Detecting long-term changes to vegetation in northern Canada using the Landsat satellite image archive

International Nuclear Information System (INIS)

Fraser, R H; Olthof, I; Carrière, M; Deschamps, A; Pouliot, D

2011-01-01

Analysis of coarse resolution (∼1 km) satellite imagery has provided evidence of vegetation changes in arctic regions since the mid-1980s that may be attributable to climate warming. Here we investigate finer-scale changes to northern vegetation over the same period using stacks of 30 m resolution Landsat TM and ETM + satellite images. Linear trends in the normalized difference vegetation index (NDVI) and tasseled cap indices are derived for four widely spaced national parks in northern Canada. The trends are related to predicted changes in fractional shrub and other vegetation covers using regression tree classifiers trained with plot measurements and high resolution imagery. We find a consistent pattern of greening (6.1–25.5% of areas increasing) and predicted increases in vascular vegetation in all four parks that is associated with positive temperature trends. Coarse resolution (3 km) NDVI trends were not detected in two of the parks that had less intense greening. A range of independent studies and observations corroborate many of the major changes observed.

Sensitivity study of land biosphere CO2 exchange through an atmospheric tracer transport model using satellite-derived vegetation index data

International Nuclear Information System (INIS)

Knorr, W.; Heimann, M.

1994-01-01

We develop a simple, globally uniform model of CO 2 exchange between the atmosphere and the terrestrial biosphere by coupling the model with a three-dimensional atmospheric tracer transport model using observed winds, and checking results against observed concentrations of CO 2 at various monitoring sites. CO 2 fluxes are derived from observed greenness using satellite-derived Global Vegetation Index data, combined with observations of temperature, radiation, and precipitation. We explore a range of CO 2 flux formulations together with some modifications of the modelled atmospheric transport. We find that while some formulations can be excluded, it cannot be decided whether or not to make CO 2 uptake and release dependent on water stress. It appears that the seasonality of net CO 2 fluxes in the tropics, which would be expected to be driven by water availability, is small and is therefore not visible in the seasonal cycle of atmospheric CO 2 . The latter is dominated largely by northern temperate and boreal vegetation, where seasonality is mostly temperature determined. We find some evidence that there is still considerable CO 2 release from soils during northern-hemisphere winter. An exponential air temperature dependence of soil release with a Q 10 of 1.5 is found to be most appropriate, with no cutoff at low freezing temperatures. This result is independent of the year from which observed winds were taken. This is remarkable insofar as year-to-year changes in modelled CO 2 concentrations caused by changes in the wind data clearly outweigh those caused by year-to-year variability in the climate and vegetation index data. (orig.)

A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis

Science.gov (United States)

Zhang, Xiya; Li, Peijun

2018-01-01

Accurate and timely information regarding the extent and spatial distribution of urban areas on regional and global scales is crucially important for both scientific and policy-making communities. Stable nighttime light (NTL) data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) provides a unique proxy of human settlement and activity, which has been used in the mapping and analysis of urban areas and urbanization dynamics. However, blooming and saturation effects of DMSP/OLS NTL data are two unresolved problems in regional urban area mapping and analysis. This study proposed a new urban index termed the Temperature and Vegetation Adjusted NTL Urban Index (TVANUI). It is intended to reduce blooming and saturation effects and to enhance urban features by combining DMSP/OLS NTL data with Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer onboard the Terra satellite. The proposed index was evaluated in two study areas by comparison with established urban indices. The results demonstrated the proposed TVANUI was effective in enhancing the variation of DMSP/OLS light in urban areas and in reducing blooming and saturation effects, showing better performance than three established urban indices. The TVANUI also significantly outperformed the established urban indices in urban area mapping using both the global-fixed threshold and the local-optimal threshold methods. Thus, the proposed TVANUI provides a useful variable for urban area mapping and analysis on regional scale, as well as for urbanization dynamics using time-series DMSP/OLS and related satellite data.

Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate

International Nuclear Information System (INIS)

Hashim, M; Pour, A B; Onn, C H

2014-01-01

Remote sensing technology is an important tool to analyze vegetation dynamics, quantifying vegetation fraction of Earth's agricultural and natural vegetation. In optical remote sensing analysis removing atmospheric interferences, particularly distribution of cloud contaminations, are always a critical task in the tropical climate. This paper suggests a fast and alternative approach to remove cloud and shadow contaminations for Landsat Enhanced Thematic Mapper + (ETM + ) multi temporal datasets. Band 3 and Band 4 from all the Landsat ETM + dataset are two main spectral bands that are very crucial in this study for cloud removal technique. The Normalise difference vegetation index (NDVI) and the normalised difference soil index (NDSI) are two main derivatives derived from the datasets. Change vector analysis is used in this study to seek the vegetation dynamics. The approach developed in this study for cloud optimizing can be broadly applicable for optical remote sensing satellite data, which are seriously obscured with heavy cloud contamination in the tropical climate

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

Directory of Open Access Journals (Sweden)

Zunjian Bian

2017-07-01

Full Text Available The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping effect of vegetation is usually present, but it is not completely considered during the inversion process. Therefore, we introduced a simple inversion procedure that uses gap frequency with a clumping index (GCI for leaf and soil temperatures over both crop and forest canopies. Simulated datasets corresponding to turbid vegetation, regularly planted crops and randomly distributed forest were generated using a radiosity model and were used to test the proposed inversion algorithm. The results indicated that the GCI algorithm performed well for both crop and forest canopies, with root mean squared errors of less than 1.0 °C against simulated values. The proposed inversion algorithm was also validated using measured datasets over orchard, maize and wheat canopies. Similar results were achieved, demonstrating that using the clumping index can improve inversion results. In all evaluations, we recommend using the GCI algorithm as a foundation for future satellite-based applications due to its straightforward form and robust performance for both crop and forest canopies using the vegetation clumping index.

Monitoring vegetation change in Abu Dhabi Emirate from 1996 to 2000 and 2004 using Landsat Satellite Imagery

International Nuclear Information System (INIS)

Starbuck, M.J.; Tamayo, J.

2007-01-01

In the fall of 2001, a study was initiated to investigate vegetation changes in the Abu Dhabi Emirates. The vast majority of vegetation present in the region is irrigated and analysis of vegetation change will support groundwater investigations in the region by indicating areas of increased water use. Satellite-based imaging systems provide a good source of data for such an analysis. The recent analysis was completed between February and November 2002 using Landsat 5 Thematic Mapper satellite imagery acquired in 1996 and Landsat 7 Enhanced Thematic Mapper Plus imagery acquired in 2000. These assessments were augmented in 2004with the study of Landsat 7 imagery acquired in early 2004. The total area of vegetation for each of seven study areas was calculated using the Normalized Difference Vegetation Index (NDVI) technique. Multiband image classification was used to differentiate general vegetation types. Change analysis consisted of simple NDVI image differencing and post-classification change matrices. Measurements of total vegetation are for the Abu Dhabi Emirate indicate an increase from 77,200 hectares in 1996 to 162,700 hectares in 2000 (110% increase). Based on comparison with manual interpretation of satellite imagery, the amount of under-reporting of irrigated land is estimated at about 15% of the actual area. From the assessment of 2004 Landset imagery, it was found that the growth of irrigated vegetation in most areas of Emirate had stabilized and had actually slightly decreased in some cases. The decreases are probably due to variability in the measurement technique and not due to actual decreases in area of vegetation. (author)

Climatic Changes Effects On Spectral Vegetation Indices For Forested Areas Analysis From Satellite Data

International Nuclear Information System (INIS)

Zoran, M.; Stefan, S.

2007-01-01

Climate-induced changes at the land surface may in turn feed back on the climate itself through changes in soil moisture, vegetation, radiative characteristics, and surface-atmosphere exchanges of water vapor. Thresholding based on biophysical variables derived from time trajectories of satellite data is a new approach to classifying forest land cover via remote . sensing .The input data are composite values of the Normalized Difference Vegetation Index (NDVI). Classification accuracies are function of the class, comparison method and season of the year. The aim of the paper is forest biomass assessment and land-cover changes analysis due to climatic effects

The effect of bi-directional reflectance distribution function on the estimation of vegetation indices and leaf area index (LAI): A case study of the vegetation in succession stages after forest fire in northwestern Canada

International Nuclear Information System (INIS)

Hasegawa, K.; Matsuyama, H.; Tsuzuki, H.; Sweda, T.

2006-01-01

The effect of the dependence of the satellite data on sun/sensor geometry must be considered in the case of monitoring vegetation from satellites. Vegetation structure causes uneven scattering of sunlight, which is expressed by bi-directional reflectance distribution function (BRDF). The purpose of this study is to estimate the effect of BRDF of monitoring vegetation using the reflectance of visible and near-infrared bands. We investigated the vegetation in succession stages after forest fire (main species: spruce) in the northwestern Canada. BRF (Bidirectional Reflectance Factor) was measured in the seven sites of some succession stages, along with the measurements of leaf area index (LAI) and biomass. The main results obtained in this study are summarized as follows. (1) In each site, the difference of Normalized Difference Vegetation Index (NDVI) value around 0.1-0.2 was caused by BRDF when the sensor angle was changed from -15deg to 15 deg, being equivalent to the standard image of IKONOS. Also, LAI estimated by NDVI varied from 22% to 65% of the average. (2) The robustness of other vegetation indices to BRDF was compared. The reflectance of the near-infrared band normalized by the sum of other bands (nNIR), and Global Environmental Monitoring Index (GEMI) were investigated along with NDVI. It is clarified that nNIR was most robust in the site where vegetation existed. GEMI was most robust in the sites of scarce vegetation, while NDVI was strongly affected by BRDF in such sites

Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method

Science.gov (United States)

Song, Wanjuan; Mu, Xihan; Ruan, Gaiyan; Gao, Zhan; Li, Linyuan; Yan, Guangjian

2017-06-01

Normalized difference vegetation index (NDVI) of highly dense vegetation (NDVIv) and bare soil (NDVIs), identified as the key parameters for Fractional Vegetation Cover (FVC) estimation, are usually obtained with empirical statistical methods However, it is often difficult to obtain reasonable values of NDVIv and NDVIs at a coarse resolution (e.g., 1 km), or in arid, semiarid, and evergreen areas. The uncertainty of estimated NDVIs and NDVIv can cause substantial errors in FVC estimations when a simple linear mixture model is used. To address this problem, this paper proposes a physically based method. The leaf area index (LAI) and directional NDVI are introduced in a gap fraction model and a linear mixture model for FVC estimation to calculate NDVIv and NDVIs. The model incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters product (MCD43B1) and LAI product, which are convenient to acquire. Two types of evaluation experiments are designed 1) with data simulated by a canopy radiative transfer model and 2) with satellite observations. The root-mean-square deviation (RMSD) for simulated data is less than 0.117, depending on the type of noise added on the data. In the real data experiment, the RMSD for cropland is 0.127, for grassland is 0.075, and for forest is 0.107. The experimental areas respectively lack fully vegetated and non-vegetated pixels at 1 km resolution. Consequently, a relatively large uncertainty is found while using the statistical methods and the RMSD ranges from 0.110 to 0.363 based on the real data. The proposed method is convenient to produce NDVIv and NDVIs maps for FVC estimation on regional and global scales.

CHARACTERISING VEGETATED SURFACES USING MODIS MULTIANGULAR SATELLITE DATA

Directory of Open Access Journals (Sweden)

G. McCamley

2012-07-01

Full Text Available Bidirectional Reflectance Distribution Functions (BRDF seek to represent variations in surface reflectance resulting from changes in a satellite's view and solar illumination angles. BRDF representations have been widely used to assist in the characterisation of vegetation. However BRDF effects are often noisy, difficult to interpret and are the spatial integral of all the individual surface features present in a pixel. This paper describes the results of an approach to understanding how BRDF effects can be used to characterise vegetation. The implementation of the Ross Thick Li Sparse BRDF model using MODIS is a stable, mature data product with a 10 year history and is a ready data source. Using this dataset, a geometric optical model is proposed that seeks to interpret the BRDF effects in terms of Normalised Difference Vegetation Index (NDVI and a height-to-width ratio of the vegetation components. The height-to-width ratio derived from this model seeks to represent the dependence of NDVI to changes in view zenith angle as a single numeric value. The model proposed within this paper has been applied to MODIS pixels in central Australia for areas in excess of 18,000 km2. The study area is predominantly arid and sparsely vegetated which provides a level of temporal and spatial homogeneity. The selected study area also minimises the effects associated with mutual obscuration of vegetation which is not considered by the model. The results are represented as a map and compared to NDVI derived from MODIS and NDVI derived from Landsat mosaics developed for Australia's National Carbon Accounting System (NCAS. The model reveals additional information not obvious in reflectance data. For example, the height-to-width ratio is able to reveal vegetation features in arid areas that do not have an accompanying significant increase in NDVI derived from MODIS, i.e. the height-to-width ratio reveals vegetation which is otherwise only apparent in NDVI derived

Camera derived vegetation greenness index as proxy for gross primary production in a low Arctic wetland area

DEFF Research Database (Denmark)

Westergaard-Nielsen, Andreas; Lund, Magnus; Hansen, Birger Ulf

2013-01-01

vegetation index (NDVI) product derived from the WorldView-2 satellite. An object-based classification based on a bi-temporal image composite was used to classify the study area into heath, copse, fen, and bedrock. Temporal evolution of vegetation greenness was evaluated and modeled with double sigmoid...... and GPP (R-2 = 0.85, p remote Arctic regions....... (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved....

Vegetation monitoring for Guatemala: a comparison between simulated VIIRS and MODIS satellite data

Science.gov (United States)

Boken, Vijendra K.; Easson, Gregory L.; Rowland, James

2010-01-01

The advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data are being widely used for vegetation monitoring across the globe. However, sensors will discontinue collecting these data in the near future. National Aeronautics and Space Administration is planning to launch a new sensor, visible infrared imaging radiometer suite (VIIRS), to continue to provide satellite data for vegetation monitoring. This article presents a case study of Guatemala and compares the simulated VIIRS-Normalized Difference Vegetation Index (NDVI) with MODIS-NDVI for four different dates each in 2003 and 2005. The dissimilarity between VIIRS-NDVI and MODIS-NDVI was examined on the basis of the percent difference, the two-tailed student's t-test, and the coefficient of determination, R 2. The per cent difference was found to be within 3%, the p-value ranged between 0.52 and 0.99, and R 2 exceeded 0.88 for all major types of vegetation (basic grains, rubber, sugarcane, coffee and forests) found in Guatemala. It was therefore concluded that VIIRS will be almost equally capable of vegetation monitoring as MODIS.

Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data

Directory of Open Access Journals (Sweden)

Tao Yu

2018-02-01

Full Text Available Accurately estimating vegetation productivity is important in research on terrestrial ecosystems, carbon cycles and climate change. Eight-day gross primary production (GPP and annual net primary production (NPP are contained in MODerate Resolution Imaging Spectroradiometer (MODIS products (MOD17, which are considered the first operational datasets for monitoring global vegetation productivity. However, the cloud-contaminated MODIS leaf area index (LAI and Fraction of Photosynthetically Active Radiation (FPAR retrievals may introduce some considerable errors to MODIS GPP and NPP products. In this paper, global eight-day GPP and eight-day NPP were first estimated based on Global LAnd Surface Satellite (GLASS LAI and FPAR products. Then, GPP and NPP estimates were validated by FLUXNET GPP data and BigFoot NPP data and were compared with MODIS GPP and NPP products. Compared with MODIS GPP, a time series showed that estimated GLASS GPP in our study was more temporally continuous and spatially complete with smoother trajectories. Validated with FLUXNET GPP and BigFoot NPP, we demonstrated that estimated GLASS GPP and NPP achieved higher precision for most vegetation types.

Seasonal analysis of precipitation, drought and Vegetation index in Indonesian paddy field based on remote sensing data

International Nuclear Information System (INIS)

Darmawan, S; Takeuchi, W; Shofiyati, R; Sari, D K; Wikantika, K

2014-01-01

Paddy field is important agriculture crop in Indonesia. Rice is a food staple for 237,6 million Indonesian people. Paddy field growth is strongly influenced by water, but the amount of precipitation is unpredictable. Annual and interannual climate variability in Indonesia is unusual. In recent years remote sensing data has been used for measurement and monitoring of precipitation, drought and Vegetation index such as Global Satellite Mapping of Precipitation (GSMaP), Multi-purpose Transmission SATellite (MTSAT) and Moderate Resolution Imaging Spectroradiometer (MODIS). The objective of this research is to investigate seasonal variability of precipitation, drought and Vegetation index in Indonesian paddy field based on remote sensing data. The methodology consists of collecting of enhanced vegetation index (EVI) from MODIS data, mosaicking of image, collecting of region of interest of paddy field, collecting of precipitation and drought index based on Keetch Bryam Drought Index (KBDI) from GSMaP and MTSAT, and seasonal analysis. The result of this research has showed seasonal variability of precipitation, KBDI and EVI on Indonesia paddy field from 2007 until 2012. Precipitation begins from January until May and October until December, and KBDI begins to increase from June and peak in September only in South Sumatera precipitation almost in all month. Seasonal analysis has showed precipitation and KBDI affect on EVI that can indicate variety phenology of Indonesian paddy field. Peak of EVI occurs before peak of KBDI occurs and increasing of KBDI followed by decreasing of EVI. In 2010 all province got higher precipitation and smaller KBDI so EVI has three peaks such as in West Java that can indicated increasing of rice production

Vegetation index anomaly response to varying lengths of drought across vegetation and climatic gradients in Hawaii

Science.gov (United States)

Lucas, M.; Miura, T.; Trauernicht, C.; Frazier, A. G.

2017-12-01

A drought which results in prolonged and extended deficit in naturally available water supply and creates multiple stresses across ecosystems is classified as an ecological drought. Detecting and understanding the dynamics and response of such droughts in tropical systems, specifically across various vegetation and climatic gradients is fairly undetermined, yet increasingly important for better understandings of the ecological effects of drought. To understanding the link between what lengths and intensities of known meteorological drought triggers detectable ecological vegetation responses, a landscape scale regression analysis evaluating the response (slope) and relationship strength (R-squared) of several cumulative SPI (standard precipitation index) lengths(1, 3, 6, 12, 18, 24, 36, 48, and 60 month), to various satellite derived monthly vegetation indices anomalies (NDVI, EVI, EVI2, and LSWI) was performed across a matrix of dominant vegetation covers (grassland, shrubland, and forest) and climatic moisture zones (arid, dry, mesic, and wet). The nine different SPI lags across these climactic and vegetation gradients was suggest that stronger relationships and steeper slopes were found in dryer climates (across all vegetation covers) and finer vegetation types (across all moisture zones). Overall NDVI, EVI and EVI2 showed the best utility in these dryer climatic zones across all vegetation types. Within arid and dry areas "best" fits showed increasing lengths of cumulative SPI were with increasing vegetation coarseness respectively. Overall these findings suggest that rainfall driven drought may have a stronger impact on the ecological condition of vegetation in water limited systems with finer vegetation types ecologically responding more rapidly to meteorological drought events than coarser woody vegetation systems. These results suggest that previously and newly documented trends of decreasing rainfall and increasing drought in Hawaiian drylands may have

A MODIS-based vegetation index climatology

Science.gov (United States)

Our motivation here is to provide information for the NASA Soil Moisture Active Passive (SMAP) satellite soil moisture retrieval algorithms (launch in 2014). Vegetation attenuates the signal and the algorithms must correct for this effect. One approach is to use data that describes the canopy water ...

On the characterization of vegetation recovery after fire disturbance using Fisher-Shannon analysis and SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series

Science.gov (United States)

Lasaponara, Rosa; Lanorte, Antonio; Lovallo, Michele; Telesca, Luciano

2015-04-01

Time series can fruitfully support fire monitoring and management from statistical analysis of fire occurrence (Tuia et al. 2008) to danger estimation (lasaponara 2005), damage evaluation (Lanorte et al 2014) and post fire recovery (Lanorte et al. 2014). In this paper, the time dynamics of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) time series are analyzed by using the statistical approach of the Fisher-Shannon (FS) information plane to assess and monitor vegetation recovery after fire disturbance. Fisher-Shannon information plane analysis allows us to gain insight into the complex structure of a time series to quantify its degree of organization and order. The analysis was carried out using 10-day Maximum Value Composites of NDVI (MVC-NDVI) with a 1 km × 1 km spatial resolution. The investigation was performed on two test sites located in Galizia (North Spain) and Peloponnese (South Greece), selected for the vast fires which occurred during the summer of 2006 and 2007 and for their different vegetation covers made up mainly of low shrubland in Galizia test site and evergreen forest in Peloponnese. Time series of MVC-NDVI have been analyzed before and after the occurrence of the fire events. Results obtained for both the investigated areas clearly pointed out that the dynamics of the pixel time series before the occurrence of the fire is characterized by a larger degree of disorder and uncertainty; while the pixel time series after the occurrence of the fire are featured by a higher degree of organization and order. In particular, regarding the Peloponneso fire, such discrimination is more evident than in the Galizia fire. This suggests a clear possibility to discriminate the different post-fire behaviors and dynamics exhibited by the different vegetation covers. Reference Lanorte A, R Lasaponara, M Lovallo, L Telesca 2014 Fisher-Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to

Calibration of UAS imagery inside and outside of shadows for improved vegetation index computation

Science.gov (United States)

Bondi, Elizabeth; Salvaggio, Carl; Montanaro, Matthew; Gerace, Aaron D.

2016-05-01

Vegetation health and vigor can be assessed with data from multi- and hyperspectral airborne and satellite- borne sensors using index products such as the normalized difference vegetation index (NDVI). Recent advances in unmanned aerial systems (UAS) technology have created the opportunity to access these same image data sets in a more cost effective manner with higher temporal and spatial resolution. Another advantage of these systems includes the ability to gather data in almost any weather condition, including complete cloud cover, when data has not been available before from traditional platforms. The ability to collect in these varied conditions, meteorological and temporal, will present researchers and producers with many new challenges. Particularly, cloud shadows and self-shadowing by vegetation must be taken into consideration in imagery collected from UAS platforms to avoid variation in NDVI due to changes in illumination within a single scene, and between collection flights. A workflow is presented to compensate for variations in vegetation indices due to shadows and variation in illumination levels in high resolution imagery collected from UAS platforms. Other calibration methods that producers may currently be utilizing produce NDVI products that still contain shadow boundaries and variations due to illumination, whereas the final NDVI mosaic from this workflow does not.

The 2010 Russian Drought Impact on Satellite Measurements of Solar-Induced Chlorophyll Fluorescence: Insights from Modeling and Comparisons with the Normalized Differential Vegetation Index (NDVI)

Science.gov (United States)

Yoshida, Y.; Joiner, J.; Tucker, C.; Berry, J.; Lee, J. -E.; Walker, G.; Reichle, R.; Koster, R.; Lyapustin, A.; Wang, Y.

2015-01-01

We examine satellite-based measurements of chlorophyll solar-induced fluorescence (SIF) over the region impacted by the Russian drought and heat wave of 2010. Like the popular Normalized Difference Vegetation Index (NDVI) that has been used for decades to measure photosynthetic capacity, SIF measurements are sensitive to the fraction of absorbed photosynthetically-active radiation (fPAR). However, in addition, SIF is sensitive to the fluorescence yield that is related to the photosynthetic yield. Both SIF and NDVI from satellite data show drought-related declines early in the growing season in 2010 as compared to other years between 2007 and 2013 for areas dominated by crops and grasslands. This suggests an early manifestation of the dry conditions on fPAR. We also simulated SIF using a global land surface model driven by observation-based meteorological fields. The model provides a reasonable simulation of the drought and heat impacts on SIF in terms of the timing and spatial extents of anomalies, but there are some differences between modeled and observed SIF. The model may potentially be improved through data assimilation or parameter estimation using satellite observations of SIF (as well as NDVI). The model simulations also offer the opportunity to examine separately the different components of the SIF signal and relationships with Gross Primary Productivity (GPP).

An optical sensor network for vegetation phenology monitoring and satellite data calibration

DEFF Research Database (Denmark)

Eklundh, L.; Jin, H.; Schubert, P.

2011-01-01

-board Aqua and Terra satellite platforms. PAR fluxes are partitioned into reflected and absorbed components for the ground and canopy. The measurements demonstrate that the instrumentation provides detailed information about the vegetation phenology and variations in reflectance due to snow cover variations......We present a network of sites across Fennoscandia for optical sampling of vegetation properties relevant for phenology monitoring and satellite data calibration. The network currently consists of five sites, distributed along an N-S gradient through Sweden and Finland. Two sites are located...... and vegetation development. Valuable information about PAR absorption of ground and canopy is obtained that may be linked to vegetation productivity....

Validation of Vegetation Index Time Series from Suomi NPP Visible Infrared Imaging Radiometer Suite Using Tower Radiation Flux Measurements

Science.gov (United States)

Miura, T.; Kato, A.; Wang, J.; Vargas, M.; Lindquist, M.

2015-12-01

Satellite vegetation index (VI) time series data serve as an important means to monitor and characterize seasonal changes of terrestrial vegetation and their interannual variability. It is, therefore, critical to ensure quality of such VI products and one method of validating VI product quality is cross-comparison with in situ flux tower measurements. In this study, we evaluated the quality of VI time series derived from Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) spacecraft by cross-comparison with in situ radiation flux measurements at select flux tower sites over North America and Europe. VIIRS is a new polar-orbiting satellite sensor series, slated to replace National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer in the afternoon overpass and to continue the highly-calibrated data streams initiated with Moderate Resolution Imaging Spectrometer of National Aeronautics and Space Administration's Earth Observing System. The selected sites covered a wide range of biomes, including croplands, grasslands, evergreen needle forest, woody savanna, and open shrublands. The two VIIRS indices of the Top-of-Atmosphere (TOA) Normalized Difference Vegetation Index (NDVI) and the atmospherically-corrected, Top-of-Canopy (TOC) Enhanced Vegetation Index (EVI) (daily, 375 m spatial resolution) were compared against the TOC NDVI and a two-band version of EVI (EVI2) calculated from tower radiation flux measurements, respectively. VIIRS and Tower VI time series showed comparable seasonal profiles across biomes with statistically significant correlations (> 0.60; p-value 0.95), with mean differences of 2.3 days and 5.0 days for the NDVI and the EVI, respectively. These results indicate that VIIRS VI time series can capture seasonal evolution of vegetated land surface as good as in situ radiometric measurements. Future studies that address biophysical or physiological interpretations

Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product

Directory of Open Access Journals (Sweden)

Zhiqiang Xiao

2016-04-01

Full Text Available The fractional vegetation cover (FCover is an essential biophysical variable and plays a critical role in the carbon cycle studies. Existing FCover products from satellite observations are spatially incomplete and temporally discontinuous, and also inaccurate for some vegetation types to meet the requirements of various applications. In this study, an operational method is proposed to calculate high-quality, accurate FCover from the Global LAnd Surface Satellite (GLASS leaf area index (LAI product to ensure physical consistency between LAI and FCover retrievals. As a result, a global FCover product (denoted by TRAGL were generated from the GLASS LAI product from 2000 to present. With no missing values, the TRAGL FCover product is spatially complete. A comparison of the TRAGL FCover product with the Geoland2/BioPar version 1 (GEOV1 FCover product indicates that these FCover products exhibit similar spatial distribution pattern. However, there were relatively large discrepancies between these FCover products over equatorial rainforests, broadleaf crops in East-central United States, and needleleaf forests in Europe and Siberia. Temporal consistency analysis indicates that TRAGL FCover product has continuous trajectories. Direct validation with ground-based FCover estimates demonstrated that TRAGL FCover values were more accurate (RMSE = 0.0865, and R2 = 0.8848 than GEOV1 (RMSE = 0.1541, and R2 = 0.7621.

Comparing MODIS and near-surface vegetation indexes for monitoring tropical dry forest phenology along a successional gradient using optical phenology towers

Science.gov (United States)

Rankine, C.; Sánchez-Azofeifa, G. A.; Guzmán, J. Antonio; Espirito-Santo, M. M.; Sharp, Iain

2017-10-01

Tropical dry forests (TDFs) present strong seasonal greenness signals ideal for tracking phenology and primary productivity using remote sensing techniques. The tightly synchronized relationship these ecosystems have with water availability offer a valuable natural experiment for observing the complex interactions between the atmosphere and the biosphere in the tropics. To investigate how well the MODIS vegetation indices (normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI)) represented the phenology of different successional stages of naturally regenerating TDFs, within a widely conserved forest fragment in the semi-arid southeast of Brazil, we installed several canopy towers with radiometric sensors to produce high temporal resolution near-surface vegetation greenness indices. Direct comparison of several years of ground measurements with a combined Aqua/Terra 8 day satellite product showed similar broad temporal trends, but MODIS often suffered from cloud contamination during the onset of the growing season and occasionally during the peak growing season. The strength of the in-situ and MODIS linear relationship was greater for NDVI than for EVI across sites but varied with forest stand age. Furthermore, we describe the onset dates and duration of canopy development phases for three years of in-situ monitoring. A seasonality analysis revealed significant discrepancies between tower and MODIS phenology transitions dates, with up to five weeks differences in growing season length estimation. Our results indicate that 8 and 16 day MODIS satellite vegetation monitoring products are suitable for tracking general patterns of tropical dry forest phenology in this region but are not temporally sufficient to characterize inter-annual differences in phenology phase onset dates or changes in productivity due to mid-season droughts. Such rapid transitions in canopy greenness are important indicators of climate change sensitivity of these

[Vegetation index estimation by chlorophyll content of grassland based on spectral analysis].

Science.gov (United States)

Xiao, Han; Chen, Xiu-Wan; Yang, Zhen-Yu; Li, Huai-Yu; Zhu, Han

2014-11-01

Comparing the methods of existing remote sensing research on the estimation of chlorophyll content, the present paper confirms that the vegetation index is one of the most practical and popular research methods. In recent years, the increasingly serious problem of grassland degradation. This paper, firstly, analyzes the measured reflectance spectral curve and its first derivative curve in the grasslands of Songpan, Sichuan and Gongger, Inner Mongolia, conducts correlation analysis between these two spectral curves and chlorophyll content, and finds out the regulation between REP (red edge position) and grassland chlorophyll content, that is, the higher the chlorophyll content is, the higher the REIP (red-edge inflection point) value would be. Then, this paper constructs GCI (grassland chlorophyll index) and selects the most suitable band for retrieval. Finally, this paper calculates the GCI by the use of satellite hyperspectral image, conducts the verification and accuracy analysis of the calculation results compared with chlorophyll content data collected from field of twice experiments. The result shows that for grassland chlorophyll content, GCI has stronger sensitivity than other indices of chlorophyll, and has higher estimation accuracy. GCI is the first proposed to estimate the grassland chlorophyll content, and has wide application potential for the remote sensing retrieval of grassland chlorophyll content. In addition, the grassland chlorophyll content estimation method based on remote sensing retrieval in this paper provides new research ideas for other vegetation biochemical parameters' estimation, vegetation growth status' evaluation and grassland ecological environment change's monitoring.

Vegetation responses to sagebrush-reduction treatments measured by satellites

Science.gov (United States)

Johnston, Aaron; Beever, Erik; Merkle, Jerod A.; Chong, Geneva W.

2018-01-01

Time series of vegetative indices derived from satellite imagery constitute tools to measure ecological effects of natural and management-induced disturbances to ecosystems. Over the past century, sagebrush-reduction treatments have been applied widely throughout western North America to increase herbaceous vegetation for livestock and wildlife. We used indices from satellite imagery to 1) quantify effects of prescribed-fire, herbicide, and mechanical treatments on vegetative cover, productivity, and phenology, and 2) describe how vegetation changed over time following these treatments. We hypothesized that treatments would increase herbaceous cover and accordingly shift phenologies towards those typical of grass-dominated systems. We expected prescribed burns would lead to the greatest and most-prolonged effects on vegetative cover and phenology, followed by herbicide and mechanical treatments. Treatments appeared to increase herbaceous cover and productivity, which coincided with signs of earlier senescence − signals expected of grass-dominated systems, relative to sagebrush-dominated systems. Spatial heterogeneity for most phenometrics was lower in treated areas relative to controls, which suggested treatment-induced homogenization of vegetative communities. Phenometrics that explain spring migrations of ungulates mostly were unaffected by sagebrush treatments. Fire had the strongest effect on vegetative cover, and yielded the least evidence for sagebrush recovery. Overall, treatment effects were small relative to those reported from field-based studies for reasons most likely related to sagebrush recovery, treatment specification, and untreated patches within mosaicked treatment applications. Treatment effects were also small relative to inter-annual variation in phenology and productivity that was explained by temperature, snowpack, and growing-season precipitation. Our results indicated that cumulative NDVI, late-season phenometrics, and spatial

Relative sensitivity of Normalized Difference Vegetation Index (NDVI) and Microwave Polarization Difference Index (MPDI) for vegetation and desertification monitoring

Science.gov (United States)

Becker, Francois; Choudhury, Bhaskar J.

1988-01-01

A simple equation relating the Microwave Polarization Difference Index (MPDI) and the Normalized Difference Vegetation Index (NDVI) is proposed which represents well data obtained from Nimbus 7/SMMR at 37 GHz and NOAA/AVHRR Channels 1 and 2. It is found that there is a limit which is characteristic of a particular type of cover for which both indices are equally sensitive to the variation of vegetation, and below which MPDI is more efficient than NDVI. The results provide insight into the relationship between water content and chlorophyll absorption at pixel size scales.

Evaluation of vegetation cover using the normalized difference vegetation index (NDVI

Directory of Open Access Journals (Sweden)

Gabriela Camargos Lima

2013-08-01

Full Text Available Soil loss by water erosion is the main cause of soil degradation in Brazil. However, erosion can be reduced by the presence of vegetation. The Normalized Difference Vegetation Index (NDVI makes it possible to identify the vegetative vigor of crops or natural vegetation which facilities the identification of areas with vegetation covers. This information is very important in identifying the phenomena which might be occurring in a particular area, especially those related to soil degradation by water erosion. Thus, the aim of this work was to assess the canopy cover by using NDVI, checking the image accuracy using the Coverage Index (CI based on the Stocking method, in the Sub-basin of Posses, which belongs to the Cantareira System, located in the Extrema municipality, Minas Gerais, Brazil. Landsat-5 TM images were used. The sub-basin of Posses was very altered in comparison to the surrounding areas. The NDVI technique proved to be a suitable tool to assess the uses that occur in the sub-basin of Posses, as validated by the Stocking methodology. The map derived from NDVI allowed the geographic distribution of different land uses to be observed and allowed for the identification of critical areas in relation to vegetation cover as well. This finding can be used to optimize efforts to recover and protect soil in areas with bare soil and degraded pasture, in order to reduce environmental degradation. The CI has not exceeded 40% for land use classes that occur in the majority of the sub-basin (91%, except in areas of woody vegetation.

[Correlation analysis on normalized difference vegetation index (NDVI) of different vegetations and climatic factors in Southwest China].

Science.gov (United States)

Zhang, Yuan-Dong; Zhang, Xiao-He; Liu, Shi-Rong

2011-02-01

Based on the 1982-2006 NDVI remote sensing data and meteorological data of Southwest China, and by using GIS technology, this paper interpolated and extracted the mean annual temperature, annual precipitation, and drought index in the region, and analyzed the correlations of the annual variation of NDVI in different vegetation types (marsh, shrub, bush, grassland, meadow, coniferous forest, broad-leaved forest, alpine vegetation, and cultural vegetation) with corresponding climatic factors. In 1982-2006, the NDVI, mean annual temperature, and annual precipitation had an overall increasing trend, and the drought index decreased. Particularly, the upward trend of mean annual temperature was statistically significant. Among the nine vegetation types, the NDVI of bush and mash decreased, and the downward trend was significant for bush. The NDVI of the other seven vegetation types increased, and the upward trend was significant for coniferous forest, meadow, and alpine vegetation, and extremely significant for shrub. The mean annual temperature in the areas with all the nine vegetation types increased significantly, while the annual precipitation had no significant change. The drought index in the areas with marsh, bush, and cultural vegetation presented an increasing trend, that in the areas with meadow and alpine vegetation decreased significantly, and this index in the areas with other four vegetation types had an unobvious decreasing trend. The NDVI of shrub and coniferous forest had a significantly positive correlation with mean annual temperature, and that of shrub and meadow had significantly negative correlation with drought index. Under the conditions of the other two climatic factors unchanged, the NDVI of coniferous forest, broad-leaved forest, and alpine vegetation showed the strongest correlation with mean annual temperature, that of grass showed the strongest correlation with annual precipitation, and the NDVI of mash, shrub, grass, meadow, and cultural

Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series

Directory of Open Access Journals (Sweden)

Laura Ulsig

2017-01-01

Full Text Available Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI. This study investigates the potential of a Photochemical Reflectance Index (PRI, which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R2 = 0.36–0.8, which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R2 > 0.6 in all cases. The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.

Estimation of Mangrove Forest Aboveground Biomass Using Multispectral Bands, Vegetation Indices and Biophysical Variables Derived from Optical Satellite Imageries: Rapideye, Planetscope and SENTINEL-2

Science.gov (United States)

Balidoy Baloloy, Alvin; Conferido Blanco, Ariel; Gumbao Candido, Christian; Labadisos Argamosa, Reginal Jay; Lovern Caboboy Dumalag, John Bart; Carandang Dimapilis, Lee, , Lady; Camero Paringit, Enrico

2018-04-01

Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10 m, 20 m and 60 m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolution. Field data for biomass were collected from a Rhizophoraceae-dominated mangrove forest in Masinloc, Zambales, Philippines where 30 test plots (1.2 ha) and 5 validation plots (0.2 ha) were established. Prior to the generation of indices, images from the three satellite systems were pre-processed using atmospheric correction tools in SNAP (Sentinel-2), ENVI (RapidEye) and python (PlanetScope). The major predictor bands tested are Blue, Green and Red, which are present in the three systems; and Red-edge band from Sentinel-2 and Rapideye. The tested vegetation index predictors are Normalized Differenced Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green-NDVI (GNDVI), Simple Ratio (SR), and Red-edge Simple Ratio (SRre). The study generated prediction models through conventional linear regression and multivariate regression. Higher coefficient of determination (r2) values were obtained using multispectral band predictors for Sentinel-2 (r2 = 0.89) and Planetscope (r2 = 0.80); and vegetation indices for RapidEye (r2 = 0.92). Multivariate Adaptive Regression Spline (MARS) models performed better than the linear regression models with r2 ranging from 0.62 to 0.92. Based on the r2 and root-mean-square errors (RMSE's), the best biomass prediction model per satellite were chosen and maps were generated. The accuracy of predicted biomass maps were high for both Sentinel-2 (r2 = 0

Satellite-based hybrid drought monitoring tool for prediction of vegetation condition in Eastern Africa: A case study for Ethiopia

Science.gov (United States)

Tadesse, Tsegaye; Demisse, Getachew Berhan; Zaitchik, Ben; Dinku, Tufa

2014-03-01

An experimental drought monitoring tool has been developed that predicts the vegetation condition (Vegetation Outlook) using a regression-tree technique at a monthly time step during the growing season in Eastern Africa. This prediction tool (VegOut-Ethiopia) is demonstrated for Ethiopia as a case study. VegOut-Ethiopia predicts the standardized values of the Normalized Difference Vegetation Index (NDVI) at multiple time steps (weeks to months into the future) based on analysis of "historical patterns" of satellite, climate, and oceanic data over historical records. The model underlying VegOut-Ethiopia capitalizes on historical climate-vegetation interactions and ocean-climate teleconnections (such as El Niño and the Southern Oscillation (ENSO)) expressed over the 24 year data record and also considers several environmental characteristics (e.g., land cover and elevation) that influence vegetation's response to weather conditions to produce 8 km maps that depict future general vegetation conditions. VegOut-Ethiopia could provide vegetation monitoring capabilities at local, national, and regional levels that can complement more traditional remote sensing-based approaches that monitor "current" vegetation conditions. The preliminary results of this case study showed that the models were able to predict the vegetation stress (both spatial extent and severity) in drought years 1-3 months ahead during the growing season in Ethiopia. The correlation coefficients between the predicted and satellite-observed vegetation condition range from 0.50 to 0.90. Based on the lessons learned from past research activities and emerging experimental forecast models, future studies are recommended that could help Eastern Africa in advancing knowledge of climate, remote sensing, hydrology, and water resources.

Large-scale assessment of soil erosion in Africa: satellites help to jointly account for dynamic rainfall and vegetation cover

Science.gov (United States)

Vrieling, Anton; Hoedjes, Joost C. B.; van der Velde, Marijn

2015-04-01

Efforts to map and monitor soil erosion need to account for the erratic nature of the soil erosion process. Soil erosion by water occurs on sloped terrain when erosive rainfall and consequent surface runoff impact soils that are not well-protected by vegetation or other soil protective measures. Both rainfall erosivity and vegetation cover are highly variable through space and time. Due to data paucity and the relative ease of spatially overlaying geographical data layers into existing models like USLE (Universal Soil Loss Equation), many studies and mapping efforts merely use average annual values for erosivity and vegetation cover as input. We first show that rainfall erosivity can be estimated from satellite precipitation data. We obtained average annual erosivity estimates from 15 yr of 3-hourly TRMM Multi-satellite Precipitation Analysis (TMPA) data (1998-2012) using intensity-erosivity relationships. Our estimates showed a positive correlation (r = 0.84) with long-term annual erosivity values of 37 stations obtained from literature. Using these TMPA erosivity retrievals, we demonstrate the large interannual variability, with maximum annual erosivity often exceeding two to three times the mean value, especially in semi-arid areas. We then calculate erosivity at a 10-daily time-step and combine this with vegetation cover development for selected locations in Africa using NDVI - normalized difference vegetation index - time series from SPOT VEGETATION. Although we do not integrate the data at this point, the joint analysis of both variables stresses the need for joint accounting for erosivity and vegetation cover for large-scale erosion assessment and monitoring.

Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis

Science.gov (United States)

Alonso, Carmelo; Tarquis, Ana M.; Zúñiga, Ignacio; Benito, Rosa M.

2017-03-01

Several studies have shown that vegetation indexes can be used to estimate root zone soil moisture. Earth surface images, obtained by high-resolution satellites, presently give a lot of information on these indexes, based on the data of several wavelengths. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends the possible data archives from the present time to several decades back. Because of this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery. In this work, four band images have been considered, which are involved in these vegetation indexes, and were taken by satellites Ikonos-2 and Landsat-7 of the same geographic location, to study the effect of both spatial (pixel size) and radiometric (number of bits coding the image) resolution on these wavelength bands as well as two vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). In order to do so, a multi-fractal analysis of these multi-spectral images was applied in each of these bands and the two indexes derived. The results showed that spatial resolution has a similar scaling effect in the four bands, but radiometric resolution has a larger influence in blue and green bands than in red and near-infrared bands. The NDVI showed a higher sensitivity to the radiometric resolution than EVI. Both were equally affected by the spatial resolution. From both factors, the spatial resolution has a major impact in the multi-fractal spectrum for all the bands and the vegetation indexes. This information should be taken in to account when vegetation indexes based on different satellite sensors are obtained.

Vegetation classification and quatification by satellite image processing. A case study in north Portugal

Energy Technology Data Exchange (ETDEWEB)

Aranha, J.T. [Dept. Florestal, UTAD, 5001-801 Vila Real (Portugal); Viana, H.F. [Instituto Politecnico de Viseu, Escola Superior Agraria, Viseu (Portugal); Rodrigues, R. [Bioflag - Consulting - Santo Tirso (Portugal)

2008-07-01

The expected increase in Forest Biomass demand for energy production leads to derive expeditious and non-expensive techniques in order to classify vegetal land cover and evaluate the available biomass like to be harvested. Satellite image processing and classification, combined to field work, is a suitable tool to achieve these aims. A vegetation index (NDVI) was created by means of a Landsat TM image, from 2006, manipulation, in order to create a general vegetation map. Then, the same image was submitted to a supervised classification process in order to produce a land cover map (overall accuracy of 85%). In a second stage, they were collected NDVI values for each sampling plot, in order to update the database previous developed with data collected within forestry stands and shrubland. This data merging enabled to transform general vegetation map into available biomass within forestry stands and shrubland. The results showed a range of values from 0.25 up to 6.00 dry ton./ha for recent and former burnt areas recovered by Pinus pinaster (maritime pine) young trees and from 2.00 up to 9.00 dry ton./ha for recent and former burnt areas recovered by shrubs (e.g. genista or broom).

Long-term decrease in satellite vegetation indices in response to environmental variables in an iconic desert riparian ecosystem: the Upper San Pedro, Arizona, United States

Science.gov (United States)

Nguyen, Uyen; Glenn, Edward P.; Nagler, Pamela L.; Scott, Russell L.

2015-01-01

The Upper San Pedro River is one of the few remaining undammed rivers that maintain a vibrant riparian ecosystem in the southwest United States. However, its riparian forest is threatened by diminishing groundwater and surface water inputs, due to either changes in watershed characteristics such as changes in riparian and upland vegetation, or human activities such as regional groundwater pumping. We used satellite vegetation indices to quantify the green leaf density of the groundwater-dependent riparian forest from 1984 to 2012. The river was divided into a southern, upstream (mainly perennial flow) reach and a northern, downstream (mainly intermittent and ephemeral flow) reach. Pre-monsoon (June) Landsat normalized difference vegetation index (NDVI) values showed a 20% drop for the northern reach (P  0·05). NDVI and enhanced vegetation index values were positively correlated (P deterioration of the riparian forest in the northern reach.

Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa

Science.gov (United States)

Sedda, Luigi; Tatem, Andrew J.; Morley, David W.; Atkinson, Peter M.; Wardrop, Nicola A.; Pezzulo, Carla; Sorichetta, Alessandro; Kuleszo, Joanna; Rogers, David J.

2015-01-01

Background Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI. Methods In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa. Results This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found. Conclusions These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality. PMID:25733559

Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment

Directory of Open Access Journals (Sweden)

Thilanki Dahigamuwa

2016-10-01

Full Text Available Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, vegetation cover is subject to phenological variations and therefore, timely classification of land cover is an essential step in effective evaluation of landslide hazard potential. The work presented here investigates methods that can be used for land cover classification based on the Normalized Difference Vegetation Index (NDVI, derived from up-to-date satellite images, and the feasibility of application in landslide risk prediction. A major benefit of this method would be the eventual ability to employ NDVI as a stand-alone parameter for accurate assessment of the impact of land cover in landslide hazard evaluation. An added benefit would be the timely detection of undesirable practices such as deforestation using satellite imagery. A landslide-prone region in Oregon, USA is used as a model for the application of the classification method. Five selected classification techniques—k-nearest neighbor, Gaussian support vector machine (GSVM, artificial neural network, decision tree and quadratic discriminant analysis support the viability of the NDVI-based land cover classification. Finally, its application in landslide risk evaluation is demonstrated.

Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico

Directory of Open Access Journals (Sweden)

D. J. Vega-Nieva

2018-04-01

Full Text Available Understanding the linkage between accumulated fuel dryness and temporal fire occurrence risk is key for improving decision-making in forest fire management, especially under growing conditions of vegetation stress associated with climate change. This study addresses the development of models to predict the number of 10-day observed Moderate-Resolution Imaging Spectroradiometer (MODIS active fire hotspots—expressed as a Fire Hotspot Density index (FHD—from an Accumulated Fuel Dryness Index (AcFDI, for 17 main vegetation types and regions in Mexico, for the period 2011–2015. The AcFDI was calculated by applying vegetation-specific thresholds for fire occurrence to a satellite-based fuel dryness index (FDI, which was developed after the structure of the Fire Potential Index (FPI. Linear and non-linear models were tested for the prediction of FHD from FDI and AcFDI. Non-linear quantile regression models gave the best results for predicting FHD using AcFDI, together with auto-regression from previously observed hotspot density values. The predictions of 10-day observed FHD values were reasonably good with R2 values of 0.5 to 0.7 suggesting the potential to be used as an operational tool for predicting the expected number of fire hotspots by vegetation type and region in Mexico. The presented modeling strategy could be replicated for any fire danger index in any region, based on information from MODIS or other remote sensors.

Vegetation cover and their functioning in dependence on the reclamation of the Velka podkrusnohorska dump during last 20 years using satellite data analysis

International Nuclear Information System (INIS)

Prochazka, J.; Nedbal, V.; Pecharova, E.; Brom, J.

2010-01-01

Vegetation plays a significant role in mass retention, solar energy dissipation, water cycle forming and local climate changes on reclamation plots of mining areas. This paper discussed the use of Landsat satellite data in order to evaluate different types of reclamation and their development for the last 20 years in the case of the Velka podkrusnohorska dump. Biophysical parameters which can be indicators of solar energy dissipation that were utilized to analyse changes of temporal development from 1991 to 2009 included land surface temperature, surface moisture expressed as wetness index tasseled cap, and normalized difference vegetation index. From these parameters, a functional index was then developed. The paper discussed the development of these parameters and their relationship to solar energy dissipation. It was concluded that since 1995, the observed parameters significantly changed, gradually converging to the state of the surrounding landscape. 16 refs., 2 tabs., 2 figs.

Vegetation cover and their functioning in dependence on the reclamation of the Velka podkrusnohorska dump during last 20 years using satellite data analysis

Energy Technology Data Exchange (ETDEWEB)

Prochazka, J.; Nedbal, V.; Pecharova, E. [South Bohemia Univ., Ceske Budejovice (Czech Republic); Brom, J. [Enki o.p.s., Trebon (Czech Republic)

2010-07-01

Vegetation plays a significant role in mass retention, solar energy dissipation, water cycle forming and local climate changes on reclamation plots of mining areas. This paper discussed the use of Landsat satellite data in order to evaluate different types of reclamation and their development for the last 20 years in the case of the Velka podkrusnohorska dump. Biophysical parameters which can be indicators of solar energy dissipation that were utilized to analyse changes of temporal development from 1991 to 2009 included land surface temperature, surface moisture expressed as wetness index tasseled cap, and normalized difference vegetation index. From these parameters, a functional index was then developed. The paper discussed the development of these parameters and their relationship to solar energy dissipation. It was concluded that since 1995, the observed parameters significantly changed, gradually converging to the state of the surrounding landscape. 16 refs., 2 tabs., 2 figs.

River flow response to changes in vegetation cover in a South ...

African Journals Online (AJOL)

It was hypothesised in this study that annual river yield (river flow as a fraction of rainfall) in the Molenaars catchment near Paarl, South Africa co-varies with an index of green vegetation cover derived from satellite data (the normalised difference vegetation index, NDVI). The catchment was partitioned into 'upland' and ...

Use of Radar Vegetation Index (RVI) in Passive Microwave Algorithms for Soil Moisture Estimates

Science.gov (United States)

Rowlandson, T. L.; Berg, A. A.

2013-12-01

The Soil Moisture Active Passive (SMAP) satellite will provide a unique opportunity for the estimation of soil moisture by having simultaneous radar and radiometer measurements available. As with the Soil Moisture and Ocean Salinity (SMOS) satellite, the soil moisture algorithms will need to account for the contribution of vegetation to the brightness temperature. Global maps of vegetation volumetric water content (VWC) are difficult to obtain, and the SMOS mission has opted to estimate the optical depth of standing vegetation by using a relationship between the VWC and the leaf area index (LAI). LAI is estimated from optical remote sensing or through soil-vegetation-atmosphere transfer modeling. During the growing season, the VWC of agricultural crops can increase rapidly, and if cloud cover exists during an optical acquisition, the estimation of LAI may be delayed, resulting in an underestimation of the VWC and overestimation of the soil moisture. Alternatively, the radar vegetation index (RVI) has shown strong correlation and linear relationship with VWC for rice and soybeans. Using the SMAP radar to produce RVI values that are coincident to brightness temperature measurements may eliminate the need for LAI estimates. The SMAP Validation Experiment 2012 (SMAPVEX12) was a cal/val campaign for the SMAP mission held in Manitoba, Canada, during a 6-week period in June and July, 2012. During this campaign, soil moisture measurements were obtained for 55 fields with varying soil texture and vegetation cover. Vegetation was sampled from each field weekly to determine the VWC. Soil moisture measurements were taken coincident to overpasses by an aircraft carrying the Passive and Active L-band System (PALS) instrumentation. The aircraft flew flight lines at both high and low altitudes. The low altitude flight lines provided a footprint size approximately equivalent to the size of the SMAPVEX12 field sites. Of the 55 field sites, the low altitude flight lines provided

Vegetation mapping with satellite data of the Forsmark and Tierp regions

Energy Technology Data Exchange (ETDEWEB)

Boresjoe-Bronge, Laine; Wester, Kjell [SwedPower, Stockholm (Sweden)

2002-04-01

SKB (Swedish Nuclear Fuel and Waste Management Co) performs a siting program for deep repository of spent nuclear fuel that includes survey of three potential sites. The SKB siting process has now reached the site investigation phase. There are several fields of investigations performed in this phase. One of them is description of the surface ecosystems. The surface ecosystems are mapped both on a regional (50-100 km{sup 2} ) and a local level (1 km{sup 2} ). Two inventory methods are used, remote sensing (satellite data/aerial photographs) for the regional level, and field inventory for the detailed level. As a part of the surface ecosystem characterisation on the regional level vegetation mapping using satellite data has been performed over the three potential deep depository sites, Forsmark, Tierp and Oskarshamn. The user requirements for the vegetation mapping of the potential sites are the following: Dominated species in the tree layer, shrub layer, field layer and ground layer shall be described both on regional and local level; Dominated species in all layers shall be quantified regarding share and percentage of ground cover, or absence of cover (vegetation free ground); The regional and the local inventory shall have identical or comparable classification systems; The classification system and the method used shall make it possible to scale the results from local to regional level and vice versa; The produced layers shall be presented in digital form and make it possible to model biomass and turnover of organic matter (carbon, nutrients, water); The produced information shall in a first phase be of use for planning and for making nature and environmental considerations. Data sources used in the study include geo-referenced SPOT4 XI data (20 m ground resolution), geo-referenced Landsat TM data (30 m ground resolution), soil type data, topographic map data and colour infrared aerial photographs. The production of vegetation layers has been carried out in two

Vegetation mapping with satellite data of the Forsmark and Tierp regions

International Nuclear Information System (INIS)

Boresjoe-Bronge, Laine; Wester, Kjell

2002-04-01

SKB (Swedish Nuclear Fuel and Waste Management Co) performs a siting program for deep repository of spent nuclear fuel that includes survey of three potential sites. The SKB siting process has now reached the site investigation phase. There are several fields of investigations performed in this phase. One of them is description of the surface ecosystems. The surface ecosystems are mapped both on a regional (50-100 km 2 ) and a local level (1 km 2 ). Two inventory methods are used, remote sensing (satellite data/aerial photographs) for the regional level, and field inventory for the detailed level. As a part of the surface ecosystem characterisation on the regional level vegetation mapping using satellite data has been performed over the three potential deep depository sites, Forsmark, Tierp and Oskarshamn. The user requirements for the vegetation mapping of the potential sites are the following: Dominated species in the tree layer, shrub layer, field layer and ground layer shall be described both on regional and local level; Dominated species in all layers shall be quantified regarding share and percentage of ground cover, or absence of cover (vegetation free ground); The regional and the local inventory shall have identical or comparable classification systems; The classification system and the method used shall make it possible to scale the results from local to regional level and vice versa; The produced layers shall be presented in digital form and make it possible to model biomass and turnover of organic matter (carbon, nutrients, water); The produced information shall in a first phase be of use for planning and for making nature and environmental considerations. Data sources used in the study include geo-referenced SPOT4 XI data (20 m ground resolution), geo-referenced Landsat TM data (30 m ground resolution), soil type data, topographic map data and colour infrared aerial photographs. The production of vegetation layers has been carried out in two steps. In

Vegetation Index, Lake Vegetation Index Regions.This layer describes the spatial extent of the North and South Lake Vegetation Index (LVI) biological regions, as described in Fore et al. 2007, Assessing the Biological Condition of Florida Lakes: Development of the Lake Veg, Published in 2008, 1:24000 (1in=2000ft) scale, Florida Department of Environmental Protection (FDEP).

