. S.

1976-01-01

The author has identified the following significant results. The Salar of Coposa is located in northern Chile along the frontier with Bolivia. The surface was divided into six general classes of materials. Analysis of LANDSAT image 1243-14001 by use of interactive multispectral computer (Image 100) enabled accurate repetition of these general classes based on reflectance. The Salar of Uyuni is the largest of the South American evaporite deposits. Using image 1243-13595, and parallel piped computer classification of reflectance units, the Salar was divided into nine classes ranging from deep to shallow water, water over salt, salt saturated with water, and several classes of dry salt.

ShapeSelectForest: a new r package for modeling landsat time series

Science.gov (United States)

Mary Meyer; Xiyue Liao; Gretchen Moisen; Elizabeth Freeman

2015-01-01

We present a new R package called ShapeSelectForest recently posted to the Comprehensive R Archival Network. The package was developed to fit nonparametric shape-restricted regression splines to time series of Landsat imagery for the purpose of modeling, mapping, and monitoring annual forest disturbance dynamics over nearly three decades. For each pixel and spectral...

Ten Years of Forest Cover Change in the Sierra Nevada Detected Using Landsat Satellite Image Analysis

Science.gov (United States)

Potter, Christopher S.

2014-01-01

A detailed geographic record of recent vegetation regrowth and disturbance patterns in forests of the Sierra Nevada remains a gap that can be filled with remote sensing data. Landsat (TM) imagery was analyzed to detect 10 years of recent changes (between 2000 and 2009) in forest vegetation cover for areas burned by wildfires between years of 1995 to 1999 in the region. Results confirmed the prevalence of regrowing forest vegetation during the period 2000 and 2009 over 17% of the combined burned areas.

Landsat and Sentinel-2A Surface Albedo Estimation and Evaluation Against In Situ Measurements Across the US SURFRAD Network

Science.gov (United States)

Franch, B.; Skakun, S.; Vermote, E.; Roger, J. C.

2017-12-01

Surface albedo is an essential parameter not only for developing climate models, but also for most energy balance studies. While climate models are usually applied at coarse resolution, the energy balance studies, which are mainly focused on agricultural applications, require a high spatial resolution. The albedo, estimated through the angular integration of the BRDF, requires an appropriate angular sampling of the surface. However, Sentinel-2A sampling characteristics, with nearly constant observation geometry and low illumination variation, prevent from deriving a surface albedo product. In this work, we apply an algorithm developed to derive a Landsat surface albedo to Sentinel-2A. It is based on the BRDF parameters estimated from the MODerate Resolution Imaging Spectroradiometer (MODIS) CMG surface reflectance product (M{O,Y}D09) using the VJB method (Vermote et al., 2009). Sentinel-2A unsupervised classification images are used to disaggregate the BRDF parameters to the Sentinel-2 spatial resolution. We test the results over five different sites of the US SURFRAD network and plot the results versus albedo field measurements. Additionally, we also test this methodology using Landsat-8 images.

LBA-ECO LC-01 Landsat TM Land Use/Land Cover, Northern Ecuadorian Amazon: 1986-1999

Data.gov (United States)

National Aeronautics and Space Administration — ABSTRACT: This data set contains Landsat TM imagery for the years 1986, 1989, 1996, and 1999, that have been classified into four land use/land cover (LULC) classes:...

LBA-ECO LC-01 Landsat TM Land Use/Land Cover, Northern Ecuadorian Amazon: 1986-1999

Data.gov (United States)

National Aeronautics and Space Administration — This data set contains Landsat TM imagery for the years 1986, 1989, 1996, and 1999, that have been classified into four land use/land cover (LULC) classes: Forest,...

Water Productivity Mapping (WPM Using Landsat ETM+ Data for the Irrigated Croplands of the Syrdarya River Basin in Central Asia

Directory of Open Access Journals (Sweden)

Sabirjan Isaev

2008-12-01

Full Text Available The overarching goal of this paper was to espouse methods and protocols for water productivity mapping (WPM using high spatial resolution Landsat remote sensing data. In a world where land and water for agriculture are becoming increasingly scarce, growing “more crop per drop” (increasing water productivity becomes crucial for food security of future generations. The study used time-series Landsat ETM+ data to produce WPMs of irrigated crops, with emphasis on cotton in the Galaba study area in the Syrdarya river basin of Central Asia. The WPM methods and protocols using remote sensing data consisted of: (1 crop productivity (ton/ha maps (CPMs involvingcrop type classification, crop yield and biophysical modeling, and extrapolating yield models to larger areas using remotely sensed data; (2 crop water use (m3/ha maps (WUMs (or actual seasonal evapotranspiration or actual ET developed through Simplified Surface Energy Balance (SSEB model; and (3 water productivity (kg/m3 maps (WPMs produced by dividing raster layers of CPMs by WUMs. The SSEB model calculated WUMs (actual ET by multiplying the ET fractionby reference ET. The ETfraction was determined using Landsat thermal imagery by selecting the “hot” pixels (zero ET and “cold” pixels (maximum ET. The grass reference ET was calculated by FAO Penman-Monteith method using meteorological data. The WPMs for the Galaba study area demonstrated a wide variations (0-0.54 kg/m3 in water productivity of cotton fields with overwhelming proportion (87% of the area having WP less than 0.30 kg/m3, 11% of the area having WP in range of 0.30-0.36 kg/m3, and only 2% of the area with WP greater than 0.36 kg/m3. These results clearly imply that there are opportunities for significant WP increases in overwhelming proportion of the existing croplands. The areas of low WP are spatially pin-pointed and can be used as focus for WP improvements

Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)

Science.gov (United States)

Hansen, M.C.; Egorov, Alexey; Potapov, P.V.; Stehman, S.V.; Tyukavina, A.; Turubanova, S.A.; Roy, David P.; Goetz, S.J.; Loveland, Thomas R.; Ju, J.; Kommareddy, A.; Kovalskyy, Valeriy; Forsyth, C.; Bents, T.

2014-01-01

Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by

Burned areas for the conterminous U.S. from 1984 through 2015, an automated approach using dense time-series of Landsat data

Science.gov (United States)

Hawbaker, T. J.; Vanderhoof, M.; Beal, Y. J. G.; Takacs, J. D.; Schmidt, G.; Falgout, J.; Brunner, N. M.; Caldwell, M. K.; Picotte, J. J.; Howard, S. M.; Stitt, S.; Dwyer, J. L.

