2016 NOAA NGS Ortho-rectified Mean High Water Color Mosaic of St. Johns River at Mile Point Turn, Florida

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

Strengthening IAEA safeguards using high-resolution commercial satellite imagery

International Nuclear Information System (INIS)

Zhang Hui

2001-01-01

Full text: In May 1997, the IAEA Board of Governors adopted the Additional Safeguards Protocol to improve its ability to detect the undeclared production of fissile material. This new strengthened safeguards system has opened the door for the IAEA to use of all types of information, including the potential use of commercial satellite imagery. We have therefore been investigating the feasibility of strengthening IAEA safeguards using commercial satellite imagery. Based on our analysis on a number of one-meter resolution IKONOS satellite images of military nuclear production facilities at nuclear states including Russia, China, India, Pakistan and Israel, we found that the new high-resolution commercial satellite imagery would play a new and valuable role in strengthening IAEA safeguards. Since 1999, images with a resolution of one meter have been available commercially from Space Imaging's IKONOS satellite. One-meter images from other companies are expected to enter the market soon. Although still an order of magnitude less capable than military imaging satellites, the capabilities of these new high-resolution commercial satellites are good enough to detect and identify the major visible characteristics of nuclear production facilities and sites. Unlike the classified spy satellite photos limited to few countries, the commercial satellite imagery is commercially available to anyone who wants to purchase it. Therefore, the new commercial satellite open a new chance that each state, international organizations, and non-governmental groups could use the commercial images to play a more proactive role in monitoring the nuclear activities in related countries and verifying the compliance of non-proliferation agreements. This could help galvanize support for intensified efforts to slow the pace of nuclear proliferation. To produce fissile materials (plutonium and highly enriched uranium) for weapons, a country would operate dedicated plutonium-production reactors and the

2016 NOAA NGS Ortho-rectified Color Mosaic of Anchorage, Alaska

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2016 NOAA NGS Ortho-rectified Color Mosaic of Whittier, Alaska

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2016 NOAA NGS Ortho-rectified Color Mosaic of Cleveland, Ohio

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2010 NOAA Ortho-rectified Mosaic of Lake Michigan - West Coast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery

Directory of Open Access Journals (Sweden)

Monica Rivas Casado

2015-11-01

Full Text Available European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management.

High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertão

Directory of Open Access Journals (Sweden)

Ran Goldblatt

2017-12-01

Full Text Available Semi-arid ecosystems play a key role in global agricultural production, seasonal carbon cycle dynamics, and longer-run climate change. Because semi-arid landscapes are heterogeneous and often sparsely vegetated, repeated and large-scale ecosystem assessments of these regions have to date been impossible. Here, we assess the potential of high-spatial resolution visible band imagery for semi-arid ecosystem mapping. We use WorldView satellite imagery at 0.3–0.5 m resolution to develop a reference data set of nearly 10,000 labeled examples of three classes—trees, shrubs/grasses, and bare land—across 1000 km 2 of the semi-arid Sertão region of northeast Brazil. Using Google Earth Engine, we show that classification with low-spectral but high-spatial resolution input (WorldView outperforms classification with the full spectral information available from Landsat 30 m resolution imagery as input. Classification with high spatial resolution input improves detection of sparse vegetation and distinction between trees and seasonal shrubs and grasses, two features which are lost at coarser spatial (but higher spectral resolution input. Our total tree cover estimates for the study area disagree with recent estimates using other methods that may underestimate treecover because they confuse trees with seasonal vegetation (shrubs and grasses. This distinction is important for monitoring seasonal and long-run carbon cycle and ecosystem health. Our results suggest that newer remote sensing products that promise high frequency global coverage at high spatial but lower spectral resolution may offer new possibilities for direct monitoring of the world’s semi-arid ecosystems, and we provide methods that could be scaled to do so.

2016 NOAA NGS Ortho-rectified Near-Infrared Mosaic of Cleveland, Ohio

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2016 NOAA NGS Ortho-rectified Color Mosaic of Baton Rouge, Louisiana

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Color Mosaic of Bremerton and Manchester, Washington

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2013 NOAA Ortho-rectified Mean High Water Color Mosaic of Sequim Bay to Foulweather Bluff, Washington: Integrated Ocean and Coastal Mapping Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

USGS High Resolution Orthoimagery Collection - Historical - National Geospatial Data Asset (NGDA) High Resolution Orthoimagery

Data.gov (United States)

U.S. Geological Survey, Department of the Interior — USGS high resolution orthorectified images from The National Map combine the image characteristics of an aerial photograph with the geometric qualities of a map. An...

2012 NOAA Ortho-rectified Color MLLW Mosaic of Alabama: Eastern Mississippi Sound

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2010 NOAA Ortho-rectified Near-infrared Mosaic of Port Arthur - Beaumont, Texas

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2013 NOAA Ortho-rectified Color Mosaic of California: Port of San Diego

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2011 NOAA Ortho-rectified Mosaic of Intracoastal Waterway, Texas (NODC Accession 0105604)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2009 NOAA Ortho-rectified RGB Mosaic of Savannah, Georgia (NODC Accession 0092435)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2017 NOAA NGS Ortho-rectified Color Mosaic of Houston Ship Channel, Texas

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2011 NOAA Ortho-rectified Mosaic of Intracoastal City, Louisiana (NODC Accession 0075831)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2010 NOAA Ortho-rectified Mosaic of Savannah River, Georgia (NODC Accession 0092435)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2011 NOAA Ortho-rectified Mosaic of Tallaboa, Puerto Rico (NODC Accession 0074381)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2016 NOAA NGS Ortho-rectified Color Mosaic of Kenai and Nikiski, Alaska

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2015 NOAA NGS Ortho-rectified Near-Infrared Mosaic of Port Canaveral, Florida

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2015 NOAA Ortho-rectified Below Mean High Water Color Mosaic of the Port of Palm Beach, Florida: Integrated Ocean and Coastal Mapping Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

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.

2012 NOAA Ortho-rectified Near-Infrared MLLW Mosaic of Alabama: Eastern Mississippi Sound

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Color Mosaic of Sacramento Deep Water Ship Channel, California

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2009 NOAA Near-Infrared Ortho-rectified Mosaic of Brunswick, Georgia (NODC Accession 0092435)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2011 NOAA Ortho-rectified Mosaic of Texas: Integrated Ocean and Coastal Mapping Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2015 NOAA Ortho-rectified Color Mosaic of the port of Silver Bay, Minnesota

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Near-Infrared Mosaic of Oregon: Lake Umatilla to Clarkson

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

Resolution Enhancement of Multilook Imagery

Energy Technology Data Exchange (ETDEWEB)

Galbraith, Amy E. [Univ. of Arizona, Tucson, AZ (United States)

2004-07-01

This dissertation studies the feasibility of enhancing the spatial resolution of multi-look remotely-sensed imagery using an iterative resolution enhancement algorithm known as Projection Onto Convex Sets (POCS). A multi-angle satellite image modeling tool is implemented, and simulated multi-look imagery is formed to test the resolution enhancement algorithm. Experiments are done to determine the optimal con guration and number of multi-angle low-resolution images needed for a quantitative improvement in the spatial resolution of the high-resolution estimate. The important topic of aliasing is examined in the context of the POCS resolution enhancement algorithm performance. In addition, the extension of the method to multispectral sensor images is discussed and an example is shown using multispectral confocal fluorescence imaging microscope data. Finally, the remote sensing issues of atmospheric path radiance and directional reflectance variations are explored to determine their effect on the resolution enhancement performance.

DSM GENERATION FROM HIGH RESOLUTION COSMO-SKYMED IMAGERY WITH RADARGRAMMETRIC MODEL

OpenAIRE

P. Capaldo; M. Crespi; F. Fratarcangeli; A. Nascetti; F. Pieralice

2012-01-01

The availability of new high resolution radar spaceborne sensors offers new interesting potentialities for the geomatics application: spatial and temporal change detection, features extraction, generation of Digital Surface (DSMs). As regards the DSMs generation from new high resolution data (as SpotLight imagery), the development and the accuracy assessment of method based on radargrammetric approach are topics of great interest and relevance. The aim of this investigation is the DSM generat...

2015 NOAA Ortho-rectified Below Mean High Water Color Mosaic of Ports of Houston, Texas City, and Galveston, Texas: Integrated Ocean and Coastal Mapping Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Color MLLW Mosaic of Pescadero Point to Bodega Bay, California

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Color Ortho-rectified Mosaic of Corpus Christi to Saint Charles Bay, Texas

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Color MLLW Mosaic of Bodega Bay to Shelter Cove, California

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Color MLLW Ortho-rectified Mosaic of Amelia Island and Nassau River, Florida

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2011 NOAA Color MHW Ortho-rectified Mosaic of Amelia Island and Nassau River, Florida

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Color MLLW Mosaic of Lopez Rock to Pescadero Point, California

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2013 NOAA Ortho-rectified Near-Infrared Mosaic of the Port of Panama City, Florida

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2015 NOAA Ortho-rectified Near-Infrared Mosaic of the port of Silver Bay, Minnesota

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Color MLLW Mosaic of Seal Rock to Lopez Rock, California

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2011 NOAA Ortho-rectified Near-Infrared Mosaic of Isle of Shoals, New Hampshire (MHW)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2010 NOAA Ortho-rectified Mosaic of Louisiana: Mississippi River - Baton Rouge to Southwest Pass

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

Alaska Orthorectified Radar Intensity Image - USGS National Map 3DEP Downloadable Data Collection

Data.gov (United States)

U.S. Geological Survey, Department of the Interior — These data are orthorectified radar intensity images (ORI) derived from interferometric synthetic aperture radar (ifsar) data. An ORI is a high-resolution image...

2012 NOAA Ortho-rectified Near-Infrared MLLW Mosaic of Pescadero Point to Bodega Bay, California

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Near-Infrared MLLW Mosaic of Lopez Rock to Pescadero Point, California

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2013 NOAA Ortho-rectified Near-Infrared Mosaic of Virginia: Norfolk, Hampton Roads,and Newport News

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2013 NOAA Ortho-rectified Color Mosaic of Intercoastal Waterway - Calcasieu Lake to Vermillion Bay, Louisiana

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Color MHW Mosaic of Washington: Seattle and Lake Washington Ship Canal

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Color Mosaic of Del Mar Boat Basin and Oceanside Harbor, California

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Near-Infrared MLLW Mosaic of Seal Rock to Lopez Rock, California

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2013 NOAA Ortho-rectified Color Mosaic of California: Port of Los Angeles and Long Beach

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

COMPARATIVE ASSESSMENT OF VERY HIGH RESOLUTION SATELLITE AND AERIAL ORTHOIMAGERY

Directory of Open Access Journals (Sweden)

P. Agrafiotis

2015-03-01

Full Text Available This paper aims to assess the accuracy and radiometric quality of orthorectified high resolution satellite imagery from Pleiades-1B satellites through a comparative evaluation of their quantitative and qualitative properties. A Pleiades-B1 stereopair of high resolution images taken in 2013, two adjacent GeoEye-1 stereopairs from 2011 and aerial orthomosaic (LSO provided by NCMA S.A (Hellenic Cadastre from 2007 have been used for the comparison tests. As control dataset orthomosaic from aerial imagery provided also by NCMA S.A (0.25m GSD from 2012 was selected. The process for DSM and orthoimage production was performed using commercial digital photogrammetric workstations. The two resulting orthoimages and the aerial orthomosaic (LSO were relatively and absolutely evaluated for their quantitative and qualitative properties. Test measurements were performed using the same check points in order to establish their accuracy both as far as the single point coordinates as well as their distances are concerned. Check points were distributed according to JRC Guidelines for Best Practice and Quality Checking of Ortho Imagery and NSSDA standards while areas with different terrain relief and land cover were also included. The tests performed were based also on JRC and NSSDA accuracy standards. Finally, tests were carried out in order to assess the radiometric quality of the orthoimagery. The results are presented with a statistical analysis and they are evaluated in order to present the merits and demerits of the imaging sensors involved for orthoimage production. The results also serve for a critical approach for the usability and cost efficiency of satellite imagery for the production of Large Scale Orthophotos.

Advanced Ecosystem Mapping Techniques for Large Arctic Study Domains Using Calibrated High-Resolution Imagery

Science.gov (United States)

Macander, M. J.; Frost, G. V., Jr.

2015-12-01

Regional-scale mapping of vegetation and other ecosystem properties has traditionally relied on medium-resolution remote sensing such as Landsat (30 m) and MODIS (250 m). Yet, the burgeoning availability of high-resolution (environments has not been previously evaluated. Image segmentation, or object-based image analysis, automatically partitions high-resolution imagery into homogeneous image regions that can then be analyzed based on spectral, textural, and contextual information. We applied eCognition software to delineate waterbodies and vegetation classes, in combination with other techniques. Texture metrics were evaluated to determine the feasibility of using high-resolution imagery to algorithmically characterize periglacial surface forms (e.g., ice-wedge polygons), which are an important physical characteristic of permafrost-dominated regions but which cannot be distinguished by medium-resolution remote sensing. These advanced mapping techniques provide products which can provide essential information supporting a broad range of ecosystem science and land-use planning applications in northern Alaska and elsewhere in the circumpolar Arctic.

2010 NOAA Ortho-rectified Mosaic from Color Aerial Imagery of CHOCTAWHATCHEE BAY

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of CHOCTAWHATCHEE BAY....

2010 NOAA Ortho-rectified Mosaic from Color Aerial Imagery of LAKE CHARLES

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of LAKE CHARLES. The...

Monitoring Termite-Mediated Ecosystem Processes Using Moderate and High Resolution Satellite Imagery

Science.gov (United States)

Lind, B. M.; Hanan, N. P.

2016-12-01

Termites are considered dominant decomposers and prominent ecosystem engineers in the global tropics and they build some of the largest and architecturally most complex non-human-made structures in the world. Termite mounds significantly alter soil texture, structure, and nutrients, and have major implications for local hydrological dynamics, vegetation characteristics, and biological diversity. An understanding of how these processes change across large scales has been limited by our ability to detect termite mounds at high spatial resolutions. Our research develops methods to detect large termite mounds in savannas across extensive geographic areas using moderate and high resolution satellite imagery. We also investigate the effect of termite mounds on vegetation productivity using Landsat-8 maximum composite NDVI data as a proxy for production. Large termite mounds in arid and semi-arid Senegal generate highly reflective `mound scars' with diameters ranging from 10 m at minimum to greater than 30 m. As Sentinel-2 has several bands with 10 m resolution and Landsat-8 has improved calibration, higher radiometric resolution, 15 m spatial resolution (pansharpened), and improved contrast between vegetated and bare surfaces compared to previous Landsat missions, we found that the largest and most influential mounds in the landscape can be detected. Because mounds as small as 4 m in diameter are easily detected in high resolution imagery we used these data to validate detection results and quantify omission errors for smaller mounds.

2010 NOAA Ortho-rectified Color MHW Mosaic of South Carolina: Hilton Head to St. Helena Sound

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2015 NOAA Ortho-rectified Color Mosaic of San Diego, California: Integrated Ocean and Coastal Mapping Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2014 NOAA NGS Ortho-rectified Mean Low Low Water Color Mosaic of Venice Inlet ICW, Florida

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Near-Infrared Mosaic of Oregon: Columbia River - Bonneville Dam to Lake Umatilla

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2011 NOAA Ortho-rectified Mosaic of Maine: Reversing Falls at Whiting Bay, Mean Lower Low Water

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Color MLLW Mosaic of Alabama: Bon Secour Bay and Weeks Bay NERR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2016 NOAA NGS Ortho-rectified Mean Low Low Water Color Mosaic of St Johns River, Florida

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Near Infrared MLLW Ortho-rectified Mosaic of Northeast Point to Murphy Island, South Carolina

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

Analysis of the impact of spatial resolution on land/water classifications using high-resolution aerial imagery

Science.gov (United States)

Enwright, Nicholas M.; Jones, William R.; Garber, Adrienne L.; Keller, Matthew J.

2014-01-01

Long-term monitoring efforts often use remote sensing to track trends in habitat or landscape conditions over time. To most appropriately compare observations over time, long-term monitoring efforts strive for consistency in methods. Thus, advances and changes in technology over time can present a challenge. For instance, modern camera technology has led to an increasing availability of very high-resolution imagery (i.e. submetre and metre) and a shift from analogue to digital photography. While numerous studies have shown that image resolution can impact the accuracy of classifications, most of these studies have focused on the impacts of comparing spatial resolution changes greater than 2 m. Thus, a knowledge gap exists on the impacts of minor changes in spatial resolution (i.e. submetre to about 1.5 m) in very high-resolution aerial imagery (i.e. 2 m resolution or less). This study compared the impact of spatial resolution on land/water classifications of an area dominated by coastal marsh vegetation in Louisiana, USA, using 1:12,000 scale colour-infrared analogue aerial photography (AAP) scanned at four different dot-per-inch resolutions simulating ground sample distances (GSDs) of 0.33, 0.54, 1, and 2 m. Analysis of the impact of spatial resolution on land/water classifications was conducted by exploring various spatial aspects of the classifications including density of waterbodies and frequency distributions in waterbody sizes. This study found that a small-magnitude change (1–1.5 m) in spatial resolution had little to no impact on the amount of water classified (i.e. percentage mapped was less than 1.5%), but had a significant impact on the mapping of very small waterbodies (i.e. waterbodies ≤ 250 m2). These findings should interest those using temporal image classifications derived from very high-resolution aerial photography as a component of long-term monitoring programs.

Land cover mapping and change detection in urban watersheds using QuickBird high spatial resolution satellite imagery

Science.gov (United States)

Hester, David Barry

The objective of this research was to develop methods for urban land cover analysis using QuickBird high spatial resolution satellite imagery. Such imagery has emerged as a rich commercially available remote sensing data source and has enjoyed high-profile broadcast news media and Internet applications, but methods of quantitative analysis have not been thoroughly explored. The research described here consists of three studies focused on the use of pan-sharpened 61-cm spatial resolution QuickBird imagery, the spatial resolution of which is the highest of any commercial satellite. In the first study, a per-pixel land cover classification method is developed for use with this imagery. This method utilizes a per-pixel classification approach to generate an accurate six-category high spatial resolution land cover map of a developing suburban area. The primary objective of the second study was to develop an accurate land cover change detection method for use with QuickBird land cover products. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and meaningful high spatial resolution land cover change analysis. The third study described here is an urban planning application of the high spatial resolution QuickBird-based land cover product developed in the first study. This work both meaningfully connects this exciting new data source to urban watershed management and makes an important empirical contribution to the study of suburban watersheds. Its analysis of residential roads and driveways as well as retail parking lots sheds valuable light on the impact of transportation-related land use on the suburban landscape. Broadly, these studies provide new methods for using state-of-the-art remote sensing data to inform land cover analysis and urban planning. These methods are widely adaptable and produce land cover products that are both meaningful and accurate. As additional high spatial resolution satellites are launched and the

[Extraction of buildings three-dimensional information from high-resolution satellite imagery based on Barista software].

Science.gov (United States)

Zhang, Pei-feng; Hu, Yuan-man; He, Hong-shi

2010-05-01

The demand for accurate and up-to-date spatial information of urban buildings is becoming more and more important for urban planning, environmental protection, and other vocations. Today's commercial high-resolution satellite imagery offers the potential to extract the three-dimensional information of urban buildings. This paper extracted the three-dimensional information of urban buildings from QuickBird imagery, and validated the precision of the extraction based on Barista software. It was shown that the extraction of three-dimensional information of the buildings from high-resolution satellite imagery based on Barista software had the advantages of low professional level demand, powerful universality, simple operation, and high precision. One pixel level of point positioning and height determination accuracy could be achieved if the digital elevation model (DEM) and sensor orientation model had higher precision and the off-Nadir View Angle was relatively perfect.

2010 NOAA Ortho-rectified Near-Infrared MHW Mosaic of South Carolina: Hilton Head to St. Helena Sound

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2011 NOAA Ortho-rectified Mosaic of Hampton Harbor to Frost Point, New Hampshire (Mean Lower Low Water)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2014 NOAA NGS Ortho-rectified Mean Low Low Water Near-Infrared Mosaic of Venice Inlet ICW, Florida

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2012 NOAA Ortho-rectified Near-Infrared MLLW Mosaic of Alabama: Bon Secour Bay and Weeks Bay NERR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

Applications and Innovations for Use of High Definition and High Resolution Digital Motion Imagery in Space Operations

Science.gov (United States)

Grubbs, Rodney

2016-01-01

The first live High Definition Television (HDTV) from a spacecraft was in November, 2006, nearly ten years before the 2016 SpaceOps Conference. Much has changed since then. Now, live HDTV from the International Space Station (ISS) is routine. HDTV cameras stream live video views of the Earth from the exterior of the ISS every day on UStream, and HDTV has even flown around the Moon on a Japanese Space Agency spacecraft. A great deal has been learned about the operations applicability of HDTV and high resolution imagery since that first live broadcast. This paper will discuss the current state of real-time and file based HDTV and higher resolution video for space operations. A potential roadmap will be provided for further development and innovations of high-resolution digital motion imagery, including gaps in technology enablers, especially for deep space and unmanned missions. Specific topics to be covered in the paper will include: An update on radiation tolerance and performance of various camera types and sensors and ramifications on the future applicability of these types of cameras for space operations; Practical experience with downlinking very large imagery files with breaks in link coverage; Ramifications of larger camera resolutions like Ultra-High Definition, 6,000 [pixels] and 8,000 [pixels] in space applications; Enabling technologies such as the High Efficiency Video Codec, Bundle Streaming Delay Tolerant Networking, Optical Communications and Bayer Pattern Sensors and other similar innovations; Likely future operations scenarios for deep space missions with extreme latency and intermittent communications links.

Estimation of the distribution of Tabebuia guayacan (Bignoniaceae) using high-resolution remote sensing imagery.

Science.gov (United States)

Sánchez-Azofeifa, Arturo; Rivard, Benoit; Wright, Joseph; Feng, Ji-Lu; Li, Peijun; Chong, Mei Mei; Bohlman, Stephanie A

2011-01-01

Species identification and characterization in tropical environments is an emerging field in tropical remote sensing. Significant efforts are currently aimed at the detection of tree species, of levels of forest successional stages, and the extent of liana occurrence at the top of canopies. In this paper we describe our use of high resolution imagery from the Quickbird Satellite to estimate the flowering population of Tabebuia guayacan trees at Barro Colorado Island (BCI), in Panama. The imagery was acquired on 29 April 2002 and 21 March 2004. Spectral Angle Mapping via a One-Class Support Vector machine was used to detect the presence of 422 and 557 flowering tress in the April 2002 and March 2004 imagery. Of these, 273 flowering trees are common to both dates. This study presents a new perspective on the effectiveness of high resolution remote sensing for monitoring a phenological response and its use as a tool for potential conservation and management of natural resources in tropical environments.

Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks

Directory of Open Access Journals (Sweden)

Rui Guo

2018-03-01

Full Text Available Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification.

First experiences with a high-resolution imagery-based adjudication approach in Ethiopia

NARCIS (Netherlands)

Lemmen, C.H.J.; Zevenbergen, J.A.

2010-01-01

Great progress has been made with rural land certification in Ethiopia. This process, however, has been mainly confined to the first phase certificates – those without a georeference. In 2008, a team conducted a simple field test using high resolution imagery. On-site tests were performed to

Mapping mountain meadow with high resolution and polarimetric SAR data

International Nuclear Information System (INIS)

Tian, Bangsen; Li, Zhen; Xu, Juan; Fu, Sitao; Liu, Jiuli

2014-01-01

This paper presents a method to map the large grassland in the eastern margin of the Tibetan Plateau with the high resolution polarimetric SAR (PolSAR) imagery. When PolSAR imagery is used for land cover classification, the brightness of a SAR image is affected by topography due to varying projection between ground and image coordinates. The objective of this paper is twofold: (1) we first extend the theory of SAR terrain correction to the polarimetric case, to utilize the entire available polarimetric signature, where correction is performed explicitly based on a matrix format like covariance matrix. (2) Next, the orthoectified PolSAR is applied to classify mountain meadow and investigate the potential of PolSAR in mapping grassland. In this paper, the gamma naught radiometric correction estimates the local illuminated area at each grid point in the radar geometry. Then, each element of the coherency matrix is divided by the local area to produce a polarimetric product. Secondly, the impact of radiometric correction upon classification accuracy is investigated. A supervised classification is performed on the orthorectified Radarsat-2 PolSAR to map the spatial distribution of meadow and evaluate monitoring capabilities of mountain meadow

High spatio-temporal resolution observations of crater-lake temperatures at Kawah Ijen volcano, East Java, Indonesia

Science.gov (United States)

Lewicki, Jennifer L.; Corentin Caudron,; Vincent van Hinsberg,; George Hilley,

2016-01-01

The crater lake of Kawah Ijen volcano, East Java, Indonesia, has displayed large and rapid changes in temperature at point locations during periods of unrest, but measurement techniques employed to-date have not resolved how the lake’s thermal regime has evolved over both space and time. We applied a novel approach for mapping and monitoring variations in crater-lake apparent surface (“skin”) temperatures at high spatial (~32 cm) and temporal (every two minutes) resolution at Kawah Ijen on 18 September 2014. We used a ground-based FLIR T650sc camera with digital and thermal infrared (TIR) sensors from the crater rim to collect (1) a set of visible imagery around the crater during the daytime and (2) a time series of co-located visible and TIR imagery at one location from pre-dawn to daytime. We processed daytime visible imagery with the Structure-from-Motion photogrammetric method to create a digital elevation model onto which the time series of TIR imagery was orthorectified and georeferenced. Lake apparent skin temperatures typically ranged from ~21 to 33oC. At two locations, apparent skin temperatures were ~ 4 and 7 oC less than in-situ lake temperature measurements at 1.5 and 5 m depth, respectively. These differences, as well as the large spatio-temporal variations observed in skin temperatures, were likely largely associated with atmospheric effects such as evaporative cooling of the lake surface and infrared absorption by water vapor and SO2. Calculations based on orthorectified TIR imagery thus yielded underestimates of volcanic heat fluxes into the lake, whereas volcanic heat fluxes estimated based on in-situ temperature measurements (68 to 111 MW) were likely more representative of Kawah Ijen in a quiescent state. The ground-based imaging technique should provide a valuable tool to continuously monitor crater-lake temperatures and contribute insight into the spatio-temporal evolution of these temperatures associated with volcanic activity.

Vegetation extraction from high-resolution satellite imagery using the Normalized Difference Vegetation Index (NDVI)

Science.gov (United States)

AlShamsi, Meera R.

2016-10-01

Over the past years, there has been various urban development all over the UAE. Dubai is one of the cities that experienced rapid growth in both development and population. That growth can have a negative effect on the surrounding environment. Hence, there has been a necessity to protect the environment from these fast pace changes. One of the major impacts this growth can have is on vegetation. As technology is evolving day by day, there is a possibility to monitor changes that are happening on different areas in the world using satellite imagery. The data from these imageries can be utilized to identify vegetation in different areas of an image through a process called vegetation detection. Being able to detect and monitor vegetation is very beneficial for municipal planning and management, and environment authorities. Through this, analysts can monitor vegetation growth in various areas and analyze these changes. By utilizing satellite imagery with the necessary data, different types of vegetation can be studied and analyzed, such as parks, farms, and artificial grass in sports fields. In this paper, vegetation features are detected and extracted through SAFIY system (i.e. the Smart Application for Feature extraction and 3D modeling using high resolution satellite ImagerY) by using high-resolution satellite imagery from DubaiSat-2 and DEIMOS-2 satellites, which provide panchromatic images of 1m resolution and spectral bands (red, green, blue and near infrared) of 4m resolution. SAFIY system is a joint collaboration between MBRSC and DEIMOS Space UK. It uses image-processing algorithms to extract different features (roads, water, vegetation, and buildings) to generate vector maps data. The process to extract green areas (vegetation) utilize spectral information (such as, the red and near infrared bands) from the satellite images. These detected vegetation features will be extracted as vector data in SAFIY system and can be updated and edited by end-users, such as

2011 NOAA Ortho-rectified Near Infrared Mosaic of Hampton Harbor to Frost Point, New Hampshire (Mean Lower Low Water)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2010 NOAA Near Infrared Ortho-rectified Mosaic of Louisiana: Mississippi River - Baton Rouge to Southwest Pass (NODC Accession 0104414)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2015 NOAA Ortho-rectified Color Mosaic of Los Angeles and Long Beach, California: Integrated Ocean and Coastal Mapping Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

2010 NOAA Ortho-rectified Mosaic from Color Aerial Imagery of BEAUMONT, ORANGE, PORT AUTHUR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of BEAUMONT, ORANGE,...

2010 NOAA Ortho-rectified Mosaic from Color Aerial Imagery of PORT OF GEORGETOWN - CSCAP

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of PORT OF GEORGETOWN...

Very high resolution UV and X-ray spectroscopy and imagery of solar active regions

Science.gov (United States)

Bruner, M.; Brown, W. A.; Haisch, B. M.

1987-01-01

A scientific investigation of the physics of the solar atmosphere, which uses the techniques of high resolution soft X-ray spectroscopy and high resolution UV imagery, is described. The experiments were conducted during a series of three sounding rocket flights. All three flights yielded excellent images in the UV range, showing unprecedented spatial resolution. The second flight recorded the X-ray spectrum of a solar flare, and the third that of an active region. A normal incidence multi-layer mirror was used during the third flight to make the first astronomical X-ray observations using this new technique.

Estimating Discharge in Low-Order Rivers With High-Resolution Aerial Imagery

OpenAIRE

King, Tyler V.; Neilson, Bethany T.; Rasmussen, Mitchell T.

2018-01-01

Remote sensing of river discharge promises to augment in situ gauging stations, but the majority of research in this field focuses on large rivers (>50 m wide). We present a method for estimating volumetric river discharge in low-order (wide) rivers from remotely sensed data by coupling high-resolution imagery with one-dimensional hydraulic modeling at so-called virtual gauging stations. These locations were identified as locations where the river contracted under low flows, exposing a substa...

2010 NOAA Ortho-rectified Mosaic from Color Aerial Imagery of MISSISSIPPI RIVER - LAPLACE TO VENICE

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of MISSISSIPPI RIVER -...

Fusion of Pixel-based and Object-based Features for Road Centerline Extraction from High-resolution Satellite Imagery

Directory of Open Access Journals (Sweden)

CAO Yungang

2016-10-01

Full Text Available A novel approach for road centerline extraction from high spatial resolution satellite imagery is proposed by fusing both pixel-based and object-based features. Firstly, texture and shape features are extracted at the pixel level, and spectral features are extracted at the object level based on multi-scale image segmentation maps. Then, extracted multiple features are utilized in the fusion framework of Dempster-Shafer evidence theory to roughly identify the road network regions. Finally, an automatic noise removing algorithm combined with the tensor voting strategy is presented to accurately extract the road centerline. Experimental results using high-resolution satellite imageries with different scenes and spatial resolutions showed that the proposed approach compared favorably with the traditional methods, particularly in the aspect of eliminating the salt noise and conglutination phenomenon.

2010 NOAA Ortho-rectified Mosaic from Color Aerial Imagery of LAKE CHARLES (NODC Accession 0075827)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of LAKE CHARLES. The...

Automated road network extraction from high spatial resolution multi-spectral imagery

Science.gov (United States)

Zhang, Qiaoping

For the last three decades, the Geomatics Engineering and Computer Science communities have considered automated road network extraction from remotely-sensed imagery to be a challenging and important research topic. The main objective of this research is to investigate the theory and methodology of automated feature extraction for image-based road database creation, refinement or updating, and to develop a series of algorithms for road network extraction from high resolution multi-spectral imagery. The proposed framework for road network extraction from multi-spectral imagery begins with an image segmentation using the k-means algorithm. This step mainly concerns the exploitation of the spectral information for feature extraction. The road cluster is automatically identified using a fuzzy classifier based on a set of predefined road surface membership functions. These membership functions are established based on the general spectral signature of road pavement materials and the corresponding normalized digital numbers on each multi-spectral band. Shape descriptors of the Angular Texture Signature are defined and used to reduce the misclassifications between roads and other spectrally similar objects (e.g., crop fields, parking lots, and buildings). An iterative and localized Radon transform is developed for the extraction of road centerlines from the classified images. The purpose of the transform is to accurately and completely detect the road centerlines. It is able to find short, long, and even curvilinear lines. The input image is partitioned into a set of subset images called road component images. An iterative Radon transform is locally applied to each road component image. At each iteration, road centerline segments are detected based on an accurate estimation of the line parameters and line widths. Three localization approaches are implemented and compared using qualitative and quantitative methods. Finally, the road centerline segments are grouped into a

Estimated Depth Maps of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery (Draft)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Estimated shallow-water, depth maps were produced using rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations in the...

2011 NOAA Ortho-rectified Mosaic of Christiansted of St. Johns, U.S. Virgin Islands: Integrated Ocean and Coastal Mapping Product (NODC Accession 0086076)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

Directory of Open Access Journals (Sweden)

Nahuel R. Peralta

2016-10-01

Full Text Available A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington of Kansas (total of 457 ha. Three basic tests were conducted on the data: (1 spatial dependence on each of the yield and vegetation indices (VIs using Moran’s I test; (2 model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS and spatial econometric (SPL models; and (3 model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG was tested positive and statistically significant for most of the fields (p < 0.05, except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02 was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to

Remote sensing and GIS integration: Towards intelligent imagery within a spatial data infrastructure

Science.gov (United States)

Abdelrahim, Mohamed Mahmoud Hosny

2001-11-01

In this research, an "Intelligent Imagery System Prototype" (IISP) was developed. IISP is an integration tool that facilitates the environment for active, direct, and on-the-fly usage of high resolution imagery, internally linked to hidden GIS vector layers, to query the real world phenomena and, consequently, to perform exploratory types of spatial analysis based on a clear/undisturbed image scene. The IISP was designed and implemented using the software components approach to verify the hypothesis that a fully rectified, partially rectified, or even unrectified digital image can be internally linked to a variety of different hidden vector databases/layers covering the end user area of interest, and consequently may be reliably used directly as a base for "on-the-fly" querying of real-world phenomena and for performing exploratory types of spatial analysis. Within IISP, differentially rectified, partially rectified (namely, IKONOS GEOCARTERRA(TM)), and unrectified imagery (namely, scanned aerial photographs and captured video frames) were investigated. The system was designed to handle four types of spatial functions, namely, pointing query, polygon/line-based image query, database query, and buffering. The system was developed using ESRI MapObjects 2.0a as the core spatial component within Visual Basic 6.0. When used to perform the pre-defined spatial queries using different combinations of image and vector data, the IISP provided the same results as those obtained by querying pre-processed vector layers even when the image used was not orthorectified and the vector layers had different parameters. In addition, the real-time pixel location orthorectification technique developed and presented within the IKONOS GEOCARTERRA(TM) case provided a horizontal accuracy (RMSE) of +/- 2.75 metres. This accuracy is very close to the accuracy level obtained when purchasing the orthorectified IKONOS PRECISION products (RMSE of +/- 1.9 metre). The latter cost approximately four

The Evaluation of High Resolution Aerial Imagery for Monitoring of ...

African Journals Online (AJOL)

The Royal Natal National Park and the Rugged Glen Nature Reserve are part of the uKhahlamba Drakensberg Park (UDP) World Heritage Site and have infestations of bracken fern (Pteridium aquilinum [L.] Kuhn). Prior image classification research on bracken fern were constrained by low resolution satellite imagery and ...

Automated Verification of Spatial Resolution in Remotely Sensed Imagery

Science.gov (United States)

Davis, Bruce; Ryan, Robert; Holekamp, Kara; Vaughn, Ronald

2011-01-01

Image spatial resolution characteristics can vary widely among sources. In the case of aerial-based imaging systems, the image spatial resolution characteristics can even vary between acquisitions. In these systems, aircraft altitude, speed, and sensor look angle all affect image spatial resolution. Image spatial resolution needs to be verified with estimators that include the ground sample distance (GSD), the modulation transfer function (MTF), and the relative edge response (RER), all of which are key components of image quality, along with signal-to-noise ratio (SNR) and dynamic range. Knowledge of spatial resolution parameters is important to determine if features of interest are distinguishable in imagery or associated products, and to develop image restoration algorithms. An automated Spatial Resolution Verification Tool (SRVT) was developed to rapidly determine the spatial resolution characteristics of remotely sensed aerial and satellite imagery. Most current methods for assessing spatial resolution characteristics of imagery rely on pre-deployed engineered targets and are performed only at selected times within preselected scenes. The SRVT addresses these insufficiencies by finding uniform, high-contrast edges from urban scenes and then using these edges to determine standard estimators of spatial resolution, such as the MTF and the RER. The SRVT was developed using the MATLAB programming language and environment. This automated software algorithm assesses every image in an acquired data set, using edges found within each image, and in many cases eliminating the need for dedicated edge targets. The SRVT automatically identifies high-contrast, uniform edges and calculates the MTF and RER of each image, and when possible, within sections of an image, so that the variation of spatial resolution characteristics across the image can be analyzed. The automated algorithm is capable of quickly verifying the spatial resolution quality of all images within a data

2014 NOAA Ortho-rectified Mean Low Low Water Color Mosaic of Puget Sound - Everett to Spring Beach, Washington: Integrated Ocean and Coastal Mapping Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

Surface Temperature Mapping of the University of Northern Iowa Campus Using High Resolution Thermal Infrared Aerial Imageries

Directory of Open Access Journals (Sweden)

Ramanathan Sugumaran

2008-08-01

Full Text Available The goal of this project was to map the surface temperature of the University of Northern Iowa campus using high-resolution thermal infrared aerial imageries. A thermal camera with a spectral bandwidth of 3.0-5.0 μm was flown at the average altitude of 600 m, achieving ground resolution of 29 cm. Ground control data was used to construct the pixelto-temperature conversion model, which was later used to produce temperature maps of the entire campus and also for validation of the model. The temperature map then was used to assess the building rooftop conditions and steam line faults in the study area. Assessment of the temperature map revealed a number of building structures that may be subject to insulation improvement due to their high surface temperatures leaks. Several hot spots were also identified on the campus for steam pipelines faults. High-resolution thermal infrared imagery proved highly effective tool for precise heat anomaly detection on the campus, and it can be used by university facility services for effective future maintenance of buildings and grounds.

Surface Temperature Mapping of the University of Northern Iowa Campus Using High Resolution Thermal Infrared Aerial Imageries

Science.gov (United States)

Savelyev, Alexander; Sugumaran, Ramanathan

2008-01-01

The goal of this project was to map the surface temperature of the University of Northern Iowa campus using high-resolution thermal infrared aerial imageries. A thermal camera with a spectral bandwidth of 3.0-5.0 μm was flown at the average altitude of 600 m, achieving ground resolution of 29 cm. Ground control data was used to construct the pixel- to-temperature conversion model, which was later used to produce temperature maps of the entire campus and also for validation of the model. The temperature map then was used to assess the building rooftop conditions and steam line faults in the study area. Assessment of the temperature map revealed a number of building structures that may be subject to insulation improvement due to their high surface temperatures leaks. Several hot spots were also identified on the campus for steam pipelines faults. High-resolution thermal infrared imagery proved highly effective tool for precise heat anomaly detection on the campus, and it can be used by university facility services for effective future maintenance of buildings and grounds. PMID:27873800

2014 Metro, Oregon 4-Band 8 Bit Imagery

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — These data are LiDAR orthorectified aerial photographs (8-bit GeoTIFF format) within the Oregon Lidar Consortium Portland project area. The imagery coverage is...

Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery

Directory of Open Access Journals (Sweden)

Yu Liu

2017-05-01

Full Text Available A new convolution neural network (CNN architecture for semantic segmentation of high resolution aerial imagery is proposed in this paper. The proposed architecture follows an hourglass-shaped network (HSN design being structured into encoding and decoding stages. By taking advantage of recent advances in CNN designs, we use the composed inception module to replace common convolutional layers, providing the network with multi-scale receptive areas with rich context. Additionally, in order to reduce spatial ambiguities in the up-sampling stage, skip connections with residual units are also employed to feed forward encoding-stage information directly to the decoder. Moreover, overlap inference is employed to alleviate boundary effects occurring when high resolution images are inferred from small-sized patches. Finally, we also propose a post-processing method based on weighted belief propagation to visually enhance the classification results. Extensive experiments based on the Vaihingen and Potsdam datasets demonstrate that the proposed architectures outperform three reference state-of-the-art network designs both numerically and visually.

2014 NOAA Ortho-rectified Mean Low Low Water Color Mosaic of Hood Canal - Port Townsend to Annas Bay, Washington: Integrated Ocean and Coastal Mapping Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

Directory of Open Access Journals (Sweden)

ZHENG Zhuo

2018-05-01

Full Text Available High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.

Very high resolution UV and x-ray spectroscopy and imagery of solar active regions. Final report

International Nuclear Information System (INIS)

Bruner, M.; Brown, W.A.; Haisch, B.M.

1987-01-01

A scientific investigation of the physics of the solar atmosphere, which uses the techniques of high resolution soft x-ray spectroscopy and high resolution UV imagery, is described. The experiments were conducted during a series of three sounding rocket flights. All three flights yielded excellent images in the UV range, showing unprecedented spatial resolution. The second flight recorded the x-ray spectrum of a solar flare, and the third that of an active region. A normal incidence multi-layer mirror was used during the third flight to make the first astronomical x-ray observations using this new technique

2014 NOAA Ortho-rectified Mean Low Low Water Near-Infrared Mosaic of Hood Canal - Port Townsend to Annas Bay, Washington: Integrated Ocean and Coastal Mapping Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery...

Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

Directory of Open Access Journals (Sweden)

Adrian Fisher

2016-06-01

Full Text Available Tree cover maps are used for many purposes, such as vegetation mapping, habitat connectivity and fragmentation studies. Small remnant patches of native vegetation are recognised as ecologically important, yet they are underestimated in remote sensing products derived from Landsat. High spatial resolution sensors are capable of mapping small patches of trees, but their use in large-area mapping has been limited. In this study, multi-temporal Satellite pour l’Observation de la Terre 5 (SPOT5 High Resolution Geometrical data was pan-sharpened to 5 m resolution and used to map tree cover for the Australian state of New South Wales (NSW, an area of over 800,000 km2. Complete coverages of SPOT5 panchromatic and multispectral data over NSW were acquired during four consecutive summers (2008–2011 for a total of 1256 images. After pre-processing, the imagery was used to model foliage projective cover (FPC, a measure of tree canopy density commonly used in Australia. The multi-temporal imagery, FPC models and 26,579 training pixels were used in a binomial logistic regression model to estimate the probability of each pixel containing trees. The probability images were classified into a binary map of tree cover using local thresholds, and then visually edited to reduce errors. The final tree map was then attributed with the mean FPC value from the multi-temporal imagery. Validation of the binary map based on visually assessed high resolution reference imagery revealed an overall accuracy of 88% (±0.51% standard error, while comparison against airborne lidar derived data also resulted in an overall accuracy of 88%. A preliminary assessment of the FPC map by comparing against 76 field measurements showed a very good agreement (r2 = 0.90 with a root mean square error of 8.57%, although this may not be representative due to the opportunistic sampling design. The map represents a regionally consistent and locally relevant record of tree cover for NSW, and

2010 NOAA Ortho-rectified Mosaic from Color Aerial Imagery of BEAUMONT, ORANGE, PORT AUTHUR (NODC Accession 0074380)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of BEAUMONT, ORANGE,...

NOAA Ortho-rectified Mosaic from Color Aerial Imagery of CHOCTAWHATCHEE BAY, FL, 2009 - 2010 (NODC Accession 0086137)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of CHOCTAWHATCHEE BAY....

2015 Big Windy, Oregon 4-Band 8 Bit Imagery

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — These data are LiDAR orthorectified aerial photographs (8-bit GeoTIFF format) within the Oregon Lidar Consortium Big Windy project area. The imagery coverage is...

Estimating spatially distributed turbulent heat fluxes from high-resolution thermal imagery acquired with a UAV system.

Science.gov (United States)

Brenner, Claire; Thiem, Christina Elisabeth; Wizemann, Hans-Dieter; Bernhardt, Matthias; Schulz, Karsten

2017-05-19

In this study, high-resolution thermal imagery acquired with a small unmanned aerial vehicle (UAV) is used to map evapotranspiration (ET) at a grassland site in Luxembourg. The land surface temperature (LST) information from the thermal imagery is the key input to a one-source and two-source energy balance model. While the one-source model treats the surface as a single uniform layer, the two-source model partitions the surface temperature and fluxes into soil and vegetation components. It thus explicitly accounts for the different contributions of both components to surface temperature as well as turbulent flux exchange with the atmosphere. Contrary to the two-source model, the one-source model requires an empirical adjustment parameter in order to account for the effect of the two components. Turbulent heat flux estimates of both modelling approaches are compared to eddy covariance (EC) measurements using the high-resolution input imagery UAVs provide. In this comparison, the effect of different methods for energy balance closure of the EC data on the agreement between modelled and measured fluxes is also analysed. Additionally, the sensitivity of the one-source model to the derivation of the empirical adjustment parameter is tested. Due to the very dry and hot conditions during the experiment, pronounced thermal patterns developed over the grassland site. These patterns result in spatially variable turbulent heat fluxes. The model comparison indicates that both models are able to derive ET estimates that compare well with EC measurements under these conditions. However, the two-source model, with a more complex treatment of the energy and surface temperature partitioning between the soil and vegetation, outperformed the simpler one-source model in estimating sensible and latent heat fluxes. This is consistent with findings from prior studies. For the one-source model, a time-variant expression of the adjustment parameter (to account for the difference between

Towards automatic SAR-optical stereogrammetry over urban areas using very high resolution imagery

Science.gov (United States)

Qiu, Chunping; Schmitt, Michael; Zhu, Xiao Xiang

2018-04-01

In this paper we discuss the potential and challenges regarding SAR-optical stereogrammetry for urban areas, using very-high-resolution (VHR) remote sensing imagery. Since we do this mainly from a geometrical point of view, we first analyze the height reconstruction accuracy to be expected for different stereogrammetric configurations. Then, we propose a strategy for simultaneous tie point matching and 3D reconstruction, which exploits an epipolar-like search window constraint. To drive the matching and ensure some robustness, we combine different established hand-crafted similarity measures. For the experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR imagery is generally feasible with 3D positioning accuracies in the meter-domain, although the matching of these strongly hetereogeneous multi-sensor data remains very challenging.

Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture

Science.gov (United States)

Elarab, Manal; Ticlavilca, Andres M.; Torres-Rua, Alfonso F.; Maslova, Inga; McKee, Mac

2015-12-01

Precision agriculture requires high-resolution information to enable greater precision in the management of inputs to production. Actionable information about crop and field status must be acquired at high spatial resolution and at a temporal frequency appropriate for timely responses. In this study, high spatial resolution imagery was obtained through the use of a small, unmanned aerial system called AggieAirTM. Simultaneously with the AggieAir flights, intensive ground sampling for plant chlorophyll was conducted at precisely determined locations. This study reports the application of a relevance vector machine coupled with cross validation and backward elimination to a dataset composed of reflectance from high-resolution multi-spectral imagery (VIS-NIR), thermal infrared imagery, and vegetative indices, in conjunction with in situ SPAD measurements from which chlorophyll concentrations were derived, to estimate chlorophyll concentration from remotely sensed data at 15-cm resolution. The results indicate that a relevance vector machine with a thin plate spline kernel type and kernel width of 5.4, having LAI, NDVI, thermal and red bands as the selected set of inputs, can be used to spatially estimate chlorophyll concentration with a root-mean-squared-error of 5.31 μg cm-2, efficiency of 0.76, and 9 relevance vectors.

A New Image Processing Procedure Integrating PCI-RPC and ArcGIS-Spline Tools to Improve the Orthorectification Accuracy of High-Resolution Satellite Imagery

Directory of Open Access Journals (Sweden)

Hongying Zhang

2016-10-01

Full Text Available Given the low accuracy of the traditional remote sensing image processing software when orthorectifying satellite images that cover mountainous areas, and in order to make a full use of mutually compatible and complementary characteristics of the remote sensing image processing software PCI-RPC (Rational Polynomial Coefficients and ArcGIS-Spline, this study puts forward a new operational and effective image processing procedure to improve the accuracy of image orthorectification. The new procedure first processes raw image data into an orthorectified image using PCI with RPC model (PCI-RPC, and then the orthorectified image is further processed using ArcGIS with the Spline tool (ArcGIS-Spline. We used the high-resolution CBERS-02C satellite images (HR1 and HR2 scenes with a pixel size of 2 m acquired from Yangyuan County in Hebei Province of China to test the procedure. In this study, when separately using PCI-RPC and ArcGIS-Spline tools directly to process the HR1/HR2 raw images, the orthorectification accuracies (root mean square errors, RMSEs for HR1/HR2 images were 2.94 m/2.81 m and 4.65 m/4.41 m, respectively. However, when using our newly proposed procedure, the corresponding RMSEs could be reduced to 1.10 m/1.07 m. The experimental results demonstrated that the new image processing procedure which integrates PCI-RPC and ArcGIS-Spline tools could significantly improve image orthorectification accuracy. Therefore, in terms of practice, the new procedure has the potential to use existing software products to easily improve image orthorectification accuracy.

Gaussian Multiple Instance Learning Approach for Mapping the Slums of the World Using Very High Resolution Imagery

Energy Technology Data Exchange (ETDEWEB)

Vatsavai, Raju [ORNL

2013-01-01

In this paper, we present a computationally efficient algo- rithm based on multiple instance learning for mapping infor- mal settlements (slums) using very high-resolution remote sensing imagery. From remote sensing perspective, infor- mal settlements share unique spatial characteristics that dis- tinguish them from other urban structures like industrial, commercial, and formal residential settlements. However, regular pattern recognition and machine learning methods, which are predominantly single-instance or per-pixel classi- fiers, often fail to accurately map the informal settlements as they do not capture the complex spatial patterns. To overcome these limitations we employed a multiple instance based machine learning approach, where groups of contigu- ous pixels (image patches) are modeled as generated by a Gaussian distribution. We have conducted several experi- ments on very high-resolution satellite imagery, represent- ing four unique geographic regions across the world. Our method showed consistent improvement in accurately iden- tifying informal settlements.

Integrating High-Resolution Taskable Imagery into a Sensorweb for Automatic Space-Based Monitoring of Flooding in Thailand

Science.gov (United States)

Chien, Steve; Mclaren, David; Doubleday, Joshua; Tran, Daniel; Tanpipat, Veerachai; Chitradon, Royol; Boonya-aroonnet, Surajate; Thanapakpawin, Porranee; Mandl, Daniel

2012-01-01

Several space-based assets (Terra, Aqua, Earth Observing One) have been integrated into a sensorweb to monitor flooding in Thailand. In this approach, the Moderate Imaging Spectrometer (MODIS) data from Terra and Aqua is used to perform broad-scale monitoring to track flooding at the regional level (250m/pixel) and EO-1 is autonomously tasked in response to alerts to acquire higher resolution (30m/pixel) Advanced Land Imager (ALI) data. This data is then automatically processed to derive products such as surface water extent and volumetric water estimates. These products are then automatically pushed to organizations in Thailand for use in damage estimation, relief efforts, and damage mitigation. More recently, this sensorweb structure has been used to request imagery, access imagery, and process high-resolution (several m to 30m), targetable asset imagery from commercial assets including Worldview-2, Ikonos, Radarsat-2, Landsat-7, and Geo-Eye-1. We describe the overall sensorweb framework as well as new workflows and products made possible via these extensions.

Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning

Directory of Open Access Journals (Sweden)

Yang Chen

2018-05-01

Full Text Available Accurate information on urban surface water is important for assessing the role it plays in urban ecosystem services in the context of human survival and climate change. The precise extraction of urban water bodies from images is of great significance for urban planning and socioeconomic development. In this paper, a novel deep-learning architecture is proposed for the extraction of urban water bodies from high-resolution remote sensing (HRRS imagery. First, an adaptive simple linear iterative clustering algorithm is applied for segmentation of the remote-sensing image into high-quality superpixels. Then, a new convolutional neural network (CNN architecture is designed that can extract useful high-level features of water bodies from input data in a complex urban background and mark the superpixel as one of two classes: an including water or no-water pixel. Finally, a high-resolution image of water-extracted superpixels is generated. Experimental results show that the proposed method achieved higher accuracy for water extraction from the high-resolution remote-sensing images than traditional approaches, and the average overall accuracy is 99.14%.

ASTER Orthorectified Digital Elevation Model (DEM) V003

Data.gov (United States)

National Aeronautics and Space Administration — The ASTER L3 DEM and Orthorectified Images form a multi-file product that contains both the Digital Elevation Model (DEM), and the Orthorectified Image products....

DSM GENERATION FROM HIGH RESOLUTION COSMO-SKYMED IMAGERY WITH RADARGRAMMETRIC MODEL

Directory of Open Access Journals (Sweden)

P. Capaldo

2012-09-01

Full Text Available The availability of new high resolution radar spaceborne sensors offers new interesting potentialities for the geomatics application: spatial and temporal change detection, features extraction, generation of Digital Surface (DSMs. As regards the DSMs generation from new high resolution data (as SpotLight imagery, the development and the accuracy assessment of method based on radargrammetric approach are topics of great interest and relevance. The aim of this investigation is the DSM generation from a COSMO-SkyMed Spotlight stereo pair with the radargrammetric technique. DSM generation procedure consists of two basic steps: the stereo pair orientation and the image matching. The suite for radargrammetric approach has been implemented in SISAR (Software per Immagini Satellitari ad Alta Risoluzione, a scientific software developed at the Geodesy and Geomatic Institute of the University of Rome "La Sapienza". As regard the image matching the critical issue is the definition of a strategy to search the corresponding points; in SISAR software, an original matching procedure has been developed, based on a coarse-to-fine hierarchical solution with an effective combination of geometrical constrains and an Area Base Matching (ABM algorithm.

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Directory of Open Access Journals (Sweden)

Nicola Clerici

2016-07-01

Full Text Available Periurban forests are key to offsetting anthropogenic carbon emissions, but they are under constant threat from urbanization. In particular, secondary Neotropical forest types in Andean periurban areas have a high potential to store carbon, but are currently poorly characterized. To address this lack of information, we developed a method to estimate periurban aboveground biomass (AGB—a proxy for multiple ecosystem services—of secondary Andean forests near Bogotá, Colombia, based on very high resolution (VHR GeoEye-1, Pleiades-1A imagery and field-measured plot data. Specifically, we tested a series of different pre-processing workflows to derive six vegetation indices that were regressed against in situ estimates of AGB. Overall, the coupling of linear models and the Ratio Vegetation Index produced the most satisfactory results. Atmospheric and topographic correction proved to be key in improving model fit, especially in high aerosol and rugged terrain such as the Andes. Methods and findings provide baseline AGB and carbon stock information for little studied periurban Andean secondary forests. The methodological approach can also be used for integrating limited forest monitoring plot AGB data with very high resolution imagery for cost-effective modelling of ecosystem service provision from forests, monitoring reforestation and forest cover change, and for carbon offset assessments.

Advances In very high resolution satellite imagery analysis for Monitoring human settlements

Energy Technology Data Exchange (ETDEWEB)

Vatsavai, Raju [ORNL; Cheriyadat, Anil M [ORNL; Bhaduri, Budhendra L [ORNL

2014-01-01

The high rate of urbanization, political conflicts and ensuing internal displacement of population, and increased poverty in the 20th century has resulted in rapid increase of informal settlements. These unplanned, unauthorized, and/or unstructured homes, known as informal settlements, shantytowns, barrios, or slums, pose several challenges to the nations, as these settlements are often located in most hazardous regions and lack basic services. Though several World Bank and United Nations sponsored studies stress the importance of poverty maps in designing better policies and interventions, mapping slums of the world is a daunting and challenging task. In this paper, we summarize our ongoing research on settlement mapping through the utilization of Very high resolution (VHR) remote sensing imagery. Most existing approaches used to classify VHR images are single instance (or pixel-based) learning algorithms, which are inadequate for analyzing VHR imagery, as single pixels do not contain sufficient contextual information (see Figure 1). However, much needed spatial contextual information can be captured via feature extraction and/or through newer machine learning algorithms in order to extract complex spatial patterns that distinguish informal settlements from formal ones. In recent years, we made significant progress in advancing the state of art in both directions. This paper summarizes these results.

Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis

Directory of Open Access Journals (Sweden)

Hanqing Zhao

2017-05-01

Full Text Available Abstract: Terraces are typical artificial landforms on the Loess Plateau, with ecological functions in water and soil conservation, agricultural production, and biodiversity. Recording the spatial distribution of terraces is the basis of monitoring their extent and understanding their ecological effects. The current terrace extraction method mainly relies on high-resolution imagery, but its accuracy is limited due to vegetation coverage distorting the features of terraces in imagery. High-resolution topographic data reflecting the morphology of true terrace surfaces are needed. Terraces extraction on the Loess Plateau is challenging because of the complex terrain and diverse vegetation after the implementation of “vegetation recovery”. This study presents an automatic method of extracting terraces based on 1 m resolution digital elevation models (DEMs and 0.3 m resolution Worldview-3 imagery as auxiliary information used for object-based image analysis (OBIA. A multi-resolution segmentation method was used where slope, positive and negative terrain index (PN, accumulative curvature slope (AC, and slope of slope (SOS were determined as input layers for image segmentation by correlation analysis and Sheffield entropy method. The main classification features based on DEMs were chosen from the terrain features derived from terrain factors and texture features by gray-level co-occurrence matrix (GLCM analysis; subsequently, these features were determined by the importance analysis on classification and regression tree (CART analysis. Extraction rules based on DEMs were generated from the classification features with a total classification accuracy of 89.96%. The red band and near-infrared band of images were used to exclude construction land, which is easily confused with small-size terraces. As a result, the total classification accuracy was increased to 94%. The proposed method ensures comprehensive consideration of terrain, texture, shape, and

Mapping coastal marine debris using aerial imagery and spatial analysis.

Science.gov (United States)

Moy, Kirsten; Neilson, Brian; Chung, Anne; Meadows, Amber; Castrence, Miguel; Ambagis, Stephen; Davidson, Kristine

2017-12-19

This study is the first to systematically quantify, categorize, and map marine macro-debris across the main Hawaiian Islands (MHI), including remote areas (e.g., Niihau, Kahoolawe, and northern Molokai). Aerial surveys were conducted over each island to collect high resolution photos, which were processed into orthorectified imagery and visually analyzed in GIS. The technique provided precise measurements of the quantity, location, type, and size of macro-debris (>0.05m 2 ), identifying 20,658 total debris items. Northeastern (windward) shorelines had the highest density of debris. Plastics, including nets, lines, buoys, floats, and foam, comprised 83% of the total count. In addition, the study located six vessels from the 2011 Tōhoku tsunami. These results created a baseline of the location, distribution, and composition of marine macro-debris across the MHI. Resource managers and communities may target high priority areas, particularly along remote coastlines where macro-debris counts were largely undocumented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery

Science.gov (United States)

Bai, Ting; Sun, Kaimin; Deng, Shiquan; Chen, Yan

2018-03-01

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.

Detailed Maps Depicting the Shallow-Water Benthic Habitats of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Detailed, shallow-water coral reef ecosystem maps were generated by rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations...

Testing methods for using high-resolution satellite imagery to monitor polar bear abundance and distribution

Science.gov (United States)

LaRue, Michelle A.; Stapleton, Seth P.; Porter, Claire; Atkinson, Stephen N.; Atwood, Todd C.; Dyck, Markus; Lecomte, Nicolas

2015-01-01

High-resolution satellite imagery is a promising tool for providing coarse information about polar species abundance and distribution, but current applications are limited. With polar bears (Ursus maritimus), the technique has only proven effective on landscapes with little topographic relief that are devoid of snow and ice, and time-consuming manual review of imagery is required to identify bears. Here, we evaluated mechanisms to further develop methods for satellite imagery by examining data from Rowley Island, Canada. We attempted to automate and expedite detection via a supervised spectral classification and image differencing to expedite image review. We also assessed what proportion of a region should be sampled to obtain reliable estimates of density and abundance. Although the spectral signature of polar bears differed from nontarget objects, these differences were insufficient to yield useful results via a supervised classification process. Conversely, automated image differencing—or subtracting one image from another—correctly identified nearly 90% of polar bear locations. This technique, however, also yielded false positives, suggesting that manual review will still be required to confirm polar bear locations. On Rowley Island, bear distribution approximated a Poisson distribution across a range of plot sizes, and resampling suggests that sampling >50% of the site facilitates reliable estimation of density (CV large-scale applications remain limited because of the challenges in automation and the limited environments in which the method can be effectively applied. Improvements in resolution may expand opportunities for its future uses.

Testing methods for using high-resolution satellite imagery to monitor polar bear abundance and distribution

Science.gov (United States)

LaRue, Michelle A.; Stapleton, Seth P.; Porter, Claire; Atkinson, Stephen N.; Atwood, Todd C.; Dyck, Markus; Lecomte, Nicolas

2015-01-01

High-resolution satellite imagery is a promising tool for providing coarse information about polar species abundance and distribution, but current applications are limited. With polar bears (Ursus maritimus), the technique has only proven effective on landscapes with little topographic relief that are devoid of snow and ice, and time-consuming manual review of imagery is required to identify bears. Here, we evaluated mechanisms to further develop methods for satellite imagery by examining data from Rowley Island, Canada. We attempted to automate and expedite detection via a supervised spectral classification and image differencing to expedite image review. We also assessed what proportion of a region should be sampled to obtain reliable estimates of density and abundance. Although the spectral signature of polar bears differed from nontarget objects, these differences were insufficient to yield useful results via a supervised classification process. Conversely, automated image differencing—or subtracting one image from another—correctly identified nearly 90% of polar bear locations. This technique, however, also yielded false positives, suggesting that manual review will still be required to confirm polar bear locations. On Rowley Island, bear distribution approximated a Poisson distribution across a range of plot sizes, and resampling suggests that sampling >50% of the site facilitates reliable estimation of density (CV in certain areas, but large-scale applications remain limited because of the challenges in automation and the limited environments in which the method can be effectively applied. Improvements in resolution may expand opportunities for its future uses.

Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning

Science.gov (United States)

Beck, J. M.; Bridges, S.; Collins, C.; Rushing, J.; Graves, S. J.

2017-12-01

Researchers at the Information Technology and Systems Center at the University of Alabama in Huntsville are using Deep Learning with Convolutional Neural Networks (CNNs) to develop a method for enhancing the spatial resolutions of moderate resolution (10-60m) multispectral satellite imagery. This enhancement will effectively match the resolutions of imagery from multiple sensors to provide increased global temporal-spatial coverage for a variety of Earth science products. Our research is centered on using Deep Learning for automatically generating transformations for increasing the spatial resolution of remotely sensed images with different spatial, spectral, and temporal resolutions. One of the most important steps in using images from multiple sensors is to transform the different image layers into the same spatial resolution, preferably the highest spatial resolution, without compromising the spectral information. Recent advances in Deep Learning have shown that CNNs can be used to effectively and efficiently upscale or enhance the spatial resolution of multispectral images with the use of an auxiliary data source such as a high spatial resolution panchromatic image. In contrast, we are using both the spatial and spectral details inherent in low spatial resolution multispectral images for image enhancement without the use of a panchromatic image. This presentation will discuss how this technology will benefit many Earth Science applications that use remotely sensed images with moderate spatial resolutions.

2010 NOAA Ortho-rectified Mosaic from Color Aerial Imagery of MISSISSIPPI RIVER - BATON ROUGE TO LAPLACE (NODC Accession 0074374)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of MISSISSIPPI RIVER -...

Research of building information extraction and evaluation based on high-resolution remote-sensing imagery

Science.gov (United States)

Cao, Qiong; Gu, Lingjia; Ren, Ruizhi; Wang, Lang

2016-09-01

Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection

Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery

Directory of Open Access Journals (Sweden)

N. C. Wright

2018-04-01

Full Text Available Snow, ice, and melt ponds cover the surface of the Arctic Ocean in fractions that change throughout the seasons. These surfaces control albedo and exert tremendous influence over the energy balance in the Arctic. Increasingly available meter- to decimeter-scale resolution optical imagery captures the evolution of the ice and ocean surface state visually, but methods for quantifying coverage of key surface types from raw imagery are not yet well established. Here we present an open-source system designed to provide a standardized, automated, and reproducible technique for processing optical imagery of sea ice. The method classifies surface coverage into three main categories: snow and bare ice, melt ponds and submerged ice, and open water. The method is demonstrated on imagery from four sensor platforms and on imagery spanning from spring thaw to fall freeze-up. Tests show the classification accuracy of this method typically exceeds 96 %. To facilitate scientific use, we evaluate the minimum observation area required for reporting a representative sample of surface coverage. We provide an open-source distribution of this algorithm and associated training datasets and suggest the community consider this a step towards standardizing optical sea ice imagery processing. We hope to encourage future collaborative efforts to improve the code base and to analyze large datasets of optical sea ice imagery.

Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery

Science.gov (United States)

Wright, Nicholas C.; Polashenski, Chris M.

2018-04-01

Snow, ice, and melt ponds cover the surface of the Arctic Ocean in fractions that change throughout the seasons. These surfaces control albedo and exert tremendous influence over the energy balance in the Arctic. Increasingly available meter- to decimeter-scale resolution optical imagery captures the evolution of the ice and ocean surface state visually, but methods for quantifying coverage of key surface types from raw imagery are not yet well established. Here we present an open-source system designed to provide a standardized, automated, and reproducible technique for processing optical imagery of sea ice. The method classifies surface coverage into three main categories: snow and bare ice, melt ponds and submerged ice, and open water. The method is demonstrated on imagery from four sensor platforms and on imagery spanning from spring thaw to fall freeze-up. Tests show the classification accuracy of this method typically exceeds 96 %. To facilitate scientific use, we evaluate the minimum observation area required for reporting a representative sample of surface coverage. We provide an open-source distribution of this algorithm and associated training datasets and suggest the community consider this a step towards standardizing optical sea ice imagery processing. We hope to encourage future collaborative efforts to improve the code base and to analyze large datasets of optical sea ice imagery.

Filtering high resolution hyperspectral imagery and analyzing it for quantification of water quality parameters and aquatic vegetation

Science.gov (United States)

Pande-Chhetri, Roshan

High resolution hyperspectral imagery (airborne or ground-based) is gaining momentum as a useful analytical tool in various fields including agriculture and aquatic systems. These images are often contaminated with stripes and noise resulting in lower signal-to-noise ratio, especially in aquatic regions where signal is naturally low. This research investigates effective methods for filtering high spatial resolution hyperspectral imagery and use of the imagery in water quality parameter estimation and aquatic vegetation classification. The striping pattern of the hyperspectral imagery is non-parametric and difficult to filter. In this research, a de-striping algorithm based on wavelet analysis and adaptive Fourier domain normalization was examined. The result of this algorithm was found superior to other available algorithms and yielded highest Peak Signal to Noise Ratio improvement. The algorithm was implemented on individual image bands and on selected bands of the Maximum Noise Fraction (MNF) transformed images. The results showed that image filtering in the MNF domain was efficient and produced best results. The study investigated methods of analyzing hyperspectral imagery to estimate water quality parameters and to map aquatic vegetation in case-2 waters. Ground-based hyperspectral imagery was analyzed to determine chlorophyll-a (Chl-a) concentrations in aquaculture ponds. Two-band and three-band indices were implemented and the effect of using submerged reflectance targets was evaluated. Laboratory measured values were found to be in strong correlation with two-band and three-band spectral indices computed from the hyperspectral image. Coefficients of determination (R2) values were found to be 0.833 and 0.862 without submerged targets and stronger values of 0.975 and 0.982 were obtained using submerged targets. Airborne hyperspectral images were used to detect and classify aquatic vegetation in a black river estuarine system. Image normalization for water

Single image super-resolution via regularized extreme learning regression for imagery from microgrid polarimeters

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Sargent, Garrett C.; Ratliff, Bradley M.; Asari, Vijayan K.

2017-08-01

The advantage of division of focal plane imaging polarimeters is their ability to obtain temporally synchronized intensity measurements across a scene; however, they sacrifice spatial resolution in doing so due to their spatially modulated arrangement of the pixel-to-pixel polarizers and often result in aliased imagery. Here, we propose a super-resolution method based upon two previously trained extreme learning machines (ELM) that attempt to recover missing high frequency and low frequency content beyond the spatial resolution of the sensor. This method yields a computationally fast and simple way of recovering lost high and low frequency content from demosaicing raw microgrid polarimetric imagery. The proposed method outperforms other state-of-the-art single-image super-resolution algorithms in terms of structural similarity and peak signal-to-noise ratio.

3D high resolution tracking of ice flow using mutli-temporal stereo satellite imagery, Franz Josef Glacier, New Zealand

Science.gov (United States)

Leprince, S.; Lin, J.; Ayoub, F.; Herman, F.; Avouac, J.

2013-12-01

We present the latest capabilities added to the Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) software, which aim at analyzing time-series of stereoscopic imagery to document 3D variations of the ground surface. We review the processing chain and present the new and improved modules for satellite pushbroom imagery, in particular the N-image bundle block adjustment to jointly optimize the viewing geometry of multiple acquisitions, the improved multi-scale image matching based on Semi-Global Matching (SGM) to extract high resolution topography, and the triangulation of multi-temporal disparity maps to derive 3D ground motion. In particular, processes are optimized to run on a cluster computing environment. This new suite of algorithms is applied to the study of Worldview stereo imagery above the Franz Josef, Fox, and Tasman Glaciers, New Zealand, acquired on 01/30/2013, 02/09/2013, and 02/28/2013. We derive high resolution (1m post-spacing) maps of ice flow in three dimensions, where ice velocities of up to 4 m/day are recorded. Images were collected in early summer during a dry and sunny period, which followed two weeks of unsettled weather with several heavy rainfall events across the Southern Alps. The 3D tracking of ice flow highlights the surface response of the glaciers to changes in effective pressure at the ice-bedrock interface due to heavy rainfall, at an unprecedented spatial resolution.

Short Communication. Using high resolution UAV imagery to estimate tree variables in Pinus pinea plantation in Portugal

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Juan Guerra Hernandez

2016-07-01

Research highlights: The results demonstrate that tree variables can be automatically extracted from high resolution imagery. We highlight the use of UAV systems as a fast, reliable and cost‑effective technique for small scale applications. Keywords: Unmanned aerial systems (UAS; forest inventory; tree crown variables; 3D image modelling; canopy height model (CHM; object‑based image analysis (OBIA, structure‑from‑motion (SfM.

TESTFIELD TRENTO: GEOMETRIC EVALUATION OF VERY HIGH RESOLUTION SATELLITE IMAGERY

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G. Agugiaro

2012-07-01

Full Text Available Today the use of spaceborne Very High Spatial Resolution (VHSR optical sensors for automatic 3D information extraction is increasing in the scientific and civil communities. The 3D Optical Metrology (3DOM Unit of the Bruno Kessler Foundation (FBK in Trento (Italy has collected stereo VHSR satellite imagery, as well as aerial and terrestrial data over Trento, with the aim to create a complete data collection with state-of-the-art datasets for investigations on image analysis, automatic digital surface model (DSM generation, 2D/3D feature extraction, city modelling and data fusion. The testfield region covers the city of Trento, characterised by very dense urban (historical centre, residential and industrial areas, and the surrounding hills and steep mountains (approximate height range 200-2100 m with cultivations, forests and bare soil. This paper reports the analysis conducted in FBK on the VHSR spaceborne imagery of Trento testfield for 3D information extraction. The data include two stereo-pairs acquired by WorldView-2 in August 2010 and by GeoEye-1 in September 2011 in panchromatic and multispectral mode, together with their original Rational Polynomial Coefficients (RPC, and the position and description of well distributed ground points. For reference and validation, a DSM from airborne LiDAR acquisition is used. The paper gives details on the project and the dataset characteristics. The results achieved by 3DOM on DSM extraction from WorldView-2 and GeoEye-1 stereo-pairs are shown and commented.

Detailed Maps Depicting the Shallow-Water Benthic Habitats of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery (Draft)

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National Oceanic and Atmospheric Administration, Department of Commerce — Detailed, shallow-water coral reef ecosystem maps were generated by rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations...

Improved wetland classification using eight-band high-resolution satellite imagery and a hybrid approach

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Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of derived wetland maps were limited or often unsatisfactory largely due to the relatively coarse spatial resolution of conventional satellite imagery. This re...

Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA (Final)

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EPA announced the availability of the final report,Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA . This report and associated land use/land cover (LULC) coverage is the result o...

Vehicle detection from very-high-resolution (VHR) aerial imagery using attribute belief propagation (ABP)

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Wang, Yanli; Li, Ying; Zhang, Li; Huang, Yuchun

2016-10-01

With the popularity of very-high-resolution (VHR) aerial imagery, the shape, color, and context attribute of vehicles are better characterized. Due to the various road surroundings and imaging conditions, vehicle attributes could be adversely affected so that vehicle is mistakenly detected or missed. This paper is motivated to robustly extract the rich attribute feature for detecting the vehicles of VHR imagery under different scenarios. Based on the hierarchical component tree of vehicle context, attribute belief propagation (ABP) is proposed to detect salient vehicles from the statistical perspective. With the Max-tree data structure, the multi-level component tree around the road network is efficiently created. The spatial relationship between vehicle and its belonging context is established with the belief definition of vehicle attribute. To effectively correct single-level belief error, the inter-level belief linkages enforce consistency of belief assignment between corresponding components at different levels. ABP starts from an initial set of vehicle belief calculated by vehicle attribute, and then iterates through each component by applying inter-level belief passing until convergence. The optimal value of vehicle belief of each component is obtained via minimizing its belief function iteratively. The proposed algorithm is tested on a diverse set of VHR imagery acquired in the city and inter-city areas of the West and South China. Experimental results show that the proposed algorithm can detect vehicle efficiently and suppress the erroneous effectively. The proposed ABP framework is promising to robustly classify the vehicles from VHR Aerial imagery.

Fast Updating National Geo-Spatial Databases with High Resolution Imagery: China's Methodology and Experience

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Chen, J.; Wang, D.; Zhao, R. L.; Zhang, H.; Liao, A.; Jiu, J.

2014-04-01

Geospatial databases are irreplaceable national treasure of immense importance. Their up-to-dateness referring to its consistency with respect to the real world plays a critical role in its value and applications. The continuous updating of map databases at 1:50,000 scales is a massive and difficult task for larger countries of the size of more than several million's kilometer squares. This paper presents the research and technological development to support the national map updating at 1:50,000 scales in China, including the development of updating models and methods, production tools and systems for large-scale and rapid updating, as well as the design and implementation of the continuous updating workflow. The use of many data sources and the integration of these data to form a high accuracy, quality checked product were required. It had in turn required up to date techniques of image matching, semantic integration, generalization, data base management and conflict resolution. Design and develop specific software tools and packages to support the large-scale updating production with high resolution imagery and large-scale data generalization, such as map generalization, GIS-supported change interpretation from imagery, DEM interpolation, image matching-based orthophoto generation, data control at different levels. A national 1:50,000 databases updating strategy and its production workflow were designed, including a full coverage updating pattern characterized by all element topographic data modeling, change detection in all related areas, and whole process data quality controlling, a series of technical production specifications, and a network of updating production units in different geographic places in the country.

Automatic Coregistration and orthorectification (ACRO) and subsequent mosaicing of NASA high-resolution imagery over the Mars MC11 quadrangle, using HRSC as a baseline

Science.gov (United States)

Sidiropoulos, Panagiotis; Muller, Jan-Peter; Watson, Gillian; Michael, Gregory; Walter, Sebastian

2018-02-01

This work presents the coregistered, orthorectified and mosaiced high-resolution products of the MC11 quadrangle of Mars, which have been processed using novel, fully automatic, techniques. We discuss the development of a pipeline that achieves fully automatic and parameter independent geometric alignment of high-resolution planetary images, starting from raw input images in NASA PDS format and following all required steps to produce a coregistered geotiff image, a corresponding footprint and useful metadata. Additionally, we describe the development of a radiometric calibration technique that post-processes coregistered images to make them radiometrically consistent. Finally, we present a batch-mode application of the developed techniques over the MC11 quadrangle to validate their potential, as well as to generate end products, which are released to the planetary science community, thus assisting in the analysis of Mars static and dynamic features. This case study is a step towards the full automation of signal processing tasks that are essential to increase the usability of planetary data, but currently, require the extensive use of human resources.

Environmental monitoring of El Hierro Island submarine volcano, by combining low and high resolution satellite imagery

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Eugenio, F.; Martin, J.; Marcello, J.; Fraile-Nuez, E.

2014-06-01

El Hierro Island, located at the Canary Islands Archipelago in the Atlantic coast of North Africa, has been rocked by thousands of tremors and earthquakes since July 2011. Finally, an underwater volcanic eruption started 300 m below sea level on October 10, 2011. Since then, regular multidisciplinary monitoring has been carried out in order to quantify the environmental impacts caused by the submarine eruption. Thanks to this natural tracer release, multisensorial satellite imagery obtained from MODIS and MERIS sensors have been processed to monitor the volcano activity and to provide information on the concentration of biological, chemical and physical marine parameters. Specifically, low resolution satellite estimations of optimal diffuse attenuation coefficient (Kd) and chlorophyll-a (Chl-a) concentration under these abnormal conditions have been assessed. These remote sensing data have played a fundamental role during field campaigns guiding the oceanographic vessel to the appropriate sampling areas. In addition, to analyze El Hierro submarine volcano area, WorldView-2 high resolution satellite spectral bands were atmospherically and deglinted processed prior to obtain a high-resolution optimal diffuse attenuation coefficient model. This novel algorithm was developed using a matchup data set with MERIS and MODIS data, in situ transmittances measurements and a seawater radiative transfer model. Multisensor and multitemporal imagery processed from satellite remote sensing sensors have demonstrated to be a powerful tool for monitoring the submarine volcanic activities, such as discolored seawater, floating material and volcanic plume, having shown the capabilities to improve the understanding of submarine volcanic processes.

Assessment of Atmospheric Algorithms to Retrieve Vegetation in Natural Protected Areas Using Multispectral High Resolution Imagery

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Javier Marcello

2016-09-01

Full Text Available The precise mapping of vegetation covers in semi-arid areas is a complex task as this type of environment consists of sparse vegetation mainly composed of small shrubs. The launch of high resolution satellites, with additional spectral bands and the ability to alter the viewing angle, offers a useful technology to focus on this objective. In this context, atmospheric correction is a fundamental step in the pre-processing of such remote sensing imagery and, consequently, different algorithms have been developed for this purpose over the years. They are commonly categorized as imaged-based methods as well as in more advanced physical models based on the radiative transfer theory. Despite the relevance of this topic, a few comparative studies covering several methods have been carried out using high resolution data or which are specifically applied to vegetation covers. In this work, the performance of five representative atmospheric correction algorithms (DOS, QUAC, FLAASH, ATCOR and 6S has been assessed, using high resolution Worldview-2 imagery and field spectroradiometer data collected simultaneously, with the goal of identifying the most appropriate techniques. The study also included a detailed analysis of the parameterization influence on the final results of the correction, the aerosol model and its optical thickness being important parameters to be properly adjusted. The effects of corrections were studied in vegetation and soil sites belonging to different protected semi-arid ecosystems (high mountain and coastal areas. In summary, the superior performance of model-based algorithms, 6S in particular, has been demonstrated, achieving reflectance estimations very close to the in-situ measurements (RMSE of between 2% and 3%. Finally, an example of the importance of the atmospheric correction in the vegetation estimation in these natural areas is presented, allowing the robust mapping of species and the analysis of multitemporal variations

Empirical Estimation of Total Nitrogen and Total Phosphorus Concentration of Urban Water Bodies in China Using High Resolution IKONOS Multispectral Imagery

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Jiaming Liu

2015-11-01

Full Text Available Measuring total nitrogen (TN and total phosphorus (TP is important in managing heavy polluted urban waters in China. This study uses high spatial resolution IKONOS imagery with four multispectral bands, which roughly correspond to Landsat/TM bands 1–4, to determine TN and TP in small urban rivers and lakes in China. By using Lake Cihu and the lower reaches of Wen-Rui Tang (WRT River as examples, this paper develops both multiple linear regressions (MLR and artificial neural network (ANN models to estimate TN and TP concentrations from high spatial resolution remote sensing imagery and in situ water samples collected concurrently with overpassing satellite. The measured and estimated values of both MLR and ANN models are in good agreement (R2 > 0.85 and RMSE < 2.50. The empirical equations selected by MLR are more straightforward, whereas the estimated accuracy using ANN model is better (R2 > 0.86 and RMSE < 0.89. Results validate the potential of using high resolution IKONOS multispectral imagery to study the chemical states of small-sized urban water bodies. The spatial distribution maps of TN and TP concentrations generated by the ANN model can inform the decision makers of variations in water quality in Lake Cihu and lower reaches of WRT River. The approaches and equations developed in this study could be applied to other urban water bodies for water quality monitoring.

The Yosemite Extreme Panoramic Imaging Project: Monitoring Rockfall in Yosemite Valley with High-Resolution, Three-Dimensional Imagery

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Stock, G. M.; Hansen, E.; Downing, G.

2008-12-01

Yosemite Valley experiences numerous rockfalls each year, with over 600 rockfall events documented since 1850. However, monitoring rockfall activity has proved challenging without high-resolution "basemap" imagery of the Valley walls. The Yosemite Extreme Panoramic Imaging Project, a partnership between the National Park Service and xRez Studio, has created an unprecedented image of Yosemite Valley's walls by utilizing gigapixel panoramic photography, LiDAR-based digital terrain modeling, and three-dimensional computer rendering. Photographic capture was accomplished by 20 separate teams shooting from key overlapping locations throughout Yosemite Valley. The shots were taken simultaneously in order to ensure uniform lighting, with each team taking over 500 overlapping shots from each vantage point. Each team's shots were then assembled into 20 gigapixel panoramas. In addition, all 20 gigapixel panoramas were projected onto a 1 meter resolution digital terrain model in three-dimensional rendering software, unifying Yosemite Valley's walls into a vertical orthographic view. The resulting image reveals the geologic complexity of Yosemite Valley in high resolution and represents one of the world's largest photographic captures of a single area. Several rockfalls have already occurred since image capture, and repeat photography of these areas clearly delineates rockfall source areas and failure dynamics. Thus, the imagery has already proven to be a valuable tool for monitoring and understanding rockfall in Yosemite Valley. It also sets a new benchmark for the quality of information a photographic image, enabled with powerful new imaging technology, can provide for the earth sciences.

Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

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Yongyang Xu

2018-01-01

Full Text Available Very high resolution (VHR remote sensing imagery has been used for land cover classification, and it tends to a transition from land-use classification to pixel-level semantic segmentation. Inspired by the recent success of deep learning and the filter method in computer vision, this work provides a segmentation model, which designs an image segmentation neural network based on the deep residual networks and uses a guided filter to extract buildings in remote sensing imagery. Our method includes the following steps: first, the VHR remote sensing imagery is preprocessed and some hand-crafted features are calculated. Second, a designed deep network architecture is trained with the urban district remote sensing image to extract buildings at the pixel level. Third, a guided filter is employed to optimize the classification map produced by deep learning; at the same time, some salt-and-pepper noise is removed. Experimental results based on the Vaihingen and Potsdam datasets demonstrate that our method, which benefits from neural networks and guided filtering, achieves a higher overall accuracy when compared with other machine learning and deep learning methods. The method proposed shows outstanding performance in terms of the building extraction from diversified objects in the urban district.

Ship detection for high resolution optical imagery with adaptive target filter

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Ju, Hongbin

2015-10-01

Ship detection is important due to both its civil and military use. In this paper, we propose a novel ship detection method, Adaptive Target Filter (ATF), for high resolution optical imagery. The proposed framework can be grouped into two stages, where in the first stage, a test image is densely divided into different detection windows and each window is transformed to a feature vector in its feature space. The Histograms of Oriented Gradients (HOG) is accumulated as a basic feature descriptor. In the second stage, the proposed ATF highlights all the ship regions and suppresses the undesired backgrounds adaptively. Each detection window is assigned a score, which represents the degree of the window belonging to a certain ship category. The ATF can be adaptively obtained by the weighted Logistic Regression (WLR) according to the distribution of backgrounds and targets of the input image. The main innovation of our method is that we only need to collect positive training samples to build the filter, while the negative training samples are adaptively generated by the input image. This is different to other classification method such as Support Vector Machine (SVM) and Logistic Regression (LR), which need to collect both positive and negative training samples. The experimental result on 1-m high resolution optical images shows the proposed method achieves a desired ship detection performance with higher quality and robustness than other methods, e.g., SVM and LR.

Applying High Resolution Imagery to Understand the Role of Dynamics in the Diminishing Arctic Sea Ice Cover

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2015-09-30

describe contemporary ice pack thickness, MODIS , AVHRR, RadarSat-2 (satellite imagery) that describe ice pack deformation features on large scales, as well...high-resolution visible-band images of the Arctic ice pack that are available at the GFL, USGS. The statistics related to the available images are...University of Maryland team as a Faculty Research Assistant, working under the guidance of Co-PI Farrell. Ms. Faber is responsible for analysis of MODIS

Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping

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Victoria Gonzalez-Dugo

2015-10-01

Full Text Available There is a growing need for developing high-throughput tools for crop phenotyping that would increase the rate of genetic improvement. In most cases, the indicators used for this purpose are related with canopy structure (often acquired with RGB cameras and multispectral sensors allowing the calculation of NDVI, but using approaches related with the crop physiology are rare. High-resolution hyperspectral remote sensing imagery provides optical indices related to physiological condition through the quantification of photosynthetic pigment and chlorophyll fluorescence emission. This study demonstrates the use of narrow-band indicators of stress as a potential tool for phenotyping under rainfed conditions using two airborne datasets acquired over a wheat experiment with 150 plots comprising two species and 50 varieties (bread and durum wheat. The flights were performed at the early stem elongation stage and during the milking stage. Physiological measurements made at the time of flights demonstrated that the second flight was made during the terminal stress, known to largely determine final yield under rainfed conditions. The hyperspectral imagery enabled the extraction of thermal, radiance, and reflectance spectra from 260 spectral bands from each plot for the calculation of indices related to photosynthetic pigment absorption in the visible and red-edge regions, the quantification of chlorophyll fluorescence emission, as well as structural indices related to canopy structure. Under the conditions of this study, the structural indices (i.e., NDVI did not show a good performance at predicting yield, probably because of the large effects of terminal water stress. Thermal indices, indices related to chlorophyll fluorescence (calculated using the FLD method, and carotenoids pigment indices (PRI and CAR demonstrated to be better suited for screening complex traits such as crop yield. The study concludes that the indicators derived from high-resolution

High-Resolution Mapping of Urban Surface Water Using ZY-3 Multi-Spectral Imagery

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Fangfang Yao

2015-09-01

Full Text Available Accurate information of urban surface water is important for assessing the role it plays in urban ecosystem services under the content of urbanization and climate change. However, high-resolution monitoring of urban water bodies using remote sensing remains a challenge because of the limitation of previous water indices and the dark building shadow effect. To address this problem, we proposed an automated urban water extraction method (UWEM which combines a new water index, together with a building shadow detection method. Firstly, we trained the parameters of UWEM using ZY-3 imagery of Qingdao, China. Then we verified the algorithm using five other sub-scenes (Aksu, Fuzhou, Hanyang, Huangpo and Huainan ZY-3 imagery. The performance was compared with that of the Normalized Difference Water Index (NDWI. Results indicated that UWEM performed significantly better at the sub-scenes with kappa coefficients improved by 7.87%, 32.35%, 12.64%, 29.72%, 14.29%, respectively, and total omission and commission error reduced by 61.53%, 65.74%, 83.51%, 82.44%, and 74.40%, respectively. Furthermore, UWEM has more stable performances than NDWI’s in a range of thresholds near zero. It reduces the over- and under-estimation issues which often accompany previous water indices when mapping urban surface water under complex environmental conditions.

2010 NOAA Ortho-rectified Mosaic from Color Aerial Imagery of MISSISSIPPI RIVER - LAPLACE TO VENICE (NODC Accession 0075830 and NODC Accession 0075829)

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National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of MISSISSIPPI RIVER -...

Evaluation of the U.S. Geological Survey Landsat burned area essential climate variable across the conterminous U.S. using commercial high-resolution imagery

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

2017-01-01

The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution (1984–2015). Validation is a critical component in the generation of such remotely sensed products. Previous efforts to validate the BAECV relied on a reference dataset derived from Landsat, which was effective in evaluating the product across its timespan but did not allow for consideration of inaccuracies imposed by the Landsat sensor itself. In this effort, the BAECV was validated using 286 high-resolution images, collected from GeoEye-1, QuickBird-2, Worldview-2 and RapidEye satellites. A disproportionate sampling strategy was utilized to ensure enough burned area pixels were collected. Errors of omission and commission for burned area averaged 22 ± 4% and 48 ± 3%, respectively, across CONUS. Errors were lowest across the western U.S. The elevated error of commission relative to omission was largely driven by patterns in the Great Plains which saw low errors of omission (13 ± 13%) but high errors of commission (70 ± 5%) and potentially a region-growing function included in the BAECV algorithm. While the BAECV reliably detected agricultural fires in the Great Plains, it frequently mapped tilled areas or areas with low vegetation as burned. Landscape metrics were calculated for individual fire events to assess the influence of image resolution (2 m, 30 m and 500 m) on mapping fire heterogeneity. As the spatial detail of imagery increased, fire events were mapped in a patchier manner with greater patch and edge densities, and shape complexity, which can influence estimates of total greenhouse gas emissions and rates of vegetation recovery. The increasing number of satellites collecting high-resolution imagery and rapid improvements in the frequency with which imagery is being collected means greater opportunities to utilize these sources

Monitoring the Invasion of Spartina alterniflora Using Very High Resolution Unmanned Aerial Vehicle Imagery in Beihai, Guangxi (China

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Huawei Wan

2014-01-01

Full Text Available Spartina alterniflora was introduced to Beihai, Guangxi (China, for ecological engineering purposes in 1979. However, the exceptional adaptability and reproductive ability of this species have led to its extensive dispersal into other habitats, where it has had a negative impact on native species and threatens the local mangrove and mudflat ecosystems. To obtain the distribution and spread of Spartina alterniflora, we collected HJ-1 CCD imagery from 2009 and 2011 and very high resolution (VHR imagery from the unmanned aerial vehicle (UAV. The invasion area of Spartina alterniflora was 357.2 ha in 2011, which increased by 19.07% compared with the area in 2009. A field survey was conducted for verification and the total accuracy was 94.0%. The results of this paper show that VHR imagery can provide details on distribution, progress, and early detection of Spartina alterniflora invasion. OBIA, object based image analysis for remote sensing (RS detection method, can enable control measures to be more effective, accurate, and less expensive than a field survey of the invasive population.

Object-based vegetation classification with high resolution remote sensing imagery

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Yu, Qian

Vegetation species are valuable indicators to understand the earth system. Information from mapping of vegetation species and community distribution at large scales provides important insight for studying the phenological (growth) cycles of vegetation and plant physiology. Such information plays an important role in land process modeling including climate, ecosystem and hydrological models. The rapidly growing remote sensing technology has increased its potential in vegetation species mapping. However, extracting information at a species level is still a challenging research topic. I proposed an effective method for extracting vegetation species distribution from remotely sensed data and investigated some ways for accuracy improvement. The study consists of three phases. Firstly, a statistical analysis was conducted to explore the spatial variation and class separability of vegetation as a function of image scale. This analysis aimed to confirm that high resolution imagery contains the information on spatial vegetation variation and these species classes can be potentially separable. The second phase was a major effort in advancing classification by proposing a method for extracting vegetation species from high spatial resolution remote sensing data. The proposed classification employs an object-based approach that integrates GIS and remote sensing data and explores the usefulness of ancillary information. The whole process includes image segmentation, feature generation and selection, and nearest neighbor classification. The third phase introduces a spatial regression model for evaluating the mapping quality from the above vegetation classification results. The effects of six categories of sample characteristics on the classification uncertainty are examined: topography, sample membership, sample density, spatial composition characteristics, training reliability and sample object features. This evaluation analysis answered several interesting scientific questions

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

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Oscar Rojas

2013-04-01

Full Text Available Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the availability of long time series as needed by the majority of the yield prediction methods used today.