Data.gov (United States)

NSGIC State | GIS Inventory — Vegetation Index dataset current as of 2008. Lake Vegetation Index Regions.This layer describes the spatial extent of the North and South Lake Vegetation Index (LVI)...

Exploiting differential vegetation phenology for satellite-based mapping of semiarid grass vegetation in the southwestern United States and northern Mexico

Science.gov (United States)

Dye, Dennis G.; Middleton, Barry R.; Vogel, John M.; Wu, Zhuoting; Velasco, Miguel G.

2016-01-01

We developed and evaluated a methodology for subpixel discrimination and large-area mapping of the perennial warm-season (C4) grass component of vegetation cover in mixed-composition landscapes of the southwestern United States and northern Mexico. We describe the methodology within a general, conceptual framework that we identify as the differential vegetation phenology (DVP) paradigm. We introduce a DVP index, the Normalized Difference Phenometric Index (NDPI) that provides vegetation type-specific information at the subpixel scale by exploiting differential patterns of vegetation phenology detectable in time-series spectral vegetation index (VI) data from multispectral land imagers. We used modified soil-adjusted vegetation index (MSAVI2) data from Landsat to develop the NDPI, and MSAVI2 data from MODIS to compare its performance relative to one alternate DVP metric (difference of spring average MSAVI2 and summer maximum MSAVI2), and two simple, conventional VI metrics (summer average MSAVI2, summer maximum MSAVI2). The NDPI in a scaled form (NDPIs) performed best in predicting variation in perennial C4 grass cover as estimated from landscape photographs at 92 sites (R2 = 0.76, p landscapes of the Southwest, and potentially for monitoring of its response to drought, climate change, grazing and other factors, including land management. With appropriate adjustments, the method could potentially be used for subpixel discrimination and mapping of grass or other vegetation types in other regions where the vegetation components of the landscape exhibit contrasting seasonal patterns of phenology.

Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)

Science.gov (United States)

Wilson, Natalie R.; Norman, Laura

2018-01-01

Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann– Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.

Spatiotemporal changes of normalized difference vegetation index (NDVI) and response to climate extremes and ecological restoration in the Loess Plateau, China

Science.gov (United States)

Zhao, Anzhou; Zhang, Anbing; Liu, Xianfeng; Cao, Sen

2018-04-01

Extreme drought, precipitation, and other extreme climatic events often have impacts on vegetation. Based on meteorological data from 52 stations in the Loess Plateau (LP) and a satellite-derived normalized difference vegetation index (NDVI) from the third-generation Global Inventory Modeling and Mapping Studies (GIMMS3g) dataset, this study investigated the relationship between vegetation change and climatic extremes from 1982 to 2013. Our results showed that the vegetation coverage increased significantly, with a linear rate of 0.025/10a ( P NDVI revealed an increasing trend from the northwest to the southeast, with about 61.79% of the LP exhibiting a significant increasing trend ( P NDVI at the yearly time scale ( P NDVI during the spring and autumn ( P NDVI and RX1day, TMAXmean, TXn, and TXx was insignificant in summer. Vegetation exhibited a significant negative relationship with precipitation extremes in winter ( P NDVI in Yan'an and Yulin during 1998-2013, r = 0.859 and 0.85, n = 16, P < 0.001.

High-latitude tree growth and satellite vegetation indices: Correlations and trends in Russia and Canada (1982-2008)

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Berner, Logan T.; Beck, Pieter S. A.; Bunn, Andrew G.; Lloyd, Andrea H.; Goetz, Scott J.

2011-03-01

Vegetation in northern high latitudes affects regional and global climate through energy partitioning and carbon storage. Spaceborne observations of vegetation, largely based on the normalized difference vegetation index (NDVI), suggest decreased productivity during recent decades in many regions of the Eurasian and North American boreal forests. To improve interpretation of NDVI trends over forest regions, we examined the relationship between NDVI from the advanced very high resolution radiometers and tree ring width measurements, a proxy of tree productivity. We collected tree core samples from spruce, pine, and larch at 22 sites in northeast Russia and northwest Canada. Annual growth rings were measured and used to generate site-level ring width index (RWI) chronologies. Correlation analysis was used to assess the association between RWI and summer NDVI from 1982 to 2008, while linear regression was used to examine trends in both measurements. The correlation between NDVI and RWI was highly variable across sites, though consistently positive (r = 0.43, SD = 0.19, n = 27). We observed significant temporal autocorrelation in both NDVI and RWI measurements at sites with evergreen conifers (spruce and pine), though weak autocorrelation at sites with deciduous conifers (larch). No sites exhibited a positive trend in both NDVI and RWI, although five sites showed negative trends in both measurements. While there are technological and physiological limitations to this approach, these findings demonstrate a positive association between NDVI and tree ring measurements, as well as the importance of considering lagged effects when modeling vegetation productivity using satellite data.

Global changes in dryland vegetation dynamics (1988–2008 assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data

Directory of Open Access Journals (Sweden)

N. Andela

2013-10-01

Full Text Available Drylands, covering nearly 30% of the global land surface, are characterized by high climate variability and sensitivity to land management. Here, two satellite-observed vegetation products were used to study the long-term (1988–2008 vegetation changes of global drylands: the widely used reflective-based Normalized Difference Vegetation Index (NDVI and the recently developed passive-microwave-based Vegetation Optical Depth (VOD. The NDVI is sensitive to the chlorophyll concentrations in the canopy and the canopy cover fraction, while the VOD is sensitive to vegetation water content of both leafy and woody components. Therefore it can be expected that using both products helps to better characterize vegetation dynamics, particularly over regions with mixed herbaceous and woody vegetation. Linear regression analysis was performed between antecedent precipitation and observed NDVI and VOD independently to distinguish the contribution of climatic and non-climatic drivers in vegetation variations. Where possible, the contributions of fire, grazing, agriculture and CO2 level to vegetation trends were assessed. The results suggest that NDVI is more sensitive to fluctuations in herbaceous vegetation, which primarily uses shallow soil water, whereas VOD is more sensitive to woody vegetation, which additionally can exploit deeper water stores. Globally, evidence is found for woody encroachment over drylands. In the arid drylands, woody encroachment appears to be at the expense of herbaceous vegetation and a global driver is interpreted. Trends in semi-arid drylands vary widely between regions, suggesting that local rather than global drivers caused most of the vegetation response. In savannas, besides precipitation, fire regime plays an important role in shaping trends. Our results demonstrate that NDVI and VOD provide complementary information and allow new insights into dryland vegetation dynamics.

Estimating foliar nitrogen in Eucalyptus using vegetation indexes

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Luiz Felipe Ramalho de Oliveira

Full Text Available ABSTRACT Nitrogen (N has commonly been applied in Eucalyptus stands in Brazil and it has a direct relation with biomass production and chlorophyll content. Foliar N concentrations are used to diagnose soil and plant fertility levels and to develop N fertilizer application rates. Normally, foliar N is obtained using destructive methods, but indirect analyses using Vegetation Indexes (VIs may be possible. The aim of this work was to evaluate VIs to estimate foliar N concentration in three Eucalyptus clones. Lower crown leaves of three clonal Eucalyptus plantations (25 months old were classified into five color patterns using the Munsell Plant Tissue Color Chart. For each color, N concentration was determined by the Kjeldahl method and foliar reflectance was measured using a CI-710 Miniature Leaf Spectrometer. Foliar reflectance data were used to obtain the VIs and the VIs were used to estimate N concentrations. In the visible region, the relationship between N concentration and reflectance percentage was negative. The highest correlations between VIs and N concentrations were obtained by the Inflection Point Position (IPP, r = 0.97, Normalized Difference Red-Edge (reNDVI, r = 0.97 and Modified Red-Edge Normalized Difference Vegetation Index (mNDI, r = 0.97. Vegetation indexes on the red edge region provided the most accurate estimates of foliar N concentration. The reNDVI index provided the best N concentration estimates in leaves of different colors of Eucalyptus urophylla × grandis and Eucalyptus urophylla × urophylla (R2 = 0.97 and RMSE = 0.91 g kg−1.

Relationship between satellite microwave radiometric data, antecedent precipitation index, and regional soil moisture

International Nuclear Information System (INIS)

Teng, W.L.; Wang, J.R.; Doraiswamy, P.C.

1993-01-01

Satellite microwave brightness temperatures (TB 'S) have been shown, in previous studies for semi-arid environments, to correlate well with the antecedent precipitation index (API), a soil moisture indicator. The current study, using the Special Sensor Microwave/Imager (SSM/I), continued this work for parts of the U.S. Corn and Wheat Belts, which included areas with a more humid climate, a denser natural vegetation cover, and a different mix of agricultural crop types. Four years (1987-1990) of SSM/I data at 19 and 37GHz, daily precipitation and temperature data from weather stations, and API calculated from the weather data were processed, geo-referenced, and averaged to equation pending latitude-longitude grid quadrants. Correlation results between TB at 19 GHz and API were highly dependent on geographical location. Correlation coefficients (r values) ranged from —0-6 to —0-85 for the semi-arid parts of the study area and from —03 to —0-7 for the more humid and more densely vegetated parts. R values were also higher for the very dry and very wet years (—0-5 to —085) than for the 'normal’ year (—0-3 to —0-65). Similar to previous results, the Microwave Polarization Difference Index (MPDI), based on the 37 GHz data, was found to correspond to variations in vegetation cover. The MPDI was used to develop a linear regression model to estimate API from TB . Correlation between estimated and calculated APIs was also geographically and time dependent. Comparison of API with some field soil moisture measurements showed a similar trend, which provided some degree of confidence in using API as an indicator of soil moisture

Study of a Vegetation Index Based on HJ CCD Data's top-of-atmosphere reflectance and FPAR Inversion

International Nuclear Information System (INIS)

Dong, Taifeng; Wu, Bingfang; Meng, Jihua

2014-01-01

The Fraction of Photosynthetically Active Radiation (FPAR)absorbed by plant canopies is a key parameter for monitoring crop condition and estimating crop yield. In general, it is necessary to obtain Top of Canopy (TOC) reflectance from optical remote sensing data in digital number through atmospheric correction procedures before retrieving FPAR. However, there are a few of uncertainties that existe in the process of atmosphere correction and reduced the quality of TOC. This paper presents a vegetation index based on Top-of-Atmosphere (TOA) reflectance derived from HJ-1 CCD satellite for estimating direct crop FPAR. The vegetation index (HJVI) was designed based on the simulated results of a canopy-atmosphere radiative transfer model, including TOA reflectance and corresponded FPAR. The HJVI had taken the advantages of information in the green, the red and the near-infrared spectral domainswith with a aim of reducing the atmospheric effect and enhancing the sensitive to green vegetation. The HJVI was used to estimate soybean FPAR directly and validated using field measurements. The result indicated that the inversion algorithm produced a good relationship between the prediction and measurement (R 2 = 0.546, RMSE = 0.083) and the HJVI showed high potential for estimating FPAR based on the HJ-1 TOA reflectance directly

Vegetation coupling to global climate: Trajectories of vegetation change and phenology modeling from satellite observations

Science.gov (United States)

Fisher, Jeremy Isaac

Important systematic shifts in ecosystem function are often masked by natural variability. The rich legacy of over two decades of continuous satellite observations provides an important database for distinguishing climatological and anthropogenic ecosystem changes. Examples from semi-arid Sudanian West Africa and New England (USA) illustrate the response of vegetation to climate and land-use. In Burkina Faso, West Africa, pastoral and agricultural practices compete for land area, while degradation may follow intensification. The Nouhao Valley is a natural experiment in which pastoral and agricultural land uses were allocated separate, coherent reserves. Trajectories of annual net primary productivity were derived from 18 years of coarse-grain (AVHRR) satellite data. Trends suggested that pastoral lands had responded rigorously to increasing rainfall after the 1980's droughts. A detailed analysis at Landsat resolution (30m) indicated that the increased vegetative cover was concentrated in the river basins of the pastoral region, implying a riparian wood expansion. In comparison, riparian cover was reduced in agricultural regions. We suggest that broad-scale patterns of increasing semi-arid West African greenness may be indicative of climate variability, whereas local losses may be anthropogenic in nature. The contiguous deciduous forests, ocean proximity, topography, and dense urban developments of New England provide an ideal landscape to examine influences of climate variability and the impact of urban development vegetation response. Spatial and temporal patterns of interannual climate variability were examined via green leaf phenology. Phenology, or seasonal growth and senescence, is driven by deficits of light, temperature, and water. In temperate environments, phenology variability is driven by interannual temperature and precipitation shifts. Average and interannual phenology analyses across southern New England were conducted at resolutions of 30m (Landsat

Vegetation index analysis of multi-source remote sensing data in coal mine wasteland

Energy Technology Data Exchange (ETDEWEB)

Han, Y.X.; Li, M.Z.; Li, D.L. [China Agricultural University, Beijing (China)

2007-12-15

Thirty-six soil samples were collected and their hyperspectral data used to calculate vegetation indices such as a normalised difference vegetation index (NDVI) and a difference vegetation index (DVI). These were evaluated for typical surface object features within the wastelands around Haizhou Opencast Coal Mine in Fuxin city. A principal component analysis to the hyperspectral data was performed, and the result showed that the first and the second principal components satisfactorily accounted for the multi-spectral image information. The panchromatic and multi-spectral images of SPOT5 were then merged. The panchromatic image replaced the first principal component to improve spatial resolution of the image. In addition, the multispectral images and the NDVI image were classified into six types using the unsupervised classification method. The linear quantitative models were built up and the highest correlation coefficients were obtained between the hyperspectral vegetation index and the vegetation index data from the SPOT5 image. The results show that the hyperspectral data and remote sensing images can be used for quantitative estimation of soil nutrients in coal mine wasteland. They can also provide large area surface information for fast and effective decision making regarding revegetation and the monitoring of dynamic change.

Using satellite data on meteorological and vegetation characteristics and soil surface humidity in the Land Surface Model for the vast territory of agricultural destination

Science.gov (United States)

Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Vasilenko, Eugene; Volkova, Elena; Kukharsky, Alexander

2017-04-01

vegetation cover (taken for vegetation temperature) Ta and efficient radiation temperature Ts.eff, as well as land surface emissivity E, normalized difference vegetation index NDVI, vegetation cover fraction B, and leaf area index LAI. The SEVIRI-based retrievals have included precipitation, LST Tls and Ta, E at daylight and nighttime, LAI (daily), and B. From the MSU-MR data there have been retrieved values of all the same characteristics as from the AVHRR data. The MSU-MR-based daily and monthly sums of precipitation have been calculated using the developed earlier and modified Multi Threshold Method (MTM) intended for the cloud detection and identification of its types around the clock as well as allocation of precipitation zones and determination of instantaneous maximum rainfall intensities for each pixel at that the transition from assessing rainfall intensity to estimating their daily values is a key element of the MTM. Measurement data from 3 IR MSU-MR channels (3.8, 11 i 12 μm) as well as their differences have been used in the MTM as predictors. Controlling the correctness of the MSU-MR-derived rainfall estimates has been carried out when comparing with analogous AVHRR- and SEVIRI-based retrievals and with precipitation amounts measured at the agricultural meteorological station of the study region. Probability of rainfall zones determination from the MSU-MR data, to match against the actual ones, has been 75-85% as well as for the AVHRR and SEVIRI data. The time behaviors of satellite-derived and ground-measured daily and monthly precipitation sums for vegetation season and yeaŗ correspondingly, have been in good agreement with each other although the first ones have been smoother than the latter. Discrepancies have existed for a number of local maxima for which satellite-derived precipitation estimates have been less than ground-measured values. It may be due to the different spatial scales of areal satellite-derived and point ground-based estimates. Some

Analysis of Decadal Vegetation Dynamics Using Multi-Scale Satellite Images

Science.gov (United States)

Chiang, Y.; Chen, K.

2013-12-01

This study aims at quantifying vegetation fractional cover (VFC) by incorporating multi-resolution satellite images, including Formosat-2(RSI), SPOT(HRV/HRG), Landsat (MSS/TM) and Terra/Aqua(MODIS), to investigate long-term and seasonal vegetation dynamics in Taiwan. We used 40-year NDVI records for derivation of VFC, with field campaigns routinely conducted to calibrate the critical NDVI threshold. Given different sensor capabilities in terms of their spatial and spectral properties, translation and infusion of NDVIs was used to assure NDVI coherence and to determine the fraction of vegetation cover at different spatio-temporal scales. Based on the proposed method, a bimodal sequence of intra-annual VFC which corresponds to the dual-cropping agriculture pattern was observed. Compared to seasonal VFC variation (78~90%), decadal VFC reveals moderate oscillations (81~86%), which were strongly linked with landuse changes and several major disturbances. This time-series mapping of VFC can be used to examine vegetation dynamics and its response associated with short-term and long-term anthropogenic/natural events.

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

Science.gov (United States)

Zeng, Fanwei; Collatz, George James; Pinzon, Jorge E.; Ivanoff, Alvaro

2013-01-01

Satellite observations of surface reflected solar radiation contain informationabout variability in the absorption of solar radiation by vegetation. Understanding thecauses of variability is important for models that use these data to drive land surface fluxesor for benchmarking prognostic vegetation models. Here we evaluated the interannualvariability in the new 30.5-year long global satellite-derived surface reflectance index data,Global Inventory Modeling and Mapping Studies normalized difference vegetation index(GIMMS NDVI3g). Pearsons correlation and multiple linear stepwise regression analyseswere applied to quantify the NDVI interannual variability driven by climate anomalies, andto evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVIsignal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systemswhere in some regions and seasons 40 of the NDVI variance could be explained byprecipitation anomalies. Temperature correlations were strongest in northern mid- to-high-latitudes in the spring and early summer where up to 70 of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America,winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wetseason precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.

Estimating riparian and agricultural evapotranspiration by reference crop evapotranspiration and MODIS Enhanced Vegetation Index

Science.gov (United States)

Nagler, Pamela L.; Glenn, Edward P.; Nguyen, Uyen; Scott, Russell; Doody, Tania

2013-01-01

Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ETa) based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ETo). The algorithm was calibrated with five years of ETa data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ETa data for alfalfa and cotton from the literature. The algorithm was based on an equation of the form ETa = ETo [a(1 − e−bEVI) − c], where the term (1 − e−bEVI) is derived from the Beer-Lambert Law to express light absorption by a canopy, with EVI replacing leaf area index as an estimate of the density of light-absorbing units. The resulting algorithm capably predicted ETa across riparian plants and crops (r2 = 0.73). It was then tested against water balance data for five irrigation districts and flux tower data for two riparian zones for which season-long or multi-year ETa data were available. Predictions were within 10% of measured results in each case, with a non-significant (P = 0.89) difference between mean measured and modeled ETa of 5.4% over all validation sites. Validation and calibration data sets were combined to present a final predictive equation for application across crops and riparian plant associations for monitoring individual irrigation districts or for conducting global water use assessments of mixed agricultural and riparian biomes.

Estimating Riparian and Agricultural Actual Evapotranspiration by Reference Evapotranspiration and MODIS Enhanced Vegetation Index

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Russell L. Scott

2013-08-01

Full Text Available Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ETa based on the Enhanced Vegetation Index (EVI from the Moderate Resolution Imaging Spectrometer (MODIS sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ETo. The algorithm was calibrated with five years of ETa data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ETa data for alfalfa and cotton from the literature. The algorithm was based on an equation of the form ETa = ETo [a(1 − e−bEVI − c], where the term (1 − e−bEVI is derived from the Beer-Lambert Law to express light absorption by a canopy, with EVI replacing leaf area index as an estimate of the density of light-absorbing units. The resulting algorithm capably predicted ETa across riparian plants and crops (r2 = 0.73. It was then tested against water balance data for five irrigation districts and flux tower data for two riparian zones for which season-long or multi-year ETa data were available. Predictions were within 10% of measured results in each case, with a non-significant (P = 0.89 difference between mean measured and modeled ETa of 5.4% over all validation sites. Validation and calibration data sets were combined to present a final predictive equation for application across crops and riparian plant associations for monitoring individual irrigation districts or for conducting global water use assessments of mixed agricultural and riparian biomes.

Vegetation Index and Phenology (VIP) Vegetation Indices Monthly Global 0.05Deg CMG V004

Data.gov (United States)

National Aeronautics and Space Administration — The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Vegetation Index and Phenology (VIP) global datasets were created using...

Ecological restoration of groundwater-dependent vegetation in the arid Ejina Delta: evidences from satellite evapotranspiration

Science.gov (United States)

Kai, Lu; Garcia, Monica; Yu, Jingjie; Zhang, Yichi; Wang, Ping; Wang, Sheng; Liu, Xiao

2017-04-01

The ecological water conveyance project (EWCP) in the Ejina delta, a typical hyper-arid area of China, aimed to restore degraded phreatophytic ecosystems. We assessed the degree of ecosystem recovery using as an ecohydrological indicator a ratio between actual and potential evapotranspiration derived from MODIS since the beginning of the project in 2001. The selected indicator was the Temperature Vegetation Dryness Index (TVDI) which was validated with Eddy covariance (EC) data confirming its applicability to monitor groundwater dependent vegetation. The spatial analyses of the evapotranspiration ratio show drying trends (2000-2015) which are stronger and also cover larger extensions than the wetting trends. Thus, the condition of key riparian areas relying mostly on surface water improved since the project began. However, groundwater dependent ecosystems located in lower river Xihe reaches present drying trends. It seems that despite of the runoff supplemented by the EWCP project, there is nowadays more inequality in the access to water by groundwater dependent ecosystems in the Ejina Delta. The study shows that energy-evaporation indices, relying on radiometric satellite temperature like the TVDI, can detect degradation signals that otherwise might go undetected by NDVI analyses especially in arid regions, where vegetation indices are greatly affected by the soil background signals. Additionally, they can provide timely information to water managers on how much water to allocate for a sustainable restoration program.

Single Tree Vegetation Depth Estimation Tool for Satellite Services Link Design

Directory of Open Access Journals (Sweden)

Z. Hasirci

2016-04-01

Full Text Available Attenuation caused by tree shadowing is an important factor for describing the propagation channel of satellite services. Thus, vegetation effects should be determined by experimental studies or empirical formulations. In this study, tree types in the Black Sea Region of Turkey are classified based on their geometrical shapes into four groups such as conic, ellipsoid, spherical and hemispherical. The variations of the vegetation depth according to different tree shapes are calculated with ray tracing method. It is showed that different geometrical shapes have different vegetation depths even if they have same foliage volume for different elevation angles. The proposed method is validated with the related literature in terms of average single tree attenuation. On the other hand, due to decrease system requirements (speed, memory usage etc. of ray tracing method, an artificial neural network is proposed as an alternative. A graphical user interface is created for the above processes in MATLAB environment named vegetation depth estimation tool (VdET.

Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring

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Meng Lu

2017-10-01

Full Text Available In recent years, sequential tests for detecting structural changes in time series have been adapted for deforestation monitoring using satellite data. The input time series of such sequential tests is typically a vegetation index (e.g., NDVI, which uses two or three bands and ignores all other bands. Being limited to a vegetation index will not benefit from the richer spectral information provided by newly launched satellites and will bring two bottle-necks for deforestation monitoring. Firstly, it is hard to select a suitable vegetation index a priori. Secondly, a single vegetation index is typically affected by seasonal signals, noise and other natural dynamics, which decrease its power for deforestation detection. A novel multispectral time series change monitoring method that combines dimension reduction methods with a sequential hypothesis test is proposed to address these limitations. For each location, the proposed method automatically chooses a “suitable” index for deforestation monitoring. To demonstrate our approach, we implemented it in two study areas: a dry tropical forest in Bolivia (time series length: 444 with strong seasonality and a moist tropical forest in Brazil (time series length: 225 with almost no seasonality. Our method significantly improves accuracy in the presence of strong seasonality, in particular the temporal lag between disturbance and its detection.

Using VEGETATION satellite data and the crop model STICS-Prairie to estimate pasture production at the national level in France

Science.gov (United States)

Di Bella, C.; Faivre, R.; Ruget, F.; Seguin, B.

In France, pastures constitute an important land cover type, sustaining principally husbandry production. The absence of low-cost methods applicable to large regions has conducted to the use of simulation models, as in the ISOP system. Remote sensing data may be considered as a potential tool to improve a correct diagnosis in a real time framework. Thirteen forage regions (FR) of France, differing in their soil, climatic and productive characteristics were selected for this purpose. SPOT4-VEGETATION images have been used to provide, using subpixel estimation models, the spectral signature corresponding to pure pasture conditions. This information has been related with some growth variables estimated by STICS-Prairie model (inside ISOP system). Beyond the good general agreement between the two types of data, we found that the best relations were observed between NDVI middle infrared based index (SWVI) and leaf area index. The results confirm the capacities of the satellite data to provide complementary productive variables and help to identify the spatial and temporal differences between satellite and model information, mainly during the harvesting periods. This could contribute to improve the evaluations of the model on a regional scale.

Utilization of satellite remote sensing data on land surface characteristics in water and heat balance component modeling for vegetation covered territories

Science.gov (United States)

Muzylev, Eugene; Uspensky, Alexander; Startseva, Zoya; Volkova, Elena; Kukharsky, Alexander; Uspensky, Sergey

2010-05-01

The model of vertical water and heat transfer in the "soil-vegetation-atmosphere" system (SVAT) for vegetation covered territory has been developed, allowing assimilating satellite remote sensing data on land surface condition as well as accounting for heterogeneities of vegetation and meteorological characteristics. The model provides the calculation of water and heat balance components (such as evapotranspiration Ev, soil water content W, sensible and latent heat fluxes and others ) as well as vertical soil moisture and temperature distributions, temperatures of soil surface and foliage, land surface brightness temperature for any time interval within vegetation season. To describe the landscape diversity soil constants and leaf area index LAI, vegetation cover fraction B, and other vegetation characteristics are used. All these values are considered to be the model parameters. Territory of Kursk region with square about 15 thousands km2 situated in the Black Earth zone of Central Russia was chosen for investigation. Satellite-derived estimates of land surface characteristics have been constructed under cloud-free condition basing AVHRR/NOAA, MODIS/EOS Terra and EOS Aqua, SEVIRI/Meteosat-8, -9 data. The developed technologies of AVHRR data thematic processing have been refined providing the retrieval of surface skin brightness temperature Tsg, air foliage temperature Ta, efficient surface temperature Ts.eff and emissivity E, as well as derivation of vegetation index NDVI, B, and LAI. The linear regression estimators for Tsg, Ta and LAI have been built using representative training samples for 2003-2009 vegetation seasons. The updated software package has been applied for AVHRR data thematic processing to generate named remote sensing products for various dates of the above vegetation seasons. The error statistics of Ta, Ts.eff and Тsg derivation has been investigated for various samples using comparison with in-situ measurements that has given RMS errors in the

Vegetation Index and Phenology (VIP) Vegetation Indices 7Days Global 0.05Deg CMG V004

Data.gov (United States)

National Aeronautics and Space Administration — The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Vegetation Index and Phenology (VIP) global datasets were created using...

Monitoring vegetation recovery in fire-affected areas using temporal profiles of spectral signal from time series MODIS and LANDSAT satellite images

Science.gov (United States)

Georgopoulou, Danai; Koutsias, Nikos

2015-04-01

Vegetation phenology is an important element of vegetation characteristics that can be useful in vegetation monitoring especially when satellite remote sensing observations are used. In that sense temporal profiles extracted from spectral signal of time series MODIS and LANDSAT satellite images can be used to characterize vegetation phenology and thus to be helpful for monitoring vegetation recovery in fire-affected areas. The aim of this study is to explore the vegetation recovery pattern of the catastrophic wildfires that occurred in Peloponnisos, southern Greece, in 2007. These fires caused the loss of 67 lives and were recognized as the most extreme natural disaster in the country's recent history. Satellite remote sensing data from MODIS and LANDSAT satellites in the period from 2000 to 2014 were acquired and processed to extract the temporal profiles of the spectral signal for selected areas within the fire-affected areas. This dataset and time period analyzed together with the time that these fires occurred gave the opportunity to create temporal profiles seven years before and seven years after the fire. The different scale of the data used gave us the chance to understand how vegetation phenology and therefore the recovery patterns are influenced by the spatial resolution of the satellite data used. Different metrics linked to key phenological events have been created and used to assess vegetation recovery in the fire-affected areas. Our analysis was focused in the main land cover types that were mostly affected by the 2007 wildland fires. Based on CORINE land-cover maps these were agricultural lands highly interspersed with large areas of natural vegetation followed by sclerophyllous vegetation, transitional woodland shrubs, complex cultivation patterns and olive groves. Apart of the use of the original spectral data we estimated and used vegetation indices commonly found in vegetation studies as well as in burned area mapping studies. In this study we

Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

Directory of Open Access Journals (Sweden)

Jan Dempewolf

2014-10-01

Full Text Available Policy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI. The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. Forecast yield for the 2007/2008 to 2012/2013 growing seasons were within 0.2% and 11.5% of final reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year's or the three- or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts.

The Global Index of Vegetation-Plot Databases 1 (GIVD): a new resource for vegetation science

NARCIS (Netherlands)

Dengler, J.; Jansen, F.; Glockler, F.; Schaminee, J.H.J.

2011-01-01

Question: How many vegetation plot observations (relevés) are available in electronic databases, how are they geographically distributed, what are their properties and how might they be discovered and located for research and application? Location: Global. Methods: We compiled the Global Index of

Satellite assessment of early-season forecasts for vegetation conditions of grazing allotments in Nevada, United States

Science.gov (United States)

Fifteen years of enhanced vegetation index data from the MODIS sensor are examined in conjunction with precipitation and the Palmer drought severity index to assess how well growing season conditions for vegetation within grazing allotments of Nevada can be predicted at different times of the year. ...

A climate index derived from satellite measured spectral infrared radiation. Ph.D. Thesis

Science.gov (United States)

Abel, M. D.; Fox, S. K.

1982-01-01

The vertical infrared radiative emitting structure (VIRES) climate index, based on radiative transfer theory and derived from the spectral radiances typically used to retrieve temperature profiles, is introduced. It is assumed that clouds and climate are closely related and a change in one will result in a change in the other. The index is a function of the cloud, temperature, and moisture distributions. It is more accurately retrieved from satellite data than is cloudiness per se. The VIRES index is based upon the shape and relative magnitude of the broadband weighting function of the infrared radiative transfer equation. The broadband weighting curves are retrieved from simulated satellite infrared sounder data (spectral radiances). The retrieval procedure is described and the error error sensitivities of the method investigated. Index measuring options and possible applications of the VIRES index are proposed.

Sensitivity of vegetation indices and gross primary production of tallgrass prairie to severe drought

Energy Technology Data Exchange (ETDEWEB)

Wagle, Pradeep; Xiao, Xiangming; Torn, Margaret S.; Cook, David R.; Matamala, Roser; Fischer, Marc L.; Jin, Cui; Dong, Jinwei; Biradar, Chandrashekhar

2014-09-01

Drought affects vegetation photosynthesis and growth.Many studies have used the normalized difference vegetation index (NDVI), which is calculated as the normalized ratio between near infrared and red spectral bands in satellite images, to evaluate the response of vegetation to drought. In this study, we examined the impacts of drought on three vegetation indices (NDVI, enhanced vegetation index, EVI, and land surface water index, LSWI) and CO2 flux from three tallgrass prairie eddy flux tower sites in the U.S. Gross primary production (GPP) was also modeled using a satellite-based Vegetation Photosynthesis Model (VPM), and the modeled GPP (GPPVPM) was compared with the GPP (GPPEC) derived from eddy covariance measurements. Precipitation at two sites in Oklahoma was 30% below the historical mean in both years of the study period (2005–2006), while the site in Illinois did not experience drought in the 2005–2007 study period. The EVI explained the seasonal dynamics of GPP better than did NDVI. The LSWI dropped below zero during severe droughts in the growing season, showing its potential to track drought. The result shows that GPP was more sensitive to drought than were vegetation indices, and EVI and LSWI were more sensitive than NDVI. We developed a modified function (Wscalar), calculated as a function of LSWI, to account for the effect of severe droughts on GPP in VPM. The GPPVPM from the modified VPM accounted for the rapid reduction in GPP during severe droughts and the seasonal dynamics of GPPVPM agreed reasonably well with GPPEC. Our analysis shows that 8-day averaged values (temperature, vapor-pressure deficit) do not reflect the short-term extreme climate events well, suggesting that satellite based models may need to be run at daily or hourly scales, especially under unfavorable climatic conditions.

A special vegetation index for the weed detection in sensor based precision agriculture.

Science.gov (United States)

Langner, Hans-R; Böttger, Hartmut; Schmidt, Helmut

2006-06-01

Many technologies in precision agriculture (PA) require image analysis and image- processing with weed and background differentiations. The detection of weeds on mulched cropland is one important image-processing task for sensor based precision herbicide applications. The article introduces a special vegetation index, the Difference Index with Red Threshold (DIRT), for the weed detection on mulched croplands. Experimental investigations in weed detection on mulched areas point out that the DIRT performs better than the Normalized Difference Vegetation Index (NDVI). The result of the evaluation with four different decision criteria indicate, that the new DIRT gives the highest reliability in weed/background differentiation on mulched areas. While using the same spectral bands (infrared and red) as the NDVI, the new DIRT is more suitable for weed detection than the other vegetation indices and requires only a small amount of additional calculation power. The new vegetation index DIRT was tested on mulched areas during automatic ratings with a special weed camera system. The test results compare the new DIRT and three other decision criteria: the difference between infrared and red intensity (Diff), the soil-adjusted quotient between infrared and red intensity (Quotient) and the NDVI. The decision criteria were compared with the definition of a worse case decision quality parameter Q, suitable for mulched croplands. Although this new index DIRT needs further testing, the index seems to be a good decision criterion for the weed detection on mulched areas and should also be useful for other image processing applications in precision agriculture. The weed detection hardware and the PC program for the weed image processing were developed with funds from the German Federal Ministry of Education and Research (BMBF).

Solar radiation measurements and Leaf Area Index (LAI) from vegetal covers

International Nuclear Information System (INIS)

Wandelli, E.V.; Marques Filho, A. de O.

1999-01-01

A method by which a physical model of the solar radiation transfer in a vegetal medium is inverted to estimate the leaf area index (LAI) for different types of vegetation is presented here, as an alternative to the destructive experiments, which are a hard task to implement on the vegetation covers. Radiation data were obtained during the dry season — 1996, at the Embrapa Experimental Station, (BR 174 - km 54, 2° 31' S, 60° 01' W), Manaus, Brazil. The method yielded convergent values for the LAI between different adopted radiation classes with more stable estimates at time when there is a predominant diffuse radiation. The application of the inversion algorithm yields the following values for the leaf area index and respective annual foliage increments: 3.5 (0.35 yr. -1 ) for the intact secondary forest; 2.0 (0.5 yr -1 ) for the palm agroforestry system; and 1.6 (0.4 yr -1 ) for the multi-layer ones [pt

Comparison of satellite-derived LAI and precipitation anomalies over Brazil with a thermal infrared-based Evaporative Stress Index for 2003-2013

Science.gov (United States)

Anderson, Martha C.; Zolin, Cornelio A.; Hain, Christopher R.; Semmens, Kathryn; Tugrul Yilmaz, M.; Gao, Feng

2015-07-01

Shortwave vegetation index (VI) and leaf area index (LAI) remote sensing products yield inconsistent depictions of biophysical response to drought and pluvial events that have occurred in Brazil over the past decade. Conflicting reports of severity of drought impacts on vegetation health and functioning have been attributed to cloud and aerosol contamination of shortwave reflectance composites, particularly over the rainforested regions of the Amazon basin which are subject to prolonged periods of cloud cover and episodes of intense biomass burning. This study compares timeseries of satellite-derived maps of LAI from the Moderate Resolution Imaging Spectroradiometer (MODIS) and precipitation from the Tropical Rainfall Mapping Mission (TRMM) with a diagnostic Evaporative Stress Index (ESI) retrieved using thermal infrared remote sensing over South America for the period 2003-2013. This period includes several severe droughts and floods that occurred both over the Amazon and over unforested savanna and agricultural areas in Brazil. Cross-correlations between absolute values and standardized anomalies in monthly LAI and precipitation composites as well as the actual-to-reference evapotranspiration (ET) ratio used in the ESI were computed for representative forested and agricultural regions. The correlation analyses reveal strong apparent anticorrelation between MODIS LAI and TRMM precipitation anomalies over the Amazon, but better coupling over regions vegetated with shorter grass and crop canopies. The ESI was more consistently correlated with precipitation patterns over both landcover types. Temporal comparisons between ESI and TRMM anomalies suggest longer moisture buffering timescales in the deeper rooted rainforest systems. Diagnostic thermal-based retrievals of ET and ET anomalies, such as used in the ESI, provide independent information on the impacts of extreme hydrologic events on vegetation health in comparison with VI and precipitation-based drought

A comparison of multi-resource remote sensing data for vegetation indices

International Nuclear Information System (INIS)

Cao, Liqin; Wei, Lifei; Liu, Tingting

2014-01-01

With the development of the satellite sensor, multi-resource observation systems have become widely used. However, there is a huge difference between quantitative remote sensing products because of the different sensing observations and the quantitative retrieval algorithms. In this paper, the quantitative relationships between the normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) and the vegetation index based on the universal pattern decomposition method (VIUPD) of Landsat ETM+ and ASTER sensors are investigated. The difference in observations was examined between the two sensors, based on a pair of images. The results showed that: 1) There was a strong correlation between the different vegetation indices for the same sensor, with the coefficient of determination being greater than 0.9. 2) Whether for ASTER or Landsat, the information of VIUPD was richer than that of NDVI and SAVI. Furthermore, in dense vegetation areas, the values of NDVI and SAVI could easily reach saturation. 3) The values of SAVI were higher than NDVI in the areas of water or bare soil, while this was the opposite in areas of lush vegetation

Use of Landsat Land Surface Temperature and Vegetation Indices for Monitoring Drought in the Salt Lake Basin Area, Turkey

Directory of Open Access Journals (Sweden)

Osman Orhan

2014-01-01

Full Text Available The main purpose of this paper is to investigate multitemporal land surface temperature (LST changes by using satellite remote sensing data. The study included a real-time field work performed during the overpass of Landsat-5 satellite on 21/08/2011 over Salt Lake, Turkey. Normalized vegetation index (NDVI, vegetation condition index (VCI, and temperature vegetation index (TVX were used for evaluating drought impact over the region between 1984 and 2011. In the image processing step, geometric and radiometric correction procedures were conducted to make satellite remote sensing data comparable with in situ measurements carried out using thermal infrared thermometer supported by hand-held GPS. The results showed that real-time ground and satellite remote sensing data were in good agreement with correlation coefficient (R2 values of 0.90. The remotely sensed and treated satellite images and resulting thematic indices maps showed that dramatic land surface temperature changes occurred (about 2∘C in the Salt Lake Basin area during the 28-year period (1984–2011. Analysis of air temperature data also showed increases at a rate of 1.5–2∘C during the same period. Intensification of irrigated agriculture particularly in the southern basin was also detected. The use of water supplies, especially groundwater, should be controlled considering particularly summer drought impacts on the basin.

Normalized difference vegetation index (NDVI) variation among cultivars and environments

Science.gov (United States)

Although Nitrogen (N) is an essential nutrient for crop production, large preplant applications of fertilizer N can result in off-field loss that causes environmental concerns. Canopy reflectance is being investigated for use in variable rate (VR) N management. Normalized difference vegetation index...

Use of satellite imagery to identify vegetation cover changes following the Waldo Canyon Fire event, Colorado, 2012-2013

Science.gov (United States)

Cole, Christopher J.; Friesen, Beverly A.; Wilson, Earl M.

2014-01-01

The Waldo Canyon Fire of 2012 was one of the most destructive wildfire events in Colorado history. The fire burned a total of 18,247 acres, claimed 2 lives, and destroyed 347 homes. The Waldo Canyon Fire continues to pose challenges to nearby communities. In a preliminary emergency assessment conducted in 2012, the U.S. Geological Survey (USGS) concluded that drainage basins within and near the area affected by the Waldo Canyon Fire pose a risk for future debris flow events. Rainfall over burned, formerly vegetated surfaces resulted in multiple flood and debris flow events that affected the cities of Colorado Springs and Manitou Springs in 2013. One fatality resulted from a mudslide near Manitou Springs in August 2013. Federal, State, and local governments continue to monitor these hazards and other post-fire effects, along with the region’s ecological recovery. At the request of the Colorado Springs Office of Emergency Management, the USGS Special Applications Science Center developed a geospatial product to identify vegetation cover changes following the 2012 Waldo Canyon Fire event. Vegetation cover was derived from July 2012 WorldView-2 and September 2013 QuickBird multispectral imagery at a spatial resolution of two meters. The 2012 image was collected after the fire had reached its maximum extent. Per-pixel increases and decreases in vegetation cover were identified by measuring spectral changes that occurred between the 2012 and 2013 image dates. A Normalized Difference Vegetation Index (NDVI), and Green-Near Infrared Index (GRNIR) were computed from each image. These spectral indices are commonly used to characterize vegetation cover and health condition, due to their sensitivity to detect foliar chlorophyll content. Vector polygons identifying surface-cover feature boundaries were derived from the 2013 imagery using image segmentation software. This geographic software groups similar image pixels into vector objects based upon their spatial and spectral

Modeling water and heat balance components of large territory for vegetation season using information from polar-orbital and geostationary meteorological satellites

Science.gov (United States)

Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Volkova, Elena; Kukharsky, Alexander; Uspensky, Sergey

2015-04-01

To date, physical-mathematical modeling processes of land surface-atmosphere interaction is considered to be the most appropriate tool for obtaining reliable estimates of water and heat balance components of large territories. The model of these processes (Land Surface Model, LSM) developed for vegetation period is destined for simulating soil water content W, evapotranspiration Ev, vertical latent LE and heat fluxes from land surface as well as vertically distributed soil temperature and moisture, soil surface Tg and foliage Tf temperatures, and land surface skin temperature (LST) Ts. The model is suitable for utilizing remote sensing data on land surface and meteorological conditions. In the study these data have been obtained from measurements by scanning radiometers AVHRR/NOAA, MODIS/EOS Terra and Aqua, SEVIRI/geostationary satellites Meteosat-9, -10 (MSG-2, -3). The heterogeneity of the land surface and meteorological conditions has been taken into account in the model by using soil and vegetation characteristics as parameters and meteorological characteristics as input variables. Values of these characteristics have been determined from ground observations and remote sensing information. So, AVHRR data have been used to build the estimates of effective land surface temperature (LST) Ts.eff and emissivity E, vegetation-air temperature (temperature at the vegetation level) Ta, normalized vegetation index NDVI, vegetation cover fraction B, the leaf area index LAI, and precipitation. From MODIS data the values of LST Tls, Å, NDVI, LAI have been derived. From SEVIRI data there have been retrieved Tls, E, Ta, NDVI, LAI and precipitation. All named retrievals covered the vast territory of the part of the agricultural Central Black Earth Region located in the steppe-forest zone of European Russia. This territory with coordinates 49°30'-54°N, 31°-43°E and a total area of 227,300 km2 has been chosen for investigation. It has been carried out for years 2009

THE ANALYSIS OF MOISTURE DEFICIT BASED ON MODIS AND LANDSAT SATELLITE IMAGES. CASE STUDY: THE OLTENIA PLAIN

Directory of Open Access Journals (Sweden)

ONȚEL IRINA

2014-03-01

Full Text Available Satellite images are an important source of information to identify and analyse some hazardous climatic phenomena such as the dryness and drought. These phenomena are characterized by scarce rainfall, increased evapotranspiration and high soil moisture deficit. The soil water reserve depletes to the wilting coefficient, soon followed by the pedological drought which has negative effects on vegetation and agricultural productivity. The MODIS satellite images (Moderate Resolution Imaging Spectroradiometer allow the monitoring of the vegetation throughout the entire vegetative period, with a frequency of 1-2 days and with a spatial resolution of 250 m, 500 m and 1 km away. Another useful source of information is the LANDSAT satellite images, with a spatial resolution of 30 m. Based on MODIS and Landsat satellite images, were calculated moisture monitoring index such as SIWSI (Shortwave Infrared Water Stress Index. Consequently, some years with low moisture such as 2000, 2002, 2007 and 2012 could be identified. Spatially, the areas with moisture deficit varied from one year to another all over the whole analised period (2000-2012. The remote sensing results was corelated with Standard Precipitation Anomaly, which gives a measure of the severity of a wet or dry event.

Development of a New BRDF-Resistant Vegetation Index for Improving the Estimation of Leaf Area Index

Directory of Open Access Journals (Sweden)

Su Zhang

2016-11-01

Full Text Available The leaf area index (LAI is one of the most important Earth surface parameters used in the modeling of ecosystems and their interaction with climate. Numerous vegetation indices have been developed to estimate the LAI. However, because of the effects of the bi-directional reflectance distribution function (BRDF, most of these vegetation indices are also sensitive to the effect of BRDF. In this study, we aim to present a new BRDF-resistant vegetation index (BRVI, which is sensitive to the LAI but insensitive to the effect of BRDF. Firstly, the BRDF effects of different bands were investigated using both simulated data and in-situ measurements of winter wheat made at different growth stages. We found bi-directional shape similarity in the solar principal plane between the green and the near-infrared (NIR bands and between the blue and red bands for farmland soil conditions and with medium chlorophyll content level. Secondly, the consistency of the shape of the BRDF across different bands was employed to develop a new BRDF-resistant vegetation index for estimating the LAI. The reflectance ratios of the NIR band to the green band and the blue band to the red band were reasonably assumed to be resistant to the BRDF effects. Nevertheless, the variation amplitude of the bi-directional reflectance in the solar principal plane was different for different bands. The divisors in the two reflectance ratios were improved by combining the reflectances at the red and green bands. The new BRVI was defined as a normalized combination of the two improved reflectance ratios. Finally, the potential of the proposed BRVI for estimation of the LAI was evaluated using both simulated data and in-situ measurements and also compared to other popular vegetation indices. The results showed that the influence of the BRDF on the BRVI was the weakest and that the BRVI retrieved LAI values well, with a coefficient of determination (R2 of 0.84 and an RMSE of 0.83 for the field

A Candidate Vegetation Index of Biological Integrity Based on Species Dominance and Habitat Fidelity

Science.gov (United States)

Gara, Brian D; Stapanian, Martin A.

2015-01-01

Indices of biological integrity of wetlands based on vascular plants (VIBIs) have been developed in many areas of the USA and are used in some states to make critical management decisions. An underlying concept of all VIBIs is that they respond negatively to disturbance. The Ohio VIBI (OVIBI) is calculated from 10 metrics, which are different for each wetland vegetation class. We present a candidate vegetation index of biotic integrity based on floristic quality (VIBI-FQ) that requires only two metrics to calculate an overall score regardless of vegetation class. These metrics focus equally on the critical ecosystem elements of diversity and dominance as related to a species’ degree of fidelity to habitat requirements. The indices were highly correlated but varied among vegetation classes. Both indices responded negatively with a published index of wetland disturbance in 261 Ohio wetlands. Unlike VIBI-FQ, however, errors in classifying wetland vegetation may lead to errors in calculating OVIBI scores. This is especially critical when assessing the ecological condition of rapidly developing ecosystems typically associated with wetland restoration and creation projects. Compared to OVIBI, the VIBI-FQ requires less field work, is much simpler to calculate and interpret, and can potentially be applied to all habitat types. This candidate index, which has been “standardized” across habitats, would make it easier to prioritize funding because it would score the “best” and “worst” of all habitats appropriately and allow for objective comparison across different vegetation classes.

Satellite Earth observation data to identify climate and anthropogenic pressures on Bucharest periurban forests

Energy Technology Data Exchange (ETDEWEB)

Zoran, Maria; Savastru, Roxana; Savastru, Dan [National Institute of R& D for Optoelectronics, MG5 Bucharest-Magurele, 077125 Romania (Romania); Dida, Adrian [University Transylvania of Brasov, Brasov (Romania)

2016-03-25

Satellite Earth observation data in the visible and near-infrared (VNIR) wavelengths represent a useful source of information for forest systems monitoring through derived biogeophysical parameters (vegetation index, leaf area index, canopy cover, fraction of absorbed photosynthetically active radiation, chlorophyll content, net primary production, canopy water stress, etc.). Use of satellite remote sensing data to assess forest spatio-temporal changes due to climatic or anthropogenic stressors is an excellent example of the value of multispectral and multitemporal observations. Fusion technique was applied to time-series multispectral and multitemporal satellite imagery (NOAA AVHRR, MODIS Terra/Aqua, Landsat ETM and IKONOS satellite data) for periurban forest areas Cernica-Branesti, placed in the neighboring of Bucharest town, Romania, over 2002-2014 period.

Satellite Earth observation data to identify climate and anthropogenic pressures on Bucharest periurban forests

International Nuclear Information System (INIS)

Zoran, Maria; Savastru, Roxana; Savastru, Dan; Dida, Adrian

2016-01-01

Satellite Earth observation data in the visible and near-infrared (VNIR) wavelengths represent a useful source of information for forest systems monitoring through derived biogeophysical parameters (vegetation index, leaf area index, canopy cover, fraction of absorbed photosynthetically active radiation, chlorophyll content, net primary production, canopy water stress, etc.). Use of satellite remote sensing data to assess forest spatio-temporal changes due to climatic or anthropogenic stressors is an excellent example of the value of multispectral and multitemporal observations. Fusion technique was applied to time-series multispectral and multitemporal satellite imagery (NOAA AVHRR, MODIS Terra/Aqua, Landsat ETM and IKONOS satellite data) for periurban forest areas Cernica-Branesti, placed in the neighboring of Bucharest town, Romania, over 2002-2014 period.

Global Food Security Index Studies and Satellite Information

Science.gov (United States)

Medina, T. A.; Ganti-Agrawal, S.; Joshi, D.; Lakhankar, T.

2017-12-01

Food yield is equal to the total crop harvest per unit cultivated area. During the elapsed time of germination and frequent harvesting, both human and climate related effects determine a country's' contribution towards global food security. Each country across the globe's annual income per capita was collected to then determine nine countries for further studies. For a location to be chosen, its income per capita needed to be considered poor, uprising or wealthy. Both physical land cover and regional climate helped categorize potential parameters thought to be studied. Once selected, Normalized Difference Vegetation Index (NDVI) data was collected for Ethiopia, Liberia, Indonesia, United States, Norway, Russia, Kuwait and Saudi Arabia over the recent 16 years for approximately every 16 days starting from early in the year 2000. Software languages such as Geographic Information System (GIS), MatLab and Excel were used to determine how population size, income and deforestation directly determines agricultural yields. Because of high maintenance requirements for large harvests when forest areas are cleared, they often have a reduction in soil quality, requiring fertilizer use to produce sufficient crop yields. Total area and vegetation index of each country is to be studied, to determine crop and deforestation percentages. To determine how deforestation impacts future income and crop yield predictions of each country studied. By using NDVI results a parameter is to be potentially found that will help define an index, to create an equation that will determine a country's annual income and ability to provide for their families and themselves.

Comparing land surface phenology derived from satellite and GPS network microwave remote sensing.

Science.gov (United States)

Jones, Matthew O; Kimball, John S; Small, Eric E; Larson, Kristine M

2014-08-01

The land surface phenology (LSP) start of season (SOS) metric signals the seasonal onset of vegetation activity, including canopy growth and associated increases in land-atmosphere water, energy and carbon (CO2) exchanges influencing weather and climate variability. The vegetation optical depth (VOD) parameter determined from satellite passive microwave remote sensing provides for global LSP monitoring that is sensitive to changes in vegetation canopy water content and biomass, and insensitive to atmosphere and solar illumination constraints. Direct field measures of canopy water content and biomass changes desired for LSP validation are generally lacking due to the prohibitive costs of maintaining regional monitoring networks. Alternatively, a normalized microwave reflectance index (NMRI) derived from GPS base station measurements is sensitive to daily vegetation water content changes and may provide for effective microwave LSP validation. We compared multiyear (2007-2011) NMRI and satellite VOD records at over 300 GPS sites in North America, and their derived SOS metrics for a subset of 24 homogenous land cover sites to investigate VOD and NMRI correspondence, and potential NMRI utility for LSP validation. Significant correlations (P<0.05) were found at 276 of 305 sites (90.5 %), with generally favorable correspondence in the resulting SOS metrics (r (2)=0.73, P<0.001, RMSE=36.8 days). This study is the first attempt to compare satellite microwave LSP metrics to a GPS network derived reflectance index and highlights both the utility and limitations of the NMRI data for LSP validation, including spatial scale discrepancies between local NMRI measurements and relatively coarse satellite VOD retrievals.