2016-12-01

Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, many existing fire datasets in the United States are known to be incomplete and that complicates efforts to understand burned area patterns and introduces a large amount of uncertainty in efforts to identify their driving processes and impacts. Because of this, the need to systematically collect burned area information has been recognized by the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change, which have both called for the production of essential climate variables. To help meet this need, we developed a novel algorithm that automatically identifies burned areas in temporally-dense time series of Landsat image stacks to produce Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm makes use of predictors derived from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference predictors. Outputs of the BAECV algorithm, generated for the conterminous United States for 1984 through 2015, consist of burn probabilities for each Landsat scene, in addition to, annual composites including: the maximum burn probability, burn classification, and the Julian date of the first Landsat scene a burn was observed. The BAECV products document patterns of fire occurrence that are not well characterized by existing fire datasets in the United States. We anticipate that these data could help to better understand past patterns of fire occurrence, the drivers that created them, and the impacts fires had on natural and human systems.

SEDIMENT ANALYSIS NETWORK FOR DECISION SUPPORT (SANDS) LANDSAT GEOLOGICAL SURVEY OF AL (GSA) ANALYSIS V1

Data.gov (United States)

National Aeronautics and Space Administration — The Sediment Analysis Network for Decision Support (SANDS) Landsat Geological Survey of AL (GSA) Analysis dataset analyzed changes in the coastal shoreline and...

Ability of LANDSAT-8 Oli Derived Texture Metrics in Estimating Aboveground Carbon Stocks of Coppice Oak Forests

Science.gov (United States)

Safari, A.; Sohrabi, H.

2016-06-01

The role of forests as a reservoir for carbon has prompted the need for timely and reliable estimation of aboveground carbon stocks. Since measurement of aboveground carbon stocks of forests is a destructive, costly and time-consuming activity, aerial and satellite remote sensing techniques have gained many attentions in this field. Despite the fact that using aerial data for predicting aboveground carbon stocks has been proved as a highly accurate method, there are challenges related to high acquisition costs, small area coverage, and limited availability of these data. These challenges are more critical for non-commercial forests located in low-income countries. Landsat program provides repetitive acquisition of high-resolution multispectral data, which are freely available. The aim of this study was to assess the potential of multispectral Landsat 8 Operational Land Imager (OLI) derived texture metrics in quantifying aboveground carbon stocks of coppice Oak forests in Zagros Mountains, Iran. We used four different window sizes (3×3, 5×5, 7×7, and 9×9), and four different offsets ([0,1], [1,1], [1,0], and [1,-1]) to derive nine texture metrics (angular second moment, contrast, correlation, dissimilar, entropy, homogeneity, inverse difference, mean, and variance) from four bands (blue, green, red, and infrared). Totally, 124 sample plots in two different forests were measured and carbon was calculated using species-specific allometric models. Stepwise regression analysis was applied to estimate biomass from derived metrics. Results showed that, in general, larger size of window for deriving texture metrics resulted models with better fitting parameters. In addition, the correlation of the spectral bands for deriving texture metrics in regression models was ranked as b4>b3>b2>b5. The best offset was [1,-1]. Amongst the different metrics, mean and entropy were entered in most of the regression models. Overall, different models based on derived texture metrics

LANDSAT remote sensing: observations of an Appalachian mountaintop surface coal mining and reclamation operation

International Nuclear Information System (INIS)

1979-10-01

The potential benefits of using LANDSAT remote sensing data by state agencies as an aide in monitoring surface coal mining operations are reviewed. A mountaintop surface mine in eastern Kentucky was surveyed over a 5 year period using satellite multispectral scanner data that were classified by computer analyses. The analyses were guided by aerial photography and by ground surveys of the surface mines procured in 1976. The application of the LANDSAT data indicates that: (1) computer classification of the various landcover categories provides information for monitoring the progress of surface mining and reclamation operations, (2) successive yearly changes in barren and revegetated areas can be qualitatively assessed for surface mines of 100 acres or more of disrupted area, (3) barren areas consisting of limestone and shale mixtures may be recognized, and revegetated areas in various stages of growth may be identified against the hilly forest background

Three-dimensional displays for natural hazards analysis, using classified Landsat Thematic Mapper digital data and large-scale digital elevation models

Science.gov (United States)

Butler, David R.; Walsh, Stephen J.; Brown, Daniel G.

1991-01-01

Methods are described for using Landsat Thematic Mapper digital data and digital elevation models for the display of natural hazard sites in a mountainous region of northwestern Montana, USA. Hazard zones can be easily identified on the three-dimensional images. Proximity of facilities such as highways and building locations to hazard sites can also be easily displayed. A temporal sequence of Landsat TM (or similar) satellite data sets could also be used to display landscape changes associated with dynamic natural hazard processes.

Analysis of Thermal Structure of Arctic Lakes at Local and Regional Scales Using in Situ and Multidate Landsat-8 Data

Science.gov (United States)

Huang, Yan; Liu, Hongxing; Hinkel, Kenneth; Yu, Bailang; Beck, Richard; Wu, Jianping

2017-11-01

The Arctic coastal plain is covered with numerous thermokarst lakes. These lakes are closely linked to climate and environmental change through their heat and water budgets. We examined the intralake thermal structure at the local scale and investigated the water temperature pattern of lakes at the regional scale by utilizing extensive in situ measurements and multidate Landsat-8 remote sensing data. Our analysis indicates that the lake skin temperatures derived from satellite thermal sensors during most of the ice-free summer period effectively represent the lake bulk temperature because the lakes are typically well-mixed and without significant vertical stratification. With the relatively high-resolution Landsat-8 thermal data, we were able to quantitatively examine intralake lateral temperature differences and gradients in relation to geographical location, topography, meteorological factors, and lake morphometry for the first time. Our results suggest that wind speed and direction not only control the vertical stratification but also influences lateral differences and gradients of lake surface temperature. Wind can considerably reduce the intralake temperature gradient. Interestingly, we found that geographical location (latitude, longitude, distance to the ocean) and lake morphometry (surface size, depth, volume) not only control lake temperature regionally but also affect the lateral temperature gradient and homogeneity level within each individual lake. For the Arctic coastal plain, at regional scales, inland and southern lakes tend to have larger horizontal temperature differences and gradients compared to coastal and northern lakes. At local scales, large and shallow lakes tend to have large lateral temperature differences relative to small and deep lakes.