Analysis of High Resolution Satellite imagery to acsees Glacier Mass Balance and Lake Hazards in Sikkim Himalayas

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Bhushan, S.; Shean, D. E.; Haritashya, U. K.; Arendt, A. A.; Syed, T. H.; Setiawan, L.

2017-12-01

Glacial lake outburst floods can impact downstream communities due to the sudden outflux of huge quantities of stored water. In this study, we develop a hazard assessment of the moraine dammed glacial lakes in Sikkim Himalayas by analyzing the morphometry of proglacial features, and the surface velocity and mass balance of glaciers. We generated high-resolution digital elevation models (DEMs) using the open-source NASA Ames Stereo Pipeline (ASP) and use other open-source tools to calculate surface velocity and patterns of glacier downwasting over time. Geodetic glacier mass balance is obtained for three periods using high-resolution WorldView/GeoEye stereo DEMs (8 m posting, 2014-2016), Cartosat-1 stereo DEMs (10 m, 2006-2008) and SRTM (30 m, 2000). Initial results reveal a region-wide mass balance of -0.31±0.13 m w.eq.a-1 for the 2007-2015 period, with some debris covered glaciers showing a very low mass loss rate. Additionally, 12 annual glacier velocity fields spanning from 1991 to 2017.derived from Landsat imagery are used to explore the relationship between glacier dynamics and changes in proglacial lakes. Multi-temporal glacial lake mapping is conducted using Landsat and Cartosat imagery. Avalanche and rockfall modeling are combined with morphometric analysis of the proglacial lake area to assess the likelihood of glacial lake dam failure. The above parameters are integrated into a decision tree approach enabling categorization of moraine-dammed lakes according to their potential for outburst events.

The fusion of satellite and UAV data: simulation of high spatial resolution band

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Jenerowicz, Agnieszka; Siok, Katarzyna; Woroszkiewicz, Malgorzata; Orych, Agata

2017-10-01

Remote sensing techniques used in the precision agriculture and farming that apply imagery data obtained with sensors mounted on UAV platforms became more popular in the last few years due to the availability of low- cost UAV platforms and low- cost sensors. Data obtained from low altitudes with low- cost sensors can be characterised by high spatial and radiometric resolution but quite low spectral resolution, therefore the application of imagery data obtained with such technology is quite limited and can be used only for the basic land cover classification. To enrich the spectral resolution of imagery data acquired with low- cost sensors from low altitudes, the authors proposed the fusion of RGB data obtained with UAV platform with multispectral satellite imagery. The fusion is based on the pansharpening process, that aims to integrate the spatial details of the high-resolution panchromatic image with the spectral information of lower resolution multispectral or hyperspectral imagery to obtain multispectral or hyperspectral images with high spatial resolution. The key of pansharpening is to properly estimate the missing spatial details of multispectral images while preserving their spectral properties. In the research, the authors presented the fusion of RGB images (with high spatial resolution) obtained with sensors mounted on low- cost UAV platforms and multispectral satellite imagery with satellite sensors, i.e. Landsat 8 OLI. To perform the fusion of UAV data with satellite imagery, the simulation of the panchromatic bands from RGB data based on the spectral channels linear combination, was conducted. Next, for simulated bands and multispectral satellite images, the Gram-Schmidt pansharpening method was applied. As a result of the fusion, the authors obtained several multispectral images with very high spatial resolution and then analysed the spatial and spectral accuracies of processed images.

Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

Science.gov (United States)

Husak, G. J.; Marshall, M. T.; Michaelsen, J.; Pedreros, D.; Funk, C.; Galu, G.

2008-07-01

Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

Rigorous Line-Based Transformation Model Using the Generalized Point Strategy for the Rectification of High Resolution Satellite Imagery

Directory of Open Access Journals (Sweden)

Kun Hu

2016-09-01

Full Text Available High precision geometric rectification of High Resolution Satellite Imagery (HRSI is the basis of digital mapping and Three-Dimensional (3D modeling. Taking advantage of line features as basic geometric control conditions instead of control points, the Line-Based Transformation Model (LBTM provides a practical and efficient way of image rectification. It is competent to build the mathematical relationship between image space and the corresponding object space accurately, while it reduces the workloads of ground control and feature recognition dramatically. Based on generalization and the analysis of existing LBTMs, a novel rigorous LBTM is proposed in this paper, which can further eliminate the geometric deformation caused by sensor inclination and terrain variation. This improved nonlinear LBTM is constructed based on a generalized point strategy and resolved by least squares overall adjustment. Geo-positioning accuracy experiments with IKONOS, GeoEye-1 and ZiYuan-3 satellite imagery are performed to compare rigorous LBTM with other relevant line-based and point-based transformation models. Both theoretic analysis and experimental results demonstrate that the rigorous LBTM is more accurate and reliable without adding extra ground control. The geo-positioning accuracy of satellite imagery rectified by rigorous LBTM can reach about one pixel with eight control lines and can be further improved by optimizing the horizontal and vertical distribution of control lines.

Sea Ice Deformation State From Synthetic Aperture Radar Imagery - Part II: Effects of Spatial Resolution and Noise Level

DEFF Research Database (Denmark)

Dierking, Wolfgang; Dall, Jørgen

2008-01-01

C- and L-band airborne synthetic aperture radar (SAR) imagery acquired at like- and cross-polarization over sea ice under winter conditions is examined with the objective to study the discrimination between level ice and ice deformation features. High-resolution low-noise data were analysed...... in the first paper. In this second paper, the main topics are the effects of spatial resolution and signal-to-noise ratio. Airborne, high-resolution SAR scenes are used to generate a sequence of images with increasingly coarser spatial resolution from 5 m to 25 m, keeping the number of looks constant....... The signal-to-noise ratio is varied between typical noise levels for airborne imagery and satellite data. Areal fraction of deformed ice and average deformation distance are determined for each image product. At L-band, the retrieved values of the areal fraction get larger as the image resolution is degraded...

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

Science.gov (United States)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure

Forest Stand Segmentation Using Airborne LIDAR Data and Very High Resolution Multispectral Imagery

Science.gov (United States)

Dechesne, Clément; Mallet, Clément; Le Bris, Arnaud; Gouet, Valérie; Hervieu, Alexandre

2016-06-01

Forest stands are the basic units for forest inventory and mapping. Stands are large forested areas (e.g., ≥ 2 ha) of homogeneous tree species composition. The accurate delineation of forest stands is usually performed by visual analysis of human operators on very high resolution (VHR) optical images. This work is highly time consuming and should be automated for scalability purposes. In this paper, a method based on the fusion of airborne laser scanning data (or lidar) and very high resolution multispectral imagery for automatic forest stand delineation and forest land-cover database update is proposed. The multispectral images give access to the tree species whereas 3D lidar point clouds provide geometric information on the trees. Therefore, multi-modal features are computed, both at pixel and object levels. The objects are individual trees extracted from lidar data. A supervised classification is performed at the object level on the computed features in order to coarsely discriminate the existing tree species in the area of interest. The analysis at tree level is particularly relevant since it significantly improves the tree species classification. A probability map is generated through the tree species classification and inserted with the pixel-based features map in an energetical framework. The proposed energy is then minimized using a standard graph-cut method (namely QPBO with α-expansion) in order to produce a segmentation map with a controlled level of details. Comparison with an existing forest land cover database shows that our method provides satisfactory results both in terms of stand labelling and delineation (matching ranges between 94% and 99%).

Estimation of mean tree stand volume using high-resolution aerial RGB imagery and digital surface model, obtained from sUAV and Trestima mobile application

Directory of Open Access Journals (Sweden)

G. K. Rybakov

2017-06-01

Full Text Available This study considers a remote sensing technique for mean volume estimation based on a very high-resolution (VHR aerial RGB imagery obtained using a small-sized unmanned aerial vehicle (sUAV and a high-resolution photogrammetric digital surface model (DSM as well as an innovative technology for field measurements (Trestima. The study area covers approx. 220 ha of forestland in Finland. The work concerns the entire process from remote sensing and field data acquisition to statistical analysis and forest volume wall-to-wall mapping. The study showed that the VHR aerial imagery and the high-resolution DSM produced based on the application of the sUAV have good prospects for forest inventory. For the sUAV based estimation of forest variables such as Height, Basal Area and mean Volume, Root Mean Square Error constituted 6.6 %, 22.6 % and 26.7 %, respectively. Application of Trestima for estimation of the mean volume of the standing forest showed minor difference over the existing Forest Management Plan at all the selected forest compartments. Simultaneously, the results of the study confirmed that the technologies and the tools applied at this work could be a reliable and potentially cost-effective means of forest data acquisition with high potential of operational use.

Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery

Directory of Open Access Journals (Sweden)

Jordi Inglada

2015-09-01

Full Text Available Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type maps at the global scale. Five concurrent strategies for automatic crop type map production have been selected and benchmarked using SPOT4 (Take5 and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia. This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the map production is fully automatic.

Evaluation of different shadow detection and restoration methods and their impact on vegetation indices using UAV high-resolution imageries over vineyards

Science.gov (United States)

Aboutalebi, M.; Torres-Rua, A. F.; McKee, M.; Kustas, W. P.; Nieto, H.

2017-12-01

Shadows are an unavoidable component of high-resolution imagery. Although shadows can be a useful source of information about terrestrial features, they are a hindrance for image processing and lead to misclassification errors and increased uncertainty in defining surface reflectance properties. In precision agriculture activities, shadows may affect the performance of vegetation indices at pixel and plant scales. Thus, it becomes necessary to evaluate existing shadow detection and restoration methods, especially for applications that makes direct use of pixel information to estimate vegetation biomass, leaf area index (LAI), plant water use and stress, chlorophyll content, just to name a few. In this study, four high-resolution imageries captured by the Utah State University - AggieAir Unmanned Aerial Vehicle (UAV) system flown in 2014, 2015, and 2016 over a commercial vineyard located in the California for the USDA-Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program are used for shadow detection and restoration. Four different methods for shadow detection are compared: (1) unsupervised classification, (2) supervised classification, (3) index-based method, and (4) physically-based method. Also, two different shadow restoration methods are evaluated: (1) linear correlation correction, and (2) gamma correction. The models' performance is evaluated over two vegetation indices: normalized difference vegetation index (NDVI) and LAI for both sunlit and shadowed pixels. Histogram and analysis of variance (ANOVA) are used as performance indicators. Results indicated that the performance of the supervised classification and the index-based method are better than other methods. In addition, there is a statistical difference between the average of NDVI and LAI on the sunlit and shadowed pixels. Among the shadow restoration methods, gamma correction visually works better than the linear correlation

Textural Segmentation of High-Resolution Sidescan Sonar Images

National Research Council Canada - National Science Library

Kalcic, Maria; Bibee, Dale

1995-01-01

.... The high resolution of the 455 kHz sonar imagery also provides much information about the surficial bottom sediments, however their acoustic scattering properties are not well understood at high frequencies...

The edge-preservation multi-classifier relearning framework for the classification of high-resolution remotely sensed imagery

Science.gov (United States)

Han, Xiaopeng; Huang, Xin; Li, Jiayi; Li, Yansheng; Yang, Michael Ying; Gong, Jianya

2018-04-01

In recent years, the availability of high-resolution imagery has enabled more detailed observation of the Earth. However, it is imperative to simultaneously achieve accurate interpretation and preserve the spatial details for the classification of such high-resolution data. To this aim, we propose the edge-preservation multi-classifier relearning framework (EMRF). This multi-classifier framework is made up of support vector machine (SVM), random forest (RF), and sparse multinomial logistic regression via variable splitting and augmented Lagrangian (LORSAL) classifiers, considering their complementary characteristics. To better characterize complex scenes of remote sensing images, relearning based on landscape metrics is proposed, which iteratively quantizes both the landscape composition and spatial configuration by the use of the initial classification results. In addition, a novel tri-training strategy is proposed to solve the over-smoothing effect of relearning by means of automatic selection of training samples with low classification certainties, which always distribute in or near the edge areas. Finally, EMRF flexibly combines the strengths of relearning and tri-training via the classification certainties calculated by the probabilistic output of the respective classifiers. It should be noted that, in order to achieve an unbiased evaluation, we assessed the classification accuracy of the proposed framework using both edge and non-edge test samples. The experimental results obtained with four multispectral high-resolution images confirm the efficacy of the proposed framework, in terms of both edge and non-edge accuracy.

Contextually guided very-high-resolution imagery classification with semantic segments

Science.gov (United States)

Zhao, Wenzhi; Du, Shihong; Wang, Qiao; Emery, William J.

2017-10-01

Contextual information, revealing relationships and dependencies between image objects, is one of the most important information for the successful interpretation of very-high-resolution (VHR) remote sensing imagery. Over the last decade, geographic object-based image analysis (GEOBIA) technique has been widely used to first divide images into homogeneous parts, and then to assign semantic labels according to the properties of image segments. However, due to the complexity and heterogeneity of VHR images, segments without semantic labels (i.e., semantic-free segments) generated with low-level features often fail to represent geographic entities (such as building roofs usually be partitioned into chimney/antenna/shadow parts). As a result, it is hard to capture contextual information across geographic entities when using semantic-free segments. In contrast to low-level features, "deep" features can be used to build robust segments with accurate labels (i.e., semantic segments) in order to represent geographic entities at higher levels. Based on these semantic segments, semantic graphs can be constructed to capture contextual information in VHR images. In this paper, semantic segments were first explored with convolutional neural networks (CNN) and a conditional random field (CRF) model was then applied to model the contextual information between semantic segments. Experimental results on two challenging VHR datasets (i.e., the Vaihingen and Beijing scenes) indicate that the proposed method is an improvement over existing image classification techniques in classification performance (overall accuracy ranges from 82% to 96%).

Visualisation of very high resolution Martian topographic data and its application on landing site selection and rover route navigation

Science.gov (United States)

Kim, J.; Lin, S.; Hong, J.; Park, D.; Yoon, S.; Kim, Y.

2010-12-01

High resolution satellite imagery acquired from orbiters are able to provide detailed topographic information and therefore are recognised as an important tool for investigating planetary and terrestrial topography. The heritage of in-orbit high resolution imaging technology is now implemented in a series of Martian Missions, such as HiRISE (High Resolution Imaging Science Experiment) and CTX (Context Camera) onboard the MRO (Mars Reconnaissance Orbiter). In order to fully utilise the data derived from image systems carried on various Mars orbiters, the generalised algorithms of image processing and photogrammetric Mars DTM extraction have been developed and implemented by Kim and Muller (2009), in which non-rigorous sensor model and hierarchical geomatics control were employed. Due to the successful “from medium to high” control strategy performed during processing, stable horizontal and vertical photogrammetric accuracy of resultant Mars DTM was achievable when compared with MOLA (Mars Obiter Laser Altimeter) DTM. Recently, the algorithms developed in Kim and Muller (2009) were further updated by employing advanced image matcher and improved sensor model. As the photogrammetric qualities of the updated topographic products are verified and the spatial solution can be up to sub-meter scale, they are of great value to be exploited for Martian rover landing site selection and rover route navigation. To this purpose, the DTMs and ortho-rectified imagery obtained from CTX and HiRISE covering potential future rovers and existing MER (Mars Exploration Rover) landing sites were firstly processed. For landing site selection, the engineering constraints such as slope and surface roughness were computed from DTMs. In addition, the combination of virtual topography and the estimated rover location was able to produce a sophisticated environment simulation of rover’s landing site. Regarding the rover navigation, the orbital DTMs and the images taken from cameras

Super-resolution for imagery from integrated microgrid polarimeters.

Science.gov (United States)

Hardie, Russell C; LeMaster, Daniel A; Ratliff, Bradley M

2011-07-04

Imagery from microgrid polarimeters is obtained by using a mosaic of pixel-wise micropolarizers on a focal plane array (FPA). Each distinct polarization image is obtained by subsampling the full FPA image. Thus, the effective pixel pitch for each polarization channel is increased and the sampling frequency is decreased. As a result, aliasing artifacts from such undersampling can corrupt the true polarization content of the scene. Here we present the first multi-channel multi-frame super-resolution (SR) algorithms designed specifically for the problem of image restoration in microgrid polarization imagers. These SR algorithms can be used to address aliasing and other degradations, without sacrificing field of view or compromising optical resolution with an anti-aliasing filter. The new SR methods are designed to exploit correlation between the polarimetric channels. One of the new SR algorithms uses a form of regularized least squares and has an iterative solution. The other is based on the faster adaptive Wiener filter SR method. We demonstrate that the new multi-channel SR algorithms are capable of providing significant enhancement of polarimetric imagery and that they outperform their independent channel counterparts.

Current Resource Imagery Projects

Data.gov (United States)

Farm Service Agency, Department of Agriculture — Map showing coverage of current Resource imagery projects. High resolution/large scale Resource imagery is typically acquired for the U.S. Forest Service and other...

Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery

Science.gov (United States)

Qin, Rongjun

2014-10-01

Due to the fast development of the urban environment, the need for efficient maintenance and updating of 3D building models is ever increasing. Change detection is an essential step to spot the changed area for data (map/3D models) updating and urban monitoring. Traditional methods based on 2D images are no longer suitable for change detection in building scale, owing to the increased spectral variability of the building roofs and larger perspective distortion of the very high resolution (VHR) imagery. Change detection in 3D is increasingly being investigated using airborne laser scanning data or matched Digital Surface Models (DSM), but rare study has been conducted regarding to change detection on 3D city models with VHR images, which is more informative but meanwhile more complicated. This is due to the fact that the 3D models are abstracted geometric representation of the urban reality, while the VHR images record everything. In this paper, a novel method is proposed to detect changes directly on LOD (Level of Detail) 2 building models with VHR spaceborne stereo images from a different date, with particular focus on addressing the special characteristics of the 3D models. In the first step, the 3D building models are projected onto a raster grid, encoded with building object, terrain object, and planar faces. The DSM is extracted from the stereo imagery by hierarchical semi-global matching (SGM). In the second step, a multi-channel change indicator is extracted between the 3D models and stereo images, considering the inherent geometric consistency (IGC), height difference, and texture similarity for each planar face. Each channel of the indicator is then clustered with the Self-organizing Map (SOM), with "change", "non-change" and "uncertain change" status labeled through a voting strategy. The "uncertain changes" are then determined with a Markov Random Field (MRF) analysis considering the geometric relationship between faces. In the third step, buildings are

High resolution remote sensing of water surface patterns

Science.gov (United States)

Woodget, A.; Visser, F.; Maddock, I.; Carbonneau, P.

2012-12-01

The assessment of in-stream habitat availability within fluvial environments in the UK traditionally includes the mapping of patterns which appear on the surface of the water, known as 'surface flow types' (SFTs). The UK's River Habitat Survey identifies ten key SFTs, including categories such as rippled flow, upwelling, broken standing waves and smooth flow. SFTs result from the interaction between the underlying channel morphology, water depth and velocity and reflect the local flow hydraulics. It has been shown that SFTs can be both biologically and hydraulically distinct. SFT mapping is usually conducted from the river banks where estimates of spatial coverage are made by eye. This approach is affected by user subjectivity and inaccuracies in the spatial extent of mapped units. Remote sensing and specifically the recent developments in unmanned aerial systems (UAS) may now offer an alternative approach for SFT mapping, with the capability for rapid and repeatable collection of very high resolution imagery from low altitudes, under bespoke flight conditions. This PhD research is aimed at investigating the mapping of SFTs using high resolution optical imagery (less than 10cm) collected from a helicopter-based UAS flown at low altitudes (less than 100m). This paper presents the initial findings from a series of structured experiments on the River Arrow, a small lowland river in Warwickshire, UK. These experiments investigate the potential for mapping SFTs from still and video imagery of different spatial resolutions collected at different flying altitudes and from different viewing angles (i.e. vertical and oblique). Imagery is processed using 3D mosaicking software to create orthophotos and digital elevation models (DEM). The types of image analysis which are tested include a simple, manual visual assessment undertaken in a GIS environment, based on the high resolution optical imagery. In addition, an object-based image analysis approach which makes use of the

Applying High-Resolution Imagery to Evaluate Restoration-Induced Changes in Stream Condition, Missouri River Headwaters Basin, Montana

Directory of Open Access Journals (Sweden)

Melanie K. Vanderhoof

2018-06-01

Full Text Available Degradation of streams and associated riparian habitat across the Missouri River Headwaters Basin has motivated several stream restoration projects across the watershed. Many of these projects install a series of beaver dam analogues (BDAs to aggrade incised streams, elevate local water tables, and create natural surface water storage by reconnecting streams with their floodplains. Satellite imagery can provide a spatially continuous mechanism to monitor the effects of these in-stream structures on stream surface area. However, remote sensing-based approaches to map narrow (e.g., <5 m wide linear features such as streams have been under-developed relative to efforts to map other types of aquatic systems, such as wetlands or lakes. We mapped pre- and post-restoration (one to three years post-restoration stream surface area and riparian greenness at four stream restoration sites using Worldview-2 and 3 images as well as a QuickBird-2 image. We found that panchromatic brightness and eCognition-based outputs (0.5 m resolution provided high-accuracy maps of stream surface area (overall accuracy ranged from 91% to 99% for streams as narrow as 1.5 m wide. Using image pairs, we were able to document increases in stream surface area immediately upstream of BDAs as well as increases in stream surface area along the restoration reach at Robb Creek, Alkali Creek and Long Creek (South. Although Long Creek (North did not show a net increase in stream surface area along the restoration reach, we did observe an increase in riparian greenness, suggesting increased water retention adjacent to the stream. As high-resolution imagery becomes more widely collected and available, improvements in our ability to provide spatially continuous monitoring of stream systems can effectively complement more traditional field-based and gage-based datasets to inform watershed management.

High-resolution Ceres Low Altitude Mapping Orbit Atlas derived from Dawn Framing Camera images

Science.gov (United States)

Roatsch, Th.; Kersten, E.; Matz, K.-D.; Preusker, F.; Scholten, F.; Jaumann, R.; Raymond, C. A.; Russell, C. T.

2017-06-01

The Dawn spacecraft Framing Camera (FC) acquired over 31,300 clear filter images of Ceres with a resolution of about 35 m/pxl during the eleven cycles in the Low Altitude Mapping Orbit (LAMO) phase between December 16 2015 and August 8 2016. We ortho-rectified the images from the first four cycles and produced a global, high-resolution, uncontrolled photomosaic of Ceres. This global mosaic is the basis for a high-resolution Ceres atlas that consists of 62 tiles mapped at a scale of 1:250,000. The nomenclature used in this atlas was proposed by the Dawn team and was approved by the International Astronomical Union (IAU). The full atlas is available to the public through the Dawn Geographical Information System (GIS) web page [http://dawngis.dlr.de/atlas] and will become available through the NASA Planetary Data System (PDS) (http://pdssbn.astro.umd.edu/).

Benthic Habitat Mapping Using Multispectral High-Resolution Imagery: Evaluation of Shallow Water Atmospheric Correction Techniques

Directory of Open Access Journals (Sweden)

Francisco Eugenio

2017-11-01

Full Text Available Remote multispectral data can provide valuable information for monitoring coastal water ecosystems. Specifically, high-resolution satellite-based imaging systems, as WorldView-2 (WV-2, can generate information at spatial scales needed to implement conservation actions for protected littoral zones. However, coastal water-leaving radiance arriving at the space-based sensor is often small as compared to reflected radiance. In this work, complex approaches, which usually use an accurate radiative transfer code to correct the atmospheric effects, such as FLAASH, ATCOR and 6S, have been implemented for high-resolution imagery. They have been assessed in real scenarios using field spectroradiometer data. In this context, the three approaches have achieved excellent results and a slightly superior performance of 6S model-based algorithm has been observed. Finally, for the mapping of benthic habitats in shallow-waters marine protected environments, a relevant application of the proposed atmospheric correction combined with an automatic deglinting procedure is presented. This approach is based on the integration of a linear mixing model of benthic classes within the radiative transfer model of the water. The complete methodology has been applied to selected ecosystems in the Canary Islands (Spain but the obtained results allow the robust mapping of the spatial distribution and density of seagrass in coastal waters and the analysis of multitemporal variations related to the human activity and climate change in littoral zones.

Exploring the Influence of Topographic Correction and SWIR Spectral Information Inclusion on Burnt Scars Detection From High Resolution EO Imagery: A Case Study Using ASTER imagery

Science.gov (United States)

Said, Yahia A.; Petropoulos, George; Srivastava, Prashant K.

2014-05-01

Information on burned area estimates is of key importance in environmental and ecological studies as well as in fire management including damage assessment and planning of post-fire recovery of affected areas. Earth Observation (EO) provides today the most efficient way in obtaining such information in a rapid, consistent and cost-effective manner. The present study aimed at exploring the effect of topographic correction to the burnt area delineation in conditions characteristic of a Mediterranean environment using ASTER high resolution multispectral remotely sensed imagery. A further objective was to investigate the potential added-value of the inclusion of the shortwave infrared (SWIR) bands in improving the retrievals of burned area cartography from the ASTER data. In particular the capability of the Maximum Likelihood (ML), the Support Vector Machines (SVMs) and Object-based Image Analysis (OBIA) classification techniques has been examined herein for the purposes of our study. As a case study is used a typical Mediterranean site on which a fire event occurred in Greece during the summer of 2007, for which post-fire ASTER imagery has been acquired. Our results indicated that the combination of topographic correction (ortho-rectification) with the inclusion of the SWIR bands returned the most accurate results in terms of burnt area mapping. In terms of image processing methods, OBIA showed the best results and found as the most promising approach for burned area mapping with least absolute difference from the validation polygon followed by SVM and ML. All in all, our study provides an important contribution to the understanding of the capability of high resolution imagery such as that from ASTER sensor and corroborates the usefulness particularly of the topographic correction as an image processing step when in delineating the burnt areas from such data. It also provides further evidence that use of EO technology can offer an effective practical tool for the

Assessing Greater Sage-Grouse Selection of Brood-Rearing Habitat Using Remotely-Sensed Imagery: Can Readily Available High-Resolution Imagery Be Used to Identify Brood-Rearing Habitat Across a Broad Landscape?

Science.gov (United States)

Westover, Matthew; Baxter, Jared; Baxter, Rick; Day, Casey; Jensen, Ryan; Petersen, Steve; Larsen, Randy

2016-01-01

Greater sage-grouse populations have decreased steadily since European settlement in western North America. Reduced availability of brood-rearing habitat has been identified as a limiting factor for many populations. We used radio-telemetry to acquire locations of sage-grouse broods from 1998 to 2012 in Strawberry Valley, Utah. Using these locations and remotely-sensed NAIP (National Agricultural Imagery Program) imagery, we 1) determined which characteristics of brood-rearing habitat could be used in widely available, high resolution imagery 2) assessed the spatial extent at which sage-grouse selected brood-rearing habitat, and 3) created a predictive habitat model to identify areas of preferred brood-rearing habitat. We used AIC model selection to evaluate support for a list of variables derived from remotely-sensed imagery. We examined the relationship of these explanatory variables at three spatial extents (45, 200, and 795 meter radii). Our top model included 10 variables (percent shrub, percent grass, percent tree, percent paved road, percent riparian, meters of sage/tree edge, meters of riparian/tree edge, distance to tree, distance to transmission lines, and distance to permanent structures). Variables from each spatial extent were represented in our top model with the majority being associated with the larger (795 meter) spatial extent. When applied to our study area, our top model predicted 75% of naïve brood locations suggesting reasonable success using this method and widely available NAIP imagery. We encourage application of our methodology to other sage-grouse populations and species of conservation concern.

Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery.

Science.gov (United States)

Martinuzzi, Sebastián; Ramos-González, Olga M; Muñoz-Erickson, Tischa A; Locke, Dexter H; Lugo, Ariel E; Radeloff, Volker C

2018-04-01

Fine-scale information about urban vegetation and social-ecological relationships is crucial to inform both urban planning and ecological research, and high spatial resolution imagery is a valuable tool for assessing urban areas. However, urban ecology and remote sensing have largely focused on cities in temperate zones. Our goal was to characterize urban vegetation cover with sub-meter (urban vegetation patterns in a tropical city, the San Juan Metropolitan Area, Puerto Rico. Our specific objectives were to (1) map vegetation cover using sub-meter spatial resolution (0.3-m) imagery, (2) quantify the amount of residential and non-residential vegetation, and (3) investigate the relationship between patterns of urban vegetation vs. socioeconomic and environmental factors. We found that 61% of the San Juan Metropolitan Area was green and that our combination of high spatial resolution imagery and object-based classification was highly successful for extracting vegetation cover in a moist tropical city (97% accuracy). In addition, simple spatial pattern analysis allowed us to separate residential from non-residential vegetation with 76% accuracy, and patterns of residential and non-residential vegetation varied greatly across the city. Both socioeconomic (e.g., population density, building age, detached homes) and environmental variables (e.g., topography) were important in explaining variations in vegetation cover in our spatial regression models. However, important socioeconomic drivers found in cities in temperate zones, such as income and home value, were not important in San Juan. Climatic and cultural differences between tropical and temperate cities may result in different social-ecological relationships. Our study provides novel information for local land use planners, highlights the value of high spatial resolution remote sensing data to advance ecological research and urban planning in tropical cities, and emphasizes the need for more studies in tropical

Automated Extraction of Crop Area Statistics from Medium-Resolution Imagery, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — This project will focus on the strategic, routine incorporation of medium-resolution satellite imagery into operational agricultural assessments for the global crop...

Super-Resolution for "Jilin-1" Satellite Video Imagery via a Convolutional Network.

Science.gov (United States)

Xiao, Aoran; Wang, Zhongyuan; Wang, Lei; Ren, Yexian

2018-04-13

Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method's practicality. Experimental results on "Jilin-1" satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods.

Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery

Directory of Open Access Journals (Sweden)

Sarah W. Cooley

2017-12-01

Full Text Available Recent deployments of CubeSat imagers by companies such as Planet may advance hydrological remote sensing by providing an unprecedented combination of high temporal and high spatial resolution imagery at the global scale. With approximately 170 CubeSats orbiting at full operational capacity, the Planet CubeSat constellation currently offers an average revisit time of <1 day for the Arctic and near-daily revisit time globally at 3 m spatial resolution. Such data have numerous potential applications for water resource monitoring, hydrologic modeling and hydrologic research. Here we evaluate Planet CubeSat imaging capabilities and potential scientific utility for surface water studies in the Yukon Flats, a large sub-Arctic wetland in north central Alaska. We find that surface water areas delineated from Planet imagery have a normalized root mean square error (NRMSE of <11% and geolocation accuracy of <10 m as compared with manual delineations from high resolution (0.3–0.5 m WorldView-2/3 panchromatic satellite imagery. For a 625 km2 subarea of the Yukon Flats, our time series analysis reveals that roughly one quarter of 268 lakes analyzed responded to changes in Yukon River discharge over the period 23 June–1 October 2016, one half steadily contracted, and one quarter remained unchanged. The spatial pattern of observed lake changes is heterogeneous. While connections to Yukon River control the hydrologically connected lakes, the behavior of other lakes is complex, likely driven by a combination of intricate flow paths, underlying geology and permafrost. Limitations of Planet CubeSat imagery include a lack of an automated cloud mask, geolocation inaccuracies, and inconsistent radiometric calibration across multiple platforms. Although these challenges must be addressed before Planet CubeSat imagery can achieve its full potential for large-scale hydrologic research, we conclude that CubeSat imagery offers a powerful new tool for the study and

An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

OpenAIRE

Chandi Witharana; Heather J. Lynch

2016-01-01

The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR) satellite imagery and closely examined the transferability of knowle...

Mapping Sub-Antarctic Cushion Plants Using Random Forests to Combine Very High Resolution Satellite Imagery and Terrain Modelling

Science.gov (United States)

Bricher, Phillippa K.; Lucieer, Arko; Shaw, Justine; Terauds, Aleks; Bergstrom, Dana M.

2013-01-01

Monitoring changes in the distribution and density of plant species often requires accurate and high-resolution baseline maps of those species. Detecting such change at the landscape scale is often problematic, particularly in remote areas. We examine a new technique to improve accuracy and objectivity in mapping vegetation, combining species distribution modelling and satellite image classification on a remote sub-Antarctic island. In this study, we combine spectral data from very high resolution WorldView-2 satellite imagery and terrain variables from a high resolution digital elevation model to improve mapping accuracy, in both pixel- and object-based classifications. Random forest classification was used to explore the effectiveness of these approaches on mapping the distribution of the critically endangered cushion plant Azorella macquariensis Orchard (Apiaceae) on sub-Antarctic Macquarie Island. Both pixel- and object-based classifications of the distribution of Azorella achieved very high overall validation accuracies (91.6–96.3%, κ = 0.849–0.924). Both two-class and three-class classifications were able to accurately and consistently identify the areas where Azorella was absent, indicating that these maps provide a suitable baseline for monitoring expected change in the distribution of the cushion plants. Detecting such change is critical given the threats this species is currently facing under altering environmental conditions. The method presented here has applications to monitoring a range of species, particularly in remote and isolated environments. PMID:23940805

Short Communication. Using high resolution UAV imagery to estimate tree variables in Pinus pinea plantation in Portugal

Energy Technology Data Exchange (ETDEWEB)

Guerra Hernandez, J.; Gonzalez-Ferreiro, E.; Sarmento, A.; Silva, J.; Nunes, A.; Correia, A.C.; Fontes, L.; Tomé, M.; Diaz-Varela, D.

2016-07-01

Aim of the study: The study aims to analyse the potential use of low‑cost unmanned aerial vehicle (UAV) imagery for the estimation of Pinus pinea L. variables at the individual tree level (position, tree height and crown diameter). Area of study: This study was conducted under the PINEA project focused on 16 ha of umbrella pine afforestation (Portugal) subjected to different treatments. Material and methods: The workflow involved: a) image acquisition with consumer‑grade cameras on board an UAV; b) orthomosaic and digital surface model (DSM) generation using structure-from-motion (SfM) image reconstruction; and c) automatic individual tree segmentation by using a mixed pixel‑ and region‑based based algorithm. Main results: The results of individual tree segmentation (position, height and crown diameter) were validated using field measurements from 3 inventory plots in the study area. All the trees of the plots were correctly detected. The RMSE values for the predicted heights and crown widths were 0.45 m and 0.63 m, respectively. Research highlights: The results demonstrate that tree variables can be automatically extracted from high resolution imagery. We highlight the use of UAV systems as a fast, reliable and cost‑effective technique for small scale applications. (Author)

Short Communication. Using high resolution UAV imagery to estimate tree variables in Pinus pinea plantation in Portugal

International Nuclear Information System (INIS)

Guerra Hernandez, J.; Gonzalez-Ferreiro, E.; Sarmento, A.; Silva, J.; Nunes, A.; Correia, A.C.; Fontes, L.; Tomé, M.; Diaz-Varela, D.

2016-01-01

Aim of the study: The study aims to analyse the potential use of low‑cost unmanned aerial vehicle (UAV) imagery for the estimation of Pinus pinea L. variables at the individual tree level (position, tree height and crown diameter). Area of study: This study was conducted under the PINEA project focused on 16 ha of umbrella pine afforestation (Portugal) subjected to different treatments. Material and methods: The workflow involved: a) image acquisition with consumer‑grade cameras on board an UAV; b) orthomosaic and digital surface model (DSM) generation using structure-from-motion (SfM) image reconstruction; and c) automatic individual tree segmentation by using a mixed pixel‑ and region‑based based algorithm. Main results: The results of individual tree segmentation (position, height and crown diameter) were validated using field measurements from 3 inventory plots in the study area. All the trees of the plots were correctly detected. The RMSE values for the predicted heights and crown widths were 0.45 m and 0.63 m, respectively. Research highlights: The results demonstrate that tree variables can be automatically extracted from high resolution imagery. We highlight the use of UAV systems as a fast, reliable and cost‑effective technique for small scale applications. (Author)

A method for geological hazard extraction using high-resolution remote sensing

International Nuclear Information System (INIS)

Wang, Q J; Chen, Y; Bi, J T; Lin, Q Z; Li, M X

2014-01-01

Taking Yingxiu, the epicentre of the Wenchuan earthquake, as the study area, a method for geological disaster extraction using high-resolution remote sensing imagery was proposed in this study. A high-resolution Digital Elevation Model (DEM) was used to create mask imagery to remove interfering factors such as buildings and water at low altitudes. Then, the mask imagery was diced into several small parts to reduce the large images' inconsistency, and they were used as the sources to be classified. After that, vector conversion was done on the classified imagery in ArcGIS. Finally, to ensure accuracy, other interfering factors such as buildings at high altitudes, bare land, and land covered by little vegetation were removed manually. Because the method can extract geological hazards in a short time, it is of great importance for decision-makers and rescuers who need to know the degree of damage in the disaster area, especially within 72 hours after an earthquake. Therefore, the method will play an important role in decision making, rescue, and disaster response planning

Recognition and characterization of networks of water bodies in the Arctic ice-wedge polygonal tundra using high-resolution satellite imagery

Science.gov (United States)

Skurikhin, A. N.; Gangodagamage, C.; Rowland, J. C.; Wilson, C. J.

2013-12-01

Arctic lowland landscapes underlain by permafrost are often characterized by polygon-like patterns such as ice-wedge polygons outlined by networks of ice wedges and complemented with polygon rims, troughs, shallow ponds and thermokarst lakes. Polygonal patterns and corresponding features are relatively easy to recognize in high spatial resolution satellite imagery by a human, but their automated recognition is challenging due to the variability in their spectral appearance, the irregularity of individual trough spacing and orientation within the patterns, and a lack of unique spectral response attributable to troughs with widths commonly between 1 m and 2 m. Accurate identification of fine scale elements of ice-wedge polygonal tundra is important as their imprecise recognition may bias estimates of water, heat and carbon fluxes in large-scale climate models. Our focus is on the problem of identification of Arctic polygonal tundra fine-scale landscape elements (as small as 1 m - 2 m width). The challenge of the considered problem is that while large water bodies (e.g. lakes and rivers) can be recognized based on spectral response, reliable recognition of troughs is more difficult. Troughs do not have unique spectral signature, their appearance is noisy (edges are not strong), their width is small, and they often form connected networks with ponds and lakes, and thus they have overlapping spectral response with other water bodies and surrounding non-water bodies. We present a semi-automated approach to identify and classify Arctic polygonal tundra landscape components across the range of spatial scales, such as troughs, ponds, river- and lake-like objects, using high spatial resolution satellite imagery. The novelty of the approach lies in: (1) the combined use of segmentation and shape-based classification to identify a broad range of water bodies, including troughs, and (2) the use of high-resolution WorldView-2 satellite imagery (with resolution of 0.6 m) for this

Object-oriented Method of Hierarchical Urban Building Extraction from High-resolution Remote-Sensing Imagery

Directory of Open Access Journals (Sweden)

TAO Chao

2016-02-01

Full Text Available An automatic urban building extraction method for high-resolution remote-sensing imagery,which combines building segmentation based on neighbor total variations with object-oriented analysis,is presented in this paper. Aimed at different extraction complexity from various buildings in the segmented image,a hierarchical building extraction strategy with multi-feature fusion is adopted. Firstly,we extract some rectangle buildings which remain intact after segmentation through shape analysis. Secondly,in order to ensure each candidate building target to be independent,multidirectional morphological road-filtering algorithm is designed which can separate buildings from the neighboring roads with similar spectrum. Finally,we take the extracted buildings and the excluded non-buildings as samples to establish probability model respectively,and Bayesian discriminating classifier is used for making judgment of the other candidate building objects to get the ultimate extraction result. The experimental results have shown that the approach is able to detect buildings with different structure and spectral features in the same image. The results of performance evaluation also support the robustness and precision of the approach developed.

UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery

Science.gov (United States)

Sturdivant, Emily; Lentz, Erika; Thieler, E. Robert; Farris, Amy; Weber, Kathryn; Remsen, David P.; Miner, Simon; Henderson, Rachel

2017-01-01

The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.

UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery

Directory of Open Access Journals (Sweden)

Emily J. Sturdivant

2017-10-01

Full Text Available The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM photogrammetry applied to imagery acquired by unmanned aerial systems (UAS offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm have little influence on the classification accuracy.

An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV Imagery, Based on Structure from Motion (SfM Point Clouds

Directory of Open Access Journals (Sweden)

Christopher Watson

2012-05-01

Full Text Available Unmanned Aerial Vehicles (UAVs are an exciting new remote sensing tool capable of acquiring high resolution spatial data. Remote sensing with UAVs has the potential to provide imagery at an unprecedented spatial and temporal resolution. The small footprint of UAV imagery, however, makes it necessary to develop automated techniques to geometrically rectify and mosaic the imagery such that larger areas can be monitored. In this paper, we present a technique for geometric correction and mosaicking of UAV photography using feature matching and Structure from Motion (SfM photogrammetric techniques. Images are processed to create three dimensional point clouds, initially in an arbitrary model space. The point clouds are transformed into a real-world coordinate system using either a direct georeferencing technique that uses estimated camera positions or via a Ground Control Point (GCP technique that uses automatically identified GCPs within the point cloud. The point cloud is then used to generate a Digital Terrain Model (DTM required for rectification of the images. Subsequent georeferenced images are then joined together to form a mosaic of the study area. The absolute spatial accuracy of the direct technique was found to be 65–120 cm whilst the GCP technique achieves an accuracy of approximately 10–15 cm.

Monitoring Oilfield Operations and GHG Emissions Sources Using Object-based Image Analysis of High Resolution Spatial Imagery

Science.gov (United States)

Englander, J. G.; Brodrick, P. G.; Brandt, A. R.

2015-12-01

Fugitive emissions from oil and gas extraction have become a greater concern with the recent increases in development of shale hydrocarbon resources. There are significant gaps in the tools and research used to estimate fugitive emissions from oil and gas extraction. Two approaches exist for quantifying these emissions: atmospheric (or 'top down') studies, which measure methane fluxes remotely, or inventory-based ('bottom up') studies, which aggregate leakage rates on an equipment-specific basis. Bottom-up studies require counting or estimating how many devices might be leaking (called an 'activity count'), as well as how much each device might leak on average (an 'emissions factor'). In a real-world inventory, there is uncertainty in both activity counts and emissions factors. Even at the well level there are significant disagreements in data reporting. For example, some prior studies noted a ~5x difference in the number of reported well completions in the United States between EPA and private data sources. The purpose of this work is to address activity count uncertainty by using machine learning algorithms to classify oilfield surface facilities using high-resolution spatial imagery. This method can help estimate venting and fugitive emissions sources from regions where reporting of oilfield equipment is incomplete or non-existent. This work will utilize high resolution satellite imagery to count well pads in the Bakken oil field of North Dakota. This initial study examines an area of ~2,000 km2 with ~1000 well pads. We compare different machine learning classification techniques, and explore the impact of training set size, input variables, and image segmentation settings to develop efficient and robust techniques identifying well pads. We discuss the tradeoffs inherent to different classification algorithms, and determine the optimal algorithms for oilfield feature detection. In the future, the results of this work will be leveraged to be provide activity

BUILT-UP AREA AND LAND COVER EXTRACTION USING HIGH RESOLUTION PLEIADES SATELLITE IMAGERY FOR MIDRAND, IN GAUTENG PROVINCE, SOUTH AFRICA

Directory of Open Access Journals (Sweden)

E. Fundisi

2017-09-01

Full Text Available Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa.

Built-Up Area and Land Cover Extraction Using High Resolution Pleiades Satellite Imagery for Midrand, in Gauteng Province, South Africa

Science.gov (United States)

Fundisi, E.; Musakwa, W.

2017-09-01

Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa.

Restoration and Super-Resolution of Diffraction-Limited Imagery Data by Bayesian and Set-Theoretic Approaches

National Research Council Canada - National Science Library

Sundareshan, Malur

2001-01-01

This project was primarily aimed at the design of novel algorithms for the restoration and super-resolution processing of imagery data to improve the resolution in images acquired from practical sensing operations...

Geo-Parcel Based Crop Identification by Integrating High Spatial-Temporal Resolution Imagery from Multi-Source Satellite Data

Directory of Open Access Journals (Sweden)

Yingpin Yang

2017-12-01

Full Text Available Geo-parcel based crop identification plays an important role in precision agriculture. It meets the needs of refined farmland management. This study presents an improved identification procedure for geo-parcel based crop identification by combining fine-resolution images and multi-source medium-resolution images. GF-2 images with fine spatial resolution of 0.8 m provided agricultural farming plot boundaries, and GF-1 (16 m and Landsat 8 OLI data were used to transform the geo-parcel based enhanced vegetation index (EVI time-series. In this study, we propose a piecewise EVI time-series smoothing method to fit irregular time profiles, especially for crop rotation situations. Global EVI time-series were divided into several temporal segments, from which phenological metrics could be derived. This method was applied to Lixian, where crop rotation was the common practice of growing different types of crops, in the same plot, in sequenced seasons. After collection of phenological features and multi-temporal spectral information, Random Forest (RF was performed to classify crop types, and the overall accuracy was 93.27%. Moreover, an analysis of feature significance showed that phenological features were of greater importance for distinguishing agricultural land cover compared to temporal spectral information. The identification results indicated that the integration of high spatial-temporal resolution imagery is promising for geo-parcel based crop identification and that the newly proposed smoothing method is effective.