Remotely Assessing Fraction of Photosynthetically Active Radiation (FPAR for Wheat Canopies Based on Hyperspectral Vegetation Indexes

Directory of Open Access Journals (Sweden)

Changwei Tan

2018-06-01

Full Text Available Fraction of photosynthetically active radiation (FPAR, as an important index for evaluating yields and biomass production, is key to providing the guidance for crop management. However, the shortage of good hyperspectral data can frequently result in the hindrance of accurate and reliable FPAR assessment, especially for wheat. In the present research, aiming at developing a strategy for accurate FPAR assessment, the relationships between wheat canopy FPAR and vegetation indexes derived from concurrent ground-measured hyperspectral data were explored. FPAR revealed the most strongly correlation with normalized difference index (NDI, and scaled difference index (N*. Both NDI and N* revealed the increase as the increase of FPAR; however, NDI value presented the stagnation as FPAR value beyond 0.70. On the other hand, N* showed a decreasing tendency when FPAR value was higher than 0.70. This special relationship between FPAR and vegetation index could be employed to establish a piecewise FPAR assessment model with NDI as a regression variable during FPAR value lower than 0.70, or N* as the regression variable during FPAR value higher than 0.70. The model revealed higher assessment accuracy up to 16% when compared with FPAR assessment models based on a single vegetation index. In summary, it is feasible to apply NDI and N* for accomplishing wheat canopy FPAR assessment, and establish an FPAR assessment model to overcome the limitations from vegetation index saturation under the condition with high FPAR value.

Application of a chlorophyll index derived from satellite data to ...

African Journals Online (AJOL)

Application of a chlorophyll index derived from satellite data to investigate the variability of phytoplankton in the Benguela ecosystem. H Demarcq, R Barlow, L Hutchings. Abstract. No Abstract. African Journal of Marine Science Vol.29(2) 2007: pp. 271-282. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD ...

Relationships between the normalised difference vegetation index and temperature fluctuations in post-mining sites

Czech Academy of Sciences Publication Activity Database

Bujalský, L.; Jirka, V.; Zemek, František; Frouz, J.

2018-01-01

Roč. 32, č. 4 (2018), s. 254-263 ISSN 1748-0930 R&D Projects: GA MŠk(CZ) LO1415 Institutional support: RVO:67179843 Keywords : temperature * normalised difference * vegetation index (NDVI) * vegetation cover * remote sensing Subject RIV: DF - Soil Science Impact factor: 1.078, year: 2016

Tundra vegetation effects on pan-Arctic albedo

International Nuclear Information System (INIS)

Loranty, Michael M; Goetz, Scott J; Beck, Pieter S A

2011-01-01

Recent field experiments in tundra ecosystems describe how increased shrub cover reduces winter albedo, and how subsequent changes in surface net radiation lead to altered rates of snowmelt. These findings imply that tundra vegetation change will alter regional energy budgets, but to date the effects have not been documented at regional or greater scales. Using satellite observations and a pan-Arctic vegetation map, we examined the effects of shrub vegetation on albedo across the terrestrial Arctic. We included vegetation classes dominated by low shrubs, dwarf shrubs, tussock-dominated graminoid tundra, and non-tussock graminoid tundra. Each class was further stratified by bioclimate subzones. Low-shrub tundra had higher normalized difference vegetation index values and earlier albedo decline in spring than dwarf-shrub tundra, but for tussock tundra, spring albedo declined earlier than for low-shrub tundra. Our results illustrate how relatively small changes in vegetation properties result in differences in albedo dynamics, regardless of shrub growth, that may lead to differences in net radiation upwards of 50 W m -2 at weekly time scales. Further, our findings imply that changes to the terrestrial Arctic energy budget during this important seasonal transition are under way regardless of whether recent satellite observed productivity trends are the result of shrub expansion. We conclude that a better understanding of changes in vegetation productivity and distribution in Arctic tundra is essential for accurately quantifying and predicting carbon and energy fluxes and associated climate feedbacks.

Satellite-Based Assessment of the spatial extent of Aquatic Vegetation in Lake Victoria

Science.gov (United States)

Clark, W.; Aligeti, N.; Jeyaprakash, T.; Martins, M.; Stodghill, J.; Winstanley, H.

2011-12-01

Lake Victoria in Africa is the second largest freshwater lake in the world and is known for its abundance of aquatic wildlife. In particular over 200 different fish species are caught and sold by local fisherman. The lake is a major contributor to the local economy as a corridor of transportation, source of drinking water, and source of hydropower. However, the invasion of aquatic vegetation such as water hyacinth in the lake has disrupted each of these markets. Aquatic vegetation now covers a substantial area of the coastline blocking waterways, disrupting hydropower, hindering the collection of drinking water and decreasing the profitability of fishing. The vegetation serves as a habitat for disease carrying mosquitoes as well as snakes and snails that spread the parasitic disease bilharzia. The current control measures of invasive aquatic vegetation rely on biological, chemical and mechanical control. The objective of this study was to utilize remote sensing to map aquatic vegetation within Lake Victoria from 2000 to 2011. MODIS, Landsat 4-5TM, and Landsat 7-ETM imagery was employed to perform change detections in vegetation and identify the extent of aquatic vegetation throughout the years. The efficiency of containment efforts were evaluated and ideal time for application of such efforts were suggested. A methodology for aquatic vegetation surveillance was created. The results of this project were presented as a workshop to the Lake Victoria Fisheries Organization, SERVIR, and other partner organizations. The workshop provided instruction into the use of NASA and other satellite derived products. Time series animations of the spatial extent of aquatic vegetation within the lake were created. By identifying seasons of decreased aquatic vegetation, ideal times to employ control efforts were identified. SERVIR will subsequently utilize the methodologies and mapping results of this study to develop operational aquatic vegetation surveillance for Lake Victoria.

Radiative transfer in shrub savanna sites in Niger: preliminary results from HAPEX-Sahel. 3. Optical dynamics and vegetation index sensitivity to biomass and plant cover

International Nuclear Information System (INIS)

Leeuwen, W.J.D. van; Huete, A.R.; Duncan, J.; Franklin, J.

1994-01-01

A shrub savannah landscape in Niger was optically characterized utilizing blue, green, red and near-infrared wavelengths. Selected vegetation indices were evaluated for their performance and sensitivity to describe the complex Sahelian soil/vegetation canopies. Bidirectional reflectance factors (BRF) of plants and soils were measured at several view angles, and used as input to various vegetation indices. Both soil and vegetation targets had strong anisotropic reflectance properties, rendering all vegetation index (VI) responses to be a direct function of sun and view geometry. Soil background influences were shown to alter the response of most vegetation indices. N-space greenness had the smallest dynamic range in VI response, but the n-space brightness index provided additional useful information. The global environmental monitoring index (GEMI) showed a large VI dynamic range for bare soils, which was undesirable for a vegetation index. The view angle response of the normalized difference vegetation index (NDVI), atmosphere resistant vegetation index (ARVI) and soil atmosphere resistant vegetation index (SARVI) were asymmetric about nadir for multiple view angles, and were, except for the SARVI, altered seriously by soil moisture and/or soil brightness effects. The soil adjusted vegetation index (SAVI) was least affected by surface soil moisture and was symmetric about nadir for grass vegetation covers. Overall the SAVI, SARVI and the n-space vegetation index performed best under all adverse conditions and were recommended to monitor vegetation growth in the sparsely vegetated Sahelian zone. (author)

Trend shifts in satellite-derived vegetation growth in Central Eurasia, 1982-2013.

Science.gov (United States)

Xu, Hao-Jie; Wang, Xin-Ping; Yang, Tai-Bao

2017-02-01

Central Eurasian vegetation is critical for the regional ecological security and the global carbon cycle. However, climatic impacts on vegetation growth in Central Eurasia are uncertain. The reason for this uncertainty lies in the fact that the response of vegetation to climate change showed nonlinearity, seasonality and differences among plant functional types. Based on remotely sensed vegetation index and in-situ meteorological data for the years 1982-2013, in conjunction with the latest land cover type product, we analyzed how vegetation growth trend varied across different seasons and evaluated vegetation response to climate variables at regional, biome and pixel scales. We found a persistent increase in the growing season NDVI over Central Eurasia during 1982-1994, whereas this greening trend has stalled since the mid-1990s in response to increased water deficit. The stalled trend in the growing season NDVI was largely attributed by summer and autumn NDVI changes. Enhanced spring vegetation growth after 2002 was caused by rapid spring warming. The response of vegetation to climatic factors varied in different seasons. Precipitation was the main climate driver for the growing season and summer vegetation growth. Changes in temperature and precipitation during winter and spring controlled the spring vegetation growth. Autumn vegetation growth was mainly dependent on the vegetation growth in summer. We found diverse responses of different vegetation types to climate drivers in Central Eurasia. Forests were more responsive to temperature than to precipitation. Grassland and desert vegetation responded more strongly to precipitation than to temperature in summer but more strongly to temperature than to precipitation in spring. In addition, the growth of desert vegetation was more dependent on winter precipitation than that of grasslands. This study has important implications for improving the performance of terrestrial ecosystem models to predict future vegetation

Assessing the vegetation condition impacts of the 2011 drought across the U.S. southern Great Plains using the vegetation drought response index (VegDRI)

Science.gov (United States)

Tadesse, Tsegaye; Wardlow, Brian D.; Brown, Jesslyn F.; Svoboda, Mark; Hayes, Michael; Fuchs, Brian; Gutzmer, Denise

2015-01-01

The vegetation drought response index (VegDRI), which combines traditional climate- and satellite-based approaches for assessing vegetation conditions, offers new insights into assessing the impacts of drought from local to regional scales. In 2011, the U.S. southern Great Plains, which includes Texas, Oklahoma, and New Mexico, was plagued by moderate to extreme drought that was intensified by an extended period of record-breaking heat. The 2011 drought presented an ideal case study to evaluate the performance of VegDRI in characterizing developing drought conditions. Assessment of the spatiotemporal drought patterns represented in the VegDRI maps showed that the severity and patterns of the drought across the region corresponded well to the record warm temperatures and much-below-normal precipitation reported by the National Climatic Data Center and the sectoral drought impacts documented by the Drought Impact Reporter (DIR). VegDRI values and maps also showed the evolution of the drought signal before the Las Conchas Fire (the largest fire in New Mexico’s history). Reports in the DIR indicated that the 2011 drought had major adverse impacts on most rangeland and pastures in Texas and Oklahoma, resulting in total direct losses of more than $12 billion associated with crop, livestock, and timber production. These severe impacts on vegetation were depicted by the VegDRI at subcounty, state, and regional levels. This study indicates that the VegDRI maps can be used with traditional drought indicators and other in situ measures to help producers and government officials with various management decisions, such as justifying disaster assistance, assessing fire risk, and identifying locations to move livestock for grazing.

Sugarcane leaf area estimate obtained from the corrected Normalized Difference Vegetation Index (NDVI

Directory of Open Access Journals (Sweden)

Rodrigo Moura Pereira

2016-06-01

Full Text Available Large farmland areas and the knowledge on the interaction between solar radiation and vegetation canopies have increased the use of data from orbital remote sensors in sugarcane monitoring. However, the constituents of the atmosphere affect the reflectance values obtained by imaging sensors. This study aimed at improving a sugarcane Leaf Area Index (LAI estimation model, concerning the Normalized Difference Vegetation Index (NDVI subjected to atmospheric correction. The model generated by the NDVI with atmospheric correction showed the best results (R2 = 0.84; d = 0.95; MAE = 0.44; RMSE = 0.55, in relation to the other models compared. LAI estimation with this model, during the sugarcane plant cycle, reached a maximum of 4.8 at the vegetative growth phase and 2.3 at the end of the maturation phase. Thus, the use of atmospheric correction to estimate the sugarcane LAI is recommended, since this procedure increases the correlations between the LAI estimated by image and by plant parameters.

Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring

Directory of Open Access Journals (Sweden)

Pauline Dusseux

2014-06-01

Full Text Available The aim of this study was to assess the ability of optical images, SAR (Synthetic Aperture Radar images and the combination of both types of data to discriminate between grasslands and crops in agricultural areas where cloud cover is very high most of the time, which restricts the use of visible and near-infrared satellite data. We compared the performances of variables extracted from four optical and five SAR satellite images with high/very high spatial resolutions acquired during the growing season. A vegetation index, namely the NDVI (Normalized Difference Vegetation Index, and two biophysical variables, the LAI (Leaf Area Index and the fCOVER (fraction of Vegetation Cover were computed using optical time series and polarization (HH, VV, HV, VH. The polarization ratio and polarimetric decomposition (Freeman–Durden and Cloude–Pottier were calculated using SAR time series. Then, variables derived from optical, SAR and both types of remotely-sensed data were successively classified using the Support Vector Machine (SVM technique. The results show that the classification accuracy of SAR variables is higher than those using optical data (0.98 compared to 0.81. They also highlight that the combination of optical and SAR time series data is of prime interest to discriminate grasslands from crops, allowing an improved classification accuracy.

Effects of Telecoupling on Global Vegetation Dynamics

Science.gov (United States)

Viña, A.; Liu, J.

2016-12-01

With the ever increasing trend in telecoupling processes, such as international trade, all countries around the world are becoming more interdependent. However, the effects of this growing interdependence on vegetation (e.g., shifts in the geographic extent and distribution) remain unknown even though vegetation dynamics are crucially important for food production, carbon sequestration, provision of other ecosystem services, and biodiversity conservation. In this study we evaluate the effects of international trade on the spatio-temporal trajectories of vegetation at national and global scales, using vegetation index imagery collected over more than three decades by the Advanced Very High Resolution Radiometer (AVHRR) satellite sensor series together with concurrent national and international data on international trade (and its associated movement of people, goods, services and information). The spatio-temporal trajectories of vegetation are obtained using the scale of fluctuation technique, which is based on the decomposition of the AVHRR image time series to obtain information on its spatial dependence structure over time. Similar to the correlation length, the scale of fluctuation corresponds to the range over which fluctuations in the vegetation index are spatially correlated. Results indicate that global vegetation has changed drastically over the last three decades. These changes are not uniform across space, with hotspots in active trading countries. This study not only has direct implications for understanding global vegetation dynamics, but also sheds important insights on the complexity of human-nature interactions across telecoupled systems.

A Subpixel Classification of Multispectral Satellite Imagery for Interpetation of Tundra-Taiga Ecotone Vegetation (Case Study on Tuliok River Valley, Khibiny, Russia)

Science.gov (United States)

Mikheeva, A. I.; Tutubalina, O. V.; Zimin, M. V.; Golubeva, E. I.

2017-12-01

The tundra-taiga ecotone plays significant role in northern ecosystems. Due to global climatic changes, the vegetation of the ecotone is the key object of many remote-sensing studies. The interpretation of vegetation and nonvegetation objects of the tundra-taiga ecotone on satellite imageries of a moderate resolution is complicated by the difficulty of extracting these objects from the spectral and spatial mixtures within a pixel. This article describes a method for the subpixel classification of Terra ASTER satellite image for vegetation mapping of the tundra-taiga ecotone in the Tuliok River, Khibiny Mountains, Russia. It was demonstrated that this method allows to determine the position of the boundaries of ecotone objects and their abundance on the basis of quantitative criteria, which provides a more accurate characteristic of ecotone vegetation when compared to the per-pixel approach of automatic imagery interpretation.

Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI composite data using agricultural measurements: an example at corn fields in western Mexico.

Science.gov (United States)

Chen, Pei-Yu; Fedosejevs, Gunar; Tiscareño-López, Mario; Arnold, Jeffrey G

2006-08-01

Although several types of satellite data provide temporal information of the land use at no cost, digital satellite data applications for agricultural studies are limited compared to applications for forest management. This study assessed the suitability of vegetation indices derived from the TERRA-Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and SPOT-VEGETATION (VGT) sensor for identifying corn growth in western Mexico. Overall, the Normalized Difference Vegetation Index (NDVI) composites from the VGT sensor based on bi-directional compositing method produced vegetation information most closely resembling actual crop conditions. The NDVI composites from the MODIS sensor exhibited saturated signals starting 30 days after planting, but corresponded to green leaf senescence in April. The temporal NDVI composites from the VGT sensor based on the maximum value method had a maximum plateau for 80 days, which masked the important crop transformation from vegetative stage to reproductive stage. The Enhanced Vegetation Index (EVI) composites from the MODIS sensor reached a maximum plateau 40 days earlier than the occurrence of maximum leaf area index (LAI) and maximum intercepted fraction of photosynthetic active radiation (fPAR) derived from in-situ measurements. The results of this study showed that the 250-m resolution MODIS data did not provide more accurate vegetation information for corn growth description than the 500-m and 1000-m resolution MODIS data.

Normalized difference vegetation index (NDVI) in the management of mountain meadows

Czech Academy of Sciences Publication Activity Database

Mašková, Z.; Zemek, František; Květ, Jan

2008-01-01

Roč. 13, - (2008), s. 417-432 ISSN 1239-6095 R&D Projects: GA ČR(CZ) GA206/99/1410 Institutional research plan: CEZ:AV0Z60870520 Keywords : normalized difference vegetation index * mountain medows * mulching Subject RIV: EH - Ecology, Behaviour Impact factor: 1.620, year: 2008 www.borenv.net

Vegetation index methods for estimating evapotranspiration by remote sensing

Science.gov (United States)

Glenn, Edward P.; Nagler, Pamela L.; Huete, Alfredo R.

2010-01-01

Evapotranspiration (ET) is the largest term after precipitation in terrestrial water budgets. Accurate estimates of ET are needed for numerous agricultural and natural resource management tasks and to project changes in hydrological cycles due to potential climate change. We explore recent methods that combine vegetation indices (VI) from satellites with ground measurements of actual ET (ETa) and meteorological data to project ETa over a wide range of biome types and scales of measurement, from local to global estimates. The majority of these use time-series imagery from the Moderate Resolution Imaging Spectrometer on the Terra satellite to project ET over seasons and years. The review explores the theoretical basis for the methods, the types of ancillary data needed, and their accuracy and limitations. Coefficients of determination between modeled ETa and measured ETa are in the range of 0.45–0.95, and root mean square errors are in the range of 10–30% of mean ETa values across biomes, similar to methods that use thermal infrared bands to estimate ETa and within the range of accuracy of the ground measurements by which they are calibrated or validated. The advent of frequent-return satellites such as Terra and planed replacement platforms, and the increasing number of moisture and carbon flux tower sites over the globe, have made these methods feasible. Examples of operational algorithms for ET in agricultural and natural ecosystems are presented. The goal of the review is to enable potential end-users from different disciplines to adapt these methods to new applications that require spatially-distributed ET estimates.

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA

Science.gov (United States)

Boyte, Stephen; Wylie, Bruce K.; Rigge, Matthew B.; Dahal, Devendra

2018-01-01

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R2 values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.

NOAA Climate Data Record (CDR) of Normalized Difference Vegetation Index (NDVI), Version 4

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This dataset contains gridded daily Normalized Difference Vegetation Index (NDVI) derived from the NOAA Climate Data Record (CDR) of Advanced Very High Resolution...

Comparison of sap flux, moisture flux tower and MODIS enhanced vegetation index methods for estimating riparian evapotranspiration

Science.gov (United States)

Nagler, Pamela L.; Glenn, Edward P.; Morino, Kiyomi; Neale, Christopher M.U; Cosh, Michael H.

2010-01-01

Riparian evapotranspiration (ET) was measured on a salt cedar (Tamarix spp.) dominated river terrace on the Lower Colorado River from 2007 to 2009 using tissue-heat-balance sap flux sensors at six sites representing very dense, medium dense, and sparse stands of plants. Salt cedar ET varied markedly across sites, and sap flux sensors showed that plants were subject to various degrees of stress, detected as mid-day depression of transpiration and stomatal conductance. Sap flux results were scaled from the leaf level of measurement to the stand level by measuring plant-specific leaf area index and fractional ground cover at each site. Results were compared to Bowen ratio moisture tower data available for three of the sites. Sap flux sensors and flux tower results ranked the sites the same and had similar estimates of ET. A regression equation, relating measured ET of salt cedar and other riparian plants and crops on the Lower Colorado River to the Enhanced Vegetation Index from the MODIS sensor on the Terra satellite and reference crop ET measured at meteorological stations, was able to predict actual ET with an accuracy or uncertainty of about 20%, despite between-site differences for salt cedar. Peak summer salt cedar ET averaged about 6 mm d-1 across sites and methods of measurement.

Using satellite fire detection to calibrate components of the fire weather index system in Malaysia and Indonesia.

Science.gov (United States)

Dymond, Caren C; Field, Robert D; Roswintiarti, Orbita; Guswanto

2005-04-01

Vegetation fires have become an increasing problem in tropical environments as a consequence of socioeconomic pressures and subsequent land-use change. In response, fire management systems are being developed. This study set out to determine the relationships between two aspects of the fire problems in western Indonesia and Malaysia, and two components of the Canadian Forest Fire Weather Index System. The study resulted in a new method for calibrating components of fire danger rating systems based on satellite fire detection (hotspot) data. Once the climate was accounted for, a problematic number of fires were related to high levels of the Fine Fuel Moisture Code. The relationship between climate, Fine Fuel Moisture Code, and hotspot occurrence was used to calibrate Fire Occurrence Potential classes where low accounted for 3% of the fires from 1994 to 2000, moderate accounted for 25%, high 26%, and extreme 38%. Further problems arise when there are large clusters of fires burning that may consume valuable land or produce local smoke pollution. Once the climate was taken into account, the hotspot load (number and size of clusters of hotspots) was related to the Fire Weather Index. The relationship between climate, Fire Weather Index, and hotspot load was used to calibrate Fire Load Potential classes. Low Fire Load Potential conditions (75% of an average year) corresponded with 24% of the hotspot clusters, which had an average size of 30% of the largest cluster. In contrast, extreme Fire Load Potential conditions (1% of an average year) corresponded with 30% of the hotspot clusters, which had an average size of 58% of the maximum. Both Fire Occurrence Potential and Fire Load Potential calibrations were successfully validated with data from 2001. This study showed that when ground measurements are not available, fire statistics derived from satellite fire detection archives can be reliably used for calibration. More importantly, as a result of this work, Malaysia and

Bayesian estimation of seasonal course of canopy leaf area index from hyperspectral satellite data

Science.gov (United States)

Varvia, Petri; Rautiainen, Miina; Seppänen, Aku

2018-03-01

In this paper, Bayesian inversion of a physically-based forest reflectance model is investigated to estimate of boreal forest canopy leaf area index (LAI) from EO-1 Hyperion hyperspectral data. The data consist of multiple forest stands with different species compositions and structures, imaged in three phases of the growing season. The Bayesian estimates of canopy LAI are compared to reference estimates based on a spectral vegetation index. The forest reflectance model contains also other unknown variables in addition to LAI, for example leaf single scattering albedo and understory reflectance. In the Bayesian approach, these variables are estimated simultaneously with LAI. The feasibility and seasonal variation of these estimates is also examined. Credible intervals for the estimates are also calculated and evaluated. The results show that the Bayesian inversion approach is significantly better than using a comparable spectral vegetation index regression.

Satellite monitoring of different vegetation types by differential optical absorption spectroscopy (DOAS in the red spectral range

Directory of Open Access Journals (Sweden)

T. Wagner

2007-01-01

Full Text Available A new method for the satellite remote sensing of different types of vegetation and ocean colour is presented. In contrast to existing algorithms relying on the strong change of the reflectivity in the red and near infrared spectral region, our method analyses weak narrow-band (few nm reflectance structures (i.e. "fingerprint" structures of vegetation in the red spectral range. It is based on differential optical absorption spectroscopy (DOAS, which is usually applied for the analysis of atmospheric trace gas absorptions. Since the spectra of atmospheric absorption and vegetation reflectance are simultaneously included in the analysis, the effects of atmospheric absorptions are automatically corrected (in contrast to other algorithms. The inclusion of the vegetation spectra also significantly improves the results of the trace gas retrieval. The global maps of the results illustrate the seasonal cycles of different vegetation types. In addition to the vegetation distribution on land, they also show patterns of biological activity in the oceans. Our results indicate that improved sets of vegetation spectra might lead to more accurate and more specific identification of vegetation type in the future.

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

Science.gov (United States)

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

2015-01-01

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

VIP Data Explorer: A Tool for Exploring 30 years of Vegetation Index and Phenology Observations

Science.gov (United States)

Barreto-munoz, A.; Didan, K.; Rivera-Camacho, J.; Yitayew, M.; Miura, T.; Tsend-Ayush, J.

2011-12-01

Continuous acquisition of global satellite imagery over the years has contributed to the creation of long term data records from AVHRR, MODIS, TM, SPOT-VGT and other sensors. These records account for 30+ years, as these archives grow, they become invaluable tools for environmental, resources management, and climate studies dealing with trends and changes from local, regional to global scale. In this project, the Vegetation Index and Phenology Lab (VIPLab) is processing 30 years of daily global surface reflectance data into an Earth Science Data Record of Vegetation Index and Phenology metrics. Data from AVHRR (N07,N09,N11 and N14) and MODIS (AQUA and TERRA collection 5) for the periods 1981-1999 and 2000-2010, at CMG resolution were processed into one seamless and sensor independent data record using various filtering, continuity and gap filling techniques (Tsend-Ayush et al., AGU 2011, Rivera-Camacho et al, AGU 2011). An interactive online tool (VIP Data Explorer) was developed to support the visualization, qualitative and quantitative exploration, distribution, and documentation of these records using a simple web 2.0 interface. The VIP Data explorer (http://vip.arizona.edu/viplab_data_explorer) can display any combination of multi temporal and multi source data, enable the quickly exploration and cross comparison of the various levels of processing of this data. It uses the Google Earth (GE) model and was developed using the GE API for images rendering, manipulation and geolocation. These ESDRs records can be quickly animated in this environment and explored for visual trends and anomalies detection. Additionally the tool enables extracting and visualizing any land pixel time series while showing the different levels of processing it went through. User can explore this ESDR database within this data explorer GUI environment, and any desired data can be placed into a dynamic "cart" to be ordered and downloaded later. More functionalities are planned and will be

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.

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

Directory of Open Access Journals (Sweden)

Xiuxia Zhang

2017-09-01

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

Comparison of Eight Techniques for Reconstructing Multi-Satellite Sensor Time-Series NDVI Data Sets in the Heihe River Basin, China

Directory of Open Access Journals (Sweden)

Liying Geng

2014-03-01

Full Text Available More than 20 techniques have been developed to de-noise time-series vegetation index data from different satellite sensors to reconstruct long time-series data sets. Although many studies have compared Normalized Difference Vegetation Index (NDVI noise-reduction techniques, few studies have compared these techniques systematically and comprehensively. This study tested eight techniques for smoothing different vegetation types using different types of multi-temporal NDVI data (Advanced Very High Resolution Radiometer (AVHRR (Global Inventory Modeling and Map Studies (GIMMS and Pathfinder AVHRR Land (PAL, Satellite Pour l’ Observation de la Terre (SPOT VEGETATION (VGT, and Moderate Resolution Imaging Spectroradiometer (MODIS (Terra with the ultimate purpose of determining the best reconstruction technique for each type of vegetation captured with four satellite sensors. These techniques include the modified best index slope extraction (M-BISE technique, the Savitzky-Golay (S-G technique, the mean value iteration filter (MVI technique, the asymmetric Gaussian (A-G technique, the double logistic (D-L technique, the changing-weight filter (CW technique, the interpolation for data reconstruction (IDR technique, and the Whittaker smoother (WS technique. These techniques were evaluated by calculating the root mean square error (RMSE, the Akaike Information Criterion (AIC, and the Bayesian Information Criterion (BIC. The results indicate that the S-G, CW, and WS techniques perform better than the other tested techniques, while the IDR, M-BISE, and MVI techniques performed worse than the other techniques. The best de-noise technique varies with different vegetation types and NDVI data sources. The S-G performs best in most situations. In addition, the CW and WS are effective techniques that were exceeded only by the S-G technique. The assessment results are consistent in terms of the three evaluation indexes for GIMMS, PAL, and SPOT data in the study

A one-layer satellite surface energy balance for estimating evapotranspiration rates and crop water stress indexes.

Science.gov (United States)

Barbagallo, Salvatore; Consoli, Simona; Russo, Alfonso

2009-01-01

Daily evapotranspiration fluxes over the semi-arid Catania Plain area (Eastern Sicily, Italy) were evaluated using remotely sensed data from Landsat Thematic Mapper TM5 images. A one-source parameterization of the surface sensible heat flux exchange using satellite surface temperature has been used. The transfer of sensible and latent heat is described by aerodynamic resistance and surface resistance. Required model inputs are brightness, temperature, fractional vegetation cover or leaf area index, albedo, crop height, roughness lengths, net radiation, air temperature, air humidity and wind speed. The aerodynamic resistance (r(ah)) is formulated on the basis of the Monin-Obukhov surface layer similarity theory and the surface resistance (r(s)) is evaluated from the energy balance equation. The instantaneous surface flux values were converted into evaporative fraction (EF) over the heterogeneous land surface to derive daily evapotranspiration values. Remote sensing-based assessments of crop water stress (CWSI) were also made in order to identify local irrigation requirements. Evapotranspiration data and crop coefficient values obtained from the approach were compared with: (i) data from the semi-empirical approach "K(c) reflectance-based", which integrates satellite data in the visible and NIR regions of the electromagnetic spectrum with ground-based measurements and (ii) surface energy flux measurements collected from a micrometeorological tower located in the experiment area. The expected variability associated with ET flux measurements suggests that the approach-derived surface fluxes were in acceptable agreement with the observations.

Metrics for determining hydrophytic vegetation in wetland delineation: a clarification on the prevalence index

Science.gov (United States)

Diane De Steven

2015-01-01

A recent publication and an article in Wetland Science & Practice (Lichvar and Gillrich 2014b, 2014a) discuss two metrics for determining if vegetation is hydrophytic for purposes of U.S. wetland delineations, the Prevalence Index (PI) and a proposed Hydrophytic Cover Index (HCI). Based on Wentworth et al. (1988), the PI is a weighted average of ordinal scores (1-5...

Post-fire vegetation recovery in Portugal based on spot/vegetation data

Science.gov (United States)

Gouveia, C.; Dacamara, C. C.; Trigo, R. M.

2010-04-01

A procedure is presented that allows identifying large burned scars and the monitoring of vegetation recovery in the years following major fire episodes. The procedure relies on 10-day fields of Maximum Value Composites of Normalized Difference Vegetation Index (MVC-NDVI), with a 1 km×1 km spatial resolution obtained from the VEGETATION instrument. The identification of fire scars during the extremely severe 2003 fire season is performed based on cluster analysis of NDVI anomalies that persist during the vegetative cycle of the year following the fire event. Two regions containing very large burned scars were selected, located in Central and Southwestern Portugal, respectively, and time series of MVC-NDVI analysed before the fire events took place and throughout the post-fire period. It is shown that post-fire vegetation dynamics in the two selected regions may be characterised based on maps of recovery rates as estimated by fitting a monoparametric model of vegetation recovery to MVC-NDVI data over each burned scar. Results indicated that the recovery process in the region located in Central Portugal is mostly related to fire damage rather than to vegetation density before 2003, whereas the latter seems to have a more prominent role than vegetation conditions after the fire episode, e.g. in the case of the region in Southwestern Portugal. These differences are consistent with the respective predominant types of vegetation. The burned area located in Central Portugal is dominated by Pinus Pinaster whose natural regeneration crucially depends on the destruction of seeds present on the soil surface during the fire, whereas the burned scar in Southwestern Portugal was populated by Eucalyptus that may quickly re-sprout from buds after fire. Besides its simplicity, the monoparametric model of vegetation recovery has the advantage of being easily adapted to other low-resolution satellite data, as well as to other types of vegetation indices.

The assessment of anthropogenic impact on the environment in East Fennoscandia based on the Normalized Difference Vegetation Index data

Science.gov (United States)

Miulgauzen, Daria; Pankratova, Lubov

2017-04-01

Being a part of Eurasian "cold sector", ecosystems of East Fennoscandia may fit in the category of the most vulnerable to any external impact, including anthropogenic one. The productivity of plant communities can serve as an indicator representing the state of ecosystems, especially in disturbed areas. The present research is aimed at the environmental impact assessment caused by the Pechenganikel Mining and Metallurgical Plant based on the plant communities' productivity data on the example of ecosystems of East Fennoscandia. Vegetation productivity was assessed on the basis of the Normalized Difference Vegetation Index (NDVI) which is often used for screenings to quantify plant canopy. The essence of the method is that of the difference between the spectral reflectance of vegetation in red and near-infrared regions. The index was calculated on the satellite images of Landsat 8 in IDRISI Kilimanjaro (Clark Labs) according to the equation: N DV I = N-IR- RED-; N IR +RED NIR - spectral reflectance measurements in near-infrared region, RED - spectral reflectance measurements in red region. To compare the index calculations with the information on the state of plant communities, the field studies were carried out in the area of 380 km2 in the vicinity of the Pechenganikel Mining and Metallurgical Plant (Kola Peninsula, Nikel urban-type settlement). As a result, there was created a map in MapInfo Professional 12.5 (Pitney Bowes Software) that represents the vegetation damage at a scale of 1:100,000. The field research has revealed the morphogenetic discrepancy between the soil-plant cover of the area in question and the one of "zonal" ecosystems. Plant communities have been widely modified or destroyed because of air pollution and there are numerous disturbances in the soil profile structure. In terms of vegetation productivity, the analysis of the NDVI figures has shown that the closer the pollution source (Pechenganikel Plant) is, the more significant the

Relationship between PC index and magnetospheric field-aligned currents measured by Swarm satellites

DEFF Research Database (Denmark)

Troshichev, О.; Sormakov, D.; Behlke, R.

2018-01-01

Abstract The relationship between the magnetospheric field-aligned currents (FAC) monitored by the Swarm satellites and the magnetic activity PC index (which is a proxy of the solar wind energy incoming into the magnetosphere) is examined. It is shown that current intensities measured in the R1...... between the PC index and the intensity of field-aligned currents in the R1 dawn and dusk layers: increase of FAC intensity in the course of substorm development is accompanied by increasing the PC index values. Correlation between PC and FAC intensities in the R2 dawn and dusk layers is also observed...

Application of Satellite Solar-Induced Chlorophyll Fluorescence to Understanding Large-Scale Variations in Vegetation Phenology and Function Over Northern High Latitude Forests

Science.gov (United States)

Jeong, Su-Jong; Schimel, David; Frankenberg, Christian; Drewry, Darren T.; Fisher, Joshua B.; Verma, Manish; Berry, Joseph A.; Lee, Jung-Eun; Joiner, Joanna

2016-01-01

This study evaluates the large-scale seasonal phenology and physiology of vegetation over northern high latitude forests (40 deg - 55 deg N) during spring and fall by using remote sensing of solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI) and observation-based estimate of gross primary productivity (GPP) from 2009 to 2011. Based on GPP phenology estimation in GPP, the growing season determined by SIF time-series is shorter in length than the growing season length determined solely using NDVI. This is mainly due to the extended period of high NDVI values, as compared to SIF, by about 46 days (+/-11 days), indicating a large-scale seasonal decoupling of physiological activity and changes in greenness in the fall. In addition to phenological timing, mean seasonal NDVI and SIF have different responses to temperature changes throughout the growing season. We observed that both NDVI and SIF linearly increased with temperature increases throughout the spring. However, in the fall, although NDVI linearly responded to temperature increases, SIF and GPP did not linearly increase with temperature increases, implying a seasonal hysteresis of SIF and GPP in response to temperature changes across boreal ecosystems throughout their growing season. Seasonal hysteresis of vegetation at large-scales is consistent with the known phenomena that light limits boreal forest ecosystem productivity in the fall. Our results suggest that continuing measurements from satellite remote sensing of both SIF and NDVI can help to understand the differences between, and information carried by, seasonal variations vegetation structure and greenness and physiology at large-scales across the critical boreal regions.

Vegetation mapping from high-resolution satellite images in the heterogeneous arid environments of Socotra Island (Yemen)

Science.gov (United States)

Malatesta, Luca; Attorre, Fabio; Altobelli, Alfredo; Adeeb, Ahmed; De Sanctis, Michele; Taleb, Nadim M.; Scholte, Paul T.; Vitale, Marcello

2013-01-01

Socotra Island (Yemen), a global biodiversity hotspot, is characterized by high geomorphological and biological diversity. In this study, we present a high-resolution vegetation map of the island based on combining vegetation analysis and classification with remote sensing. Two different image classification approaches were tested to assess the most accurate one in mapping the vegetation mosaic of Socotra. Spectral signatures of the vegetation classes were obtained through a Gaussian mixture distribution model, and a sequential maximum a posteriori (SMAP) classification was applied to account for the heterogeneity and the complex spatial pattern of the arid vegetation. This approach was compared to the traditional maximum likelihood (ML) classification. Satellite data were represented by a RapidEye image with 5 m pixel resolution and five spectral bands. Classified vegetation relevés were used to obtain the training and evaluation sets for the main plant communities. Postclassification sorting was performed to adjust the classification through various rule-based operations. Twenty-eight classes were mapped, and SMAP, with an accuracy of 87%, proved to be more effective than ML (accuracy: 66%). The resulting map will represent an important instrument for the elaboration of conservation strategies and the sustainable use of natural resources in the island.

Assessment of drought risk by using vegetation indices from remotely sensed data a perspective from hot and arid district of pakistan

International Nuclear Information System (INIS)

Tabassum, R.; Kahlid, A.

2016-01-01

The Shaheed Benazir Abad District is situated at the center of Sindh Province, which is one of the hottest and driest part of Pakistan. In the past few decades, the extreme and moderate droughts had been reported in the district with peak value -2.4 recorded using the Standardized Precipitation Index (SPI). In this study, satellite remote sensing and digital image processing techniques were used to monitor the drought conditions in the district. Multiple drought indices were calculated by using Thematic Mapper (TM) data of the Landsat satellite program, jointly managed by the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), including Land Surface Temperature (LST), Normalized Vegetation Index (NDVI), Vegetation Condition Index (VCI) and Temperature Vegetation Index (TVX). These indices provided the agricultural drought conditions for the duration of 1992-2011. The VCI maps indicated the high drought conditions in the plain land, away from the built-up areas, while the proximity of the built-up land is under a moderate drought. However, in cultivated lands, the agriculture drought condition is not obvious due to canal irrigated cultivation. A drought in year 2011, was more severe than in the year 2000. It is an indication of climate change impacts in the region. (author)

A FRAMEWORK OF CHANGE DETECTION BASED ON COMBINED MORPHOLOGICA FEATURES AND MULTI-INDEX CLASSIFICATION

Directory of Open Access Journals (Sweden)

S. Li

2017-09-01

Full Text Available Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI, the differential water index (NDWI are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

Science.gov (United States)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

An Intercomparison of Vegetation Products from Satellite-based Observations used for Soil Moisture Retrievals

Science.gov (United States)

Vreugdenhil, Mariette; de Jeu, Richard; Wagner, Wolfgang; Dorigo, Wouter; Hahn, Sebastian; Bloeschl, Guenter

2013-04-01

Vegetation and its water content affect active and passive microwave soil moisture retrievals and need to be taken into account in such retrieval methodologies. This study compares the vegetation parameterisation that is used in the TU-Wien soil moisture retrieval algorithm to other vegetation products, such as the Vegetation Optical Depth (VOD), Net Primary Production (NPP) and Leaf Area Index (LAI). When only considering the retrieval algorithm for active microwaves, which was developed by the TU-Wien, the effect of vegetation on the backscattering coefficient is described by the so-called slope [1]. The slope is the first derivative of the backscattering coefficient in relation to the incidence angle. Soil surface backscatter normally decreases quite rapidly with the incidence angle over bare or sparsely vegetated soils, whereas the contribution of dense vegetation is fairly uniform over a large range of incidence angles. Consequently, the slope becomes less steep with increasing vegetation. Because the slope is a derivate of noisy backscatter measurements, it is characterised by an even higher level of noise. Therefore, it is averaged over several years assuming that the state of the vegetation doesn't change inter-annually. The slope is compared to three dynamic vegetation products over Australia, the VOD, NPP and LAI. The VOD was retrieved from AMSR-E passive microwave data using the VUA-NASA retrieval algorithm and provides information on vegetation with a global coverage of approximately every two days [2]. LAI is defined as half the developed area of photosynthetically active elements of the vegetation per unit horizontal ground area. In this study LAI is used from the Geoland2 products derived from SPOT Vegetation*. The NPP is the net rate at which plants build up carbon through photosynthesis and is a model-based estimate from the BiosEquil model [3, 4]. Results show that VOD and slope correspond reasonably well over vegetated areas, whereas in arid

A survey of drought and Variation of Vegetation by statistical indexes and remote sensing (Case study: Jahad forest in Bandar Abbas)

International Nuclear Information System (INIS)

Tamassoki, E; Soleymani, Z; Bahrami, F; Abbasgharemani, H

2014-01-01

The damages of drought as a climatic and creeping phenomenon are very enormous specially in deserts. Necessity of management and conflict with it is clear. In this case vegetation are damaged too, and even are changed faster. This paper describes the process of vegetation changes and surveys it with drought indexes such as statistical and remote sensing indexes and correlation between temperature and relative humidity by Geographical Information System (GIS) and Remote Sensing (RS) in forest park of Bandar Abbas in successive years. At the end the regression and determination-coefficient for showing the importance of droughts survey are computed. Results revealed that the correlation between vegetation and indexes was 0.5. The humidity had maximum correlation and when we close to 2009 the period of droughts increase and time intervals decrease that influence vegetation enormously and cause the more area lost its vegetation

Coastwide Reference Monitoring System (CRMS) Vegetation Volume Index: An assessment tool for marsh habitat focused on the three-dimensional structure at CRMS vegetation monitoring stations

Science.gov (United States)

Wood, William B.; Visser, Jenneke M.; Piazza, Sarai C.; Sharp, Leigh A.; Hundy, Laura C.; McGinnis, Tommy E.

2015-12-04

A Vegetation Volume (VV) variable and Vegetation Volume Index (VVI) have been developed for the Coastwide Reference Monitoring System (CRMS). The VV is a measure of the amount of three-dimensional vegetative structure present at each CRMS site and is based on vegetation data collected annually. The VV uses 10 stations per CRMS site to quantify four vegetation layers: carpet, herbaceous, shrub, and tree. For each layer an overall live vegetation percent cover and height are collected to create a layer volume; the individual layer volumes are then summed to generate a site vegetation volume profile. The VV uses the two-dimensional area of live vegetative cover (in square meters) multiplied by the height (in meters) of each layer to produce a volume (in cubic meters) for each layer present in a 2-meter by 2-meter station. These layers are additive, yielding a total volume for each of the 10 herbaceous vegetation stations and an overall CRMS marsh site average.

The 2005 and 2012 major drought events in Iberia: monitoring vegetation dynamics and crop yields using satellite data.

Science.gov (United States)

Gouveia, Célia M.; Trigo, Ricardo M.

2014-05-01

The Iberian Peninsula is recurrently affected by drought episodes and therefore by the adverse effects associated that range from severe water shortages to economic losses and related social impacts. During the hydrological years of 2004/2005 and 2011/2012, Iberia was hit by two of the worst drought episodes ever recording in this semi-arid region (Garcia-Herrera at al., 2007; Trigo et al., 2013). These two drought episodes were extreme in both its magnitude and spatial extent. A tendency towards a drier Mediterranean for the period 1970-2010 in comparison with 1901-70 has been identified (Hoerling et al., 2012), reinforcing the need for a continuous monitoring of vegetation stress and reliable estimates of the drought impacts. The strong effect of water scarcity on vegetation dynamics is well documented in Mediterranean and other semi-arid regions. Despite the usual link established between the decrease of vegetation greenness and the lack of precipitation during a considerably long period, the impact on vegetation activity may be amplified by other climatic anomalies, such as high temperature, high wind, and low relative humidity. The recent availability of consistent satellite imagery covering large regions over long periods of time has progressively reinforced the role of remote sensing in environmental studies, in particular in those related to drought episodes (e.g. Gouveia et al., 2009). The aim of the present work is to assess and monitor the cumulative impact over time of drought conditions on vegetation over Iberian Peninsula. For this purpose we have used the regional fields of the Normalized Difference Vegetation Index (NDVI) as obtained from the VEGETATION-SPOT5 instrument, from 1999 to 2013. The entire 15-yr long period was analysed, but particular attention was devoted to the two extreme drought episodes of 2004-2005 and 2011-2012. During the hydrological years of 2004-2005 and 2011-2012 drought episodes negative anomalies of NDVI were observed over

Vegetation Earth System Data Record from DSCOVR EPIC Observations

Science.gov (United States)

Knyazikhin, Y.; Song, W.; Yang, B.; Mottus, M.; Rautiainen, M.; Stenberg, P.

2017-12-01

The NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR) mission was launched on February 11, 2015 to the Sun-Earth Lagrangian L1 point where it began to collect radiance data of the entire sunlit Earth every 65 to 110 min in June 2015. It provides imageries in near backscattering directions with the scattering angle between 168° and 176° at ten ultraviolet to near infrared (NIR) narrow spectral bands centered at 317.5 (band width 1.0) nm, 325.0 (2.0) nm, 340.0 (3.0) nm, 388.0 (3.0) nm, 433.0 (3.0) nm, 551.0 (3.0) nm, 680.0 (3.0) nm, 687.8 (0.8) nm, 764.0 (1.0) nm and 779.5 (2.0) nm. This poster presents current status of the Vegetation Earth System Data Record of global Leaf Area Index (LAI), solar zenith angle dependent Sunlit Leaf Area Index (SLAI), Fraction vegetation absorbed Photosynthetically Active Radiation (FPAR) and Normalized Difference Vegetation Index (NDVI) derived from the DSCOVR EPIC observations. Whereas LAI is a standard product of many satellite missions, the SLAI is a new satellite-derived parameter. Sunlit and shaded leaves exhibit different radiative response to incident Photosynthetically Active Radiation (400-700 nm), which in turn triggers various physiological and physical processes required for the functioning of plants. FPAR, LAI and SLAI are key state parameters in most ecosystem productivity models and carbon/nitrogen cycle. The product at 10 km sinusoidal grid and 65 to 110 min temporal frequency as well as accompanying Quality Assessment (QA) variables will be publicly available from the NASA Langley Atmospheric Science Data Center. The Algorithm Theoretical Basis (ATBD) and product validation strategy are also discussed in this poster.

Analysis of Agricultural Drought in East Java Using Vegetation Health Index

OpenAIRE

Amalo, Luisa Febrina; Hidayat, Rahmat; Sulma, Sayidah

2018-01-01

Drought is a natural hazard indicated by the decreasing of rainfall and water storage and impacting agricultural sector. Agricultural drought assessment has been used to monitor agricultural sustainability, particularly in East Java as national agricultural production center. Identification of drought characteristics –correlated with El Niño-Southern Oscillation, and agricultural impact on paddy fields and rice production using VHI (Vegetation Health Index) were conducted. VHI is produced by ...

Atmospheric effects on the NDVI - Strategies for its removal. [Normalized Difference Vegetation Index

Science.gov (United States)

Kaufman, Y. J.; Tanre, D.; Holben, B. N.; Markham, B.; Gitelson, A.

1992-01-01

The compositing technique used to derive global vegetation index (NDVI) from the NOAA AVHRR radiances reduces the residual effect of water vapor and aerosol on the NDVI. The reduction in the atmospheric effect is shown using a comprehensive measured data set for desert conditions, and a simulation for grass with continental aerosol. A statistical analaysis of the probability of occurrence of aerosol optical thickness and precipitable water vapor measured in different climatic regimes is used for this simulation. It is concluded that for a long compositing period (e.g., 27 days), the residual aerosol optical thickness and precipitable water vapor are usually too small to be corrected. For a 9-day compositing, the residual average aerosol effect may be about twice the correction uncertainty. For Landsat TM or Earth Observing System Moderate Resolution Imaging Spectrometer (EOS-MODIS) data, the newly defined atmospherically resistant vegetation index (ARVI) is more promising than possible direct atmospheric correction schemes, except for heavy desert dust conditions.

Determination of Leaf Area Index, Total Foliar N, and Normalized Difference Vegetation Index for Arctic Ecosystems Dominated by Cassiope tetragona

DEFF Research Database (Denmark)

Campioli, M; Street, LE; Michelsen, Anders

2009-01-01

have not been accurately quantified. We address this knowledge gap by (i) direct measurements of LAI and TFN for C. tetragona, and (ii) determining TFN-LAI and LAI–normalized difference vegetation index (NDVI) relationships for typical C. tetragona tundras in the subarctic (Sweden) and High Arctic...... leaf N and biomass. The LAI-NDVI and TFN-LAI relationships showed high correlation and can be used to estimate indirectly LAI and TFN. The LAI-NDVI relationship for C. tetragona vegetation differed from a generic LAI-NDVI relationship for arctic tundra, whereas the TFN-LAI relationship did not. Overall...

Mapping Satellite Inherent Optical Properties Index in Coastal Waters of the Yucatán Peninsula (Mexico

Directory of Open Access Journals (Sweden)

Jesús A. Aguilar-Maldonado

2018-06-01

Full Text Available The Yucatán Peninsula hosts worldwide-known tourism destinations that concentrate most of the Mexico tourism activity. In this region, tourism has exponentially increased over the last years, including wildlife oriented tourism. Rapid tourism development, involving the consequent construction of hotels and tourist commodities, is associated with domestic sewage discharges from septic tanks. In this karstic environment, submarine groundwater discharges are very important and highly vulnerable to anthropogenic pollution. Nutrient loadings are linked to harmful algal blooms, which are an issue of concern to local and federal authorities due to their recurrence and socioeconomic and human health costs. In this study, we used satellite products from MODIS (Moderate Resolution Imaging Spectroradiometer to calculate and map the satellite Inherent Optical Properties (IOP Index. We worked with different scenarios considering both holiday and hydrological seasons. Our results showed that the satellite IOP Index allows one to build baseline information in a sustainable mid-term or long-term basis which is key for ecosystem-based management.

Assessing Riparian Vegetation Condition and Function in Disturbed Sites of the Arid Northwestern Mexico

Directory of Open Access Journals (Sweden)

Lara Cornejo-Denman

2018-01-01

Full Text Available Transformation or modification of vegetation distribution and structure in arid riparian ecosystems can lead to the loss of ecological function. Mexico has 101,500,000 ha of arid lands, however there is a general lack of information regarding how arid riparian ecosystems are being modified. To assess these modifications, we use eight sites in the San Miguel River (central Sonora to analyze (1 riparian vegetation composition, structure and distribution using field sampling and remote sensing data from Unmanned Aerial Vehicles (UAV; (2 productivity (proxies, using vegetation indices derived from satellite data; and (3 variability posed by riparian vegetation and vegetation adjacent to riparian habitats. The development of a simple yet informative Anthropogenic-disturbance Index (ADI allowed us to classify and describe each study site. We found sharp differences in vegetation composition and structure between sites due to the absence/presence of obligate-riparian species. We also report significant difference between EVI (Enhanced Vegetation Index values for the dry season among vegetation types that develop near the edges of the river but differ in composition, suggesting that land cover changes form obligate-riparian to facultative-riparian species can lead to a loss in potential productivity. Finally, our tests suggest that sites with higher disturbance present lower photosynthetic activity.

Environment, vegetation and greenness (NDVI) along the North America and Eurasia Arctic transects

International Nuclear Information System (INIS)

Walker, D A; Raynolds, M K; Kuss, P; Kade, A N; Epstein, H E; Frost, G V; Kopecky, M A; Daniëls, F J A; Leibman, M O; Moskalenko, N G; Khomutov, A V; Matyshak, G V; Khitun, O V; Forbes, B C; Bhatt, U S; Vonlanthen, C M; Tichý, L

2012-01-01

Satellite-based measurements of the normalized difference vegetation index (NDVI; an index of vegetation greenness and photosynthetic capacity) indicate that tundra environments are generally greening and becoming more productive as climates warm in the Arctic. The greening, however, varies and is even negative in some parts of the Arctic. To help interpret the space-based observations, the International Polar Year (IPY) Greening of the Arctic project conducted ground-based surveys along two >1500 km transects that span all five Arctic bioclimate subzones. Here we summarize the climate, soil, vegetation, biomass, and spectral information collected from the North America Arctic transect (NAAT), which has a more continental climate, and the Eurasia Arctic transect (EAT), which has a more oceanic climate. The transects have broadly similar summer temperature regimes and overall vegetation physiognomy, but strong differences in precipitation, especially winter precipitation, soil texture and pH, disturbance regimes, and plant species composition and structure. The results indicate that summer warmth and NDVI increased more strongly along the more continental transect. (letter)

Vegetation Dynamics and Rainfall Sensitivity of the Amazon

Science.gov (United States)

Hilker, Thomas; Lyapustin, Alexei I.; Tucker, Compton J.; Hall, Forrest G.; Myneni, Ranga B.; Wang, Yujie; Bi, Jian; Mendes de Moura, Yhasmin; Sellers, Piers J.

2014-01-01

We show that the vegetation canopy of the Amazon rainforest is highly sensitive to changes in precipitation patterns and that reduction in rainfall since 2000 has diminished vegetation greenness across large parts of Amazonia. Large-scale directional declines in vegetation greenness may indicate decreases in carbon uptake and substantial changes in the energy balance of the Amazon. We use improved estimates of surface reflectance from satellite data to show a close link between reductions in annual precipitation, El Nino southern oscillation events, and photosynthetic activity across tropical and subtropical Amazonia. We report that, since the year 2000, precipitation has declined across 69% of the tropical evergreen forest (5.4 million sq km) and across 80% of the subtropical grasslands (3.3 million sq km). These reductions, which coincided with a decline in terrestrial water storage, account for about 55% of a satellite-observed widespread decline in the normalized difference vegetation index (NDVI). During El Nino events, NDVI was reduced about 16.6% across an area of up to 1.6 million sq km compared with average conditions. Several global circulation models suggest that a rise in equatorial sea surface temperature and related displacement of the intertropical convergence zone could lead to considerable drying of tropical forests in the 21st century. Our results provide evidence that persistent drying could degrade Amazonian forest canopies, which would have cascading effects on global carbon and climate dynamics.

Integrated study of biomass index in La Herreria (Sierra de Guadarrama)

Science.gov (United States)

Hernandez Díaz-Ambrona, Carlos G.