55-year (1960-2015) spatiotemporal shoreline change analysis using historical DISP and Landsat time series data in Shanghai

Science.gov (United States)

Qiao, Gang; Mi, Huan; Wang, Weian; Tong, Xiaohua; Li, Zhongbin; Li, Tan; Liu, Shijie; Hong, Yang

2018-06-01

Shoreline change has been an increasing concern for low-lying and vulnerable coastal zones worldwide, especially in estuarine delta regions, which generally have significant economic development, large human settlements and infrastructures. Thus, long time-series shoreline change data are useful for understanding how shorelines respond to natural and anthropogenic activities, as well as for providing greater insights into coastal protection and sustainable development in the future. For the first time, this study analyzes 55 years of spatiotemporal shoreline changes in Shanghai, China, by integrating the historical Declassified Intelligence Satellite Photography (DISP) and Landsat time series data at five-year intervals from 1960 to 2015. Twelve shorelines were interpreted from DISP and Landsat images. The spatiotemporal changes in the shorelines were explored at five-year intervals within the study period for the Shanghai mainland and islands. The results indicate that shorelines in Shanghai accreted significantly over the last 55 years, but different accretion patterns were observed in Chongming Dongtan. The rate of shoreline change varied in different areas, and the most noticeable expansions were Chongming Beitan, Chongming Dongtan, Hengsha Dongtan, and Nanhuizui. The length of the entire shoreline increased by 25.7% from 472.6 km in 1960 to 594.2 km in 2015. Due to the shoreline changes, the Shanghai area expanded by 1,192.5 km2 by 2015, which was an increase of 19.9% relative to its 1960 area. The Digital Shoreline Analysis System (DSAS) was used to compute rate-of-change statistics. Between 1960 and 2015, 10.6% of the total transects exceeded 3 km of Net Shoreline Movement (NSM), with a maximum value of approximately 20 km at eastern Hengsha Island. The average Weighted Linear Regression Rate (WLR) of the Shanghai shoreline was 52.2 m/yr from 1960 to 2015; there was 94.1% accretion, 3.1% erosion, and 2.8% with no significant change. In addition, the driving

Regional and landscape-scale variability of Landsat-observed vegetation dynamics in northwest Siberian tundra

International Nuclear Information System (INIS)

Frost, Gerald V; Epstein, Howard E; Walker, Donald A

2014-01-01

Widespread increases in Arctic tundra productivity have been documented for decades using coarse-scale satellite observations, but finer-scale observations indicate that changes have been very uneven, with a high degree of landscape- and regional-scale heterogeneity. Here we analyze time-series of the Normalized Difference Vegetation Index (NDVI) observed by Landsat (1984–2012), to assess landscape- and regional-scale variability of tundra vegetation dynamics in the northwest Siberian Low Arctic, a little-studied region with varied soils, landscape histories, and permafrost attributes. We also estimate spatio-temporal rates of land-cover change associated with expansion of tall alder (Alnus) shrublands, by integrating Landsat time-series with very-high-resolution imagery dating to the mid-1960s. We compiled Landsat time-series for eleven widely-distributed landscapes, and performed linear regression of NDVI values on a per-pixel basis. We found positive net NDVI trends (‘greening’) in nine of eleven landscapes. Net greening occurred in alder shrublands in all landscapes, and strong greening tended to correspond to shrublands that developed since the 1960s. Much of the spatial variability of greening within landscapes was linked to landscape physiography and permafrost attributes, while between-landscape variability largely corresponded to differences in surficial geology. We conclude that continued increases in tundra productivity in the region are likely in upland tundra landscapes with fine-textured, cryoturbated soils; these areas currently tend to support discontinuous vegetation cover, but are highly susceptible to rapid increases in vegetation cover, as well as land-cover changes associated with the development of tall shrublands. (paper)

Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat TM data

Science.gov (United States)

Sader, Steven A.; Waide, Robert B.; Lawrence, William T.; Joyce, Armond T.

1989-01-01

Forest stand structure and biomass data were collected using conventional forest inventory techniques in tropical, subtropical, and warm temperate forest biomes. The feasibility of detecting tropical forest successional age class and total biomass differences using Landsat-Thematic mapper (TM) data, was evaluated. The Normalized Difference Vegetation Index (NDVI) calculated from Landsat-TM data were not significantly correlated with forest regeneration age classes in the mountain terrain of the Luquillo Experimental Forest, Puerto Rico. The low sun angle and shadows cast on steep north and west facing slopes reduced spectral reflectance values recorded by TM orbital altitude. The NDVI, calculated from low altitude aircraft scanner data, was significatly correlated with forest age classes. However, analysis of variance suggested that NDVI differences were not detectable for successional forests older than approximately 15-20 years. Also, biomass differences in young successional tropical forest were not detectable using the NDVI. The vegetation index does not appear to be a good predictor of stand structure variables (e.g., height, diameter of main stem) or total biomass in uneven age, mixed broadleaf forest. Good correlation between the vegetation index and low biomass in even age pine plantations were achieved for a warm temperate study site. The implications of the study for the use of NDVI for forest structure and biomass estimation are discussed.

THE ANALYSIS OF MOISTURE DEFICIT BASED ON MODIS AND LANDSAT SATELLITE IMAGES. CASE STUDY: THE OLTENIA PLAIN

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ONȚEL IRINA

2014-03-01

Full Text Available Satellite images are an important source of information to identify and analyse some hazardous climatic phenomena such as the dryness and drought. These phenomena are characterized by scarce rainfall, increased evapotranspiration and high soil moisture deficit. The soil water reserve depletes to the wilting coefficient, soon followed by the pedological drought which has negative effects on vegetation and agricultural productivity. The MODIS satellite images (Moderate Resolution Imaging Spectroradiometer allow the monitoring of the vegetation throughout the entire vegetative period, with a frequency of 1-2 days and with a spatial resolution of 250 m, 500 m and 1 km away. Another useful source of information is the LANDSAT satellite images, with a spatial resolution of 30 m. Based on MODIS and Landsat satellite images, were calculated moisture monitoring index such as SIWSI (Shortwave Infrared Water Stress Index. Consequently, some years with low moisture such as 2000, 2002, 2007 and 2012 could be identified. Spatially, the areas with moisture deficit varied from one year to another all over the whole analised period (2000-2012. The remote sensing results was corelated with Standard Precipitation Anomaly, which gives a measure of the severity of a wet or dry event.

LANDSAT/MMS propulsion module design. Tas4.4: Concept design

Science.gov (United States)

Mansfield, J. M.; Etheridge, F. G.; Indrikis, J.

1976-01-01

Evaluations are presented of alternative LANDSAT follow-on launch configurations to derive the propulsion requirements for the multimission modular spacecraft (MMS). Two basic types were analyzed including use of conventional launch vehicles and shuttle supported missions. It was concluded that two sizes of modular hydrazine propulsion modules would provide the most cost-effective combination for future missions of this spacecraft. Conceptual designs of the selected propulsion modules were performed to the depth permitting determination of mass properties and estimated costs.