Monitoring the Invasion of Spartina alterniflora Using Multi-source High-resolution Imagery in the Zhangjiang Estuary, China

Directory of Open Access Journals (Sweden)

Mingyue Liu

2017-05-01

Full Text Available Spartina alterniflora (S. alterniflora is one of the most harmful invasive plants in China. Google Earth (GE, as a free software, hosts high-resolution imagery for many areas of the world. To explore the use of GE imagery for monitoring S. alterniflora invasion and developing an understanding of the invasion process of S. alterniflora in the Zhangjiang Estuary, the object-oriented method and visual interpretation were applied to GE, SPOT-5, and Gaofen-1 (GF-1 images. In addition, landscape metrics of S. alterniflora patches adjacent to mangrove forests were calculated and mangrove gaps were recorded by checking whether S. alterniflora exists. The results showed that from 2003–2015, the areal extent of S. alterniflora in the Zhangjiang Estuary increased from 57.94 ha to 116.11 ha, which was mainly converted from mudflats and moved seaward significantly. Analyses of the S. alterniflora expansion patterns in the six subzones indicated that the expansion trends varied with different environmental circumstances and human activities. Land reclamation, mangrove replantation, and mudflat aquaculture caused significant losses of S. alterniflora. The number of invaded gaps increased and S. alterniflora patches adjacent to mangrove forests became much larger and more aggregated during 2003–2015 (the class area increased from 12.13 ha to 49.76 ha and the aggregation index increased from 91.15 to 94.65. We thus concluded that S. alterniflora invasion in the Zhangjiang Estuary had seriously increased and that measures should be taken considering the characteristics shown in different subzones. This study provides an example of applying GE imagery to monitor invasive plants and illustrates that this approach can aid in the development of governmental policies employed to control S. alterniflora invasion.

High-Resolution Sonars: What Resolution Do We Need for Target Recognition?

Directory of Open Access Journals (Sweden)

Pailhas Yan

2010-01-01

Full Text Available Target recognition in sonar imagery has long been an active research area in the maritime domain, especially in the mine-counter measure context. Recently it has received even more attention as new sensors with increased resolution have been developed; new threats to critical maritime assets and a new paradigm for target recognition based on autonomous platforms have emerged. With the recent introduction of Synthetic Aperture Sonar systems and high-frequency sonars, sonar resolution has dramatically increased and noise levels decreased. Sonar images are distance images but at high resolution they tend to appear visually as optical images. Traditionally algorithms have been developed specifically for imaging sonars because of their limited resolution and high noise levels. With high-resolution sonars, algorithms developed in the image processing field for natural images become applicable. However, the lack of large datasets has hampered the development of such algorithms. Here we present a fast and realistic sonar simulator enabling development and evaluation of such algorithms.We develop a classifier and then analyse its performances using our simulated synthetic sonar images. Finally, we discuss sensor resolution requirements to achieve effective classification of various targets and demonstrate that with high resolution sonars target highlight analysis is the key for target recognition.

Mapping urban impervious surface using object-based image analysis with WorldView-3 satellite imagery

Science.gov (United States)

Iabchoon, Sanwit; Wongsai, Sangdao; Chankon, Kanoksuk

2017-10-01

Land use and land cover (LULC) data are important to monitor and assess environmental change. LULC classification using satellite images is a method widely used on a global and local scale. Especially, urban areas that have various LULC types are important components of the urban landscape and ecosystem. This study aims to classify urban LULC using WorldView-3 (WV-3) very high-spatial resolution satellite imagery and the object-based image analysis method. A decision rules set was applied to classify the WV-3 images in Kathu subdistrict, Phuket province, Thailand. The main steps were as follows: (1) the image was ortho-rectified with ground control points and using the digital elevation model, (2) multiscale image segmentation was applied to divide the image pixel level into image object level, (3) development of the decision ruleset for LULC classification using spectral bands, spectral indices, spatial and contextual information, and (4) accuracy assessment was computed using testing data, which sampled by statistical random sampling. The results show that seven LULC classes (water, vegetation, open space, road, residential, building, and bare soil) were successfully classified with overall classification accuracy of 94.14% and a kappa coefficient of 92.91%.

Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

Science.gov (United States)

Yang, He; Ma, Ben; Du, Qian; Yang, Chenghai

2010-08-01

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.

High resolution color imagery for orthomaps and remote sensing

Energy Technology Data Exchange (ETDEWEB)

Fricker, Peter [Leica Geosystems GIS and Mapping, LLC (Switzerland); Gallo, M. Guillermo [Leica Geosystems GIS and Mapping, LLC (United States)

2005-07-01

The ADS40 Airborne Digital Pushbroom Sensor is currently the only commercial sensor capable of acquiring color and false color strip images in the low decimeter range at the same high resolution as the black and white stereo images. This high resolution of 12,000 pixels across the entire swath and 100% forward overlap in the image strips result in high quality DSM's, True Ortho's and at the same time allow unbiased remote sensing applications due to color strip images unchanged by pan-sharpening. The paper gives details on how the pushbroom sensor achieves these seemingly difficult technical challenges. It describes how a variety of mapping applications benefit from this sensor, a sensor which acts as a satellite pushbroom sensor within the airborne environment. (author)

RADIOMETRIC CALIBRATION OF MARS HiRISE HIGH RESOLUTION IMAGERY BASED ON FPGA

Directory of Open Access Journals (Sweden)

Y. Hou

2016-06-01

Full Text Available Due to the large data amount of HiRISE imagery, traditional radiometric calibration method is not able to meet the fast processing requirements. To solve this problem, a radiometric calibration system of HiRISE imagery based on field program gate array (FPGA is designed. The montage gap between two channels caused by gray inconsistency is removed through histogram matching. The calibration system is composed of FPGA and DSP, which makes full use of the parallel processing ability of FPGA and fast computation as well as flexible control characteristic of DSP. Experimental results show that the designed system consumes less hardware resources and the real-time processing ability of radiometric calibration of HiRISE imagery is improved.

Using High Resolution Commercial Satellite Imagery to Quantify Spatial Features of Urban Areas and their Relationship to Quality of Life Indicators in Accra, Ghana

Science.gov (United States)

Sandborn, A.; Engstrom, R.; Yu, Q.

2014-12-01

Mapping urban areas via satellite imagery is an important task for detecting and anticipating land cover and land use change at multiple scales. As developing countries experience substantial urban growth and expansion, remotely sensed based estimates of population and quality of life indicators can provide timely and spatially explicit information to researchers and planners working to determine how cities are changing. In this study, we use commercial high spatial resolution satellite imagery in combination with fine resolution census data to determine the ability of using remotely sensed data to reveal the spatial patterns of quality of life in Accra, Ghana. Traditionally, spectral characteristics are used on a per-pixel basis to determine land cover; however, in this study, we test a new methodology that quantifies spatial characteristics using a variety of spatial features observed in the imagery to determine the properties of an urban area. The spatial characteristics used in this study include histograms of oriented gradients, PanTex, Fourier transform, and line support regions. These spatial features focus on extracting structural and textural patterns of built-up areas, such as homogeneous building orientations and straight line indices. Information derived from aggregating the descriptive statistics of the spatial features at both the fine-resolution census unit and the larger neighborhood level are then compared to census derived quality of life indicators including information about housing, education, and population estimates. Preliminary results indicate that there are correlations between straight line indices and census data including available electricity and literacy rates. Results from this study will be used to determine if this methodology provides a new and improved way to measure a city structure in developing cities and differentiate between residential and commercial land use zones, as well as formal versus informal housing areas.

Integrating Landsat Data and High-Resolution Imagery for Applied Conservation Assessment of Forest Cover in Latin American Heterogenous Landscapes

Science.gov (United States)

Thomas, N.; Rueda, X.; Lambin, E.; Mendenhall, C. D.

2012-12-01

Large intact forested regions of the world are known to be critical to maintaining Earth's climate, ecosystem health, and human livelihoods. Remote sensing has been successfully implemented as a tool to monitor forest cover and landscape dynamics over broad regions. Much of this work has been done using coarse resolution sensors such as AVHRR and MODIS in combination with moderate resolution sensors, particularly Landsat. Finer scale analysis of heterogeneous and fragmented landscapes is commonly performed with medium resolution data and has had varying success depending on many factors including the level of fragmentation, variability of land cover types, patch size, and image availability. Fine scale tree cover in mixed agricultural areas can have a major impact on biodiversity and ecosystem sustainability but may often be inadequately captured with the global to regional (coarse resolution and moderate resolution) satellite sensors and processing techniques widely used to detect land use and land cover changes. This study investigates whether advanced remote sensing methods are able to assess and monitor percent tree canopy cover in spatially complex human-dominated agricultural landscapes that prove challenging for traditional mapping techniques. Our study areas are in high altitude, mixed agricultural coffee-growing regions in Costa Rica and the Colombian Andes. We applied Random Forests regression tree analysis to Landsat data along with additional spectral, environmental, and spatial variables to predict percent tree canopy cover at 30m resolution. Image object-based texture, shape, and neighborhood metrics were generated at the Landsat scale using eCognition and included in the variable suite. Training and validation data was generated using high resolution imagery from digital aerial photography at 1m to 2.5 m resolution. Our results are promising with Pearson's correlation coefficients between observed and predicted percent tree canopy cover of .86 (Costa

Access High Quality Imagery from the NOAA View Portal

Science.gov (United States)

Pisut, D.; Powell, A. M.; Loomis, T.; Goel, V.; Mills, B.; Cowan, D.

2013-12-01

NOAA curates a vast treasure trove of environmental data, but one that is sometimes not easily accessed, especially for education, outreach, and media purposes. Traditional data portals in NOAA require extensive knowledge of the specific names of observation platforms, models, and analyses, along with nomenclature for variable outputs. A new website and web mapping service (WMS) from NOAA attempts to remedy such issues. The NOAA View data imagery portal provides a seamless entry point into data from across the agency: satellite, models, in-situ analysis, etc. The system provides the user with ability to browse, animate, and download high resolution (e.g., 4,000 x 2,000 pixel) imagery, Google Earth, and even proxy data files. The WMS architecture also allows the resources to be ingested into other software systems or applications.

Geometric Accuracy Investigations of SEVIRI High Resolution Visible (HRV Level 1.5 Imagery

Directory of Open Access Journals (Sweden)

Sultan Kocaman Aksakal

2013-05-01

Full Text Available GCOS (Global Climate Observing System is a long-term program for monitoring the climate, detecting the changes, and assessing their impacts. Remote sensing techniques are being increasingly used for climate-related measurements. Imagery of the SEVIRI instrument on board of the European geostationary satellites Meteosat-8 and Meteosat-9 are often used for the estimation of essential climate variables. In a joint project between the Swiss GCOS Office and ETH Zurich, geometric accuracy and temporal stability of 1-km resolution HRV channel imagery of SEVIRI have been evaluated over Switzerland. A set of tools and algorithms has been developed for the investigations. Statistical analysis and blunder detection have been integrated in the process for robust evaluation. The relative accuracy is evaluated by tracking large numbers of feature points in consecutive HRV images taken at 15-minute intervals. For the absolute accuracy evaluation, lakes in Switzerland and surroundings are used as reference. 20 lakes digitized from Landsat orthophotos are transformed into HRV images and matched via 2D translation terms at sub-pixel level. The algorithms are tested using HRV images taken on 24 days in 2008 (2 days per month. The results show that 2D shifts that are up to 8 pixels are present both in relative and absolute terms.

USING COMBINATION OF PLANAR AND HEIGHT FEATURES FOR DETECTING BUILT-UP AREAS FROM HIGH-RESOLUTION STEREO IMAGERY

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F. Peng

2017-09-01

Full Text Available Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades the performance of built-up area detection when using planar texture, shape, and spectral features. Terrain slope and building height are often used to optimize the results, but extracted from auxiliary data (e.g. LIDAR data, DSM. Moreover, the auxiliary data must be acquired around the same time as image acquisition. Otherwise, built-up area detection accuracy is affected. Stereo imagery incorporates both planar and height information unlike single remotely sensed images. Stereo imagery acquired by many satellites (e.g. Worldview-4, Pleiades-HR, ALOS-PRISM, and ZY-3 can be used as data source of identifying built-up areas. A new method of identifying high-accuracy built-up areas from stereo imagery is achieved by using a combination of planar and height features. The digital surface model (DSM and digital orthophoto map (DOM are first generated from stereo images. Then, height values of above-ground objects (e.g. buildings are calculated from the DSM, and used to obtain raw built-up areas. Other raw built-up areas are obtained from the DOM using Pantex and Gabor, respectively. Final high-accuracy built-up area results are achieved from these raw built-up areas using the decision level fusion. Experimental results show that accurate built-up areas can be achieved from stereo imagery. The height information used in the proposed method is derived from stereo imagery itself, with no need to require auxiliary height data (e.g. LIDAR data. The proposed method is suitable for spaceborne and airborne stereo pairs and triplets.

Using Combination of Planar and Height Features for Detecting Built-Up Areas from High-Resolution Stereo Imagery

Science.gov (United States)

Peng, F.; Cai, X.; Tan, W.

2017-09-01

Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades the performance of built-up area detection when using planar texture, shape, and spectral features. Terrain slope and building height are often used to optimize the results, but extracted from auxiliary data (e.g. LIDAR data, DSM). Moreover, the auxiliary data must be acquired around the same time as image acquisition. Otherwise, built-up area detection accuracy is affected. Stereo imagery incorporates both planar and height information unlike single remotely sensed images. Stereo imagery acquired by many satellites (e.g. Worldview-4, Pleiades-HR, ALOS-PRISM, and ZY-3) can be used as data source of identifying built-up areas. A new method of identifying high-accuracy built-up areas from stereo imagery is achieved by using a combination of planar and height features. The digital surface model (DSM) and digital orthophoto map (DOM) are first generated from stereo images. Then, height values of above-ground objects (e.g. buildings) are calculated from the DSM, and used to obtain raw built-up areas. Other raw built-up areas are obtained from the DOM using Pantex and Gabor, respectively. Final high-accuracy built-up area results are achieved from these raw built-up areas using the decision level fusion. Experimental results show that accurate built-up areas can be achieved from stereo imagery. The height information used in the proposed method is derived from stereo imagery itself, with no need to require auxiliary height data (e.g. LIDAR data). The proposed method is suitable for spaceborne and airborne stereo pairs and triplets.

Spectral Difference in the Image Domain for Large Neighborhoods, a GEOBIA Pre-Processing Step for High Resolution Imagery

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Roeland de Kok

2012-08-01

Full Text Available Contrast plays an important role in the visual interpretation of imagery. To mimic visual interpretation and using contrast in a Geographic Object Based Image Analysis (GEOBIA environment, it is useful to consider an analysis for single pixel objects. This should be done before applying homogeneity criteria in the aggregation of pixels for the construction of meaningful image objects. The habit or “best practice” to start GEOBIA with pixel aggregation into homogeneous objects should come with the awareness that feature attributes for single pixels are at risk of becoming less accessible for further analysis. Single pixel contrast with image convolution on close neighborhoods is a standard technique, also applied in edge detection. This study elaborates on the analysis of close as well as much larger neighborhoods inside the GEOBIA domain. The applied calculations are limited to the first segmentation step for single pixel objects in order to produce additional feature attributes for objects of interest to be generated in further aggregation processes. The equation presented functions at a level that is considered an intermediary product in the sequential processing of imagery. The procedure requires intensive processor and memory capacity. The resulting feature attributes highlight not only contrasting pixels (edges but also contrasting areas of local pixel groups. The suggested approach can be extended and becomes useful in classifying artificial areas at national scales using high resolution satellite mosaics.

C-CAP Kahoolawe 2005 era High Resolution Land Cover Metadata

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of land derived from high resolution imagery and was analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine...

Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach

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Charles R. Lane

2014-12-01

Full Text Available Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2 for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA. We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85 for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system.

Appraisal of Opportunities and Perspectives for the Systematic Condition Assessment of Heritage Sites with Copernicus Sentinel-2 High-Resolution Multispectral Imagery

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Deodato Tapete

2018-04-01

Full Text Available Very high-resolution (VHR optical satellite imagery (≤5 m is nowadays an established source of information to monitor cultural and archaeological heritage that is exposed to hazards and anthropogenic threats to their conservation, whereas few publications specifically investigate the role that regularly acquired images from high-resolution (HR satellite sensors (5–30 m may play in this application domain. This paper aims to appraise the potential of the multispectral constellation Sentinel-2 of the European Commission Earth observation programme Copernicus to detect prominent features and changes in heritage sites, during both ordinary times and crisis. We test the 10 m spatial resolution of the 3 visible spectral bands of Sentinel-2 for substantiation of single local events—that is, wall collapses in the UNESCO World Heritage site of the Old City of Aleppo (Syria—and for hotspot mapping of recurrent incidents—that is, the archaeological looting in the archaeological site of Apamea (Syria. By screening long Sentinel-2 time series consisting of 114 images for Aleppo and 57 images for Apamea, we demonstrate that changes of textural properties and surface reflectance can be logged accurately in time and space and can be associated to events relevant for conservation. VHR imagery from Google Earth was used for the validation and identification of trends occurring prior to the Sentinel-2 launch. We also demonstrate how to exploit the Sentinel-2 short revisiting time (5 days and large swath (290 km for multi-temporal tracking of spatial patterns of urban sprawl across the cultural landscape of the World Heritage Site of Cyrene (Libya, and the three coastal ancient Greek sites of Tocra, Ptolemais, and Apollonia in Cyrenaica. With the future development of tailored machine learning approaches of feature extraction and pattern detection, Sentinel-2 can become extremely useful to screen wider regions with short revisiting times and to undertake

Cloud detection method for Chinese moderate high resolution satellite imagery (Conference Presentation)

Science.gov (United States)

Zhong, Bo; Chen, Wuhan; Wu, Shanlong; Liu, Qinhuo

2016-10-01

Cloud detection of satellite imagery is very important for quantitative remote sensing research and remote sensing applications. However, many satellite sensors don't have enough bands for a quick, accurate, and simple detection of clouds. Particularly, the newly launched moderate to high spatial resolution satellite sensors of China, such as the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) and the wide field of view (WFV) sensor on-board the Gao Fen 1 (GF-1), only have four available bands including blue, green, red, and near infrared bands, which are far from the requirements of most could detection methods. In order to solve this problem, an improved and automated cloud detection method for Chinese satellite sensors called OCM (Object oriented Cloud and cloud-shadow Matching method) is presented in this paper. It firstly modified the Automatic Cloud Cover Assessment (ACCA) method, which was developed for Landsat-7 data, to get an initial cloud map. The modified ACCA method is mainly based on threshold and different threshold setting produces different cloud map. Subsequently, a strict threshold is used to produce a cloud map with high confidence and large amount of cloud omission and a loose threshold is used to produce a cloud map with low confidence and large amount of commission. Secondly, a corresponding cloud-shadow map is also produced using the threshold of near-infrared band. Thirdly, the cloud maps and cloud-shadow map are transferred to cloud objects and cloud-shadow objects. Cloud and cloud-shadow are usually in pairs; consequently, the final cloud and cloud-shadow maps are made based on the relationship between cloud and cloud-shadow objects. OCM method was tested using almost 200 HJ-1/CCD images across China and the overall accuracy of cloud detection is close to 90%.

Integrated thermal infrared imaging and Structure-from-Motion photogrametry to map apparent temperature and radiant hydrothermal heat flux at Mammoth Mountain, CA USA

Science.gov (United States)

Lewis, Aaron; George Hilley,; Lewicki, Jennifer L.

2015-01-01

This work presents a method to create high-resolution (cm-scale) orthorectified and georeferenced maps of apparent surface temperature and radiant hydrothermal heat flux and estimate the radiant hydrothermal heat emission rate from a study area. A ground-based thermal infrared (TIR) camera was used to collect (1) a set of overlapping and offset visible imagery around the study area during the daytime and (2) time series of co-located visible and TIR imagery at one or more sites within the study area from pre-dawn to daytime. Daytime visible imagery was processed using the Structure-from-Motion photogrammetric method to create a digital elevation model onto which pre-dawn TIR imagery was orthorectified and georeferenced. Three-dimensional maps of apparent surface temperature and radiant hydrothermal heat flux were then visualized and analyzed from various computer platforms (e.g., Google Earth, ArcGIS). We demonstrate this method at the Mammoth Mountain fumarole area on Mammoth Mountain, CA. Time-averaged apparent surface temperatures and radiant hydrothermal heat fluxes were observed up to 73.7 oC and 450 W m-2, respectively, while the estimated radiant hydrothermal heat emission rate from the area was 1.54 kW. Results should provide a basis for monitoring potential volcanic unrest and mitigating hydrothermal heat-related hazards on the volcano.

Employing high resolution satellite imagery to document a rapid glacier surge in the Karakoram - risks and opportunities for hazard assessment

Science.gov (United States)

Steiner, J. F.; Kraaijenbrink, P. D. A.; Jiduc, S. G.; Immerzeel, W. W.

2017-12-01

Glacier surges occur regularly in the Karakoram but their driving mechanisms, recurrence and its relation to climatic change remain unclear. Since many glacier tongues in the region reach to very low elevations, local populations are often exposed to glacial hazards. While the scientific interpretation of hazard is one challenge, adequately communicating results to possibly affected stakeholders poses a different set of hurdles. Using DEMs as well as Landsat imagery in combination with high-resolution Planet imagery we quantify surface elevation changes and flow velocities to document a glacier surge of the Khurdopin glacier, located in a remote valley in Pakistan, in the first half of 2017. Results reveal that an accumulation of ice mass leads to a rapid surge in peaking with velocities above 5000 m a-1 or 0.5 m h-1 during a few days. Velocities increase steadily during a four-year build-up phase prior to the actual surge, while the remaining 15 years of the recurring cycle the glacier is quiescent. It is hypothesized that the surge is mainly initiated as a result of increased pressure melting caused by ice accumulation. However, surface observations show increased crevassing and disappearance of supra glacial ponds, which could have led to increased lubrication of the glacier bed. As a consequence of the surging tongue blocking the main valley a lake has formed and grown continuously in size over two months at a rate of up to 3000 m2 per day. Using satellite imagery with a frequent overpass rate we are able to (a) characterize the nature of glacier surges in the region with greater detail and (b) monitor the surge as well as the formation of the lake as it develops. Having developed a connection to local stakeholders we were able to provide rapid hazard assessments to affected communities, which can be employed to define possible actions. We show the potential of satellite imagery - freely available Landsat in combination with commercial Planet imagery -, which

Super-Resolution for “Jilin-1” Satellite Video Imagery via a Convolutional Network

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Aoran Xiao

2018-04-01

Full Text Available Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method’s practicality. Experimental results on “Jilin-1” satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods.

Automated protocols for spaceborne sub-meter resolution "Big Data" products for Earth Science

Science.gov (United States)

Neigh, C. S. R.; Carroll, M.; Montesano, P.; Slayback, D. A.; Wooten, M.; Lyapustin, A.; Shean, D. E.; Alexandrov, O.; Macander, M. J.; Tucker, C. J.

2017-12-01

The volume of available remotely sensed data has grown exceeding Petabytes per year and the cost for data, storage systems and compute power have both dropped exponentially. This has opened the door for "Big Data" processing systems with high-end computing (HEC) such as the Google Earth Engine, NASA Earth Exchange (NEX), and NASA Center for Climate Simulation (NCCS). At the same time, commercial very high-resolution (VHR) satellites have grown into a constellation with global repeat coverage that can support existing NASA Earth observing missions with stereo and super-spectral capabilities. Through agreements with the National Geospatial-Intelligence Agency NASA-Goddard Space Flight Center is acquiring Petabytes of global sub-meter to 4 meter resolution imagery from WorldView-1,2,3 Quickbird-2, GeoEye-1 and IKONOS-2 satellites. These data are a valuable no-direct cost for the enhancement of Earth observation research that supports US government interests. We are currently developing automated protocols for generating VHR products to support NASA's Earth observing missions. These include two primary foci: 1) on demand VHR 1/2° ortho mosaics - process VHR to surface reflectance, orthorectify and co-register multi-temporal 2 m multispectral imagery compiled as user defined regional mosaics. This will provide an easy access dataset to investigate biodiversity, tree canopy closure, surface water fraction, and cropped area for smallholder agriculture; and 2) on demand VHR digital elevation models (DEMs) - process stereo VHR to extract VHR DEMs with the NASA Ames stereo pipeline. This will benefit Earth surface studies on the cryosphere (glacier mass balance, flow rates and snow depth), hydrology (lake/water body levels, landslides, subsidence) and biosphere (forest structure, canopy height/cover) among others. Recent examples of products used in NASA Earth Science projects will be provided. This HEC API could foster surmounting prior spatial-temporal limitations while

New Visualization Techniques to Analyze Ultra-High Resolution Four-dimensional Surface Deformation Imagery Collected With Ground-based Tripod LiDAR

Science.gov (United States)

Kreylos, O.; Bawden, G. W.; Kellogg, L. H.

2005-12-01

We are developing a visualization application to display and interact with very large (tens of millions of points) four-dimensional point position datasets in an immersive environment such that point groups from repeated Tripod LiDAR (Light Detection And Ranging) surveys can be selected, measured, and analyzed for land surface change using 3D~interactions. Ground-based tripod or terrestrial LiDAR (T-LiDAR) can remotely collect ultra-high resolution (centimeter to subcentimeter) and accurate (± 4 mm) digital imagery of the scanned target, and at scanning rates of 2,000 (x, y, z, i) (3D~position~+ intensity) points per second over 7~million points can be collected for a given target in an hour. We developed a multiresolution point set data representation based on octrees to display large T-LiDAR point cloud datasets at the frame rates required for immersive display (between 60 Hz and 120 Hz). Data inside an observer's region of interest is shown in full detail, whereas data outside the field of view or far away from the observer is shown at reduced resolution to provide context. Using 3D input devices at the University of California Davis KeckCAVES, users can navigate large point sets, accurately select related point groups in two or more point sets by sweeping regions of space, and guide the software in deriving positional information from point groups to compute their displacements between surveys. We used this new software application in the KeckCAVES to analyze 4D T-LiDAR imagery from the June~1, 2005 Blue Bird Canyon landslide in Laguna Beach, southern California. Over 50~million (x, y, z, i) data points were collected between 10 and 21~days after the landslide to evaluate T-LiDAR as a natural hazards response tool. The visualization of the T-LiDAR scans within the immediate landslide showed minor readjustments in the weeks following the primarily landslide with no observable continued motion on the primary landslide. Recovery and demolition efforts across the

2014 NOAA Ortho-rectified Mosaic of Hurricane Sandy Coastal Impact Area

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles at 0.35m GSD created for NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative in Hurricane Sandy coastal...

A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery.

Science.gov (United States)

Meng, Fan; Yang, Xiaomei; Zhou, Chenghu; Li, Zhi

2017-09-15

Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features.

C-CAP Santa Cruz 2001 era High Resolution Land Cover Metadata

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This dataset represents land cover for the San Lorenzo River basin in Santa Cruz County, California derived from high resolution imagery. The land cover features in...

High Resolution Topography of Polar Regions from Commercial Satellite Imagery, Petascale Computing and Open Source Software

Science.gov (United States)

Morin, Paul; Porter, Claire; Cloutier, Michael; Howat, Ian; Noh, Myoung-Jong; Willis, Michael; Kramer, WIlliam; Bauer, Greg; Bates, Brian; Williamson, Cathleen

2017-04-01

Surface topography is among the most fundamental data sets for geosciences, essential for disciplines ranging from glaciology to geodynamics. Two new projects are using sub-meter, commercial imagery licensed by the National Geospatial-Intelligence Agency and open source photogrammetry software to produce a time-tagged 2m posting elevation model of the Arctic and an 8m posting reference elevation model for the Antarctic. When complete, this publically available data will be at higher resolution than any elevation models that cover the entirety of the Western United States. These two polar projects are made possible due to three equally important factors: 1) open-source photogrammetry software, 2) petascale computing, and 3) sub-meter imagery licensed to the United States Government. Our talk will detail the technical challenges of using automated photogrammetry software; the rapid workflow evolution to allow DEM production; the task of deploying the workflow on one of the world's largest supercomputers; the trials of moving massive amounts of data, and the management strategies the team needed to solve in order to meet deadlines. Finally, we will discuss the implications of this type of collaboration for future multi-team use of leadership-class systems such as Blue Waters, and for further elevation mapping.

Coastal habitat mapping in the Aegean Sea using high resolution orthophoto maps

Science.gov (United States)

Topouzelis, Konstantinos; Papakonstantinou, Apostolos; Doukari, Michaela; Stamatis, Panagiotis; Makri, Despina; Katsanevakis, Stelios

2017-09-01

The significance of coastal habitat mapping lies in the need to prevent from anthropogenic interventions and other factors. Until 2015, Landsat-8 (30m) imagery were used as medium spatial resolution satellite imagery. So far, Sentinel-2 satellite imagery is very useful for more detailed regional scale mapping. However, the use of high resolution orthophoto maps, which are determined from UAV data, is expected to improve the mapping accuracy. This is due to small spatial resolution of the orthophoto maps (30 cm). This paper outlines the integration of UAS for data acquisition and Structure from Motion (SfM) pipeline for the visualization of selected coastal areas in the Aegean Sea. Additionally, the produced orthophoto maps analyzed through an object-based image analysis (OBIA) and nearest-neighbor classification for mapping the coastal habitats. Classification classes included the main general habitat types, i.e. seagrass, soft bottom, and hard bottom The developed methodology applied at the Koumbara beach (Ios Island - Greece). Results showed that UAS's data revealed the sub-bottom complexity in large shallow areas since they provide such information in the spatial resolution that permits the mapping of seagrass meadows with extreme detail. The produced habitat vectors are ideal as reference data for studies with satellite data of lower spatial resolution.

Assessing Wildfire Risk in Cultural Heritage Properties Using High Spatial and Temporal Resolution Satellite Imagery and Spatially Explicit Fire Simulations: The Case of Holy Mount Athos, Greece

Directory of Open Access Journals (Sweden)

Giorgos Mallinis

2016-02-01

Full Text Available Fire management implications and the design of conservation strategies on fire prone landscapes within the UNESCO World Heritage Properties require the application of wildfire risk assessment at landscape level. The objective of this study was to analyze the spatial variation of wildfire risk on Holy Mount Athos in Greece. Mt. Athos includes 20 monasteries and other structures that are threatened by increasing frequency of wildfires. Site-specific fuel models were created by measuring in the field several fuel parameters in representative natural fuel complexes, while the spatial extent of the fuel types was determined using a synergy of high-resolution imagery and high temporal information from medium spatial resolution imagery classified through object-based analysis and a machine learning classifier. The Minimum Travel Time (MTT algorithm, as it is embedded in FlamMap software, was applied in order to evaluate Burn Probability (BP, Conditional Flame Length (CFL, Fire Size (FS, and Source-Sink Ratio (SSR. The results revealed low burn probabilities for the monasteries; however, nine out of the 20 monasteries have high fire potential in terms of fire intensity, which means that if an ignition occurs, an intense fire is expected. The outputs of this study may be used for decision-making for short-term predictions of wildfire risk at an operational level, contributing to fire suppression and management of UNESCO World Heritage Properties.

Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery

Science.gov (United States)

Chen, Gang; Metz, Margaret R.; Rizzo, David M.; Dillon, Whalen W.; Meentemeyer, Ross K.

2015-04-01

Forest ecosystems are subject to a variety of disturbances with increasing intensities and frequencies, which may permanently change the trajectories of forest recovery and disrupt the ecosystem services provided by trees. Fire and invasive species, especially exotic disease-causing pathogens and insects, are examples of disturbances that together could pose major threats to forest health. This study examines the impacts of fire and exotic disease (sudden oak death) on forests, with an emphasis on the assessment of post-fire burn severity in a forest where trees have experienced three stages of disease progression pre-fire: early-stage (trees retaining dried foliage and fine twigs), middle-stage (trees losing fine crown fuels), and late-stage (trees falling down). The research was conducted by applying Geographic Object-Based Image Analysis (GEOBIA) to MASTER airborne images that were acquired immediately following the fire for rapid assessment and contained both high-spatial (4 m) and high-spectral (50 bands) resolutions. Although GEOBIA has gradually become a standard tool for analyzing high-spatial resolution imagery, high-spectral resolution data (dozens to hundreds of bands) can dramatically reduce computation efficiency in the process of segmentation and object-based variable extraction, leading to complicated variable selection for succeeding modeling. Hence, we also assessed two widely used band reduction algorithms, PCA (principal component analysis) and MNF (minimum noise fraction), for the delineation of image objects and the subsequent performance of burn severity models using either PCA or MNF derived variables. To increase computation efficiency, only the top 5 PCA and MNF and top 10 PCA and MNF components were evaluated, which accounted for 10% and 20% of the total number of the original 50 spectral bands, respectively. Results show that if no band reduction was applied the models developed for the three stages of disease progression had relatively

Classification of High Spatial Resolution, Hyperspectral ...

Science.gov (United States)

EPA announced the availability of the final report,Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA . This report and associated land use/land cover (LULC) coverage is the result of a collaborative effort among an interdisciplinary team of scientists with the U.S. Environmental Protection Agency's (U.S. EPA's) Office of Research and Development in Cincinnati, Ohio. A primary goal of this project is to enhance the use of geography and spatial analytic tools in risk assessment, and to improve the scientific basis for risk management decisions affecting drinking water and water quality. The land use/land cover classification is derived from 82 flight lines of Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery acquired from July 24 through August 9, 2002 via fixed-wing aircraft.

Global Fiducials Program Imagery: New Opportunities for Geospatial Research, Outreach, and Education

Science.gov (United States)

Price, S. D.

2012-12-01

MOLNIA, Bruce F., PRICE, Susan D. and, KING, Stephen E., U.S. Geological Survey (USGS), 562 National Center, Reston, VA 20192, sprice@usgs.gov The Civil Applications Committee (CAC), operated by the U.S. Geological Survey (USGS), is the Federal interagency committee that facilitates Federal civil agency access to U.S. National Systems space-based electro-optical (EO) imagery for natural disaster response; global change investigations; ecosystem monitoring; mapping, charting, and geodesy; and related topics. The CAC's Global Fiducials Program (GFP) has overseen the systematic collection of high-resolution imagery to provide geospatial data time series spanning a decade or more at carefully selected sites to study and monitor changes, and to facilitate a comprehensive understanding of dynamic and sensitive areas of our planet. Since 2008, more than 4,500 one-meter resolution EO images which comprise time series from 85 GFP sites have been released for unrestricted public use. Initial site selections were made by Federal and academic scientists based on each site's unique history, susceptibility, or environmental value. For each site, collection strategies were carefully defined to maximize information extraction capabilities. This consistency enhances our ability to understand Earth's dynamic processes and long-term trends. Individual time series focus on Arctic sea ice change; temperate glacier behavior; mid-continent wetland dynamics; barrier island response to hurricanes; coastline evolution; wildland fire recovery; Long-Term Ecological Resource (LTER) site processes; and many other topics. The images are available from a USGS website at no cost, in an orthorectified GeoTIFF format with supporting metadata, making them ideal for use in Earth science education and GIS projects. New on-line tools provide enhanced analysis of these time-series imagery. For additional information go to http://gfp.usgs.gov or http://gfl.usgs.gov.Bering Glacier is the largest and

Role of light satellites in the high-resolution Earth observation domain

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Fishman, Moshe

1999-12-01

Current 'classic' applications using and exploring space based earth imagery are exclusive, narrow niche tailored, expensive and hardly accessible. On the other side new, inexpensive and widely used 'consumable' applications will be only developed concurrently to the availability of appropriate imagery allowing that process. A part of these applications can be imagined today, like WWW based 'virtual tourism' or news media, but the history of technological, cultural and entertainment evolution teaches us that most of future applications are unpredictable -- they emerge together with the platforms enabling their appearance. The only thing, which can be ultimately stated, is that the definitive condition for such applications is the availability of the proper imagery platform providing low cost, high resolution, large area, quick response, simple accessibility and quick dissemination of the raw picture. This platform is a constellation of Earth Observation satellites. Up to 1995 the Space Based High Resolution Earth Observation Domain was dominated by heavy, super-expensive and very inflexible birds. The launch of Israeli OFEQ-3 Satellite by MBT Division of Israel Aircraft Industries (IAI) marked the entrance to new era of light, smart and cheap Low Earth Orbited Imaging satellites. The Earth Resource Observation System (EROS) initiated by West Indian Space, is based on OFEQ class Satellites design and it is capable to gather visual data of Earth Surface both at high resolution and large image capacity. The main attributes, derived from its compact design, low weight and sophisticated logic and which convert the EROS Satellite to valuable and productive system, are discussed. The major advantages of Light Satellites in High Resolution Earth Observation Domain are presented and WIS guidelines featuring the next generation of LEO Imaging Systems are included.

Does the Data Resolution/origin Matter? Satellite, Airborne and Uav Imagery to Tackle Plant Invasions

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Müllerová, Jana; Brůna, Josef; Dvořák, Petr; Bartaloš, Tomáš; Vítková, Michaela

2016-06-01

Invasive plant species represent a serious threat to biodiversity and landscape as well as human health and socio-economy. To successfully fight plant invasions, new methods enabling fast and efficient monitoring, such as remote sensing, are needed. In an ongoing project, optical remote sensing (RS) data of different origin (satellite, aerial and UAV), spectral (panchromatic, multispectral and color), spatial (very high to medium) and temporal resolution, and various technical approaches (object-, pixelbased and combined) are tested to choose the best strategies for monitoring of four invasive plant species (giant hogweed, black locust, tree of heaven and exotic knotweeds). In our study, we address trade-offs between spectral, spatial and temporal resolutions required for balance between the precision of detection and economic feasibility. For the best results, it is necessary to choose best combination of spatial and spectral resolution and phenological stage of the plant in focus. For species forming distinct inflorescences such as giant hogweed iterative semi-automated object-oriented approach was successfully applied even for low spectral resolution data (if pixel size was sufficient) whereas for lower spatial resolution satellite imagery or less distinct species with complicated architecture such as knotweed, combination of pixel and object based approaches was used. High accuracies achieved for very high resolution data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparably precise, fast and efficient. This knowledge serves as a basis for prediction, monitoring and prioritization of management targets.

High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors

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Hamideh Nouri

2014-01-01

Full Text Available Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The study site was Veale Gardens in Adelaide, Australia. Image processing was applied on five images captured from February 2012 to February 2013 using ERDAS Imagine. From 64 possible two band combinations of WorldView-2, the most reliable one (with the maximum median differences was selected. Normalized Difference Vegetation Index (NDVI values were derived for each category of landscape cover, namely trees, shrubs, turf grasses, impervious pavements, and water bodies. Urban landscape evapotranspiration rates for Veale Gardens were estimated through field monitoring using observational-based landscape coefficients. The relationships between remotely sensed NDVIs for the entire Veale Gardens and for individual NDVIs of different vegetation covers were compared with field measured urban landscape evapotranspiration rates. The water stress conditions experienced in January 2013 decreased the correlation between ET and NDVI with the highest relationship of ET-Landscape NDVI (Landscape Normalized Difference Vegetation Index for shrubs (r2 = 0.66 and trees (r2 = 0.63. However, when the January data was excluded, there was a significant correlation between ET and NDVI. The highest correlation for ET-Landscape NDVI was found for the entire Veale Gardens regardless of vegetation type (r2 = 0.95, p > 0.05 and the lowest one was for turf (r2 = 0.88, p > 0.05. In support of the feasibility of ET estimation by WV2 over a longer period, an algorithm recently developed that estimates evapotranspiration rates based on the Enhanced Vegetation Index (EVI from MODIS was employed. The results revealed a

High spatial resolution WorldView-2 imagery for mapping NDVI and its relationship to temporal urban landscape evapotranspiration factors

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Nouri, Hamideh; Beecham, Simon; Anderson, Sharolyn; Nagler, Pamela

2014-01-01

Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET) and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The study site was Veale Gardens in Adelaide, Australia. Image processing was applied on five images captured from February 2012 to February 2013 using ERDAS Imagine. From 64 possible two band combinations of WorldView-2, the most reliable one (with the maximum median differences) was selected. Normalized Difference Vegetation Index (NDVI) values were derived for each category of landscape cover, namely trees, shrubs, turf grasses, impervious pavements, and water bodies. Urban landscape evapotranspiration rates for Veale Gardens were estimated through field monitoring using observational-based landscape coefficients. The relationships between remotely sensed NDVIs for the entire Veale Gardens and for individual NDVIs of different vegetation covers were compared with field measured urban landscape evapotranspiration rates. The water stress conditions experienced in January 2013 decreased the correlation between ET and NDVI with the highest relationship of ET-Landscape NDVI (Landscape Normalized Difference Vegetation Index) for shrubs (r2 = 0.66) and trees (r2 = 0.63). However, when the January data was excluded, there was a significant correlation between ET and NDVI. The highest correlation for ET-Landscape NDVI was found for the entire Veale Gardens regardless of vegetation type (r2 = 0.95, p > 0.05) and the lowest one was for turf (r2 = 0.88, p > 0.05). In support of the feasibility of ET estimation by WV2 over a longer period, an algorithm recently developed that estimates evapotranspiration rates based on the Enhanced Vegetation Index (EVI) from MODIS was employed. The results revealed a significant positive

Virginia Base Mapping Program (VBMP) 2002; Multiple Resolutions (1"=100',1"= 200',1"=400' scale) Digital Orthophotography for the South Zone of the Virginia State Plane Grid

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — These files contain 2-foot GSD high-resolution orthorectified aerial image map products in GeoTIFF version 6.0 file format. GeoTIFF files are uncompressed raster...

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.

Ribbon scanning confocal for high-speed high-resolution volume imaging of brain.

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Alan M Watson

Full Text Available Whole-brain imaging is becoming a fundamental means of experimental insight; however, achieving subcellular resolution imagery in a reasonable time window has not been possible. We describe the first application of multicolor ribbon scanning confocal methods to collect high-resolution volume images of chemically cleared brains. We demonstrate that ribbon scanning collects images over ten times faster than conventional high speed confocal systems but with equivalent spectral and spatial resolution. Further, using this technology, we reconstruct large volumes of mouse brain infected with encephalitic alphaviruses and demonstrate that regions of the brain with abundant viral replication were inaccessible to vascular perfusion. This reveals that the destruction or collapse of large regions of brain micro vasculature may contribute to the severe disease caused by Venezuelan equine encephalitis virus. Visualization of this fundamental impact of infection would not be possible without sampling at subcellular resolution within large brain volumes.

2014 NOAA Ortho-rectified Mosaic of Delaware Coastline: Hurricane Sandy Impact Area

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National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles at 0.10m GSD. This data set was created for NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative in the...

Developing a semi/automated protocol to post-process large volume, High-resolution airborne thermal infrared (TIR) imagery for urban waste heat mapping

Science.gov (United States)

Rahman, Mir Mustafizur

In collaboration with The City of Calgary 2011 Sustainability Direction and as part of the HEAT (Heat Energy Assessment Technologies) project, the focus of this research is to develop a semi/automated 'protocol' to post-process large volumes of high-resolution (H-res) airborne thermal infrared (TIR) imagery to enable accurate urban waste heat mapping. HEAT is a free GeoWeb service, designed to help Calgary residents improve their home energy efficiency by visualizing the amount and location of waste heat leaving their homes and communities, as easily as clicking on their house in Google Maps. HEAT metrics are derived from 43 flight lines of TABI-1800 (Thermal Airborne Broadband Imager) data acquired on May 13--14, 2012 at night (11:00 pm--5:00 am) over The City of Calgary, Alberta (˜825 km 2) at a 50 cm spatial resolution and 0.05°C thermal resolution. At present, the only way to generate a large area, high-spatial resolution TIR scene is to acquire separate airborne flight lines and mosaic them together. However, the ambient sensed temperature within, and between flight lines naturally changes during acquisition (due to varying atmospheric and local micro-climate conditions), resulting in mosaicked images with different temperatures for the same scene components (e.g. roads, buildings), and mosaic join-lines arbitrarily bisect many thousands of homes. In combination these effects result in reduced utility and classification accuracy including, poorly defined HEAT Metrics, inaccurate hotspot detection and raw imagery that are difficult to interpret. In an effort to minimize these effects, three new semi/automated post-processing algorithms (the protocol) are described, which are then used to generate a 43 flight line mosaic of TABI-1800 data from which accurate Calgary waste heat maps and HEAT metrics can be generated. These algorithms (presented as four peer-reviewed papers)---are: (a) Thermal Urban Road Normalization (TURN)---used to mitigate the microclimatic

Potential of High-Resolution Satellite Imagery for Mapping Distribution and Evaluating Ecological Characteristics of Tree Species at the Angkor Monument, Cambodia

Directory of Open Access Journals (Sweden)

Tomita Mizuki

2015-01-01

Full Text Available Large trees play several vital roles in the Angkor monuments landscape. They protect biodiversity, enhance the tourism experience, and provide various ecosystem services to local residents. A clear understanding of forest composition and distribution of individual species, as well as timely monitoring of changes, is necessary for conservation of these trees. using traditional field work, obtaining this sort of data is time-consuming and labour-intensive. This research investigates classification of very high resolution remote sensing data as a tool for efficient analyses. QuickBird satellite imagery was used to clarify the tree species community in and around Preah Khan temple, to elucidate differences in ecological traits among the three dominant species (Dipterocarpus alatus, Lagerstroemia calyculata and Tetrameles nudiflora, and to identify crowns of the dominant species.

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

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Fan Hu

2016-06-01

Full Text Available Scene classification of high-resolution remote sensing (HRRS imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal “filters” from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.

Science with High Spatial Resolution Far-Infrared Data

Science.gov (United States)

Terebey, Susan (Editor); Mazzarella, Joseph M. (Editor)

1994-01-01

The goal of this workshop was to discuss new science and techniques relevant to high spatial resolution processing of far-infrared data, with particular focus on high resolution processing of IRAS data. Users of the maximum correlation method, maximum entropy, and other resolution enhancement algorithms applicable to far-infrared data gathered at the Infrared Processing and Analysis Center (IPAC) for two days in June 1993 to compare techniques and discuss new results. During a special session on the third day, interested astronomers were introduced to IRAS HIRES processing, which is IPAC's implementation of the maximum correlation method to the IRAS data. Topics discussed during the workshop included: (1) image reconstruction; (2) random noise; (3) imagery; (4) interacting galaxies; (5) spiral galaxies; (6) galactic dust and elliptical galaxies; (7) star formation in Seyfert galaxies; (8) wavelet analysis; and (9) supernova remnants.

Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

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Wang, Min; Cui, Qi; Wang, Jie; Ming, Dongping; Lv, Guonian

2017-01-01

In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

Spatiotemporal estimation of air temperature patterns at the street level using high resolution satellite imagery.