2016-04-01

Drought severity has many implications for society, including its impacts on the water supply, water pollution, reservoir management and ecosystem. There have been many attempts to characterize its severity, resulting in the numerous drought indices that have been developed (Niemeyer 2008). The'biomass index', based on satellite image derived Normalized Difference Vegetation Index (NDVI) has been used in several countries for pasture and forage crops for some years (Rao, 2010; Escribano-Rodriguez et al., 2014). NDVI generally provides a broad overview of the vegetation condition and spatial vegetation distribution in a region. Vegetative drought is closely related with weather impacts. However, in NDVI, the weather component gets subdued by the strong ecological component. Another vegetation index is Vegetation Condition Index (VCI) that separates the short-term weather-related NDVI fluctuations from the long-term ecosystem changes (Kogan, 1990). Therefore, while NDVI shows seasonal vegetation dynamics, VCI rescales vegetation dynamics between 0 and 100 to reflect relative changes in the vegetation condition from extremely bad to optimal (Kogan et al., 2003). In this work a pasture area at La Herreria (Sierra de Guadarrama, Spain) has been delimited. Then, NDVI historical data are reconstructed based on remote sensing imaging MODIS, with 500x500m2 resolution. From the closest meteorological station (Santolaria-Canales, 2015) records of weekly precipitation, temperature and evapotranspiration from 2001 till 2012 were obtained. Standard Precipitation Index (SPI), Crop Moisture Index (CMI) (Palmer, 1968) and Evapotranspiration-Precipitation Ratio (EPR) are calculated in an attempt to relate them to several vegetation indexes: NDVI, VCI and NDVI Change Ratio to Median (RMNDVI). The results are discussed in the context of pasture index insurance. References Escribano Rodriguez, J.Agustín, Carlos Gregorio Hernández Díaz-Ambrona and Ana María Tarquis Alfonso

Derivation of global vegetation biophysical parameters from EUMETSAT Polar System

Science.gov (United States)

García-Haro, Francisco Javier; Campos-Taberner, Manuel; Muñoz-Marí, Jordi; Laparra, Valero; Camacho, Fernando; Sánchez-Zapero, Jorge; Camps-Valls, Gustau

2018-05-01

This paper presents the algorithm developed in LSA-SAF (Satellite Application Facility for Land Surface Analysis) for the derivation of global vegetation parameters from the AVHRR (Advanced Very High Resolution Radiometer) sensor on board MetOp (Meteorological-Operational) satellites forming the EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Polar System (EPS). The suite of LSA-SAF EPS vegetation products includes the leaf area index (LAI), the fractional vegetation cover (FVC), and the fraction of absorbed photosynthetically active radiation (FAPAR). LAI, FAPAR, and FVC characterize the structure and the functioning of vegetation and are key parameters for a wide range of land-biosphere applications. The algorithm is based on a hybrid approach that blends the generalization capabilities offered by physical radiative transfer models with the accuracy and computational efficiency of machine learning methods. One major feature is the implementation of multi-output retrieval methods able to jointly and more consistently estimate all the biophysical parameters at the same time. We propose a multi-output Gaussian process regression (GPRmulti), which outperforms other considered methods over PROSAIL (coupling of PROSPECT and SAIL (Scattering by Arbitrary Inclined Leaves) radiative transfer models) EPS simulations. The global EPS products include uncertainty estimates taking into account the uncertainty captured by the retrieval method and input errors propagation. A sensitivity analysis is performed to assess several sources of uncertainties in retrievals and maximize the positive impact of modeling the noise in training simulations. The paper discusses initial validation studies and provides details about the characteristics and overall quality of the products, which can be of interest to assist the successful use of the data by a broad user's community. The consistent generation and distribution of the EPS vegetation products will

Evaluation and cross-comparison of vegetation indices for crop monitoring from sentinel-2 and worldview-2 images

Science.gov (United States)

Psomiadis, Emmanouil; Dercas, Nicholas; Dalezios, Nicolas R.; Spyropoulos, Nikolaos V.

2017-10-01

Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing applications are designed to provide farmers with timely crop monitoring and production information. Such information can be used to identify crop vigor problems. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state and biophysical properties of vegetation. However, due to the various sensor characteristics, there are differences among VIs derived from multiple sensors for the same target. Therefore, multi-sensor VI capability and effectiveness are critical but complicated issues in the application of multi-sensor vegetation observations. Various factors such as the atmospheric conditions during acquisition, sensor and geometric characteristics, such as viewing angle, field of view, and sun elevation influence direct comparability of vegetation indicators among different sensors. In the present study, two experimental areas were used which are located near the villages Nea Lefki and Melia of Larissa Prefecture in Thessaly Plain area, containing a wheat and a cotton crop, respectively. Two satellite systems with different spatial resolution, WorldView-2 (W2) and Sentinel-2 (S2) with 2 and 10 meters pixel size, were used. Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) were calculated and a statistical comparison of the VIs was made to designate their correlation and dependency. Finally, several other innovative indices were calculated and compared to evaluate their effectiveness in the detection of problematic plant growth areas.

The Effect of Vegetation Productivity on Millet Prices in the Informal Markets of Mali, Burkina Faso and Niger

Energy Technology Data Exchange (ETDEWEB)

Brown, M.E. [Department of Geography, University of Maryland, NASA Goddard Space Flight Center, Code 923, Greenbelt, MD 20771 (United States); Pinzon, J.E. [Science Systems and Applications Inc., NASA Goddard Space Flight Center, Code 923, Greenbelt, MD (United States); Prince, S.D. [Department of Geography, University of Maryland, College Park, MD (United States)

2006-09-15

Systematic evaluation of food security throughout the Sahel has been attempted for nearly two decades. Food security analyses have used both food prices to determine the ability of the population to access food, and satellite-derived vegetation indices that measure vegetation production to establish how much food is available each year. The relationship between these two food security indicators is explored here using correspondence analysis and through the use of Markov chain models. Two sources of quantitative data were used: 8 km normalized difference vegetation index (NDVI) data from the Advanced Very High Resolution Radiometers (AVHRR) carried on the NOAA series of satellites, and monthly millet prices from 445 markets in Mali, Niger and Burkina Faso. The results show that the growing season vegetation production is related to the price of millet at the annual and the seasonal time scales. If the growing season was characterized by erratic, sparse rainfall, it resulted in higher prices, and well-distributed, abundant rainfall resulted in lower prices. The correspondence between vegetation production and millet prices is used to produce maps of millet prices for West Africa.

Extending a field-based Sonoran desert vegetation classification to a regional scale using optical and microwave satellite imagery

Science.gov (United States)

Shupe, Scott Marshall

2000-10-01

Vegetation mapping in and regions facilitates ecological studies, land management, and provides a record to which future land changes can be compared. Accurate and representative mapping of desert vegetation requires a sound field sampling program and a methodology to transform the data collected into a representative classification system. Time and cost constraints require that a remote sensing approach be used if such a classification system is to be applied on a regional scale. However, desert vegetation may be sparse and thus difficult to sense at typical satellite resolutions, especially given the problem of soil reflectance. This study was designed to address these concerns by conducting vegetation mapping research using field and satellite data from the US Army Yuma Proving Ground (USYPG) in Southwest Arizona. Line and belt transect data from the Army's Land Condition Trend Analysis (LCTA) Program were transformed into relative cover and relative density classification schemes using cluster analysis. Ordination analysis of the same data produced two and three-dimensional graphs on which the homogeneity of each vegetation class could be examined. It was found that the use of correspondence analysis (CA), detrended correspondence analysis (DCA), and non-metric multidimensional scaling (NMS) ordination methods was superior to the use of any single ordination method for helping to clarify between-class and within-class relationships in vegetation composition. Analysis of these between-class and within-class relationships were of key importance in examining how well relative cover and relative density schemes characterize the USYPG vegetation. Using these two classification schemes as reference data, maximum likelihood and artificial neural net classifications were then performed on a coregistered dataset consisting of a summer Landsat Thematic Mapper (TM) image, one spring and one summer ERS-1 microwave image, and elevation, slope, and aspect layers

[Retrieval of Copper Pollution Information from Hyperspectral Satellite Data in a Vegetation Cover Mining Area].

Science.gov (United States)

Qu, Yong-hua; Jiao, Si-hong; Liu, Su-hong; Zhu, Ye-qing

2015-11-01

Heavy metal mining activities have caused the complex influence on the ecological environment of the mining regions. For example, a large amount of acidic waste water containing heavy metal ions have be produced in the process of copper mining which can bring serious pollution to the ecological environment of the region. In the previous research work, bare soil is mainly taken as the research target when monitoring environmental pollution, and thus the effects of land surface vegetation have been ignored. It is well known that vegetation condition is one of the most important indictors to reflect the ecological change in a certain region and there is a significant linkage between the vegetation spectral characteristics and the heavy metal when the vegetation is effected by the heavy metal pollution. It means the vegetation is sensitive to heavy metal pollution by their physiological behaviors in response to the physiological ecology change of their growing environment. The conventional methods, which often rely on large amounts of field survey data and laboratorial chemical analysis, are time consuming and costing a lot of material resources. The spectrum analysis method using remote sensing technology can acquire the information of the heavy mental content in the vegetation without touching it. However, the retrieval of that information from the hyperspectral data is not an easy job due to the difficulty in figuring out the specific band, which is sensitive to the specific heavy metal, from a huge number of hyperspectral bands. Thus the selection of the sensitive band is the key of the spectrum analysis method. This paper proposed a statistical analysis method to find the feature band sensitive to heavy metal ion from the hyperspectral data and to then retrieve the metal content using the field survey data and the hyperspectral images from China Environment Satellite HJ-1. This method selected copper ion content in the leaves as the indicator of copper pollution

Estimation and Validation of RapidEye-Based Time-Series of Leaf Area Index for Winter Wheat in the Rur Catchment (Germany

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Muhammad Ali

2015-03-01

Full Text Available Leaf Area Index (LAI is an important variable for numerous processes in various disciplines of bio- and geosciences. In situ measurements are the most accurate source of LAI among the LAI measuring methods, but the in situ measurements have the limitation of being labor intensive and site specific. For spatial-explicit applications (from regional to continental scales, satellite remote sensing is a promising source for obtaining LAI with different spatial resolutions. However, satellite-derived LAI measurements using empirical models require calibration and validation with the in situ measurements. In this study, we attempted to validate a direct LAI retrieval method from remotely sensed images (RapidEye with in situ LAI (LAIdestr. Remote sensing LAI (LAIrapideye were derived using different vegetation indices, namely SAVI (Soil Adjusted Vegetation Index and NDVI (Normalized Difference Vegetation Index. Additionally, applicability of the newly available red-edge band (RE was also analyzed through Normalized Difference Red-Edge index (NDRE and Soil Adjusted Red-Edge index (SARE. The LAIrapideye obtained from vegetation indices with red-edge band showed better correlation with LAIdestr (r = 0.88 and Root Mean Square Devation, RMSD = 1.01 & 0.92. This study also investigated the need to apply radiometric/atmospheric correction methods to the time-series of RapidEye Level 3A data prior to LAI estimation. Analysis of the the RapidEye Level 3A data set showed that application of the radiometric/atmospheric correction did not improve correlation of the estimated LAI with in situ LAI.

Assessing Sahelian vegetation and stress from seasonal time series of polar orbiting and geostationary satellite imagery

DEFF Research Database (Denmark)

Olsen, Jørgen Lundegaard

that short term variations in anomalies from seasonally detrended time series of indices could carry information on vegetation stress was examined and confirmed. However, it was not found sufficiently robust on pixel level to be implemented for monitoring vegetation water stress on a per-pixel basis...... provide good sensitivity to canopy water content, which can make vegetation stress detection possible. Furthermore, the high frequency observations in the optical spectrum now available from geostationary instruments have the potential for detection of changes in vegetation related surface properties...... on short timescales, which are challenging from polar orbiting instruments. Geostationary NDVI and the NIR and SWIR based Shortwave Infrared Water Stress Index (SIWSI) indices are compared with extensive field data from the Dahra site, supplemented by data from the Agoufou and Demokeya sites. The indices...

Improving satellite-based post-fire evapotranspiration estimates in semi-arid regions

Science.gov (United States)

Poon, P.; Kinoshita, A. M.

2017-12-01

Climate change and anthropogenic factors contribute to the increased frequency, duration, and size of wildfires, which can alter ecosystem and hydrological processes. The loss of vegetation canopy and ground cover reduces interception and alters evapotranspiration (ET) dynamics in riparian areas, which can impact rainfall-runoff partitioning. Previous research evaluated the spatial and temporal trends of ET based on burn severity and observed an annual decrease of 120 mm on average for three years after fire. Building upon these results, this research focuses on the Coyote Fire in San Diego, California (USA), which burned a total of 76 km2 in 2003 to calibrate and improve satellite-based ET estimates in semi-arid regions affected by wildfire. The current work utilizes satellite-based products and techniques such as the Google Earth Engine Application programming interface (API). Various ET models (ie. Operational Simplified Surface Energy Balance Model (SSEBop)) are compared to the latent heat flux from two AmeriFlux eddy covariance towers, Sky Oaks Young (US-SO3), and Old Stand (US-SO2), from 2000 - 2015. The Old Stand tower has a low burn severity and the Young Stand tower has a moderate to high burn severity. Both towers are used to validate spatial ET estimates. Furthermore, variables and indices, such as Enhanced Vegetation Index (EVI), Normalized Difference Moisture Index (NDMI), and the Normalized Burn Ratio (NBR) are utilized to evaluate satellite-based ET through a multivariate statistical analysis at both sites. This point-scale study will able to improve ET estimates in spatially diverse regions. Results from this research will contribute to the development of a post-wildfire ET model for semi-arid regions. Accurate estimates of post-fire ET will provide a better representation of vegetation and hydrologic recovery, which can be used to improve hydrologic models and predictions.

Disturbance metrics predict a wetland Vegetation Index of Biotic Integrity

Science.gov (United States)

Stapanian, Martin A.; Mack, John; Adams, Jean V.; Gara, Brian; Micacchion, Mick

2013-01-01

Indices of biological integrity of wetlands based on vascular plants (VIBIs) have been developed in many areas in the USA. Knowledge of the best predictors of VIBIs would enable management agencies to make better decisions regarding mitigation site selection and performance monitoring criteria. We use a novel statistical technique to develop predictive models for an established index of wetland vegetation integrity (Ohio VIBI), using as independent variables 20 indices and metrics of habitat quality, wetland disturbance, and buffer area land use from 149 wetlands in Ohio, USA. For emergent and forest wetlands, predictive models explained 61% and 54% of the variability, respectively, in Ohio VIBI scores. In both cases the most important predictor of Ohio VIBI score was a metric that assessed habitat alteration and development in the wetland. Of secondary importance as a predictor was a metric that assessed microtopography, interspersion, and quality of vegetation communities in the wetland. Metrics and indices assessing disturbance and land use of the buffer area were generally poor predictors of Ohio VIBI scores. Our results suggest that vegetation integrity of emergent and forest wetlands could be most directly enhanced by minimizing substrate and habitat disturbance within the wetland. Such efforts could include reducing or eliminating any practices that disturb the soil profile, such as nutrient enrichment from adjacent farm land, mowing, grazing, or cutting or removing woody plants.

A New Temperature-Vegetation Triangle Algorithm with Variable Edges (TAVE for Satellite-Based Actual Evapotranspiration Estimation

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Hua Zhang

2016-09-01

Full Text Available The estimation of spatially-variable actual evapotranspiration (AET is a critical challenge to regional water resources management. We propose a new remote sensing method, the Triangle Algorithm with Variable Edges (TAVE, to generate daily AET estimates based on satellite-derived land surface temperature and the vegetation index NDVI. The TAVE captures heterogeneity in AET across elevation zones and permits variability in determining local values of wet and dry end-member classes (known as edges. Compared to traditional triangle methods, TAVE introduces three unique features: (i the discretization of the domain as overlapping elevation zones; (ii a variable wet edge that is a function of elevation zone; and (iii variable values of a combined-effect parameter (that accounts for aerodynamic and surface resistance, vapor pressure gradient, and soil moisture availability along both wet and dry edges. With these features, TAVE effectively addresses the combined influence of terrain and water stress on semi-arid environment AET estimates. We demonstrate the effectiveness of this method in one of the driest countries in the world—Jordan, and compare it to a traditional triangle method (TA and a global AET product (MOD16 over different land use types. In irrigated agricultural lands, TAVE matched the results of the single crop coefficient model (−3%, in contrast to substantial overestimation by TA (+234% and underestimation by MOD16 (−50%. In forested (non-irrigated, water consuming regions, TA and MOD16 produced AET average deviations 15.5 times and −3.5 times of those based on TAVE. As TAVE has a simple structure and low data requirements, it provides an efficient means to satisfy the increasing need for evapotranspiration estimation in data-scarce semi-arid regions. This study constitutes a much needed step towards the satellite-based quantification of agricultural water consumption in Jordan.

Response of vegetation NDVI to climatic extremes in the arid region of Central Asia: a case study in Xinjiang, China

Science.gov (United States)

Yao, Junqiang; Chen, Yaning; Zhao, Yong; Mao, Weiyi; Xu, Xinbing; Liu, Yang; Yang, Qing

2018-02-01

Observed data showed the climatic transition from warm-dry to warm-wet in Xinjiang during the past 30 years and will probably affect vegetation dynamics. Here, we analyze the interannual change of vegetation index based on the satellite-derived normalized difference vegetation index (NDVI) with temperature and precipitation extreme over the Xinjiang, using the 8-km NDVI third-generation (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) from 1982 to 2010. Few previous studies analyzed the link between climate extremes and vegetation response. From the satellite-based results, annual NDVI significantly increased in the first two decades (1981-1998) and then decreased after 1998. We show that the NDVI decrease over the past decade may conjointly be triggered by the increases of temperature and precipitation extremes. The correlation analyses demonstrated that the trends of NDVI was close to the trend of extreme precipitation; that is, consecutive dry days (CDD) and torrential rainfall days (R24) positively correlated with NDVI during 1998-2010. For the temperature extreme, while the decreases of NDVI correlate positively with warmer mean minimum temperature ( Tnav), it correlates negatively with the number of warmest night days ( Rwn). The results suggest that the climatic extremes have possible negative effects on the ecosystem.

The Seasonal Cycle of Satellite Chlorophyll Fluorescence Observations and its Relationship to Vegetation Phenology and Ecosystem Atmosphere Carbon Exchange

Science.gov (United States)

Joiner, J.; Yoshida, Y.; Vasilkov, A. P.; Schaefer, K.; Jung, M.; Guanter, L.; Zhang, Y; Garrity, S.; Middleton, E. M.; Huemmrich, K. F.;

Ten Years of Post-Fire Vegetation Recovery following the 2007 Zaca Fire using Landsat Satellite Imagery

Science.gov (United States)

Hallett, J. K. E.; Miller, D.; Roberts, D. A.

2017-12-01

Forest fires play a key role in shaping eco-systems. The risk to vegetation depends on the fire regime, fuel conditions (age and amount), fire temperature, and physiological characteristics such as bark thickness and stem diameter. The 2007 Zaca Fire (24 kilometers NE of Buellton, Santa Barbara County, California) burned 826.4 km2 over the course of 2 months. In this study, we used a time series of Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager imagery, to evaluate plant burn severity and post fire recovery as defined into classes of above average recovery, normal recovery, and below average recovery. We spectrally unmixed the images into green vegetation (GV), non-photosynthetic vegetation (NPV), soil surface (SOIL), and ash with a spectral library developed using Constrained Reference Endmember Selection (CRES). We delineated the fire perimeter using the differenced Normalized Burn Ratio (dNBR) and evaluated changes in this index and the Normalized Difference Vegetation Index through time. The results showed an immediate decline in GV and NPV fractions, with a rise in soil and ash fractions directly following the fire, with a slow recovery in GV fraction and a loss of bare soil cover. The was a sharp increase in the ash fraction following the fire and gradual decrease in the year after. Most areas have recovered as of 2017, with prominent recovery in the center of the burn scar and reduced recovery in areas to the south. These results indicate how post-fire vegetation varies based on initial burn severity and pre-fire GV and NPV fractions.

Integrated NDVI images for Niger 1986-1987. [Normalized Difference Vegetation Index

Science.gov (United States)

Harrington, John A., Jr.; Wylie, Bruce K.; Tucker, Compton J.

1988-01-01

Two NOAA AVHRR images are presented which provide a comparison of the geographic distribution of an integration of the normalized difference vegetation index (NDVI) for the Sahel zone in Niger for the growing seasons of 1986 and 1987. The production of the images and the application of the images for resource management are discussed. Daily large area coverage with a spatial resolution of 1.1 km at nadir were transformed to the NDVI and geographically registered to produce the images.

Evaluation of the data of vegetable covering using fraction images and multitemporal vegetation index, derived of orbital data of moderate resolution of the sensor MODIS

International Nuclear Information System (INIS)

Murillo Mejia, Mario Humberto

2006-01-01

The objective was to evaluate the data obtained by sensor MODIS onboard the EOS terra satellite land cover units. The study area is the republic of Colombia in South America. The methodology consisted of analyzing the multitemporal (vegetation, soil and shade-water) fraction images and vegetation indices (NDVI) apply the lineal spectral mixture model to products derived from derived images by sensor MODIS data obtained in years 2001 and 2003. The mosaics of the original and the transformed vegetation (soil and shade-water) bands were generated for the whole study area using SPRING 4. 0 software, developed by INPE then these mosaics were segmented, classified, mapped, and edited to obtain a moderate resolution land cover map. The results derived from MODIS analysis were compared with Landsat ETM+ data acquire for a single test site. The results of the project showed the usefulness of MODIS images for large-scale land cover mapping and monitoring studies

Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

Science.gov (United States)

Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang

2016-09-01

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for

Post-fire vegetation recovery in Portugal based ewline on spot/vegetation data

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C. Gouveia

2010-04-01

Full Text Available A procedure is presented that allows identifying large burned scars and the monitoring of vegetation recovery in the years following major fire episodes. The procedure relies on 10-day fields of Maximum Value Composites of Normalized Difference Vegetation Index (MVC-NDVI, with a 1 km×1 km spatial resolution obtained from the VEGETATION instrument. The identification of fire scars during the extremely severe 2003 fire season is performed based on cluster analysis of NDVI anomalies that persist during the vegetative cycle of the year following the fire event. Two regions containing very large burned scars were selected, located in Central and Southwestern Portugal, respectively, and time series of MVC-NDVI analysed before the fire events took place and throughout the post-fire period. It is shown that post-fire vegetation dynamics in the two selected regions may be characterised based on maps of recovery rates as estimated by fitting a monoparametric model of vegetation recovery to MVC-NDVI data over each burned scar. Results indicated that the recovery process in the region located in Central Portugal is mostly related to fire damage rather than to vegetation density before 2003, whereas the latter seems to have a more prominent role than vegetation conditions after the fire episode, e.g. in the case of the region in Southwestern Portugal. These differences are consistent with the respective predominant types of vegetation. The burned area located in Central Portugal is dominated by Pinus Pinaster whose natural regeneration crucially depends on the destruction of seeds present on the soil surface during the fire, whereas the burned scar in Southwestern Portugal was populated by Eucalyptus that may quickly re-sprout from buds after fire. Besides its simplicity, the monoparametric model of vegetation recovery has the advantage of being easily adapted to other low-resolution satellite data, as well as to other types of vegetation

Automated Recognition of Vegetation and Water Bodies on the Territory of Megacities in Satellite Images of Visible and IR Bands

Science.gov (United States)

Mozgovoy, Dmitry k.; Hnatushenko, Volodymyr V.; Vasyliev, Volodymyr V.

2018-04-01

Vegetation and water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. A methodology of automated recognition of vegetation and water bodies on the territory of megacities in satellite images of sub-meter spatial resolution of the visible and IR bands is proposed. By processing multispectral images from the satellite SuperView-1A, vector layers of recognized plant and water objects were obtained. Analysis of the results of image processing showed a sufficiently high accuracy of the delineation of the boundaries of recognized objects and a good separation of classes. The developed methodology provides a significant increase of the efficiency and reliability of updating maps of large cities while reducing financial costs. Due to the high degree of automation, the proposed methodology can be implemented in the form of a geo-information web service functioning in the interests of a wide range of public services and commercial institutions.

NOAA JPSS Visible Infrared Imaging Radiometer Suite (VIIRS) Smoothed Normalized Difference Vegetation Index (NDVI) from NDE

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Visible Infrared Imaging Radiometer Suite (VIIRS) Smoothed Normalized Difference Vegetation Index (NDVI) from NDE is a weekly product derived from the VIIRS...

Assessments of Drought Impacts on Vegetation in China with the Optimal Time Scales of the Climatic Drought Index

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Zheng Li

2015-07-01

Full Text Available Drought is expected to increase in frequency and severity due to global warming, and its impacts on vegetation are typically extensively evaluated with climatic drought indices, such as multi-scalar Standardized Precipitation Evapotranspiration Index (SPEI. We analyzed the covariation between the SPEIs of various time scales and the anomalies of the normalized difference vegetation index (NDVI, from which the vegetation type-related optimal time scales were retrieved. The results indicated that the optimal time scales of needle-leaved forest, broadleaf forest and shrubland were between 10 and 12 months, which were considerably longer than the grassland, meadow and cultivated vegetation ones (2 to 4 months. When the optimal vegetation type-related time scales were used, the SPEI could better reflect the vegetation’s responses to water conditions, with the correlation coefficients between SPEIs and NDVI anomalies increased by 5.88% to 28.4%. We investigated the spatio-temporal characteristics of drought and quantified the different responses of vegetation growth to drought during the growing season (April–October. The results revealed that the frequency of drought has increased in the 21st century with the drying trend occurring in most of China. These results are useful for ecological assessments and adapting management steps to mitigate the impact of drought on vegetation. They are helpful to employ water resources more efficiently and reduce potential damage to human health caused by water shortages.

Analysis of Behavior of Vegetation in the Year of 2016 for the Municipality of Remanso- BA

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Ismael Farias de Freitas

2017-07-01

Full Text Available Droughts are a natural problem in the Northeastern Brazilian region, in addition the rainfall distribution poorly distributed spatially and temporally results in seasonal changes in the surface vegetation. Consequently, the monitoring and evaluation of vegetation in the northeast region of Brazil has become increasingly constant. For this evaluation several techniques are used, but the use of environmental satellites is increasingly applied, such as the Landsat 8 satellite, where the products generated for the calculation of the Normalized Difference Vegetation Index (NDVI were used. In this circumstance, the objective of this work was to evaluate the vegetation behavior through the NDVI and to analyze the interaction of the same with the occurrence of precipitation in the municipality of Remanso-BA throughout the year 2016. For the calculation and elaboration of the thematic maps of NDVI were respectively, the software Erdas 9.2 and Qgis 2.14.2. In the study, 11 images of the Landsat 8 satellite corresponding to orbit 218 and quadrant 067 were used. The results showed high NDVI values in the rainy season, while in the dry season the values were lower, a significant reduction occurred during the year in the area Of body of water in which is the Lago de Sobradinho. It was also evident the decrease of dense vegetation in the first months of the year and the increase of areas devoid of vegetation due to lack of rain. However, the variations of NDVI were due to the occurrence of precipitation over the period studied

Impact of vegetation variability on potential predictability and skill of EC-Earth simulations

Energy Technology Data Exchange (ETDEWEB)

Weiss, Martina; Hurk, Bart van den; Haarsma, Reindert; Hazeleger, Wilco [Royal Netherlands Meteorological Institute (KNMI), De Bilt (Netherlands)

2012-12-15

Climate models often use a simplified and static representation of vegetation characteristics to determine fluxes of energy, momentum and water vapour between surface and lower atmosphere. In order to analyse the impact of short term variability in vegetation phenology, we use remotely-sensed leaf area index and albedo products to examine the role of vegetation in the coupled land-atmosphere system. Perfect model experiments are carried out to determine the impact of realistic temporal variability of vegetation on potential predictability of evaporation and temperature, as well as model skill of EC-Earth simulations. The length of the simulation period is hereby limited by the availability of satellite products to 2000-2010. While a realistic representation of vegetation positively influences the simulation of evaporation and its potential predictability, a positive impact on 2 m temperature is of smaller magnitude, regionally confined and more pronounced in climatically extreme years. (orig.)

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

Science.gov (United States)

Taufik, Afirah; Sakinah Syed Ahmad, Sharifah

2016-06-01

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

Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

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Sheng Chang

2017-06-01

Full Text Available In Mongolia, drought is a major natural disaster that can influence and devastate large regions, reduce livestock production, cause economic damage, and accelerate desertification in association with destructive human activities. The objective of this article is to determine the optimal satellite-derived drought indices for accurate and real-time expression of grassland drought in Mongolia. Firstly, an adaptability analysis was performed by comparing nine remote sensing-derived drought indices with reference indicators obtained from field observations using several methods (correlation, consistency percentage (CP, and time-space analysis. The reference information included environmental data, vegetation growth status, and region drought-affected (RDA information at diverse scales (pixel, county, and region for three types of land cover (forest steppe, steppe, and desert steppe. Second, a meteorological index (PED, a normalized biomass (NorBio reference indicator, and the RDA-based drought CP method were adopted for describing Mongolian drought. Our results show that in forest steppe regions the normalized difference water index (NDWI is most sensitive to NorBio (maximum correlation coefficient (MAX_R: up to 0.92 and RDA (maximum CP is 87%, and is most consistent with RDA spatial distribution. The vegetation health index (VHI and temperature condition index (TCI are most correlated with the PED index (MAX_R: 0.75 and soil moisture (MAX_R: 0.58, respectively. In steppe regions, the NDWI is most closely related to soil moisture (MAX_R: 0.69 and the VHI is most related to the PED (MAX_R: 0.76, NorBio (MCC: 0.95, and RDA data (maximum CP is 89%, exhibiting the most consistency with RDA spatial distribution. In desert steppe areas, the vegetation condition index (VCI correlates best with NorBio (MAX_R: 0.92, soil moisture (MAX_R: 0.61, and RDA spatial distribution, while TCI correlates best with the PED (MAX_R: 0.75 and the RDA data (maximum CP is 79

Satellite Monitoring of Vegetation Response to Precipitation and Dust Storm Outbreaks in Gobi Desert Regions

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Yuki Sofue

2018-02-01

Full Text Available Recently, droughts have become widespread in the Northern Hemisphere, including in Mongolia. The ground surface condition, particularly vegetation coverage, affects the occurrence of dust storms. The main sources of dust storms in the Asian region are the Taklimakan and Mongolian Gobi desert regions. In these regions, precipitation is one of the most important factors for growth of plants especially in arid and semi-arid land. The purpose of this study is to clarify the relationship between precipitation and vegetation cover dynamics over 29 years in the Gobi region. We compared the patterns between precipitation and Normalized Difference Vegetation Index (NDVI for a period of 29 years. The precipitation and vegetation datasets were examined to investigate the trends during 1985–2013. Cross correlation analysis between the precipitation and the NDVI anomalies was performed. Data analysis showed that the variations of NDVI anomalies in the east region correspond well with the precipitation anomalies during this period. However, in the southwest region of the Gobi region, the NDVI had decreased regardless of the precipitation amount, especially since 2010. This result showed that vegetation in this region was more degraded than in the other areas.

Close relationship between spectral vegetation indices and Vcmax in deciduous and mixed forests

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Yanlian Zhou

2014-04-01

Full Text Available Seasonal variations of photosynthetic capacity parameters, notably the maximum carboxylation rate, Vcmax, play an important role in accurate estimation of CO2 assimilation in gas-exchange models. Satellite-derived normalised difference vegetation index (NDVI, enhanced vegetation index (EVI and model-data fusion can provide means to predict seasonal variation in Vcmax. In this study, Vcmax was obtained from a process-based model inversion, based on an ensemble Kalman filter (EnKF, and gross primary productivity, and sensible and latent heat fluxes measured using eddy covariance technique at two deciduous broadleaf forest sites and a mixed forest site. Optimised Vcmax showed considerable seasonal and inter-annual variations in both mixed and deciduous forest ecosystems. There was noticeable seasonal hysteresis in Vcmax in relation to EVI and NDVI from 8 d composites of satellite data during the growing period. When the growing period was phenologically divided into two phases (increasing VIs and decreasing VIs phases, significant seasonal correlations were found between Vcmax and VIs, mostly showing R2>0.95. Vcmax varied exponentially with increasing VIs during the first phase (increasing VIs, but second and third-order polynomials provided the best fits of Vcmax to VIs in the second phase (decreasing VIs. The relationships between NDVI and EVI with Vcmax were different. Further efforts are needed to investigate Vcmax–VIs relationships at more ecosystem sites to the use of satellite-based VIs for estimating Vcmax.

Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices

Science.gov (United States)

Hamzeh, S.; Naseri, A. A.; AlaviPanah, S. K.; Mojaradi, B.; Bartholomeus, H. M.; Clevers, J. G. P. W.; Behzad, M.

2013-04-01

The presence of salt in the soil profile negatively affects the growth and development of vegetation. As a result, the spectral reflectance of vegetation canopies varies for different salinity levels. This research was conducted to (1) investigate the capability of satellite-based hyperspectral vegetation indices (VIs) for estimating soil salinity in agricultural fields, (2) evaluate the performance of 21 existing VIs and (3) develop new VIs based on a combination of wavelengths sensitive for multiple stresses and find the best one for estimating soil salinity. For this purpose a Hyperion image of September 2, 2010, and data on soil salinity at 108 locations in sugarcane (Saccharum officina L.) fields were used. Results show that soil salinity could well be estimated by some of these VIs. Indices related to chlorophyll absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with soil salinity. In contrast, indices that are only based on water absorption bands had low to medium correlations, while indices that use only visible bands did not perform well. From the investigated indices the optimized soil-adjusted vegetation index (OSAVI) had the strongest relationship (R2 = 0.69) with soil salinity for the training data, but it did not perform well in the validation phase. The validation procedure showed that the new salinity and water stress indices (SWSI) implemented in this study (SWSI-1, SWSI-2, SWSI-3) and the Vogelmann red edge index yielded the best results for estimating soil salinity for independent fields with root mean square errors of 1.14, 1.15, 1.17 and 1.15 dS/m, respectively. Our results show that soil salinity could be estimated by satellite-based hyperspectral VIs, but validation of obtained models for independent data is essential for selecting the best model.

Suppression of vegetation in LANDSAT ETM+ remote sensing images

Science.gov (United States)

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

2010-05-01

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

Development of satellite green vegetation fraction time series for use in mesoscale modeling: application to the European heat wave 2006

DEFF Research Database (Denmark)

Nielsen, Joakim Refslund; Dellwik, Ebba; Hahmann, Andrea N.

2014-01-01

A method is presented for development of satellite green vegetation fraction (GVF) time series for use in the Weather Research and Forecasting (WRF) model. The GVF data is in the WRF model used to describe the temporal evolution of many land surface parameters, in addition to the evolution of veg...

Mangrove forests submitted to depositional processes and salinity variation investigated using satellite images and vegetation structure surveys

OpenAIRE

Cunha-Lignon, M.; Kampel, M.; Menghini, R.P.; Schaeffer-Novelli, Y.; Cintrón, G.; Dahdouh-Guebas, F.

2011-01-01

The current paper examines the growth and spatio-temporal variation of mangrove forests in response to depositional processes and different salinity conditions. Data from mangrove vegetation structure collected at permanent plots and satellite images were used. In the northern sector important environmental changes occurred due to an artificial channel producing modifications in salinity. The southern sector is considered the best conserved mangrove area along the coast of São Paulo State, Br...

Satellite-based detection of global urban heat-island temperature influence

Science.gov (United States)

Gallo, K.P.; Adegoke, Jimmy O.; Owen, T.W.; Elvidge, C.D.

2002-01-01

This study utilizes a satellite-based methodology to assess the urban heat-island influence during warm season months for over 4400 stations included in the Global Historical Climatology Network of climate stations. The methodology includes local and regional satellite retrievals of an indicator of the presence green photosynthetically active vegetation at and around the stations. The difference in local and regional samples of the normalized difference vegetation index (NDVI) is used to estimate differences in mean air temperature. Stations classified as urban averaged 0.90??C (N. Hemisphere) and 0.92??C (S. Hemisphere) warmer than the surrounding environment on the basis of the NDVI-derived temperature estimates. Additionally, stations classified as rural averaged 0.19??C (N. Hemisphere) and 0.16??C (S. Hemisphere) warmer than the surrounding environment. The NDVI-derived temperature estimates were found to be in reasonable agreement with temperature differences observed between climate stations. The results suggest that satellite-derived data sets can be used to estimate the urban heat-island temperature influence on a global basis and that a more detailed analysis of rural stations and their surrounding environment may be necessary to assure that temperature trends derived from assumed rural environments are not influenced by changes in land use/land cover. Copyright 2002 by the American Geophysical Union.

Estimation of the soil heat flux/net radiation ratio based on spectral vegetation indexes in high-latitude Arctic areas

International Nuclear Information System (INIS)

Jacobsen, A.; Hansen, B.U.

1999-01-01

The vegetation communities in the Arctic environment are very sensitive to even minor climatic variations and therefore the estimation of surface energy fluxes from high-latitude vegetated areas is an important subject to be pursued. This study was carried out in July-August and used micro meteorological data, spectral reflectance signatures, and vegetation biomass to establish the relation between the soil heat flux/net radiation (G / Rn) ratio and spectral vegetation indices (SVIs). Continuous measurements of soil temperature and soil heat flux were used to calculate the surface ground heat flux by use of conventional methods, and the relation to surface temperature was investigated. Twenty-seven locations were established, and six samples per location, including the measurement of the surface temperature and net radiation to establish the G/Rn ratio and simultaneous spectral reflectance signatures and wet biomass estimates, were registered. To obtain regional reliability, the locations were chosen in order to represent the different Arctic vegetation communities in the study area; ranging from dry tundra vegetation communities (fell fields and dry dwarf scrubs) to moist/wet tundra vegetation communities (snowbeds, grasslands and fens). Spectral vegetation indices, including the simple ratio vegetation index (RVI) and the normalized difference vegetation index (NDVI), were calculated. A comparison of SVIs to biomass proved that RVI gave the best linear expression, and NDVI the best exponential expression. A comparison of SVIs and the surface energy flux ratio G / Rn proved that NDVI gave the best linear expression. SPOT HRV images from July 1989 and 1992 were used to map NDVI and G / Rn at a regional scale. (author)

Shelter Index and a simple wind speed parameter to characterize vegetation control of sand transport threshold and Flu

Science.gov (United States)

Gillies, J. A.; Nield, J. M.; Nickling, W. G.; Furtak-Cole, E.

2014-12-01

Wind erosion and dust emissions occur in many dryland environments from a range of surfaces with different types and amounts of vegetation. Understanding how vegetation modulates these processes remains a research challenge. Here we present results from a study that examines the relationship between an index of shelter (SI=distance from a point to the nearest upwind vegetation/vegetation height) and particle threshold expressed as the ratio of wind speed measured at 0.45 times the mean plant height divided by the wind speed at 17 m when saltation commences, and saltation flux. The results are used to evaluate SI as a parameter to characterize the influence of vegetation on local winds and sediment transport conditions. Wind speed, wind direction, saltation activity and point saltation flux were measured at 35 locations in defined test areas (~13,000 m2) in two vegetation communities: mature streets of mesquite covered nebkhas and incipient nebkhas dominated by low mesquite plants. Measurement positions represent the most open areas, and hence those places most susceptible to wind erosion among the vegetation elements. Shelter index was calculated for each measurement position for each 10° wind direction bin using digital elevation models for each site acquired using terrestrial laser scanning. SI can show the susceptibility to wind erosion at different time scales, i.e., event, seasonal, or annual, but in a supply-limited system it can fail to define actual flux amounts due to a lack of knowledge of the distribution of sediment across the surface of interest with respect to the patterns of SI.

Advanced Very High Resolution Radiometer (AVHRR) data evaluation for use in monitoring vegetation. Volume 1: Channels 1 and 2

Science.gov (United States)

Horvath, N. C.; Gray, T. I.; Mccrary, D. G. (Principal Investigator)

1982-01-01

Data from the National Oceanic and Atmospheric Administration satellite system (NOAA-6 satellite) were analyzed to study their nonmeteorological uses. A file of charts, graphs, and tables was created form the products generated. It was found that the most useful data lie between pixel numbers 400 and 2000 on a given scan line. The analysis of the generated products indicates that the Gray-McCrary Index can discern vegetation and associated daily and seasonal changes. The solar zenith-angle correction used in previous studies was found to be a useful adjustment to the index. The METSAT system seems best suited for providing large-area analyses of surface features on a daily basis.

An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics from MODIS Data

Science.gov (United States)

Tan, Bin; Morisette, Jeffrey T.; Wolfe, Robert E.; Gao, Feng; Ederer, Gregory A.; Nightingale, Joanne; Pedelty, Jeffrey A.

2012-01-01

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates.

Testing the Potential of Vegetation Indices for Land Use/cover Classification Using High Resolution Data

Science.gov (United States)

Karakacan Kuzucu, A.; Bektas Balcik, F.

2017-11-01

Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

THE NUMBER OF TIDAL DWARF SATELLITE GALAXIES IN DEPENDENCE OF BULGE INDEX

International Nuclear Information System (INIS)

López-Corredoira, Martín; Kroupa, Pavel

2016-01-01

We show that a significant correlation (up to 5σ) emerges between the bulge index, defined to be larger for a larger bulge/disk ratio, in spiral galaxies with similar luminosities in the Galaxy Zoo 2 of the Sloan Digital Sky Survey and the number of tidal-dwarf galaxies in the catalog by Kaviraj et al. In the standard cold or warm dark matter cosmological models, the number of satellite galaxies correlates with the circular velocity of the dark matter host halo. In generalized gravity models without cold or warm dark matter, such a correlation does not exist, because host galaxies cannot capture infalling dwarf galaxies due to the absence of dark-matter-induced dynamical friction. However, in such models, a correlation is expected to exist between the bulge mass and the number of satellite galaxies because bulges and tidal-dwarf satellite galaxies form in encounters between host galaxies. This is not predicted by dark matter models in which bulge mass and the number of satellites are a priori uncorrelated because higher bulge/disk ratios do not imply higher dark/luminous ratios. Hence, our correlation reproduces the prediction of scenarios without dark matter, whereas an explanation is not found readily from the a priori predictions of the standard scenario with dark matter. Further research is needed to explore whether some application of the standard theory may explain this correlation

HUBUNGAN ANTARA INDEKS VEGETASI NDVI (NORMALIZED DIFFERENCE VEGETATION INDEX DAN KOEFISIEN RESESI BASEFLOW PADA BEBERAPA SUBDAS PROPINSI JAWA TENGAH DAN DAERAH ISTIMEWA YOGYAKARTA

Directory of Open Access Journals (Sweden)

Bokiraiya Latuamury

2013-06-01

Full Text Available The background of this research is the decrease of environment capacity in cacthment ecosystem, especially impact of vegetation forest on behavior streamflow. The indicators of cacthment destruction can be seen through hydrograph characteristics. Evaluation of cactment respons of flow hydrographic as an evaluation tools of river catchment responses becomes very important to analyze because it is a benchmark in determination several policy about flood, drough, sedimentation and landslide handling. The research purpose is to analyze the relationship between vegetation index NDVI (Normalized Difference Vegetation Index and the characteristic of baseflow recession coefficient at several subcatchment areas in province of Central Java and Specific District of Yogjakarta.The method of this research is surveillance on data recording of AWLR (Automatic Water Level Recorder and data of River Flow Measuring Stations in order to separate the baseflow by calibration curve, and image interpretation of Landsat ETM+ for the transformation of vegetation index (NDVI-Normalized Difference Vegetation Index.The analysis on recession coefficient data (Krb and NDVI were correlated to analyze the strength of relationship between these two parameters. The results of statistical analysis on index NDVI and recession coefficient showsthat NDVI and recession coefficient value at R2 is 0.1427, F = 2.17 which is not significant at 1% significance level of 0.1646. The result shows a very weak correlation of 0.077 which mean that vegetation density (NDVI indexhas a very weak control on low flows. Basically, river baseflow is a genetic component of river flow which comes from aquifer storage and/or other low flow sources. Thus, geology and soil have a significant effect on baseflow.

Use of middle infrared radiation to estimate the leaf area index of a boreal forest

Energy Technology Data Exchange (ETDEWEB)

Boyd, D.S. [Kingston Univ., Surrey (United Kingdom). Centre for Earth and Environmental Science Research, School of Geography; Wicks, T. E.; Curran, P.J. [Southampton Univ., Southampton, Hampshire (United Kingdom). Dept. of Geography

2000-06-01

Reflected radiation recorded by satellite sensors is a common procedure to estimate the leaf area index (LAI) of boreal forest. The normalized difference vegetation index (NDVI), derived from measurements of visible and near infrared radiation were commonly used to estimate LAI. But research in tropical forest has shown that LAI is more closely related to radiation of middle infrared wavelengths than that of visible wavelengths. This research calculated a vegetation index (VI3) using radiation from vegetation recorded at near and middle infrared wavelengths. In the case of boreal forest, VI3 and LAI displayed a closer relationship than NDVI and LAI. Also, the use of VI3 explained approximately 76 per cent of the variation in field estimates of LAI, versus approximately 46 per cent for NDVI. The authors concluded that consideration should be given to information provided by middle infrared radiation to estimate the leaf area index of boreal forest. The research area was located in the Southern Study Area (SSA) of the BOReal Ecosystem-Atmospher Study (BOREAS), situated on the southern edge of the Canadian boreal forest, 40 km north of Prince Albert, Saskatchewan. 1 tab., 4 figs., 46 refs.

Patchiness in semi-arid dwarf shrublands: evidence from satellite ...

African Journals Online (AJOL)

... Plants; Remote sensing; Rhigozum obovatum Burch; Satellite-derived vegetation indices; Woody species; patchiness; semi-arid; dwarf shrubland; shrublands; co2; assimilation; karoo; south africa; ndvi; satellite imagery; geochemical mound; rhigozum obovatum; eriocephalus ericoides; pentzia incana; vegetation; botany

Analysis of vegetation from satellite images correlated to the bird species presence and the state of health of the ecosystems of Bucharest during the period from 1991 to 2006

Directory of Open Access Journals (Sweden)

Dragoș Mirela

2017-01-01

Full Text Available The urban vegetation needs adequate monitoring and conservation, being a critical resource of urban landscape. To its deeply esthetic values, the practical values and, respectively, ecosystem services delivered by the urban biodiversity are added (amelioration of the environment and urban microclimate, flood control, diminishing of the environmental pollution, increasing of biodiversity and habitats etc.. Accurate remote sensing techniques have been used widely in locating and mapping urban vegetation (Light Detection And Ranging-LiDAR, satellite images. The purpose of this study is to point out the vegetation status in correlation with the number of the bird species (as indicator of the ecosystem's health, using remote sensing techniques (Landsat satellite images, between 1991-2006 in Bucharest, Romania's capital. Rapid urban evolution of Bucharest led to important changes within the structure of the city, underlined by the increasing of the built area to the detriment of the green one. The intensity of the urbanization rate also led to the decreasing of the number of the bird species. The results obtained through analysis of satellite images indicate the necessity to acquire the up-to-date information related to the vegetation status in order to establish in the future, through urban landscape projects, protection measures for the vegetation cover and for the bird habitats in Bucharest Municipality.

SAT-WIND project. Final report[Winds from satellites for offshore and coastal wind energy mapping and wind-indexing

Energy Technology Data Exchange (ETDEWEB)

Hasager, C.B.; Astrup, P.; Nielsen, M. (and others)

2007-04-15

The SAT-WIND project 'Winds from satellites for offshore and coastal wind energy mapping and wind-indexing' was a research project funded by STVF/DSF in the years 2003 to 2006 (Sagsnr. 2058-03-0006). The goal of the project was to verify the applicability of satellite wind maps derived from passive microwave, altimeter, scatterometer and imaging Synthetic Aperture Radar (SAR) technologies for wind energy tools for wind resources and wind-indexing. The study area was the Danish Seas including the North Sea, interior seas and the Baltic Sea. The report describes technical details on the satellite data sources including: 1) passive microwave (SSM/I, AMSR-E), 2) passive microwave polarimetric (WindSat), 3) scatterometer (ERS, QuikSCAT, Midori-2 and NSCAT), 4) altimeter (ERS, Topex, Poseidon, GFO-1, Jason-1), 5) SAR (ERS, Envisat). The SAR wind maps were treated in S-WAsP developed by Risoe National Laboratory in cooperation with GRAS A/S in the innovative project SAT-WIND-SMV (Sagsnr. 2104-05-0084) in the years 2005 and 2006 in parallel with SAT-WIND. The results from the SAT-WIND project are presented. These include ocean wind statistics, offshore wind resource estimates and comparison results for wind-indexing. (au)

SAT-WIND project. Final report[Winds from satellites for offshore and coastal wind energy mapping and wind-indexing

Energy Technology Data Exchange (ETDEWEB)

Hasager, C B; Astrup, P; Nielsen, M [and others

2007-04-15

The SAT-WIND project 'Winds from satellites for offshore and coastal wind energy mapping and wind-indexing' was a research project funded by STVF/DSF in the years 2003 to 2006 (Sagsnr. 2058-03-0006). The goal of the project was to verify the applicability of satellite wind maps derived from passive microwave, altimeter, scatterometer and imaging Synthetic Aperture Radar (SAR) technologies for wind energy tools for wind resources and wind-indexing. The study area was the Danish Seas including the North Sea, interior seas and the Baltic Sea. The report describes technical details on the satellite data sources including: 1) passive microwave (SSM/I, AMSR-E), 2) passive microwave polarimetric (WindSat), 3) scatterometer (ERS, QuikSCAT, Midori-2 and NSCAT), 4) altimeter (ERS, Topex, Poseidon, GFO-1, Jason-1), 5) SAR (ERS, Envisat). The SAR wind maps were treated in S-WAsP developed by Risoe National Laboratory in cooperation with GRAS A/S in the innovative project SAT-WIND-SMV (Sagsnr. 2104-05-0084) in the years 2005 and 2006 in parallel with SAT-WIND. The results from the SAT-WIND project are presented. These include ocean wind statistics, offshore wind resource estimates and comparison results for wind-indexing. (au)

Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat TM data

Science.gov (United States)

Sader, Steven A.; Waide, Robert B.; Lawrence, William T.; Joyce, Armond T.

1989-01-01

Forest stand structure and biomass data were collected using conventional forest inventory techniques in tropical, subtropical, and warm temperate forest biomes. The feasibility of detecting tropical forest successional age class and total biomass differences using Landsat-Thematic mapper (TM) data, was evaluated. The Normalized Difference Vegetation Index (NDVI) calculated from Landsat-TM data were not significantly correlated with forest regeneration age classes in the mountain terrain of the Luquillo Experimental Forest, Puerto Rico. The low sun angle and shadows cast on steep north and west facing slopes reduced spectral reflectance values recorded by TM orbital altitude. The NDVI, calculated from low altitude aircraft scanner data, was significatly correlated with forest age classes. However, analysis of variance suggested that NDVI differences were not detectable for successional forests older than approximately 15-20 years. Also, biomass differences in young successional tropical forest were not detectable using the NDVI. The vegetation index does not appear to be a good predictor of stand structure variables (e.g., height, diameter of main stem) or total biomass in uneven age, mixed broadleaf forest. Good correlation between the vegetation index and low biomass in even age pine plantations were achieved for a warm temperate study site. The implications of the study for the use of NDVI for forest structure and biomass estimation are discussed.

The ecology of malaria--as seen from Earth-observation satellites.

Science.gov (United States)

Thomson, M C; Connor, S J; Milligan, P J; Flasse, S P

1996-06-01

Data from sensors on board geostationary and polar-orbiting, meteorological satellites (Meteosat and NOAA series) are routinely obtained free, via local reception systems, in an increasing number of African countries. Data collected by these satellites are processed to produce proxy ecological variables which have been extensively investigated for monitoring changes in the distribution and condition of different natural resources, including rainfall and vegetation state. How these data products (once incorporated, along with other data, into a geographical information system) could contribute to the goals of monitoring patterns of malaria transmission, predicting epidemics and planning control strategies is the subject of the present review. By way of illustration, an analysis of two of these products, normalized difference vegetation index (NVDI) and cold-cloud duration (CCD), is given in conjunction with epidemiological and entomological data from The Gambia, a country where extensive studies on malaria transmission have been undertaken in recent years. Preliminary results indicate that even simple analysis of proxy ecological variables derived from satellite data can indicate variation in environmental factors affecting malaria-transmission indices. However, it is important to note that the associations observed will vary depending on the local ecology, season and species of vector. Whilst further quantitative research is required to validate the relationship between satellite-data products and malaria-transmission indices, this approach offers a means by which detailed knowledge of the underlying spatial and temporal variation in the environment can be incorporated into a decision-support system for malaria control.

Chlorophyll fluorescence, photochemical reflective index and normalized difference vegetative index during plant senescence.