Landsat-based trend analysis of lake dynamics across northern permafrost regions

Science.gov (United States)

Nitze, Ingmar; Grosse, Guido; Jones, Benjamin M.; Arp, Christopher D.; Ulrich, Mathias; Federov, Alexander; Veremeeva, Alexandra

2017-01-01

Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions. Here we present, a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM,ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (-0.69%), Western Alaska (-2.82%), and Kolyma Lowland (-0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e. upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region.

Extent and Area of Swidden in Montane Mainland Southeast Asia: Estimation by Multi-Step Thresholds with Landsat-8 OLI Data

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Peng Li

2016-01-01

Full Text Available Information on the distribution, area and extent of swidden agriculture landscape is necessary for implementing the program of Reducing Emissions from Deforestation and Forest Degradation (REDD, biodiversity conservation and local livelihood improvement. To our knowledge, explicit spatial maps and accurate area data on swidden agriculture remain surprisingly lacking. However, this traditional farming practice has been transforming into other profit-driven land use, like tree plantations and permanent cash agriculture. Swidden agriculture is characterized by a rotational and dynamic nature of agroforestry, with land cover changing from natural forests, newly-cleared swiddens to different-aged fallows. The Operational Land Imager (OLI onboard the Landsat-8 satellite has visible, near-infrared and shortwave infrared bands, which are sensitive to the changes in vegetation cover, land surface moisture content and soil exposure, and therefore, four vegetation indices (VIs were calculated, including the Normalized Difference Vegetation Index (NDVI, the Normalized Difference Moisture Index (NDMI, the Normalized Burn Ratio (NBR and the Soil Adjusted Vegetation Index (SAVI. In this study, we developed a multi-step threshold approach that uses a combination of thresholds of four VIs and local elevation range (LER and applied it to detect and map newly-opened swiddens and different-aged fallows using OLI imagery acquired between 2013 and 2015. The resultant Landsat-derived swidden agriculture maps have high accuracy with an overall accuracy of 86.9% and a Kappa coefficient of 0.864. The results of this study indicated that the Landsat-based multi-step threshold algorithms could potentially be applied to monitor the long-term change pattern of swidden agriculture in montane mainland Southeast Asia since the late 1980s and also in other tropical regions, like insular Southeast Asia, South Asia, Latin America and Central Africa, where swidden agriculture is

Landsat thematic mapper (TM) soil variability analysis over Webster County, Iowa

Science.gov (United States)

Thompson, D. R.; Henderson, K. E.; Pitts, D. E.

1984-01-01

Thematic mapper simulator (TMS) data acquired June 7, June 23, and July 31, 1982, and Landsat thematic mapper (TM) data acquired August 2, September 3, and October 21, 1982, over Webster County, Iowa, were examined for within-field soil effects on corn and soybean spectral signatures. It was found that patterns displayed on various computer-generated map products were in close agreement with the detailed soil survey of the area. The difference in spectral values appears to be due to a combination of subtle soil properties and crop growth patterns resulting from the different soil properties. Bands 4 (0.76-.90 micron), 5 (1.55-1.75 micron), and 7 (2.08-2.35 micron) were found to be responding to the within-field soil variability even with increasing ground cover. While these results are preliminary, they do indicate that the soil influence on the vegetation is being detected by TM and should provide improved information relating to crop and soil properties.

Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

Directory of Open Access Journals (Sweden)

Dengsheng Lu

2012-01-01

Full Text Available Landsat Thematic mapper (TM image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.

Ten Years of Land Cover Change on the California Coast Detected using Landsat Satellite Image Analysis

Science.gov (United States)

Potter, Christopher S.

2013-01-01

Landsat satellite imagery was analyzed to generate a detailed record of 10 years of vegetation disturbance and regrowth for Pacific coastal areas of Marin and San Francisco Counties. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) methodology, a transformation of Tasseled-Cap data space, was applied to detected changes in perennial coastal shrubland, woodland, and forest cover from 1999 to 2009. Results showed several principal points of interest, within which extensive contiguous areas of similar LEDAPS vegetation change (either disturbed or restored) were detected. Regrowth areas were delineated as burned forest areas in the Point Reyes National Seashore (PRNS) from the 1995 Vision Fire. LEDAPS-detected disturbance patterns on Inverness Ridge, PRNS in areas observed with dieback of tanoak and bay laurel trees was consistent with defoliation by sudden oak death (Phytophthora ramorum). LEDAPS regrowth pixels were detected over much of the predominantly grassland/herbaceous cover of the Olema Valley ranchland near PRNS. Extensive restoration of perennial vegetation cover on Crissy Field, Baker Beach and Lobos Creek dunes in San Francisco was identified. Based on these examples, the LEDAPS methodology will be capable of fulfilling much of the need for continual, low-cost monitoring of emerging changes to coastal ecosystems.

Land and Land-use Change in the Climate Sensitive High Plains: An Automated Approach with Landsat

Science.gov (United States)

Goetz, Alexander F.; Williams, D. L. (Technical Monitor)

2002-01-01

The High Plains is an economically important and climatologically sensitive region of the United States and Canada. The High Plains contain 100,000 sq km of Holocene sand dunes and sand sheets that are currently stabilized by natural vegetation. Droughts and the larger threat of global warming are climate phenomena that could cause depletion of natural vegetation and make this region susceptible to sand dune reactivation. The original proposal was directed toward the use of Landsat TM data to establish the state and ongoing changes of the surface in the 1.2 million sq. km, semi-arid High Plains region of the central US, A key objective was to develop a model to predict the reactivation of the 100,000 sq. km of Holocene dunes found on the High Plains during an extended drought. At least one Landsat 5 image per year for 1985, 1988 and 1996 was obtained for 32 scenes on the High Plains to coincide with wet and dry years. Additional Landsat 7 data were acquired for 1999 and 2000 primarily for Colorado and Nebraska. As luck would have it, there was no severe drought during the study period 1985-2000. Attention was focused on developing methods for mapping dry vs. green vegetation on sparsely vegetated rangelands in sandy soils, since these were the areas most susceptible to surface reactivation during a drought.

Wetland fire scar monitoring and analysis using archival Landsat data for the Everglades

Science.gov (United States)

Jones, John W.; Hall, Annette E.; Foster, Ann M.; Smith, Thomas J.