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Pelta, Ran; Chudnovsky, Alexandra A

2017-02-01

Although meteorological monitoring stations provide accurate measurements of Air Temperature (AT), their spatial coverage within a given region is limited and thus is often insufficient for exposure and epidemiological studies. In many applications, satellite imagery measures energy flux, which is spatially continuous, and calculates Brightness Temperature (BT) that used as an input parameter. Although both quantities (AT-BT) are physically related, the correlation between them is not straightforward, and varies daily due to parameters such as meteorological conditions, surface moisture, land use, satellite-surface geometry and others. In this paper we first investigate the relationship between AT and BT as measured by 39 meteorological stations in Israel during 1984-2015. Thereafter, we apply mixed regression models with daily random slopes to calibrate Landsat BT data with monitored AT measurements for the period 1984-2015. Results show that AT can be predicted with high accuracy by using BT with high spatial resolution. The model shows relatively high accuracy estimation of AT (R 2 =0.92, RMSE=1.58°C, slope=0.90). Incorporating meteorological parameters into the model generates better accuracy (R 2 =0.935) than the AT-BT model (R 2 =0.92). Furthermore, based on the relatively high model accuracy, we investigated the spatial patterns of AT within the study domain. In the latter we focused on July-August, as these two months are characterized by relativity stable synoptic conditions in the study area. In addition, a temporal change in AT during the last 30years was estimated and verified using available meteorological stations and two additional remote sensing platforms. Finally, the impact of different land coverage on AT were estimated, as an example of future application of the presented approach. Copyright © 2016 Elsevier B.V. All rights reserved.

Estimation of Evapotraspiration of Tamarisk using Energy Balance Models with High Resolution Airborne Imagery and LIDAR Data

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Geli, H. M.; Taghvaeian, S.; Neale, C. M.; Pack, R.; Watts, D. R.; Osterberg, J.

2010-12-01

The wide uncontrolled spread of the invasive species of Tamarisk (Salt Cedar) in the riparian areas of the southwest of the United States has become a source of concern to the water resource management community. This tree which was imported for ornamental purposes and to control bank erosion during the 1800’s later became problematic and unwanted due to its biophysical properties: Its vigorous growth out-competes native species for moisture, lowering water tables, increasing the soil salinity and hence becomes the dominant riparian vegetation especially over arid to semi-arid floodplain environments. Most importantly they consume large amounts of water leading to reduction of river flows and lowering the groundwater table. We implemented this study in an effort to provide reliable estimates of the amount of water consumed or “lost” by such species through evapotranspiration (ET) as well as to a better understand of the related land surface and near atmosphere interactions. The recent advances in remote sensing techniques and the related data quality made it possible to provide spatio-temporal estimates of ET at a considerably higher resolution and reliable accuracy over a wide range of surface heterogeneity. We tested two different soil-vegetation atmosphere transfer models (SVAT) that are based on thermal remote sensing namely: the two source model (TSM) of Norman et al. (1995) with its recent modifications and the Surface Energy balance algorithm (SEBAL) of Bastiaanssen et al. (1998) to estimate the different surface energy balance components and the evapotranspiration (ET) spatially. We used high resolution (1.0 meter pixel size) shortwave reflectance and longwave thermal airborne imagery acquired by the research aircraft at the Remote Sensing Services Lab at Utah State University (USU) and land use map classified from these images as well as a detailed vegetation height image acquired by the LASSI Lidar also developed at USU. We also compared estimates

An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery

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Shean, David E.; Alexandrov, Oleg; Moratto, Zachary M.; Smith, Benjamin E.; Joughin, Ian R.; Porter, Claire; Morin, Paul

2016-06-01

We adapted the automated, open source NASA Ames Stereo Pipeline (ASP) to generate digital elevation models (DEMs) and orthoimages from very-high-resolution (VHR) commercial imagery of the Earth. These modifications include support for rigorous and rational polynomial coefficient (RPC) sensor models, sensor geometry correction, bundle adjustment, point cloud co-registration, and significant improvements to the ASP code base. We outline a processing workflow for ˜0.5 m ground sample distance (GSD) DigitalGlobe WorldView-1 and WorldView-2 along-track stereo image data, with an overview of ASP capabilities, an evaluation of ASP correlator options, benchmark test results, and two case studies of DEM accuracy. Output DEM products are posted at ˜2 m with direct geolocation accuracy of process individual stereo pairs on a local workstation, the methods presented here were developed for large-scale batch processing in a high-performance computing environment. We are leveraging these resources to produce dense time series and regional mosaics for the Earth's polar regions.

HIGH RESOLUTION LANDCOVER MODELLING WITH PLÉIADES IMAGERY AND DEM DATA IN SUPPORT OF FINE SCALE LANDSCAPE THERMAL MODELLING

Directory of Open Access Journals (Sweden)

M. Thompson

2017-11-01

Full Text Available In the evaluation of air-borne thermal infrared imaging sensors, the use of simulated spectral infrared scenery is a cost-effective way to provide input to the sensor. The benefit of simulated scenes includes control over parameters governing the spectral and related thermal behaviour of the terrain as well as atmospheric conditions. Such scenes need to have a high degree of radiometric and geometric accuracy, as well as high resolution to account for small objects having different spectral and associated thermal properties. In support of this, innovative use of tri-stereo, ultra-high resolution Pléiades satellite imagery is being used to generated high detail, small scale quantitative terrain surface data to compliment comparable optical data in order to produce detailed urban and rural landscape datasets representative of different landscape features, within which spectrally defined characteristics can be subsequently matched to thermal signatures. Pléiades tri-stereo mode, acquired from the same orbit during the same pass, is particularly favourable for reaching the required metric accuracy because images are radiometrically and geometrically very homogeneous, which allows a very good radiometric matching for relief computation. The tri-stereo approach reduces noise and allows significantly enhanced relief description in landscapes where simple stereo imaging cannot see features, such as in dense urban areas or valley bottoms in steep, mountainous areas. This paper describes the datasets that have been generated for DENEL over the Hartebeespoort Dam region, west of Pretoria, South Africa. The final terrain datasets are generated by integrated modelling of both height and spectral surface characteristics within an object-based modelling environment. This approach provides an operational framework for rapid and highly accurate mapping of building and vegetation structure of wide areas, as is required in support of the evaluation of thermal

Object-based methods for individual tree identification and tree species classification from high-spatial resolution imagery

Science.gov (United States)

Wang, Le

2003-10-01

textures occurring due to branches and twigs. As a result from the inverse wavelet transform, the tree crown boundary is enhanced while the unwanted textures are suppressed. Based on the enhanced image, an improvement is achieved when applying the two-stage methods to a high resolution aerial photograph. To improve tree species classification, we develop a new method to choose the optimal scale parameter with the aid of Bhattacharya Distance (BD), a well-known index of class separability in traditional pixel-based classification. The optimal scale parameter is then fed in the process of a region-growing-based segmentation as a break-off value. Our object classification achieves a better accuracy in separating tree species when compared to the conventional Maximum Likelihood Classification (MLC). In summary, we develop two object-based methods for identifying individual trees and classifying tree species from high-spatial resolution imagery. Both methods achieve promising results and will promote integration of Remote Sensing and GIS in forest applications.

Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning

Science.gov (United States)

Debats, Stephanie Renee

Smallholder farms dominate in many parts of the world, including Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural landcover. In this thesis, we developed a benchmark labeled data set of high-resolution satellite imagery of agricultural fields in South Africa. We presented a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. The algorithm achieved similar high performance across agricultural types, including spectrally indistinct smallholder fields, and demonstrated the ability to generalize across large geographic areas. In sensitivity analyses, we determined multi-temporal images provided greater performance gains than the addition of multi-spectral bands. We also demonstrated how active learning can be incorporated in the algorithm to create smaller, more efficient training data sets, which reduced computational resources, minimized the need for humans to hand-label data, and boosted performance. We designed a patch-based uncertainty metric to drive the active learning framework, based on the regular grid of a crowdsourcing platform, and demonstrated how subject matter experts can be replaced with fleets of crowdsourcing workers. Our active learning algorithm achieved similar performance as an algorithm trained with randomly selected data, but with 62% less data samples. This thesis furthers the goal of providing accurate agricultural landcover maps, at a scale that is relevant for the dominant smallholder class. Accurate maps are crucial for monitoring and promoting agricultural production. Furthermore, improved agricultural landcover maps will aid a host of other applications, including landcover change assessments, cadastral surveys to strengthen smallholder land rights, and constraints for crop modeling

Mapping Urban Tree Canopy Coverage and Structure using Data Fusion of High Resolution Satellite Imagery and Aerial Lidar

Science.gov (United States)

Elmes, A.; Rogan, J.; Williams, C. A.; Martin, D. G.; Ratick, S.; Nowak, D.

2015-12-01

Urban tree canopy (UTC) coverage is a critical component of sustainable urban areas. Trees provide a number of important ecosystem services, including air pollution mitigation, water runoff control, and aesthetic and cultural values. Critically, urban trees also act to mitigate the urban heat island (UHI) effect by shading impervious surfaces and via evaporative cooling. The cooling effect of urban trees can be seen locally, with individual trees reducing home HVAC costs, and at a citywide scale, reducing the extent and magnitude of an urban areas UHI. In order to accurately model the ecosystem services of a given urban forest, it is essential to map in detail the condition and composition of these trees at a fine scale, capturing individual tree crowns and their vertical structure. This paper presents methods for delineating UTC and measuring canopy structure at fine spatial resolution (body of methods, relying on a data fusion method to combine the information contained in high resolution WorldView-3 satellite imagery and aerial lidar data using an object-based image classification approach. The study area, Worcester, MA, has recently undergone a large-scale tree removal and reforestation program, following a pest eradication effort. Therefore, the urban canopy in this location provides a wide mix of tree age class and functional type, ideal for illustrating the effectiveness of the proposed methods. Early results show that the object-based classifier is indeed capable of identifying individual tree crowns, while continued research will focus on extracting crown structural characteristics using lidar-derived metrics. Ultimately, the resulting fine resolution UTC map will be compared with previously created UTC maps of the same area but for earlier dates, producing a canopy change map corresponding to the Worcester area tree removal and replanting effort.

Using Very High Resolution Remotely Sensed Imagery to Estimate Agricultural Production: A comparison of food insecure and secure growing areas in Kenya

Science.gov (United States)

Grace, K.; Husak, G. J.; Bogle, S.

2013-12-01

Determining the amount of food produced in a food insecure, isolated, subsistence farming community can be used to help identify households or communities who may be in need of additional food resources. Measuring annual food production in developing countries, much less at a sub-national level, is complicated by lack of data. It can be difficult and costly to access all of the farming households engaged in subsistence farming. However, recent research has focused on the use of remotely sensed data to aid in the estimation of area under cultivation and because food production is the measure of yield (production per hectare) multiplied by area (number of hectares), we can use the area measure to reduce uncertainty in food production estimates. One strategy for estimating cultivated area relies on a fairly time intensive manual interpretation of very high resolution data. Due to the availability of very high resolution data it is possible to construct estimates of cultivated area, even in communities where fields are small. While this strategy has been used to effectively estimate cultivated area in a timely manner, questions remain about the spatial and temporal generalizability of this approach. The purpose of this paper is to produce and compare estimates of cultivated area in two very different agricultural areas of Kenya, a highly food insecure country in East Africa, during two different agricultural seasons. The areas selected represent two different livelihood zones: a marginal growing area where poor farmers rely on inconsistent rainfall and a lush growing area near the mountainous region of the middle-West area of the country where rainfall is consistent and therefore more suited to cultivation. The overarching goal is to determine the effectiveness of very high resolution remotely sensed imagery in calculating estimates of cultivated area in areas where food production strategies are different. Additionally the results of this research will explore the

High precision analysis of an embryonic extensional fault-related fold using 3D orthorectified virtual outcrops: The viewpoint importance in structural geology

Science.gov (United States)

Tavani, Stefano; Corradetti, Amerigo; Billi, Andrea

2016-05-01

Image-based 3D modeling has recently opened the way to the use of virtual outcrop models in geology. An intriguing application of this method involves the production of orthorectified images of outcrops using almost any user-defined point of view, so that photorealistic cross-sections suitable for numerous geological purposes and measurements can be easily generated. These purposes include the accurate quantitative analysis of fault-fold relationships starting from imperfectly oriented and partly inaccessible real outcrops. We applied the method of image-based 3D modeling and orthorectification to a case study from the northern Apennines, Italy, where an incipient extensional fault affecting well-layered limestones is exposed on a 10-m-high barely accessible cliff. Through a few simple steps, we constructed a high-quality image-based 3D model of the outcrop. In the model, we made a series of measurements including fault and bedding attitudes, which allowed us to derive the bedding-fault intersection direction. We then used this direction as viewpoint to obtain a distortion-free photorealistic cross-section, on which we measured bed dips and thicknesses as well as fault stratigraphic separations. These measurements allowed us to identify a slight difference (i.e. only 0.5°) between the hangingwall and footwall cutoff angles. We show that the hangingwall strain required to compensate the upward-decreasing displacement of the fault was accommodated by this 0.5° rotation (i.e. folding) and coeval 0.8% thickening of strata in the hangingwall relatively to footwall strata. This evidence is consistent with trishear fault-propagation folding. Our results emphasize the viewpoint importance in structural geology and therefore the potential of using orthorectified virtual outcrops.

A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land

Directory of Open Access Journals (Sweden)

Feng Gao

2012-10-01

Full Text Available Thermal infrared (TIR imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes that are at significantly finer spatial scales. Consequently, thermal sharpening techniques have been developed to sharpen TIR imagery to shortwave band pixel resolutions, which are often fine enough for field-scale applications. A classic thermal sharpening technique, TsHARP, uses a relationship between land surface temperature (LST and Normalized Difference Vegetation Index (NDVI developed empirically at the TIR pixel resolution and applied at the NDVI pixel resolution. However, recent studies show that unique relationships between temperature and NDVI may only exist for a limited class of landscapes, with mostly green vegetation and homogeneous air and soil conditions. To extend application of thermal sharpening to more complex conditions, a new data mining sharpener (DMS technique is developed. The DMS approach builds regression trees between TIR band brightness temperatures and shortwave spectral reflectances based on intrinsic sample characteristics. A comparison of sharpening techniques applied over a rainfed agricultural area in central Iowa, an irrigated agricultural region in the Texas High Plains, and a heterogeneous naturally vegetated landscape in Alaska indicates that the DMS outperformed TsHARP in all cases. The artificial box-like patterns in LST generated by the TsHARP approach are greatly reduced using the DMS scheme, especially for areas containing irrigated crops, water bodies, thin clouds or terrain. While the DMS technique can provide fine resolution TIR imagery, there are limits to the sharpening ratios that can be reasonably implemented. Consequently, sharpening techniques cannot replace actual thermal band imagery at fine resolutions or missions that

A sun-crown-sensor model and adapted C-correction logic for topographic correction of high resolution forest imagery

Science.gov (United States)

Fan, Yuanchao; Koukal, Tatjana; Weisberg, Peter J.

2014-10-01

Canopy shadowing mediated by topography is an important source of radiometric distortion on remote sensing images of rugged terrain. Topographic correction based on the sun-canopy-sensor (SCS) model significantly improved over those based on the sun-terrain-sensor (STS) model for surfaces with high forest canopy cover, because the SCS model considers and preserves the geotropic nature of trees. The SCS model accounts for sub-pixel canopy shadowing effects and normalizes the sunlit canopy area within a pixel. However, it does not account for mutual shadowing between neighboring pixels. Pixel-to-pixel shadowing is especially apparent for fine resolution satellite images in which individual tree crowns are resolved. This paper proposes a new topographic correction model: the sun-crown-sensor (SCnS) model based on high-resolution satellite imagery (IKONOS) and high-precision LiDAR digital elevation model. An improvement on the C-correction logic with a radiance partitioning method to address the effects of diffuse irradiance is also introduced (SCnS + C). In addition, we incorporate a weighting variable, based on pixel shadow fraction, on the direct and diffuse radiance portions to enhance the retrieval of at-sensor radiance and reflectance of highly shadowed tree pixels and form another variety of SCnS model (SCnS + W). Model evaluation with IKONOS test data showed that the new SCnS model outperformed the STS and SCS models in quantifying the correlation between terrain-regulated illumination factor and at-sensor radiance. Our adapted C-correction logic based on the sun-crown-sensor geometry and radiance partitioning better represented the general additive effects of diffuse radiation than C parameters derived from the STS or SCS models. The weighting factor Wt also significantly enhanced correction results by reducing within-class standard deviation and balancing the mean pixel radiance between sunlit and shaded slopes. We analyzed these improvements with model

Estimating Sediment Delivery to The Rio Maranon, Peru Prior to Large-Scale Hydropower Developments Using High Resolution Imagery from Google Earth and a DJI Phantom 3 Drone

Science.gov (United States)

Goode, J. R.; Candelaria, T.; Kramer, N. R.; Hill, A. F.

2016-12-01

As global energy demands increase, generating hydroelectric power by constructing dams and reservoirs on large river systems is increasingly seen as a renewable alternative to fossil fuels, especially in emerging economies. Many large-scale hydropower projects are located in steep mountainous terrain, where environmental factors have the potential to conspire against the sustainability and success of such projects. As reservoir storage capacity decreases when sediment builds up behind dams, high sediment yields can limit project life expectancy and overall hydropower viability. In addition, episodically delivered sediment from landslides can make quantifying sediment loads difficult. These factors, combined with remote access, limit the critical data needed to effectively evaluate development decisions. In the summer of 2015, we conducted a basic survey to characterize the geomorphology, hydrology and ecology of 620 km of the Rio Maranon, Peru - a major tributary to the Amazon River, which flows north from the semi-arid Peruvian Andes - prior to its dissection by several large hydropower dams. Here we present one component of this larger study: a first order analysis of potential sediment inputs to the Rio Maranon, Peru. To evaluate sediment delivery and storage in this system, we used high resolution Google Earth imagery to delineate landslides, combined with high resolution imagery from a DJI Phantom 3 Drone, flown at alluvial fan inputs to the river in the field. Because hillslope-derived sediment inputs from headwater tributaries are important to overall ecosystem health in large river systems, our study has the potential to contribute to the understanding the impacts of large Andean dams on sediment connectivity to the Amazon basin.

Identifying and Allocating Geodetic Systems to historical oil gas wells by using high-resolution satellite imagery

Science.gov (United States)

Alvarez, Gabriel O.

2018-05-01

Hydrocarbon exploration in Argentina started long before the IGM created a single, high-precision geodetic reference network for the whole country. Several geodetic surveys were conducted in every producing basin, which have ever since then supported well placement. Currently, every basin has a huge amount of information referenced to the so-called "local" geodetic systems, such as Chos Malal - Quiñi Huao in the Neuquén Basin, and Pampa del Castillo in the San Jorge Basin, which differ to a greater or lesser extent from the national Campo Inchauspe datum established by the IGM in 1969 as the official geodetic network. However, technology development over the last few years and the expansion of satellite positioning systems such as GPS resulted in a new world geodetic order. Argentina rapidly joined this new geodetic order through the implementation of a new national geodetic system by the IGM: POSGAR network, which replaced the old national Campo Inchauspe system. However, this only helped to worsen the data georeferencing issue for oil companies, as a third reference system was added to each basin. Now every basin has a local system, the national system until 1997 (Campo Inchauspe), and finally the newly created POSGAR network national satellite system, which is geocentric unlike the former two planimetric datums. The purpose of this paper is to identify and allocate geodetic systems of coordinates to historical wells, whose geodetic system is missing or has been erroneously allocated, by using currently available technological resources such as geographic information systems and high-resolution satellite imagery.

Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis

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Jane Southworth

2010-12-01

Full Text Available Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. This research utilizes object based classification (OBC for a region in Namibia, using IKONOS imagery, to help differentiate tree canopies and therefore woodland savanna, from shrub or grasslands. The methodology involved the identification and isolation of tree canopies within the imagery and the creation of tree polygon layers had an overall accuracy of 84%. In addition, the results were scaled up to a corresponding Landsat image of the same region, and the OBC results compared to corresponding pixel values of NDVI. The results were not compelling, indicating once more the problems of these traditional image analysis techniques for savanna ecosystems. Overall, the use of the OBC holds great promise for this ecosystem and could be utilized more frequently in studies of vegetation structure.

Semi-automatic building extraction in informal settlements from high-resolution satellite imagery

Science.gov (United States)

Mayunga, Selassie David

The extraction of man-made features from digital remotely sensed images is considered as an important step underpinning management of human settlements in any country. Man-made features and buildings in particular are required for varieties of applications such as urban planning, creation of geographical information systems (GIS) databases and Urban City models. The traditional man-made feature extraction methods are very expensive in terms of equipment, labour intensive, need well-trained personnel and cannot cope with changing environments, particularly in dense urban settlement areas. This research presents an approach for extracting buildings in dense informal settlement areas using high-resolution satellite imagery. The proposed system uses a novel strategy of extracting building by measuring a single point at the approximate centre of the building. The fine measurement of the building outlines is then effected using a modified snake model. The original snake model on which this framework is based, incorporates an external constraint energy term which is tailored to preserving the convergence properties of the snake model; its use to unstructured objects will negatively affect their actual shapes. The external constrained energy term was removed from the original snake model formulation, thereby, giving ability to cope with high variability of building shapes in informal settlement areas. The proposed building extraction system was tested on two areas, which have different situations. The first area was Tungi in Dar Es Salaam, Tanzania where three sites were tested. This area is characterized by informal settlements, which are illegally formulated within the city boundaries. The second area was Oromocto in New Brunswick, Canada where two sites were tested. Oromocto area is mostly flat and the buildings are constructed using similar materials. Qualitative and quantitative measures were employed to evaluate the accuracy of the results as well as the performance

Detecting blind building façades from highly overlapping wide angle aerial imagery

Science.gov (United States)

Burochin, Jean-Pascal; Vallet, Bruno; Brédif, Mathieu; Mallet, Clément; Brosset, Thomas; Paparoditis, Nicolas

2014-10-01

This paper deals with the identification of blind building façades, i.e. façades which have no openings, in wide angle aerial images with a decimeter pixel size, acquired by nadir looking cameras. This blindness characterization is in general crucial for real estate estimation and has, at least in France, a particular importance on the evaluation of legal permission of constructing on a parcel due to local urban planning schemes. We assume that we have at our disposal an aerial survey with a relatively high stereo overlap along-track and across-track and a 3D city model of LoD 1, that can have been generated with the input images. The 3D model is textured with the aerial imagery by taking into account the 3D occlusions and by selecting for each façade the best available resolution texture seeing the whole façade. We then parse all 3D façades textures by looking for evidence of openings (windows or doors). This evidence is characterized by a comprehensive set of basic radiometric and geometrical features. The blindness prognostic is then elaborated through an (SVM) supervised classification. Despite the relatively low resolution of the images, we reach a classification accuracy of around 85% on decimeter resolution imagery with 60 × 40 % stereo overlap. On the one hand, we show that the results are very sensitive to the texturing resampling process and to vegetation presence on façade textures. On the other hand, the most relevant features for our classification framework are related to texture uniformity and horizontal aspect and to the maximal contrast of the opening detections. We conclude that standard aerial imagery used to build 3D city models can also be exploited to some extent and at no additional cost for facade blindness characterisation.

Satellite imagery in safeguards: progress and prospects

International Nuclear Information System (INIS)

Niemeyer, I.; Listner, C.

2013-01-01

The use of satellite imagery has become very important for the verification of the safeguards implementation under the Nuclear Non-Proliferation Treaty (NPT). The main applications of satellite imagery are to verify the correctness and completeness of the member states' declarations, and to provide preparatory information for inspections, complimentary access and other technical visits. If the area of interest is not accessible, remote sensing sensors provide one of the few opportunities of gathering data for nuclear monitoring, as for example in Iraq between 1998 and 2002 or currently in North Korea. Satellite data of all available sensor types contains a considerable amount of safeguard-relevant information. Very high-resolution optical satellite imagery provides the most detailed spatial information on nuclear sites and activities up to 0.41 m resolution, together with up to 8 spectral bands from the visible light and near infrared. Thermal infrared (TIR) images can indicate the operational status of nuclear facilities and help to identify undeclared activities. Hyper-spectral imagery allows a quantitative estimation of geophysical, geochemical and biochemical characteristics of the earth's surface and is therefore useful for assessing, for example, surface cover changes due to drilling, mining and milling activities. Synthetic Aperture Radar (SAR) image data up to 1 m spatial resolution provides an all-weather, day and night monitoring capability. However, the absence (or existence) of nuclear activities can never be confirmed completely based on satellite imagery. (A.C.)

Monitoring of oil pollution in the Arabian Gulf based on medium resolution satellite imagery

Science.gov (United States)

Zhao, J.; Ghedira, H.

2013-12-01

A large number of inland and offshore oil fields are located in the Arabian Gulf where about 25% of the world's oil is produced by the countries surrounding the Arabian Gulf region. Almost all of this oil production is shipped by sea worldwide through the Strait of Hormuz making the region vulnerable to environmental and ecological threats that might arise from accidental or intentional oil spills. Remote sensing technologies have the unique capability to detect and monitor oil pollutions over large temporal and spatial scales. Synoptic satellite imaging can date back to 1972 when Landsat-1 was launched. Landsat satellite missions provide long time series of imagery with a spatial resolution of 30 m. MODIS sensors onboard NASA's Terra and Aqua satellites provide a wide and frequent coverage at medium spatial resolution, i.e. 250 m and 500, twice a day. In this study, the capability of medium resolution MODIS and Landsat data in detecting and monitoring oil pollutions in the Arabian Gulf was tested. Oil spills and slicks show negative or positive contrasts in satellite derived RGB images compared with surrounding clean waters depending on the solar/viewing geometry, oil thickness and evolution, etc. Oil-contaminated areas show different spectral characteristics compared with surrounding waters. Rayleigh-corrected reflectance at the seven medium resolution bands of MODIS is lower in oil affected areas. This is caused by high light absorption of oil slicks. 30-m Landsat image indicated the occurrence of oil spill on May 26 2000 in the Arabian Gulf. The oil spill showed positive contrast and lower temperature than surrounding areas. Floating algae index (FAI) images are also used to detect oil pollution. Oil-contaminated areas were found to have lower FAI values. To track the movement of oil slicks found on October 21 2007, ocean circulations from a HYCOM model were examined and demonstrated that the oil slicks were advected toward the coastal areas of United Arab

Beyond Population Distribution: Enhancing Sociocultural Resolution from Remote Sensing

Science.gov (United States)

Bhaduri, B. L.; Rose, A.

2017-12-01

At Oak Ridge National Laboratory, since late 1990s, we have focused on developing high resolution population distribution and dynamics data from local to global scales. Increasing resolutions of geographic data has been mirrored by population data sets developed across the community. However, attempts to increase temporal and sociocultural resolutions have been limited given the lack of high resolution data on human settlements and activities. While recent advancements in moderate to high resolution earth observation have led to better physiographic data, the approach of exploiting very high resolution (sub-meter resolution) imagery has also proven useful for generating accurate human settlement maps. It allows potential (social and vulnerability) characterization of population from settlement structures by exploiting image texture and spectral features. Our recent research utilizing machine learning and geocomputation has not only validated "poverty mapping from imagery" hypothesis, but has delineated a new paradigm of rapid analysis of high resolution imagery to enhance such "neighborhood" mapping techniques. Such progress in GIScience is allowing us to move towards the goal of creating a global foundation level database for impervious surfaces and "neighborhoods," and holds tremendous promise for key applications focusing on sustainable development including many social science applications.

Algorithm and Application of Gcp-Independent Block Adjustment for Super Large-Scale Domestic High Resolution Optical Satellite Imagery

Science.gov (United States)

Sun, Y. S.; Zhang, L.; Xu, B.; Zhang, Y.

2018-04-01

The accurate positioning of optical satellite image without control is the precondition for remote sensing application and small/medium scale mapping in large abroad areas or with large-scale images. In this paper, aiming at the geometric features of optical satellite image, based on a widely used optimization method of constraint problem which is called Alternating Direction Method of Multipliers (ADMM) and RFM least-squares block adjustment, we propose a GCP independent block adjustment method for the large-scale domestic high resolution optical satellite image - GISIBA (GCP-Independent Satellite Imagery Block Adjustment), which is easy to parallelize and highly efficient. In this method, the virtual "average" control points are built to solve the rank defect problem and qualitative and quantitative analysis in block adjustment without control. The test results prove that the horizontal and vertical accuracy of multi-covered and multi-temporal satellite images are better than 10 m and 6 m. Meanwhile the mosaic problem of the adjacent areas in large area DOM production can be solved if the public geographic information data is introduced as horizontal and vertical constraints in the block adjustment process. Finally, through the experiments by using GF-1 and ZY-3 satellite images over several typical test areas, the reliability, accuracy and performance of our developed procedure will be presented and studied in this paper.

OrthoImagery Submission for Isabella county, MI

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — This data set contains 1-meter resolution imagery derived from the 2005 National Agriculture Imagery Program (NAIP) statewide aerial imagery acquisition. Data have...

River floodplain vegetation classification using multi-temporal high-resolution colour infrared UAV imagery.

NARCIS (Netherlands)

van Iersel, W.K.; Straatsma, M.W.; Addink, E.A.; Middelkoop, H.

2016-01-01

To evaluate floodplain functioning, monitoring of its vegetation is essential. Although airborne imagery is widely applied for this purpose, classification accuracy (CA) remains low for grassland (< 88%) and herbaceous vegetation (<57%) due to the spectral and structural similarity of these

A Semi-Automated Machine Learning Algorithm for Tree Cover Delineation from 1-m Naip Imagery Using a High Performance Computing Architecture

Science.gov (United States)

Basu, S.; Ganguly, S.; Nemani, R. R.; Mukhopadhyay, S.; Milesi, C.; Votava, P.; Michaelis, A.; Zhang, G.; Cook, B. D.; Saatchi, S. S.; Boyda, E.

2014-12-01

Accurate tree cover delineation is a useful instrument in the derivation of Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree cover delineation in high to coarse resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR datasets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree cover estimates for the whole of Continental United States, using a High Performance Computing Architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field (CRF), which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the state of California, which covers a total of 11,095 NAIP tiles and spans a total geographical area of 163,696 sq. miles. Our framework produced correct detection rates of around 85% for fragmented forests and 70% for urban tree cover areas, with false positive rates lower than 3% for both regions. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR high-resolution canopy height model shows the effectiveness of our algorithm in generating accurate high-resolution tree cover maps.

User Validation of VIIRS Satellite Imagery

Directory of Open Access Journals (Sweden)

Don Hillger

2015-12-01

Full Text Available Visible/Infrared Imaging Radiometer Suite (VIIRS Imagery from the Suomi National Polar-orbiting Partnership (S-NPP satellite is the finest spatial resolution (375 m multi-spectral imagery of any operational meteorological satellite to date. The Imagery environmental data record (EDR has been designated as a Key Performance Parameter (KPP for VIIRS, meaning that its performance is vital to the success of a series of Joint Polar Satellite System (JPSS satellites that will carry this instrument. Because VIIRS covers the high-latitude and Polar Regions especially well via overlapping swaths from adjacent orbits, the Alaska theatre in particular benefits from VIIRS more than lower-latitude regions. While there are no requirements that specifically address the quality of the EDR Imagery aside from the VIIRS SDR performance requirements, the value of VIIRS Imagery to operational users is an important consideration in the Cal/Val process. As such, engaging a wide diversity of users constitutes a vital part of the Imagery validation strategy. The best possible image quality is of utmost importance. This paper summarizes the Imagery Cal/Val Team’s quality assessment in this context. Since users are a vital component to the validation of VIIRS Imagery, specific examples of VIIRS imagery applied to operational needs are presented as an integral part of the post-checkout Imagery validation.

An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

Directory of Open Access Journals (Sweden)

Chandi Witharana

2016-04-01

Full Text Available The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked “without adaptation” and “with adaptation” on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational “image-to-assessment pipeline” that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census.

Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery

Directory of Open Access Journals (Sweden)

Fan Hu

2015-11-01

Full Text Available Learning efficient image representations is at the core of the scene classification task of remote sensing imagery. The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineered features or unsupervised feature learning, can only generate mid-level image features with limited representative ability, which essentially prevents them from achieving better performance. Recently, the deep convolutional neural networks (CNNs, which are hierarchical architectures trained on large-scale datasets, have shown astounding performance in object recognition and detection. However, it is still not clear how to use these deep convolutional neural networks for high-resolution remote sensing (HRRS scene classification. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for HRRS scene classification. We propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully-connected layers are regarded as the final image features; in the second scenario, we extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios, even with a simple linear classifier, can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the low- and mid-level features. Moreover, we tentatively combine features extracted from different CNN models for better performance.

Spatially explicit rangeland erosion monitoring using high-resolution digital aerial imagery

Science.gov (United States)

Gillan, Jeffrey K.; Karl, Jason W.; Barger, Nichole N.; Elaksher, Ahmed; Duniway, Michael C.

2016-01-01

Nearly all of the ecosystem services supported by rangelands, including production of livestock forage, carbon sequestration, and provisioning of clean water, are negatively impacted by soil erosion. Accordingly, monitoring the severity, spatial extent, and rate of soil erosion is essential for long-term sustainable management. Traditional field-based methods of monitoring erosion (sediment traps, erosion pins, and bridges) can be labor intensive and therefore are generally limited in spatial intensity and/or extent. There is a growing effort to monitor natural resources at broad scales, which is driving the need for new soil erosion monitoring tools. One remote-sensing technique that can be used to monitor soil movement is a time series of digital elevation models (DEMs) created using aerial photogrammetry methods. By geographically coregistering the DEMs and subtracting one surface from the other, an estimate of soil elevation change can be created. Such analysis enables spatially explicit quantification and visualization of net soil movement including erosion, deposition, and redistribution. We constructed DEMs (12-cm ground sampling distance) on the basis of aerial photography immediately before and 1 year after a vegetation removal treatment on a 31-ha Piñon-Juniper woodland in southeastern Utah to evaluate the use of aerial photography in detecting soil surface change. On average, we were able to detect surface elevation change of ± 8−9cm and greater, which was sufficient for the large amount of soil movement exhibited on the study area. Detecting more subtle soil erosion could be achieved using the same technique with higher-resolution imagery from lower-flying aircraft such as unmanned aerial vehicles. DEM differencing and process-focused field methods provided complementary information and a more complete assessment of soil loss and movement than any single technique alone. Photogrammetric DEM differencing could be used as a technique to

An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery

Directory of Open Access Journals (Sweden)

Xiangguang Leng

2016-08-01

Full Text Available With the rapid development of spaceborne synthetic aperture radar (SAR and the increasing need of ship detection, research on adaptive ship detection in spaceborne SAR imagery is of great importance. Focusing on practical problems of ship detection, this paper presents a highly adaptive ship detection scheme for spaceborne SAR imagery. It is able to process a wide range of sensors, imaging modes and resolutions. Two main stages are identified in this paper, namely: ship candidate detection and ship discrimination. Firstly, this paper proposes an adaptive land masking method using ship size and pixel size. Secondly, taking into account the imaging mode, incidence angle, and polarization channel of SAR imagery, it implements adaptive ship candidate detection in spaceborne SAR imagery by applying different strategies to different resolution SAR images. Finally, aiming at different types of typical false alarms, this paper proposes a comprehensive ship discrimination method in spaceborne SAR imagery based on confidence level and complexity analysis. Experimental results based on RADARSAT-1, RADARSAT-2, TerraSAR-X, RS-1, and RS-3 images demonstrate that the adaptive scheme proposed in this paper is able to detect ship targets in a fast, efficient and robust way.

Feature extraction and classification of clouds in high resolution panchromatic satellite imagery

Science.gov (United States)

Sharghi, Elan

The development of sophisticated remote sensing sensors is rapidly increasing, and the vast amount of satellite imagery collected is too much to be analyzed manually by a human image analyst. It has become necessary for a tool to be developed to automate the job of an image analyst. This tool would need to intelligently detect and classify objects of interest through computer vision algorithms. Existing software called the Rapid Image Exploitation Resource (RAPIER®) was designed by engineers at Space and Naval Warfare Systems Center Pacific (SSC PAC) to perform exactly this function. This software automatically searches for anomalies in the ocean and reports the detections as a possible ship object. However, if the image contains a high percentage of cloud coverage, a high number of false positives are triggered by the clouds. The focus of this thesis is to explore various feature extraction and classification methods to accurately distinguish clouds from ship objects. An examination of a texture analysis method, line detection using the Hough transform, and edge detection using wavelets are explored as possible feature extraction methods. The features are then supplied to a K-Nearest Neighbors (KNN) or Support Vector Machine (SVM) classifier. Parameter options for these classifiers are explored and the optimal parameters are determined.

THE ACCURACY OF AUTOMATIC PHOTOGRAMMETRIC TECHNIQUES ON ULTRA-LIGHT UAV IMAGERY

Directory of Open Access Journals (Sweden)

O. Küng

2012-09-01

Full Text Available This paper presents an affordable, fully automated and accurate mapping solutions based on ultra-light UAV imagery. Several datasets are analysed and their accuracy is estimated. We show that the accuracy highly depends on the ground resolution (flying height of the input imagery. When chosen appropriately this mapping solution can compete with traditional mapping solutions that capture fewer high-resolution images from airplanes and that rely on highly accurate orientation and positioning sensors on board. Due to the careful integration with recent computer vision techniques, the post processing is robust and fully automatic and can deal with inaccurate position and orientation information which are typically problematic with traditional techniques.

Real-time person detection in low-resolution thermal infrared imagery with MSER and CNNs

Science.gov (United States)

Herrmann, Christian; Müller, Thomas; Willersinn, Dieter; Beyerer, Jürgen

2016-10-01

In many camera-based systems, person detection and localization is an important step for safety and security applications such as search and rescue, reconnaissance, surveillance, or driver assistance. Long-wave infrared (LWIR) imagery promises to simplify this task because it is less affected by background clutter or illumination changes. In contrast to a lot of related work, we make no assumptions about any movement of persons or the camera, i.e. persons may stand still and the camera may move or any combination thereof. Furthermore, persons may appear arbitrarily in near or far distances to the camera leading to low-resolution persons in far distances. To address this task, we propose a two-stage system, including a proposal generation method and a classifier to verify, if the detected proposals really are persons. In contradiction to use all possible proposals as with sliding window approaches, we apply Maximally Stable Extremal Regions (MSER) and classify the detected proposals afterwards with a Convolutional Neural Network (CNN). The MSER algorithm acts as a hot spot detector when applied to LWIR imagery. Because the body temperature of persons is usually higher than the background, they appear as hot spots in the image. However, the MSER algorithm is unable to distinguish between different kinds of hot spots. Thus, all further LWIR sources such as windows, animals or vehicles will be detected, too. Still by applying MSER, the number of proposals is reduced significantly in comparison to a sliding window approach which allows employing the high discriminative capabilities of deep neural networks classifiers that were recently shown in several applications such as face recognition or image content classification. We suggest using a CNN as classifier for the detected hot spots and train it to discriminate between person hot spots and all further hot spots. We specifically design a CNN that is suitable for the low-resolution person hot spots that are common with

Hurricane Ophelia Aerial Photography: High-Resolution Imagery of the North Carolina Coast After Landfall

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the North Carolina coast after Hurricane Ophelia made landfall. The regions photographed range from Hubert, North Carolina to...

JEarth | Analytical Remote Sensing Imagery Application for Researchers and Practitioners

Science.gov (United States)

Prashad, L.; Christensen, P. R.; Anwar, S.; Dickenshied, S.; Engle, E.; Noss, D.

2009-12-01

The ASU 100 Cities Project and the ASU Mars Space Flight Facility (MSFF) present JEarth, a set of analytical Geographic Information System (GIS) tools for viewing and processing Earth-based remote sensing imagery and vectors, including high-resolution and hyperspectral imagery such as TIMS and MASTER. JEarth is useful for a wide range of researchers and practitioners who need to access, view, and analyze remote sensing imagery. JEarth stems from existing MSFF applications: the Java application JMars (Java Mission-planning and Analysis for Remote Sensing) for viewing and analyzing remote sensing imagery and THMPROC, a web-based, interactive tool for processing imagery to create band combinations, stretches, and other imagery products. JEarth users can run the application on their desktops by installing Java-based open source software on Windows, Mac, or Linux operating systems.

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

Directory of Open Access Journals (Sweden)

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.

NOAA Integrated Ocean and Coastal Mapping (IOCM) orthorectified mosaic image tiles, Empire, Louisiana 2010 (NODC Accession 0075830)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of the Mississippi -...

Sea-Ice Feature Mapping using JERS-1 Imagery

Science.gov (United States)

Maslanik, James; Heinrichs, John

1994-01-01

JERS-1 SAR and OPS imagery are examined in combination with other data sets to investigate the utility of the JERS-1 sensors for mapping fine-scale sea ice conditions. Combining ERS-1 C band and JERS-1 L band SAR aids in discriminating multiyear and first-year ice. Analysis of OPS imagery for a field site in the Canadian Archipelago highlights the advantages of OPS's high spatial and spectral resolution for mapping ice structure, melt pond distribution, and surface albedo.

Low-resolution ship detection from high-altitude aerial images

Science.gov (United States)

Qi, Shengxiang; Wu, Jianmin; Zhou, Qing; Kang, Minyang

2018-02-01

Ship detection from optical images taken by high-altitude aircrafts such as unmanned long-endurance airships and unmanned aerial vehicles has broad applications in marine fishery management, ship monitoring and vessel salvage. However, the major challenge is the limited capability of information processing on unmanned high-altitude platforms. Furthermore, in order to guarantee the wide detection range, unmanned aircrafts generally cruise at high altitudes, resulting in imagery with low-resolution targets and strong clutters suffered by heavy clouds. In this paper, we propose a low-resolution ship detection method to extract ships from these high-altitude optical images. Inspired by a recent research on visual saliency detection indicating that small salient signals could be well detected by a gradient enhancement operation combined with Gaussian smoothing, we propose the facet kernel filtering to rapidly suppress cluttered backgrounds and delineate candidate target regions from the sea surface. Then, the principal component analysis (PCA) is used to compute the orientation of the target axis, followed by a simplified histogram of oriented gradient (HOG) descriptor to characterize the ship shape property. Finally, support vector machine (SVM) is applied to discriminate real targets and false alarms. Experimental results show that the proposed method actually has high efficiency in low-resolution ship detection.

Comparing the Dry Season In-Situ Leaf Area Index (LAI Derived from High-Resolution RapidEye Imagery with MODIS LAI in a Namibian Savanna

Directory of Open Access Journals (Sweden)

Manuel J. Mayr

2015-04-01

Full Text Available The Leaf Area Index (LAI is one of the most frequently applied measures to characterize vegetation and its dynamics and functions with remote sensing. Satellite missions, such as NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS operationally produce global datasets of LAI. Due to their role as an input to large-scale modeling activities, evaluation and verification of such datasets are of high importance. In this context, savannas appear to be underrepresented with regards to their heterogeneous appearance (e.g., tree/grass-ratio, seasonality. Here, we aim to examine the LAI in a heterogeneous savanna ecosystem located in Namibia’s Owamboland during the dry season. Ground measurements of LAI are used to derive a high-resolution LAI model with RapidEye satellite data. This model is related to the corresponding MODIS LAI/FPAR (Fraction of Absorbed Photosynthetically Active Radiation scene (MOD15A2 in order to evaluate its performance at the intended annual minimum during the dry season. Based on a field survey we first assessed vegetation patterns from species composition and elevation for 109 sites. Secondly, we measured in situ LAI to quantitatively estimate the available vegetation (mean = 0.28. Green LAI samples were then empirically modeled (LAImodel with high resolution RapidEye imagery derived Difference Vegetation Index (DVI using a linear regression (R2 = 0.71. As indicated by several measures of model performance, the comparison with MOD15A2 revealed moderate consistency mostly due to overestimation by the aggregated LAImodel. Model constraints aside, this study may point to important issues for MOD15A2 in savannas concerning the underlying MODIS Land Cover product (MCD12Q1 and a potential adjustment by means of the MODIS Burned Area product (MCD45A1.

Spatial and temporal changes in household structure locations using high-resolution satellite imagery for population assessment: an analysis in southern Zambia, 2006-2011

Directory of Open Access Journals (Sweden)

Timothy Shields

2016-05-01

Full Text Available Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase. Comparison of the images indicated that 971 (25.4% structures were added and 536 (14.0% removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery.

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

Science.gov (United States)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Coastal and Inland Water Applications of High Resolution Optical Satellite Data from Landsat-8 and Sentinel-2

Science.gov (United States)

Vanhellemont, Q.

2016-02-01

Since the launch of Landsat-8 (L8) in 2013, a joint NASA/USGS programme, new applications of high resolution imagery for coastal and inland waters have become apparent. The optical imaging instrument on L8, the Operational Land Imager (OLI), is much improved compared to its predecessors on L5 and L7, especially with regards to SNR and digitization, and is therefore well suited for retrieving water reflectances and derived parameters such as turbidity and suspended sediment concentration. In June 2015, the European Space Agency (ESA) successfully launched a similar instrument, the MultiSpectral Imager (MSI), on board of Sentinel-2A (S2A). Imagery from both L8 and S2A are free of charge and publicly available (S2A starting at the end of 2015). Atmospheric correction schemes and processing software is under development in the EC-FP7 HIGHROC project. The spatial resolution of these instruments (10-60 m) is a great improvement over typical moderate resolution ocean colour sensors such as MODIS and MERIS (0.25 - 1 km). At higher resolution, many more lakes, rivers, ports and estuaries are spatially resolved, and can thus now be studied using satellite data, unlocking potential for mandatory monitoring e.g. under European Directives such as the Marine Strategy Framework Directive and the Water Framework Directive. We present new applications of these high resolution data, such as monitoring of offshore constructions, wind farms, sediment transport, dredging and dumping, shipping and fishing activities. The spatial variability at sub moderate resolution (0.25 - 1 km) scales can be assessed, as well as the impact of sub grid scale variability (including ships and platforms used for validation) on the moderate pixel retrieval. While the daily revisit time of the moderate resolution sensors is vastly superior to those of the high resolution satellites, at the equator respectively 16 and 10 days for L8 and S2A, the low revisit times can be partially mitigated by combining data

Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

Science.gov (United States)

Alshehhi, Rasha; Marpu, Prashanth Reddy

2017-04-01

Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

Satellite imagery in a nuclear age

International Nuclear Information System (INIS)

Baines, P.J.