Science.gov (United States)

Cordon, Gabriela; Lagorio, M Gabriela; Paruelo, José M

2016-07-20

The relationship between the Photochemical Reflectance Index (PRI), Normalized Difference Vegetation Index (NDVI) and chlorophyll fluorescence along senescence was investigated in this work. Reflectance and radiance measurements were performed at canopy level in grass species presenting different photosynthetic metabolism: Avena sativa (C3) and Setaria italica (C4), at different stages of the natural senescence process. Sun induced-chlorophyll fluorescence at 760nm (SIF 760 ) and the apparent fluorescence yield (SIF 760 /a, with a=irradiance at time of measurement) were extracted from the radiance spectra of canopies using the Fraunhofer Line Discrimination-method. The photosynthetic parameters derived from Kautsky kinetics and pigment content were also calculated at leaf level. Whilst stand level NDVI patterns were related to changes in the structure of canopies and not in pigment content, stand level PRI patterns suggested changes both in terms of canopy and of pigment content in leaves. Both SIF 760 /a and Φ PSII decreased progressively along senescence in both species. A strong increment in NPQ was evident in A. sativa while in S. italica NPQ values were lower. Our most important finding was that two chlorophyll fluorescence signals, Φ PSII and SIF 760 /a, correlated with the canopy PRI values in the two grasses assessed, even when tissues at different ontogenic stages were present. Even though significant changes occurred in the Total Chlr/Car ratio along senescence in both studied species, significant correlations between PRI and chlorophyll fluorescence signals might indicate the usefulness of this reflectance index as a proxy of photosynthetic RUE, at least under the conditions of this study. The relationships between stand level PRI and the fluorescence estimators (Φ PSII and SIF 760 /a) were positive in both cases. Therefore, an increase in PRI values as in the fluorescence parameters would indicate higher RUE. Copyright © 2016 Elsevier GmbH. All

Assessing satellite-based start-of-season trends in the US High Plains

International Nuclear Information System (INIS)

Lin, X; Sassenrath, G F; Hubbard, K G; Mahmood, R

2014-01-01

To adequately assess the effects of global warming it is necessary to address trends and impacts at the local level. This study examines phenological changes in the start-of-season (SOS) derived from satellite observations from 1982–2008 in the US High Plains region. The surface climate-based SOS was also evaluated. The averaged profiles of SOS from 37° to 49°N latitude by satellite- and climate-based methods were in reasonable agreement, especially for areas where croplands were masked out and an additional frost date threshold was adopted. The statistically significant trends of satellite-based SOS show a later spring arrival ranging from 0.1 to 4.9 days decade −1 over nine Level III ecoregions. We found the croplands generally exhibited larger trends (later arrival) than the non-croplands. The area-averaged satellite-based SOS for non-croplands (i.e. mostly grasslands) showed no significant trends. We examined the trends of temperatures, precipitation, and standardized precipitation index (SPI), as well as the strength of correlation between the satellite-based SOS and these climatic drivers. Our results indicate that satellite-based SOS trends are spatially and primarily related to annual maximum normalized difference vegetation index (NDVI, mostly in summertime) and/or annual minimum NDVI (mostly in wintertime) and these trends showed the best correlation with six-month SPI over the period 1982–2008 in the US High Plains region. (letter)

Critical Analysis of Forest Degradation in the Southern Eastern Ghats of India: Comparison of Satellite Imagery and Soil Quality Index

Science.gov (United States)

Ramachandran, Andimuthu; Radhapriya, Parthasarathy; Jayakumar, Shanmuganathan; Dhanya, Praveen; Geetha, Rajadurai

2016-01-01

India has one of the largest assemblages of tropical biodiversity, with its unique floristic composition of endemic species. However, current forest cover assessment is performed via satellite-based forest surveys, which have many limitations. The present study, which was performed in the Eastern Ghats, analysed the satellite-based inventory provided by forest surveys and inferred from the results that this process no longer provides adequate information for quantifying forest degradation in an empirical manner. The study analysed 21 soil properties and generated a forest soil quality index of the Eastern Ghats, using principal component analysis. Using matrix modules and geospatial technology, we compared the forest degradation status calculated from satellite-based forest surveys with the degradation status calculated from the forest soil quality index. The Forest Survey of India classified about 1.8% of the Eastern Ghats’ total area as degraded forests and the remainder (98.2%) as open, dense, and very dense forests, whereas the soil quality index results found that about 42.4% of the total area is degraded, with the remainder (57.6%) being non-degraded. Our ground truth verification analyses indicate that the forest soil quality index along with the forest cover density data from the Forest Survey of India are ideal tools for evaluating forest degradation. PMID:26812397

Critical Analysis of Forest Degradation in the Southern Eastern Ghats of India: Comparison of Satellite Imagery and Soil Quality Index.

Science.gov (United States)

Ramachandran, Andimuthu; Radhapriya, Parthasarathy; Jayakumar, Shanmuganathan; Dhanya, Praveen; Geetha, Rajadurai

2016-01-01

India has one of the largest assemblages of tropical biodiversity, with its unique floristic composition of endemic species. However, current forest cover assessment is performed via satellite-based forest surveys, which have many limitations. The present study, which was performed in the Eastern Ghats, analysed the satellite-based inventory provided by forest surveys and inferred from the results that this process no longer provides adequate information for quantifying forest degradation in an empirical manner. The study analysed 21 soil properties and generated a forest soil quality index of the Eastern Ghats, using principal component analysis. Using matrix modules and geospatial technology, we compared the forest degradation status calculated from satellite-based forest surveys with the degradation status calculated from the forest soil quality index. The Forest Survey of India classified about 1.8% of the Eastern Ghats' total area as degraded forests and the remainder (98.2%) as open, dense, and very dense forests, whereas the soil quality index results found that about 42.4% of the total area is degraded, with the remainder (57.6%) being non-degraded. Our ground truth verification analyses indicate that the forest soil quality index along with the forest cover density data from the Forest Survey of India are ideal tools for evaluating forest degradation.

Quantifying the Vegetation Health Based on the Resilience in an Arid System

Directory of Open Access Journals (Sweden)

Ranjbar Abolfazl

2018-03-01

Full Text Available Proper management of natural ecosystems is not possible without the knowledge of the health status of its components. Vegetation, as the main component of the ecosystem, plays an important role in its health. One of the key determinants of vegetation health is its resilience in the face of environmental disorders. This research was conducted in parts of the Namakzar-e Khaf watershed in Northeast of South Khorasan Province with the aim of quantifying the vegetative resilience on behalf of the ecosystem health in response to long-term precipitation changes. First, the annual precipitation standardization was performed during a thirty-year period by the SPI method. Then, the average variation in TNDVI index obtained from the Landsat satellite images was examined and the resilience was tested by calculating the four effective factors (amplitude, malleability, damping and hysteresis. According to the results, the amplitude in the survey period was 6.04% and the vegetation has had different values of damping over the years. The most prominent example of vegetation resilience occurred between 1986 and 1996, with malleability of 0.7 and damping of zero. Vegetation in this period, after the elimination of drought effects (1986, has not only returned to the amount of vegetation of reference year with severe precipitation (1996 but also increased by 0.25%. This increase, as the index of hysteresis, has been presented for the first time in the ecosystem health discussion quantitatively in the present study. A set of quantitative calculations showed that despite reduced annual precipitation and drought events, the vegetation has been able to maintain its resilience, which indicates the health of vegetation in the studied ecosystem.

Assessment of Land Use-Cover Changes and Successional Stages of Vegetation in the Natural Protected Area Altas Cumbres, Northeastern Mexico, Using Landsat Satellite Imagery

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Uriel Jeshua Sánchez-Reyes

2017-07-01

Full Text Available Loss of vegetation cover is a major factor that endangers biodiversity. Therefore, the use of geographic information systems and the analysis of satellite images are important for monitoring these changes in Natural Protected Areas (NPAs. In northeastern Mexico, the Natural Protected Area Altas Cumbres (NPAAC represents a relevant floristic and faunistic patch on which the impact of loss of vegetation cover has not been assessed. This work aimed to analyze changes of land use and coverage (LULCC over the last 42 years on the interior and around the exterior of the area, and also to propose the time of succession for the most important types of vegetation. For the analysis, LANDSAT satellite images from 1973, 1986, 2000, 2005 and 2015 were used, they were classified in seven categories through a segmentation and maximum likelihood analysis. A cross-tabulation analysis was performed to determine the succession gradient. Towards the interior of the area, a significant reduction of tropical vegetation and, to a lesser extent, temperate forests was found, as well as an increase in scrub cover from 1973 to 2015. In addition, urban and vegetation-free areas, as well as modified vegetation, increased to the exterior. Towards the interior of the NPA, the processes of perturbation and recovery were mostly not linear, while in the exterior adjacent area, the presence of secondary vegetation with distinct definite time of succession was evident. The analysis carried out is the first contribution that evaluates LULCC in this important NPA of northeastern Mexico. Results suggest the need to evaluate the effects of these modifications on species.

Estimating Winter Annual Biomass in the Sonoran and Mojave Deserts with Satellite- and Ground-Based Observations

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Bradley C. Reed

2013-02-01

Full Text Available Winter annual plants in southwestern North America influence fire regimes, provide forage, and help prevent erosion. Exotic annuals may also threaten native species. Monitoring winter annuals is difficult because of their ephemeral nature, making the development of a satellite monitoring tool valuable. We mapped winter annual aboveground biomass in the Desert Southwest from satellite observations, evaluating 18 algorithms using time-series vegetation indices (VI. Field-based biomass estimates were used to calibrate and evaluate each algorithm. Winter annual biomass was best estimated by calculating a base VI across the period of record and subtracting it from the peak VI for each winter season (R2 = 0.92. The normalized difference vegetation index (NDVI derived from 8-day reflectance data provided the best estimate of winter annual biomass. It is important to account for the timing of peak vegetation when relating field-based estimates to satellite VI data, since post-peak field estimates may indicate senescent biomass which is inaccurately represented by VI-based estimates. Images generated from the best-performing algorithm show both spatial and temporal variation in winter annual biomass. Efforts to manage this variable resource would be enhanced by a tool that allows the monitoring of changes in winter annual resources over time.

Vegetation role in controlling the ecoenvironmental conditions for sustainable urban environments: a comparison of Beijing and Islamabad

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Naeem, Shahid; Cao, Chunxiang; Waqar, Mirza Muhammad; Wei, Chen; Acharya, Bipin Kumar

2018-01-01

The rapid increase in urbanization due to population growth leads to the degradation of vegetation in major cities. This study investigated the spatial patterns of the ecoenvironmental conditions of inhabitants of two distinct Asian capital cities, Beijing of China and Islamabad of Pakistan, by utilizing Earth observation data products. The significance of urban vegetation for the cooling effect was studied in local climate zones, i.e., urban, suburban, and rural areas within 1-km2 quantiles. Landsat-8 (OLI) and Gaofen-1 satellite imagery were used to assess vegetation cover and land surface temperature, while population datasets were used to evaluate environmental impact. Comparatively, a higher cooling effect of vegetation presence was observed in rural and suburban zones of Beijing as compared to Islamabad, while the urban zone of Islamabad was found comparatively cooler than Beijing's urban zone. The urban thermal field variance index calculated from satellite imagery was ranked into the ecological evaluation index. The worst ecoenvironmental conditions were found in urban zones of both cities where the fraction of vegetation is very low. Meanwhile, this condition is more serious in Beijing, as more than 90% of the total population is living under the worst ecoenvironment conditions, while only 7% of the population is enjoying comfortable conditions. Ecoenvironmental conditions of Islamabad are comparatively better than Beijing where ˜61% of the total population live under the worst ecoenvironmental conditions, and ˜24% are living under good conditions. Thus, Islamabad at this early growth stage can learn from Beijing's ecoenvironmental conditions to improve the quality of living by controlling the associated factors in the future.

Comparing forest measurements from tree rings and a space-based index of vegetation activity in Siberia

International Nuclear Information System (INIS)

Bunn, Andrew G; Hughes, Malcolm K; Losleben, Mark; Kirdyanov, Alexander V; Shishov, Vladimir V; Vaganov, Eugene A; Berner, Logan T; Oltchev, Alexander

2013-01-01

Different methods have been developed for measuring carbon stocks and fluxes in the northern high latitudes, ranging from intensively measured small plots to space-based methods that use reflectance data to drive production efficiency models. The field of dendroecology has used samples of tree growth from radial increments to quantify long-term variability in ecosystem productivity, but these have very limited spatial domains. Since the cambium material in tree cores is itself a product of photosynthesis in the canopy, it would be ideal to link these two approaches. We examine the associations between the normalized differenced vegetation index (NDVI) and tree growth using 19 pairs of tree-ring widths (TRW) and maximum latewood density (MXD) across much of Siberia. We find consistent correlations between NDVI and both measures of tree growth and no systematic difference between MXD and TRW. At the regional level we note strong correspondence between the first principal component of tree growth and NDVI for MXD and TRW in a temperature-limited bioregion, indicating that canopy reflectance and cambial production are broadly linked. Using a network of 21 TRW chronologies from south of Lake Baikal, we find a similarly strong regional correspondence with NDVI in a markedly drier region. We show that tree growth is dominated by variation at decadal and multidecadal time periods, which the satellite record is incapable of recording given its relatively short record. (letter)

Multi-index time series monitoring of drought and fire effects on desert grasslands

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Villarreal, Miguel; Norman, Laura M.; Buckley, Steven; Wallace, Cynthia S.A.; Coe, Michelle A.

2016-01-01

The Western United States is expected to undergo both extended periods of drought and longer wildfire seasons under forecasted global climate change and it is important to understand how these disturbances will interact and affect recovery and composition of plant communities in the future. In this research paper we describe the temporal response of grassland communities to drought and fire in southern Arizona, where land managers are using repeated, prescribed fire as a habitat restoration tool. Using a 25-year atlas of fire locations, we paired sites with multiple fires to unburned control areas and compare satellite and field-based estimates of vegetation cover over time. Two hundred and fifty Landsat TM images, dating from 1985–2011, were used to derive estimates of Total Vegetation Fractional Cover (TVFC) of live and senescent grass using the Soil-Adjusted Total Vegetation Index (SATVI) and post-fire vegetation greenness using the Normalized Difference Vegetation Index (NDVI). We also implemented a Greenness to Cover Index that is the difference of time-standardized SATVI-TVFC and NDVI values at a given time and location to identify post-fire shifts in native, non-native, and annual plant cover. The results highlight anomalous greening and browning during drought periods related to amounts of annual and non-native plant cover present. Results suggest that aggressive application of prescribed fire may encourage spread of non-native perennial grasses and annual plants, particularly during droughts.

Simulating Visible/Infrared Imager Radiometer Suite Normalized Difference Vegetation Index Data Using Hyperion and MODIS

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Ross, Kenton W.; Russell, Jeffrey; Ryan, Robert E.

2006-01-01

The success of MODIS (the Moderate Resolution Imaging Spectrometer) in creating unprecedented, timely, high-quality data for vegetation and other studies has created great anticipation for data from VIIRS (the Visible/Infrared Imager Radiometer Suite). VIIRS will be carried onboard the joint NASA/Department of Defense/National Oceanic and Atmospheric Administration NPP (NPOESS (National Polar-orbiting Operational Environmental Satellite System) Preparatory Project). Because the VIIRS instruments will have lower spatial resolution than the current MODIS instruments 400 m versus 250 m at nadir for the channels used to generate Normalized Difference Vegetation Index data, scientists need the answer to this question: how will the change in resolution affect vegetation studies? By using simulated VIIRS measurements, this question may be answered before the VIIRS instruments are deployed in space. Using simulated VIIRS products, the U.S. Department of Agriculture and other operational agencies can then modify their decision support systems appropriately in preparation for receipt of actual VIIRS data. VIIRS simulations and validations will be based on the ART (Application Research Toolbox), an integrated set of algorithms and models developed in MATLAB(Registerd TradeMark) that enables users to perform a suite of simulations and statistical trade studies on remote sensing systems. Specifically, the ART provides the capability to generate simulated multispectral image products, at various scales, from high spatial hyperspectral and/or multispectral image products. The ART uses acquired ( real ) or synthetic datasets, along with sensor specifications, to create simulated datasets. For existing multispectral sensor systems, the simulated data products are used for comparison, verification, and validation of the simulated system s actual products. VIIRS simulations will be performed using Hyperion and MODIS datasets. The hyperspectral and hyperspatial properties of Hyperion

Integration of satellite-induced fluorescence and vegetation optical depth to improve the retrieval of land evaporation

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Pagán, B. R.; Martens, B.; Maes, W. H.; Miralles, D. G.

2017-12-01

Global satellite-based data sets of land evaporation overcome limitations in coverage of in situ measurements while retaining some observational nature. Although their potential for real world applications are promising, their value during dry conditions is still poorly understood. Most evaporation retrieval algorithms are not directly sensitive to soil moisture. An exception is the Global Land Evaporation Amsterdam Model (GLEAM), which uses satellite surface soil moisture and precipitation to account for land water availability. The existing methodology may greatly benefit from the optimal integration of novel observations of the land surface. Microwave vegetation optical depth (VOD) and near-infrared solar-induced fluorescence (SIF) are expected to reflect different aspects of evaporative stress. While the former is considered to be a proxy of vegetation water content, the latter is indicative of the activity of photosynthetic machinery. As stomata regulate both photosynthesis and transpiration, we expect a relationship between SIF and transpiration. An important motivation to incorporate observations in land evaporation calculations is that plant transpiration - usually the largest component of the flux - is extremely challenging to model due to species-dependent responses to drought. Here we present an innovative integration of VOD and SIF into the GLEAM evaporative stress function. VOD is utilized as a measurement of isohydricity to improve the representation of species specific drought responses. SIF is used for transpiration modelling, a novel application, and standardized by incoming solar radiation to better account for radiation-limited periods. Results are validated with global FLUXNET and International Soil Moisture Network data and demonstrate that the incorporation of VOD and SIF can yield accurate estimates of transpiration over large-scales, which are essential to further understand ecosystem-atmosphere feedbacks and the response of terrestrial

Method for validating cloud mask obtained from satellite measurements using ground-based sky camera.

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Letu, Husi; Nagao, Takashi M; Nakajima, Takashi Y; Matsumae, Yoshiaki

2014-11-01

Error propagation in Earth's atmospheric, oceanic, and land surface parameters of the satellite products caused by misclassification of the cloud mask is a critical issue for improving the accuracy of satellite products. Thus, characterizing the accuracy of the cloud mask is important for investigating the influence of the cloud mask on satellite products. In this study, we proposed a method for validating multiwavelength satellite data derived cloud masks using ground-based sky camera (GSC) data. First, a cloud cover algorithm for GSC data has been developed using sky index and bright index. Then, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived cloud masks by two cloud-screening algorithms (i.e., MOD35 and CLAUDIA) were validated using the GSC cloud mask. The results indicate that MOD35 is likely to classify ambiguous pixels as "cloudy," whereas CLAUDIA is likely to classify them as "clear." Furthermore, the influence of error propagations caused by misclassification of the MOD35 and CLAUDIA cloud masks on MODIS derived reflectance, brightness temperature, and normalized difference vegetation index (NDVI) in clear and cloudy pixels was investigated using sky camera data. It shows that the influence of the error propagation by the MOD35 cloud mask on the MODIS derived monthly mean reflectance, brightness temperature, and NDVI for clear pixels is significantly smaller than for the CLAUDIA cloud mask; the influence of the error propagation by the CLAUDIA cloud mask on MODIS derived monthly mean cloud products for cloudy pixels is significantly smaller than that by the MOD35 cloud mask.

Satellite and ground-based analysis of the effects on vegetation of continuous SO2 degassing at Turrialba volcano (Costa Rica) and its application to hazard management

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Tortini, R.; van Manen, S. M.; Burson, B.; Carn, S. A.

2014-12-01

Turrialba is an active stratovolcano located 35 km northeast of San Jose, Costa Rica's capital city and socioeconomic hub. After over 100 years of quiescence Turrialba resumed activity in 1996 progressively increasing its degassing and seismic activity, showing continuous gas emissions since 2007. Intermittent phreatic explosions with ash emissions that have reached the capital have occurred since 2010. This activity has resulted in the temporary evacuation of two villages, closure of the National Park that comprises the summit region of the volcano and devastation of the local ecosystem. We combined a variety of satellite-based time series with ground-based measurements of ambient gas concentrations, element deposition and surveys of species richness to enable a comprehensive assessment of SO2 emissions and changes in vegetation. Satellite-based time-series were obtained from Landsat ETM+, Terra ASTER, Terra/Aqua MODIS and Aura OMI, with some of the data dating back to 2000. From 2007-2010 we observed emissions of SO2 and loss of vegetation healthiness (i.e. decrease of EVI2) downwind of the vents. From 2010 onwards these stabilized, but we observe an apparent decrease in agriculture. Other multi-temporal products, such as the ALOS PALSAR FNF data, confirm our observations. The exposure to the volcanic plume resulted in high soil acidity and significant uptake of certain heavy metals by vegetation; in contrast other elements are leached from the soil as a result of the acid deposition. These factors are likely to be responsible for decreased species richness and physiological damage observed at Turrialba. Our study shows ecological impacts, in terms of soil characteristics, vegetation composition and diversity and physiological damage of vegetation, which all correlate to fumigation by Turrialba's plume. Analyzing and relating the remote observations to conditions and impacts on the ground provides a better understanding of volcanic degassing, its impacts on

Case study of atmospheric correction on CCD data of HJ-1 satellite based on 6S model

International Nuclear Information System (INIS)

Xue, Xiaoiuan; Meng, Oingyan; Xie, Yong; Sun, Zhangli; Wang, Chang; Zhao, Hang

2014-01-01

In this study, atmospheric radiative transfer model 6S was used to simulate the radioactive transfer process in the surface-atmosphere-sensor. An algorithm based on the look-up table (LUT) founded by 6S model was used to correct (HJ-1) CCD image pixel by pixel. Then, the effect of atmospheric correction on CCD data of HJ-1 satellite was analyzed in terms of the spectral curves and evaluated against the measured reflectance acquired during HJ-1B satellite overpass, finally, the normalized difference vegetation index (NDVI) before and after atmospheric correction were compared. The results showed: (1) Atmospheric correction on CCD data of HJ-1 satellite can reduce the ''increase'' effect of the atmosphere. (2) Apparent reflectance are higher than those of surface reflectance corrected by 6S model in band1∼band3, but they are lower in the near-infrared band; the surface reflectance values corrected agree with the measured reflectance values well. (3)The NDVI increases significantly after atmospheric correction, which indicates the atmospheric correction can highlight the vegetation information

Analyzing Vegetation Change in an Elephant-Impacted Landscape Using the Moving Standard Deviation Index

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Timothy J. Fullman

2014-01-01

Full Text Available Northern Botswana is influenced by various socio-ecological drivers of landscape change. The African elephant (Loxodonta africana is one of the leading sources of landscape shifts in this region. Developing the ability to assess elephant impacts on savanna vegetation is important to promote effective management strategies. The Moving Standard Deviation Index (MSDI applies a standard deviation calculation to remote sensing imagery to assess degradation of vegetation. Used previously for assessing impacts of livestock on rangelands, we evaluate the ability of the MSDI to detect elephant-modified vegetation along the Chobe riverfront in Botswana, a heavily elephant-impacted landscape. At broad scales, MSDI values are positively related to elephant utilization. At finer scales, using data from 257 sites along the riverfront, MSDI values show a consistent negative relationship with intensity of elephant utilization. We suggest that these differences are due to varying effects of elephants across scales. Elephant utilization of vegetation may increase heterogeneity across the landscape, but decrease it within heavily used patches, resulting in the observed MSDI pattern of divergent trends at different scales. While significant, the low explanatory power of the relationship between the MSDI and elephant utilization suggests the MSDI may have limited use for regional monitoring of elephant impacts.

Interannual variations and trends in global land surface phenology derived from enhanced vegetation index during 1982-2010

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Zhang, Xiaoyang; Tan, Bin; Yu, Yunyue

2014-05-01

Land surface phenology is widely retrieved from satellite observations at regional and global scales, and its long-term record has been demonstrated to be a valuable tool for reconstructing past climate variations, monitoring the dynamics of terrestrial ecosystems in response to climate impacts, and predicting biological responses to future climate scenarios. This study detected global land surface phenology from the advanced very high resolution radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 1982 to 2010. Based on daily enhanced vegetation index at a spatial resolution of 0.05 degrees, we simulated the seasonal vegetative trajectory for each individual pixel using piecewise logistic models, which was then used to detect the onset of greenness increase (OGI) and the length of vegetation growing season (GSL). Further, both overall interannual variations and pixel-based trends were examined across Koeppen's climate regions for the periods of 1982-1999 and 2000-2010, respectively. The results show that OGI and GSL varied considerably during 1982-2010 across the globe. Generally, the interannual variation could be more than a month in precipitation-controlled tropical and dry climates while it was mainly less than 15 days in temperature-controlled temperate, cold, and polar climates. OGI, overall, shifted early, and GSL was prolonged from 1982 to 2010 in most climate regions in North America and Asia while the consistently significant trends only occurred in cold climate and polar climate in North America. The overall trends in Europe were generally insignificant. Over South America, late OGI was consistent (particularly from 1982 to 1999) while either positive or negative GSL trends in a climate region were mostly reversed between the periods of 1982-1999 and 2000-2010. In the Northern Hemisphere of Africa, OGI trends were mostly insignificant, but prolonged GSL was evident over individual climate regions during the last 3

Spatial and Temporal Variation in Primary Productivity (NDVI) of Coastal Alaskan Tundra: Decreased Vegetation Growth Following Earlier Snowmelt

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Gamon, John A.; Huemmrich, K. Fred; Stone, Robert S.; Tweedie, Craig E.

2015-01-01

In the Arctic, earlier snowmelt and longer growing seasons due to warming have been hypothesized to increase vegetation productivity. Using the Normalized Difference Vegetation Index (NDVI) from both field and satellite measurements as an indicator of vegetation phenology and productivity, we monitored spatial and temporal patterns of vegetation growth for a coastal wet sedge tundra site near Barrow, Alaska over three growing seasons (2000-2002). Contrary to expectation, earlier snowmelt did not lead to increased productivity. Instead, productivity was associated primarily with precipitation and soil moisture, and secondarily with growing degree days, which, during this period, led to reduced growth in years with earlier snowmelt. Additional moisture effects on productivity and species distribution, operating over a longer time scale, were evident in spatial NDVI patterns associated with microtopography. Lower, wetter regions dominated by graminoids were more productive than higher, drier locations having a higher percentage of lichens and mosses, despite the earlier snowmelt at the more elevated sites. These results call into question the oft-stated hypothesis that earlier arctic growing seasons will lead to greater vegetation productivity. Rather, they agree with an emerging body of evidence from recent field studies indicating that early-season, local environmental conditions, notably moisture and temperature, are primary factors determining arctic vegetation productivity. For this coastal arctic site, early growing season conditions are strongly influenced by microtopography, hydrology, and regional sea ice dynamics, and may not be easily predicted from snowmelt date or seasonal average air temperatures alone. Our comparison of field to satellite NDVI also highlights the value of in-situ monitoring of actual vegetation responses using field optical sampling to obtain detailed information on surface conditions not possible from satellite observations alone.

Estimation of vegetation cover resilience from satellite time series

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T. Simoniello

2008-07-01

Full Text Available Resilience is a fundamental concept for understanding vegetation as a dynamic component of the climate system. It expresses the ability of ecosystems to tolerate disturbances and to recover their initial state. Recovery times are basic parameters of the vegetation's response to forcing and, therefore, are essential for describing realistic vegetation within dynamical models. Healthy vegetation tends to rapidly recover from shock and to persist in growth and expansion. On the contrary, climatic and anthropic stress can reduce resilience thus favouring persistent decrease in vegetation activity.

In order to characterize resilience, we analyzed the time series 1982–2003 of 8 km GIMMS AVHRR-NDVI maps of the Italian territory. Persistence probability of negative and positive trends was estimated according to the vegetation cover class, altitude, and climate. Generally, mean recovery times from negative trends were shorter than those estimated for positive trends, as expected for vegetation of healthy status. Some signatures of inefficient resilience were found in high-level mountainous areas and in the Mediterranean sub-tropical ones. This analysis was refined by aggregating pixels according to phenology. This multitemporal clustering synthesized information on vegetation cover, climate, and orography rather well. The consequent persistence estimations confirmed and detailed hints obtained from the previous analyses. Under the same climatic regime, different vegetation resilience levels were found. In particular, within the Mediterranean sub-tropical climate, clustering was able to identify features with different persistence levels in areas that are liable to different levels of anthropic pressure. Moreover, it was capable of enhancing reduced vegetation resilience also in the southern areas under Warm Temperate sub-continental climate. The general consistency of the obtained results showed that, with the help of suited analysis

Vegetation index cartography as a methodology complement to the terroir zoning for its use in precision viticulture

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Alvaro Martínez

2017-09-01

Full Text Available Aim: Precision Viticulture (PV is a form of vineyard management based on tools that offer winegrowers georeferenced information of each vineyard, mainly sector mapping (sub-areas differentiated by characteristics capable of influencing vineyard usage. This provides knowledge of the variations in these sectors and PV treats each one of them in an independent and optimised manner. This allows, amongst many other possibilities, to monitor fruit ripening with the objective of performing site-specific harvest based on the characteristics of each given sector. Local variations in soil features and natural environmental factors, such as climate, lithology, geomorphology and soil, determine the units that drive or limit PV. Methods and results: In this paper, multispectral images are used. These have been obtained between veraison and harvest in three different years in order to calculate four vegetation indexes (VI that have been used since the end of the last century to delimit homogenous sectors in vineyards: the Normalized Difference Vegetation Index (NDVI, the Improved Soil Adjusted Vegetation Index (MSAVI, the Simple Ratio Index (SR and the Modified Simple Ratio Index (MSR. Mapping of these VI has allowed to relate their distribution with natural environmental factors with the objective of valuing their use in the discrimination of homogenous sectors as a complement and/or alternative to traditional methodologies to terroir zoning. Results show that, in the area studied, the vineyards planted in alluvial soil and conglomerated zones, over dominant fine-loamy, mixed, mesic, Calcixerollic Xerochrept soil series, at elevations between 519 and 604 m, oriented east and on slopes less than 5º present higher values for all four indexes throughout the three years of study. Conclusions: It is precisely these environmental elements (lithology, soil, elevation, orientation and slope and many soil features that must be relatively uniform in order to make an

Enhanced vegetation growth peak and its key mechanisms

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Huang, K.; Xia, J.; Wang, Y.; Ahlström, A.; Schwalm, C.; Huntzinger, D. N.; Chen, J.; Cook, R. B.; Fang, Y.; Fisher, J. B.; Jacobson, A. R.; Michalak, A.; Schaefer, K. M.; Wei, Y.; Yan, L.; Luo, Y.

2017-12-01

It remains unclear that whether and how the vegetation growth peak has been shifted globally during the past three decades. Here we used two global datasets of gross primary productivity (GPP) and a satellite-derived Normalized Difference Vegetation Index (NDVI) to characterize recent changes in seasonal peak vegetation growth. The attribution of changes in peak growth to their driving factors was examined with several datasets. We demonstrated that the growth peak of global vegetation has been linearly increasing during the past three decades. About 65% of this trend is evenly explained by the expanding croplands (21%), rising atmospheric [CO2] (22%), and intensifying nitrogen deposition (22%). The contribution of expanding croplands to the peak growth trend was substantiated by measurements from eddy-flux towers, sun-induced chlorophyll fluorescence and a global database of plant traits, all of which demonstrated that croplands have a higher photosynthetic capacity than other vegetation types. The contribution of rising atmospheric [CO2] and nitrogen deposition are consistent with the positive response of leaf growth to elevated [CO2] (25%) and nitrogen addition (8%) from 346 manipulated experiments. The positive effect of rising atmospheric [CO2] was also well captured by 15 terrestrial biosphere models. However, most models underestimated the contributions of land-cover change and nitrogen deposition, but overestimated the positive effect of climate change.

Correlation of meteorological parameters and remotely sensed normalized difference vegetation index (NDVI) with cotton leaf curl virus (CLCV) in Multan

International Nuclear Information System (INIS)

Ahmed, A; Akhtar, A; Khalid, B; Shamim, A

2013-01-01

Climate change and weather has a profound effect on the spread of Cotton Leaf Curl Virus (CLCV) which is transmitted by whitefly. Climate change is altering temperature and precipitation patterns, resulting in the shift of some insect/pest from small population to large population thus effecting crops yield. To find out the relationship between the weather conditions, outburst of CLCV and changes in Normalized Difference Vegetation Index (NDVI) values due to the outburst of CLCV, a study was carried out for tehsil Multan. Data was acquired for the months of June, July, August and September for the year 2010. Regression analysis between CLCV and meteorological conditions as well as between CLCV and NDVI was performed. Meteorological parameters included temperature, humidity, precipitation, cloud cover, wind direction, pan evaporation and sunshine hours. NDVI values were calculated from SPOT satellite imagery (1km) using ArcMap10 and WinDisp v5.1. Correlation coefficients obtained in most of the cases were acceptable however the significance F and P-value were higher than their critical value at 95% level of significance. Therefore significant correlation was found only between CLCV and temperature and between CLCV and PAN evaporation during the month of July.

Variations in global land surface phenology: a comparison of satellite optical and passive microwave data

Science.gov (United States)

Tong, X.; Tian, F.; Brandt, M.; Zhang, W.; Liu, Y.; Fensholt, R.

2017-12-01

Changes in vegetation phenological events are among the most sensitive biological responses to climate change. In last decades, facilitating by satellite remote sensing techniques, land surface phenology (LSP) have been monitored at global scale using proxy approaches as tracking the temporal change of a satellite-derived vegetation index. However, the existing global assessments of changes in LSP are all established on the basis of leaf phenology using NDVI derived from optical sensors, being responsive to vegetation canopy cover and greenness. Instead, the vegetation optical depth (VOD) parameter from passive microwave sensors, which is sensitive to the aboveground vegetation water content by including as well the woody components in the observations, provides an alternative, independent and comprehensive means for global vegetation phenology monitoring. We used the unique long-term global VOD record available for the period 1992-2012 to monitoring the dynamics of LSP metrics (length of season, start of season and end of season) in comparison with the dynamics of LSP metrics derived from the latest GIMMS NDVI3G V1. We evaluated the differences in the linear trends of LSP metrics between two datasets. Currently, our results suggest that the level of seasonality variation of vegetation water content is less than the vegetation greenness. We found significant phenological changes in vegetation water content in African woodlands, where has been reported with little leaf phenological change regardless of the delays in rainfall onset. Therefore, VOD might allow us to detect temporal shifts in the timing difference of vegetation water storage vs. leaf emergence and to see if some ecophysiological thresholds seem to be reached, that could cause species turnover as climate change-driven alterations to the African monsoon proceed.

Testing high spatial resolution WorldView-2 imagery for retrieving the leaf area index

Science.gov (United States)

Tarantino, Eufemia; Novelli, Antonio; Laterza, Maurizio; Gioia, Andrea

2015-06-01

This work analyzes the potentiality of WorldView-2 satellite data for retrieving the Leaf Area Index (LAI) area located in Apulia, the most Eastern region of Italy, overlooking the Adriatic and Ionian seas. Lacking contemporary in-situ measurements, the semi-empiric method of Clevers (1989) (CLAIR model) was chosen as a feasible image-based LAI retrieval method, which is based on an inverse exponential relationship between the LAI and the WDVI (Weighted Difference Vegetation Index) with relation to different land covers. Results were examined in homogeneous land cover classes and compared with values obtained in recent literature.

Satellite Soil Moisture and Water Storage Observations Identify Early and Late Season Water Supply Influencing Plant Growth in the Missouri Watershed

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A, G.; Velicogna, I.; Kimball, J. S.; Du, J.; Kim, Y.; Colliander, A.; Njoku, E. G.

2017-12-01

We employ an array of continuously overlapping global satellite sensor observations including combined surface soil moisture (SM) estimates from SMAP, AMSR-E and AMSR-2, GRACE terrestrial water storage (TWS), and satellite precipitation measurements, to characterize seasonal timing and inter-annual variations of the regional water supply pattern and its associated influence on vegetation growth estimates from MODIS enhanced vegetation index (EVI), AMSR-E/2 vegetation optical depth (VOD) and GOME-2 solar-induced florescence (SIF). Satellite SM is used as a proxy of plant-available water supply sensitive to relatively rapid changes in surface condition, GRACE TWS measures seasonal and inter-annual variations in regional water storage, while precipitation measurements represent the direct water input to the analyzed ecosystem. In the Missouri watershed, we find surface SM variations are the dominant factor controlling vegetation growth following the peak of the growing season. Water supply to growth responds to both direct precipitation inputs and groundwater storage carry-over from prior seasons (winter and spring), depending on land cover distribution and regional climatic condition. For the natural grassland in the more arid central and northwest watershed areas, an early season anomaly in precipitation or surface temperature can have a lagged impact on summer vegetation growth by affecting the surface SM and the underlying TWS supplies. For the croplands in the more humid eastern portions of the watershed, the correspondence between surface SM and plant growth weakens. The combination of these complementary remote-sensing observations provides an effective means for evaluating regional variations in the timing and availability of water supply influencing vegetation growth.

Early drought detection by spectral analysis of satellite time series of precipitation and Normalized Difference Vegetation Index (NDVI)

NARCIS (Netherlands)

Van Hoek, Mattijn; Jia, Li; Zhou, J.; Zheng, Chaolei; Menenti, M.

2016-01-01

The time lag between anomalies in precipitation and vegetation activity plays a critical role in early drought detection as agricultural droughts are caused by precipitation shortages. The aim of this study is to explore a new approach to estimate the time lag between a forcing (precipitation)

The Oslo Health Study: A Dietary Index Estimating Frequent Intake of Soft Drinks and Rare Intake of Fruit and Vegetables Is Negatively Associated with Bone Mineral Density

Science.gov (United States)

Høstmark, Arne Torbjørn; Søgaard, Anne Johanne; Alvær, Kari; Meyer, Haakon E.

2011-01-01

Background. Since nutritional factors may affect bone mineral density (BMD), we have investigated whether BMD is associated with an index estimating the intake of soft drinks, fruits, and vegetables. Methods. BMD was measured in distal forearm in a subsample of the population-based Oslo Health Study. 2126 subjects had both valid BMD measurements and answered all the questions required for calculating a Dietary Index = the sum of intake estimates of colas and non-cola beverages divided by the sum of intake estimates of fruits and vegetables. We did linear regression analyses to study whether the Dietary Index and the single food items included in the index were associated with BMD. Results. There was a consistent negative association between the Dietary Index and forearm BMD. Among the single index components, colas and non-cola soft drinks were negatively associated with BMD. The negative association between the Dietary Index and BMD prevailed after adjusting for gender, age, and body mass index, length of education, smoking, alcohol intake, and physical activity. Conclusion. An index reflecting frequent intake of soft drinks and rare intake of fruit and vegetables was inversely related to distal forearm bone mineral density. PMID:21772969

Remotely sensed vegetation indices for seasonal crop yields predictions in the Czech Republic

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Hlavinka, Petr; Semerádová, Daniela; Balek, Jan; Bohovic, Roman; Žalud, Zdeněk; Trnka, Miroslav

2015-04-01

Remotely sensed vegetation indices by satellites are valuable tool for vegetation conditions assessment also in the case of field crops. This study is based on the use of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) aboard Terra satellite. Data available from the year 2000 were analyzed and tested for seasonal yields predictions within selected districts of the Czech Republic (Central Europe). Namely the yields of spring barley, winter wheat and oilseed winter rape during the period from 2000 to 2014 were assessed. Observed yields from 14 districts (NUTS 4) were collected and thus 210 seasons were included. Selected districts differ considerably in their soil fertility and terrain configuration and represent transect across various agroclimatic conditions (from warm and dry to relative cool and wet regions). Two approaches were tested: 1) using of composite remotely sensed data (available in 16 day time step) provided by the USGS (https://lpdaac.usgs.gov/); 2) using daily remotely sensed data in combination with originally developed smoothing method. The yields were successfully predicted based on established regression models (remotely sensed data used as independent parameter). Besides others the impact of severe drought episodes within vegetation were identified and yield reductions at district level predicted (even before harvest). As a result the periods with the best relationship between remotely sensed data and yields were identified. The impact of drought conditions as well as normal or above normal yields of field crops could be predicted by proposed method within study region up to 30 days prior to the harvest. It could be concluded that remotely sensed vegetation conditions assessment should be important part of early warning systems focused on drought. Such information should be widely available for various users (decision makers, farmers, etc.) in

MITRA Virtual laboratory for operative application of satellite time series for land degradation risk estimation

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Nole, Gabriele; Scorza, Francesco; Lanorte, Antonio; Manzi, Teresa; Lasaponara, Rosa

2015-04-01

This paper aims to present the development of a tool to integrate time series from active and passive satellite sensors (such as of MODIS, Vegetation, Landsat, ASTER, COSMO, Sentinel) into a virtual laboratory to support studies on landscape and archaeological landscape, investigation on environmental changes, estimation and monitoring of natural and anthropogenic risks. The virtual laboratory is composed by both data and open source tools specifically developed for the above mentioned applications. Results obtained for investigations carried out using the implemented tools for monitoring land degradation issues and subtle changes ongoing on forestry and natural areas are herein presented. In detail MODIS, SPOT Vegetation and Landsat time series were analyzed comparing results of different statistical analyses and the results integrated with ancillary data and evaluated with field survey. The comparison of the outputs we obtained for the Basilicata Region from satellite data analyses and independent data sets clearly pointed out the reliability for the diverse change analyses we performed, at the pixel level, using MODIS, SPOT Vegetation and Landsat TM data. Next steps are going to be implemented to further advance the current Virtual Laboratory tools, by extending current facilities adding new computational algorithms and applying to other geographic regions. Acknowledgement This research was performed within the framework of the project PO FESR Basilicata 2007/2013 - Progetto di cooperazione internazionale MITRA "Remote Sensing tecnologies for Natural and Cultural heritage Degradation Monitoring for Preservation and valorization" funded by Basilicata Region Reference 1. A. Lanorte, R Lasaponara, M Lovallo, L Telesca 2014 Fisher-Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbance International Journal of Applied Earth Observation and

Use of satellite imagery to assess the trophic state of Miyun Reservoir, Beijing, China

International Nuclear Information System (INIS)

Wang Zhengjun; Hong Jianming; Du Guisen

2008-01-01

The objective of this research is to explore an appropriate way of monitoring and assessing water quality by satellite remote sensing techniques in the Miyun reservoir of Beijing, China. Two scene Thematic Mapper images in May and October of 2003 were acquired and simultaneous in situ measurements, sampling and analysis were conducted. Statistical analysis indicates that satellite-based normalized ratio vegetation index (NRVI) and in situ measured water chlorophyll a (Chl-a) concentration have very high correlation. Two linear regression models with high determination coefficients were constructed for NRVI and Chl-a of sample points. According to the modified trophic state index map, water quality in the western section of Miyun reservoir was consistently higher than in the eastern section during the two months tested. The trophic grade of the eastern reservoir remained mesotrophic with a tendency for eutrophication. - Remote sensing techniques can effectively monitor the change of water quality with time and space

Growing Degree Vegetation Production Index (GDVPI): A Novel and Data-Driven Approach to Delimit Season Cycles

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Graham, W. D.; Spruce, J.; Ross, K. W.; Gasser, J.; Grulke, N.

2014-12-01

Growing Degree Vegetation Production Index (GDVPI) is a parametric approach to delimiting vegetation seasonal growth and decline cycles using incremental growing degree days (GDD), and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 8-day composite cumulative integral data. We obtain a specific location's daily minimum and maximum temperatures from the nearest National Oceanic and Atmospheric Administration (NOAA) weather stations posted on the National Climate Data Center (NCDC) Climate Data Online (CDO) archive and compute GDD. The date range for this study is January 1, 2000 through December 31, 2012. We employ a novel process, a repeating logistic product (RLP), to compensate for short-term weather variability and data drops from the recording stations and fit a curve to the median daily GDD values, adjusting for asymmetry, amplitude, and phase shift that minimize the sum of squared errors when comparing the observed and predicted GDD. The resulting curve, here referred to as the surrogate GDD, is the time-temperature phasing parameter used to convert Cartesian NDVI values into polar coordinate pairs, multiplying the NDVI values as the radial by the cosine and sine of the surrogate GDD as the angular. Depending on the vegetation type and the original NDVI curve, the polar NDVI curve may be nearly circular, kidney-shaped, or pear-shaped in the case of conifers, deciduous, or agriculture, respectively. We examine the points of tangency about the polar coordinate NDVI curve, identifying values of 1, 0, -1, or infinity, as each of these represent natural inflection points. Lines connecting the origin to each tangent point illustrate and quantify the parametrically segmentation of the growing season based on the GDD and NDVI ostensible dependency. Furthermore, the area contained by each segment represents the apparent vegetation production. A particular benefit is that the inflection points are determined

Inversion of Farmland Soil Moisture in Large Region Based on Modified Vegetation Index

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Wang, J. X.; Yu, B. S.; Zhang, G. Z.; Zhao, G. C.; He, S. D.; Luo, W. R.; Zhang, C. C.

2018-04-01

Soil moisture is an important parameter for agricultural production. Efficient and accurate monitoring of soil moisture is an important link to ensure the safety of agricultural production. Remote sensing technology has been widely used in agricultural moisture monitoring because of its timeliness, cyclicality, dynamic tracking of changes in things, easy access to data, and extensive monitoring. Vegetation index and surface temperature are important parameters for moisture monitoring. Based on NDVI, this paper introduces land surface temperature and average temperature for optimization. This article takes the soil moisture in winter wheat growing area in Henan Province as the research object, dividing Henan Province into three main regions producing winter wheat and dividing the growth period of winter wheat into the early, middle and late stages on the basis of phenological characteristics and regional characteristics. Introducing appropriate correction factor during the corresponding growth period of winter wheat, correcting the vegetation index in the corresponding area, this paper establishes regression models of soil moisture on NDVI and soil moisture on modified NDVI based on correlation analysis and compare models. It shows that modified NDVI is more suitable as a indicator of soil moisture because of the better correlation between soil moisture and modified NDVI and the higher prediction accuracy of the regression model of soil moisture on modified NDVI. The research in this paper has certain reference value for winter wheat farmland management and decision-making.

Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images

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Rougier, Simon; Puissant, Anne; Stumpf, André; Lachiche, Nicolas

2016-09-01

Vegetation monitoring is becoming a major issue in the urban environment due to the services they procure and necessitates an accurate and up to date mapping. Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical learning techniques have provided good results for the detection of urban vegetation but necessitate a large amount of training data. In this context, this study proposes to investigate the performances of different sampling strategies in order to reduce the number of examples needed. Two windows based active learning algorithms from state-of-art are compared to a classical stratified random sampling and a third combining active learning and stratified strategies is proposed. The efficiency of these strategies is evaluated on two medium size French cities, Strasbourg and Rennes, associated to different datasets. Results demonstrate that classical stratified random sampling can in some cases be just as effective as active learning methods and that it should be used more frequently to evaluate new active learning methods. Moreover, the active learning strategies proposed in this work enables to reduce the computational runtime by selecting multiple windows at each iteration without increasing the number of windows needed.

Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors. Part 2; Implementation, Analysis and Validation

Science.gov (United States)

Ganguly, Sangram; Samanta, Arindam; Schull, Mitchell A.; Shabanov, Nikolay V.; Milesi, Cristina; Nemani, Ramajrushna R,; Knyazikhin, Yuri; Myneni, Ranga B.

2008-01-01

The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the intercomparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies.

Use of geostationary satellite imagery in optical and thermal bands for the estimation of soil moisture status and land evapotranspiration

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Ghilain, N.; Arboleda, A.; Gellens-Meulenberghs, F.

2009-04-01

For water and agricultural management, there is an increasing demand to monitor the soil water status and the land evapotranspiration. In the framework of the LSA-SAF project (http://landsaf.meteo.pt), we are developing an energy balance model forced by remote sensing products, i.e. radiation components and vegetation parameters, to monitor in quasi real-time the evapotranspiration rate over land (Gellens-Meulenberghs et al, 2007; Ghilain et al, 2008). The model is applied over the full MSG disk, i.e. including Europe and Africa. Meteorological forcing, as well as the soil moisture status, is provided by the forecasts of the ECMWF model. Since soil moisture is computed by a forecast model not dedicated to the monitoring of the soil water status, inadequate soil moisture input can occur, and can cause large effects on evapotranspiration rates, especially over semi-arid or arid regions. In these regions, a remotely sensed-based method for the soil moisture retrieval can therefore be preferable, to avoid too strong dependency in ECMWF model estimates. Among different strategies, remote sensing offers the advantage of monitoring large areas. Empirical methods of soil moisture assessment exist using remotely sensed derived variables either from the microwave bands or from the thermal bands. Mainly polar orbiters are used for this purpose, and little attention has been paid to the new possibilities offered by geosynchronous satellites. In this contribution, images of the SEVIRI instrument on board of MSG geosynchronous satellites are used. Dedicated operational algorithms were developed for the LSA-SAF project and now deliver images of land surface temperature (LST) every 15-minutes (Trigo et al, 2008) and vegetations indices (leaf area index, LAI; fraction of vegetation cover, FVC; fraction of absorbed photosynthetically active radiation, FAPAR) every day (Garcia-Haro et al, 2005) over Africa and Europe. One advantage of using products derived from geostationary

Observational Quantification of Climatic and Human Influences on Vegetation Greening in China

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Wenjian Hua

2017-04-01

Full Text Available This study attempts to quantify the relative contributions of vegetation greening in China due to climatic and human influences from multiple observational datasets. Satellite measured vegetation greenness, Normalized Difference Vegetation Index (NDVI, and relevant climate, land cover, and socioeconomic data since 1982 are analyzed using a multiple linear regression (MLR method. A statistically significant positive trend of average growing-season (April–October NDVI is found over more than 34% of the vegetated areas, mainly in North China, while significant decreases in NDVI are only seen in less than 5% of the areas. The relationships between vegetation and climate (temperature, precipitation, and radiation vary by geographical location and vegetation type. We estimate the NDVI changes in association with the non-climatic effects by removing the climatic effects from the original NDVI time series using the MLR analysis. Our results indicate that land use change is the dominant factor driving the long-term changes in vegetation greenness. The significant greening in North China is due to the increase in crops, grasslands, and forests. The socioeconomic datasets provide consistent and supportive results for the non-climatic effects at the provincial level that afforestation and reduced fire events generally have a major contribution. This study provides a basis for quantifying the non-climatic effects due to possible human influences on the vegetation greening in China.

Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery

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M. C. Anderson

2011-01-01

Full Text Available Thermal infrared (TIR remote sensing of land-surface temperature (LST provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (e.g., air temperature, advection are affecting plant functioning. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil + canopy land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5 to 10-km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions on the order of 10 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe, Africa

Advances in monitoring vegetation and land use dynamics in the Sahel

DEFF Research Database (Denmark)

Mbow, Cheikh; Fensholt, Rasmus; Nielsen, Thomas Theis

2014-01-01

of CO2 in the atmosphere, grazing pressure, bush fires and agricultural expansion or contraction. The use of satellite data in combination with field data played a major role in the monitoring of vegetation dynamics and land use in the Sahel, since the mega drought of the 1970s and the 1980s. This paper...... briefly reviews the advance of satellite-based monitoring of vegetation dynamics over these 40 years. We discuss the promises of current and likely future data sources and analysis tools, as well as the need to strengthen in situ data collection to support and validate satellite-based vegetation and land...

Vegetation Water Content Mapping in a Diverse Agricultural Landscape: National Airborne Field Experiment 2006

Science.gov (United States)

Cosh, Michael H.; Jing Tao; Jackson, Thomas J.; McKee, Lynn; O'Neill, Peggy

2011-01-01

Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE 06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE 06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/sq m. The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy. Keywords: Vegetation, field experimentation, thematic mapper, NDWI, agriculture.

Interannual variability of the normalized difference vegetation index on the Tibetan Plateau and its relationship with climate change

Science.gov (United States)

Zhou, Dingwen; Fan, Guangzhou; Huang, Ronghui; Fang, Zhifang; Liu, Yaqin; Li, Hongquan

2007-05-01

The Qinghai-Xizang Plateau, or Tibetan Plateau, is a sensitive region for climate change, where the manifestation of global warming is particularly noticeable. The wide climate variability in this region significantly affects the local land ecosystem and could consequently lead to notable vegetation changes. In this paper, the interannual variations of the plateau vegetation are investigated using a 21-year normalized difference vegetation index (NDVI) dataset to quantify the consequences of climate warming for the regional ecosystem and its interactions. The results show that vegetation coverage is best in the eastern and southern plateau regions and deteriorates toward the west and north. On the whole, vegetation activity demonstrates a gradual enhancement in an oscillatory manner during 1982 2002. The temporal variation also exhibits striking regional differences: an increasing trend is most apparent in the west, south, north and southeast, whereas a decreasing trend is present along the southern plateau boundary and in the central-east region. Covariance analysis between the NDVI and surface temperature/precipitation suggests that vegetation change is closely related to climate change. However, the controlling physical processes vary geographically. In the west and east, vegetation variability is found to be driven predominantly by temperature, with the impact of precipitation being of secondary importance. In the central plateau, however, temperature and precipitation factors are equally important in modulating the interannual vegetation variability.

Development of indicators of vegetation recovery based on time series analysis of SPOT Vegetation data

Science.gov (United States)

Lhermitte, S.; Tips, M.; Verbesselt, J.; Jonckheere, I.; Van Aardt, J.; Coppin, Pol

2005-10-01

Large-scale wild fires have direct impacts on natural ecosystems and play a major role in the vegetation ecology and carbon budget. Accurate methods for describing post-fire development of vegetation are therefore essential for the understanding and monitoring of terrestrial ecosystems. Time series analysis of satellite imagery offers the potential to quantify these parameters with spatial and temporal accuracy. Current research focuses on the potential of time series analysis of SPOT Vegetation S10 data (1999-2001) to quantify the vegetation recovery of large-scale burns detected in the framework of GBA2000. The objective of this study was to provide quantitative estimates of the spatio-temporal variation of vegetation recovery based on remote sensing indicators. Southern Africa was used as a pilot study area, given the availability of ground and satellite data. An automated technique was developed to extract consistent indicators of vegetation recovery from the SPOT-VGT time series. Reference areas were used to quantify the vegetation regrowth by means of Regeneration Indices (RI). Two kinds of recovery indicators (time and value- based) were tested for RI's of NDVI, SR, SAVI, NDWI, and pure band information. The effects of vegetation structure and temporal fire regime features on the recovery indicators were subsequently analyzed. Statistical analyses were conducted to assess whether the recovery indicators were different for different vegetation types and dependent on timing of the burning season. Results highlighted the importance of appropriate reference areas and the importance of correct normalization of the SPOT-VGT data.

Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors. Part 1; Theory

Science.gov (United States)

Ganguly, Sangram; Schull, Mitchell A.; Samanta, Arindam; Shabanov, Nikolay V.; Milesi, Cristina; Nemani, Ramakrishna R.; Knyazikhin, Yuri; Myneni, Ranga B.

2008-01-01

The generation of multi-decade long Earth System Data Records (ESDRs) of Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from remote sensing measurements of multiple sensors is key to monitoring long-term changes in vegetation due to natural and anthropogenic influences. Challenges in developing such ESDRs include problems in remote sensing science (modeling of variability in global vegetation, scaling, atmospheric correction) and sensor hardware (differences in spatial resolution, spectral bands, calibration, and information content). In this paper, we develop a physically based approach for deriving LAI and FPAR products from the Advanced Very High Resolution Radiometer (AVHRR) data that are of comparable quality to the Moderate resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products, thus realizing the objective of producing a long (multi-decadal) time series of these products. The approach is based on the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components and obeys the energy conservation law. The approach is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters, namely, the single scattering albedo and data uncertainty. According to the theory of spectral invariants, the single scattering albedo is a function of the spatial scale, and thus, accounts for the variation in BRF with sensor spatial resolution. Likewise, the single scattering albedo accounts for the variation in spectral BRF with sensor bandwidths. The second adjustable parameter is data uncertainty, which accounts for varying information content of the remote sensing measurements, i.e., Normalized Difference Vegetation Index (NDVI, low information content), vs. spectral BRF (higher

Loss of surface horizon of an irrigated soil detected by radiometric images of normalized difference vegetation index.