2013-01-01

The ability to document the frequency, extent, and severity of fires in wetlands, as well as the dynamics of post-fire wetland land cover, informs fire and wetland science, resource management, and ecosystem protection. Available information on Everglades burn history has been based on field data collection methods that evolved through time and differ by land management unit. Our objectives were to (1) design and test broadly applicable and repeatable metrics of not only fire scar delineation but also post-fire land cover dynamics through exhaustive use of the Landsat satellite data archives, and then (2) explore how those metrics relate to various hydrologic and anthropogenic factors that may influence post-fire land cover dynamics. Visual interpretation of every Landsat scene collected over the study region during the study time frame produced a new, detailed database of burn scars greater than 1.6 ha in size in the Water Conservation Areas and post-fire land cover dynamics for Everglades National Park fires greater than 1.6 ha in area. Median burn areas were compared across several landscape units of the Greater Everglades and found to differ as a function of administrative unit and fire history. Some burned areas transitioned to open water, exhibiting water depths and dynamics that support transition mechanisms proposed in the literature. Classification tree techniques showed that time to green-up and return to pre-burn character were largely explained by fire management practices and hydrology. Broadly applicable as they use data from the global, nearly 30-year-old Landsat archive, these methods for documenting wetland burn extent and post-fire land cover change enable cost-effective collection of new data on wetland fire ecology and independent assessment of fire management practice effectiveness.

IceTrendr: a linear time-series approach to monitoring glacier environments using Landsat

Science.gov (United States)

Nelson, P.; Kennedy, R. E.; Nolin, A. W.; Hughes, J. M.; Braaten, J.

2017-12-01

Arctic glaciers in Alaska and Canada have experienced some of the greatest ice mass loss of any region in recent decades. A challenge to understanding these changing ecosystems, however, is developing globally-consistent, multi-decadal monitoring of glacier ice. We present a toolset and approach that captures, labels, and maps glacier change for use in climate science, hydrology, and Earth science education using Landsat Time Series (LTS). The core step is "temporal segmentation," wherein a yearly LTS is cleaned using pre-processing steps, converted to a snow/ice index, and then simplified into the salient shape of the change trajectory ("temporal signature") using linear segmentation. Such signatures can be characterized as simple `stable' or `transition of glacier ice to rock' to more complex multi-year changes like `transition of glacier ice to debris-covered glacier ice to open water to bare rock to vegetation'. This pilot study demonstrates the potential for interactively mapping, visualizing, and labeling glacier changes. What is truly innovative is that IceTrendr not only maps the changes but also uses expert knowledge to label the changes and such labels can be applied to other glaciers exhibiting statistically similar temporal signatures. Our key findings are that the IceTrendr concept and software can provide important functionality for glaciologists and educators interested in studying glacier changes during the Landsat TM timeframe (1984-present). Issues of concern with using dense Landsat time-series approaches for glacier monitoring include many missing images during the period 1984-1995 and that automated cloud mask are challenged and require the user to manually identify cloud-free images. IceTrendr is much more than just a simple "then and now" approach to glacier mapping. This process is a means of integrating the power of computing, remote sensing, and expert knowledge to "tell the story" of glacier changes.

Mapping 2000 2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

Science.gov (United States)

Wang, Panshi; Huang, Chengquan; Brown De Colstoun, Eric C.

2017-01-01

Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 200-02010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approach An independent validation dataset was also developed using Google Earth imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 +/- 7.84 sq km.

A Landsat-Based Assessment of Mobile Bay Land Use and Land Cover Change from 1974 to 2008

Science.gov (United States)

Spruce, Joseph; Ellis, Jean; Smoot, James; Swann, Roberta; Graham, William

2009-01-01

The Mobile Bay region has experienced noteworthy land use and land cover (LULC) change in the latter half of the 20th century. Accompanying this change has been urban expansion and a reduction of rural land uses. Much of this LULC change has reportedly occurred since the landfall of Hurricane Frederic in 1979. The Mobile Bay region provides great economic and ecologic benefits to the Nation, including important coastal habitat for a broad diversity of fisheries and wildlife. Regional urbanization threatens the estuary s water quality and aquatic-habitat dependent biota, including commercial fisheries and avian wildlife. Coastal conservation and urban land use planners require additional information on historical LULC change to support coastal habitat restoration and resiliency management efforts. This presentation discusses results of a Gulf of Mexico Application Pilot project that was conducted in 2008 to quantify and assess LULC change from 1974 to 2008. This project was led by NASA Stennis Space Center and involved multiple Gulf of Mexico Alliance (GOMA) partners, including the Mobile Bay National Estuary Program (NEP), the U.S. Army Corps of Engineers, the National Oceanic and Atmospheric Administration s (NOAA s) National Coastal Data Development Center (NCDDC), and the NOAA Coastal Services Center. Nine Landsat images were employed to compute LULC products because of their availability and suitability for the application. The project also used Landsat-based national LULC products, including coastal LULC products from NOAA s Coastal Change & Analysis Program (C-CAP), available at 5-year intervals since 1995. Our study was initiated in part because C-CAP LULC products were not available to assess the region s urbanization prior to 1995 and subsequent to post Hurricane Katrina in 2006. This project assessed LULC change across the 34-year time frame and at decadal and middecadal scales. The study area included the majority of Mobile and Baldwin counties that

Modeling urban expansion in Yangon, Myanmar using Landsat time-series and stereo GeoEye Images

Science.gov (United States)

Sritarapipat, Tanakorn; Takeuchi, Wataru

2016-06-01

This research proposed a methodology to model the urban expansion based dynamic statistical model using Landsat and GeoEye Images. Landsat Time-Series from 1978 to 2010 have been applied to extract land covers from the past to the present. Stereo GeoEye Images have been employed to obtain the height of the building. The class translation was obtained by observing land cover from the past to the present. The height of the building can be used to detect the center of the urban area (mainly commercial area). It was assumed that the class translation and the distance of multi-centers of the urban area also the distance of the roads affect the urban growth. The urban expansion model based on the dynamic statistical model was defined to refer to three factors; (1) the class translation, (2) the distance of the multicenters of the urban areas, and (3) the distance from the roads. Estimation and prediction of urban expansion by using our model were formulated and expressed in this research. The experimental area was set up in Yangon, Myanmar. Since it is the major of country's economic with more than five million population and the urban areas have rapidly increased. The experimental results indicated that our model of urban expansion estimated urban growth in both estimation and prediction steps in efficiency.

Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series

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Thomas Koellner

2012-12-01

Full Text Available The expansion of irrigated agriculture during the Soviet Union (SU era made Central Asia a leading cotton production region in the world. However, the successor states of the SU in Central Asia face on-going environmental damages and soil degradation that are endangering the sustainability of agricultural production. With Landsat MSS and TM data from 1972/73, 1977, 1987, 1998, and 2000 the expansion and densification of the irrigated cropland could be reconstructed in the Kashkadarya Province of Uzbekistan, Central Asia. Classification trees were generated by interpreting multitemporal normalized difference vegetation index data and crop phenological knowledge. Assessments based on image-derived validation samples showed good accuracy. Official statistics were found to be of limited use for analyzing the plausibility of the results, because they hardly represent the area that is cropped in the very dry study region. The cropping area increased from 134,800 ha in 1972/73 to 470,000 ha in 2009. Overlaying a historical soil map illustrated that initially sierozems were preferred for irrigated agriculture, but later the less favorable solonchaks and solonetzs were also explored, illustrating the strategy of agricultural expansion in the Aral Sea Basin. Winter wheat cultivation doubled between 1987 and 1998 to approximately 211,000 ha demonstrating its growing relevance for modern Uzbekistan. The spatial-temporal approach used enhances the understanding of natural conditions before irrigation is employed and supports decision-making for investments in irrigation infrastructure and land cultivation throughout the Landsat era.

Forest cover of North America in the 1970s mapped using Landsat MSS data

Science.gov (United States)

Feng, M.; Sexton, J. O.; Channan, S.; Townshend, J. R.

2015-12-01

The distribution and changes in Earth's forests impact hydrological, biogeochemical, and energy fluxes, as well as ecosystems' capacity to support biodiversity and human economies. Long-term records of forest cover are needed across a broad range of investigation, including climate and carbon-cycle modeling, hydrological studies, habitat analyzes, biological conservation, and land-use planning. Satellite-based observations enable mapping and monitoring of forests at ecologically and economically relevant resolutions and continental or even global extents. Following early forest-mapping efforts using coarser resolution remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS), forests have been mapped regionally at developed by the Global Land Cover Facility (GLCF) as reference, we developed an automated approach to detect forests using MSS data by leveraging the multispectral and phenological characteristics of forests observed in MSS time-series. The forest-cover map is produced with layers representing the year of observation, detection of forest-cover change relative to 1990, and the uncertainty of forest-cover and -change layers. The approach has been implemented with open-source libraries to facilitate processing large volumes of Landsat MSS images on high-performance computing machines. As the first result of our global mapping effort, we present the forest cover for North America. More than 25,000 Landsat MSS scenes were processed to provide a 120-meter resolution forest cover for North America, which will be made publicly available on the GLCF website (http://www.landcover.org).

Assessing and mapping the severity of soil erosion using the 30-m Landsat multispectral satellite data in the former South African homelands of Transkei

Science.gov (United States)

Seutloali, Khoboso E.; Dube, Timothy; Mutanga, Onisimo

2017-08-01

Soil erosion is increasingly recognised as the principal cause of land degradation, loss of agricultural land area and siltation of surrounding water waterbodies. Accurate and up-to-date soil erosion mapping is key in understanding its severity if these negative impacts are to be minimised and affected areas rehabilitated. The aim of this work was to map the severity of soil erosion, based on the 30-m Landsat series multispectral satellite data in the former South African homelands of Transkei between the year 1994 and 2010. Further, the study assessed if the observed soil erosion trends and morphology that existed in this area could be explained by biophysical factors (i.e. slope, stream erosivity, topographic wetness index) retrieved from the 30-m ASTER Digital Elevation Model (DEM). The results of this study indicate that the Transkei region experiences varying erosion levels from moderate to very severe. The large portion of the land area under the former homelands was largely affected by rill erosion with approximately 74% occurring in the year 1984 and 54% in 2010. The results also revealed specific thresholds of soil erosion drivers. These include steeper areas (≥30°), high stream power index greater than 2.0 (stream erosivity), relatively lower vegetation cover (≤15%) and low topographic wetness index (≤5%). The results of this work demonstrate the severity of soil erosion in the Southern African former homelands of Transkei for the year 1984 and 2010. Additionally, this work has demonstrated the significance of the 30-m Landsat multispectral sensor in examining soil erosion occurrence at a regional scale where in-depth field work still remains a challenging task.

A Generic Approach for Inversion of Surface Reflectance over Land: Overview, Application and Validation Using MODIS and LANDSAT8 Data

Science.gov (United States)

Vermote, E.; Roger, J. C.; Justice, C. O.; Franch, B.; Claverie, M.

2016-01-01

This paper presents a generic approach developed to derive surface reflectance over land from a variety of sensors. This technique builds on the extensive dataset acquired by the Terra platform by combining MODIS and MISR to derive an explicit and dynamic map of band ratio's between blue and red channels and is a refinement of the operational approach used for MODIS and LANDSAT over the past 15 years. We will present the generic approach and the application to MODIS and LANDSAT data and its validation using the AERONET data.

An investigation of several aspects of LANDSAT-5 data quality. [Palmer County, Shelby, mt; White sands, NM; Great Salt Lake, UT; San Matted Bridge and Sacramento, California

Science.gov (United States)

Wrigley, R. C. (Principal Investigator)

1984-01-01

Band-to-band registration, geodetic registration, interdector noise, and the modulation transfer function (MTE) are discussed for the Palmer County; TX scene. Band combinations for several LANDSAT 4 and LANDSAT 5 scenes; the geodetic registration test for the Sacramento, CA area; periodic noise components in TM band 5; and grey level measurements by detector for Great Salt Lake (UT) dark water forescans and backscans are considered. Results of MTF analyses of the San Mateo Bridge and of TM high resolution and aerial Daedalus scanner imagery are consistent and appear to be repeatable. An oil-on-sand target was constructed on the White Sands Missile Range in New Mexico. The two-image analysis procedure used is summarized.

MONITORING MANGROVE AREA IN BENOA BAY USING LANDSAT TM AND ETM + DATA

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Ni Luh Made Ari Sugianthi

2012-11-01

Full Text Available Mangrove ecosystems are crucial for the management of some coastal resources in Indonesia. Thisresearch used Landsat TM 1994, Landsat ETM+ 2002 with the purpose to know mangrove area change foreight years, mangrove density and accuracy of image as source of data to mangrove area in Benoa Bay. Fromimage analysis that using maximum likelihood method, the mangrove is classified into 3 classes i.e.:mangroves with high density, medium density and low density. For the ground check, used single plotmethod by using 6 trees.The extent of mangrove area in Benoa Bay were 447.69 ha in 1994 and 622.08 ha in 2002. Thechange of the extent of mangrove area during 8 years (1994 – 2002 increased by 174.41 ha. The area ofdensities in 1994, high density was 225.15 ha, medium density was 122.48 ha and low density was 130.05ha. In 2002, high density was 262.8 ha, medium density was 265.95 ha, and low density was 133.30 ha.Based on the regression analysis between mangrove density and the value of interpretation, the density ofmangrove in Benoa Bay which the criteria of high density is 364.723 – 466.311 tree/ha, medium density is237.738 - 364.723 tree/ha and low density is 186.944 – 237.738 tree/ha. The determination coefficient (r2was 0.6312. Based on the regression analysis in 2002 used in interpretation of mangrove density in 1994,which the criteria of high density is 357.10 tree/ha –316.47 tree/ha, medium density is 273.29 tree/ha –316.47 tree/ha and low density is 252.98 tree/ha –273.29 tree/ha.The accuracy of the Landsat ETM+ 2002 for mangrove area classification in Benoa Bay was 90%.These values were above the acceptable limit of accuracy stated of 80 %, so that this classification accuracywas acceptable.