1998-01-01

Increasingly, high resolution satellite imaging systems are becoming available from multiple and diverse sources with capabilities useful for answering security questions. With increased supply, data availability and data authenticity may be assured. In a commercial market a supplier can ill afford the loss in market share that would result from any falsification of data. Similarly rising competitors willing to sell imagery of national security sites will decrease the tendency to endure self-imposed restrictions on sales of those sites. International organizations operating in the security interests of all nations might also gain preferential access. Costa for imagery will also fall to the point were individuals can afford purchases of satellite images. International organizations will find utility in exploiting imagery for solving international security problems. Housed within international organizations possessing competent staff, procedures, and 'shared destiny' stakes in resolving compliance discrepancies, the use of satellite imagery may provide a degree of stability in a world in which individuals, non-governmental organizations and governments may choose to exploit the available information for political gain. The use of satellite imagery outside these international organizations might not necessarily be aimed at seeking mutually beneficial solutions for international problems

Commercial Satellite Imagery Analysis for Countering Nuclear Proliferation

Science.gov (United States)

Albright, David; Burkhard, Sarah; Lach, Allison

2018-05-01

High-resolution commercial satellite imagery from a growing number of private satellite companies allows nongovernmental analysts to better understand secret or opaque nuclear programs of countries in unstable or tense regions, called proliferant states. They include North Korea, Iran, India, Pakistan, and Israel. By using imagery to make these countries’ aims and capabilities more transparent, nongovernmental groups like the Institute for Science and International Security have affected the policies of governments and the course of public debate. Satellite imagery work has also strengthened the efforts of the International Atomic Energy Agency, thereby helping this key international agency build its case to mount inspections of suspect sites and activities. This work has improved assessments of the nuclear capabilities of proliferant states. Several case studies provide insight into the use of commercial satellite imagery as a key tool to educate policy makers and affect policy.

Using high resolution satellite multi-temporal interferometry for landslide hazard detection in tropical environments: the case of Haiti

Science.gov (United States)

Wasowski, Janusz; Nutricato, Raffaele; Nitti, Davide Oscar; Bovenga, Fabio; Chiaradia, Maria Teresa; Piard, Boby Emmanuel; Mondesir, Philemon

2015-04-01

Synthetic aperture radar (SAR) multi-temporal interferometry (MTI) is one of the most promising satellite-based remote sensing techniques for fostering new opportunities in landslide hazard detection and assessment. MTI is attractive because it can provide very precise quantitative information on slow slope displacements of the ground surface over huge areas with limited vegetation cover. Although MTI is a mature technique, we are only beginning to realize the benefits of the high-resolution imagery that is currently acquired by the new generation radar satellites (e.g., COSMO-SkyMed, TerraSAR-X). In this work we demonstrate the potential of high resolution X-band MTI for wide-area detection of slope instability hazards even in tropical environments that are typically very harsh (eg. coherence loss) for differential interferometry applications. This is done by presenting an example from the island of Haiti, a tropical region characterized by dense and rapidly growing vegetation, as well as by significant climatic variability (two rainy seasons) with intense precipitation events. Despite the unfavorable setting, MTI processing of nearly 100 COSMO-SkyMed (CSK) mages (2011-2013) resulted in the identification of numerous radar targets even in some rural (inhabited) areas thanks to the high resolution (3 m) of CSK radar imagery, the adoption of a patch wise processing SPINUA approach and the presence of many man-made structures dispersed in heavily vegetated terrain. In particular, the density of the targets resulted suitable for the detection of some deep-seated and shallower landslides, as well as localized, very slow slope deformations. The interpretation and widespread exploitation of high resolution MTI data was facilitated by Google EarthTM tools with the associated high resolution optical imagery. Furthermore, our reconnaissance in situ checks confirmed that MTI results provided useful information on landslides and marginally stable slopes that can represent a

Use of Aerial high resolution visible imagery to produce large river bathymetry: a multi temporal and spatial study over the by-passed Upper Rhine

Science.gov (United States)

Béal, D.; Piégay, H.; Arnaud, F.; Rollet, A.; Schmitt, L.

2011-12-01

Aerial high resolution visible imagery allows producing large river bathymetry assuming that water depth is related to water colour (Beer-Bouguer-Lambert law). In this paper we aim at monitoring Rhine River geometry changes for a diachronic study as well as sediment transport after an artificial injection (25.000 m3 restoration operation). For that a consequent data base of ground measurements of river depth is used, built on 3 different sources: (i) differential GPS acquisitions, (ii) sounder data and (iii) lateral profiles realized by experts. Water depth is estimated using a multi linear regression over neo channels built on a principal component analysis over red, green and blue bands and previously cited depth data. The study site is a 12 km long reach of the by-passed section of the Rhine River that draws French and German border. This section has been heavily impacted by engineering works during the last two centuries: channelization since 1842 for navigation purposes and the construction of a 45 km long lateral canal and 4 consecutive hydroelectric power plants of since 1932. Several bathymetric models are produced based on 3 different spatial resolutions (6, 13 and 20 cm) and 5 acquisitions (January, March, April, August and October) since 2008. Objectives are to find the optimal spatial resolution and to characterize seasonal effects. Best performances according to the 13 cm resolution show a 18 cm accuracy when suspended matters impacted less water transparency. Discussions are oriented to the monitoring of the artificial reload after 2 flood events during winter 2010-2011. Bathymetric models produced are also useful to build 2D hydraulic model's mesh.

Essential Technology and Application of Jitter Detection and Compensation for High Resolution Satellites

Directory of Open Access Journals (Sweden)

TONG Xiaohua

2017-10-01

Full Text Available Satellite jitter is a common and complex phenomenon for the on-orbit high resolution satellites, which may affect the mapping accuracy and quality of imagery. A framework of jitter detection and compensation integrating data processing of multiple sensors is proposed in this paper. Jitter detection is performed based on multispectral imagery, three-line-array imagery, dense ground control and attitude measurement data, and jitter compensation is conducted both on image and on attitude with the sensor model. The platform jitter of ZY-3 satellite is processed and analyzed using the proposed technology, and the results demonstrate the feasibility and reliability of jitter detection and compensation. The variation law analysis of jitter indicates that the frequencies of jitter of ZY-3 satellite hold in the range between 0.6 and 0.7 Hz, while the amplitudes of jitter of ZY-3 satellite drop from 1 pixel in the early stage to below 0.4 pixels and tend to remain stable in the following stage.

Satellite Imagery Analysis for Automated Global Food Security Forecasting

Science.gov (United States)

Moody, D.; Brumby, S. P.; Chartrand, R.; Keisler, R.; Mathis, M.; Beneke, C. M.; Nicholaeff, D.; Skillman, S.; Warren, M. S.; Poehnelt, J.

2017-12-01

The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. Cloud computing and storage, combined with recent advances in machine learning, are enabling understanding of the world at a scale and at a level of detail never before feasible. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and that can scale with the high-rate and dimensionality of imagery being collected. We focus on the problem of monitoring food crop productivity across the Middle East and North Africa, and show how an analysis-ready, multi-sensor data platform enables quick prototyping of satellite imagery analysis algorithms, from land use/land cover classification and natural resource mapping, to yearly and monthly vegetative health change trends at the structural field level.

DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

Directory of Open Access Journals (Sweden)

S. Eken

2017-11-01

Full Text Available In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

USGS NAIP Imagery Overlay Map Service from The National Map

Data.gov (United States)

U.S. Geological Survey, Department of the Interior — The USGS NAIP Imagery service from The National Map (TNM) consists of high resolution images that combine the visual attributes of an aerial photograph with the...

IAEA Safeguards: Cost/benefit analysis of commercial satellite imagery

International Nuclear Information System (INIS)

Andersson, Christer

1999-03-01

A major milestone in the efforts to strengthen the Safeguards System was reached in May 1997 when the Board of Governors approved a 'Model Protocol Additional to Safeguards Agreements'. The Protocol provides the legal basis necessary to enhance the Agency's ability to detect undeclared nuclear material and activities by using information available from open sources to complement the declarations made by Member States. Commercially available high-resolution satellite data has emerged as one potential complementary open information source to support the traditional and extended Safeguard activities of IAEA. This document constitutes a first report from SSC Satellitbild giving the Agency tentative and initial estimates of the potential cost and time-savings possible with the new proposed technology. The initial cost/benefit simulation will be further finalised in the following 'Implementation Blueprint' study. The general foundation and starting point for the cost/benefit calculation is to simulate a new efficient and relatively small 'imagery unit' within the IAEA, capable of performing advanced image processing as a tool for various safeguards tasks. The image processing capacity is suggested to be task- and interpretation-oriented. The study was performed over a period of 1,5 weeks in late 1998, and is based upon interviews of IAEA staff, reviews of existing IAEA documentation as well as from SSC Satellitbild's long-standing experience of satellite imagery and field missions. The cost/benefit analysis is based on a spreadsheet simulation of five potential applications of commercial satellite imagery: Reference information; Confirmation of Agency acquired and Member State supplied data; Change detection and on-going monitoring; Assessing open source information available to the Agency; Detecting undeclared activities and undeclared sites. The study confirms that the proposed concept of a relatively small 'imagery unit' using high-resolution data will be a sound and

Geomorphology and vegetation mapping the ice-free terrains of the Western Antarctic Peninsula region using very high resolution imagery from an UAV

Science.gov (United States)

Vieira, G.; Mora, C.; Pina, P.; Bandeira, L.; Hong, S. G.

2014-12-01

The West Antarctic Peninsula (WAP) is one of the Earth's regions with a fastest warming signal since the 1950's with an increase of over +2.5 ºC in MAAT. Significant changes have been reported for glaciers, ice-shelves, sea-ice and also for the permafrost environment. Mapping and monitoring the ice-free areas of the WAP has been until recently limited by the available aerial photo surveys, but also by the scarce high resolution satellite imagery (e.g. QuickBird, WorldView, etc.) that are seriously constrained by the high cloudiness of the region. Recent developments in Unmanned Aerial Vehicles (UAV's), which have seen significant technological advances and price reduction in the last few years, allow for its systematical use for mapping and monitoring in remote environments. In the framework of projects PERMANTAR-3 (PTDC/AAG-GLO/3908/2012 - FCT) and 3DAntártida (Ciência Viva), we complement traditional terrain surveying and mapping, satellite remote sensing (SAR and optical) and D-GPS deformation monitoring, with the application of an UAV. In this communication, we present the results from the application of a Sensefly ebee UAV in mapping the vegetation and geomorphological processes (e.g. sorted circles), as well as for digital elevation model generation in a test site in Barton Pen., King George Isl.. The UAV is a lightweight (ci. 700g) aircraft, with a 96 cm wingspan, which is portable and easy to transport. It allows for up to 40 min flight time, with application of RGB or NIR cameras. We have tested the ebee successfully with winds up to 10 m/s and obtained aerial photos with a ground resolution of 4 cm/pixel. The digital orthophotomaps, high resolution DEM's together with field observations have allowed for deriving geomorphological maps with unprecedented detail and accuracy, providing new insight into the controls on the spatial distribution of geomorphological processes. The talk will focus on the first results from the field surveys of February and

Assessing Field-Specific Risk of Soybean Sudden Death Syndrome Using Satellite Imagery in Iowa.

Science.gov (United States)

Yang, S; Li, X; Chen, C; Kyveryga, P; Yang, X B

2016-08-01

Moderate resolution imaging spectroradiometer (MODIS) satellite imagery from 2004 to 2013 were used to assess the field-specific risks of soybean sudden death syndrome (SDS) caused by Fusarium virguliforme in Iowa. Fields with a high frequency of significant decrease (>10%) of the normalized difference vegetation index (NDVI) observed in late July to middle August on historical imagery were hypothetically considered as high SDS risk. These high-risk fields had higher slopes and shorter distances to flowlines, e.g., creeks and drainages, particularly in the Des Moines lobe. Field data in 2014 showed a significantly higher SDS level in the high-risk fields than fields selected without considering NDVI information. On average, low-risk fields had 10 times lower F. virguliforme soil density, determined by quantitative polymerase chain reaction, compared with other surveyed fields. Ordinal logistic regression identified positive correlations between SDS and slope, June NDVI, and May maximum temperature, but high June maximum temperature hindered SDS. A modeled SDS risk map showed a clear trend of potential disease occurrences across Iowa. Landsat imagery was analyzed similarly, to discuss the ability to utilize higher spatial resolution data. The results demonstrated the great potential of both MODIS and Landsat imagery for SDS field-specific risk assessment.

USGS Imagery Only Base Map Service from The National Map

Data.gov (United States)

U.S. Geological Survey, Department of the Interior — USGS Imagery Only is a tile cache base map of orthoimagery in The National Map visible to the 1:18,000 scale. Orthoimagery data are typically high resolution images...

Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study.

Science.gov (United States)

De Roeck, Els; Van Coillie, Frieke; De Wulf, Robert; Soenen, Karen; Charlier, Johannes; Vercruysse, Jozef; Hantson, Wouter; Ducheyne, Els; Hendrickx, Guy

2014-12-01

The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke, as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m2 and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations.

Mapping turbidity in the Charles River, Boston using a high-resolution satellite.

Science.gov (United States)

Hellweger, Ferdi L; Miller, Will; Oshodi, Kehinde Sarat

2007-09-01

The usability of high-resolution satellite imagery for estimating spatial water quality patterns in urban water bodies is evaluated using turbidity in the lower Charles River, Boston as a case study. Water turbidity was surveyed using a boat-mounted optical sensor (YSI) at 5 m spatial resolution, resulting in about 4,000 data points. The ground data were collected coincidently with a satellite imagery acquisition (IKONOS), which consists of multispectral (R, G, B) reflectance at 1 m resolution. The original correlation between the raw ground and satellite data was poor (R2 = 0.05). Ground data were processed by removing points affected by contamination (e.g., sensor encounters a particle floc), which were identified visually. Also, the ground data were corrected for the memory effect introduced by the sensor's protective casing using an analytical model. Satellite data were processed to remove pixels affected by permanent non-water features (e.g., shoreline). In addition, water pixels within a certain buffer distance from permanent non-water features were removed due to contamination by the adjacency effect. To determine the appropriate buffer distance, a procedure that explicitly considers the distance of pixels to the permanent non-water features was applied. Two automatic methods for removing the effect of temporary non-water features (e.g., boats) were investigated, including (1) creating a water-only mask based on an unsupervised classification and (2) removing (filling) all local maxima in reflectance. After the various processing steps, the correlation between the ground and satellite data was significantly better (R2 = 0.70). The correlation was applied to the satellite image to develop a map of turbidity in the lower Charles River, which reveals large-scale patterns in water clarity. However, the adjacency effect prevented the application of this method to near-shore areas, where high-resolution patterns were expected (e.g., outfall plumes).

Laser radar cross-section estimation from high-resolution image data.

Science.gov (United States)

Osche, G R; Seeber, K N; Lok, Y F; Young, D S

1992-05-10

A methodology for the estimation of ladar cross sections from high-resolution image data of geometrically complex targets is presented. Coherent CO(2) laser radar was used to generate high-resolution amplitude imagery of a UC-8 Buffalo test aircraft at a range of 1.3 km at nine different aspect angles. The average target ladar cross section was synthesized from these data and calculated to be sigma(T) = 15.4 dBsm, which is similar to the expected microwave radar cross sections. The aspect angle dependence of the cross section shows pronounced peaks at nose on and broadside, which are also in agreement with radar results. Strong variations in both the mean amplitude and the statistical distributions of amplitude with the aspect angle have also been observed. The relative mix of diffuse and specular returns causes significant deviations from a simple Lambertian or Swerling II target, especially at broadside where large normal surfaces are present.

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).

Automatic Blocked Roads Assessment after Earthquake Using High Resolution Satellite Imagery

Science.gov (United States)

Rastiveis, H.; Hosseini-Zirdoo, E.; Eslamizade, F.

2015-12-01

In 2010, an earthquake in the city of Port-au-Prince, Haiti, happened quite by chance an accident and killed over 300000 people. According to historical data such an earthquake has not occurred in the area. Unpredictability of earthquakes has necessitated the need for comprehensive mitigation efforts to minimize deaths and injuries. Blocked roads, caused by debris of destroyed buildings, may increase the difficulty of rescue activities. In this case, a damage map, which specifies blocked and unblocked roads, can be definitely helpful for a rescue team. In this paper, a novel method for providing destruction map based on pre-event vector map and high resolution world view II satellite images after earthquake, is presented. For this purpose, firstly in pre-processing step, image quality improvement and co-coordination of image and map are performed. Then, after extraction of texture descriptor from the image after quake and SVM classification, different terrains are detected in the image. Finally, considering the classification results, specifically objects belong to "debris" class, damage analysis are performed to estimate the damage percentage. In this case, in addition to the area objects in the "debris" class their shape should also be counted. The aforementioned process are performed on all the roads in the road layer.In this research, pre-event digital vector map and post-event high resolution satellite image, acquired by Worldview-2, of the city of Port-au-Prince, Haiti's capital, were used to evaluate the proposed method. The algorithm was executed on 1200×800 m2 of the data set, including 60 roads, and all the roads were labelled correctly. The visual examination have authenticated the abilities of this method for damage assessment of urban roads network after an earthquake.

Hurricane Ivan Aerial Photography: High-Resolution Imagery of the Florida Panhandle and Surrounding Regions After Landfall

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the Florida panhandle and surrounding regions after Hurricane Ivan made landfall. The regions photographed range from Gulf...

IAEA Safeguards: Cost/benefit analysis of commercial satellite imagery

Energy Technology Data Exchange (ETDEWEB)

Andersson, Christer [SSC Satellitbild AB, Kiruna (Sweden)

1999-03-01

A major milestone in the efforts to strengthen the Safeguards System was reached in May 1997 when the Board of Governors approved a `Model Protocol Additional to Safeguards Agreements`. The Protocol provides the legal basis necessary to enhance the Agency`s ability to detect undeclared nuclear material and activities by using information available from open sources to complement the declarations made by Member States. Commercially available high-resolution satellite data has emerged as one potential complementary open information source to support the traditional and extended Safeguard activities of IAEA. This document constitutes a first report from SSC Satellitbild giving the Agency tentative and initial estimates of the potential cost and time-savings possible with the new proposed technology. The initial cost/benefit simulation will be further finalised in the following `Implementation Blueprint` study. The general foundation and starting point for the cost/benefit calculation is to simulate a new efficient and relatively small `imagery unit` within the IAEA, capable of performing advanced image processing as a tool for various safeguards tasks. The image processing capacity is suggested to be task- and interpretation-oriented. The study was performed over a period of 1,5 weeks in late 1998, and is based upon interviews of IAEA staff, reviews of existing IAEA documentation as well as from SSC Satellitbild`s long-standing experience of satellite imagery and field missions. The cost/benefit analysis is based on a spreadsheet simulation of five potential applications of commercial satellite imagery: Reference information; Confirmation of Agency acquired and Member State supplied data; Change detection and on-going monitoring; Assessing open source information available to the Agency; Detecting undeclared activities and undeclared sites. The study confirms that the proposed concept of a relatively small `imagery unit` using high-resolution data will be a sound and

Self-imagery in individuals with high body dissatisfaction: the effect of positive and negative self-imagery on aspects of the self-concept.

Science.gov (United States)

Farrar, Stephanie; Stopa, Lusia; Turner, Hannah

2015-03-01

Cognitive behavioural models of eating disorders highlight low self-esteem as a maintaining factor. This study explored the impact of positive and negative self-imagery on aspects of the working self (implicit and explicit self-esteem and self-concept clarity) in individuals with high body dissatisfaction (an important aspect of eating disorders). The impact of these images on state body satisfaction and affect was also explored. A group of participants with high body dissatisfaction completed measures of explicit self-esteem, self-concept clarity, state body satisfaction and affect prior to completing a negative (n = 33) or positive (n = 33) self-imagery retrieval task. Following this they completed the baseline measures and a measure of implicit self-esteem. Holding a negative self-image in mind had a negative effect on explicit self-esteem, whilst holding a positive self-image had a beneficial effect. There were no effects of imagery on implicit self-esteem. Holding a negative image in mind led to a significant reduction in self-concept clarity; however, positive self-imagery did not affect self-concept clarity. Holding a negative self-image in mind led to a decrease in body satisfaction and state affect. The opposite was found for the positive self-imagery group. Implicit self-esteem was not measured at baseline. Imagery techniques which promote positive self-images may help improve aspects of the working self, body satisfaction and affect in individuals with high levels of body dissatisfaction. As such, these imagery techniques warrant further investigation in a clinical population. Copyright © 2014 Elsevier Ltd. All rights reserved.

Identification of the potential gap areas for the developing green infrastructure in the Urban area using High resolution satellite Imagery

Science.gov (United States)

Kanaparthi, M. B.

2017-12-01

In India urban population is growing day by day which is causing air pollution less air quality finally leading to climate change and global warming. To mitigate the effect of the climate change we need to plant more trees in the urban area. The objective of this study is develop a plan to improve the urban Green Infrastructure (GI) to fight against the climate change and global warming. Improving GI is a challenging and difficult task in the urban areas because land unavailability of land, to overcome the problem greenways is a good the solution. Greenway is a linear open space developed along the rivers, canals, roads in the urban areas to form a network of green spaces. Roads are the most common structures in the urban area. The idea is to develop the greenways alongside the road to connecting the different green spaces. Tree crowns will act as culverts to connect the green spaces. This will require the spatial structure of the green space, distribution of trees along the roads and the gap areas along the road where more trees can be planted. This can be achieved with help of high resolution Satellite Imagery and the object extraction techniques. This study was carried in the city Bhimavaram which is located in state Andhra Pradesh. The final outcome of this study is potential gap areas for planting trees in the city.

The National Map - Orthoimagery

Science.gov (United States)

Mauck, James; Brown, Kim; Carswell, William J.

2009-01-01

Orthorectified digital aerial photographs and satellite images of 1-meter (m) pixel resolution or finer make up the orthoimagery component of The National Map. The process of orthorectification removes feature displacements and scale variations caused by terrain relief and sensor geometry. The result is a combination of the image characteristics of an aerial photograph or satellite image and the geometric qualities of a map. These attributes allow users to: *Measure distance *Calculate areas *Determine shapes of features *Calculate directions *Determine accurate coordinates *Determine land cover and use *Perform change detection *Update maps The standard digital orthoimage is a 1-m or finer resolution, natural color or color infra-red product. Most are now produced as GeoTIFFs and accompanied by a Federal Geographic Data Committee (FGDC)-compliant metadata file. The primary source for 1-m data is the National Agriculture Imagery Program (NAIP) leaf-on imagery. The U.S. Geological Survey (USGS) utilizes NAIP imagery as the image layer on its 'Digital- Map' - a new generation of USGS topographic maps (http://nationalmap.gov/digital_map). However, many Federal, State, and local governments and organizations require finer resolutions to meet a myriad of needs. Most of these images are leaf-off, natural-color products at resolutions of 1-foot (ft) or finer.

The employment of weather satellite imagery in an effort to identify and locate the forest-tundra ecotone in Canada

Science.gov (United States)

Aldrich, S. A.; Aldrich, F. T.; Rudd, R. D.

1969-01-01

Weather satellite imagery provides the only routinely available orbital imagery depicting the high latitudes. Although resolution is low on this imagery, it is believed that a major natural feature, notably linear in expression, should be mappable on it. The transition zone from forest to tundra, the ecotone, is such a feature. Locational correlation is herein established between a linear signature on the imagery and several ground truth positions of the ecotone in Canada.

Hurricane Rita Aerial Photography: High-Resolution Imagery of the Texas and Louisiana Gulf Coast After Landfall

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the Texas and Louisiana Gulf Coast after Hurricane Rita made landfall. The regions photographed range from San Luis Pass, Texas...

NOAA Integrated Ocean and Coastal Mapping (IOCM) orthorectified mosaic image tiles, LaPlace to Venice, Louisiana 2010 (NODC Accession 0075829)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of Mississippi River -...

Carbon Transfers and Emissions Following Harvest and Pile Burning in Coastal Douglas-fir Forests Determined from Analysis of High-Resolution UAV Imagery and Point Clouds and from Field Surveys.

Science.gov (United States)

Trofymow, J. A.; Gougeon, F.; Kelley, J. W.

2017-12-01

Forest carbon (C) models require knowledge on C transfers due to intense disturbances such as fire, harvest, and slash burning. In such events, live trees die and C transferred to detritus or exported as round wood. With burning, live and detrital C is lost as emissions. Burning can be incomplete, leaving wood, charred and scattered or in unburnt rings and piles. For harvests, all round wood volume is routinely measured, while dispersed and piled residue volumes are typically assessed in field surveys, scaled to a block. Recently, geospatial methods have been used to determine, for an entire block, piled residues using LiDAR or image point clouds (PC) and dispersed residues by analysis of high-resolution imagery. Second-growth Douglas-fir forests on eastern Vancouver Island were examined, 4 blocks at Oyster River (OR) and 2 at Northwest Bay (NB). OR blocks were cut winter 2011, piled spring 2011, field survey, aerial RGB imagery and LiDAR PC acquired fall 2011, piles burned, burn residues surveyed, and post-burn aerial RGB imagery acquired 2012. NB blocks were cut fall 2014, piled spring 2015, field survey, UAV RGB imagery and image PC acquired summer 2015, piles burned and burn residues surveyed spring 2016, and post-burn UAV RGB imagery and PC acquired fall 2016. Volume to biomass conversion used survey species proportions and wood density. At OR, round wood was 261.7 SE 13.1, firewood 1.7 SE 0.3, and dispersed residue by survey, 13.8 SE 3.6 tonnes dry mass (t dm) ha-1. Piled residues were 8.2 SE 0.9 from pile surveys vs. 25.0 SE 5.9 t dm ha-1 from LiDAR PC bulk pile volumes and packing ratios. Post-burn, piles lost 5.8 SE 0.5 from survey of burn residues vs. 18.2 SE 4.7 t dm ha-1 from pile volume changes using 2011 LiDAR PC and 2012 imagery. The percentage of initial merchantable biomass exported as round & fire wood, remaining as dispersed & piled residue, and lost to burning was, respectively, 92.5%, 5.5% and 2% using only field methods vs. 87%, 7% and 6% from

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas.

Science.gov (United States)

Liu, Bo; Wu, Huayi; Wang, Yandong; Liu, Wenming

2015-01-01

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas.

Directory of Open Access Journals (Sweden)

Bo Liu

Full Text Available Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1 using directional mathematical morphology to enhance the contrast between roads and non-roads; (2 using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

Science.gov (United States)

Liu, Bo; Wu, Huayi; Wang, Yandong; Liu, Wenming

2015-01-01

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction. PMID:26397832

Sport Transition of JPSS VIIRS Imagery for Night-time Applications

Science.gov (United States)

Fuell, Kevin; LeRoy, Anita; Smith, Matt; Miller, Steve; Kann, Diedre; Bernhardt, David; Reydell, Nezette; Cox, Robert

2014-01-01

The NASA/Short-term Prediction, Research, and Transition (SPoRT) Program and NOAA/Cooperative Institute for Research in the Atmosphere (CIRA) work within the NOAA/Joint Polar Satellite System (JPSS) Proving Ground to demonstrate the unique capabilities of the VIIRS instrument. Very similar to MODIS, the VIIRS instrument provides many high-resolution visible and infrared channels in a broad spectrum. In addition, VIIRS is equipped with a low-light sensor that is able to detect light emissions from the land and atmosphere as well as reflected sunlight by the lunar surface. This band is referred to as the Day-Night Band due to the sunlight being used at night to see cloud and topographic features just as one would typically see in day-time visible imagery. NWS forecast offices that collaborate with SPoRT and CIRA have utilized MODIS imagery in operations, but have longed for more frequent passes of polar-orbiting data. The VIIRS instrument enhances SPoRT collaborations with WFOs by providing another day and night-time pass, and at times two additional passes due to its large swath width. This means that multi-spectral, RGB imagery composites are more readily available to prepare users for their use in GOES-R era and high-resolution imagery for use in high-latitudes is more frequently able to supplement standard GOES imagery within the SPoRT Hybrid GEO-LEO product. The transition of VIIRS also introduces the new Day-Night Band capability to forecast operations. An Intensive Evaluation Period (IEP) was conducted in Summer 2013 with a group of "Front Range" NWS offices related to VIIRS night-time imagery. VIIRS single-channel imagery is able to better analyze the specific location of fire hotspots and other land features, as well as provide a more true measurement of various cloud and aerosol properties than geostationary measurements, especially at night. Viewed within the SPoRT Hybrid imagery, the VIIRS data allows forecasters to better interpret the more frequent, but

An efficient cloud detection method for high resolution remote sensing panchromatic imagery

Science.gov (United States)

Li, Chaowei; Lin, Zaiping; Deng, Xinpu

2018-04-01

In order to increase the accuracy of cloud detection for remote sensing satellite imagery, we propose an efficient cloud detection method for remote sensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.

Creating high-resolution bare-earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for lidar point cloud filtering

Science.gov (United States)

DeWitt, Jessica D.; Warner, Timothy A.; Chirico, Peter G.; Bergstresser, Sarah E.

2017-01-01

For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.

Coarse Resolution SAR Imagery to Support Flood Inundation Models in Near Real Time

Science.gov (United States)

Di Baldassarre, Giuliano; Schumann, Guy; Brandimarte, Luigia; Bates, Paul

2009-11-01

In recent years, the availability of new emerging data (e.g. remote sensing, intelligent wireless sensors, etc) has led to a sudden shift from a data-sparse to a data-rich environment for hydrological and hydraulic modelling. Furthermore, the increased socioeconomic relevance of river flood studies has motivated the development of complex methodologies for the simulation of the hydraulic behaviour of river systems. In this context, this study aims at assessing the capability of coarse resolution SAR (Synthetic Aperture Radar) imagery to support and quickly validate flood inundation models in near real time. A hydraulic model of a 98km reach of the River Po (Italy), previously calibrated on a high-magnitude flood event with extensive and high quality field data, is tested using a SAR flood image, acquired and processed in near real time, during the June 2008 low-magnitude event. Specifically, the image is an acquisition by the ENVISAT-ASAR sensor in wide swath mode and has been provided through ESA (European Space Agency) Fast Registration system at no cost 24 hours after the acquisition. The study shows that the SAR image enables validation and improvement of the model in a time shorter than the flood travel time. This increases the reliability of model predictions (e.g. water elevation and inundation width along the river reach) and, consequently, assists flood management authorities in undertaking the necessary prevention activities.

Extraction of Rocky Desertification from Disp Imagery: a Case Study of Liupanshui, Guizhou, China

Science.gov (United States)

Zhou, G.; Wu, Z.; Wang, W.; Shi, Y.; Mao, G.; Huang, Y.; Jia, B.; Gao, G.; Chen, P.

2017-09-01

Karst rocky desertification is a typical type of land degradation in Guizhou Province, China. It causes great ecological and economical implications to the local people. This paper utilized the declassified intelligence satellite photography (DISP) of 1960s to extract the karst rocky desertification area to analyze the early situation of karst rocky desertification in Liupanshui, Guizhou, China. Due to the lack of ground control points and parameters of the satellite, a polynomial orthographic correction model with considering altitude difference correction is proposed for orthorectification of DISP imagery. With the proposed model, the 96 DISP images from four missions are orthorectified. The images are assembled into a seamless image map of the karst area of Guizhou, China. The assembled image map is produced to thematic map of karst rocky desertification by visual interpretation in Liupanshui city. With the assembled image map, extraction of rocky desertification is conducted.

EXTRACTION OF ROCKY DESERTIFICATION FROM DISP IMAGERY: A CASE STUDY OF LIUPANSHUI, GUIZHOU, CHINA

Directory of Open Access Journals (Sweden)

G. Zhou

2017-09-01

Full Text Available Karst rocky desertification is a typical type of land degradation in Guizhou Province, China. It causes great ecological and economical implications to the local people. This paper utilized the declassified intelligence satellite photography (DISP of 1960s to extract the karst rocky desertification area to analyze the early situation of karst rocky desertification in Liupanshui, Guizhou, China. Due to the lack of ground control points and parameters of the satellite, a polynomial orthographic correction model with considering altitude difference correction is proposed for orthorectification of DISP imagery. With the proposed model, the 96 DISP images from four missions are orthorectified. The images are assembled into a seamless image map of the karst area of Guizhou, China. The assembled image map is produced to thematic map of karst rocky desertification by visual interpretation in Liupanshui city. With the assembled image map, extraction of rocky desertification is conducted.

Information from imagery: ISPRS scientific vision and research agenda

Science.gov (United States)

Chen, Jun; Dowman, Ian; Li, Songnian; Li, Zhilin; Madden, Marguerite; Mills, Jon; Paparoditis, Nicolas; Rottensteiner, Franz; Sester, Monika; Toth, Charles; Trinder, John; Heipke, Christian

2016-05-01

With the increased availability of very high-resolution satellite imagery, terrain based imaging and participatory sensing, inexpensive platforms, and advanced information and communication technologies, the application of imagery is now ubiquitous, playing an important role in many aspects of life and work today. As a leading organisation in this field, the International Society for Photogrammetry and Remote Sensing (ISPRS) has been devoted to effectively and efficiently obtaining and utilising information from imagery since its foundation in the year 1910. This paper examines the significant challenges currently facing ISPRS and its communities, such as providing high-quality information, enabling advanced geospatial computing, and supporting collaborative problem solving. The state-of-the-art in ISPRS related research and development is reviewed and the trends and topics for future work are identified. By providing an overarching scientific vision and research agenda, we hope to call on and mobilise all ISPRS scientists, practitioners and other stakeholders to continue improving our understanding and capacity on information from imagery and to deliver advanced geospatial knowledge that enables humankind to better deal with the challenges ahead, posed for example by global change, ubiquitous sensing, and a demand for real-time information generation.

Quantifying the Evolution of Melt Ponds in the Marginal Ice Zone Using High Resolution Optical Imagery and Neural Networks

Science.gov (United States)

Ortiz, M.; Pinales, J. C.; Graber, H. C.; Wilkinson, J.; Lund, B.

2016-02-01

Melt ponds on sea ice play a significant and complex role on the thermodynamics in the Marginal Ice Zone (MIZ). Ponding reduces the sea ice's ability to reflect sunlight, and in consequence, exacerbates the albedo positive feedback cycle. In order to understand how melt ponds work and their effect on the heat uptake of sea ice, we must quantify ponds through their seasonal evolution first. A semi-supervised neural network three-class learning scheme using a gradient descent with momentum and adaptive learning rate backpropagation function is applied to classify melt ponds/melt areas in the Beaufort Sea region. The network uses high resolution panchromatic satellite images from the MEDEA program, which are collocated with autonomous platform arrays from the Marginal Ice Zone Program, including ice mass-balance buoys, arctic weather stations and wave buoys. The goal of the study is to capture the spatial variation of melt onset and freeze-up of the ponds within the MIZ, and gather ponding statistics such as size and concentration. The innovation of this work comes from training the neural network as the melt ponds evolve over time; making the machine learning algorithm time-dependent, which has not been previously done. We will achieve this by analyzing the image histograms through quantification of the minima and maxima intensity changes as well as linking textural variation information of the imagery. We will compare the evolution of the melt ponds against several different array sites on the sea ice to explore if there are spatial differences among the separated platforms in the MIZ.

Tracking Large Area Mangrove Deforestation with Time-Series of High Fidelity MODIS Imagery

Science.gov (United States)

Rahman, A. F.; Dragoni, D.; Didan, K.

2011-12-01

Mangrove forests are important coastal ecosystems of the tropical and subtropical regions. These forests provide critical ecosystem services, fulfill important socio-economic and environmental functions, and support coastal livelihoods. But these forest are also among the most vulnerable ecosystems, both to anthropogenic disturbance and climate change. Yet, there exists no map or published study showing detailed spatiotemporal trends of mangrove deforestation at local to regional scales. There is an immediate need of producing such detailed maps to further study the drivers, impacts and feedbacks of anthropogenic and climate factors on mangrove deforestation, and to develop local and regional scale adaptation/mitigation strategies. In this study we use a time-series of high fidelity imagery from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) for tracking changes in the greenness of mangrove forests of Kalimantan Island of Indonesia. A novel method of filtering satellite data for cloud, aerosol, and view angle effects was used to produce high fidelity MODIS time-series images at 250-meter spatial resolution and three-month temporal resolution for the period of 2000-2010. Enhanced Vegetation Index 2 (EVI2), a measure of vegetation greenness, was calculated from these images for each pixel at each time interval. Temporal variations in the EVI2 of each pixel were tracked as a proxy to deforestaton of mangroves using the statistical method of change-point analysis. Results of these change detection were validated using Monte Carlo simulation, photographs from Google-Earth, finer spatial resolution images from Landsat satellite, and ground based GIS data.

Satellite imagery and the Department of Safeguards

International Nuclear Information System (INIS)

Chitumbo, K.; Bunney, J.; Leve, G.; Robb, S.

2001-01-01

Full text: The presentation examines some of the challenges the Satellite Imagery and Analysis Laboratory (SIAL) is facing in supporting Strengthened Safeguards. It focuses on the analytical process, starting with specifying initial tasking and continuing through to end products that are a direct result of in-house analysis. In addition it also evaluates the advantages and disadvantages of SIAL's mission and introduces external forces that the agency must consider, but cannot itself, predict or control. Although SIAL's contribution to tasks relating to Article 2a(iii) of the Additional Protocol are known and are presently of great benefit to operations areas, this is only one aspect of its work. SIAL's ability to identify and analyze historical satellite imagery data has the advantage of permitting operations to take a more in depth view of a particular area of interest's (AOI) development, and thus may permit operations to confirm or refute specific assertions relating to the AOI's function or abilities. These assertions may originate in-house or may be open source reports the agency feels it is obligated to explore. SIAL's mission is unique in the world of imagery analysis. Its aim is to support all operations areas equally and in doing so it must maintain global focus. The task is tremendous, but the resultant coverage and concentration of unique expertise will allow SIAL to develop and provide operations with datasets that can be exploited in standalone mode or be incorporated into new cutting edge tools to be developed in SGIT. At present SIAL relies on two remote sensors, IKONOS-2 and EROS-AI, for present high- resolution imagery data and is using numerous sources for historical, pre 1999, data. A multiplicity of sources for high-resolution data is very important to SIAL, but is something that it cannot influence. It is hoped that the planned launch of two new sensors by Summer 2002 will be successful and will offer greater flexibility for image collection

Volumetric Forest Change Detection Through Vhr Satellite Imagery

Science.gov (United States)

Akca, Devrim; Stylianidis, Efstratios; Smagas, Konstantinos; Hofer, Martin; Poli, Daniela; Gruen, Armin; Sanchez Martin, Victor; Altan, Orhan; Walli, Andreas; Jimeno, Elisa; Garcia, Alejandro

2016-06-01

Quick and economical ways of detecting of planimetric and volumetric changes of forest areas are in high demand. A research platform, called FORSAT (A satellite processing platform for high resolution forest assessment), was developed for the extraction of 3D geometric information from VHR (very-high resolution) imagery from satellite optical sensors and automatic change detection. This 3D forest information solution was developed during a Eurostars project. FORSAT includes two main units. The first one is dedicated to the geometric and radiometric processing of satellite optical imagery and 2D/3D information extraction. This includes: image radiometric pre-processing, image and ground point measurement, improvement of geometric sensor orientation, quasiepipolar image generation for stereo measurements, digital surface model (DSM) extraction by using a precise and robust image matching approach specially designed for VHR satellite imagery, generation of orthoimages, and 3D measurements in single images using mono-plotting and in stereo images as well as triplets. FORSAT supports most of the VHR optically imagery commonly used for civil applications: IKONOS, OrbView - 3, SPOT - 5 HRS, SPOT - 5 HRG, QuickBird, GeoEye-1, WorldView-1/2, Pléiades 1A/1B, SPOT 6/7, and sensors of similar type to be expected in the future. The second unit of FORSAT is dedicated to 3D surface comparison for change detection. It allows users to import digital elevation models (DEMs), align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes between epochs. To this end our 3D surface matching method LS3D is being used. FORSAT is a single source and flexible forest information solution with a very competitive price/quality ratio, allowing expert and non-expert remote sensing users to monitor forests in three and four dimensions from VHR optical imagery for many forest information needs. The capacity and benefits of FORSAT have been tested in

Investigating Hydrocarbon Seep Environments with High-Resolution, Three-Dimensional Geographic Visualizations.

Science.gov (United States)

Doolittle, D. F.; Gharib, J. J.; Mitchell, G. A.

2015-12-01

Detailed photographic imagery and bathymetric maps of the seafloor acquired by deep submergence vehicles such as Autonomous Underwater Vehicles (AUV) and Remotely Operated Vehicles (ROV) are expanding how scientists and the public view and ultimately understand the seafloor and the processes that modify it. Several recently acquired optical and acoustic datasets, collected during ECOGIG (Ecosystem Impacts of Oil and Gas Inputs to the Gulf) and other Gulf of Mexico expeditions using the National Institute for Undersea Science Technology (NIUST) Eagle Ray, and Mola Mola AUVs, have been fused with lower resolution data to create unique three-dimensional geovisualizations. Included in these data are multi-scale and multi-resolution visualizations over hydrocarbon seeps and seep related features. Resolution of the data range from 10s of mm to 10s of m. When multi-resolution data is integrated into a single three-dimensional visual environment, new insights into seafloor and seep processes can be obtained from the intuitive nature of three-dimensional data exploration. We provide examples and demonstrate how integration of multibeam bathymetry, seafloor backscatter data, sub-bottom profiler data, textured photomosaics, and hull-mounted multibeam acoustic midwater imagery are made into a series a three-dimensional geovisualizations of actively seeping sites and associated chemosynthetic communities. From these combined and merged datasets, insights on seep community structure, morphology, ecology, fluid migration dynamics, and process geomorphology can be investigated from new spatial perspectives. Such datasets also promote valuable inter-comparisons of sensor resolution and performance.

Do we miss the hot spots? – The use of very high resolution aerial photographs to quantify carbon fluxes in peatlands

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T. Becker

2008-10-01

Full Text Available Accurate determination of carbon balances in heterogeneous ecosystems often requires the extrapolation of point based measurements. The ground resolution (pixel size of the extrapolation base, e.g. a land-cover map, might thus influence the calculated carbon balance, in particular if biogeochemical hot spots are small in size. In this paper, we test the effects of varying ground resolution on the calculated carbon balance of a boreal peatland consisting of hummocks (dry, lawns (intermediate and flarks (wet surfaces. The generalizations in lower resolution imagery led to biased area estimates for individual micro-site types. While areas of lawns and hummocks were stable below a threshold resolution of ~60 cm, the maximum of the flark area was located at resolutions below 25 cm and was then decreasing with coarsening resolution. Using a resolution of 100 cm instead of 6 cm led to an overestimation of total CO₂ uptake of the studied peatland area (approximately 14 600 m² of ~5% and an underestimation of total CH₄ emission of ~6%. To accurately determine the surface area of scattered and small-sized micro-site types in heterogeneous ecosystems (e.g. flarks in peatlands, a minimum ground resolution appears necessary. In our case this leads to a recommended resolution of 25 cm, which can be derived by conventional airborne imagery. The usage of high resolution imagery from commercial satellites, e.g. Quickbird, however, is likely to underestimate the surface area of biogeochemical hot spots. It is important to note that the observed resolution effect on the carbon balance estimates can be much stronger for other ecosystems than for the investigated peatland. In the investigated peatland the relative hot spot area of the flarks is very small and their hot spot characteristics with respect to CH₄ and CO₂ fluxes is rather modest.

Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

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Shasha Jiang

2016-05-01

Full Text Available Automated remote sensing methods have not gained widespread usage for damage assessment after hurricane events, especially for low-rise buildings, such as individual houses and small businesses. Hurricane wind, storm surge with waves, and inland flooding have unique damage signatures, further complicating the development of robust automated assessment methodologies. As a step toward realizing automated damage assessment for multi-hazard hurricane events, this paper presents a mono-temporal image classification methodology that quickly and accurately differentiates urban debris from non-debris areas using post-event images. Three classification approaches are presented: spectral, textural, and combined spectral–textural. The methodology is demonstrated for Gulfport, Mississippi, using IKONOS panchromatic satellite and NOAA aerial colour imagery collected after 2005 Hurricane Katrina. The results show that multivariate texture information significantly improves debris class detection performance by decreasing the confusion between debris and other land cover types, and the extracted debris zone accurately captures debris distribution. Additionally, the extracted debris boundary is approximately equivalent regardless of imagery type, demonstrating the flexibility and robustness of the debris mapping methodology. While the test case presents results for hurricane hazards, the proposed methodology is generally developed and expected to be effective in delineating debris zones for other natural hazards, including tsunamis, tornadoes, and earthquakes.

GLCF: Landsat GeoCover

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satellite imagery provided in a standardized, orthorectified format, covering the entire land surface of the * Orthorectification * Distribution Status * Hard Media Orders * Letters Delivered Quick Links * Create True Color

NOAA Integrated Ocean and Coastal Mapping (IOCM) true color (RGB) orthorectified mosaic image tiles, Lake Charles, Louisiana 2009-2010 (NODC Accession 0075827)

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National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of Lake Charles,...

Essential climatic variables estimation with satellite imagery

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Kolotii, A.; Kussul, N.; Shelestov, A.; Lavreniuk, M. S.