Science.gov (United States)

Fabian Sallesses, Leonardo; Aparicio, Virginia Carolina; Costa, Jose Luis

2017-04-01

The use of the soil in the Humid Pampa of Argentina has changed since the mid-1990s from agricultural-livestock production (that included pastures with direct grazing) to a purely agricultural production. Also, in recent years the area under irrigation by central pivot has been increased to 150%. The waters used for irrigation are sodium carbonates. The combination of irrigation and rain increases the sodium absorption ratio of soil (SARs), consequently raising the clay dispersion and reducing infiltration. This implies an increased risk of soil loss. A reduction in the development of white clover crop (Trifolium repens L.) was observed at an irrigation plot during 2015 campaign. The clover was planted in order to reduce the impact of two maize (Zea mays L.) campaigns under irrigation, which had increased soil SAR and deteriorated soil structure. SPOT-5 radiometric normalized difference vegetation index (NDVI) images were used to determine two zones of high and low production. In each zone, four random points were selected for further geo-referenced field sampling. Two geo-referenced measures of effective depth and surface soil sampling were carried out in each point. Texture of soil samples was determined by Pipette Method of Sedimentation Analysis. Data exploratory analysis showed that low production zone had a media effective depth = 80 cm and silty clay loam texture, while high production zone had a media effective depth > 140 cm and silt loam texture. The texture class of the low production zone did not correspond to prior soil studies carried out by the INTA (National Institute of Agricultural Technology), which showed that those soil textures were silt loam at surface and silty clay loam at sub-surface. The loss of the A horizon is proposed as a possible explanation, but further research is required. Besides, the need of a soil cartography actualization, which integrates new satellite imaging technologies and geo-referenced measurements with soil sensors is

Response of heterogeneous vegetation to aerosol radiative forcing over a northeast Indian station.

Science.gov (United States)

Latha, R; Vinayak, B; Murthy, B S

2018-01-15

Importance of atmospheric aerosols through direct and indirect effects on hydrological cycle is highlighted through multiple studies. This study tries to find how much the aerosols can affect evapo-transpiration (ET), a key component of the hydrological cycle over high NDVI (normalized difference vegetation index)/dense canopy, over Dibrugarh, known for vast tea plantation. The radiative effects of aerosols are calculated using satellite (Terra-MODIS) and reanalysis data on daily and monthly scales. Aerosol optical depth (AOD) obtained from satellite and ground observations compares well. Aerosol radiative forcing (ARF), calculated using MERRA data sets of 'clean-clear radiation' and 'clear-radiation' at the surface, shows a lower forcing efficiency, 35 Wm -zs , that is about half of that of ground observations. As vegetation controls ET over high NDVI area to the maximum and that gets modified through ARF, a regression equation is fitted between ET, AOD and NDVI for this station as ET = 0.25 + (-84.27) × AOD + (131.51) × NDVI that explains 82% of 'daily' ET variation using easily available satellite data. ET is found to follow net radiation closely and the direct relation between soil moisture and ET is weak on daily scale over this station as it may be acting through NDVI. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluation of Land Surface Models in Reproducing Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs

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Ning Zeng

2013-10-01

Full Text Available Leaf Area Index (LAI represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering a period of several decades. This provides a unique opportunity to validate LAI estimates from multiple vegetation models. The objective of this paper is to compare new, satellite-derived LAI measurements with modeled output for the Northern Hemisphere. We compare monthly LAI output from eight land surface models from the TRENDY compendium with satellite data from an Artificial Neural Network (ANN from the latest version (third generation of GIMMS AVHRR NDVI data over the period 1986–2005. Our results show that all the models overestimate the mean LAI, particularly over the boreal forest. We also find that seven out of the eight models overestimate the length of the active vegetation-growing season, mostly due to a late dormancy as a result of a late summer phenology. Finally, we find that the models report a much larger positive trend in LAI over this period than the satellite observations suggest, which translates into a higher trend in the growing season length. These results highlight the need to incorporate a larger number of more accurate plant functional types in all models and, in particular, to improve the phenology of deciduous trees.

Global Crop Monitoring: A Satellite-Based Hierarchical Approach

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Bingfang Wu

2015-04-01

Full Text Available Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, the CropWatch system has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The approach adopts a hierarchical system covering four spatial levels of detail: global, regional, national (thirty-one key countries including China and “sub-countries” (for the nine largest countries. The thirty-one countries encompass more that 80% of both production and exports of maize, rice, soybean and wheat. The methodology resorts to climatic and remote sensing indicators at different scales. The global patterns of crop environmental growing conditions are first analyzed with indicators for rainfall, temperature, photosynthetically active radiation (PAR as well as potential biomass. At the regional scale, the indicators pay more attention to crops and include Vegetation Health Index (VHI, Vegetation Condition Index (VCI, Cropped Arable Land Fraction (CALF as well as Cropping Intensity (CI. Together, they characterize crop situation, farming intensity and stress. CropWatch carries out detailed crop condition analyses at the national scale with a comprehensive array of variables and indicators. The Normalized Difference Vegetation Index (NDVI, cropped areas and crop conditions are integrated to derive food production estimates. For the nine largest countries, CropWatch zooms into the sub-national units to acquire detailed information on crop condition and production by including new indicators (e.g., Crop type proportion. Based on trend analysis, CropWatch also issues crop production supply outlooks, covering both long-term variations and short-term dynamic changes in key food exporters and importers. The hierarchical approach adopted by CropWatch is the basis of the analyses of climatic and crop conditions assessments published in the quarterly “CropWatch bulletin” which

Interacting Effects of Leaf Water Potential and Biomass on Vegetation Optical Depth

Science.gov (United States)

Momen, M.; Wood, J. D.; Novick, K. A.; Pockman, W.; Konings, A. G.

2017-12-01

Remotely-sensed microwave observations of vegetation optical depth (VOD) have been widely used to examine vegetation responses to climate. Such studies have alternately found that VOD is sensitive to both biomass and canopy water content. However, the relative impacts of changes in phenology or water stress on VOD have not been disentangled. In particular, understanding whether leaf water potential (LWP) affects VOD may permit the assimilation of satellite observations into new large-scale plant hydraulic models. Despite extensive validation of the relationship between satellite-derived VOD estimates and vegetation density, relatively few studies have explicitly sought to validate the sensitivity of VOD to canopy water status, and none have studied the effect of variations in LWP on VOD. In this work, we test the sensitivity of VOD to variations in LWP, and present a conceptual framework which relates VOD to a combination of leaf water potential and total biomass including leaves, whose dynamics can be measured through leaf area index, and woody biomass. We used in-situ measurements of LWP data to validate the conceptual model in mixed deciduous forests in Indiana and Missouri, as well as a pinion-juniper woodland in New Mexico. Observed X-band VOD from the AMSR-E and AMSR2 satellites showed dynamics similar to those reconstructed VOD signals based on the new conceptual model which employs in-situ LWP data (R2=0.60-0.80). Because LWP data are not available at global scales, we further estimated ecosystem LWP based on remotely sensed surface soil moisture to better understand the sensitivity of VOD across ecosystems. At the global scale, incorporating a combination of biomass and water potential in the reconstructed VOD signal increased correlations with VOD about 15% compared to biomass alone and about 30% compared to water potential alone. In wetter regions with denser and taller canopy heights, VOD has a higher correlation with leaf area index than with water

Changes in Vegetation Growth Dynamics and Relations with Climate over China’s Landmass from 1982 to 2011

Directory of Open Access Journals (Sweden)

Guang Xu

2014-04-01

Full Text Available Understanding how the dynamics of vegetation growth respond to climate change at different temporal and spatial scales is critical to projecting future ecosystem dynamics and the adaptation of ecosystems to global change. In this study, we investigated vegetated growth dynamics (annual productivity, seasonality and the minimum amount of vegetated cover in China and their relations with climatic factors during 1982–2011, using the updated Global Inventory Modeling and Mapping Studies (GIMMS third generation global satellite Advanced Very High Resolution Radiometer (AVHRR Normalized Difference Vegetation Index (NDVI dataset and climate data acquired from the National Centers for Environmental Prediction (NCEP. Major findings are as follows: (1 annual mean NDVI over China significantly increased by about 0.0006 per year from 1982 to 2011; (2 of the vegetated area in China, over 33% experienced a significant positive trend in vegetation growth, mostly located in central and southern China; about 21% experienced a significant positive trend in growth seasonality, most of which occurred in northern China (>35°N; (3 changes in vegetation growth dynamics were significantly correlated with air temperature and precipitation (p < 0.001 at a region scale; (4 at the country scale, changes in NDVI was significantly and positively correlated with annual air temperature (r = 0.52, p < 0.01 and not associated with annual precipitation (p > 0.1; (5 of the vegetated area, about 24% showed significant correlations between annual mean NDVI and air temperature (93% positive and remainder negative, and 12% showed significant correlations of annual mean NDVI with annual precipitation (65% positive and 35% negative. The spatiotemporal variations in vegetation growth dynamics were controlled primarily by temperature and secondly by precipitation. Vegetation growth was also affected by human activities; and (6 monthly NDVI was significantly correlated with the

Crop classification based on multi-temporal satellite remote sensing data for agro-advisory services

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Karale, Yogita; Mohite, Jayant; Jagyasi, Bhushan

2014-11-01

In this paper, we envision the use of satellite images coupled with GIS to obtain location specific crop type information in order to disseminate crop specific advises to the farmers. In our ongoing mKRISHI R project, the accurate information about the field level crop type and acreage will help in the agro-advisory services and supply chain planning and management. The key contribution of this paper is the field level crop classification using multi temporal images of Landsat-8 acquired during November 2013 to April 2014. The study area chosen is Vani, Maharashtra, India, from where the field level ground truth information for various crops such as grape, wheat, onion, soybean, tomato, along with fodder and fallow fields has been collected using the mobile application. The ground truth information includes crop type, crop stage and GPS location for 104 farms in the study area with approximate area of 42 hectares. The seven multi-temporal images of the Landsat-8 were used to compute the vegetation indices namely: Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR) and Difference Vegetation Index (DVI) for the study area. The vegetation indices values of the pixels within a field were then averaged to obtain the field level vegetation indices. For each crop, binary classification has been carried out using the feed forward neural network operating on the field level vegetation indices. The classification accuracy for the individual crop was in the range of 74.5% to 97.5% and the overall classification accuracy was found to be 88.49%.

Northern Everglades, Florida, satellite image map

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Thomas, Jean-Claude; Jones, John W.

2002-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program with support from the Everglades National Park. The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia

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Tadesse, Tsegaye; Senay, Gabriel B.; Berhan, Getachew; Regassa, Teshome; Beyene, Shimelis

2015-08-01

Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia's cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation (R2 > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships (R2 products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal crop yields and early warning of food insecurity during drought years for these identified zones.

CROP YIELD AND CO2 FIXATION MONITORING IN ASIA USING A PHOTOSYNTHETICSTERILITY MODEL WITH SATELLITES AND METEOROLOGICAL DATA

Energy Technology Data Exchange (ETDEWEB)

Daijiro Kaneko [Department of Civil and Environmental Engineering, Matsue National College of Technology, Matsue (Japan); Toshiro Kumakura [Department of Civil and Environmental Engineering, Nagaoka University of Technology, Nagaoka (Japan); Peng Yang [Laboratory of Resources Remote Sensing and Digital Agriculture, Ministry of Agriculture, Beijing (China)

2008-09-30

This study is intended to develop a model for estimating carbon dioxide (CO{sub 2}) fixation in the carbon cycle and for monitoring grain yields using a photosynthetic-sterility model, which integrates solar radiation and air temperature effects on photosynthesis, along with grain-filling from heading to ripening. Grain production monitoring would support orderly crisis management to maintain food security in Asia, which is facing climate fluctuation through this century of global warming. The author improved a photosynthesis-and-sterility model to compute both the crop yield and crop situation index CSI, which gives a percentage of rice yields compared to normal annual production. The model calculates photosynthesis rates including biomass effects, lowtemperature sterility, and high-temperature injury by incorporating solar radiation, effective air temperature, the normalized difference vegetation index NDVI, and the effect of temperature on photosynthesis by grain plant leaves. A decision-tree method classifies the distribution of crop fields in Asia using MODIS fundamental landcover and SPOT VEGETATION data, which include the Normalized Vegetation index (NDVI) and Land Surface Water Index (LSWI). This study provides daily distributions of the photosynthesis rate, which is the CO2 fixation in Asian areas combined with the land-cover distribution, the Japanese geostationary meteorological satellite (GMS), and meteorological re-analysis data by National Centers for Environmental Prediction (NCEP). The method is based on routine observation data, enabling automated monitoring of crop yields.

Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing.

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Richardson, Andrew D; Hufkens, Koen; Milliman, Tom; Frolking, Steve

2018-04-09

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both "greenness rising" and "greenness falling" transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground.

Soil Erosion Risk Map based on irregularity of the vegetative activity

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Saa-Requejo, Antonio; Tarquis, Ana Maria; Martín-Sotoca, Juan J.; Valencia, Jose L.; Gobin, Anne; Rodriguez-Sinobas, Leonor

2016-04-01

Because of the difficulties to build on both daily rainfall and base shorter time, we explored the possibilities of building indexes based on land cover, which also provide us the opportunity to evaluate their evolution over time. We consider the Fournier index (Fournier, 1960) which is used to assess the rainfall erosivity based on monthly rainfall, alternatively to use of the rainfall intensity in time bases under one hour (eg., van der Knijff et al., 1999; Shamshad et al, 2008). This index can also be interpreted as an index of irregularity and representing a ratio between maximum monthly precipitation and annual rainfall. We propose to calculate this irregularity in terms of irregularity of the vegetative activity. This activity is related to precipitation, but also with the availability of water in the soil reservoir and land use. Therefore, we propose a kind of Fournier index on the effective use of water, which is also closely related to variations in infiltration. Higher is the presence of vegetation higher is the effective use of water. For this "modified Fourier index" we used the NDVI (Normalized Difference Vegetation Index) as index of available vegetative activity, which is widely reported in the literature (Jensen, 2000). Initial calculations have been done with MODIS 500 x 500 m satellite data. The selected area was Cega-Eresma-Adaja subbasin during the period from 2009 to 2012. We selected 8 days composite images product. The calculation of the valid values to eliminate areas with clouds or snow is performed according to the criteria of Martinez Sotoca (2014), ie with a Saturation (based on HSL color model) greater or equal to 0.15. Then, an average of these values was estimated to represent each month of the year. The results are very interesting when we compare Modified Fournier Index on NDVIs with the map of potential soil loss. We have found surprisingly similar patterns and practical equivalence between several classes. Therefore, the Modified

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

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

2017-12-01

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

Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: ecological variability and regional differences

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Beck, Pieter S A; Goetz, Scott J, E-mail: pbeck@whrc.org [Woods Hole Research Center, 149 Woods Hole Road, Falmouth, MA 02540 (United States)

2011-10-15

To assess ongoing changes in high latitude vegetation productivity we compared spatiotemporal patterns in remotely sensed vegetation productivity in the tundra and boreal zones of North America and Eurasia. We compared the long-term GIMMS (Global Inventory Modeling and Mapping Studies) NDVI (Normalized Difference Vegetation Index) to the more recent and advanced MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI data set, and mapped circumpolar trends in a gross productivity metric derived from the former. We then analyzed how temporal changes in productivity differed along an evergreen-deciduous gradient in boreal Alaska, along a shrub cover gradient in Arctic Alaska, and during succession after fire in boreal North America and northern Eurasia. We find that the earlier reported contrast between trends of increasing tundra and decreasing boreal forest productivity has amplified in recent years, particularly in North America. Decreases in boreal forest productivity are most prominent in areas of denser tree cover and, particularly in Alaska, evergreen forest stands. On the North Slope of Alaska, however, increases in tundra productivity do not appear restricted to areas of higher shrub cover, which suggests enhanced productivity across functional vegetation types. Differences in the recovery of post-disturbance vegetation productivity between North America and Eurasia are described using burn chronosequences, and the potential factors driving regional differences are discussed.

Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: ecological variability and regional differences

International Nuclear Information System (INIS)

Beck, Pieter S A; Goetz, Scott J

2011-01-01

To assess ongoing changes in high latitude vegetation productivity we compared spatiotemporal patterns in remotely sensed vegetation productivity in the tundra and boreal zones of North America and Eurasia. We compared the long-term GIMMS (Global Inventory Modeling and Mapping Studies) NDVI (Normalized Difference Vegetation Index) to the more recent and advanced MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI data set, and mapped circumpolar trends in a gross productivity metric derived from the former. We then analyzed how temporal changes in productivity differed along an evergreen-deciduous gradient in boreal Alaska, along a shrub cover gradient in Arctic Alaska, and during succession after fire in boreal North America and northern Eurasia. We find that the earlier reported contrast between trends of increasing tundra and decreasing boreal forest productivity has amplified in recent years, particularly in North America. Decreases in boreal forest productivity are most prominent in areas of denser tree cover and, particularly in Alaska, evergreen forest stands. On the North Slope of Alaska, however, increases in tundra productivity do not appear restricted to areas of higher shrub cover, which suggests enhanced productivity across functional vegetation types. Differences in the recovery of post-disturbance vegetation productivity between North America and Eurasia are described using burn chronosequences, and the potential factors driving regional differences are discussed.

Seasonal relationship between normalized difference vegetation index and abundance of the Phlebotomus kala-azar vector in an endemic focus in Bihar, India

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Gouri S. Bhunia

2012-11-01

Full Text Available Remote sensing was applied for the collection of spatio-temporal data to increase our understanding of the potential distribution of the kala-azar vector Phlebotomus argentipes in endemic areas of the Vaishali district of Bihar, India. We produced monthly distribution maps of the normalized difference vegetation index (NDVI based on data from the thematic mapper (TM sensor onboard the Landsat-5 satellite. Minimum, maximum and mean NDVI values were computed for each month and compared with the concurrent incidence of kala-azar and the vector density. Maximum and mean NDVI values (R2 = 0.55 and R2 = 0.60, respectively, as well as the season likelihood ratio (X2 = 17.51; P <0.001, were found to be strongly associated with kala-azar, while the correlation with between minimum NDVI values and kala-azar was weak (R2 = 0.25. Additionally, a strong association was found between the mean and maximum NDVI values with seasonal vector abundance (R2 = 0.60 and R2 = 0.55, respectively but there was only a marginal association between minimum NDVI value and the spatial distribution of kala-azar vis-à-vis P. argentipes density.

Past and future effects of climate change on spatially heterogeneous vegetation activity in China

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Gao, Jiangbo; Jiao, Kewei; Wu, Shaohong; Ma, Danyang; Zhao, Dongsheng; Yin, Yunhe; Dai, Erfu

2017-07-01

Climate change is a major driver of vegetation activity but its complex ecological relationships impede research efforts. In this study, the spatial distribution and dynamic characteristics of climate change effects on vegetation activity in China from the 1980s to the 2010s and from 2021 to 2050 were investigated using a geographically weighted regression (GWR) model. The GWR model was based on combined datasets of satellite vegetation index, climate observation and projection, and future vegetation productivity simulation. Our results revealed that the significantly positive precipitation-vegetation relationship was and will be mostly distributed in North China. However, the regions with temperature-dominated distribution of vegetation activity were and will be mainly located in South China. Due to the varying climate features and vegetation cover, the spatial correlation between vegetation activity and climate change may be altered. There will be different dominant climatic factors for vegetation activity distribution in some regions such as Northwest China, and even opposite correlations in Northeast China. Additionally, the response of vegetation activity to precipitation will move southward in the next three decades. In contrast, although the high warming rate will restrain the vegetation activity, precipitation variability could modify hydrothermal conditions for vegetation activity. This observation is exemplified in the projected future enhancement of vegetation activity in the Tibetan Plateau and weakened vegetation activity in East and Middle China. Furthermore, the vegetation in most parts of North China may adapt to an arid environment, whereas in many southern areas, vegetation will be repressed by water shortage in the future.

Satellite time-series data for vegetation phenology detection and environmental assessment in Southeast Asia

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Suepa, Tanita

The relationship between temporal and spatial data is considered the major advantage of remote sensing in research related to biophysical characteristics. With temporally formatted remote sensing products, it is possible to monitor environmental changes as well as global climate change through time and space by analyzing vegetation phenology. Although a number of different methods have been developed to determine the seasonal cycle using time series of vegetation indices, these methods were not designed to explore and monitor changes and trends of vegetation phenology in Southeast Asia (SEA). SEA is adversely affected by impacts of climate change, which causes considerable environmental problems, and the increase in agricultural land conversion and intensification also adds to those problems. Consequently, exploring and monitoring phenological change and environmental impacts are necessary for a better understanding of the ecosystem dynamics and environmental change in this region. This research aimed to investigate inter-annual variability of vegetation phenology and rainfall seasonality, analyze the possible drivers of phenological changes from both climatic and anthropogenic factors, assess the environmental impacts in agricultural areas, and develop an enhanced visualization method for phenological information dissemination. In this research, spatio-temporal patterns of vegetation phenology were analyzed by using MODIS-EVI time series data over the period of 2001-2010. Rainfall seasonality was derived from TRMM daily rainfall rate. Additionally, this research assessed environmental impacts of GHG emissions by using the environmental model (DNDC) to quantify emissions from rice fields in Thailand. Furthermore, a web mapping application was developed to present the output of phenological and environmental analysis with interactive functions. The results revealed that satellite time-series data provided a great opportunity to study regional vegetation variability

Evaluating the quality of riparian forest vegetation: the Riparian Forest Evaluation (RFV index

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Fernando Magdaleno

2014-08-01

Full Text Available Aim of study: This paper presents a novel index, the Riparian Forest Evaluation (RFV index, for assessing the ecological condition of riparian forests. The status of riparian ecosystems has global importance due to the ecological and social benefits and services they provide. The initiation of the European Water Framework Directive (2000/60/CE requires the assessment of the hydromorphological quality of natural channels. The Directive describes riparian forests as one of the fundamental components that determine the structure of riverine areas. The RFV index was developed to meet the aim of the Directive and to complement the existing methodologies for the evaluation of riparian forests.Area of study: The RFV index was applied to a wide range of streams and rivers (170 water bodies inSpain.Materials and methods: The calculation of the RFV index is based on the assessment of both the spatial continuity of the forest (in its three core dimensions: longitudinal, transversal and vertical and the regeneration capacity of the forest, in a sampling area related to the river hydromorphological pattern. This index enables an evaluation of the quality and degree of alteration of riparian forests. In addition, it helps to determine the scenarios that are necessary to improve the status of riparian forests and to develop processes for restoring their structure and composition.Main results: The results were compared with some previous tools for the assessment of riparian vegetation. The RFV index got the highest average scores in the basins of northernSpain, which suffer lower human influence. The forests in central and southern rivers got worse scores. The bigger differences with other tools were found in complex and partially altered streams and rivers.Research highlights: The study showed the index’s applicability under diverse hydromorphological and ecological conditions and the main advantages of its application. The utilization of the index allows a

Estimating vegetation vulnerability to detect areas prone to land degradation in the Mediterranean basin

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Imbrenda, Vito; Coluzzi, Rosa; D'Emilio, Mariagrazia; Lanfredi, Maria; Simoniello, Tiziana

2013-04-01

Vegetation is one of the key components to study land degradation vulnerability because of the complex interactions and feedbacks that link it to soil. In the Mediterranean region, degradation phenomena are due to a mix of predisposing factors (thin soil horizons, low soil organic matter, increasing aridity, etc.) and bad management practices (overgrazing, deforestation, intensification of agriculture, tourism development). In particular, in areas threatened by degradation processes but still covered by vegetation, large scale soil condition evaluation is a hard task and the detection of stressed vegetation can be useful to identify on-going soil degradation phenomena and to reduce their impacts through interventions for recovery/rehabilitation. In this context the use of satellite time series can increase the efficacy and completeness of the land degradation assessment, providing precious information to understand vegetation dynamics. In order to estimate vulnerability levels in Basilicata (a Mediterranean region of Southern Italy) in the framework of PRO-LAND project (PO-FESR Basilicata 2007-2013), we crossed information on potential vegetation vulnerability with information on photosynthetic activity dynamics. Potential vegetation vulnerability represents the vulnerability related to the type of present cover in terms of fire risk, erosion protection, drought resistance and plant cover distribution. It was derived from an updated land cover map by separately analyzing each factor, and then by combining them to obtain concise information on the possible degradation exposure. The analysis of photosynthetic activity dynamics provides information on the status of vegetation, that is fundamental to discriminate the different vulnerability levels within the same land cover, i.e. the same potential vulnerability. For such a purpose, we analyzed a time series (2000-2010) of a satellite vegetation index (MODIS NDVI) with 250m resolution, available as 16-day composite

Mid-term fire danger index based on satellite imagery and ancillary geographic data

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Stefanidou, A.; Dragozi, E.; Tompoulidou, M.; Stepanidou, L.; Grigoriadis, D.; Katagis, T.; Stavrakoudis, D.; Gitas, I.

2017-09-01

Fire danger forecast constitutes one of the most important components of integrated fire management since it provides crucial information for efficient pre-fire planning, alertness and timely response to a possible fire event. The aim of this work is to develop an index that has the capability of predicting accurately fire danger on a mid-term basis. The methodology that is currently under development is based on an innovative approach that employs dry fuel spatial connectivity as well as biophysical and topological variables for the reliable prediction of fire danger. More specifically, the estimation of the dry fuel connectivity is based on a previously proposed automated procedure implemented in R software that uses Moderate Resolution Imaging Spectrometer (MODIS) time series data. Dry fuel connectivity estimates are then combined with other ancillary data such as fuel type and proximity to roads in order to result in the generation of the proposed mid-term fire danger index. The innovation of the proposed index—which will be evaluated by comparison to historical fire data—lies in the fact that its calculation is almost solely affected by the availability of satellite data. Finally, it should be noted that the index is developed within the framework of the National Observatory of Forest Fires (NOFFi) project.

Long-term change analysis of satellite-based evapotranspiration over Indian vegetated surface

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Gupta, Shweta; Bhattacharya, Bimal K.; Krishna, Akhouri P.

2016-05-01

In the present study, trend of satellite based annual evapotranspiration (ET) and natural forcing factors responsible for this were analyzed. Thirty years (1981-2010) of ET data at 0.08° grid resolution, generated over Indian region from opticalthermal observations from NOAA PAL and MODIS AQUA satellites, were used. Long-term data on gridded (0.5° x 0.5°) annual rainfall (RF), annual mean surface soil moisture (SSM) ERS scatterometer at 25 km resolution and annual mean incoming shortwave radiation from MERRA-2D reanalysis were also analyzed. Mann-Kendall tests were performed with time series data for trend analysis. Mean annual ET loss from Indian ago-ecosystem was found to be almost double (1100 Cubic Km) than Indian forest ecosystem (550 Cubic Km). Rainfed vegetation systems such as forest, rainfed cropland, grassland showed declining ET trend @ - 4.8, -0.6 &-0.4 Cubic Kmyr-1, respectively during 30 years. Irrigated cropland initially showed ET decline upto 1995 @ -0.8 cubic Kmyr-1 which could possibly be due to solar dimming followed by increasing ET @ 0.9 cubic Kmyr-1 after 1995. A cross-over point was detected between forest ET decline and ET increase in irrigated cropland during 2008. During 2001-2010, the four agriculturally important Indian states eastern, central, western and southern showed significantly increasing ET trend with S-score of 15-25 and Z-score of 1.09-2.9. Increasing ET in western and southern states was found to be coupled with increase in annual rainfall and SSM. But in eastern and central states no significant trend in rainfall was observed though significant increase in ET was noticed. The study recommended to investigate the influence of anthropogenic factors such as increase in area under irrigation, increased use of water for irrigation through ground water pumping, change in cropping pattern and cultivars on increasing ET.

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

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

2018-01-01

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

A morphometric analysis of vegetation patterns in dryland ecosystems

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Mander, Luke; Dekker, Stefan C.; Li, Mao; Mio, Washington; Punyasena, Surangi W.; Lenton, Timothy M.

2017-02-01

Vegetation in dryland ecosystems often forms remarkable spatial patterns. These range from regular bands of vegetation alternating with bare ground, to vegetated spots and labyrinths, to regular gaps of bare ground within an otherwise continuous expanse of vegetation. It has been suggested that spotted vegetation patterns could indicate that collapse into a bare ground state is imminent, and the morphology of spatial vegetation patterns, therefore, represents a potentially valuable source of information on the proximity of regime shifts in dryland ecosystems. In this paper, we have developed quantitative methods to characterize the morphology of spatial patterns in dryland vegetation. Our approach is based on algorithmic techniques that have been used to classify pollen grains on the basis of textural patterning, and involves constructing feature vectors to quantify the shapes formed by vegetation patterns. We have analysed images of patterned vegetation produced by a computational model and a small set of satellite images from South Kordofan (South Sudan), which illustrates that our methods are applicable to both simulated and real-world data. Our approach provides a means of quantifying patterns that are frequently described using qualitative terminology, and could be used to classify vegetation patterns in large-scale satellite surveys of dryland ecosystems.

Detecting plague-host abundance from space: Using a spectral vegetation index to identify occupancy of great gerbil burrows

Science.gov (United States)

Wilschut, Liesbeth I.; Heesterbeek, Johan A. P.; Begon, Mike; de Jong, Steven M.; Ageyev, Vladimir; Laudisoit, Anne; Addink, Elisabeth A.

2018-02-01

In Kazakhstan, plague outbreaks occur when its main host, the great gerbil, exceeds an abundance threshold. These live in family groups in burrows, which can be mapped using remote sensing. Occupancy (percentage of burrows occupied) is a good proxy for abundance and hence the possibility of an outbreak. Here we use time series of satellite images to estimate occupancy remotely. In April and September 2013, 872 burrows were identified in the field as either occupied or empty. For satellite images acquired between April and August, 'burrow objects' were identified and matched to the field burrows. The burrow objects were represented by 25 different polygon types, then classified (using a majority vote from 10 Random Forests) as occupied or empty, using Normalized Difference Vegetation Indices (NDVI) calculated for all images. Throughout the season NDVI values were higher for empty than for occupied burrows. Occupancy status of individual burrows that were continuously occupied or empty, was classified with producer's and user's accuracy values of 63 and 64% for the optimum polygon. Occupancy level was predicted very well and differed 2% from the observed occupancy. This establishes firmly the principle that occupancy can be estimated using satellite images with the potential to predict plague outbreaks over extensive areas with much greater ease and accuracy than previously.

Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions

Directory of Open Access Journals (Sweden)

Thomas P. Higginbottom

2014-10-01

Full Text Available Land degradation and desertification has been ranked as a major environmental and social issue for the coming decades. Thus, the observation and early detection of degradation is a primary objective for a number of scientific and policy organisations, with remote sensing methods being a candidate choice for the development of monitoring systems. This paper reviews the statistical and ecological frameworks of assessing land degradation and desertification using vegetation index data. The development of multi-temporal analysis as a desertification assessment technique is reviewed, with a focus on how current practice has been shaped by controversy and dispute within the literature. The statistical techniques commonly employed are examined from both a statistical as well as ecological point of view, and recommendations are made for future research directions. The scientific requirements for degradation and desertification monitoring systems identified here are: (I the validation of methodologies in a robust and comparable manner; and (II the detection of degradation at minor intensities and magnitudes. It is also established that the multi-temporal analysis of vegetation index data can provide a sophisticated measure of ecosystem health and variation, and that, over the last 30 years, considerable progress has been made in the respective research.

Normalized difference vegetation index (ndvi) analysis for land cover types using landsat 8 oli in besitang watershed, Indonesia

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Zaitunah, A.; Samsuri; Ahmad, A. G.; Safitri, R. A.

2018-03-01

Watershed is an ecosystem area confined by topography and has function as a catcher, storage, and supplier of water, sediments, pollutants and nutrients in the river system and exit through a single outlet. Various activities around watershed areas of Besitang have changed the land cover and vegetation index (NDVI) that exist in the region. In order to detect changes in land cover and NDVI quickly and accurately, we used remote sensing technology and geographic information systems (GIS). The study aimed to assess changes in land cover and vegetation density (NDVI) between 2005 and 2015, as well as obtaining the density of vegetation (NDVI) on each of the land cover of 2005 and 2015. The research showed the extensive of forest area of 949.65 Ha and a decline of mangrove forest area covering an area of 2,884.06 Ha. The highest vegetation density reduced 39,714.58 Ha, and rather dense increased 24,410.72 Ha between 2005 and 2015. The land cover that have the highest NDVI value range with very dense vegetation density class is the primary dry forest (0.804 to 0.876), followed by secondary dry forest (0.737 to 0.804) for 2015. In 2015 the land cover has NDVI value range the primary dry forest (0.513 to 0.57), then secondary dry forest (0.456 to 0.513) with dense vegetation density class

Monitoring of vegetation dynamics and assessing vegetation response to drought in the Iberian Peninsula

Energy Technology Data Exchange (ETDEWEB)

Garcia-Haro, F. J.; Moreno, A.; Perez-Hoyos, A.; Gilabert, M. A.; Melia, J.; Belda, F.; Poquet, D.; Martinez, B.; Verger, A.

2009-07-01

Monitoring the vegetation activity over long time-scales is necessary to discern ecosystem response to climate variability. Spatial and temporally consistent estimates of the biophysical variables such as fractional vegetation cover (FVC) and leaf area index (LAI) have been obtained in the context of DULCINEA Project. We used long-term monthly climate statistics to build simple climatic indices (SPI, moisture index) at different time scales. From these indices, we estimated that the climatic disturbances affected both the growing season and the total amount of vegetation. This implies that the anomaly of vegetation cover is a good indicator of moisture condition and can be an important data source when used for detecting an monitoring drought in the Iberian Peninsula. The impact of climate variability on the vegetation dynamics has shown not to be the same for every region. We concluded that the relationships between vegetation anomaly and moisture availability are significant for the arid and semiarid areas. (Author) 6 refs.

Monitoring of vegetation dynamics and assessing vegetation response to drought in the Iberian Peninsula

International Nuclear Information System (INIS)

Garcia-Haro, F. J.; Moreno, A.; Perez-Hoyos, A.; Gilabert, M. A.; Melia, J.; Belda, F.; Poquet, D.; Martinez, B.; Verger, A.

2009-01-01

Monitoring the vegetation activity over long time-scales is necessary to discern ecosystem response to climate variability. Spatial and temporally consistent estimates of the biophysical variables such as fractional vegetation cover (FVC) and leaf area index (LAI) have been obtained in the context of DULCINEA Project. We used long-term monthly climate statistics to build simple climatic indices (SPI, moisture index) at different time scales. From these indices, we estimated that the climatic disturbances affected both the growing season and the total amount of vegetation. This implies that the anomaly of vegetation cover is a good indicator of moisture condition and can be an important data source when used for detecting an monitoring drought in the Iberian Peninsula. The impact of climate variability on the vegetation dynamics has shown not to be the same for every region. We concluded that the relationships between vegetation anomaly and moisture availability are significant for the arid and semiarid areas. (Author) 6 refs.

Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area

Science.gov (United States)

Norman, Laura M.; Middleton, Barry R.; Wilson, Natalie R.

2018-01-01

Mapping of vegetation types is of great importance to the San Carlos Apache Tribe and their management of forestry and fire fuels. Various remote sensing techniques were applied to classify multitemporal Landsat 8 satellite data, vegetation index, and digital elevation model data. A multitiered unsupervised classification generated over 900 classes that were then recoded to one of the 16 generalized vegetation/land cover classes using the Southwest Regional Gap Analysis Project (SWReGAP) map as a guide. A supervised classification was also run using field data collected in the SWReGAP project and our field campaign. Field data were gathered and accuracy assessments were generated to compare outputs. Our hypothesis was that a resulting map would update and potentially improve upon the vegetation/land cover class distributions of the older SWReGAP map over the 24,000  km2 study area. The estimated overall accuracies ranged between 43% and 75%, depending on which method and field dataset were used. The findings demonstrate the complexity of vegetation mapping, the importance of recent, high-quality-field data, and the potential for misleading results when insufficient field data are collected.

Mapping Aquatic Vegetation in a Large, Shallow Eutrophic Lake: A Frequency-Based Approach Using Multiple Years of MODIS Data

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Xiaohan Liu

2015-08-01

Full Text Available Aquatic vegetation serves many important ecological and socioeconomic functions in lake ecosystems. The presence of floating algae poses difficulties for accurately estimating the distribution of aquatic vegetation in eutrophic lakes. We present an approach to map the distribution of aquatic vegetation in Lake Taihu (a large, shallow eutrophic lake in China and reduce the influence of floating algae on aquatic vegetation mapping. Our approach involved a frequency analysis over a 2003–2013 time series of the floating algal index (FAI based on moderate-resolution imaging spectroradiometer (MODIS data. Three phenological periods were defined based on the vegetation presence frequency (VPF and the growth of algae and aquatic vegetation: December and January composed the period of wintering aquatic vegetation; February and March composed the period of prolonged coexistence of algal blooms and wintering aquatic vegetation; and June to October was the peak period of the coexistence of algal blooms and aquatic vegetation. By comparing and analyzing the satellite-derived aquatic vegetation distribution and 244 in situ measurements made in 2013, we established a FAI threshold of −0.025 and VPF thresholds of 0.55, 0.45 and 0.85 for the three phenological periods. We validated the accuracy of our approach by comparing the results between the satellite-derived maps and the in situ results obtained from 2008–2012. The overall classification accuracy was 87%, 81%, 77%, 88% and 73% in the five years from 2008–2012, respectively. We then applied the approach to the MODIS images from 2003–2013 and obtained the total area of the aquatic vegetation, which varied from 265.94 km2 in 2007 to 503.38 km2 in 2008, with an average area of 359.62 ± 69.20 km2 over the 11 years. Our findings suggest that (1 the proposed approach can be used to map the distribution of aquatic vegetation in eutrophic algae-rich waters and (2 dramatic changes occurred in the

Review: advances in in situ and satellite phenological observations in Japan

Science.gov (United States)

Nagai, Shin; Nasahara, Kenlo Nishida; Inoue, Tomoharu; Saitoh, Taku M.; Suzuki, Rikie

2016-04-01

To accurately evaluate the responses of spatial and temporal variation of ecosystem functioning (evapotranspiration and photosynthesis) and services (regulating and cultural services) to the rapid changes caused by global warming, we depend on long-term, continuous, near-surface, and satellite remote sensing of phenology over wide areas. Here, we review such phenological studies in Japan and discuss our current knowledge, problems, and future developments. In contrast with North America and Europe, Japan has been able to evaluate plant phenology along vertical and horizontal gradients within a narrow area because of the country's high topographic relief. Phenological observation networks that support scientific studies and outreach activities have used near-surface tools such as digital cameras and spectral radiometers. Differences in phenology among ecosystems and tree species have been detected by analyzing the seasonal variation of red, green, and blue digital numbers (RGB values) extracted from phenological images, as well as spectral reflectance and vegetation indices. The relationships between seasonal variations in RGB-derived indices or spectral characteristics and the ecological and CO2 flux measurement data have been well validated. In contrast, insufficient satellite remote-sensing observations have been conducted because of the coarse spatial resolution of previous datasets, which could not detect the heterogeneous plant phenology that results from Japan's complex topography and vegetation. To improve Japanese phenological observations, multidisciplinary analysis and evaluation will be needed to link traditional phenological observations with "index trees," near-surface and satellite remote-sensing observations, "citizen science" (observations by citizens), and results published on the Internet.

South Florida Everglades: satellite image map

Science.gov (United States)

Jones, John W.; Thomas, Jean-Claude; Desmond, G.B.

2001-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program (http://access.usgs.gov/) with support from the Everglades National Park (http://www.nps.gov/ever/). The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

Multifractal Downscaling of Rainfall Using Normalized Difference Vegetation Index (NDVI) in the Andes Plateau.

Science.gov (United States)

Duffaut Espinosa, L A; Posadas, A N; Carbajal, M; Quiroz, R

2017-01-01

In this paper, a multifractal downscaling technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This downscaling procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 and 11 days, and the desired final scale is 1 day. The spatial resolution of approximately 1 km remains the same throughout the downscaling process. The results were validated against on-site measurements of meteorological stations distributed in the area under study.

Dynamic Response of Satellite-Derived Vegetation Growth to Climate Change in the Three North Shelter Forest Region in China

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Bin He

2015-08-01

Full Text Available Since the late 1970s, the Chinese government has initiated ecological restoration programs in the Three North Shelter Forest System Project (TNSFSP area. Whether accelerated climate change will help or hinder these efforts is still poorly understood. Using the updated and extended AVHRR NDVI3g dataset from 1982 to 2011 and corresponding climatic data, we investigated vegetation variations in response to climate change. The results showed that the overall state of vegetation in the study region has improved over the past three decades. Vegetation cover significantly decreased in 23.1% and significantly increased in 21.8% of the study area. An increase in all three main vegetation types (forest, grassland, and cropland was observed, but the trend was only statistically significant in cropland. In addition, bare and sparsely vegetated areas, mainly located in the western part of the study area, have significantly expanded since the early 2000s. A moisture condition analysis indicated that the study area experienced significant climate variations, with warm-wet conditions in the western region and warm-dry conditions in the eastern region. Correlation analysis showed that variations in the Normalized Difference Vegetation Index (NDVI were positively correlated with precipitation and negatively correlated with temperature. Ultimately, climate change influenced vegetation growth by controlling the availability of soil moisture. Further investigation suggested that the positive impacts of precipitation on NDVI have weakened in the study region, whereas the negative impacts from temperature have been enhanced in the eastern study area. However, over recent years, the negative temperature impacts have been converted to positive impacts in the western region. Considering the variations in the relationship between NDVI and climatic variables, the warm–dry climate in the eastern region is likely harmful to vegetation growth, whereas the warm�

Development of new index for forest fire risk using satellite images in Indonesia through the direct spectral measurements of soil

Science.gov (United States)

Hashimoto, A.; Akita, M.; Takahashi, Y.; Suzuki, H.; Hasegawa, Y.; Ogino, Y.; Naruse, N.; Takahashi, Y.

2016-12-01

In recent years, the smoke caused by the forest fires in Indonesia has become a serious problem. Most of the land in Indonesia is covered with peat moss, which occurs the expanding of fires due to the burning itself. Thus, the surface soil water, reflecting the amount of precipitation in the area, can become the indication of the risk of fires. This study aims to develop a new index reflecting the risk of forest fires in Indonesia using satellite remote sensing through the direct spectral measurements of peat moss soil.We have prepared the peat moss in 7 steps of soil water content measured at an accuracy of ±15 percent (Field pro, WD-3). We obtained spectra between 400nm and 1050nm (Source: halogen lamp, spectroscope: self-made space time, spectral analysis kit) from the peat moss.The obtained spectra show the difference from the previous spectral measurement for the soil in various water content. There are the features, especially, in the wavelength range of ultraviolet (400-450nm) and infrared (530-800nm) as shown in the figure; the more the soil water increases, the lower the reflectance becomes. We have developed a new index using the New deep blue band (433 453nm and NIR band 845 885nm of Landsat 8. The resulting satellite images calculated by our original index appears to reflect the risk of forest fires rather than well-known indices such as Normalized Difference Water Index and Normalized difference Soil Index.In conclusion, we have created a new index that highly reflects to the degree of soil water of a peat soil in Indonesia.

Statistical modeling of phenological phases in Poland based on coupling satellite derived products and gridded meteorological data

Science.gov (United States)

Czernecki, Bartosz; Jabłońska, Katarzyna; Nowosad, Jakub

2016-04-01

The aim of the study was to create and evaluate different statistical models for reconstructing and predicting selected phenological phases. This issue is of particular importance in Poland where national-wide phenological monitoring was abandoned in the middle of 1990s and the reactivated network was established in 2006. Authors decided to evaluate possibilities of using a wide-range of statistical modeling techniques to create synthetic archive dataset. Additionally, a robust tool for predicting the most distinguishable phenophases using only free of charge data as predictors was created. Study period covers the years 2007-2014 and contains only quality-controlled dataset of 10 species and 14 phenophases. Phenological data used in this study originates from the manual observations network run by the Institute of Meteorology and Water Management - National Research Institute (IMGW-PIB). Three kind of data sources were used as predictors: (i) satellite derived products, (ii) preprocessed gridded meteorological data, and (iii) spatial properties (longitude, latitude, altitude) of the monitoring site. Moderate-Resolution Imaging Spectroradiometer (MODIS) level-3 vegetation products were used for detecting onset dates of particular phenophases. Following indices were used: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (fPAR). Additionally, Interactive Multisensor Snow and Ice Mapping System (IMS) products were chosen to detect occurrence of snow cover. Due to highly noisy data, authors decided to take into account pixel reliability information. Besides satellite derived products (NDVI, EVI, FPAR, LAI, Snow cover), a wide group of observational data and agrometeorological indices derived from the European Climate Assessment & Dataset (ECA&D) were used as a potential predictors: cumulative growing degree days (GDD), cumulative growing precipitation days (GPD

The greening of the McGill Paleoclimate Model. Part I: Improved land surface scheme with vegetation dynamics

Energy Technology Data Exchange (ETDEWEB)

Wang, Yi; Mysak, Lawrence A.; Wang, Zhaomin [McGill University, Department of Atmospheric and Oceanic Sciences, Global Environmental and Climate Change Centre (GEC3), Montreal, QC (Canada); Brovkin, Victor [Potsdam Institute for Climate Impact Research (PIK), Potsdam (Germany)

2005-04-01

The formulation of a new land surface scheme (LSS) with vegetation dynamics for coupling to the McGill Paleoclimate Model (MPM) is presented. This LSS has the following notable improvements over the old version: (1) parameterization of deciduous and evergreen trees by using the model's climatology and the output of the dynamic global vegetation model, VECODE (Brovkin et al. in Ecological Modelling 101:251-261 (1997), Global Biogeochemical Cycles 16(4):1139, (2002)); (2) parameterization of tree leaf budburst and leaf drop by using the model's climatology; (3) parameterization of the seasonal cycle of the grass leaf area index; (4) parameterization of the seasonal cycle of tree leaf area index by using the time-dependent growth of the leaves; (5) calculation of land surface albedo by using vegetation-related parameters, snow depth and the model's climatology. The results show considerable improvement of the model's simulation of the present-day climate as compared with that simulated in the original physically-based MPM. In particular, the strong seasonality of terrestrial vegetation and the associated land surface albedo variations are in good agreement with several satellite observations of these quantities. The application of this new version of the MPM (the ''green'' MPM) to Holocene millennial-scale climate changes is described in a companion paper, Part II. (orig.)

Mapping canopy gap fraction and leaf area index at continent-scale from satellite lidar

Science.gov (United States)

Mahoney, C.; Hopkinson, C.; Held, A. A.

2015-12-01

Information on canopy cover is essential for understanding spatial and temporal variability in vegetation biomass, local meteorological processes and hydrological transfers within vegetated environments. Gap fraction (GF), an index of canopy cover, is often derived over large areas (100's km2) via airborne laser scanning (ALS), estimates of which are reasonably well understood. However, obtaining country-wide estimates is challenging due to the lack of spatially distributed point cloud data. The Geoscience Laser Altimeter System (GLAS) removes spatial limitations, however, its large footprint nature and continuous waveform data measurements make derivations of GF challenging. ALS data from 3 Australian sites are used as a basis to scale-up GF estimates to GLAS footprint data by the use of a physically-based Weibull function. Spaceborne estimates of GF are employed in conjunction with supplementary predictor variables in the predictive Random Forest algorithm to yield country-wide estimates at a 250 m spatial resolution; country-wide estimates are accompanied with uncertainties at the pixel level. Preliminary estimates of effective Leaf Area Index (eLAI) are also presented by converting GF via the Beer-Lambert law, where an extinction coefficient of 0.5 is employed; deemed acceptable at such spatial scales. The need for such wide-scale quantification of GF and eLAI are key in the assessment and modification of current forest management strategies across Australia. Such work also assists Australia's Terrestrial Ecosystem Research Network (TERN), a key asset to policy makers with regards to the management of the national ecosystem, in fulfilling their government issued mandates.

Effects of Warming Hiatuses on Vegetation Growth in the Northern Hemisphere

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Hong Wei

2018-04-01

Full Text Available There have been hiatuses in global warming since the 1990s, and their potential impacts have attracted extensive attention and discussion. Changes in temperature not only directly affect the greening of vegetation but can also indirectly alter both the growth state and the growth tendency of vegetation by altering other climatic elements. The middle-high latitudes of the Northern Hemisphere (NH constitute the region that has experienced the most warming in recent decades; therefore, identifying the effects of warming hiatuses on the vegetation greening in that region is of great importance. Using satellite-derived Normalized Difference Vegetation Index (NDVI data and climatological observation data from 1982–2013, we investigated hiatuses in warming trends and their impact on vegetation greenness in the NH. Our results show that the regions with warming hiatuses in the NH accounted for 50.1% of the total area and were concentrated in Mongolia, central China, and other areas. Among these regions, 18.8% of the vegetation greenness was inhibited in the warming hiatus areas, but 31.3% of the vegetation grew faster. Because temperature was the main positive climatic factor in central China, the warming hiatuses caused the slow vegetation greening rate. However, precipitation was the main positive climatic factor affecting vegetation greenness in Mongolia; an increase in precipitation accelerated vegetation greening. The regions without a warming hiatus, which were mainly distributed in northern Russia, northern central Asia, and other areas, accounted for 49.9% of the total area. Among these regions, 21.4% of the vegetation grew faster over time, but 28.5% of the vegetation was inhibited. Temperature was the main positive factor affecting vegetation greenness in northern Russia; an increase in temperature promoted vegetation greening. However, radiation was the main positive climatic factor in northern central Asia; reductions in radiation

Application of a regularized model inversion system (REGFLEC) to multi-temporal RapidEye imagery for retrieving vegetation characteristics

KAUST Repository

Houborg, Rasmus

2015-10-14

Accurate retrieval of canopy biophysical and leaf biochemical constituents from space observations is critical to diagnosing the functioning and condition of vegetation canopies across spatio-temporal scales. Retrieved vegetation characteristics may serve as important inputs to precision farming applications and as constraints in spatially and temporally distributed model simulations of water and carbon exchange processes. However significant challenges remain in the translation of composite remote sensing signals into useful biochemical, physiological or structural quantities and treatment of confounding factors in spectrum-trait relations. Bands in the red-edge spectrum have particular potential for improving the robustness of retrieved vegetation properties. The development of observationally based vegetation retrieval capacities, effectively constrained by the enhanced information content afforded by bands in the red-edge, is a needed investment towards optimizing the benefit of current and future satellite sensor systems. In this study, a REGularized canopy reFLECtance model (REGFLEC) for joint leaf chlorophyll (Chll) and leaf area index (LAI) retrieval is extended to sensor systems with a band in the red-edge region for the first time. Application to time-series of 5 m resolution multi-spectral RapidEye data is demonstrated over an irrigated agricultural region in central Saudi Arabia, showcasing the value of satellite-derived crop information at this fine scale for precision management. Validation against in-situ measurements in fields of alfalfa, Rhodes grass, carrot and maize indicate improved accuracy of retrieved vegetation properties when exploiting red-edge information in the model inversion process. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Evaluation and attribution of vegetation contribution to seasonal climate predictability

Science.gov (United States)

Catalano, Franco; Alessandri, Andrea; De Felice, Matteo

2015-04-01

The land surface model of EC-Earth has been modified to include dependence of vegetation densities on the Leaf Area Index (LAI), based on the Lambert-Beer formulation. Effective vegetation fractional coverage can now vary at seasonal and interannual time-scales and therefore affect biophysical parameters such as the surface roughness, albedo and soil field capacity. The modified model is used to perform a real predictability seasonal hindcast experiment. LAI is prescribed using a recent observational dataset based on the third generation GIMMS and MODIS satellite data. Hindcast setup is: 7 months forecast length, 2 start dates (1st May and 1st November), 10 members, 28 years (1982-2009). The effect of the realistic LAI prescribed from observation is evaluated with respect to a control experiment where LAI does not vary. Hindcast results demonstrate that a realistic representation of vegetation significantly improves the forecasts of temperature and precipitation. The sensitivity is particularly large for temperature during boreal winter over central North America and Central Asia. This may be attributed in particular to the effect of the high vegetation component on the snow cover. Summer forecasts are improved in particular for precipitation over Europe, Sahel, North America, West Russia and Nordeste. Correlation improvements depends on the links between targets (temperature and precipitation) and drivers (surface heat fluxes, albedo, soil moisture, evapotranspiration, moisture divergence) which varies from region to region.

Understanding the Seasonal Greenness Trends and Controls in South Asia Using Satellite Based Observations

Science.gov (United States)

Sarmah, S.; Jia, G.; Zhang, A.; Singha, M.