Bora Bora, Tahaa, and Raiatea, French Polynesia, Landsat and SIR-C Images Compared to SRTM Shaded

Science.gov (United States)

2005-01-01

Bora Bora, Tahaa, and Raiatea (top to bottom) are Polynesian Islands about 220 kilometers (135 miles) west-northwest of Tahiti in the South Pacific. Each of the islands is surrounded by a coral reef and its associated islets ('motus') that enclose a lagoon. Actually, as seen here, Tahaa and Raiatea are close enough together to share a common lagoon and reef. These islands are volcanic in origin and were built up from the sea floor by lava extrusions millions of years ago. None is now active, and all are deeply eroded. This display compares three differing 'views from space' of these islands. On the left, an image from the Landsat 7 satellite shows the islands as they might have appeared to an astronaut in orbit in 1999 (but a little sharper and with atmospheric haze suppressed). In the middle is an image created from data gathered by the third-generation Shuttle Imaging Radar (SIR-C), flown in 1994. On the right is a graphic illustrating elevation data gathered by the Shuttle Radar Topography Mission (SRTM) in 2000. Each of these images shows very different information as compared to the other two. Landsat sees clouds, which are almost always above these islands, blocking the view of the terrain. It also readily sees through shallow water down to the reefs. SIR-C sees the waves and other effects of winds upon the ocean surface. It does not look through water to see the reefs, but it clearly separates land and water. It also provides a bolder (but distorted) view of the islands' topographic patterns. With the ability of radar to see through clouds and provision of its own illumination, the SIR-C view is not limited by clouds nor their shadows. SRTM was designed to provide new information that is missing in the Landsat and SIR-C views. Specifically, SRTM created the world's first near-global, detailed elevation model. Natural topographic shading in Landsat imagery and radar topographic shadowing of SIR-C give some evidence of the shape of the ground but do not

Monitoring Quarry Area with Landsat Long Time-Series for Socioeconomic Study

Directory of Open Access Journals (Sweden)

Haoteng Zhao

2018-03-01

Full Text Available Quarry sites result from human activity, which includes the removal of original vegetation and the overlying soil to dig out stones for building use. Therefore, the dynamics of the quarry area provide a unique view of human mining activities. Actually, the topographic changes caused by mining activities are also a result of the development of the local economy. Thus, monitoring the quarry area can provide information about the policies of the economy and environmental protection. In this paper, we developed a combined method of machine learning classification and quarry region analysis to estimate the quarry area in a quarry region near Beijing. A temporal smoothing based on the classification results of all years was applied in post-processing to remove outliers and obtain gently changing sequences along the monitoring term. The method was applied to Landsat images to derive a quarry distribution map and quarry area time series from 1984 to 2017, revealing significant inter-annual variability. The time series revealed a five-stage development of the quarry area with different growth patterns. As the study region lies on two jurisdictions—Tianjin and Hebei—a comparison of the quarry area changes in the two jurisdictions was applied, which revealed that the different policies in the two regions could impose different impacts on the development of a quarry area. An analysis concerning the relationship between quarry area and gross regional product (GRP was performed to explore the potential application on socioeconomic studies, and we found a strong positive correlation between quarry area and GRP in Langfang City, Hebei Province. These results demonstrate the potential benefit of annual monitoring over the long-term for socioeconomic studies, which can be used for mining decision making.

Downscaling of Aircraft, Landsat, and MODIS-bases Land Surface Temperature Images with Support Vector Machines

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High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at s...

Mapping areas invaded by Prosopis juliflora in Somaliland on Landsat 8 imagery

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Rembold, Felix; Leonardi, Ugo; Ng, Wai-Tim; Gadain, Hussein; Meroni, Michele; Atzberger, Clement

2015-10-01

Prosopis juliflora is a fast growing tree species originating from South and Central America with a high invasion potential in semi-arid areas around the globe. It was introduced to East Africa for the stabilization of dune systems and for providing fuel wood after prolonged droughts and deforestation in the 1970s and 1980s. In many dry lands in East Africa the species has expanded rapidly and has become challenging to control. The species generally starts its colonization on deep soils with high water availability while in later stages or on poorer soils, its thorny thickets expand into drier grasslands and rangelands. Abandoned or low input farmland is also highly susceptible for invasion as P. juliflora has competitive advantages to native species and is extremely drought tolerant. In this work we describe a rapid approach to detect and map P. juliflora invasion at country level for the whole of Somaliland. Field observations were used to delineate training sites for a supervised classification of Landsat 8 imagery collected during the driest period of the year (i.e., from late February to early April). The choice of such a period allowed to maximise the spectral differences between P. juliflora and other species present in the area, as P. juliflora tends to maintain a higher vigour and canopy water content than native vegetation, when exposed to water stress. The results of our classification map the current status of invasion of Prosopis in Somaliland showing where the plant is invading natural vegetation or agricultural areas. These results have been verified for two spatial subsets of the whole study area with very high resolution (VHR) imagery, proving that Landsat 8 imagery is highly adequate to map P. juliflora. The produced map represents a baseline for understanding spatial distribution of P. juliflora across Somaliland but also for change detection and monitoring of long term dynamics in support to P. juliflora management and control activities.

DEVELOPMENT OF WATER QUALITY PARAMETER RETRIEVAL ALGORITHMS FOR ESTIMATING TOTAL SUSPENDED SOLIDS AND CHLOROPHYLL-A CONCENTRATION USING LANDSAT-8 IMAGERY AT POTERAN ISLAND WATER

Directory of Open Access Journals (Sweden)

N. Laili

2015-10-01

Full Text Available The Landsat-8 satellite imagery is now highly developed compares to the former of Landsat projects. Both land and water area are possibly mapped using this satellite sensor. Considerable approaches have been made to obtain a more accurate method for extracting the information of water area from the images. It is difficult to generate an accurate water quality information from Landsat images by using some existing algorithm provided by researchers. Even though, those algorithms have been validated in some water area, but the dynamic changes and the specific characteristics of each area make it necessary to get them evaluated and validated over another water area. This paper aims to make a new algorithm by correlating the measured and estimated TSS and Chla concentration. We collected in-situ remote sensing reflectance, TSS and Chl-a concentration in 9 stations surrounding the Poteran islands as well as Landsat 8 data on the same acquisition time of April 22, 2015. The regression model for estimating TSS produced high accuracy with determination coefficient (R2, NMAE and RMSE of 0.709; 9.67 % and 1.705 g/m3 respectively. Whereas, Chla retrieval algorithm produced R2 of 0.579; NMAE of 10.40% and RMSE of 51.946 mg/m3. By implementing these algorithms to Landsat 8 image, the estimated water quality parameters over Poteran island water ranged from 9.480 to 15.801 g/m3 and 238.546 to 346.627 mg/m3 for TSS and Chl-a respectively.