2016-12-01

According to Sendai Framework for Disaster Risk Reduction 2015 - 2030 Leaf Area Index (LAI) is considered as one of essential climatic variables. This variable represents the amount of leaf material in ecosystems and controls the links between biosphere and atmosphere through various processes and enables monitoring and quantitative assessment of vegetation state. LAI has added value for such important global resources monitoring tasks as drought mapping and crop yield forecasting with use of data from different sources [1-2]. Remote sensing data from space can be used to estimate such biophysical parameter at regional and national scale. High temporal satellite imagery is usually required to capture main parameters of crop growth [3]. Sentinel-2 mission launched in 2015 be ESA is a source of high spatial and temporal resolution satellite imagery for mapping biophysical parameters. Products created with use of automated Sen2-Agri system deployed during Sen2-Agri country level demonstration project for Ukraine will be compared with our independent results of biophysical parameters mapping. References Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736 Kolotii, A., Kussul, N., Shelestov, A., Skakun, S., Yailymov, B., Basarab, R., ... & Ostapenko, V. (2015). Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 39-44. Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508.

ROV seafloor surveys combining 5-cm lateral resolution multibeam bathymetry with color stereo photographic imagery

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Caress, D. W.; Hobson, B.; Thomas, H. J.; Henthorn, R.; Martin, E. J.; Bird, L.; Rock, S. M.; Risi, M.; Padial, J. A.

2013-12-01

The Monterey Bay Aquarium Research Institute is developing a low altitude, high-resolution seafloor mapping capability that combines multibeam sonar with stereo photographic imagery. The goal is to obtain spatially quantitative, repeatable renderings of the seafloor with fidelity at scales of 5 cm or better from altitudes of 2-3 m. The initial test surveys using this sensor system are being conducted from a remotely operated vehicle (ROV). Ultimately we intend to field this survey system from an autonomous underwater vehicle (AUV). This presentation focuses on the current sensor configuration, methods for data processing, and results from recent test surveys. Bathymetry data are collected using a 400-kHz Reson 7125 multibeam sonar. This configuration produces 512 beams across a 135° wide swath; each beam has a 0.5° acrosstrack by 1.0° alongtrack angular width. At a 2-m altitude, the nadir beams have a 1.7-cm acrosstrack and 3.5 cm alongtrack footprint. Dual Allied Vision Technology GX1920 2.8 Mpixel color cameras provide color stereo photography of the seafloor. The camera housings have been fitted with corrective optics achieving a 90° field of view through a dome port. Illumination is provided by dual 100J xenon strobes. Position, depth, and attitude data are provided by a Kearfott SeaDevil Inertial Navigation System (INS) integrated with a 300 kHz RDI Doppler velocity log (DVL). A separate Paroscientific pressure sensor is mounted adjacent to the INS. The INS Kalman filter is aided by the DVL velocity and pressure data, achieving navigational drift rates less than 0.05% of the distance traveled during surveys. The sensors are mounted onto a toolsled fitted below MBARI's ROV Doc Ricketts with the sonars, cameras and strobes all pointed vertically down. During surveys the ROV flies at a 2-m altitude at speeds of 0.1-0.2 m/s. During a four-day R/V Western Flyer cruise in June 2013, we successfully collected multibeam and camera survey data from a 2-m altitude

Spatial Resolution Assessment of the Telops Airborne TIR Imagery

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Mousakhani, S.; Eslami, M.; Saadatseresht, M.

2017-09-01

Having a high spatial resolution of Thermal InfraRed (TIR) Sensors is a challenge in remote sensing applications. Airborne high spatial resolution TIR is a novel source of data that became available lately. Recent developments in spatial resolution of the TIR sensors have been an interesting topic for scientists. TIR sensors are very sensitive to the energies emitted from objects. Past researches have been shown that increasing the spatial resolution of an airborne image will decrease the spectral content of the data and will reduce the Signal to Noise Ratio (SNR). Therefore, in this paper a comprehensive assessment is adapted to estimate an appropriate spatial resolution of the TIR data (TELOPS TIR data), in consideration of the SNR. So, firstly, a low-pass filter is applied on TIR data and the achieved products fed to a classification method for analysing of the accuracy improvement. The obtained results show that, there is no significant change in classification accuracy by applying low-pass filter. Furthermore, estimation of the appropriate spatial resolution of the TIR data is evaluated for obtaining higher spectral content and SNR. For this purpose, different resolutions of the TIR data are created and fed to the maximum likelihood classification method separately. The results illustrated in the case of using images with ground pixel size four times greater than the original image, the classification accuracy is not reduced. Also, SNR and spectral contents are improved. But the corners sharpening is declined.

High-resolution topography using SfM-photogrammetry from UAV for coastal mudflat geomorphic surveys

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Fleury, Jules; Brunier, Guillaume; Michaud, Emma; Anthony, Edward; Morvan, Sylvain; Dussouillez, Philippe; Gardel, Antoine

2016-04-01

on a set of Ground Control Points acquired by RTK-GPS is between 2 and 3 times the DSM pixel resolution. This SfM implementation allows for: (1) a fine estimate, from the high resolution topographic data, of gentle mudflat morphologies including heterogeneous benthic surficial structures (channels, creeks, pools, burrows, pits) and (2) an assessment, from the high resolution visible imagery, of the benthic macrofauna burrowing activity by crabs and the density of their burrows. Affordable, lightweight and reproducible, SfM photogrammetric implementation based on UAVs offers interesting new perspectives in coastal geomorphology studies requiring high-resolution and accurate topographic data and imagery.

NAIP Aerial Imagery (Resampled), Salton Sea - 2005 [ds425

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California Natural Resource Agency — NAIP 2005 aerial imagery that has been resampled from 1-meter source resolution to approximately 30-meter resolution. This is a mosaic composed from several NAIP...

Structure recognition from high resolution images of ceramic composites

Energy Technology Data Exchange (ETDEWEB)

Ushizima, Daniela; Perciano, Talita; Krishnan, Harinarayan; Loring, Burlen; Bale, Hrishikesh; Parkinson, Dilworth; Sethian, James

2015-01-05

Fibers provide exceptional strength-to-weight ratio capabilities when woven into ceramic composites, transforming them into materials with exceptional resistance to high temperature, and high strength combined with improved fracture toughness. Microcracks are inevitable when the material is under strain, which can be imaged using synchrotron X-ray computed micro-tomography (mu-CT) for assessment of material mechanical toughness variation. An important part of this analysis is to recognize fibrillar features. This paper presents algorithms for detecting and quantifying composite cracks and fiber breaks from high-resolution image stacks. First, we propose recognition algorithms to identify the different structures of the composite, including matrix cracks and fibers breaks. Second, we introduce our package F3D for fast filtering of large 3D imagery, implemented in OpenCL to take advantage of graphic cards. Results show that our algorithms automatically identify micro-damage and that the GPU-based implementation introduced here takes minutes, being 17x faster than similar tools on a typical image file.

New Possibilities for High-Resolution, Large-Scale Ecosystem Assessment of the World's Semi-Arid Regions

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Burney, J. A.; Goldblatt, R.

2016-12-01

Understanding drivers of land use change - and in particular, levels of ecosystem degradation - in semi-arid regions is of critical importance because these agroecosystems (1) are home to the world's poorest populations, almost all of whom depend on agriculture for their livelihoods, (2) play a critical role in the global carbon and climate cycles, and (3) have in many cases seen dramatic changes in temperature and precipitation, relative to global averages, over the past several decades. However, assessing ecosystem health (or, conversely, degradation) presents a difficult measurement problem. Established methods are very labor intensive and rest on detailed questionnaires and field assessments. High-resolution satellite imagery has a unique role semi-arid ecosystem assessment in that it can be used for rapid (or repeated) and very simple measurements of tree and shrub density, an excellent overall indicator for dryland ecosystem health. Because trees and large shrubs are more sparse in semi-arid regions, sub-meter resolution imagery in conjunction with automated image analysis can be used to assess density differences at high spatial resolution without expensive and time-consuming ground-truthing. This could be used down to the farm level, for example, to better assess the larger-scale ecosystem impacts of different management practices, to assess compliance with REDD+ carbon offset protocols, or to evaluate implementation of conservation goals. Here we present results comparing spatial and spectral remote sensing methods for semi-arid ecosystem assessment across new data sources, using the Brazilian Sertão as an example, and the implications for large-scale use in semi-arid ecosystem science.

Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

Science.gov (United States)

Weiqi Zhou; Austin Troy; Morgan Grove

2008-01-01

Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...

RADARGRAMMETRIC DIGITAL SURFACE MODELS GENERATION FROM TERRASAR-X IMAGERY: CASE STUDIES, PROBLEMS AND POTENTIALITIES

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P. Capaldo

2012-07-01

Full Text Available The interest for the radargrammetric approach to Digital Surface Models (DSMs generation has been growing in last years thanks to the availability of very high resolution imagery acquired by new SAR (Synthetic Aperture Radar sensors, as COSMO-SkyMed, Radarsat-2 and TerraSAR-X, which are able to supply imagery up to 1 m ground resolution. DSMs radargrammetric generation approach consists of two basic steps, as for the standard photogrammetry applied to optical imagery: the imagery (at least a stereo pair orientation and the image matching for the generation of the points cloud. The steps of the radargrammetric DSMs generation have been implemented into SISAR (Software per Immagini Satellitari ad Alta Risoluzione, a scientific software developed at Geodesy and Geomatics Institute of the University of Rome “La Sapienza”. Moreover, starting from the radargrammetric orientation model, a tool for the Rational Polynomial Coefficients (RPCs for SAR images have been implemented. The possibility to generate RPCs, re-parametrizing a rigorous orientation model through a standardized set of coefficients which can be managed by a Rational Polynomial Coefficients (RPFs model (similarly to optical high resolution imagery sounds of particular interest since, at present, the most part of SAR imagery (except from Radarsat-2 is not supplied with RPCs, although the corresponding RPFs model is available in several commercial software. In particular the RPCs model has been used in the matching process and in the stereo restitution for the DSMs generation, with the advantage of shorter computational time. This paper discusses the application and the results of the implemented algorithm for radargrammetric DSMs generation from TerraSAR-X SpotLight imagery, acquired in Spotlight mode over Trento (Northern Italy. Urban and extra-urban (forested, cultivated areas were considered in two different tiles, and a final overall accuracy ranging from 4.5 to 6 meters was

Emergency Response Imagery Related to Hurricanes Harvey, Irma, and Maria

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Worthem, A. V.; Madore, B.; Imahori, G.; Woolard, J.; Sellars, J.; Halbach, A.; Helmricks, D.; Quarrick, J.

2017-12-01

NOAA's National Geodetic Survey (NGS) and Remote Sensing Division acquired and rapidly disseminated emergency response imagery related to the three recent hurricanes Harvey, Irma, and Maria. Aerial imagery was collected using a Trimble Digital Sensor System, a high-resolution digital camera, by means of NOAA's King Air 350ER and DeHavilland Twin Otter (DHC-6) Aircraft. The emergency response images are used to assess the before and after effects of the hurricanes' damage. The imagery aids emergency responders, such as FEMA, Coast Guard, and other state and local governments, in developing recovery strategies and efforts by prioritizing areas most affected and distributing appropriate resources. Collected imagery is also used to provide damage assessment for use in long-term recovery and rebuilding efforts. Additionally, the imagery allows for those evacuated persons to see images of their homes and neighborhoods remotely. Each of the individual images are processed through ortho-rectification and merged into a uniform mosaic image. These remotely sensed datasets are publically available, and often used by web-based map servers as well as, federal, state, and local government agencies. This poster will show the imagery collected for these three hurricanes and the processes involved in getting data quickly into the hands of those that need it most.

MODELING AND SIMULATION OF HIGH RESOLUTION OPTICAL REMOTE SENSING SATELLITE GEOMETRIC CHAIN

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Z. Xia

2018-04-01

Full Text Available The high resolution satellite with the longer focal length and the larger aperture has been widely used in georeferencing of the observed scene in recent years. The consistent end to end model of high resolution remote sensing satellite geometric chain is presented, which consists of the scene, the three line array camera, the platform including attitude and position information, the time system and the processing algorithm. The integrated design of the camera and the star tracker is considered and the simulation method of the geolocation accuracy is put forward by introduce the new index of the angle between the camera and the star tracker. The model is validated by the geolocation accuracy simulation according to the test method of the ZY-3 satellite imagery rigorously. The simulation results show that the geolocation accuracy is within 25m, which is highly consistent with the test results. The geolocation accuracy can be improved about 7 m by the integrated design. The model combined with the simulation method is applicable to the geolocation accuracy estimate before the satellite launching.

A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

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Huai Yu

2016-03-01

Full Text Available Scene classification plays an important role in understanding high-resolution satellite (HRS remotely sensed imagery. For remotely sensed scenes, both color information and texture information provide the discriminative ability in classification tasks. In recent years, substantial performance gains in HRS image classification have been reported in the literature. One branch of research combines multiple complementary features based on various aspects such as texture, color and structure. Two methods are commonly used to combine these features: early fusion and late fusion. In this paper, we propose combining the two methods under a tree of regions and present a new descriptor to encode color, texture and structure features using a hierarchical structure-Color Binary Partition Tree (CBPT, which we call the CTS descriptor. Specifically, we first build the hierarchical representation of HRS imagery using the CBPT. Then we quantize the texture and color features of dense regions. Next, we analyze and extract the co-occurrence patterns of regions based on the hierarchical structure. Finally, we encode local descriptors to obtain the final CTS descriptor and test its discriminative capability using object categorization and scene classification with HRS images. The proposed descriptor contains the spectral, textural and structural information of the HRS imagery and is also robust to changes in illuminant color, scale, orientation and contrast. The experimental results demonstrate that the proposed CTS descriptor achieves competitive classification results compared with state-of-the-art algorithms.

Automated detection and mapping of crown discolouration caused by jack pine budworm with 2.5 m resolution multispectral imagery

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Leckie, Donald G.; Cloney, Ed; Joyce, Steve P.

2005-05-01

Jack pine budworm ( Choristoneura pinus pinus (Free.)) is a native insect defoliator of mainly jack pine ( Pinus banksiana Lamb.) in North America east of the Rocky Mountains. Periodic outbreaks of this insect, which generally last two to three years, can cause growth loss and mortality and have an important impact ecologically and economically in terms of timber production and harvest. The jack pine budworm prefers to feed on current year needles. Their characteristic feeding habits cause discolouration or reddening of the canopy. This red colouration is used to map the distribution and intensity of defoliation that has taken place that year (current defoliation). An accurate and consistent map of the distribution and intensity of budworm defoliation (as represented by the red discolouration) at the stand and within stand level is desirable. Automated classification of multispectral imagery, such as is available from airborne and new high resolution satellite systems, was explored as a viable tool for objectively classifying current discolouration. Airborne multispectral imagery was acquired at a 2.5 m resolution with the Multispectral Electro-optical Imaging Sensor (MEIS). It recorded imagery in six nadir looking spectral bands specifically designed to detect discolouration caused by budworm and a near-infrared band viewing forward at 35° was also used. A 2200 nm middle infrared image was acquired with a Daedalus scanner. Training and test areas of different levels of discolouration were created based on field observations and a maximum likelihood supervized classification was used to estimate four classes of discolouration (nil-trace, light, moderate and severe). Good discrimination was achieved with an overall accuracy of 84% for the four discolouration levels. The moderate discolouration class was the poorest at 73%, because of confusion with both the severe and light classes. Accuracy on a stand basis was also good, and regional and within stand

Object-based Morphological Building Index for Building Extraction from High Resolution Remote Sensing Imagery

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LIN Xiangguo

2017-06-01

Full Text Available Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. In this article, an object-based morphological building index (OBMBI is constructed based on both image segmentation and graph-based top-hat reconstruction, and OBMBI is used for building extraction from high resolution remote sensing images. First, bidirectional mapping relationship between pixels, objects and graph-nodes are constructed. Second, the OBMBI image is built based on both graph-based top-hat reconstruction and the above mapping relationship. Third, a binary thresholding is performed on the OBMBI image, and the binary image is converted into vector format to derive the building polygons. Finally, the post-processing is made to optimize the extracted building polygons. Two images, including an aerial image and a panchromatic satellite image, are used to test both the proposed method and classic PanTex method. The experimental results suggest that our proposed method has a higher accuracy in building extraction than the classic PanTex method. On average, the correctness, the completeness and the quality of our method are respectively 9.49%, 11.26% and 14.11% better than those of the PanTex.

Mapping of Agricultural Crops from Single High-Resolution Multispectral Images—Data-Driven Smoothing vs. Parcel-Based Smoothing

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Asli Ozdarici-Ok

2015-05-01

Full Text Available Mapping agricultural crops is an important application of remote sensing. However, in many cases it is based either on hyperspectral imagery or on multitemporal coverage, both of which are difficult to scale up to large-scale deployment at high spatial resolution. In the present paper, we evaluate the possibility of crop classification based on single images from very high-resolution (VHR satellite sensors. The main objective of this work is to expose performance difference between state-of-the-art parcel-based smoothing and purely data-driven conditional random field (CRF smoothing, which is yet unknown. To fulfill this objective, we perform extensive tests with four different classification methods (Support Vector Machines, Random Forest, Gaussian Mixtures, and Maximum Likelihood to compute the pixel-wise data term; and we also test two different definitions of the pairwise smoothness term. We have performed a detailed evaluation on different multispectral VHR images (Ikonos, QuickBird, Kompsat-2. The main finding of this study is that pairwise CRF smoothing comes close to the state-of-the-art parcel-based method that requires parcel boundaries (average difference ≈ 2.5%. Our results indicate that a single multispectral (R, G, B, NIR image is enough to reach satisfactory classification accuracy for six crop classes (corn, pasture, rice, sugar beet, wheat, and tomato in Mediterranean climate. Overall, it appears that crop mapping using only one-shot VHR imagery taken at the right time may be a viable alternative, especially since high-resolution multitemporal or hyperspectral coverage as well as parcel boundaries are in practice often not available.

Coastal change analysis of Lovells Island using high resolution ground based LiDAR imagery

Science.gov (United States)

Ly, Jennifer K.

Many methods have been employed to study coastline change. These methods range from historical map analysis to GPS surveys to modern airborne LiDAR and satellite imagery. These previously used methods can be time consuming, labor intensive, and expensive and have varying degrees of accuracy and temporal coverage. Additionally, it is often difficult to apply such techniques in direct response to an isolated event within an appropriate temporal framework. Here we utilize a new ground based Canopy Biomass LiDAR (CBL) system built at The University of Massachusetts Boston (in collaboration with the Rochester Institute of Technology) in order to identify and analyze coastal change on Lovells Island, Boston Harbor. Surveys of a bluff developing in an eroding drumlin and beach cusps on a high-energy cobble beach on Lovells Island were conducted in June, September and December of 2013. At each site for each survey, the CBL was set up and multiple scans of each feature were taken on a predetermined transect that was established parallel to the high-water mark at distances relative to the scale of the bluff and cusps. The scans from each feature were compiled, integrated and visualized using Meshlab. Results from our surveys indicate that the highly portable and easy to deploy CBL system produces images of exceptional clarity, with the capacity to resolve small-scale changes to coastal features and systems. The CBL, while still under development (and coastal surveying protocols with it are just being established), appears to be an ideal tool for analyzing coastal geological features and is anticipated to prove to be a useful tool for the observation and analysis of coastal change. Furthermore, there is significant potential for utilizing the low cost ultra-portable CBL in frequent deployments to develop small-scale erosion rate and sediment budget analyses.

Hurricane Katrina Aerial Photography: High-Resolution Imagery of the Gulf Coast of Louisiana, Mississippi and Alabama After Landfall

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National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the Gulf Coast of Louisiana, Mississippi and Alabama after Hurricane Katrina made landfall. The regions photographed range from...

Evaluation of deep neural networks for single image super-resolution in a maritime context

NARCIS (Netherlands)

Nieuwenhuizen, R.P.J.; Kruithof, M.; Schutte, K.

2017-01-01

High resolution imagery is of crucial importance for the performance on visual recognition tasks. Super-resolution (SR) reconstruction algorithms aim to enhance the image resolution beyond the capability of the image sensor being used. Traditional SR algorithms approach this inverse problem using

NOAA Integrated Ocean and Coastal Mapping (IOCM) orthorectified mosaic image tiles, Merrimack River and Plum Island Sound, Massachusetts, June 2011 (NODC Accession 0103944)

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National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains both true color (RGB) and infrared (IR) ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping...

The investigation of active Martian dune fields using very high resolution photogrammetric measurements

Science.gov (United States)

Kim, Jungrack; Kim, Younghwi; Park, Minseong

2016-10-01

At the present time, arguments continue regarding the migration speeds of Martian dune fields and their correlation with atmospheric circulation. However, precisely measuring the spatial translation of Martian dunes has succeeded only a very few times—for example, in the Nili Patera study (Bridges et al. 2012) using change-detection algorithms and orbital imagery. Therefore, in this study, we developed a generic procedure to precisely measure the migration of dune fields with recently introduced 25-cm resolution orbital imagery specifically using a high-accuracy photogrammetric processor. The processor was designed to trace estimated dune migration, albeit slight, over the Martian surface by 1) the introduction of very high resolution ortho images and stereo analysis based on hierarchical geodetic control for better initial point settings; 2) positioning error removal throughout the sensor model refinement with a non-rigorous bundle block adjustment, which makes possible the co-alignment of all images in a time series; and 3) improved sub-pixel co-registration algorithms using optical flow with a refinement stage conducted on a pyramidal grid processor and a blunder classifier. Moreover, volumetric changes of Martian dunes were additionally traced by means of stereo analysis and photoclinometry. The established algorithms have been tested using high-resolution HIRISE time-series images over several Martian dune fields. Dune migrations were iteratively processed both spatially and volumetrically, and the results were integrated to be compared to the Martian climate model. Migrations over well-known crater dune fields appeared to be almost static for the considerable temporal periods and were weakly correlated with wind directions estimated by the Mars Climate Database (Millour et al. 2015). As a result, a number of measurements over dune fields in the Mars Global Dune Database (Hayward et al. 2014) covering polar areas and mid-latitude will be demonstrated

Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and Vietnam

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Christiane Weber

2011-09-01

Full Text Available The multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR is needed for urban form detection from remote sensing imagery. By applying the local variance method to our datasets (pan-sharpened images, we can identify OSR at two levels of observation: individual urban elements and urban districts in two agglomerations in West Europe (Strasbourg, France and in Southeast Asia (Da Nang, Vietnam. The OSR corresponds to the minimal variance of largest number of spectral bands. We carry out three categories of interval values of spatial resolutions for identifying OSR: from 0.8 m to 3 m for isolated objects, from 6 m to 8 m for vegetation area and equal or higher than 20 m for urban district. At the urban district level, according to spatial patterns, form, size and material of elements, we propose the range of OSR between 30 m and 40 m for detecting administrative districts, new residential districts and residential discontinuous districts. The detection of industrial districts refers to a coarser OSR from 50 m to 60 m. The residential continuous dense districts effectively need a finer OSR of between 20 m and 30 m for their optimal identification. We also use fractal dimensions to identify the threshold of homogeneity/heterogeneity of urban structure at urban district level. It seems therefore that our approaches are robust and transferable to different urban contexts.

Roads Data Conflation Using Update High Resolution Satellite Images

Science.gov (United States)

Abdollahi, A.; Riyahi Bakhtiari, H. R.

2017-11-01

Urbanization, industrialization and modernization are rapidly growing in developing countries. New industrial cities, with all the problems brought on by rapid population growth, need infrastructure to support the growth. This has led to the expansion and development of the road network. A great deal of road network data has made by using traditional methods in the past years. Over time, a large amount of descriptive information has assigned to these map data, but their geometric accuracy and precision is not appropriate to today's need. In this regard, the improvement of the geometric accuracy of road network data by preserving the descriptive data attributed to them and updating of the existing geo databases is necessary. Due to the size and extent of the country, updating the road network maps using traditional methods is time consuming and costly. Conversely, using remote sensing technology and geographic information systems can reduce costs, save time and increase accuracy and speed. With increasing the availability of high resolution satellite imagery and geospatial datasets there is an urgent need to combine geographic information from overlapping sources to retain accurate data, minimize redundancy, and reconcile data conflicts. In this research, an innovative method for a vector-to-imagery conflation by integrating several image-based and vector-based algorithms presented. The SVM method for image classification and Level Set method used to extract the road the different types of road intersections extracted from imagery using morphological operators. For matching the extracted points and to find the corresponding points, matching function which uses the nearest neighborhood method was applied. Finally, after identifying the matching points rubber-sheeting method used to align two datasets. Two residual and RMSE criteria used to evaluate accuracy. The results demonstrated excellent performance. The average root-mean-square error decreased from 11.8 to 4.1 m.

Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

Science.gov (United States)

Kit, Oleksandr; Lüdeke, Matthias

2013-09-01

This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery

Science.gov (United States)

Mitri, George H.; Gitas, Ioannis Z.

2013-02-01

Careful evaluation of forest regeneration and vegetation recovery after a fire event provides vital information useful in land management. The use of remotely sensed data is considered to be especially suitable for monitoring ecosystem dynamics after fire. The aim of this work was to map post-fire forest regeneration and vegetation recovery on the Mediterranean island of Thasos by using a combination of very high spatial (VHS) resolution (QuickBird) and hyperspectral (EO-1 Hyperion) imagery and by employing object-based image analysis. More specifically, the work focused on (1) the separation and mapping of three major post-fire classes (forest regeneration, other vegetation recovery, unburned vegetation) existing within the fire perimeter, and (2) the differentiation and mapping of the two main forest regeneration classes, namely, Pinus brutia regeneration, and Pinus nigra regeneration. The data used in this study consisted of satellite images and field observations of homogeneous regenerated and revegetated areas. The methodology followed two main steps: a three-level image segmentation, and, a classification of the segmented images. The process resulted in the separation of classes related to the aforementioned objectives. The overall accuracy assessment revealed very promising results (approximately 83.7% overall accuracy, with a Kappa Index of Agreement of 0.79). The achieved accuracy was 8% higher when compared to the results reported in a previous work in which only the EO-1 Hyperion image was employed in order to map the same classes. Some classification confusions involving the classes of P. brutia regeneration and P. nigra regeneration were observed. This could be attributed to the absence of large and dense homogeneous areas of regenerated pine trees in the study area.

Crowdsourcing earthquake damage assessment using remote sensing imagery

Directory of Open Access Journals (Sweden)

Stuart Gill

2011-06-01

Full Text Available This paper describes the evolution of recent work on using crowdsourced analysis of remote sensing imagery, particularly high-resolution aerial imagery, to provide rapid, reliable assessments of damage caused by earthquakes and potentially other disasters. The initial effort examined online imagery taken after the 2008 Wenchuan, China, earthquake. A more recent response to the 2010 Haiti earthquake led to the formation of an international consortium: the Global Earth Observation Catastrophe Assessment Network (GEO-CAN. The success of GEO-CAN in contributing to the official damage assessments made by the Government of Haiti, the United Nations, and the World Bank led to further development of a web-based interface. A current initiative in Christchurch, New Zealand, is underway where remote sensing experts are analyzing satellite imagery, geotechnical engineers are marking liquefaction areas, and structural engineers are identifying building damage. The current site includes online training to improve the accuracy of the assessments and make it possible for even novice users to contribute to the crowdsourced solution. The paper discusses lessons learned from these initiatives and presents a way forward for using crowdsourced remote sensing as a tool for rapid assessment of damage caused by natural disasters around the world.

NOAA Integrated Ocean and Coastal Mapping (IOCM) true color (RGB) orthorectified mosaic image tiles, Baton Rouge to LaPlace, Louisiana 2010 (NODC Accession 0074374)

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National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative of the Mississippi...

FULLY AUTOMATED GENERATION OF ACCURATE DIGITAL SURFACE MODELS WITH SUB-METER RESOLUTION FROM SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

J. Wohlfeil

2012-07-01

Full Text Available Modern pixel-wise image matching algorithms like Semi-Global Matching (SGM are able to compute high resolution digital surface models from airborne and spaceborne stereo imagery. Although image matching itself can be performed automatically, there are prerequisites, like high geometric accuracy, which are essential for ensuring the high quality of resulting surface models. Especially for line cameras, these prerequisites currently require laborious manual interaction using standard tools, which is a growing problem due to continually increasing demand for such surface models. The tedious work includes partly or fully manual selection of tie- and/or ground control points for ensuring the required accuracy of the relative orientation of images for stereo matching. It also includes masking of large water areas that seriously reduce the quality of the results. Furthermore, a good estimate of the depth range is required, since accurate estimates can seriously reduce the processing time for stereo matching. In this paper an approach is presented that allows performing all these steps fully automated. It includes very robust and precise tie point selection, enabling the accurate calculation of the images’ relative orientation via bundle adjustment. It is also shown how water masking and elevation range estimation can be performed automatically on the base of freely available SRTM data. Extensive tests with a large number of different satellite images from QuickBird and WorldView are presented as proof of the robustness and reliability of the proposed method.

Rapid Target Detection in High Resolution Remote Sensing Images Using Yolo Model

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Wu, Z.; Chen, X.; Gao, Y.; Li, Y.

2018-04-01

Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

High-resolution tree canopy mapping for New York City using LIDAR and object-based image analysis

Science.gov (United States)

MacFaden, Sean W.; O'Neil-Dunne, Jarlath P. M.; Royar, Anna R.; Lu, Jacqueline W. T.; Rundle, Andrew G.

2012-01-01

Urban tree canopy is widely believed to have myriad environmental, social, and human-health benefits, but a lack of precise canopy estimates has hindered quantification of these benefits in many municipalities. This problem was addressed for New York City using object-based image analysis (OBIA) to develop a comprehensive land-cover map, including tree canopy to the scale of individual trees. Mapping was performed using a rule-based expert system that relied primarily on high-resolution LIDAR, specifically its capacity for evaluating the height and texture of aboveground features. Multispectral imagery was also used, but shadowing and varying temporal conditions limited its utility. Contextual analysis was a key part of classification, distinguishing trees according to their physical and spectral properties as well as their relationships to adjacent, nonvegetated features. The automated product was extensively reviewed and edited via manual interpretation, and overall per-pixel accuracy of the final map was 96%. Although manual editing had only a marginal effect on accuracy despite requiring a majority of project effort, it maximized aesthetic quality and ensured the capture of small, isolated trees. Converting high-resolution LIDAR and imagery into usable information is a nontrivial exercise, requiring significant processing time and labor, but an expert system-based combination of OBIA and manual review was an effective method for fine-scale canopy mapping in a complex urban environment.

KOLAM: a cross-platform architecture for scalable visualization and tracking in wide-area imagery

Science.gov (United States)

Fraser, Joshua; Haridas, Anoop; Seetharaman, Guna; Rao, Raghuveer M.; Palaniappan, Kannappan

2013-05-01

KOLAM is an open, cross-platform, interoperable, scalable and extensible framework supporting a novel multi- scale spatiotemporal dual-cache data structure for big data visualization and visual analytics. This paper focuses on the use of KOLAM for target tracking in high-resolution, high throughput wide format video also known as wide-area motion imagery (WAMI). It was originally developed for the interactive visualization of extremely large geospatial imagery of high spatial and spectral resolution. KOLAM is platform, operating system and (graphics) hardware independent, and supports embedded datasets scalable from hundreds of gigabytes to feasibly petabytes in size on clusters, workstations, desktops and mobile computers. In addition to rapid roam, zoom and hyper- jump spatial operations, a large number of simultaneously viewable embedded pyramid layers (also referred to as multiscale or sparse imagery), interactive colormap and histogram enhancement, spherical projection and terrain maps are supported. The KOLAM software architecture was extended to support airborne wide-area motion imagery by organizing spatiotemporal tiles in very large format video frames using a temporal cache of tiled pyramid cached data structures. The current version supports WAMI animation, fast intelligent inspection, trajectory visualization and target tracking (digital tagging); the latter by interfacing with external automatic tracking software. One of the critical needs for working with WAMI is a supervised tracking and visualization tool that allows analysts to digitally tag multiple targets, quickly review and correct tracking results and apply geospatial visual analytic tools on the generated trajectories. One-click manual tracking combined with multiple automated tracking algorithms are available to assist the analyst and increase human effectiveness.

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

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Basu, Saikat; Ganguly, Sangram; Michaelis, Andrew; Votava, Petr; Roy, Anshuman; Mukhopadhyay, Supratik; Nemani, Ramakrishna

2015-01-01

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery using a Probabilistic Learning Framework

Science.gov (United States)

Basu, S.; Ganguly, S.; Michaelis, A.; Votava, P.; Roy, A.; Mukhopadhyay, S.; Nemani, R. R.

2015-12-01

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

Redefining nondiscriminatory access to remote sensing imagery and its impact on global transparency

Science.gov (United States)

Aten, Michelle L.

2003-04-01

Global transparency is founded on the Open Skies philosophy and its precept of non-discriminatory access. Global transparency implies that anyone can have anytime, anyplace access to a wide-array of remotely sensed imagery. The custom of non-discriminatory access requires that datasets of interest must be affordable, usable, and obtainable in a timely fashion devoid of political, economic or technical obstacles. Thus, an assessment of the correlation between the availability of satellite imagery and changes in governmental policies, pricing fluctuations of data, and advances in technology is critical to assessing the viability of global transparency. The Open Skies philosophy was originally proposed at the 1955 Geneva Summit to advocate mutually beneficial aerial reconnaissance missions over the USSR and the US as a verification tool for arms control and non-proliferation agreements. However, due to Cold War tensions, this philosophy and the custom of non-discriminatory were not widely adopted in the civilian remote sensing community until the commissioning of the Landsat Program in 1972. Since this time, commercial high-resolution satellites have drastically changed the circumstances on which the fundamental tenets of this philosophy are based. Since the successful launch of the first of this satellite class, the IKONOS satellite, high-resolution imagery is now available to non-US governments and an unlimited set of non-state actors. As more advanced capabilities are added to the growing assortment of remote sensing satellites, the reality of global transparency will rapidly evolve. This assessment includes an overview of historical precedents and a brief explanation of relevant US policy decisions that define non-discriminatory access with respect to US government and US based corporate assets. It also presents the dynamics of the political, economic, and technical barriers that may dictate or influence the remote sensing community's access to satellite data. In

Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image

OpenAIRE

Wang, Jianhua; Qin, Qiming; Zhao, Jianghua; Ye, Xin; Feng, Xiao; Qin, Xuebin; Yang, Xiucheng

2015-01-01

Road damage detection and assessment from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pairing of pre-disaster and post-disaster road data for change detection and assessment is difficult to achieve due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e., remote sensing imagery or vector map) are hard to obtain. In this study, a knowledge-based method for ...

Characterizing Forest Succession Stages for Wildlife Habitat Assessment Using Multispectral Airborne Imagery

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Wen Zhang

2017-06-01

Full Text Available In this study, we demonstrate the potential of using high spatial resolution airborne imagery to characterize the structural development stages of forest canopies. Four forest succession stages were adopted: stand initiation, young multistory, understory reinitiation, and old growth. Remote sensing metrics describing the spatial patterns of forest structures were derived and a Random Forest learning algorithm was used to classify forest succession stages. These metrics included texture variables from Gray Level Co-occurrence Measures (GLCM, range and sill from the semi-variogram, and the fraction of shadow and its spatial distribution. Among all the derived variables, shadow fractions and the GLCM variables of contrast, mean, and dissimilarity were the most important for characterizing the forest succession stages (classification accuracy of 89%. In addition, a LiDAR (Light Detection and Ranging derived forest structural index (predicted Lorey’s height was employed to validate the classification result. The classification using imagery spatial variables was shown to be consistent with the LiDAR derived variable (R2 = 0.68 and Root Mean Square Error (RMSE = 2.39. This study demonstrates that high spatial resolution imagery was able to characterize forest succession stages with promising accuracy and may be considered an alternative to LiDAR data for this kind of application. Also, the results of stand development stages build a framework for future wildlife habitat mapping.

Delineation of wetland areas from high resolution WorldView-2 data by object-based method

International Nuclear Information System (INIS)

Hassan, N; Hamid, J R A; Adnan, N A; Jaafar, M

2014-01-01

Various classification methods are available that can be used to delineate land cover types. Object-based is one of such methods for delineating the land cover from satellite imageries. This paper focuses on the digital image processing aspects of discriminating wetland areas via object-based method using high resolution satellite multispectral WorldView-2 image data taken over part of Penang Island region. This research is an attempt to improve the wetland area delineation in conjunction with a range of classification techniques which can be applied to satellite data with high spatial and spectral resolution such as World View 2. The intent is to determine a suitable approach to delineate and map these wetland areas more appropriately. There are common parameters to take into account that are pivotal in object-based method which are the spatial resolution and the range of spectral channels of the imaging sensor system. The preliminary results of the study showed object-based analysis is capable of delineating wetland region of interest with an accuracy that is acceptable to the required tolerance for land cover classification

Computational Combination of the Optical Properties of Fenestration Layers at High Directional Resolution

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Lars Oliver Grobe

2017-03-01

Full Text Available Complex fenestration systems typically comprise co-planar, clear and scattering layers. As there are many ways to combine layers in fenestration systems, a common approach in building simulation is to store optical properties separate for each layer. System properties are then computed employing a fast matrix formalism, often based on a directional basis devised by JHKlems comprising 145 incident and 145 outgoing directions. While this low directional resolution is found sufficient to predict illuminance and solar gains, it is too coarse to replicate the effects of directionality in the generation of imagery. For increased accuracy, a modification of the matrix formalism is proposed. The tensor-tree format of RADIANCE, employing an algorithm subdividing the hemisphere at variable resolutions, replaces the directional basis. The utilization of the tensor-tree with interfaces to simulation software allows sharing and re-use of data. The light scattering properties of two exemplary fenestration systems as computed employing the matrix formalism at variable resolution show good accordance with the results of ray-tracing. Computation times are reduced to 0.4% to 2.5% compared to ray-tracing through co-planar layers. Imagery computed employing the method illustrates the effect of directional resolution. The method is supposed to foster research in the field of daylighting, as well as applications in planning and design.

High-resolution mapping based on an Unmanned Aerial Vehicle (UAV) to capture paleoseismic offsets along the Altyn-Tagh fault, China.

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Gao, Mingxing; Xu, Xiwei; Klinger, Yann; van der Woerd, Jerome; Tapponnier, Paul

2017-08-15

The recent dramatic increase in millimeter- to centimeter- resolution topographic datasets obtained via multi-view photogrammetry raises the possibility of mapping detailed offset geomorphology and constraining the spatial characteristics of active faults. Here, for the first time, we applied this new method to acquire high-resolution imagery and generate topographic data along the Altyn Tagh fault, which is located in a remote high elevation area and shows preserved ancient earthquake surface ruptures. A digital elevation model (DEM) with a resolution of 0.065 m and an orthophoto with a resolution of 0.016 m were generated from these images. We identified piercing markers and reconstructed offsets based on both the orthoimage and the topography. The high-resolution UAV data were used to accurately measure the recent seismic offset. We obtained the recent offset of 7 ± 1 m. Combined with the high resolution satellite image, we measured cumulative offsets of 15 ± 2 m, 20 ± 2 m, 30 ± 2 m, which may be due to multiple paleo-earthquakes. Therefore, UAV mapping can provide fine-scale data for the assessment of the seismic hazards.

Estimation of Wheat Plant Density at Early Stages Using High Resolution Imagery

Directory of Open Access Journals (Sweden)

Shouyang Liu

2017-05-01

Full Text Available Crop density is a key agronomical trait used to manage wheat crops and estimate yield. Visual counting of plants in the field is currently the most common method used. However, it is tedious and time consuming. The main objective of this work is to develop a machine vision based method to automate the density survey of wheat at early stages. RGB images taken with a high resolution RGB camera are classified to identify the green pixels corresponding to the plants. Crop rows are extracted and the connected components (objects are identified. A neural network is then trained to estimate the number of plants in the objects using the object features. The method was evaluated over three experiments showing contrasted conditions with sowing densities ranging from 100 to 600 seeds⋅m-2. Results demonstrate that the density is accurately estimated with an average relative error of 12%. The pipeline developed here provides an efficient and accurate estimate of wheat plant density at early stages.

Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data.

Science.gov (United States)

Wu, Mingquan; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-09-18

Owing to low temporal resolution and cloud interference, there is a shortage of high spatial resolution remote sensing data. To address this problem, this study introduces a modified spatial and temporal data fusion approach (MSTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the limitations of the conditional spatial temporal data fusion approach (STDFA) including the constant window for disaggregation and the sensor difference. An adaptive window size selection method is proposed in this study to select the best window size and moving steps for the disaggregation of coarse pixels. The linear regression method is used to remove the influence of differences in sensor systems using disaggregated mean coarse reflectance by testing and validation in two study areas located in Xinjiang Province, China. The results show that the MSTDFA algorithm can generate daily synthetic Landsat imagery with a high correlation coefficient (R) ranged from 0.646 to 0.986 between synthetic images and the actual observations. We further show that MSTDFA can be applied to 250 m 16-day MODIS MOD13Q1 products and the Landsat Normalized Different Vegetation Index (NDVI) data by generating a synthetic NDVI image highly similar to actual Landsat NDVI observation with a high R of 0.97.

Earth mapping - aerial or satellite imagery comparative analysis

Science.gov (United States)

Fotev, Svetlin; Jordanov, Dimitar; Lukarski, Hristo

Nowadays, solving the tasks for revision of existing map products and creation of new maps requires making a choice of the land cover image source. The issue of the effectiveness and cost of the usage of aerial mapping systems versus the efficiency and cost of very-high resolution satellite imagery is topical [1, 2, 3, 4]. The price of any remotely sensed image depends on the product (panchromatic or multispectral), resolution, processing level, scale, urgency of task and on whether the needed image is available in the archive or has to be requested. The purpose of the present work is: to make a comparative analysis between the two approaches for mapping the Earth having in mind two parameters: quality and cost. To suggest an approach for selection of the map information sources - airplane-based or spacecraft-based imaging systems with very-high spatial resolution. Two cases are considered: area that equals approximately one satellite scene and area that equals approximately the territory of Bulgaria.

Using High-Resolution Imagery to Characterize Disturbance from Hurricane Irma in South Florida Wetlands

Science.gov (United States)

Lagomasino, D.; Cook, B.; Fatoyinbo, T.; Morton, D. C.; Montesano, P.; Neigh, C. S. R.; Wooten, M.; Gaiser, E.; Troxler, T.

2017-12-01

Hurricane Irma, one of the strongest hurricanes recorded in the Atlantic, first made landfall in the Florida Keys before coming ashore in southwestern Florida near Everglades National Park (ENP) on September 9th and 10th of this year. Strong winds and storm surge impacted a 100+ km stretch of the southern Florida Gulf Coast, resulting in extensive damages to coastal and inland ecosystems. Impacts from previous catastrophic storms in the region have led to irreversible changes to vegetation communities and in some areas, ecosystem collapse. The processes that drive coastal wetland vulnerability and resilience are largely a function of the severity of the impact to forest structure and ground elevation. Remotely sensed imagery plays an important role in measuring changes to the landscape, particularly for extensive and inaccessible regions like the mangroves in ENP. We have estimated changes in coastal vegetation structure and soil elevation using a combination of repeat measurements from ground, airborne, and satellite platforms. At the ground level, we used before and after Structure-from-Motion models to capture the change in below canopy structure as result of stem breakage and fallen branches. Using airborne imagery collected before and after Hurricane Irma by Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, we measured the change in forest structure and soil elevation. This unique data acquisition covered an area over 130,000 ha in regions most heavily impacted storm surge. Lastly, we also combined commercial and NASA satellite Earth observations to measure forest structural changes across the entire South Florida coast. An analysis of long-term observations from the Landsat data archive highlights the heterogeneity of hurricane and other environmental disturbances along the Florida coast. These findings captured coastal disturbance legacies that have the potential to influence the trajectory of mangrove resilience and vulnerability

Use of high-resolution imagery acquired from an unmanned aircraft system for fluvial mapping and estimating water-surface velocity in rivers

Science.gov (United States)

Kinzel, P. J.; Bauer, M.; Feller, M.; Holmquist-Johnson, C.; Preston, T.

2013-12-01

The use of unmanned aircraft systems (UAS) for environmental monitoring in the United States is anticipated to increase in the coming years as the Federal Aviation Administration (FAA) further develops guidelines to permit their integration into the National Airspace System. The U.S. Geological Survey's (USGS) National Unmanned Aircraft Systems Project Office routinely obtains Certificates of Authorization from the FAA for utilizing UAS technology for a variety of natural resource applications for the U.S. Department of the Interior (DOI). We evaluated the use of a small UAS along two reaches of the Platte River near Overton Nebraska, USA, to determine the accuracy of the system for mapping the extent and elevation of emergent sandbars and to test the ability of a hovering UAS to identify and track tracers to estimate water-surface velocity. The UAS used in our study is the Honeywell Tarantula Hawk RQ16 (T-Hawk), developed for the U.S. Army as a reconnaissance and surveillance platform. The T-Hawk has been recently modified by USGS, and certified for airworthiness by the DOI - Office of Aviation Services, to accommodate a higher-resolution imaging payload than was originally deployed with the system. The T-Hawk is currently outfitted with a Canon PowerShot SX230 HS with a 12.1 megapixel resolution and intervalometer to record images at a user defined time step. To increase the accuracy of photogrammetric products, orthoimagery and DEMs using structure-from-motion (SFM) software, we utilized ground control points in the study reaches and acquired imagery using flight lines at various altitudes (200-400 feet above ground level) and oriented both parallel and perpendicular to the river. Our results show that the mean error in the elevations derived from SFM in the upstream reach was 17 centimeters and horizontal accuracy was 6 centimeters when compared to 4 randomly distributed targets surveyed on emergent sandbars. In addition to the targets, multiple transects were

Improved VIIRS and MODIS SST Imagery

Directory of Open Access Journals (Sweden)

Irina Gladkova

2016-01-01

Full Text Available Moderate Resolution Imaging Spectroradiometers (MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS radiometers, flown onboard Terra/Aqua and Suomi National Polar-orbiting Partnership (S-NPP/Joint Polar Satellite System (JPSS satellites, are capable of providing superior sea surface temperature (SST imagery. However, the swath data of these multi-detector sensors are subject to several artifacts including bow-tie distortions and striping, and require special pre-processing steps. VIIRS additionally does two irreversible data reduction steps onboard: pixel aggregation (to reduce resolution changes across the swath and pixel deletion, which complicate both bow-tie correction and destriping. While destriping was addressed elsewhere, this paper describes an algorithm, adopted in the National Oceanic and Atmospheric Administration (NOAA Advanced Clear-Sky Processor for Oceans (ACSPO SST system, to minimize the bow-tie artifacts in the SST imagery and facilitate application of the pattern recognition algorithms for improved separation of ocean from cloud and mapping fine SST structure, especially in the dynamic, coastal and high-latitude regions of the ocean. The algorithm is based on a computationally fast re-sampling procedure that ensures a continuity of corresponding latitude and longitude arrays. Potentially, Level 1.5 products may be generated to benefit a wide range of MODIS and VIIRS users in land, ocean, cryosphere, and atmosphere remote sensing.