2017-12-01

South Asia (SA) is one of the most remarkable regions in changing vegetation greenness along with its major expansion of agricultural activity, especially irrigated farming. However, SA is predicted to be a vulnerable agricultural regions to future climate changes. The influence of monsoon climate on the seasonal trends and anomalies of vegetation greenness are not well understood in the region which can provide valuable information about climate-ecosystem interaction. This study analyzed the spatio-temporal patterns of seasonal vegetation trends and variability using satellite vegetation indices (VI) including AVHRR Normalized Difference Vegetation Index (NDVI) (1982-2013) and MODIS Enhanced Vegetation Index (EVI) (2000-2013) in summer monsoon (SM) (June-Sept) and winter monsoon (WM) (Dec-Apr) seasons among irrigated cropland (IC), rainfed cropland (RC) and natural vegetation (NV). Seasonal VI variations with climatic factors (precipitation and temperature) and LULC changes have been investigated to identify the forcings behind the vegetation trends and variability. We found that major greening occurred in the last three decades due to the increase in IC productivity noticeably in WM, however, recent (2000-2013) greening trends were lower than the previous decades (1982-1999) in both the IC and RC indicating the stresses on them. The browning trends, mainly concentrated in NV areas were prominent during WM and rigorous since 2000, confirmed from the moderate resolution EVI and LULC datasets. Winter time maximal temperature had been increasing tremendously whereas precipitation trend was not significant over SA. Both the climate variability and LULC changes had integrated effects on the vegetation changes in NV areas specifically in hilly regions. However, LULC impact was intensified since 2000, mostly in north east India. This study also revealed a distinct seasonal variation in spatial distribution of correlation between VI's and climate anomalies over SA

The response of Arctic vegetation to the summer climate: relation between shrub cover, NDVI, surface albedo and temperature

Energy Technology Data Exchange (ETDEWEB)

Blok, Daan; Heijmans, Monique M P D; Berendse, Frank [Nature Conservation and Plant Ecology Group, Wageningen University, PO Box 47, 6700 AA, Wageningen (Netherlands); Schaepman-Strub, Gabriela [Institute of Evolutionary Biology and Environmental Studies, University of Zuerich, Winterthurerstrasse 190, 8057 Zuerich (Switzerland); Bartholomeus, Harm [Centre for Geo-Information, Wageningen University, PO Box 47, 6700 AA, Wageningen (Netherlands); Maximov, Trofim C, E-mail: daan.blok@wur.nl [Biological Problems of the Cryolithozone, Russian Academy of Sciences, Siberian Division, 41, Lenin Prospekt, Yakutsk, The Republic of Sakha, Yakutia 677980 (Russian Federation)

2011-07-15

Recently observed Arctic greening trends from normalized difference vegetation index (NDVI) data suggest that shrub growth is increasing in response to increasing summer temperature. An increase in shrub cover is expected to decrease summer albedo and thus positively feed back to climate warming. However, it is unknown how albedo and NDVI are affected by shrub cover and inter-annual variations in the summer climate. Here, we examine the relationship between deciduous shrub fractional cover, NDVI and albedo using field data collected at a tundra site in NE Siberia. Field data showed that NDVI increased and albedo decreased with increasing deciduous shrub cover. We then selected four Arctic tundra study areas and compiled annual growing season maximum NDVI and minimum albedo maps from MODIS satellite data (2000-10) and related these satellite products to tundra vegetation types (shrub, graminoid, barren and wetland tundra) and regional summer temperature. We observed that maximum NDVI was greatest in shrub tundra and that inter-annual variation was negatively related to summer minimum albedo but showed no consistent relationship with summer temperature. Shrub tundra showed higher albedo than wetland and barren tundra in all four study areas. These results suggest that a northwards shift of shrub tundra might not lead to a decrease in summer minimum albedo during the snow-free season when replacing wetland tundra. A fully integrative study is however needed to link results from satellite data with in situ observations across the Arctic to test the effect of increasing shrub cover on summer albedo in different tundra vegetation types.

The response of Arctic vegetation to the summer climate: relation between shrub cover, NDVI, surface albedo and temperature

International Nuclear Information System (INIS)

Blok, Daan; Heijmans, Monique M P D; Berendse, Frank; Schaepman-Strub, Gabriela; Bartholomeus, Harm; Maximov, Trofim C

2011-01-01

Recently observed Arctic greening trends from normalized difference vegetation index (NDVI) data suggest that shrub growth is increasing in response to increasing summer temperature. An increase in shrub cover is expected to decrease summer albedo and thus positively feed back to climate warming. However, it is unknown how albedo and NDVI are affected by shrub cover and inter-annual variations in the summer climate. Here, we examine the relationship between deciduous shrub fractional cover, NDVI and albedo using field data collected at a tundra site in NE Siberia. Field data showed that NDVI increased and albedo decreased with increasing deciduous shrub cover. We then selected four Arctic tundra study areas and compiled annual growing season maximum NDVI and minimum albedo maps from MODIS satellite data (2000-10) and related these satellite products to tundra vegetation types (shrub, graminoid, barren and wetland tundra) and regional summer temperature. We observed that maximum NDVI was greatest in shrub tundra and that inter-annual variation was negatively related to summer minimum albedo but showed no consistent relationship with summer temperature. Shrub tundra showed higher albedo than wetland and barren tundra in all four study areas. These results suggest that a northwards shift of shrub tundra might not lead to a decrease in summer minimum albedo during the snow-free season when replacing wetland tundra. A fully integrative study is however needed to link results from satellite data with in situ observations across the Arctic to test the effect of increasing shrub cover on summer albedo in different tundra vegetation types.

Monitoring of Conservation Tillage and Tillage Intensity by Ground and Satellite Imagery

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M.A Rostami

2014-09-01

Full Text Available Local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. The current methods for assessing crop residue coverage and tillage intensity such as residue weighing methods, line-transect and photo comparison methods are tedious and time-consuming. The present study was devoted to investigate accurate methods for monitoring residue management and tillage practices. The satellite imagery technique was used as a rapid and spatially explicit method for delineating crop residue coverage and as an estimator of conservation tillage adoption and intensity. The potential of multispectral high-spatial resolution WorldView-2 local data was evaluated using the total of eleven satellite spectral indices and Linear Spectral Unmixing Analysis (LSUA. The total of ninety locations was selected for this study and for each location the residue coverage was measured by the image processing method and recorded as ground control. The output of indices and LSUA method were individually correlated to the control and the relevant R2 was calculated. Results indicated that crop residue cover was related to IPVI, RVI1, RVI2 and GNDVI spectral indices and satisfactory correlations were established (0.74 - 0.81. The crop residue coverage estimated from the LSUA approach was found to be correlated with the ground residue data (0.75. Two effective indices named as Infrared Percentage Vegetation Index (IPVI and Ratio Vegetation Index (RVI with maximum R2 were considered for classification of tillage intensity. Results indicated that the classification accuracy with IPVI and RVI indices in different conditions varied from 78-100 percent and therefore in good agreement with ground measurement, observations and field records.

CROP SPECIES RECOGNITION AND DISCRIMINATION PADDY-RICE-GROWINGFIELDS FROM REAPED-FIELDS BY THE RADAR VEGETATION INDEX (RVI OF ALOS-2/PALSAR2

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Y. Yamada

2016-06-01

Full Text Available The Japanese ALOS-2 satellite was launched on May 24th, 2014. It has the L-band SAR, PALSAR-2. Kim,Y. and van Zyl, J.J. proposed a kind of Radar Vegetation Index (RVI as RVI = 8 * σ0hv / (σ0hh + σ0vv + 2* σ0hv by L-band full-polarimetric radar data. Kim, Y. and Jackson, T.J., et al. applied the equation into rice and soybean by multi-frequency polarimetric scatterometer above 4.16 meters from the ground. Their report showed the L-band was the most promising wave length for estimating LAI and NDVI from RVI. The author tried to apply the analysis to the actual paddy field areas, both Inashiki region and Miyagi region in the eastern main island, “Honshu”, areas of Japan by ALOS-2/PALSAR-2 full-polarimetry data in the summer season, the main crop growing time, of 2015. Judging from conventional methods, it will be possible to discriminate paddy rice growing fields from reaped fields or the other crops growing fields by the PALSAR-2 data. But the RVI value is vaguely related to such land use or biomass at the present preliminary experiment. The continuous research by the additional PALSAR-2 full-polarimetry data should be desired.

A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers.

Science.gov (United States)

Gamon, John A; Huemmrich, K Fred; Wong, Christopher Y S; Ensminger, Ingo; Garrity, Steven; Hollinger, David Y; Noormets, Asko; Peñuelas, Josep

2016-11-15

In evergreen conifers, where the foliage amount changes little with season, accurate detection of the underlying "photosynthetic phenology" from satellite remote sensing has been difficult, presenting challenges for global models of ecosystem carbon uptake. Here, we report a close correspondence between seasonally changing foliar pigment levels, expressed as chlorophyll/carotenoid ratios, and evergreen photosynthetic activity, leading to a "chlorophyll/carotenoid index" (CCI) that tracks evergreen photosynthesis at multiple spatial scales. When calculated from NASA's Moderate Resolution Imaging Spectroradiometer satellite sensor, the CCI closely follows the seasonal patterns of daily gross primary productivity of evergreen conifer stands measured by eddy covariance. This discovery provides a way of monitoring evergreen photosynthetic activity from optical remote sensing, and indicates an important regulatory role for carotenoid pigments in evergreen photosynthesis. Improved methods of monitoring photosynthesis from space can improve our understanding of the global carbon budget in a warming world of changing vegetation phenology.

Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability.

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Wu, Chih-Da; Chen, Yu-Cheng; Pan, Wen-Chi; Zeng, Yu-Ting; Chen, Mu-Jean; Guo, Yue Leon; Lung, Shih-Chun Candice

2017-05-01

This study utilized a long-term satellite-based vegetation index, and considered culture-specific emission sources (temples and Chinese restaurants) with Land-use Regression (LUR) modelling to estimate the spatial-temporal variability of PM 2.5 using data from Taipei metropolis, which exhibits typical Asian city characteristics. Annual average PM 2.5 concentrations from 2006 to 2012 of 17 air quality monitoring stations established by Environmental Protection Administration of Taiwan were used for model development. PM 2.5 measurements from 2013 were used for external data verification. Monthly Normalized Difference Vegetation Index (NDVI) images coupled with buffer analysis were used to assess the spatial-temporal variations of greenness surrounding the monitoring sites. The distribution of temples and Chinese restaurants were included to represent the emission contributions from incense and joss money burning, and gas cooking, respectively. Spearman correlation coefficient and stepwise regression were used for LUR model development, and 10-fold cross-validation and external data verification were applied to verify the model reliability. The results showed a strongly negative correlation (r: -0.71 to -0.77) between NDVI and PM 2.5 while temples (r: 0.52 to 0.66) and Chinese restaurants (r: 0.31 to 0.44) were positively correlated to PM 2.5 concentrations. With the adjusted model R 2 of 0.89, a cross-validated adj-R 2 of 0.90, and external validated R 2 of 0.83, the high explanatory power of the resultant model was confirmed. Moreover, the averaged NDVI within a 1750 m circular buffer (p < 0.01), the number of Chinese restaurants within a 1750 m buffer (p < 0.01), and the number of temples within a 750 m buffer (p = 0.06) were selected as important predictors during the stepwise selection procedures. According to the partial R 2 , NDVI explained 66% of PM 2.5 variation and was the dominant variable in the developed model. We suggest future studies

Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest

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Chen, Xuexia; Vogelmann, James E.; Rollins, Matt; Ohlen, Donald; Key, Carl H.; Yang, Limin; Huang, Chengquan; Shi, Hua

2011-01-01

It is challenging to detect burn severity and vegetation recovery because of the relatively long time period required to capture the ecosystem characteristics. Multitemporal remote sensing data can providemultitemporal observations before, during and after a wildfire, and can improve the change detection accuracy. The goal of this study is to examine the correlations between multitemporal spectral indices and field-observed burn severity, and to provide a practical method to estimate burn severity and vegetation recovery. The study site is the Jasper Fire area in the Black Hills National Forest, South Dakota, that burned during August and September 2000. Six multitemporal Landsat images acquired from 2000 (pre-fire), 2001 (post-fire), 2002, 2003, 2005 and 2007 were used to assess burn severity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized burn ratio (NBR), integrated forest index (IFI) and the differences of these indices between the pre-fire and post-fire years were computed and analysed with 66 field-based composite burn index (CBI) plots collected in 2002. Results showed that differences of NDVI and differences of EVI between the pre-fire year and the first two years post-fire were highly correlated with the CBI scores. The correlations were low beyond the second year post-fire. Differences of NBR had good correlation with CBI scores in all study years. Differences of IFI had low correlation with CBI in the first year post-fire and had good correlation in later years. A CBI map of the burnt area was produced using regression tree models and the multitemporal images. The dynamics of four spectral indices from 2000 to 2007 indicated that both NBR and IFI are valuable for monitoring long-term vegetation recovery. The high burn severity areas had a much slower recovery than the moderate and low burn areas.

[Differences of vegetation phenology monitoring by remote sensing based on different spectral vegetation indices.

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Zuo, Lu; Wang, Huan Jiong; Liu, Rong Gao; Liu, Yang; Shang, Rong

2018-02-01

Vegetation phenology is a comprehensive indictor for the responses of terrestrial ecosystem to climatic and environmental changes. Remote sensing spectrum has been widely used in the extraction of vegetation phenology information. However, there are many differences between phenology extracted by remote sensing and site observations, with their physical meaning remaining unclear. We selected one tile of MODIS data in northeastern China (2000-2014) to examine the SOS and EOS differences derived from the normalized difference vegetation index (NDVI) and the simple ratio vegetation index (SR) based on both the red and near-infrared bands. The results showed that there were significant differences between NDVI-phenology and SR-phenology. SOS derived from NDVI averaged 18.9 days earlier than that from SR. EOS derived from NDVI averaged 19.0 days later than from SR. NDVI-phenology had a longer growing season. There were significant differences in the inter-annual variation of phenology from NDVI and SR. More than 20% of the pixel SOS and EOS derived from NDVI and SR showed the opposite temporal trend. These results caused by the seasonal curve characteristics and noise resistance differences of NDVI and SR. The observed data source of NDVI and SR were completely consistent, only the mathematical expressions were different, but phenology results were significantly different. Our results indicated that vegetation phenology monitoring by remote sensing is highly dependent on the mathematical expression of vegetation index. How to establish a reliable method for extracting vegetation phenology by remote sensing needs further research.

Detecting robust signals of interannual variability of gross primary productivity in Asia from multiple terrestrial carbon cycle models and long-term satellite-based vegetation data

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Ichii, K.; Kondo, M.; Ueyama, M.; Kato, T.; Ito, A.; Sasai, T.; Sato, H.; Kobayashi, H.; Saigusa, N.

2014-12-01

Long term record of satellite-based terrestrial vegetation are important to evaluate terrestrial carbon cycle models. In this study, we demonstrate how multiple satellite observation can be used for evaluating past changes in gross primary productivity (GPP) and detecting robust anomalies in terrestrial carbon cycle in Asia through our model-data synthesis analysis, Asia-MIP. We focused on the two different temporal coverages: long-term (30 years; 1982-2011) and decadal (10 years; 2001-2011; data intensive period) scales. We used a NOAA/AVHRR NDVI record for long-term analysis and multiple satellite data and products (e.g. Terra-MODIS, SPOT-VEGETATION) as historical satellite data, and multiple terrestrial carbon cycle models (e.g. BEAMS, Biome-BGC, ORCHIDEE, SEIB-DGVM, and VISIT). As a results of long-term (30 years) trend analysis, satellite-based time-series data showed that approximately 40% of the area has experienced a significant increase in the NDVI, while only a few areas have experienced a significant decreasing trend over the last 30 years. The increases in the NDVI were dominant in the sub-continental regions of Siberia, East Asia, and India. Simulations using the terrestrial biosphere models also showed significant increases in GPP, similar to the results for the NDVI, in boreal and temperate regions. A modeled sensitivity analysis showed that the increases in GPP are explained by increased temperature and precipitation in Siberia. Precipitation, solar radiation, CO2fertilization and land cover changes are important factors in the tropical regions. However, the relative contributions of each factor to GPP changes are different among the models. Year-to-year variations of terrestrial GPP were overall consistently captured by the satellite data and terrestrial carbon cycle models if the anomalies are large (e.g. 2003 summer GPP anomalies in East Asia and 2002 spring GPP anomalies in mid to high latitudes). The behind mechanisms can be consistently

Automated Land Cover Change Detection and Mapping from Hidden Parameter Estimates of Normalized Difference Vegetation Index (NDVI) Time-Series

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Chakraborty, S.; Banerjee, A.; Gupta, S. K. S.; Christensen, P. R.; Papandreou-Suppappola, A.

2017-12-01

Multitemporal observations acquired frequently by satellites with short revisit periods such as the Moderate Resolution Imaging Spectroradiometer (MODIS), is an important source for modeling land cover. Due to the inherent seasonality of the land cover, harmonic modeling reveals hidden state parameters characteristic to it, which is used in classifying different land cover types and in detecting changes due to natural or anthropogenic factors. In this work, we use an eight day MODIS composite to create a Normalized Difference Vegetation Index (NDVI) time-series of ten years. Improved hidden parameter estimates of the nonlinear harmonic NDVI model are obtained using the Particle Filter (PF), a sequential Monte Carlo estimator. The nonlinear estimation based on PF is shown to improve parameter estimation for different land cover types compared to existing techniques that use the Extended Kalman Filter (EKF), due to linearization of the harmonic model. As these parameters are representative of a given land cover, its applicability in near real-time detection of land cover change is also studied by formulating a metric that captures parameter deviation due to change. The detection methodology is evaluated by considering change as a rare class problem. This approach is shown to detect change with minimum delay. Additionally, the degree of change within the change perimeter is non-uniform. By clustering the deviation in parameters due to change, this spatial variation in change severity is effectively mapped and validated with high spatial resolution change maps of the given regions.

Monitoring soil wetness variations by means of satellite passive microwave observations: the HYDROPTIMET study cases

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T. Lacava

2005-01-01

Full Text Available Soil moisture is an important component of the hydrological cycle. In the framework of modern flood warning systems, the knowledge of soil moisture is crucial, due to the influence on the soil response in terms of infiltration-runoff. Precipitation-runoff processes, in fact, are related to catchment's hydrological conditions before the precipitation. Thus, an estimation of these conditions is of significant importance to improve the reliability of flood warning systems. Combining such information with other weather-related satellite products (i.e. rain rate estimation might represent a useful exercise in order to improve our capability to handle (and possibly mitigate or prevent hydro-geological hazards. Remote sensing, in the last few years, has supported several techniques for soil moisture/wetness monitoring. Most of the satellite-based techniques use microwave data, thanks to the all-weather and all-time capability of these data, as well as to their high sensitivity to water content in the soil. On the other hand, microwave data are unfortunately highly affected by the presence of surface roughness or vegetation coverage within the instantaneous satellite field of view (IFOV. Those problems, consequently, strongly limit the efficiency and the reliability of traditional satellite techniques. Recently, using data coming from AMSU (Advanced Microwave Sounding Unit, flying aboard NOAA (National Oceanic and Atmospheric Administration satellites, a new methodology for soil wetness estimation has been proposed. The proposed index, called Soil Wetness Variation Index (SWVI, developed by a multi-temporal analysis of AMSU records, seems able to reduce the problems related to vegetation and/or roughness effects. Such an approach has been tested, with promising results, on the analysis of some flooding events which occurred in Europe in the past. In this study, results achieved for the HYDROPTIMET test cases will be analysed and discussed in detail

Satellite view of seasonal greenness trends and controls in South Asia

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Sarmah, Sangeeta; Jia, Gensuo; Zhang, Anzhi

2018-03-01

South Asia (SA) has been considered one of the most remarkable regions for changing vegetation greenness, accompanying its major expansion of agricultural activities, especially irrigated farming. The influence of the monsoon climate on the seasonal trends and anomalies of vegetation greenness is poorly understood in this area. Herein, we used the satellite-based Normalized Difference Vegetation Index (NDVI) to investigate various spatiotemporal patterns in vegetation activity during summer and winter monsoon (SM and WM) seasons and among irrigated croplands (IC), rainfed croplands (RC), and natural vegetation (NV) areas during 1982–2013. Seasonal NDVI variations with climatic factors (precipitation and temperature) and land use and cover changes (LUCC) have also been investigated. This study demonstrates that the seasonal dynamics of vegetation could improve the detailed understanding of vegetation productivity over the region. We found distinct greenness trends between two monsoon seasons and among the major land use/cover classes. Winter monsoons contributed greater variability to the overall vegetation dynamics of SA. Major greening occurred due to the increased productivity over irrigated croplands during the winter monsoon season; meanwhile, browning trends were prominent over NV areas during the same season. Maximum temperatures had been increasing tremendously during the WM season; however, the precipitation trend was not significant over SA. Both the climate variability and LUCC revealed coupled effects on the long term NDVI trends in NV areas, especially in the hilly regions, whereas anthropogenic activities (agricultural advancements) played a pivotal role in the rest of the area. Until now, advanced cultivation techniques have proven to be beneficial for the region in terms of the productivity of croplands. However, the crop productivity is at risk under climate change.

Phenological Characterization of Desert Sky Island Vegetation Communities with Remotely Sensed and Climate Time Series Data

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Stuart E. Marsh

2010-01-01

Full Text Available Climate change and variability are expected to impact the synchronicity and interactions between the Sonoran Desert and the forested sky islands which represent steep biological and environmental gradients. The main objectives were to examine how well satellite greenness time series data and derived phenological metrics (e.g., season start, peak greenness can characterize specific vegetation communities across an elevation gradient, and to examine the interactions between climate and phenological metrics for each vegetation community. We found that representative vegetation types (11, varying between desert scrub, mesquite, grassland, mixed oak, juniper and pine, often had unique seasonal and interannual phenological trajectories and spatial patterns. Satellite derived land surface phenometrics (11 for each of the vegetation communities along the cline showed numerous distinct significant relationships in response to temperature (4 and precipitation (7 metrics. Satellite-derived sky island vegetation phenology can help assess and monitor vegetation dynamics and provide unique indicators of climate variability and patterns of change.

Satellite Image Time Series Decomposition Based on EEMD

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Yun-long Kong

2015-11-01

Full Text Available Satellite Image Time Series (SITS have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble Empirical Mode Decomposition (EEMD is proposed. EEMD is achieved by sifting an ensemble of adaptive orthogonal components called Intrinsic Mode Functions (IMFs. EEMD is noise-assisted and overcomes the drawback of mode mixing in conventional Empirical Mode Decomposition (EMD. Inspired by these advantages, the aim of this work is to employ EEMD to decompose SITS into IMFs and to choose relevant IMFs for the separation of seasonal and trend components. In a series of simulations, IMFs extracted by EEMD achieved a clear representation with physical meaning. The experimental results of 16-day compositions of Moderate Resolution Imaging Spectroradiometer (MODIS, Normalized Difference Vegetation Index (NDVI, and Global Environment Monitoring Index (GEMI time series with disturbance illustrated the effectiveness and stability of the proposed approach to monitoring tasks, such as applications for the detection of abrupt changes.

A MODIS-based begetation index climatology

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Passive microwave soil moisture algorithms must account for vegetation attenuation of the signal in the retrieval process. One approach to accounting for vegetation is to use vegetation indices such as the Normalized Difference Vegetation Index (NDVI) to estimate the vegetation optical depth. The pa...

Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems

Science.gov (United States)

Glenn, E.P.; Neale, C. M. U.; Hunsaker, D.J.; Nagler, P.L.

2011-01-01

Crop coefficients were developed to determine crop water needs based on the evapotranspiration (ET) of a reference crop under a given set of meteorological conditions. Starting in the 1980s, crop coefficients developed through lysimeter studies or set by expert opinion began to be supplemented by remotely sensed vegetation indices (VI) that measured the actual status of the crop on a field-by-field basis. VIs measure the density of green foliage based on the reflectance of visible and near infrared (NIR) light from the canopy, and are highly correlated with plant physiological processes that depend on light absorption by a canopy such as ET and photosynthesis. Reflectance-based crop coefficients have now been developed for numerous individual crops, including corn, wheat, alfalfa, cotton, potato, sugar beet, vegetables, grapes and orchard crops. Other research has shown that VIs can be used to predict ET over fields of mixed crops, allowing them to be used to monitor ET over entire irrigation districts. VI-based crop coefficients can help reduce agricultural water use by matching irrigation rates to the actual water needs of a crop as it grows instead of to a modeled crop growing under optimal conditions. Recently, the concept has been applied to natural ecosystems at the local, regional and continental scales of measurement, using time-series satellite data from the MODIS sensors on the Terra satellite. VIs or other visible-NIR band algorithms are combined with meteorological data to predict ET in numerous biome types, from deserts, to arctic tundra, to tropical rainforests. These methods often closely match ET measured on the ground at the global FluxNet array of eddy covariance moisture and carbon flux towers. The primary advantage of VI methods for estimating ET is that transpiration is closely related to radiation absorbed by the plant canopy, which is closely related to VIs. The primary disadvantage is that they cannot capture stress effects or soil

High accuracy satellite drag model (HASDM)

Science.gov (United States)

Storz, Mark F.; Bowman, Bruce R.; Branson, Major James I.; Casali, Stephen J.; Tobiska, W. Kent

The dominant error source in force models used to predict low-perigee satellite trajectories is atmospheric drag. Errors in operational thermospheric density models cause significant errors in predicted satellite positions, since these models do not account for dynamic changes in atmospheric drag for orbit predictions. The Air Force Space Battlelab's High Accuracy Satellite Drag Model (HASDM) estimates and predicts (out three days) a dynamically varying global density field. HASDM includes the Dynamic Calibration Atmosphere (DCA) algorithm that solves for the phases and amplitudes of the diurnal and semidiurnal variations of thermospheric density near real-time from the observed drag effects on a set of Low Earth Orbit (LEO) calibration satellites. The density correction is expressed as a function of latitude, local solar time and altitude. In HASDM, a time series prediction filter relates the extreme ultraviolet (EUV) energy index E10.7 and the geomagnetic storm index ap, to the DCA density correction parameters. The E10.7 index is generated by the SOLAR2000 model, the first full spectrum model of solar irradiance. The estimated and predicted density fields will be used operationally to significantly improve the accuracy of predicted trajectories for all low-perigee satellites.

Phenological characteristics of the main vegetation types on the Tibetan Plateau based on vegetation and water indices

International Nuclear Information System (INIS)

Peng, D L; Huang, W J; Zhou, B; Li, C J; Wu, Y P; Yang, X H

2014-01-01

Plant phenology is considered one of the most sensitive and easily observable natural indicators of climate change, though few studies have focused on the heterogeneities of phenology across the different vegetation types. In this study, we tried to find the phenological characteristics of the main vegetation types on the Tibetan Plateau. MCD12Q1 images over the Tibetan Plateau from 2001 to 2010 were used to extract the main vegetation types. The Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) were calculated using surface reflectance values from the blue, red, near-infrared, short-wave infrared (SWIR) 6 (for LSIW6), and SWIR7 (for LSIW7) bands derived from MOD09A1 and used to explore the phenological characteristics of the main vegetation types on the Tibetan Plateau. The results showed that there were eight constant vegetation types on the Tibetan Plateau from 2001 to 2010 demonstrating multiple phenological characteristics. Evergreen needleleaf forest, evergreen broadleaf forest, and permanent wetland had the minimum NDVI values during the summer season, while open shrubland and grassland had the maximum NDVI/EVI values during this period. NDVI and EVI of cropland/natural vegetation had two peaks for their seasonal variations. EVI showed a more significant correlation with LSWI6/LSWI7 than NDVI. Compared to LSWI7, larger EVI values occurred in evergreen needleleaf forest, evergreen broadleaf forest, mixed forest, and permanent wetland, while smaller values occurred in shrubland and barren or sparsely vegetated cover, and nearly equal values occurred in grassland and cropland

Long-term energy balance and vegetation water stress monitoring of Mediterranean oak savanna using satellite thermal data

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González-Dugo, Maria P.; Chen, Xuelong; Andreu, Ana; Carpintero, Elisabet; Gómez-Giraldez, Pedro; Su, Z.(Bob)

2017-04-01

Drought is one of the major hazards faced by natural and cropped vegetation in the Mediterranean Sea Basin. Water scarcity is likely to be worsened under the predicted conditions of climate change, which is expected to make this region both warmer and drier. A Holm oak savanna, known as dehesa in Spain and montado in Portugal, is an agro-silvo-pastoral system occupying more than 3 million hectares the Iberian Peninsula and Greece. It consists of widely-spaced oak trees (mostly Quercus ilex L.), combined with crops, pasture and Mediterranean shrubs. This ecosystem is considered an example of sustainable land use, supporting a large number of species and diversity of habitats and for its importance in rural economy. A similar ecosystem is worldwide distributed in areas with Mediterranean climate (as California or South Africa) and shares structural and functional properties with tropical savannas in Africa, Australia and South America. Remote sensing time series can assist the monitoring of the energy balance components, with special attention to the evapotranspiration and vegetation water stress over these areas. Long-term data analysis may improve our understanding of the functioning of the system, helping to assess drought impacts and leading to reduce the economic and environmental vulnerability of this ecosystem. This work analyzes the evolution the surface energy balance components, mapping the evapotranspiration and moisture stress of holm oak woodlands of Spain and Portugal during the last 15 years (2001-2015). The surface energy balance model (SEBS) has been applied over the Iberian Peninsula on a monthly time scale and 0.05° spatial resolution, using multi-satellite and meteorological forcing data. Modelled energy and water fluxes have been validated using ground measurements of two eddy covariance towers located in oak savanna sites during 3 years, resulting in moderate deviations from observations (10-25 W/m2). The departure of actual ET from the

Improving evapotranspiration in a land surface model using biophysical variables derived from MSG/SEVIRI satellite

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N. Ghilain

2012-08-01

Full Text Available Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG and distributed by the Satellite Application Facility on Land surface Analysis (LSA-SAF are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I, showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI and Fractional Vegetation Cover (FVC products for evapotranspiration monitoring with a land surface model at 3–5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north–south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land

Monitoring phenology of photosynthesis in temperate evergreen and mixed deciduous forests using the normalized difference vegetation index (NDVI) and the photochemical reflectance index (PRI) at leaf and canopy scales

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Wong, C. Y.; Arain, M. A.; Ensminger, I.

2016-12-01

Evergreen conifers in boreal and temperate regions undergo strong seasonal changes in photoperiod and temperatures, which determines their phenology of high photosynthetic activity in the growing season and downregulation during the winter. Monitoring the timing of the transition between summer activity and winter downregulation in evergreens is difficult since this is a largely invisible process, unlike in deciduous trees that have a visible budding and a sequence of leaf unfolding in the spring and leaf abscission in the fall. The light-use efficiency (LUE) model estimates gross primary productivity (GPP) and may be parameterized using remotely sensed vegetation indices. Using spectral reflectance data, we derived the normalized difference vegetation index (NDVI), a measure of leaf "greenness", and the photochemical reflectance index (PRI), a proxy for chlorophyll:carotenoid ratios which is related to photosynthetic activity. To better understand the relationship between these vegetation indices and photosynthetic activity and to contrast this relationship between plant functional types, the phenology of NDVI, PRI and photosynthesis was monitored in an evergreen forest and a mixed deciduous forest at the leaf and canopy scale. Our data indicates that the LUE model can be parameterized by NDVI and PRI to track forest phenology. Differences in the sensitivity of PRI and NDVI will be discussed. These findings have implications to address the phenology of evergreen conifers by using PRI to complement NDVI in the LUE model, potentially improving model productivity estimates in northern hemisphere forests, that are dominated by conifers.

A Land Product Characterization System for Comparative Analysis of Satellite Data and Products

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Kevin Gallo

2017-12-01

Full Text Available A Land Product Characterization System (LPCS has been developed to provide land data and products to the community of individuals interested in validating space-based land products by comparing them with similar products available from other sensors or surface-based observations. The LPCS facilitates the application of global multi-satellite and in situ data for characterization and validation of higher-level, satellite-derived, land surface products (e.g., surface reflectance, normalized difference vegetation index, and land surface temperature. The LPCS includes data search, inventory, access, and analysis functions that will permit data to be easily identified, retrieved, co-registered, and compared statistically through a single interface. The system currently includes data and products available from Landsat 4 through 8, Moderate Resolution Imaging Spectroradiometer (MODIS Terra and Aqua, Suomi National Polar-Orbiting Partnership (S-NPP/Joint Polar Satellite System (JPSS Visible Infrared Imaging Radiometer Suite (VIIRS, and simulated data for the Geostationary Operational Environmental Satellite (GOES-16 Advanced Baseline Imager (ABI. In addition to the future inclusion of in situ data, higher-level land products from the European Space Agency (ESA Sentinel-2 and -3 series of satellites, and other high and medium resolution spatial sensors, will be included as available. When fully implemented, any of the sensor data or products included in the LPCS would be available for comparative analysis.

Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data

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Willem J. D. van Leeuwen

2008-03-01

Full Text Available This study examines how satellite based time-series vegetation greenness data and phenological measurements can be used to monitor and quantify vegetation recovery after wildfire disturbances and examine how pre-fire fuel reduction restoration treatments impact fire severity and impact vegetation recovery trajectories. Pairs of wildfire affected sites and a nearby unburned reference site were chosen to measure the post-disturbance recovery in relation to climate variation. All site pairs were chosen in forested uplands in Arizona and were restricted to the area of the Rodeo-Chediski fire that occurred in 2002. Fuel reduction treatments were performed in 1999 and 2001. The inter-annual and seasonal vegetation dynamics before, during, and after wildfire events can be monitored using a time series of biweekly composited MODIS NDVI (Moderate Resolution Imaging Spectroradiometer - Normalized Difference Vegetation Index data. Time series analysis methods included difference metrics, smoothing filters, and fitting functions that were applied to extract seasonal and inter-annual change and phenological metrics from the NDVI time series data from 2000 to 2007. Pre- and post-fire Landsat data were used to compute the Normalized Burn Ratio (NBR and examine burn severity at the selected sites. The phenological metrics (pheno-metrics included the timing and greenness (i.e. NDVI for the start, peak and end of the growing season as well as proxy measures for the rate of green-up and senescence and the annual vegetation productivity. Pre-fire fuel reduction treatments resulted in lower fire severity, which reduced annual productivity much less than untreated areas within the Rodeo-Chediski fire perimeter. The seasonal metrics were shown to be useful for estimating the rate of post-fire disturbance recovery and the timing of phenological greenness phases. The use of satellite time series NDVI data and derived pheno-metrics show potential for tracking vegetation

Leaf area index retrieval using Hyperion EO-1 data-based vegetation indices in Himalayan forest system

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Singh, Dharmendra; Singh, Sarnam

2016-04-01

Present Study is being taken to retrieve Leaf Area Indexn(LAI) in Himalayan forest system using vegetation indices developed from Hyperion EO-1 hyperspectral data. Hemispherical photograph were captured in the month of March and April, 2012 at 40 locations, covering moist tropical Sal forest, subtropical Bauhinia and pine forest and temperate Oak forest and analysed using an open source GLA software. LAI in the study region was ranging in between 0.076 m2/m2 to 6.00 m2/m2. These LAI values were used to develop spectral models with the FLAASH corrected Hyperion measurements.Normalized difference vegetation index (NDVI) was used taking spectral reflectance values of all the possible combinations of 170 atmospherically corrected channels. The R2 was ranging from lowest 0.0 to highest 0.837 for the band combinations of spectral region 640 nm and 670 nm. The spectral model obtained was, spectral reflectance (y) = 0.02x LAI(x) - 0.0407.

Fusion of Satellite Multispectral Images Based on Ground-Penetrating Radar (GPR Data for the Investigation of Buried Concealed Archaeological Remains

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Athos Agapiou

2017-06-01

Full Text Available The paper investigates the superficial layers of an archaeological landscape based on the integration of various remote sensing techniques. It is well known in the literature that shallow depths may be rich in archeological remains, which generate different signal responses depending on the applied technique. In this study three main technologies are examined, namely ground-penetrating radar (GPR, ground spectroscopy, and multispectral satellite imagery. The study aims to propose a methodology to enhance optical remote sensing satellite images, intended for archaeological research, based on the integration of ground based and satellite datasets. For this task, a regression model between the ground spectroradiometer and GPR is established which is then projected to a high resolution sub-meter optical image. The overall methodology consists of nine steps. Beyond the acquirement of the in-situ measurements and their calibration (Steps 1–3, various regression models are examined for more than 70 different vegetation indices (Steps 4–5. The specific data analysis indicated that the red-edge position (REP hyperspectral index was the most appropriate for developing a local fusion model between ground spectroscopy data and GPR datasets (Step 6, providing comparable results with the in situ GPR measurements (Step 7. Other vegetation indices, such as the normalized difference vegetation index (NDVI, have also been examined, providing significant correlation between the two datasets (R = 0.50. The model is then projected to a high-resolution image over the area of interest (Step 8. The proposed methodology was evaluated with a series of field data collected from the Vésztő-Mágor Tell in the eastern part of Hungary. The results were compared with in situ magnetic gradiometry measurements, indicating common interpretation results. The results were also compatible with the preliminary archaeological investigations of the area (Step 9. The overall

Advantages of Using Microwave Satellite Soil Moisture over Gridded Precipitation Products and Land Surface Model Output in Assessing Regional Vegetation Water Availability and Growth Dynamics for a Lateral Inflow Receiving Landscape

NARCIS (Netherlands)

Chen, T.; McVicar, T.R.; Wang, G.J.; Chen, X.; de Jeu, R.A.M.; Liu, Y.; Shen, H.; Zhang, F.; Dolman, A.J.

2016-01-01

To improve the understanding of water-vegetation relationships, direct comparative studies assessing the utility of satellite remotely sensed soil moisture, gridded precipitation products, and land surface model output are needed. A case study was investigated for a water-limited, lateral inflow

INTEGRATION OF PALSAR AND ASTER SATELLITE DATA FOR GEOLOGICAL MAPPING IN TROPICS

Directory of Open Access Journals (Sweden)

A. Beiranvand Pour

2015-10-01

Full Text Available This research investigates the integration of the Phased Array type L-band Synthetic Aperture Radar (PALSAR and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER satellite data for geological mapping applications in tropical environments. The eastern part of the central belt of peninsular Malaysia has been investigated to identify structural features and mineral mapping using PALSAR and ASTER data. Adaptive local sigma and directional filters were applied to PALSAR data for detecting geological structure elements in the study area. The vegetation, mineralogic and lithologic indices for ASTER bands were tested in tropical climate. Lineaments (fault and fractures and curvilinear (anticline or syncline were detected using PALSAR fused image of directional filters (N-S, NE-SW, and NW-SE.Vegetation index image map show vegetation cover by fusing ASTER VNIR bands. High concentration of clay minerals zone was detected using fused image map derived from ASTER SWIR bands. Fusion of ASTER TIR bands produced image map of the lithological units. Results indicate that data integration and data fusion from PALSAR and ASTER sources enhanced information extraction for geological mapping in tropical environments.

Egypt satellite images for land surface characterization

DEFF Research Database (Denmark)

Hasager, Charlotte Bay

images used for mapping the vegetation cover types and other land cover types in Egypt. The mapping ranges from 1 km resolution to 30 m resolution. The aim is to provide satellite image mapping with land surface characteristics relevant for roughness mapping.......Satellite images provide information on the land surface properties. From optical remote sensing images in the blue, green, red and near-infrared part of the electromagnetic spectrum it is possible to identify a large number of surface features. The report briefly describes different satellite...

Spatio-temporal distribution of vegetation index and its influencing factors—a case study of the Jiaozhou Bay, China

Science.gov (United States)

Zheng, Yang; Yu, Ge

2017-11-01

The coastal zone is an area characterized by intense interaction between land and sea, high sensitivity to regional environmental changes, and concentrated human activities. Little research has investigated vegetation cover changes in coastal zones resulting from climate change and land-use change, with a lack of knowledge about the driving mechanism. Normalized difference vegetation index (NDVI) can be used as an indicator for change of the coastal environment. In this study, we analyzed the interannual changes and spatial distribution of NDVI in the coastal zone around Jiaozhou Bay in Qingdao, a coastal city undergoing rapid urbanization in northeast China. The underlying causes of NDVI variations were discussed in the context of climate change and land-use change. Results showed that the spatio-temporal distribution of NDVI displayed high spatial variability in the study area and showed a typical trend of gradually increasing from coastal to inland regions. The significant increase area of NDVI was mainly found in newly added construction land, extending along the coastline towards the inland. Land vegetation cover demonstrated a certain response relationship to sea-land climate change and land-based activities. The impact of land-based human activities was slightly greater than that of sea-land climate change for land vegetation cover. The results indicate that promoting ecological policies can build an ecological security framework of vegetation suitable for the resource characteristics of coastal cities. The framework will buffer the negative effects of sea-land climate change and land-based human activities on vegetation cover and thereby achieve the balance of regional development and ecological benefits in the coastal zone.

Enhanced Vegetation Index (EVI na análise da dinâmica da vegetação da reserva biológica de Sooretama, ES Use of Enhanced Vegetation Index (EVI in the analysis os vegetation dynamics of the Sooretama biological reservation, ES

Directory of Open Access Journals (Sweden)

André Quintão de Almeida

2008-12-01

Full Text Available Técnicas de análises de séries temporais são utilizadas para caracterizar o comportamento de fenômenos naturais no domínio do tempo. Neste artigo, segundo a metodologia proposta por Box et al. (1994, 125 observações do Enhanced Vegetation Index (EVI foram analisadas. Os valores modelados correspondem às variações temporais ocorridas no dossel florestal da reserva biológica de Sooretama, localizada ao Norte do Estado do Espírito Santo, no Município de Linhares. Os resultados indicaram que a metodologia foi adequada. Os resíduos do modelo ajustado são não correlacionados com distribuição normal, média zero e variância s². Com o menor valor do Critério de Informação de Akaike (AIC -570,51, o modelo ajustado foi o Sazonal Auto-Regressivo Integrado de Médias Móveis (1,0,1(1,0,112.Temporal series analysis techniques are used to characterize the behavior of natural phenomenon in time domain. In this paper, 125 Enhanced Vegetation Index (EVI observations were analyzed according to the methodology proposed by Box et al.(1994. The values modeled correspond to the temporal variations that occurred in the forest canopy of the Sooretama Biological Reserve, in northern Espírito Santo, in the district of Linhares. The results indicated that such methodology was adequate. The residues of the adjusted model are not correlated with normal distribution, zero average and s² variance. At the lowest value of the Akaike Information Criteria (AIC -570. 51, the model adjusted was the Mobile Average Integrated Self-Regressive Seasonal model (1, 0, 1 (1, 0, 1-12.

Modeling the distribution of Schistosoma mansoni and host snails in Uganda using satellite sensor data and Geographical Information Systems

DEFF Research Database (Denmark)

Stensgaard, Anna-Sofie; Jørgensen, A; Kabatereine, N B

2005-01-01

The potential value of MODIS satellite sensor data on Normalized Difference Vegetation Index (NDVI) and land surface temperatures (LST) for describing the distribution of the Schistosoma mansoni-"Biomphalaria pfeifferi"/Biomphalaria sudanica parasite-snail system in inland Uganda, were tested...... by developing annual and seasonal composite models, and iteratively analysing for their relationship with parasite and snail distribution. The dry season composite model predicted an endemic area that produced the best fit with the distribution of schools with > or =5% prevalence. NDVI values of 151-174, day...

Estimating Leaf Area Index for an arid region using Spectral Data ...

African Journals Online (AJOL)

In this study, spectral reflectance of pearl millet was computed at various wavelengths and at different times during the cropping season, using a spectroradiometer. Three main indices (Normalised Difference Vegetation Index, Ratio Vegetation Index, and Perpendicular Vegetation Index)were derived from the spectral data.

Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results

Science.gov (United States)

Tadesse, Tsegaye; Champagne, Catherine; Wardlow, Brian D.; Hadwen, Trevor A.; Brown, Jesslyn; Demisse, Getachew B.; Bayissa, Yared A.; Davidson, Andrew M.

2017-01-01

Drought is a natural climatic phenomenon that occurs throughout the world and impacts many sectors of society. To help decision-makers reduce the impacts of drought, it is important to improve monitoring tools that provide relevant and timely information in support of drought mitigation decisions. Given that drought is a complex natural hazard that manifests in different forms, monitoring can be improved by integrating various types of information (e.g., remote sensing and climate) that is timely and region specific to identify where and when droughts are occurring. The Vegetation Drought Response Index for Canada (VegDRI-Canada) is a recently developed drought monitoring tool for Canada. VegDRI-Canada extends the initial VegDRI concept developed for the conterminous United States to a broader transnational coverage across North America. VegDRI-Canada models are similar to those developed for the United States, integrating satellite observations of vegetation status, climate data, and biophysical information on land use and land cover, soil characteristics, and other environmental factors. Collectively, these different types of data are integrated into the hybrid VegDRI-Canada to isolate the effects of drought on vegetation. Twenty-three weekly VegDRI-Canada models were built for the growing season (April–September) through the weekly analysis of these data using a regression tree-based data mining approach. A 15-year time series of VegDRI-Canada results (s to 2014) was produced using these models and the output was validated by randomly selecting 20% of the historical data, as well as holdout year (15% unseen data) across the growing season that the Pearson’s correlation ranged from 0.6 to 0.77. A case study was also conducted to evaluate the VegDRI-Canada results over the prairie region of Canada for two drought years and one non-drought year for three weekly periods of the growing season (i.e., early-, mid-, and late season). The comparison of the Veg

Terrestrial Carbon Sinks in the Brazilian Amazon and Cerrado Region Predicted from MODIS Satellite Data and Ecosystem Modeling

Science.gov (United States)

Potter, C.; Klooster, S.; Huete, A.; Genovese, V.; Bustamante, M.; Ferreira, L. Guimaraes; deOliveira, R. C., Jr.; Zepp, R.

2009-01-01

A simulation model based on satellite observations of monthly vegetation cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate monthly carbon fluxes in terrestrial ecosystems of Brazilian Amazon and Cerrado regions over the period 2000-2004. Net ecosystem production (NEP) flux for atmospheric CO2 in the region for these years was estimated. Consistently high carbon sink fluxes in terrestrial ecosystems on a yearly basis were found in the western portions of the states of Acre and Rondonia and the northern portions of the state of Par a. These areas were not significantly impacted by the 2002-2003 El Nino event in terms of net annual carbon gains. Areas of the region that show periodically high carbon source fluxes from terrestrial ecosystems to the atmosphere on yearly basis were found throughout the state of Maranhao and the southern portions of the state of Amazonas. As demonstrated though tower site comparisons, NEP modeled with monthly MODIS Enhanced Vegetation Index (EVI) inputs closely resembles the measured seasonal carbon fluxes at the LBA Tapajos tower site. Modeling results suggest that the capacity for use of MODIS Enhanced Vegetation Index (EVI) data to predict seasonal uptake rates of CO2 in Amazon forests and Cerrado woodlands is strong.

Drought impact on vegetation in pre and post fire events in Iberian Peninsula

Science.gov (United States)

Gouveia, C. M.; Bastos, A.; Trigo, R. M.; DaCamara, C.

2012-04-01

In 2004/2005, the Iberian Peninsula was stricken by an exceptional drought that affected more than one third of Portugal and part of southern Spain during more than 9 months. This severe drought had a strong negative impact on vegetation dynamics, as it coincided with the period of high photosynthetic activity (Gouveia et al., 2009). Since water availability is a crucial factor in post-fire vegetation recovery, it is desirable to assess the impact that such water-stress conditions had on fire sensitivity and post-fire vegetation recovery. Fire events in the European Mediterranean areas have become a serious problem and a major ecosystem disturbance, increasing erosion and soil degradation. In Portugal, the years 2003 and 2005 were particularly devastating. In 2003 it was registered the maximal burnt area since 1980, with more than 425000 ha burned, representing about 5% of Portuguese mainland. The 2005 fire season registered the highest number of fire occurrences in Portugal and the second year with the greatest number of fires in Spain. The high number of fire events observed during the summer 2005 in the Iberian Peninsula is linked, in part, to the extreme drought conditions that prevailed during the preceding winter and spring seasons of 2004/2005. Vegetation recovery after the 2003 and 2005 fire seasons was estimated using the mono-parametric model developed by Gouveia et al. (2010), which relies on monthly values of Normalized Difference Vegetation Index (NDVI), from 1999 to 2009, at 1kmresolution, as obtained from the VEGETATION-SPOT5 instrument.. This model was further used to evaluate the effect of drought in pre and post vegetation activity. Besides the standard NDVI, the Normalized Difference Water Index (NDWI) and the Normalized Difference Drought Index (NDDI) were computed in order to evaluate drought intensity. In the case of the burnt scars of 2003, when data corresponding to the months of drought are removed, recovery times are considerably shorter

New Approaches to Irrigation Scheduling of Vegetables

Directory of Open Access Journals (Sweden)

Michael D. Cahn

2017-04-01

Full Text Available Using evapotranspiration (ET data for scheduling irrigations on vegetable farms is challenging due to imprecise crop coefficients, time consuming computations, and the need to simultaneously manage many fields. Meanwhile, the adoption of soil moisture monitoring in vegetables has historically been limited by sensor accuracy and cost, as well as labor required for installation, removal, and collection of readings. With recent improvements in sensor technology, public weather-station networks, satellite and aerial imaging, wireless communications, and cloud computing, many of the difficulties in using ET data and soil moisture sensors for irrigation scheduling of vegetables can now be addressed. Web and smartphone applications have been developed that automate many of the calculations involved in ET-based irrigation scheduling. Soil moisture sensor data can be collected through wireless networks and accessed using web browser or smartphone apps. Energy balance methods of crop ET estimation, such as eddy covariance and Bowen ratio, provide research options for further developing and evaluating crop coefficient guidelines of vegetables, while recent advancements in surface renewal instrumentation have led to a relatively low-cost tool for monitoring crop water requirement in commercial farms. Remote sensing of crops using satellite, manned aircraft, and UAV platforms may also provide useful tools for vegetable growers to evaluate crop development, plant stress, water consumption, and irrigation system performance.

Biophysical applications of satellite remote sensing

CERN Document Server

Hanes, Jonathan

2014-01-01

Including an introduction and historical overview of the field, this comprehensive synthesis of the major biophysical applications of satellite remote sensing includes in-depth discussion of satellite-sourced biophysical metrics such as leaf area index.

Results of agriclimatological studies using multiple satellite sensors like NOAA AVHRR; GMS IR and LANDSAT MSS and TM

International Nuclear Information System (INIS)

Choudhury, A.M.

1990-08-01

Bangladesh Space Research and Remote Sensing Organization (SPARRSO) routinely receives NOAA and GMS imagery and uses them in agrometeorological monitoring, it also uses LANDSAT MSS and TM data for this purpose. Analysis of multiple satellite sensor data shows advantages for high resolution sensors. However, in the ease of crop monitoring, a good correlation has been obtained between results obtained with NOAA AVHRR and LANDSAT MSS for vegetation index. Crop estimation has been made using all kinds of sensors and it has been found that higher resolution data always give more accurate results. (author). 3 refs

Estimation of absorbed photosynthetically active radiation and vegetation net production efficiency using satellite data

International Nuclear Information System (INIS)

Hanan, N.P.; Prince, S.D.; Begue, A.

1995-01-01

The amount of photosynthetically active radiation (PAR) absorbed by green vegetation is an important determinant of photosynthesis and growth. Methods for the estimation of fractional absorption of PAR (iff PAR ) for areas greater than 1 km 2 using satellite data are discussed, and are applied to sites in the Sahel that have a sparse herb layer and tree cover of less than 5%. Using harvest measurements of seasonal net production, net production efficiencies are calculated. Variation in estimates of seasonal PAR absorption (APAR) caused by the atmospheric correction method and relationship between surface reflectances and iff PAR is considered. The use of maximum value composites of satellite NDVI to reduce the effect of the atmosphere is shown to produce inaccurate APAR estimates. In this data set, however, atmospheric correction using average optical depths was found to give good approximations of the fully corrected data. A simulation of canopy radiative transfer using the SAIL model was used to derive a relationship between canopy NDVI and iff PAR . Seasonal APAR estimates assuming a 1:1 relationship between iff PAR and NDVI overestimated the SAIL modeled results by up to 260%. The use of a modified 1:1 relationship, where iff PAR was assumed to be linearly related to NDVI scaled between minimum (soil) and maximum (infinite canopy) values, underestimated the SAIL modeled results by up to 35%. Estimated net production efficiencies (ϵ n , dry matter per unit APAR) fell in the range 0.12–1.61 g MJ −1 for above ground production, and in the range 0.16–1.88 g MJ −1 for total production. Sites with lower rainfall had reduced efficiencies, probably caused by physiological constraints on photosynthesis during dry conditions. (author)

Spectral Reflectance and Vegetation Index Changes in Deciduous Forest Foliage Following Tree Removal: Potential for Deforestation Monitoring

Science.gov (United States)

Peng, D.; Hu, Y.; Li, Z.

2016-05-01

It is important to detect and quantify deforestation to guide strategic decisions regarding environment, socioeconomic development, and climate change. In the present study, we conducted a field experiment to examine spectral reflectance and vegetation index changes in poplar and locust tree foliage with different leaf area indices over the course of three sunny days, following tree removal from the canopy. The spectral reflectance of foliage from harvested trees was measured using an ASD FieldSpec Prospectroradiometer; synchronous meteorological data were also obtained. We found that reflectance in short-wave infrared and red-edge reflectance was more time sensitive after tree removal than reflectance in other spectral regions, and that the normalized difference water index (NDWI) and the red-edge chlorophyll index (CIRE) were the preferred indicators of these changes from several indices evaluated. Synthesized meteorological environments were found to influence water and chlorophyll contents after tree removal, and this subsequently changed the spectral canopy reflectance. Our results indicate the potential for such tree removal to be detected with NDWI or CIRE from the second day of a deforestation event.

AUTOMATIC APPROACH TO VHR SATELLITE IMAGE CLASSIFICATION

Directory of Open Access Journals (Sweden)

P. Kupidura

2016-06-01

Full Text Available In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture, which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the

Recent Vegetation Fire Incidence in Russia

OpenAIRE

Hayasaka, Hiroshi

2011-01-01

MODIS hotspot data from NASA have now become a standard means of evaluating vegetation fires worldwide. Remote sensing is the most effective tool for large countries like Russia because it is hard to obtain exact, detailed forest fire data. Accumulated MODIS hotspot data of the nine years from 2002 to 2010 may allow us to assess recent changes in the vegetation fire incidence in Russia. This kind of analysis using various satellites is useful in estimating fire intensity and sever...