Fusion of Terra-MODIS and Landsat TM data for geothermal sites investigation in Jiangsu Province, China

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Chen, Shengbo

2006-01-01

Geothermal resources are generally confined to areas of the Earth's crust where heat flow higher than in surrounding areas heats the water contained in permeable rocks (reservoirs) at depth. It is becoming one of attractive solutions for clean and sustainable energy future for the world. The geothermal fields commonly occurs at the boundaries of plates, and only occasionally in the middle of a plate. The study area, Jiangsu Province, as an example, located in the east of China, is a potential area of geothermal energy. In this study, Landsat thematic Mapper (TM) data were georeferenced to position spatially the geothermal energy in the study area. Multi-spectral infrared data of Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra platform were georeferenced to Landsat TM images. Based on the Wien Displacement Law, these infrared data indicate the surface emitted radiance under the same atmospheric condition, and stand for surface bright temperature respectively. Thus, different surface bright temperature data from Terra-MODIS band 20 or band 31 (R), together with Landsat TM band 4 (G) and band 3 (B) separately, were made up false color composite images (RGB) to generate the distribution maps of surface bright temperatures. Combing with geologic environment and geophysical anomalies, the potential area of geothermal energy with different geo-temperature were mapped respectively. Specially, one geothermal spot in Qinhu Lake Scenery Area in Taizhou city was validated by drilling, and its groundwater temperature is up to some 51°.

Development of an image processing system at the Technology Applications Center, UNM: Landsat image processing in mineral exploration and related activities. Final report

International Nuclear Information System (INIS)

Budge, T.K.

1980-09-01

This project was a demonstration of the capabilities of Landsat satellite image processing applied to the monitoring of mining activity in New Mexico. Study areas included the Navajo coal surface mine, the Jackpile uranium surface mine, and the potash mining district near Carlsbad, New Mexico. Computer classifications of a number of land use categories in these mines were presented and discussed. A literature review of a number of case studies concerning the use of Landsat image processing in mineral exploration and related activities was prepared. Included in this review is a discussion of the Landsat satellite system and the basics of computer image processing. Topics such as destriping, contrast stretches, atmospheric corrections, ratioing, and classification techniques are addressed. Summaries of the STANSORT II and ELAS software packages and the Technology Application Center's Digital Image Processing System (TDIPS) are presented

Spatio-Temporal Change of Vegetation Coverage and its Driving Forces Based on Landsat Images: a Case Study of Changchun City

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Dong, L.; Jiang, H.; Yang, L.

2018-04-01

Based on the Landsat images in 2006, 2011 and 2015, and the method of dimidiate pixel model, the Normalized Difference Vegetation Index (NDVI) and the vegetation coverage, this paper analyzes the spatio-temporal variation of vegetation coverage in Changchun, China from 2006 to 2015, and investigates the response of vegetation coverage change to natural and artificial factors. The research results show that in nearly 10 years, the vegetation coverage in Changchun dropped remarkably, and reached the minimum in 2011. Moreover, the decrease of maximum NDVI was significant, with a decrease of about 27.43 %, from 2006 to 2015. The vegetation coverage change in different regions of the research area was significantly different. Among them, the vegetation change in Changchun showed a little drop, and it decreased firstly and then increased slowly in Yushu, Nong'an and Dehui. In addition, the temperature and precipitation change, land reclamation all affect the vegetation coverage. In short, the study of vegetation coverage change contributes scientific and technical support to government and environmental protection department, so as to promote the coordinated development of ecology and economy.

A comparative study on generating simulated Landsat NDVI images using data fusion and regression method-the case of the Korean Peninsula.

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Lee, Mi Hee; Lee, Soo Bong; Eo, Yang Dam; Kim, Sun Woong; Woo, Jung-Hun; Han, Soo Hee

2017-07-01

Landsat optical images have enough spatial and spectral resolution to analyze vegetation growth characteristics. But, the clouds and water vapor degrade the image quality quite often, which limits the availability of usable images for the time series vegetation vitality measurement. To overcome this shortcoming, simulated images are used as an alternative. In this study, weighted average method, spatial and temporal adaptive reflectance fusion model (STARFM) method, and multilinear regression analysis method have been tested to produce simulated Landsat normalized difference vegetation index (NDVI) images of the Korean Peninsula. The test results showed that the weighted average method produced the images most similar to the actual images, provided that the images were available within 1 month before and after the target date. The STARFM method gives good results when the input image date is close to the target date. Careful regional and seasonal consideration is required in selecting input images. During summer season, due to clouds, it is very difficult to get the images close enough to the target date. Multilinear regression analysis gives meaningful results even when the input image date is not so close to the target date. Average R 2 values for weighted average method, STARFM, and multilinear regression analysis were 0.741, 0.70, and 0.61, respectively.

Use of topographic and climatological models in a geographical data base to improve Landsat MSS classification for Olympic National Park

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Cibula, William G.; Nyquist, Maurice O.

1987-01-01

An unsupervised computer classification of vegetation/landcover of Olympic National Park and surrounding environs was initially carried out using four bands of Landsat MSS data. The primary objective of the project was to derive a level of landcover classifications useful for park management applications while maintaining an acceptably high level of classification accuracy. Initially, nine generalized vegetation/landcover classes were derived. Overall classification accuracy was 91.7 percent. In an attempt to refine the level of classification, a geographic information system (GIS) approach was employed. Topographic data and watershed boundaries (inferred precipitation/temperature) data were registered with the Landsat MSS data. The resultant boolean operations yielded 21 vegetation/landcover classes while maintaining the same level of classification accuracy. The final classification provided much better identification and location of the major forest types within the park at the same high level of accuracy, and these met the project objective. This classification could now become inputs into a GIS system to help provide answers to park management coupled with other ancillary data programs such as fire management.

Using the Landsat Archive to Estimate and Map Changes in Agriculture, Forests, and other Land Cover Types in East Africa

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