Derivation of high spatial resolution albedo from UAV digital imagery: application over the Greenland Ice Sheet

Science.gov (United States)

Ryan, Jonathan C.; Hubbard, Alun; Box, Jason E.; Brough, Stephen; Cameron, Karen; Cook, Joseph M.; Cooper, Matthew; Doyle, Samuel H.; Edwards, Arwyn; Holt, Tom; Irvine-Fynn, Tristram; Jones, Christine; Pitcher, Lincoln H.; Rennermalm, Asa K.; Smith, Laurence C.; Stibal, Marek; Snooke, Neal

2017-05-01

Measurements of albedo are a prerequisite for modelling surface melt across the Earth's cryosphere, yet available satellite products are limited in spatial and/or temporal resolution. Here, we present a practical methodology to obtain centimetre resolution albedo products with accuracies of 5% using consumer-grade digital camera and unmanned aerial vehicle (UAV) technologies. Our method comprises a workflow for processing, correcting and calibrating raw digital images using a white reference target, and upward and downward shortwave radiation measurements from broadband silicon pyranometers. We demonstrate the method with a set of UAV sorties over the western, K-sector of the Greenland Ice Sheet. The resulting albedo product, UAV10A1, covers 280 km2, at a resolution of 20 cm per pixel and has a root-mean-square difference of 3.7% compared to MOD10A1 and 4.9% compared to ground-based broadband pyranometer measurements. By continuously measuring downward solar irradiance, the technique overcomes previous limitations due to variable illumination conditions during and between surveys over glaciated terrain. The current miniaturization of multispectral sensors and incorporation of upward facing radiation sensors on UAV packages means that this technique will likely become increasingly attractive in field studies and used in a wide range of applications for high temporal and spatial resolution surface mapping of debris, dust, cryoconite and bioalbedo and for directly constraining surface energy balance models.

Modeling vegetation heights from high resolution stereo aerial photography: an application for broad-scale rangeland monitoring.

Science.gov (United States)

Gillan, Jeffrey K; Karl, Jason W; Duniway, Michael; Elaksher, Ahmed

2014-11-01

Vertical vegetation structure in rangeland ecosystems can be a valuable indicator for assessing rangeland health and monitoring riparian areas, post-fire recovery, available forage for livestock, and wildlife habitat. Federal land management agencies are directed to monitor and manage rangelands at landscapes scales, but traditional field methods for measuring vegetation heights are often too costly and time consuming to apply at these broad scales. Most emerging remote sensing techniques capable of measuring surface and vegetation height (e.g., LiDAR or synthetic aperture radar) are often too expensive, and require specialized sensors. An alternative remote sensing approach that is potentially more practical for managers is to measure vegetation heights from digital stereo aerial photographs. As aerial photography is already commonly used for rangeland monitoring, acquiring it in stereo enables three-dimensional modeling and estimation of vegetation height. The purpose of this study was to test the feasibility and accuracy of estimating shrub heights from high-resolution (HR, 3-cm ground sampling distance) digital stereo-pair aerial images. Overlapping HR imagery was taken in March 2009 near Lake Mead, Nevada and 5-cm resolution digital surface models (DSMs) were created by photogrammetric methods (aerial triangulation, digital image matching) for twenty-six test plots. We compared the heights of individual shrubs and plot averages derived from the DSMs to field measurements. We found strong positive correlations between field and image measurements for several metrics. Individual shrub heights tended to be underestimated in the imagery, however, accuracy was higher for dense, compact shrubs compared with shrubs with thin branches. Plot averages of shrub height from DSMs were also strongly correlated to field measurements but consistently underestimated. Grasses and forbs were generally too small to be detected with the resolution of the DSMs. Estimates of

D Surface Generation from Aerial Thermal Imagery

Science.gov (United States)

Khodaei, B.; Samadzadegan, F.; Dadras Javan, F.; Hasani, H.

2015-12-01

Aerial thermal imagery has been recently applied to quantitative analysis of several scenes. For the mapping purpose based on aerial thermal imagery, high accuracy photogrammetric process is necessary. However, due to low geometric resolution and low contrast of thermal imaging sensors, there are some challenges in precise 3D measurement of objects. In this paper the potential of thermal video in 3D surface generation is evaluated. In the pre-processing step, thermal camera is geometrically calibrated using a calibration grid based on emissivity differences between the background and the targets. Then, Digital Surface Model (DSM) generation from thermal video imagery is performed in four steps. Initially, frames are extracted from video, then tie points are generated by Scale-Invariant Feature Transform (SIFT) algorithm. Bundle adjustment is then applied and the camera position and orientation parameters are determined. Finally, multi-resolution dense image matching algorithm is used to create 3D point cloud of the scene. Potential of the proposed method is evaluated based on thermal imaging cover an industrial area. The thermal camera has 640×480 Uncooled Focal Plane Array (UFPA) sensor, equipped with a 25 mm lens which mounted in the Unmanned Aerial Vehicle (UAV). The obtained results show the comparable accuracy of 3D model generated based on thermal images with respect to DSM generated from visible images, however thermal based DSM is somehow smoother with lower level of texture. Comparing the generated DSM with the 9 measured GCPs in the area shows the Root Mean Square Error (RMSE) value is smaller than 5 decimetres in both X and Y directions and 1.6 meters for the Z direction.

High-angular-resolution stellar imaging with occultations from the Cassini spacecraft - III. Mira

Science.gov (United States)

Stewart, Paul N.; Tuthill, Peter G.; Nicholson, Philip D.; Hedman, Matthew M.

2016-04-01

We present an analysis of spectral and spatial data of Mira obtained by the Cassini spacecraft, which not only observed the star's spectra over a broad range of near-infrared wavelengths, but was also able to obtain high-resolution spatial information by watching the star pass behind Saturn's rings. The observed spectral range of 1-5 microns reveals the stellar atmosphere in the crucial water-bands which are unavailable to terrestrial observers, and the simultaneous spatial sampling allows the origin of spectral features to be located in the stellar environment. Models are fitted to the data, revealing the spectral and spatial structure of molecular layers surrounding the star. High-resolution imagery is recovered revealing the layered and asymmetric nature of the stellar atmosphere. The observational data set is also used to confront the state-of-the-art cool opacity-sampling dynamic extended atmosphere models of Mira variables through a detailed spectral and spatial comparison, revealing in general a good agreement with some specific departures corresponding to particular spectral features.

The absolute radiometric calibration of the advanced very high resolution radiometer

Science.gov (United States)

Slater, P. N.; Teillet, P. M.; Ding, Y.

1988-01-01

An increasing number of remote sensing investigations require radiometrically calibrated imagery from NOAA Advanced Very High Resolution Radiation (AVHRR) sensors. Although a prelaunch calibration is done for these sensors, there is no capability for monitoring any changes in the in-flight absolute calibration for the visible and near infrared spectral channels. Hence, the possibility of using the reflectance-based method developed at White Sands for in-orbit calibration of LANDSAT Thematic Mapper (TM) and SPOT Haute Resolution Visible (HVR) data to calibrate the AVHRR sensor was investigated. Three diffrent approaches were considered: Method 1 - ground and atmospheric measurements and reference to another calibrated satellite sensor; Method 2 - ground and atmospheric measurements with no reference to another sensor; and Method 3 - no ground and atmospheric measurements but reference to another satellite sensor. The purpose is to describe an investigation on the use of Method 2 to calibrate NOAA-9 AVHRR channels 1 and 2 with the help of ground and atmospheric measurements at Rogers (dry) Lake, Edwards Air Force Base (EAFB) in the Mojave desert of California.

The absolute radiometric calibration of the advanced very high resolution radiometer

Science.gov (United States)

Slater, P. N.; Teillet, P. M.; Ding, Y.

1988-10-01

An increasing number of remote sensing investigations require radiometrically calibrated imagery from NOAA Advanced Very High Resolution Radiation (AVHRR) sensors. Although a prelaunch calibration is done for these sensors, there is no capability for monitoring any changes in the in-flight absolute calibration for the visible and near infrared spectral channels. Hence, the possibility of using the reflectance-based method developed at White Sands for in-orbit calibration of LANDSAT Thematic Mapper (TM) and SPOT Haute Resolution Visible (HVR) data to calibrate the AVHRR sensor was investigated. Three diffrent approaches were considered: Method 1 - ground and atmospheric measurements and reference to another calibrated satellite sensor; Method 2 - ground and atmospheric measurements with no reference to another sensor; and Method 3 - no ground and atmospheric measurements but reference to another satellite sensor. The purpose is to describe an investigation on the use of Method 2 to calibrate NOAA-9 AVHRR channels 1 and 2 with the help of ground and atmospheric measurements at Rogers (dry) Lake, Edwards Air Force Base (EAFB) in the Mojave desert of California.

High-resolution X-ray television and high-resolution video recorders

International Nuclear Information System (INIS)

Haendle, J.; Horbaschek, H.; Alexandrescu, M.

1977-01-01

The improved transmission properties of the high-resolution X-ray television chain described here make it possible to transmit more information per television image. The resolution in the fluoroscopic image, which is visually determined, depends on the dose rate and the inertia of the television pick-up tube. This connection is discussed. In the last few years, video recorders have been increasingly used in X-ray diagnostics. The video recorder is a further quality-limiting element in X-ray television. The development of function patterns of high-resolution magnetic video recorders shows that this quality drop may be largely overcome. The influence of electrical band width and number of lines on the resolution in the X-ray television image stored is explained in more detail. (orig.) [de

Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery

OpenAIRE

Moran, Emilio Federico.

2010-01-01

High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervi...

A Direct and Fast Methodology for Ship Recognition in Sentinel-2 Multispectral Imagery

Directory of Open Access Journals (Sweden)

Henning Heiselberg

2016-12-01

Full Text Available The European Space Agency satellite Sentinel-2 provides multispectral images with pixel sizes down to 10 m. This high resolution allows for ship detection and recognition by determining a number of important ship parameters. We are able to show how a ship position, its heading, length and breadth can be determined down to a subpixel resolution. If the ship is moving, its velocity can also be determined from its Kelvin waves. The 13 spectrally different visual and infrared images taken using multispectral imagery (MSI are “fingerprints” that allow for the recognition and identification of ships. Furthermore, the multispectral image profiles along the ship allow for discrimination between the ship, its turbulent wakes, and the Kelvin waves, such that the ship’s length and breadth can be determined more accurately even when sailing. The ship’s parameters are determined by using satellite imagery taken from several ships, which are then compared to known values from the automatic identification system. The agreement is on the order of the pixel resolution or better.

Aerial Photography and Imagery, Ortho-Corrected - FDOT 2003 Orthophotography

Data.gov (United States)

NSGIC Local Govt | GIS Inventory — These aerials were flown by the Florida Department of Transportation from December 23, 2002 to February 13, 2003 NAD 83 Mosaic format Semi Orthorectified black and...

Motor imagery training: Kinesthetic imagery strategy and inferior parietal fMRI activation.

Science.gov (United States)

Lebon, Florent; Horn, Ulrike; Domin, Martin; Lotze, Martin

2018-04-01

Motor imagery (MI) is the mental simulation of action frequently used by professionals in different fields. However, with respect to performance, well-controlled functional imaging studies on MI training are sparse. We investigated changes in fMRI representation going along with performance changes of a finger sequence (error and velocity) after MI training in 48 healthy young volunteers. Before training, we tested the vividness of kinesthetic and visual imagery. During tests, participants were instructed to move or to imagine moving the fingers of the right hand in a specific order. During MI training, participants repeatedly imagined the sequence for 15 min. Imaging analysis was performed using a full-factorial design to assess brain changes due to imagery training. We also used regression analyses to identify those who profited from training (performance outcome and gain) with initial imagery scores (vividness) and fMRI activation magnitude during MI at pre-test (MI pre ). After training, error rate decreased and velocity increased. We combined both parameters into a common performance index. FMRI activation in the left inferior parietal lobe (IPL) was associated with MI and increased over time. In addition, fMRI activation in the right IPL during MI pre was associated with high initial kinesthetic vividness. High kinesthetic imagery vividness predicted a high performance after training. In contrast, occipital activation, associated with visual imagery strategies, showed a negative predictive value for performance. Our data echo the importance of high kinesthetic vividness for MI training outcome and consider IPL as a key area during MI and through MI training. © 2018 Wiley Periodicals, Inc.

Estimation and modeling of forest attributes across large spatial scales using BiomeBGC, high-resolution imagery, LiDAR data, and inventory data

Science.gov (United States)

Golinkoff, Jordan Seth

The accurate estimation of forest attributes at many different spatial scales is a critical problem. Forest landowners may be interested in estimating timber volume, forest biomass, and forest structure to determine their forest's condition and value. Counties and states may be interested to learn about their forests to develop sustainable management plans and policies related to forests, wildlife, and climate change. Countries and consortiums of countries need information about their forests to set global and national targets to deal with issues of climate change and deforestation as well as to set national targets and understand the state of their forest at a given point in time. This dissertation approaches these questions from two perspectives. The first perspective uses the process model Biome-BGC paired with inventory and remote sensing data to make inferences about a current forest state given known climate and site variables. Using a model of this type, future climate data can be used to make predictions about future forest states as well. An example of this work applied to a forest in northern California is presented. The second perspective of estimating forest attributes uses high resolution aerial imagery paired with light detection and ranging (LiDAR) remote sensing data to develop statistical estimates of forest structure. Two approaches within this perspective are presented: a pixel based approach and an object based approach. Both approaches can serve as the platform on which models (either empirical growth and yield models or process models) can be run to generate inferences about future forest state and current forest biogeochemical cycling.

A semi-automated approach to derive elevation time-series and calculate glacier mass balance from historical aerial imagery

Science.gov (United States)

Whorton, E.; Headman, A.; Shean, D. E.; McCann, E.

2017-12-01

Understanding the implications of glacier recession on water resources in the western U.S. requires quantifying glacier mass change across large regions over several decades. Very few glaciers in North America have long-term continuous field measurements of glacier mass balance. However, systematic aerial photography campaigns began in 1957 on many glaciers in the western U.S. and Alaska. These historical, vertical aerial stereo-photographs documenting glacier evolution have recently become publically available. Digital elevation models (DEM) of the transient glacier surface preserved in each imagery timestamp can be derived, then differenced to calculate glacier volume and mass change to improve regional geodetic solutions of glacier mass balance. In order to batch process these data, we use Python-based algorithms and Agisoft Photoscan structure from motion (SfM) photogrammetry software to semi-automate DEM creation, and orthorectify and co-register historical aerial imagery in a high-performance computing environment. Scanned photographs are rotated to reduce scaling issues, cropped to the same size to remove fiducials, and batch histogram equalization is applied to improve image quality and aid pixel-matching algorithms using the Python library OpenCV. Processed photographs are then passed to Photoscan through the Photoscan Python library to create DEMs and orthoimagery. To extend the period of record, the elevation products are co-registered to each other, airborne LiDAR data, and DEMs derived from sub-meter commercial satellite imagery. With the exception of the placement of ground control points, the process is entirely automated with Python. Current research is focused on: one, applying these algorithms to create geodetic mass balance time series for the 90 photographed glaciers in Washington State and two, evaluating the minimal amount of positional information required in Photoscan to prevent distortion effects that cannot be addressed during co

Calibrating a numerical model's morphology using high-resolution spatial and temporal datasets from multithread channel flume experiments.

Science.gov (United States)

Javernick, L.; Bertoldi, W.; Redolfi, M.

2017-12-01

Accessing or acquiring high quality, low-cost topographic data has never been easier due to recent developments of the photogrammetric techniques of Structure-from-Motion (SfM). Researchers can acquire the necessary SfM imagery with various platforms, with the ability to capture millimetre resolution and accuracy, or large-scale areas with the help of unmanned platforms. Such datasets in combination with numerical modelling have opened up new opportunities to study river environments physical and ecological relationships. While numerical models overall predictive accuracy is most influenced by topography, proper model calibration requires hydraulic data and morphological data; however, rich hydraulic and morphological datasets remain scarce. This lack in field and laboratory data has limited model advancement through the inability to properly calibrate, assess sensitivity, and validate the models performance. However, new time-lapse imagery techniques have shown success in identifying instantaneous sediment transport in flume experiments and their ability to improve hydraulic model calibration. With new capabilities to capture high resolution spatial and temporal datasets of flume experiments, there is a need to further assess model performance. To address this demand, this research used braided river flume experiments and captured time-lapse observed sediment transport and repeat SfM elevation surveys to provide unprecedented spatial and temporal datasets. Through newly created metrics that quantified observed and modeled activation, deactivation, and bank erosion rates, the numerical model Delft3d was calibrated. This increased temporal data of both high-resolution time series and long-term temporal coverage provided significantly improved calibration routines that refined calibration parameterization. Model results show that there is a trade-off between achieving quantitative statistical and qualitative morphological representations. Specifically, statistical

Classification of high resolution imagery based on fusion of multiscale texture features

International Nuclear Information System (INIS)

Liu, Jinxiu; Liu, Huiping; Lv, Ying; Xue, Xiaojuan

2014-01-01

In high resolution data classification process, combining texture features with spectral bands can effectively improve the classification accuracy. However, the window size which is difficult to choose is regarded as an important factor influencing overall classification accuracy in textural classification and current approaches to image texture analysis only depend on a single moving window which ignores different scale features of various land cover types. In this paper, we propose a new method based on the fusion of multiscale texture features to overcome these problems. The main steps in new method include the classification of fixed window size spectral/textural images from 3×3 to 15×15 and comparison of all the posterior possibility values for every pixel, as a result the biggest probability value is given to the pixel and the pixel belongs to a certain land cover type automatically. The proposed approach is tested on University of Pavia ROSIS data. The results indicate that the new method improve the classification accuracy compared to results of methods based on fixed window size textural classification

EU-FP7-iMARS: analysis of Mars multi-resolution images using auto-coregistration, data mining and crowd source techniques: A Mid-term Report

Science.gov (United States)

Muller, J.-P.; Yershov, V.; Sidiropoulos, P.; Gwinner, K.; Willner, K.; Fanara, L.; Waelisch, M.; van Gasselt, S.; Walter, S.; Ivanov, A.; Cantini, F.; Morley, J. G.; Sprinks, J.; Giordano, M.; Wardlaw, J.; Kim, J.-R.; Chen, W.-T.; Houghton, R.; Bamford, S.

2015-10-01

Understanding the role of different solid surface formation processes within our Solar System is one of the fundamental goals of planetary science research. There has been a revolution in planetary surface observations over the last 8 years, especially in 3D imaging of surface shape (down to resolutions of 10s of cms) and subsequent terrain correction of imagery from orbiting spacecraft. This has led to the potential to be able to overlay different epochs back to the mid-1970s. Within iMars, a processing system has been developed to generate 3D Digital Terrain Models (DTMs) and corresponding OrthoRectified Images (ORIs) fully automatically from NASA MRO HiRISE and CTX stereo-pairs which are coregistered to corresponding HRSC ORI/DTMs. In parallel, iMars has developed a fully automated processing chain for co-registering level-1 (EDR) images from all previous NASA orbital missions to these HRSC ORIs and in the case of HiRISE these are further co-registered to previously co-registered CTX-to-HRSC ORIs. Examples will be shown of these multi-resolution ORIs and the application of different data mining algorithms to change detection using these co-registered images. iMars has recently launched a citizen science experiment to evaluate best practices for future citizen scientist validation of such data mining processed results. An example of the iMars website will be shown along with an embedded Version 0 prototype of a webGIS based on OGC standards.

Aerial Photography and Imagery, Ortho-Corrected, Washington County, NC true color orthophotography - 1/4 foot resolution over selected areas, Published in 2009, 1:1200 (1in=100ft) scale, Washington County Government.

Data.gov (United States)

NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2009. Washington County, NC true color orthophotography - 1/4 foot resolution over selected...

Aerial Photography and Imagery, Ortho-Corrected, Washington County, NC true color orhophotography - 1/2 foot resolution over selected areas, Published in 2009, 1:2400 (1in=200ft) scale, Washington County Government.

Data.gov (United States)

NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2009. Washington County, NC true color orhophotography - 1/2 foot resolution over selected areas.

Wetland Vegetation Integrity Assessment with Low Altitude Multispectral Uav Imagery

Science.gov (United States)

Boon, M. A.; Tesfamichael, S.

2017-08-01

The use of multispectral sensors on Unmanned Aerial Vehicles (UAVs) was until recently too heavy and bulky although this changed in recent times and they are now commercially available. The focus on the usage of these sensors is mostly directed towards the agricultural sector where the focus is on precision farming. Applications of these sensors for mapping of wetland ecosystems are rare. Here, we evaluate the performance of low altitude multispectral UAV imagery to determine the state of wetland vegetation in a localised spatial area. Specifically, NDVI derived from multispectral UAV imagery was used to inform the determination of the integrity of the wetland vegetation. Furthermore, we tested different software applications for the processing of the imagery. The advantages and disadvantages we experienced of these applications are also shortly presented in this paper. A JAG-M fixed-wing imaging system equipped with a MicaScene RedEdge multispectral camera were utilised for the survey. A single surveying campaign was undertaken in early autumn of a 17 ha study area at the Kameelzynkraal farm, Gauteng Province, South Africa. Structure-from-motion photogrammetry software was used to reconstruct the camera position's and terrain features to derive a high resolution orthoretified mosaic. MicaSense Atlas cloud-based data platform, Pix4D and PhotoScan were utilised for the processing. The WET-Health level one methodology was followed for the vegetation assessment, where wetland health is a measure of the deviation of a wetland's structure and function from its natural reference condition. An on-site evaluation of the vegetation integrity was first completed. Disturbance classes were then mapped using the high resolution multispectral orthoimages and NDVI. The WET-Health vegetation module completed with the aid of the multispectral UAV products indicated that the vegetation of the wetland is largely modified ("D" PES Category) and that the condition is expected to

RIGOROUS GEOREFERENCING OF ALSAT-2A PANCHROMATIC AND MULTISPECTRAL IMAGERY

Directory of Open Access Journals (Sweden)

I. Boukerch

2013-04-01

Full Text Available The exploitation of the full geometric capabilities of the High-Resolution Satellite Imagery (HRSI, require the development of an appropriate sensor orientation model. Several authors studied this problem; generally we have two categories of geometric models: physical and empirical models. Based on the analysis of the metadata provided with ALSAT-2A, a rigorous pushbroom camera model can be developed. This model has been successfully applied to many very high resolution imagery systems. The relation between the image and ground coordinates by the time dependant collinearity involving many coordinates systems has been tested. The interior orientation parameters must be integrated in the model, the interior parameters can be estimated from the viewing angles corresponding to the pointing directions of any detector, these values are derived from cubic polynomials provided in the metadata. The developed model integrates all the necessary elements with 33 unknown. All the approximate values of the 33 unknowns parameters may be derived from the informations contained in the metadata files provided with the imagery technical specifications or they are simply fixed to zero, so the condition equation is linearized and solved using SVD in a least square sense in order to correct the initial values using a suitable number of well-distributed GCPs. Using Alsat-2A images over the town of Toulouse in the south west of France, three experiments are done. The first is about 2D accuracy analysis using several sets of parameters. The second is about GCPs number and distribution. The third experiment is about georeferencing multispectral image by applying the model calculated from panchromatic image.

NOAA Integrated Ocean and Coastal Mapping (IOCM) orthorectified mosaic image tiles for Hampton Harbor to Frost Point and the Isle of Shoals, NH, 2011 (NODC Accession 0092292)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains orthorectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. Data were collected at...

Satellite images of the September 2013 flood event in Lyons, Colorado

Science.gov (United States)

Cole, Christopher J.; Friesen, Beverly A.; Wilds, Stanley; Noble, Suzanne; Warner, Harumi; Wilson, Earl M.

2013-01-01

The U.S. Geological Survey (USGS) Special Applications Science Center (SASC) produced an image base map showing high-resolution remotely sensed data over Lyons, Colorado—a city that was severely affected by the flood event that occurred throughout much of the Colorado Front Range in September of 2013. The 0.5-meter WorldView-2 data products were created from imagery collected by DigitalGlobe on September 13 and September 24, 2013, during and following the flood event. The images shown on this map were created to support flood response efforts, specifically for use in determining damage assessment and mitigation decisions. The raw, unprocessed imagery were orthorectified and pan-sharpened to enhance mapping accuracy and spatial resolution, and reproduced onto a cartographic base map. These maps are intended to provide a snapshot representation of post-flood ground conditions, which may be useful to decisionmakers and the general public. The SASC also provided data processing and analysis support for other Colorado flood-affected areas by creating cartographic products, geo-corrected electro-optical and radar image mosaics, and GIS water cover files for use by the Colorado National Guard, the National Park Service, the U.S. Forest Service, and the flood response community. All products for this International Charter event were uploaded to the USGS Hazards Data Distribution System (HDDS) website (http://hdds.usgs.gov/hdds2/) for distribution.

Parallel computation for blood cell classification in medical hyperspectral imagery

International Nuclear Information System (INIS)

Li, Wei; Wu, Lucheng; Qiu, Xianbo; Ran, Qiong; Xie, Xiaoming

2016-01-01

With the advantage of fine spectral resolution, hyperspectral imagery provides great potential for cell classification. This paper provides a promising classification system including the following three stages: (1) band selection for a subset of spectral bands with distinctive and informative features, (2) spectral-spatial feature extraction, such as local binary patterns (LBP), and (3) followed by an effective classifier. Moreover, these three steps are further implemented on graphics processing units (GPU) respectively, which makes the system real-time and more practical. The GPU parallel implementation is compared with the serial implementation on central processing units (CPU). Experimental results based on real medical hyperspectral data demonstrate that the proposed system is able to offer high accuracy and fast speed, which are appealing for cell classification in medical hyperspectral imagery. (paper)

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kunduz mineral district in Afghanistan: Chapter S in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

Science.gov (United States)

Davis, Philip A.; Arko, Scott A.; Harbin, Michelle L.

2013-01-01

DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Dudkash mineral district in Afghanistan: Chapter R in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

Science.gov (United States)

Davis, Philip A.; Arko, Scott A.; Harbin, Michelle L.

2013-01-01

. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Aynak mineral district in Afghanistan: Chapter E in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

Science.gov (United States)

Davis, Philip A.; Cagney, Laura E.; Arko, Scott A.; Harbin, Michelle L.

2012-01-01

products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kundalyan mineral district in Afghanistan: Chapter H in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

Science.gov (United States)

Davis, Philip A.; Cagney, Laura E.; Arko, Scott A.; Harbin, Michelle L.

2012-01-01

such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Herat mineral district in Afghanistan: Chapter T in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

Science.gov (United States)

Davis, Philip A.; Arko, Scott A.; Harbin, Michelle L.

2013-01-01

such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Tourmaline mineral district in Afghanistan: Chapter J in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

Science.gov (United States)

Davis, Philip A.; Cagney, Laura E.; Arko, Scott A.; Harbin, Michelle L.

2012-01-01

match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Badakhshan mineral district in Afghanistan: Chapter F in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

Science.gov (United States)

Davis, Philip A.; Arko, Scott A.; Harbin, Michelle L.

2012-01-01

match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or

Imagery for Disaster Response and Recovery

Science.gov (United States)

Bethel, G. R.

2011-12-01

Exposing the remotely sensed imagery for disaster response and recovery can provide the basis for an unbiased understanding of current conditions. Having created consolidated remotely sensed and geospatial data sources documents for US and Foreign disasters over the past six years, availability and usability are continuing to evolve. By documenting all existing sources of imagery and value added products, the disaster response and recovery community can develop actionable information. The past two years have provided unique situations to use imagery including a major humanitarian disaster and response effort in Haiti, a major environmental disaster in the Gulf of Mexico, a killer tornado in Joplin Missouri and long-term flooding in the Midwest. Each disaster presents different challenges and requires different spatial resolutions, spectral properties and/or multi-temporal collections. The community of data providers continues to expand with organized actives such as the International Charter for Space and Major Disasters and acquisitions by the private sector for the public good rather than for profit. However, data licensing, the lack of cross-calibration and inconsistent georeferencing hinder optimal use. Recent pre-event imagery is a critial component to any disaster response.

High resolution population maps for low income nations: combining land cover and census in East Africa.

Directory of Open Access Journals (Sweden)

Andrew J Tatem

2007-12-01

Full Text Available Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas.We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps.We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km(2. The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk and are freely available.

High Resolution Population Maps for Low Income Nations: Combining Land Cover and Census in East Africa

Science.gov (United States)

Tatem, Andrew J.; Noor, Abdisalan M.; von Hagen, Craig; Di Gregorio, Antonio; Hay, Simon I.

2007-01-01

Background Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas. Methodology/Principal Findings We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania) and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps. Conclusions We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km2. The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk) and are freely available. PMID:18074022

Using object-based image analysis to conduct high-resolution conifer extraction at regional spatial scales

Science.gov (United States)

Coates, Peter S.; Gustafson, K. Benjamin; Roth, Cali L.; Chenaille, Michael P.; Ricca, Mark A.; Mauch, Kimberly; Sanchez-Chopitea, Erika; Kroger, Travis J.; Perry, William M.; Casazza, Michael L.

2017-08-10

The distribution and abundance of pinyon (Pinus monophylla) and juniper (Juniperus osteosperma, J. occidentalis) trees (hereinafter, "pinyon-juniper") in sagebrush (Artemisia spp.) ecosystems of the Great Basin in the Western United States has increased substantially since the late 1800s. Distributional expansion and infill of pinyon-juniper into sagebrush ecosystems threatens the ecological function and economic viability of these ecosystems within the Great Basin, and is now a major contemporary challenge facing land and wildlife managers. Particularly, pinyon-juniper encroachment into intact sagebrush ecosystems has been identified as a primary threat facing populations of greater sage-grouse (Centrocercus urophasianus; hereinafter, "sage-grouse"), which is a sagebrush obligate species. Even seemingly innocuous scatterings of isolated pinyon-juniper in an otherwise intact sagebrush landscape can negatively affect survival and reproduction of sage-grouse. Therefore, accurate and high-resolution maps of pinyon-juniper distribution and abundance (indexed by canopy cover) across broad geographic extents would help guide land management decisions that better target areas for pinyon-juniper removal projects (for example, fuel reduction, habitat improvement for sage-grouse, and other sagebrush species) and facilitate science that further quantifies ecological effects of pinyon-juniper encroachment on sage-grouse populations and sagebrush ecosystem processes. Hence, we mapped pinyon-juniper (referred to as conifers for actual mapping) at a 1 × 1-meter (m) high resolution across the entire range of previously mapped sage-grouse habitat in Nevada and northeastern California.We used digital orthophoto quad tiles from National Agriculture Imagery Program (2010, 2013) as base imagery, and then classified conifers using automated feature extraction methodology with the program Feature Analyst™. This method relies on machine learning algorithms that extract features from

Prairie Monitoring Protocol Development: North Coast and Cascades Network

Science.gov (United States)

McCoy, Allen; Dalby, Craig

2009-01-01

The purpose of the project was to conduct research that will guide development of a standard approach to monitoring several components of prairies within the North Coast and Cascades Network (NCCN) parks. Prairies are an important element of the natural environment at many parks, including San Juan Island National Historical Park (NHP) and Ebey's Landing National Historical Reserve (NHR). Forests have been encroaching on these prairies for many years, and so monitoring of the prairies is an important resource issue. This project specifically focused on San Juan Island NHP. Prairies at Ebey's Landing NHR will be monitored in the future, but that park was not mapped as part of this prototype project. In the interest of efficiency, the Network decided to investigate two main issues before launching a full protocol development effort: (1) the imagery requirements for monitoring prairie components, and (2) the effectiveness of software to assist in extracting features from the imagery. Several components of prairie monitoring were initially identified as being easily tracked using aerial imagery. These components included prairie/forest edge, broad prairie composition (for example, shrubs, scattered trees), and internal exclusions (for example, shrubs, bare ground). In addition, we believed that it might be possible to distinguish different grasses in the prairies if the imagery were of high enough resolution. Although the areas in question at San Juan Island NHP are small enough that mapping on the ground with GPS (Global Positioning System) would be feasible, other applications could benefit from aerial image acquisition on a regular, recurring basis and thereby make the investment in aerial imagery worthwhile. The additional expense of orthorectifying the imagery also was determined to be cost-effective.

Use of Airborne Hyperspectral Imagery to Map Soil Properties in Tilled Agricultural Fields

International Nuclear Information System (INIS)

Hively, W.D; McCarty, G.W; Reeves, J.B; Lang, M.W; Oesterling, R.A; Delwiche, S.R

2011-01-01

Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400-2450 nm, -10 nm resolution, 2.5 m spatial resolution). Surface soil samples (n=315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R 2 >0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3 x 3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.

ANL high resolution injector

International Nuclear Information System (INIS)

Minehara, E.; Kutschera, W.; Hartog, P.D.; Billquist, P.

1985-01-01

The ANL (Argonne National Laboratory) high-resolution injector has been installed to obtain higher mass resolution and higher preacceleration, and to utilize effectively the full mass range of ATLAS (Argonne Tandem Linac Accelerator System). Preliminary results of the first beam test are reported briefly. The design and performance, in particular a high-mass-resolution magnet with aberration compensation, are discussed. 7 refs., 5 figs., 2 tabs

Canopy Density Mapping on Ultracam-D Aerial Imagery in Zagros Woodlands, Iran

Science.gov (United States)

Erfanifard, Y.; Khodaee, Z.

2013-09-01

Canopy density maps express different characteristics of forest stands, especially in woodlands. Obtaining such maps by field measurements is so expensive and time-consuming. It seems necessary to find suitable techniques to produce these maps to be used in sustainable management of woodland ecosystems. In this research, a robust procedure was suggested to obtain these maps by very high spatial resolution aerial imagery. It was aimed to produce canopy density maps by UltraCam-D aerial imagery, newly taken in Zagros woodlands by Iran National Geographic Organization (NGO), in this study. A 30 ha plot of Persian oak (Quercus persica) coppice trees was selected in Zagros woodlands, Iran. The very high spatial resolution aerial imagery of the plot purchased from NGO, was classified by kNN technique and the tree crowns were extracted precisely. The canopy density was determined in each cell of different meshes with different sizes overlaid on the study area map. The accuracy of the final maps was investigated by the ground truth obtained by complete field measurements. The results showed that the proposed method of obtaining canopy density maps was efficient enough in the study area. The final canopy density map obtained by a mesh with 30 Ar (3000 m2) cell size had 80% overall accuracy and 0.61 KHAT coefficient of agreement which shows a great agreement with the observed samples. This method can also be tested in other case studies to reveal its capability in canopy density map production in woodlands.

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.

A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery.

Science.gov (United States)

Huang, Huasheng; Deng, Jizhong; Lan, Yubin; Yang, Aqing; Deng, Xiaoling; Zhang, Lei

2018-01-01

Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.

Improved coastal wetland mapping using very-high 2-meter spatial resolution imagery

Science.gov (United States)

McCarthy, Matthew J.; Merton, Elizabeth J.; Muller-Karger, Frank E.

2015-08-01

Accurate wetland maps are a fundamental requirement for land use management and for wetland restoration planning. Several wetland map products are available today; most of them based on remote sensing images, but their different data sources and mapping methods lead to substantially different estimations of wetland location and extent. We used two very high-resolution (2 m) WorldView-2 satellite images and one (30 m) Landsat 8 Operational Land Imager (OLI) image to assess wetland coverage in two coastal areas of Tampa Bay (Florida): Fort De Soto State Park and Weedon Island Preserve. An initial unsupervised classification derived from WorldView-2 was more accurate at identifying wetlands based on ground truth data collected in the field than the classification derived from Landsat 8 OLI (82% vs. 46% accuracy). The WorldView-2 data was then used to define the parameters of a simple and efficient decision tree with four nodes for a more exacting classification. The criteria for the decision tree were derived by extracting radiance spectra at 1500 separate pixels from the WorldView-2 data within field-validated regions. Results for both study areas showed high accuracy in both wetland (82% at Fort De Soto State Park, and 94% at Weedon Island Preserve) and non-wetland vegetation classes (90% and 83%, respectively). Historical, published land-use maps overestimate wetland surface cover by factors of 2-10 in the study areas. The proposed methods improve speed and efficiency of wetland map production, allow semi-annual monitoring through repeat satellite passes, and improve the accuracy and precision with which wetlands are identified.

Assessing the value of Landsat imagery: Results from a 2012 comprehensive user survey

Science.gov (United States)

Miller, H. M.; Richardson, L.; Loomis, J.; Koontz, S.; Koontz, L.

2012-12-01

Landsat satellite imagery has long been recognized as unique among remotely sensed data due to the combination of its extensive archive, global coverage, and relatively high spatial and temporal resolution. Since the imagery became available at no cost in 2008, the number of users registered with the U.S. Geological Survey (USGS) has increased tenfold while the number of scenes downloaded annually has increased a hundredfold. It is clear that the imagery is being used extensively, and understanding the benefits provided by this imagery can help inform decisions involving its provision. However, the value of Landsat imagery is difficult to measure for a variety of reasons, one of which stems from the fact that the imagery has characteristics of a public good and does not have a direct market price to reflect its value to society. Further, there is not a clear understanding of the full range of users of the imagery, as well as how these users are distributed across the many different end uses this data is applied to. To assess the value of Landsat imagery, we conducted a survey of users registered with USGS in early 2012. Over 11,000 current users of Landsat imagery responded to the survey. The value of the imagery was measured both qualitatively and quantitatively. To explore the qualitative value of the imagery, users were asked about the importance of the imagery to their work, their dependence on the imagery, and the impacts on their work if there was no Landsat imagery. The majority of users deemed Landsat imagery important to their work and stated they were dependent on Landsat imagery to do their work. Additionally, if Landsat imagery was no longer available, over half of the users would have to discontinue some of their work. On average, these users would discontinue half of their current work if the imagery was no longer available. The focus of this presentation will be the quantitative results of a double-bounded contingent valuation analysis which reveals

A data mining approach for sharpening satellite thermal imagery over land

Science.gov (United States)

Thermal infrared (TIR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes which are at significant...

Phase 2 Final Report. IAEA Safeguards: Implementation blueprint of commercial satellite imagery

Energy Technology Data Exchange (ETDEWEB)

Andersson, Christer [SSC Satellitbild AB, Solna (Sweden)

2000-01-01

This document - IAEA Safeguards: Implementation Blueprint of Commercial Satellite Imagery - constitutes the second report from SSC Satellitbild giving a structured view and solid guidelines on how to proceed with a conceivable implementation of satellite imagery to support Safeguards activities of the Agency. This Phase 2 report presents a large number of concrete recommendations regarding suggested management issues, work organisation, imagery purchasing and team building. The study has also resulted in several lists of actions and preliminary project plans with GANT schedules concerning training, hardware and software, as well as for the initial pilot studies. In both the Phase 1 and Phase 2 studies it is confirmed that the proposed concept of a relatively small Imagery Unit using high-resolution data will be a sound and feasible undertaking. Such a unit capable of performing advanced image processing as a tool for various safeguard tasks will give the Agency an effective instrument for reference, monitoring, verification, and detection of declared and undeclared activities. The total cost for implementing commercial satellite imagery at the Department for Safeguards, as simulated in these studies, is approximately MUSD 1,5 per year. This cost is founded on an activity scenario with a staff of 4 experts working in an IAEA Imagery Unit with a workload of three dossiers or issues per week. The imagery unit is built around an advanced PC image processing system capable of handling several hundreds of pre-processed images per year. Alternatively a Reduced Scenario with a staff of 3 would need a budget of approximately MUSD 0,9 per year, whereas an Enhanced Imagery Unit including 5 experts and a considerably enlarged capacity would cost MUSD 1,7 per year. The Imagery Unit should be organised so it clearly reflects the objectives and role as set by the Member States and the management of the Agency. We recommend the Imagery Unit to be organised into four main work

Phase 2 Final Report. IAEA Safeguards: Implementation blueprint of commercial satellite imagery

International Nuclear Information System (INIS)

Andersson, Christer

2000-01-01

This document - IAEA Safeguards: Implementation Blueprint of Commercial Satellite Imagery - constitutes the second report from SSC Satellitbild giving a structured view and solid guidelines on how to proceed with a conceivable implementation of satellite imagery to support Safeguards activities of the Agency. This Phase 2 report presents a large number of concrete recommendations regarding suggested management issues, work organisation, imagery purchasing and team building. The study has also resulted in several lists of actions and preliminary project plans with GANT schedules concerning training, hardware and software, as well as for the initial pilot studies. In both the Phase 1 and Phase 2 studies it is confirmed that the proposed concept of a relatively small Imagery Unit using high-resolution data will be a sound and feasible undertaking. Such a unit capable of performing advanced image processing as a tool for various safeguard tasks will give the Agency an effective instrument for reference, monitoring, verification, and detection of declared and undeclared activities. The total cost for implementing commercial satellite imagery at the Department for Safeguards, as simulated in these studies, is approximately MUSD 1,5 per year. This cost is founded on an activity scenario with a staff of 4 experts working in an IAEA Imagery Unit with a workload of three dossiers or issues per week. The imagery unit is built around an advanced PC image processing system capable of handling several hundreds of pre-processed images per year. Alternatively a Reduced Scenario with a staff of 3 would need a budget of approximately MUSD 0,9 per year, whereas an Enhanced Imagery Unit including 5 experts and a considerably enlarged capacity would cost MUSD 1,7 per year. The Imagery Unit should be organised so it clearly reflects the objectives and role as set by the Member States and the management of the Agency. We recommend the Imagery Unit to be organised into four main work

Spectro-spatial relationship between UAV derived high resolution DEM and SWIR hyperspectral data: application to an ombrotrophic peatland

Science.gov (United States)

Arroyo-Mora, J. Pablo; Kalacska, Margaret; Lucanus, Oliver; Soffer, Raymond; Leblanc, George

2017-10-01

Peatlands cover 3% of the globe and are key ecosystems for climate regulation. To better understand the potential effects of climate change in peatlands, a major challenge is to determine the complex relationship between hydrology, microtopography, vegetation patterns, and gas exchange. Here we study the spectral and spatial relationship of microtopographic features (e.g. hollows and hummocks) and near-surface water through narrow-band spectral indices derived from hyperspectral imagery. We used a very high resolution digital elevation model (2.5 cm horizontal, 2.2 cm vertical resolution) derived from an UAV based Structure from Motion photogrammetry to map hollows and hummocks in the peatland area. We also created a 2 cm spatial resolution orthophoto mosaic to enhance the visual identification of these hollows and hummocks. Furthermore, we collected SWIR airborne hyperspectral (880-2450 nm) imagery at 1 m pixel resolution over four time periods, from April to June 2016 (phenological gradient: vegetation greening). Our results revealed an increase in the water indices values (NDWI1640 and NDWI2130) and a decrease in the moisture stress index (MSI) between April and June. In addition, for the same period the NDWI2130 shows a bimodal distribution indicating potential to quantitatively assess moisture differences between mosses and vascular plants. Our results, using the digital surface model to extract NDWI2130 values, showed significant differences between hollows and hummocks for each time period, with higher moisture values for hollows (i.e. moss dominated). However, for June, the water index for hummocks approximated the values found in hollows. Our study shows the advantages of using fine spatial and spectral scales to detect temporal trends in near surface water in a peatland.

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Haji-Gak mineral district in Afghanistan: Chapter C in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

Science.gov (United States)

Davis, Philip A.; Cagney, Laura E.; Arko, Scott A.; Harbin, Michelle L.

2012-01-01

match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then co-registered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image-coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kharnak-Kanjar mineral district in Afghanistan: Chapter K in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

Science.gov (United States)

Davis, Philip A.; Arko, Scott A.; Harbin, Michelle L.

2012-01-01

, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Dusar-Shaida mineral district in Afghanistan: Chapter I in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

Science.gov (United States)

Davis, Philip A.; Arko, Scott A.; Harbin, Michelle L.

2012-01-01

DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other

High-resolution DEMs for High-mountain Asia: A systematic, region-wide assessment of geodetic glacier mass balance and dynamics

Science.gov (United States)

Shean, D. E.; Arendt, A. A.; Osmanoglu, B.; Montesano, P.

2017-12-01

High Mountain Asia (HMA) constitutes the largest glacierized region outside of the Earth's polar regions. Although available observations are limited, long-term records indicate sustained regional glacier mass loss since 1850, with increased loss in recent decades. Recent satellite data (e.g., GRACE, ICESat-1) show spatially variable glacier mass balance, with significant mass loss in the Himalaya and Hindu Kush and slight mass gain in the Karakoram. We generated 4000 high-resolution digital elevation models (DEMs) from sub-meter commercial stereo imagery (DigitalGlobe WorldView/GeoEye) acquired over glaciers in High-mountain Asia from 2002-present (mostly 2013-present). We produced a regional 8-m DEM mosaic for 2015 and estimated 15-year geodetic mass balance for 40000 glaciers larger than 0.1 km2. We are combining with other regional DEM sources to systematically document the spatiotemporal evolution of glacier mass balance for the entire HMA region. We also generated monthly to interannual DEM and velocity time series for high-priority sites distributed across the region, with >15-20 DEMs available for some locations from 2010-present. These records document glacier dynamics, seasonal snow accumulation/redistribution, and processes that affect glacier mass balance (e.g., ice-cliff retreat, debris cover evolution). These efforts will provide basin-scale assessments of snow/ice melt runoff contributions for model cal/val and downstream water resources applications. We will continue processing all archived and newly available commercial stereo imagery for HMA, and will release all DEMs through the HiMAT DAAC.