Monitoring responses of Mason Pine to acid rain in China based on remote sensing vegetation index

International Nuclear Information System (INIS)

Jin, Jiaxin; Jiang, Hong; Zhang, Xiuying; Wang, Ying; Hou, Chunliang

2014-01-01

Since the 1970s, acid rain has remained in the public spotlight in both Europe and the United States and recently has emerged as an important problem in other regions such as Southeast Asia. To reveal responses of Masson Pine to acid rain during a long time series in central China, we used the interpolation dataset of acid rain and the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) data to derive the monthly pH and NDVI trajectories based on acidity gradients from 1992 to 2006. Then we analyzed inter-annual and seasonal variation of vegetation growth by improved sinusoidal fitting and regression analysis. In the environment of strong acidity and moderate acidity, the growth of Masson Pine was inhibited during the study period, while the slight acidity promoted growth of Masson Pine to some extent. For the multi-year monthly changing trend of NDVI, late spring to mid autumn, the NDVI showed a decreasing trend, especially in June, while from late autumn to the following spring, the NDVI showed a rising tendency, specifically in December and March

Remote sensing study of the impact of vegetation on thermal environment in different contexts

Science.gov (United States)

Xie, Qijiao; Wu, Yingjiao; Zhou, Zhixiang; Wang, Zhengxiang

2018-02-01

Satellite remote sensing technology provides informative data for detecting the land surface temperature (LST) distribution and urban heat island (UHI) effect remotely and regionally. In this study, two Landsat Thematic Mapper (TM) images acquired on September 26, 1987 and September 17, 2013 were used to derive LST and the normalized difference vegetation index (NDVI) values in Wuhan, China. The relationships between NDVI and LST were examined in different contexts, namely built-up area, farmland, grassland and forest. Results showed that negative correlations between the mean NDVI and LST were detected in all observed land covers, which meant that vegetation was efficient in decreasing surface temperatures and mitigating UHI effect. The cooling efficiency of vegetation on thermal environment varied with different contexts. As mean NDVI increased at each 0.1, the decreased LST values in built-up area, farmland, grassland and forest were 1.4 °C, 1.4 °C, 1.1 °C, 1.9 °C in 1987 and 1.4 °C, 1.7 °C, 1.3 °C, 1.8 °C in 2013, respectively. This finding encourages urban planners and greening designers to devote more efforts in protecting urban forests.

Effects of vegetation structure on biomass accumulation in a Balanced Optimality Structure Vegetation Model (BOSVM v1.0

Directory of Open Access Journals (Sweden)

Z. Yin

2014-05-01

Full Text Available A myriad of interactions exist between vegetation and local climate for arid and semi-arid regions. Vegetation function, structure and individual behavior have large impacts on carbon–water–energy balances, which consequently influence local climate variability that, in turn, feeds back to the vegetation. In this study, a conceptual vegetation structure scheme is formulated and tested in the new Balanced Optimality Structure Vegetation Model (BOSVM to explore the importance of vegetation structure and vegetation adaptation to water stress on equilibrium biomass states. Surface energy, water and carbon fluxes are simulated for a range of vegetation structures across a precipitation gradient in West Africa and optimal vegetation structures that maximize biomass for each precipitation regime are determined. Two different strategies of vegetation adaptation to water stress are included. Under dry conditions vegetation tries to maximize the water use efficiency and leaf area index as it tries to maximize carbon gain. However, a negative feedback mechanism in the vegetation–soil water system is found as the vegetation also tries to minimize its cover to optimize the surrounding bare ground area from which water can be extracted, thereby forming patches of vertical vegetation. Under larger precipitation, a positive feedback mechanism is found in which vegetation tries to maximize its cover as it then can reduce water loss from bare soil while having maximum carbon gain due to a large leaf area index. The competition between vegetation and bare soil determines a transition between a "survival" state to a "growing" state.

Downscaling Satellite Land Surface Temperatures in Urban Regions for Surface Energy Balance Study and Heat Index Development

Science.gov (United States)

Norouzi, H.; Bah, A.; Prakash, S.; Nouri, N.; Blake, R.

2017-12-01

A great percentage of the world's population reside in urban areas that are exposed to the threats of global and regional climate changes and associated extreme weather events. Among them, urban heat islands have significant health and economic impacts due to higher thermal gradients of impermeable surfaces in urban regions compared to their surrounding rural areas. Therefore, accurate characterization of the surface energy balance in urban regions are required to predict these extreme events. High spatial resolution Land surface temperature (LST) in the scale of street level in the cities can provide wealth of information to study surface energy balance and eventually providing a reliable heat index. In this study, we estimate high-resolution LST maps using combination of LandSat 8 and infrared based satellite products such as Moderate Resolution Imaging Spectroradiometer (MODIS) and newly launched Geostationary Operational Environmental Satellite-R Series (GOES-R). Landsat 8 provides higher spatial resolution (30 m) estimates of skin temperature every 16 days. However, MODIS and GOES-R have lower spatial resolution (1km and 4km respectively) with much higher temporal resolution. Several statistical downscaling methods were investigated to provide high spatiotemporal LST maps in urban regions. The results reveal that statistical methods such as Principal Component Analysis (PCA) can provide reliable estimations of LST downscaling with 2K accuracy. Other methods also were tried including aggregating (up-scaling) the high-resolution data to a coarse one to examine the limitations and to build the model. Additionally, we deployed flux towers over distinct materials such as concrete, asphalt, and rooftops in New York City to monitor the sensible and latent heat fluxes through eddy covariance method. To account for the incoming and outgoing radiation, a 4-component radiometer is used that can observe both incoming and outgoing longwave and shortwave radiation. This

Spatial and temporal patterns of greenness on the Yamal Peninsula, Russia: interactions of ecological and social factors affecting the Arctic normalized difference vegetation index

International Nuclear Information System (INIS)

Walker, D A; Bhatt, U S; Raynolds, M K; Romanovsky, V E; Leibman, M O; Gubarkov, A A; Khomutov, A V; Moskalenko, N G; Orekhov, P; Ukraientseva, N G; Epstein, H E; Yu, Q; Forbes, B C; Kaarlejaervi, E; Comiso, J C; Jia, G J; Kaplan, J O; Kumpula, T; Kuss, P; Matyshak, G

2009-01-01

The causes of a greening trend detected in the Arctic using the normalized difference vegetation index (NDVI) are still poorly understood. Changes in NDVI are a result of multiple ecological and social factors that affect tundra net primary productivity. Here we use a 25 year time series of AVHRR-derived NDVI data (AVHRR: advanced very high resolution radiometer), climate analysis, a global geographic information database and ground-based studies to examine the spatial and temporal patterns of vegetation greenness on the Yamal Peninsula, Russia. We assess the effects of climate change, gas-field development, reindeer grazing and permafrost degradation. In contrast to the case for Arctic North America, there has not been a significant trend in summer temperature or NDVI, and much of the pattern of NDVI in this region is due to disturbances. There has been a 37% change in early-summer coastal sea-ice concentration, a 4% increase in summer land temperatures and a 7% change in the average time-integrated NDVI over the length of the satellite observations. Gas-field infrastructure is not currently extensive enough to affect regional NDVI patterns. The effect of reindeer is difficult to quantitatively assess because of the lack of control areas where reindeer are excluded. Many of the greenest landscapes on the Yamal are associated with landslides and drainage networks that have resulted from ongoing rapid permafrost degradation. A warming climate and enhanced winter snow are likely to exacerbate positive feedbacks between climate and permafrost thawing. We present a diagram that summarizes the social and ecological factors that influence Arctic NDVI. The NDVI should be viewed as a powerful monitoring tool that integrates the cumulative effect of a multitude of factors affecting Arctic land-cover change.

Spatial and temporal patterns of greenness on the Yamal Peninsula, Russia: interactions of ecological and social factors affecting the Arctic normalized difference vegetation index

Energy Technology Data Exchange (ETDEWEB)

Walker, D A; Bhatt, U S; Raynolds, M K; Romanovsky, V E [University of Alaska Fairbanks, Fairbanks, AK (United States); Leibman, M O; Gubarkov, A A; Khomutov, A V; Moskalenko, N G; Orekhov, P; Ukraientseva, N G [Earth Cryosphere Institute, Russian Academy of Science, Siberian Branch, Tyumen (Russian Federation); Epstein, H E; Yu, Q [University of Virginia, Charlottesville, VA (United States); Forbes, B C; Kaarlejaervi, E [Arctic Center, University of Lapland, Rovaniemi (Finland); Comiso, J C [NASA Goddard Space Flight Center, MD (United States); Jia, G J [Chinese Academy of Sciences, Institute for Atmospheric Physics, Beijing (China); Kaplan, J O [Swiss Federal Institute for Forest Snow and Landscape Research, Birmensdorf (Switzerland); Kumpula, T [University of Joensuu, Joensuu (Finland); Kuss, P [University of Berne, Berne (Switzerland); Matyshak, G [Moscow State University, Moscow (Russian Federation)

2009-10-15

The causes of a greening trend detected in the Arctic using the normalized difference vegetation index (NDVI) are still poorly understood. Changes in NDVI are a result of multiple ecological and social factors that affect tundra net primary productivity. Here we use a 25 year time series of AVHRR-derived NDVI data (AVHRR: advanced very high resolution radiometer), climate analysis, a global geographic information database and ground-based studies to examine the spatial and temporal patterns of vegetation greenness on the Yamal Peninsula, Russia. We assess the effects of climate change, gas-field development, reindeer grazing and permafrost degradation. In contrast to the case for Arctic North America, there has not been a significant trend in summer temperature or NDVI, and much of the pattern of NDVI in this region is due to disturbances. There has been a 37% change in early-summer coastal sea-ice concentration, a 4% increase in summer land temperatures and a 7% change in the average time-integrated NDVI over the length of the satellite observations. Gas-field infrastructure is not currently extensive enough to affect regional NDVI patterns. The effect of reindeer is difficult to quantitatively assess because of the lack of control areas where reindeer are excluded. Many of the greenest landscapes on the Yamal are associated with landslides and drainage networks that have resulted from ongoing rapid permafrost degradation. A warming climate and enhanced winter snow are likely to exacerbate positive feedbacks between climate and permafrost thawing. We present a diagram that summarizes the social and ecological factors that influence Arctic NDVI. The NDVI should be viewed as a powerful monitoring tool that integrates the cumulative effect of a multitude of factors affecting Arctic land-cover change.

Using the satellite-derived normalized difference vegetation index (NDVI) to explain ranging patterns in a lek-breeding antelope: the importance of scale.

Science.gov (United States)

Bro-Jørgensen, Jakob; Brown, Molly E; Pettorelli, Nathalie

2008-11-01

Lek-breeding species are characterized by a negative association between territorial resource availability and male mating success; however, the impact of resources on the overall distribution patterns of the two sexes in lek systems is not clear. The normalized difference vegetation index (NDVI) has recently emerged as a powerful proxy measure for primary productivity, allowing the links between the distributions of animals and resources to be explored. Using NDVI at four spatial resolutions, we here investigate how the distribution of the two sexes in a lek-breeding population of topi antelopes relates to resource abundance before and during the rut. We found that in the dry season preceding the rut, topi density correlated positively with NDVI at the large, but not the fine, scale. This suggests that before the rut, when resources were relatively scant, topi preferred pastures where green grass was widely abundant. The pattern was less pronounced in males, suggesting that the need for territorial attendance prevents males from tracking resources as freely as females do. During the rut, which occurs in the wet season, both male and female densities correlated negatively with NDVI at the fine scale. At this time, resources were generally plentiful and the results suggest that, rather than by resource maximization, distribution during the rut was determined by benefits of aggregating on relatively resource-poor leks for mating, and possibly antipredator, purposes. At the large scale, no correlation between density and NDVI was found during the rut in either sex, which can be explained by leks covering areas too small to be reflected at this resolution. The study illustrates that when investigating spatial organization, it is important: (1) to choose the appropriate analytic scale, and (2) to consider behavioural as well as strictly ecological factors.

Tree Canopy Light Interception Estimates in Almond and a Walnut Orchards Using Ground, Low Flying Aircraft, and Satellite Based Methods to Improve Irrigation Scheduling Programs

Science.gov (United States)

Rosecrance, Richard C.; Johnson, Lee; Soderstrom, Dominic

2016-01-01

Canopy light interception is a main driver of water use and crop yield in almond and walnut production. Fractional green canopy cover (Fc) is a good indicator of light interception and can be estimated remotely from satellite using the normalized difference vegetation index (NDVI) data. Satellite-based Fc estimates could be used to inform crop evapotranspiration models, and hence support improvements in irrigation evaluation and management capabilities. Satellite estimates of Fc in almond and walnut orchards, however, need to be verified before incorporating them into irrigation scheduling or other crop water management programs. In this study, Landsat-based NDVI and Fc from NASA's Satellite Irrigation Management Support (SIMS) were compared with four estimates of canopy cover: 1. light bar measurement, 2. in-situ and image-based dimensional tree-crown analyses, 3. high-resolution NDVI data from low flying aircraft, and 4. orchard photos obtained via Google Earth and processed by an Image J thresholding routine. Correlations between the various estimates are discussed.

Relation of MODIS EVI and LAI across time, vegetation types and hydrological regimes

Science.gov (United States)

Alexandridis, Thomas; Ovakoglou, George

2015-04-01

Estimation of the Leaf Area Index (LAI) of a landscape is considered important to describe the ecosystems activity and is used as an important input parameter in hydrological and biogeochemical models related to water and carbon cycle, desertification risk, etc. The measurement of LAI in the field is a laborious and costly process and is mainly done by indirect methods, such as hemispherical photographs that are processed by specialized software. For this reason there have been several attempts to estimate LAI with multispectral satellite images, using theoretical biomass development models, or empirical equations using vegetation indices and land cover maps. The aim of this work is to study the relation of MODIS EVI and LAI across time, vegetation type, and hydrological regime. This was achieved by studying 120 maps of EVI and LAI which cover a hydrological year and five hydrologically diverse areas: river Nestos in Greece, Queimados catchment in Brazil, Rijnland catchment in The Netherlands, river Tamega in Portugal, and river Umbeluzi in Mozambique. The following Terra MODIS composite datasets were downloaded for the hydrological year 2012-2013: MOD13A2 "Vegetation Indices" and MCD15A2 "LAI and FPAR", as well as the equivalent quality information layers (QA). All the pixels that fall in a vegetation land cover (according to the MERIS GLOBCOVER map) were sampled for the analysis, with the exception of those that fell at the border between two vegetation or other land cover categories, to avoid the influence of mixed pixels. Using linear regression analysis, the relationship between EVI and LAI was identified per date, vegetation type and study area. Results show that vegetation type has the highest influence in the variation of the relationship between EVI and LAI in each study area. The coefficient of determination (R2) is high and statistically significant (ranging from 0.41 to 0.83 in 90% of the cases). When plotting the EVI factor from the regression equation

Research note: Grazing-index method procedures of vegetation ...

African Journals Online (AJOL)

In the past, veld condition in the Karoo was assessed using the ecological index methods. This recently changed to the graxing-index method on account of the of the differently estimated grazing-index values being used. The principles governing the method of survey remain the same. The method employs ...

The long-term Global LAnd Surface Satellite (GLASS) product suite and applications

Science.gov (United States)

Liang, S.

2015-12-01

Our Earth's environment is experiencing rapid changes due to natural variability and human activities. To monitor, understand and predict environment changes to meet the economic, social and environmental needs, use of long-term high-quality satellite data products is critical. The Global LAnd Surface Satellite (GLASS) product suite, generated at Beijing Normal University, currently includes 12 products, including leaf area index (LAI), broadband shortwave albedo, broadband longwave emissivity, downwelling shortwave radiation and photosynthetically active radiation, land surface skin temperature, longwave net radiation, daytime all-wave net radiation, fraction of absorbed photosynetically active radiation absorbed by green vegetation (FAPAR), fraction of green vegetation coverage, gross primary productivity (GPP), and evapotranspiration (ET). Most products span from 1981-2014. The algorithms for producing these products have been published in the top remote sensing related journals and books. More and more applications have being reported in the scientific literature. The GLASS products are freely available at the Center for Global Change Data Processing and Analysis of Beijing Normal University (http://www.bnu-datacenter.com/), and the University of Maryland Global Land Cover Facility (http://glcf.umd.edu). After briefly introducing the basic characteristics of GLASS products, we will present some applications on the long-term environmental changes detected from GLASS products at both global and local scales. Detailed analysis of regional hotspots, such as Greenland, Tibetan plateau, and northern China, will be emphasized, where environmental changes have been mainly associated with climate warming, drought, land-atmosphere interactions, and human activities.

Vegetation of the Hantam-Tanqua-Roggeveld subregion, South Africa Part 2: Succulent Karoo Biome related vegetation

Directory of Open Access Journals (Sweden)

Helga van der Merwe

2009-03-01

Full Text Available The Hantam-Tanqua-Roggeveld subregion lies within the Succulent Karoo Hotspot that stretches along the western side of the Republic of South Africa and Namibia. This project, carried out to document the botanical diversity in the Hantam-Tanqua-Roggeveld subregion, was part of a project identified as a priority during the SKEP (Succulent Karoo Ecosystem Programme initiative in this Hotspot. Botanical surveys were conducted in an area covering over three million hectares. Satellite images of the area and topocadastral, land type and geology maps were used to stratify the area into relatively homogeneous units. An analysis of the floristic data of 390 sample plots identified two major floristic units, i.e. the Fynbos Biome related vegetation and the Succulent Karoo Biome related vegetation. A description of the vegetation related to the Succulent Karoo Biome is presented in this article. Seven associations, 16 subassociations and several mosaic vegetation units, consisting of more than one vegetation unit, were identified and mapped. Various threats to the vegetation in the region were identified during the survey and are briefly discussed.

Marsh collapse thresholds for coastal Louisiana estimated using elevation and vegetation index data

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Couvillion, Brady R.; Beck, Holly

2013-01-01

Forecasting marsh collapse in coastal Louisiana as a result of changes in sea-level rise, subsidence, and accretion deficits necessitates an understanding of thresholds beyond which inundation stress impedes marsh survival. The variability in thresholds at which different marsh types cease to occur (i.e., marsh collapse) is not well understood. We utilized remotely sensed imagery, field data, and elevation data to help gain insight into the relationships between vegetation health and inundation. A Normalized Difference Vegetation Index (NDVI) dataset was calculated using remotely sensed data at peak biomass (August) and used as a proxy for vegetation health and productivity. Statistics were calculated for NDVI values by marsh type for intermediate, brackish, and saline marsh in coastal Louisiana. Marsh-type specific NDVI values of 1.5 and 2 standard deviations below the mean were used as upper and lower limits to identify conditions indicative of collapse. As marshes seldom occur beyond these values, they are believed to represent a range within which marsh collapse is likely to occur. Inundation depth was selected as the primary candidate for evaluation of marsh collapse thresholds. Elevation relative to mean water level (MWL) was calculated by subtracting MWL from an elevation dataset compiled from multiple data types including light detection and ranging (lidar) and bathymetry. A polynomial cubic regression was used to examine a random subset of pixels to determine the relationship between elevation (relative to MWL) and NDVI. The marsh collapse uncertainty range values were found by locating the intercept of the regression line with the 1.5 and 2 standard deviations below the mean NDVI value for each marsh type. Results indicate marsh collapse uncertainty ranges of 30.7–35.8 cm below MWL for intermediate marsh, 20–25.6 cm below MWL for brackish marsh, and 16.9–23.5 cm below MWL for saline marsh. These values are thought to represent the ranges of

[Responses of normalized difference vegetation index (NDVI) to precipitation changes on the grassland of Tibetan Plateau from 2000 to 2015.

Science.gov (United States)

Wang, Zhi Peng; Zhang, Xian Zhou; He, Yong Tao; Li, Meng; Shi, Pei Li; Zu, Jia Xing; Niu, Ben

2018-01-01

Precipitation change is an important factor in the inter-annual variation of grassland growth on the Tibetan Plateau. The total amount, distribution pattern and concentration time are three basic characteristics of precipitation change. The temporal and spatial characteristics of precipitation change were analyzed based on climate data of 145 meteorological stations on the Tibetan Plateau and nearby areas from 2000 to 2015. The total precipitation amount was characterized by annual precipitation, distribution pattern of precipitation during the year was characterized by improved precipitation concentration index (PCI), and precipitation centroid (PC) was defined to indicate the change in precipitation concentrated time. To better illustrate the response of grassland to precipitation change, vegetation growth status was characterized by the maximum value of normalized difference vegetation index (NDVI max ). Results indicated that the annual precipitation and PCI had an apparent gradient across the whole plateau and the latest PC occurred in the southern plateau. NDVI max of alpine shrub grassland was significantly correlated with the change of PCI,increased with even distribution of precipitation during growth period, and limited by the total annual precipitation. Alpine meadow did not show significantly correlations with these three indices. The inter-annual variability of NDVI max of steppe was controlled by both PCI and PC. NDVI max of alpine desert grassland was mainly controlled by annual precipitation. In addition to annual total amount of precipitation, the distribution characteristics of precipitation should be further considered when the influence of precipitation change on different types of vegetation on the Qinghai Tibet Plateau was studied.

Models for the prediction of the cetane index of biofuels obtained from different vegetable oils using their fatty acid composition

International Nuclear Information System (INIS)

Sanchez Borroto, Yisel; Piloto Rodriguez, Ramon; Goyos Perez, Leonardo

2011-01-01

The objective of the present work is to obtain a physical-mathematical model that establishes a relationship between the cetane index of biofuels obtained from different vegetable oils and its composition of essential fatty acid. This model is based on experimental data obtained by the authors of the present work and an experimental data reported by different extracted authors of indexed databases. The adjustment of the coefficients of the model is based on the obtaining of residual minima in the capacity of prediction of the model. Starting from these results it is established a very useful tool for the determination of such an important parameter for the fuel diesel as it is the cetane index obtained from an analysis of chemical composition and not obtained from tests in engines banks, to save time and economic resources. (author)

Monitoring Corals and Submerged Aquatic Vegetation in Western Pacific Using Satellite Remote Sensing Integrated with Field Data

Science.gov (United States)

Roelfsema, C. M.; Phinn, S. R.; Lyons, M. B.; Kovacs, E.; Saunders, M. I.; Leon, J. X.

2013-12-01

Corals and Submerged Aquatic Vegetation (SAV) are typically found in highly dynamic environments where the magnitude and types of physical and biological processes controlling their distribution, diversity and function changes dramatically. Recent advances in the types of satellite image data and the length of their archives that are available globally, coupled with new techniques for extracting environmental information from these data sets has enabled significant advances to be made in our ability to map and monitor coral and SAV environments. Object Based Image Analysis techniques are one of the most significant advances in information extraction techniques for processing images to deliver environmental information at multiple spatial scales. This poster demonstrates OBIA applied to high spatial resolution satellite image data to map and monitor coral and SAV communities across a variety of environments in the Western Pacific that vary in their extent, biological composition, forcing physical factors and location. High spatial resolution satellite imagery (Quickbird, Ikonos and Worldview2) were acquired coincident with field surveys on each reef to collect georeferenced benthic photo transects, over various areas in the Western Pacific. Base line maps were created, from Roviana Lagoon Solomon island (600 km2), Bikini Atoll Marshall Island (800 Km2), Lizard Island, Australia (30 km2) and time series maps for geomorphic and benthic communities were collected for Heron Reef, Australia (24 km2) and Eastern Banks area of Moreton Bay, Australia (200 km2). The satellite image data were corrected for radiometric and atmospheric distortions to at-surface reflectance. Georeferenced benthic photos were acquired by divers or Autonomous Underwater Vehicles, analysed for benthic cover composition, and used for calibration and validation purposes. Hierarchical mapping from: reef/non-reef (1000's - 10000's m); reef type (100's - 1000's m); 'geomorphic zone' (10's - 100's m); to

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.

Resolving uncertainties in the urban air quality, climate, and vegetation nexus through citizen science, satellite imagery, and atmospheric modeling

Science.gov (United States)

Jenerette, D.; Wang, J.; Chandler, M.; Ripplinger, J.; Koutzoukis, S.; Ge, C.; Castro Garcia, L.; Kucera, D.; Liu, X.

2017-12-01

Large uncertainties remain in identifying the distribution of urban air quality and temperature risks across neighborhood to regional scales. Nevertheless, many cities are actively expanding vegetation with an expectation to moderate both climate and air quality risks. We address these uncertainties through an integrated analysis of satellite data, atmospheric modeling, and in-situ environmental sensor networks maintained by citizen scientists. During the summer of 2017 we deployed neighborhood-scale networks of air temperature and ozone sensors through three campaigns across urbanized southern California. During each five-week campaign we deployed six sensor nodes that included an EPA federal equivalent method ozone sensor and a suite of meteorological sensors. Each node was further embedded in a network of 100 air temperature sensors that combined a randomized design developed by the research team and a design co-created by citizen scientists. Between 20 and 60 citizen scientists were recruited for each campaign, with local partners supporting outreach and training to ensure consistent deployment and data gathering. We observed substantial variation in both temperature and ozone concentrations at scales less than 4km, whole city, and the broader southern California region. At the whole city scale the average spatial variation with our ozone sensor network just for city of Long Beach was 26% of the mean, while corresponding variation in air temperature was only 7% of the mean. These findings contrast with atmospheric model estimates of variation at the regional scale of 11% and 1%. Our results show the magnitude of fine-scale variation underestimated by current models and may also suggest scaling functions that can connect neighborhood and regional variation in both ozone and temperature risks in southern California. By engaging citizen science with high quality sensors, satellite data, and real-time forecasting, our results help identify magnitudes of climate and

A Method for Application of Classification Tree Models to Map Aquatic Vegetation Using Remotely Sensed Images from Different Sensors and Dates

Directory of Open Access Journals (Sweden)

Ying Cai

2012-09-01

Full Text Available In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT, the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model development came, greatly limiting the application of CT. We have developed an effective normalization method to improve the robustness of CT models when applied to images originating from different sensors and dates. A total of 965 ground-truth samples of aquatic vegetation types were obtained in 2009 and 2010 in Taihu Lake, China. Using relevant spectral indices (SI as classifiers, we manually developed a stable CT model structure and then applied a standard CT algorithm to obtain quantitative (optimal thresholds from 2009 ground-truth data and images from Landsat7-ETM+, HJ-1B-CCD, Landsat5-TM and ALOS-AVNIR-2 sensors. Optimal CT thresholds produced average classification accuracies of 78.1%, 84.7% and 74.0% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. However, the optimal CT thresholds for different sensor images differed from each other, with an average relative variation (RV of 6.40%. We developed and evaluated three new approaches to normalizing the images. The best-performing method (Method of 0.1% index scaling normalized the SI images using tailored percentages of extreme pixel values. Using the images normalized by Method of 0.1% index scaling, CT models for a particular sensor in which thresholds were replaced by those from the models developed for images originating from other sensors provided average classification accuracies of 76.0%, 82.8% and 68.9% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. Applying the CT models developed for normalized 2009 images to 2010 images resulted in high classification (78.0%–93.3% and overall (92.0%–93.1% accuracies. Our

Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia

Science.gov (United States)

Tadesse, Tsegaye; Senay, Gabriel B.; Berhan, Getachew; Regassa, Teshome; Beyene, Shimelis

2015-01-01

Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia’s cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation (R2 > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships (R2 < 0.7) for all cropping zones. However, for each individual crop zones, the correlation between NDVI and ETa ranged between 0.3 and 0.84 for about 44% of the cropping zones. Similarly, for each individual crop zones, the correlation (R2) between the seasonal ETa anomaly and de-trended cereal crop yield was between 0.4 and 0.82 for 76% (31 out of 41) of the crop growing zones. The preliminary results indicated that the ETa products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal

Recent change of vegetation growth trend in China

International Nuclear Information System (INIS)

Peng Shushi; Fang Jingyun; Piao Shilong; Chen, Anping; Xu Liang; Myneni, Ranga B; Cao Chunxiang; Pinzon, Jorge E; Tucker, Compton J

2011-01-01

Using satellite-derived normalized difference vegetation index (NDVI) data, several previous studies have indicated that vegetation growth significantly increased in most areas of China during the period 1982–99. In this letter, we extended the study period to 2010. We found that at the national scale the growing season (April–October) NDVI significantly increased by 0.0007 yr −1 from 1982 to 2010, but the increasing trend in NDVI over the last decade decreased in comparison to that of the 1982–99 period. The trends in NDVI show significant seasonal and spatial variances. The increasing trend in April and May (AM) NDVI (0.0013 yr −1 ) is larger than those in June, July and August (JJA) (0.0003 yr −1 ) and September and October (SO) (0.0008 yr −1 ). This relatively small increasing trend of JJA NDVI during 1982–2010 compared with that during 1982–99 (0.0012 yr −1 ) (Piao et al 2003 J. Geophys. Res.—Atmos. 108 4401) implies a change in the JJA vegetation growth trend, which significantly turned from increasing (0.0039 yr −1 ) to slightly decreasing ( − 0.0002 yr −1 ) in 1988. Regarding the spatial pattern of changes in NDVI, the growing season NDVI increased (over 0.0020 yr −1 ) from 1982 to 2010 in southern China, while its change was close to zero in northern China, as a result of a significant changing trend reversal that occurred in the 1990s and early 2000s. In northern China, the growing season NDVI significantly increased before the 1990s as a result of warming and enhanced precipitation, but decreased after the 1990s due to drought stress strengthened by warming and reduced precipitation. Our results also show that the responses of vegetation growth to climate change vary across different seasons and ecosystems.

Recent Change of Vegetation Growth Trend in China

Science.gov (United States)

Peng, Shushi; Chen, Anping; Xu, Liang; Cao, Chunxiang; Fang, Jingyun; Myneni, Ranga B.; Pinzon, Jorge E.; Tucker, COmpton J.; Piao, Shilong

2011-01-01

Using satellite-derived normalized difference vegetation index (NDVI) data, several previous studies have indicated that vegetation growth significantly increased in most areas of China during the period 1982-99. In this letter, we extended the study period to 2010. We found that at the national scale the growing season (April-October) NDVI significantly increased by 0.0007/yr from 1982 to 2010, but the increasing trend in NDVI over the last decade decreased in comparison to that of the 1982-99 period. The trends in NDVI show significant seasonal and spatial variances. The increasing trend in April and May (AM) NDVI (0.0013/yr is larger than those in June, July and August (JJA) (0.0003/yr) and September and October (SO) (0.0008/yr). This relatively small increasing trend of JJA NDVI during 1982-2010 compared with that during 1982-99 (0.0012/yr) (Piao et al 2003 J. Geophys. Res.-Atmos. 108 4401) implies a change in the JJA vegetation growth trend, which significantly turned from increasing (0.0039/yr) to slightly decreasing (0:0002/yr) in 1988. Regarding the spatial pattern of changes in NDVI, the growing season NDVI increased (over 0.0020/yr) from 1982 to 2010 in southern China, while its change was close to zero in northern China, as a result of a significant changing trend reversal that occurred in the 1990s and early 2000s. In northern China, the growing season NDVI significantly increased before the 1990s as a result of warming and enhanced precipitation, but decreased after the 1990s due to drought stress strengthened by warming and reduced precipitation. Our results also show that the responses of vegetation growth to climate change vary across different seasons and ecosystems.

Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images

International Nuclear Information System (INIS)

Arellano, Paul; Tansey, Kevin; Balzter, Heiko; Boyd, Doreen S.

2015-01-01

The global demand for fossil energy is triggering oil exploration and production projects in remote areas of the world. During the last few decades hydrocarbon production has caused pollution in the Amazon forest inflicting considerable environmental impact. Until now it is not clear how hydrocarbon pollution affects the health of the tropical forest flora. During a field campaign in polluted and pristine forest, more than 1100 leaf samples were collected and analysed for biophysical and biochemical parameters. The results revealed that tropical forests exposed to hydrocarbon pollution show reduced levels of chlorophyll content, higher levels of foliar water content and leaf structural changes. In order to map this impact over wider geographical areas, vegetation indices were applied to hyperspectral Hyperion satellite imagery. Three vegetation indices (SR, NDVI and NDVI 705 ) were found to be the most appropriate indices to detect the effects of petroleum pollution in the Amazon forest. - Highlights: • Leaf biochemical alterations in the rainforest are caused by petroleum pollution. • Lower levels of chlorophyll content are symptom of vegetation stress in polluted sites. • Increased foliar water content was found in vegetation near polluted sites. • Vegetation stress was detected by using vegetation indices from satellite images. • Polluted sites and hydrocarbon seepages in rainforest can be identified from space. - Hydrocarbon pollution in the Amazon forest is observed for first time from satellite data

LAND SURFACE TEMPERATURES ESTIMATED ON GROUNDOBSERVED DATA AND SATELLITE IMAGES, DURING THE VEGETATION PERIOD IN THE OLTENIA PLAIN

Directory of Open Access Journals (Sweden)

ONŢEL IRINA

2015-03-01

Full Text Available The purpose of this study is to analyze the land surface temperatures by using climatological and remote sensing data during the vegetation period in the Oltenia Plain. The data used in this study refer both to climatological data (namely monthly and seasonal air and soil temperatures, and to remote sensing data delivered by MODIS Land Surface Temperature (LST, with a spatial resolution of 1 km. The analyzed period spans from 2000 to 2013 and the vegetation period considered is April-September. As main results, there were observed four years with high temperatures, namely 2000 (20.4oC-air T, 24.6oC soil T, and 26oC LST, 2003 (20.2oC air T, 23.9oC soil T and 24.5oC LST, 2007 (20.5oC air T, 24.3oC soil T and 25oC LST and 2012 (21.3oC air T, 25.7oC soil T and 26.5oC LST. The correlations between air temperature, soil temperature and LST were statisticaly significant. The diference between air temperature and soil temperature values ranked within 3-4oC, while the difference between soil temperature and land surface temperature obtained from MODIS images was about 0.8oC. Spatially, the highest temperatures were recorded on the Leu-Rotunda Field, the Caracal Plain and the Nedeia Field, and pretty high variations of observed temperatures seemed to depend on vegetation cover. The MODIS images represent one of the most important types of satellite data available for free, which can be successfully used in determining the climatic parameters and can help to predict the changes in plant activity, due to weather phenomena.

Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh

OpenAIRE

Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y.; Nizamuddin, Mohammad; Goldberg, Mitch

2009-01-01

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991...

Temporal analysis of vegetation indices related to biophysical parameters using Sentinel 2A images to estimate maize production

Science.gov (United States)

Macedo, Lucas Saran; Kawakubo, Fernando Shinji

2017-10-01

Agricultural production is one of the most important Brazilian economic activities accounting for about 21,5% of total Gross Domestic Product. In this scenario, the use of satellite images for estimating biophysical parameters along the phenological development of agricultural crops allows the conclusion about the sanity of planting and helps the projection on design production trends. The objective of this study is to analyze the temporal patterns and variation of six vegetion indexes obtained from the bands of Sentinel 2A satellite, associated with greenness (NDVI and ClRE), senescence (mARI and PSRI) and water content (DSWI and NDWI) to estimate maize production. The temporal pattern of the indices was analyzed in function of productivity data collected in-situ. The results obtained evidenced the importance of the SWIR and Red Edge ranges with Pearson correlation values of the temporal mean for NDWI 0.88 and 0.76 for CLRE.

Using Landsat Vegetation Indices to Estimate Impervious Surface Fractions for European Cities

DEFF Research Database (Denmark)

Kaspersen, Per Skougaard; Fensholt, Rasmus; Drews, Martin

2015-01-01

and applicability of vegetation indices (VI), from Landsat imagery, to estimate IS fractions for European cities. The accuracy of three different measures of vegetation cover is examined for eight urban areas at different locations in Europe. The Normalized Difference Vegetation Index (NDVI) and Soil Adjusted...... Vegetation Index (SAVI) are converted to IS fractions using a regression modelling approach. Also, NDVI is used to estimate fractional vegetation cover (FR), and consequently IS fractions. All three indices provide fairly accurate estimates (MAEs ≈ 10%, MBE’s

Improvement in remote sensing of low vegetation cover in arid regions by correcting vegetation indices for soil ''noise''

International Nuclear Information System (INIS)

Escadafal, R.; Huete, A.

1991-01-01

The variations of near-infrared red reflectance ratios of ten aridic soil samples were correlated with a ''redness index'' computed from red and green spectral bands. These variations have been shown to limit the performances of vegetation indices (NDVI and SAVI) in discriminating low vegetation covers. The redness index is used to adjust for this ''soil noise''. Dala simulated for vegetation densities of 5 to 15% cover showed that the sensitivity of the corrected vegetation indices was significantly improved. Specifically, the ''noise-corrected'' SAVI was able to assess vegetation amounts with an error four times smaller than the uncorrected NDVI. These promising results should lead to a significant improvement in assessing biomass in arid lands from remotely sensed data. (author) [fr

Analysis of Behavior of Vegetation in the Year of 2016 for the Municipality of Remanso- BA

OpenAIRE

Ismael Farias de Freitas; Laurizio E. R. Alves; Heliofábio B. Gomes; Jeová R. S. Júnior; Dimas B. Santiago; Rafael A. Silva

2017-01-01

Droughts are a natural problem in the Northeastern Brazilian region, in addition the rainfall distribution poorly distributed spatially and temporally results in seasonal changes in the surface vegetation. Consequently, the monitoring and evaluation of vegetation in the northeast region of Brazil has become increasingly constant. For this evaluation several techniques are used, but the use of environmental satellites is increasingly applied, such as the Landsat 8 satellite, where the products...

Angular Normalization of Ground and Satellite Observations of Sun-induced Chlorophyll Fluorescence for Assessing Vegetation Productivity

Science.gov (United States)

Chen, J. M.; He, L.; Chou, S.; Ju, W.; Zhang, Y.; Joiner, J.; Liu, J.; Mo, G.

2017-12-01

Sun-induced chlorophyll fluorescence (SIF) measured from plant canopies originates mostly from sunlit leaves. Observations of SIF by satellite sensors, such as GOME-2 and GOSAT, are often made over large view zenith angle ranges, causing large changes in the viewed sunlit leaf fraction across the scanning swath. Although observations made by OCO-2 are near nadir, the observed sunlit leaf fraction could still vary greatly due to changes in the solar zenith angle with latitude and time of overpass. To demonstrate the importance of considering the satellite-target-view geometry in using SIF for assessing vegetation productivity, we conducted multi-angle measurements of SIF using a hyperspectral sensor mounted on an automated rotating system over a rice field near Nanjing, China. A method is developed to separate SIF measurements at each angle into sunlit and shaded leaf components, and an angularly normalized canopy-level SIF is obtained as the weighted sum of sunlit and shaded SIF. This normalized SIF is shown to be a much better proxy of GPP of the rice field measured by an eddy covariance system than the unnormalized SIF observations. The same normalization scheme is also applied to the far-red GOME-2 SIF observations on sunny days, and we found that the normalized SIF is better correlated with model-simulated GPP than the original SIF observations. The coefficient of determination (R2) is improved by 0.07±0.04 on global average using the normalization scheme. The most significant improvement in R2 by 0.09±0.04 is found in deciduous broadleaf forests, where the observed sunlit leaf fraction is highly sensitive to solar zenith angle.

Assessing Wildlife Habitat And Range Utilization in Arizona Using Satellite Data

Science.gov (United States)

Hutchinson, C. F.; Marsh, S. E.; Krausman, P. R.; Enns, R. M.; Howery, L. D.; Trobia, E.; Wallace, C. S.; Walker, J. J.; Mauz, K.; Boyd, H.; Salazar, H.

2001-05-01

Since their reintroduction in 1914, elk (Cervus elaphus) have grown to be a major issue in the western United States. Most land is controlled by federal or state agencies, but individual ranchers have agreements that permit them to graze cattle on much of this land. Elk often compete with cattle for forage, and damage infrastructure (i.e. fences, watering points, and crops). Conversely, environmentalists and hunters also have an interest in the management of elk populations. As a result, consequence of these conflicting interests, there is little agreement about the size of the elk population or the nature, location, and timing of conflicts that elk might cause. This study was intended to provide information that might help managers understand the distribution of elk in Arizona as a consequence of seasonal variation and in response to extreme climatic events (i.e. El Niño and La Niña). The first task involved modeling elk populations over time. There are no long term or large-scale studies of elk movements through continuous observation (i.e. radiocollars). A technique for modeling elk population has been developed that is based on harvest data, gender ratios, and estimates of male mortality. This provided estimates of elk populations for individual game management units (areas for which harvest is reported and within which elk are managed by the Arizona Game and Fish Department). The second task involved the use of satellite data to characterize vegetation responses to seasonal and interannual climate variation among vegetation associations within game management units. This involved the use of NOAA Advanced Very High Resolution Radiometer (AVHRR) time series data to describe temporal vegetation behavior, Landsat and Ikonos data to describe spatial vegetation distribution in conjunction with U.S. Forest Service vegetation maps. Elk population estimates were correlated with satellite-derived vegetation measures by vegetation association through time. The patterns

Upscaling reflectance information of lichens and mosses using a singularity index: a case study of the Hudson Bay Lowlands, Canada

Directory of Open Access Journals (Sweden)

T. Neta

2010-08-01

Full Text Available Assessing moisture contents of lichens and mosses using ground-based high spectral resolution spectrometers (400–2500 nm offers immense opportunities for a comprehensive monitoring of peatland moisture status by satellite/airborne imagery. This information may be valuable for present and future carbon balance modeling. Previous studies are based upon point measurements of vegetation moisture content and water table position, and therefore a detailed moisture status of entire northern peatlands is not available. Consequently, upscaling ground and remotely sensed data to the desired spatial resolutions is inevitable. This study continues our previous investigation of the impact of various moisture conditions of common sub-Arctic lichen and moss species (i.e., Cladina stellaris, Cladina rangiferina, Dicranum elongatum, and Tomenthypnum nitens upon the spectral signatures obtained in the Hudson Bay Lowlands, Canada. Upscaling reflectance measurements of the above species were conducted in the field, and reflectance analysis using a singularity index was made, since this study serves as a basis for future aircraft/satellite research. An attempt to upscale current and new spectral reflectance indices developed in our previous studies was made as well. Our findings indicate that the spectral index C. rangiferina is to a lesser amount influenced by scale since it has a small R² values between the log of the index and the log of the resolution, reduced slopes between the log of the index and the log of the resolution, and similar slopes between log reflectance and log resolution (α of two wavelengths employed by the index. Future study should focus on concurrent monitoring of moisture variations in lichens and mosses both in situ and from satellite and airborne images, as well as analysis of fractal models in relations to the upscaling experiments.

Using remote sensing satellite data and artificial neural network for prediction of potato yield in Bangladesh

Science.gov (United States)

Akhand, Kawsar; Nizamuddin, Mohammad; Roytman, Leonid; Kogan, Felix

2016-09-01

Potato is one of the staple foods and cash crops in Bangladesh. It is widely cultivated in all of the districts and ranks second after rice in production. Bangladesh is the fourth largest potato producer in Asia and is among the world's top 15 potato producing countries. The weather condition for potato cultivation is favorable during the sowing, growing and harvesting period. It is a winter crop and is cultivated during the period of November to March. Bangladesh is mainly an agricultural based country with respect to agriculture's contribution to GDP, employment and consumption. Potato is a prominent crop in consideration of production, its internal demand and economic value. Bangladesh has a big economic activities related to potato cultivation and marketing, especially the economic relations among farmers, traders, stockers and cold storage owners. Potato yield prediction before harvest is an important issue for the Government and the stakeholders in managing and controlling the potato market. Advanced very high resolution radiometer (AVHRR) based satellite data product vegetation health indices VCI (vegetation condition index) and TCI (temperature condition index) are used as predictors for early prediction. Artificial neural network (ANN) is used to develop a prediction model. The simulated result from this model is encouraging and the error of prediction is less than 10%.

Physics teaching by infrared remote sensing of vegetation

Science.gov (United States)

Schüttler, Tobias; Maman, Shimrit; Girwidz, Raimund

2018-05-01

Context- and project-based teaching has proven to foster different affective and cognitive aspects of learning. As a versatile and multidisciplinary scientific research area with diverse applications for everyday life, satellite remote sensing is an interesting context for physics education. In this paper we give a brief overview of satellite remote sensing of vegetation and how to obtain your own, individual infrared remote sensing data with affordable converted digital cameras. This novel technique provides the opportunity to conduct individual remote sensing measurement projects with students in their respective environment. The data can be compared to real satellite data and is of sufficient accuracy for educational purposes.

Using satellite remote sensing to model and map the distribution of Bicknell's thrush (Catharus bicknelli) in the White Mountains of New Hampshire

Science.gov (United States)

Hale, Stephen Roy

Landsat-7 Enhanced Thematic Mapper satellite imagery was used to model Bicknell's Thrush (Catharus bicknelli) distribution in the White Mountains of New Hampshire. The proof-of-concept was established for using satellite imagery in species-habitat modeling, where for the first time imagery spectral features were used to estimate a species-habitat model variable. The model predicted rising probabilities of thrush presence with decreasing dominant vegetation height, increasing elevation, and decreasing distance to nearest Fir Sapling cover type. To solve the model at all locations required regressor estimates at every pixel, which were not available for the dominant vegetation height and elevation variables. Topographically normalized imagery features Normalized Difference Vegetation Index and Band 1 (blue) were used to estimate dominant vegetation height using multiple linear regression; and a Digital Elevation Model was used to estimate elevation. Distance to nearest Fir Sapling cover type was obtained for each pixel from a land cover map specifically constructed for this project. The Bicknell's Thrush habitat model was derived using logistic regression, which produced the probability of detecting a singing male based on the pattern of model covariates. Model validation using Bicknell's Thrush data not used in model calibration, revealed that the model accurately estimated thrush presence at probabilities ranging from 0 to account for more total individuals and reproductive output than higher density less abundant land area. Efforts to conserve areas of highest individual density under the assumption that density reflects habitat quality could target the smallest fraction of the total population.

Coupling Analysis of Heat Island Effects, Vegetation Coverage and Urban Flood in Wuhan

Science.gov (United States)

Liu, Y.; Liu, Q.; Fan, W.; Wang, G.

2018-04-01

In this paper, satellite image, remote sensing technique and geographic information system technique are main technical bases. Spectral and other factors comprehensive analysis and visual interpretation are main methods. We use GF-1 and Landsat8 remote sensing satellite image of Wuhan as data source, and from which we extract vegetation distribution, urban heat island relative intensity distribution map and urban flood submergence range. Based on the extracted information, through spatial analysis and regression analysis, we find correlations among heat island effect, vegetation coverage and urban flood. The results show that there is a high degree of overlap between of urban heat island and urban flood. The area of urban heat island has buildings with little vegetation cover, which may be one of the reasons for the local heavy rainstorms. Furthermore, the urban heat island has a negative correlation with vegetation coverage, and the heat island effect can be alleviated by the vegetation to a certain extent. So it is easy to understand that the new industrial zones and commercial areas which under constructions distribute in the city, these land surfaces becoming bare or have low vegetation coverage, can form new heat islands easily.

Regional vegetation dynamics and its response to climate change—a case study in the Tao River Basin in Northwestern China

International Nuclear Information System (INIS)

Li, Changbin; Yang, Linshan; Wang, Shuaibing; Yang, Wenjin; Zhu, Gaofeng; Qi, Jiaguo; Zou, Songbing; Zhang, Feng

2014-01-01

The 30-year normalized-difference vegetation index (NDVI) time series from AVHRR/MODIS satellite sensors was used in this study to assess the regional vegetation dynamic changes in the Tao River Basin, which cuts across the Eastern Tibetan Plateau (ETP) and the Southwestern Loess Plateau (SLP). First, principal component and correlation analyses were carried out to determine the key climatic variables driving ecological change in the region. Then, regression models were tested to correlate NDVI with the selected climatic variables to determine their predictive power. Finally, Sen’s slope method was used to determine how terrestrial vegetation has responded to regional climate change in the region. The results indicated an average winter season NDVI value of 0.14 in the ETP but only 0.04 in the SLP. Primarily driven by increasing temperature, vegetation growth has generally been enhanced since 1981; spring NDVI increased by 0.03 every 10 years in the ETP and 0.02 in the SLP. Further, results from trend analyses suggest vegetation growth in the ETP shifted to earlier-start and earlier-end dates, however in the SLP, the growing season has been extended with an earlier-start and later-end date. The precipitation threshold for vegetation germination, measured by the cumulative spring rainfall, was found to be 44 mm for both the ETP and SLP. (paper)

An evaluation of hyperspectral vegetation indices for detecting soil salinity in sugarcane fields using EO-1 Hyperion Data

Science.gov (United States)

Hamzeh, S.; Naseri, A. A.; Alavi Panah, S. K.; Bartholomeus, H.; Mojaradi, B.; Clevers, J.; Behzad, M.

2012-04-01

Sugarcane is the major agricultural crops in the Khuzestan province, in the southwest of Iran. But soil salinity is a major problem affecting the sugarcane yield, and therefore, monitoring and assessment of soil salinity is necessary. This research was carried out to investigate the performance of several hyperspectral vegetation indices to assess salinity stress in sugarcane fields and to determine the suitable indicators and statistical models for detecting various soil salinity levels. For this purpose one Hyperion image was acquired on Sept 2, 2010 and soil salinity was measured in 108 points 5 to 15 days from this date. 60 Samples were used for modeling and 48 samples were used for validation. Values of the soil salinity were linked with the corresponding pixel at the satellite imagery and 16 (hyperspectral) spectral indices were calculated. Then, the potential of these indices for estimating the soil salinity were analyzed and results show that soil salinity can well be estimated by vegetation indices derived from Hyperion data. Indices that are based on the chlorophyll and water absorption bands have medium to high relationship with soil salinity, while indices that only use visible bands or combination of visible and NIR bands don't perform well. From the investigated indices the Optimized Soil-Adjusted Vegetation Index (OSAVI) has the strongest relationship (R2 = 0.69) with soil salinity, because this index minimizes the variations in reflectance characteristics of soil background.

Environmental quality evaluation. Indexing tools to evaluate environmental quality from biological data, floristic and vegetational data in Ponte Galeria (Rome, Italy)

International Nuclear Information System (INIS)

Mazzocchi, F.; Castorina, M.; De Mei, M.

1998-01-01

In the present work the study of indexing tools to evaluate environmental quality from biological data has been performed using a certain number of floristic and vegetational indices near Macchia Grande of Ponte Galeria (Rome, Italy). The indices have been applied on the basis of the data coming from a phyto sociological study of the area. Multivariate statistics methodologies have been utilized to obtain a synthetic evaluation of the indices [it

Sixteen years of agricultural drought assessment of the BioBío region in Chile using a 250 m resolution Vegetation Condition Index (VCI)

Science.gov (United States)

Zambrano, Francisco; Lillo-Saavedra, Mario; Verbist, Koen; Lagos, Octavio

2016-10-01

Drought is one of the most complex natural hazards because of its slow onset and long-term impact; it has the potential to negatively affect many people. There are several advantages to using remote sensing to monitor drought, especially in developing countries with limited historical meteorological records and a low weather station density. In the present study, we assessed agricultural drought in the croplands of the BioBio Region in Chile. The vegetation condition index (VCI) allows identifying the temporal and spatial variations of vegetation conditions associated with stress because of rainfall deficit. The VCI was derived at a 250m spatial resolution for the 2000-2015 period with the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 product. We evaluated VCI for cropland areas using the land cover MCD12Q1 version 5.1 product and compared it to the in situ Standardized Precipitation Index (SPI) for six-time scales (1-6 months) from 26 weather stations. Results showed that the 3-month SPI (SPI-3), calculated for the modified growing season (Nov-Apr) instead of the regular growing season (Sept-Apr), has the best Pearson correlation with VCI values with an overall correlation of 0.63 and between 0.40 and 0.78 for the administrative units. These results show a very short-term vegetation response to rainfall deficit in September, which is reflected in the vegetation in November, and also explains to a large degree the variation in vegetation stress. It is shown that for the last 16 years in the BioBio Region we could identify the 2007/2008, 2008/2009, and 2014/2015 seasons as the three most important drought events; this is reflected in both the overall regional and administrative unit analyses. These results concur with drought emergencies declared by the regional government. Future studies are needed to associate the remote sensing values observed at high resolution (250m) with the measured crop yield to identify more detailed individual crop

Seasonally asymmetric enhancement of northern vegetation productivity

Science.gov (United States)

Park, T.; Myneni, R.

2017-12-01

Multiple evidences of widespread greening and increasing terrestrial carbon uptake have been documented. In particular, enhanced gross productivity of northern vegetation has been a critical role leading to observed carbon uptake trend. However, seasonal photosynthetic activity and its contribution to observed annual carbon uptake trend and interannual variability are not well understood. Here, we introduce a multiple-source of datasets including ground, atmospheric and satellite observations, and multiple process-based global vegetation models to understand how seasonal variation of land surface vegetation controls a large-scale carbon exchange. Our analysis clearly shows a seasonally asymmetric enhancement of northern vegetation productivity in growing season during last decades. Particularly, increasing gross productivity in late spring and early summer is obvious and dominant driver explaining observed trend and variability. We observe more asymmetric productivity enhancement in warmer region and this spatially varying asymmetricity in northern vegetation are likely explained by canopy development rate, thermal and light availability. These results imply that continued warming may facilitate amplifying asymmetric vegetation activity and cause these trends to become more pervasive, in turn warming induced regime shift in northern land.

Multitemporal satellite change detection investigations for documentation and valorization of cultural landscape

Science.gov (United States)

Lasaponara, R.; Masini, n.

2012-04-01

archaeological prospection. Int J Remote Sens 27: 3607-3614. Lasaponara R, Masini N (2006b) Identification of archaeological buried remains based on Normalized Difference Vegetation Index (NDVI) from Quickbird satellite data. IEEE Geosci Remote S 3(3): 325-328. Lasaponara R, Masini N (2007a) Detection of archaeological crop marks by using satellite QuickBird multispectral imagery. J Archaeol Sci 34: 214-21. Lasaponara R, Masini N (2007b) Improving satellite Quickbird - based identification of landscape archaeological features trough tasselled cup transformation and PCA. 21st CIPA Symposium, Atene, 1-6 giugno 2007. Lasaponara R, Masini N (2010) Facing the archaeological looting in Peru by local spatial autocorrelation statistics of Very high resolution satellite imagery. In: Taniar D et al (Eds), Proceedings of ICSSA, The 2010 International Conference on Computational Science and its Application (Fukuoka-Japan, March 23 - 26, 2010), Springer, Berlin, 261-269. Lasaponara R, Masini N (2011) Satellite Remote Sensing in Archaeology : past, present and future. J Archaeol Sc 38: 1995-2002. Lasaponara R, Masini N, Rizzo E, Orefici G (2011) New discoveries in the Piramide Naranjada in Cahuachi (Peru) using satellite, Ground Probing Radar and magnetic investigations. J Archaeol Sci 38: 2031-2039. Lasaponara R, Masini N, Scardozzi G (2008) Satellite based archaeological research in ancient territory of Hierapolis. 1st International EARSeL Workshop. Advances in Remote Sensing for Archaeology and Cultural Heritage Management", CNR, Rome, September 30-October 4, Aracne, Rome, pp.11-16. Lillesand T M, Kiefer R W (2000) Remote Sensing and Image interpretation. John Wiley and Sons, New York. Masini N, Lasaponara R (2006) Satellite-based recognition of landscape archaeological features related to ancient human transformation. J Geophys Eng 3: 230-235, doi:10.1088/1742-2132/3/3/004. Masini N, Lasaponara R (2007) Investigating the spectral capability of QuickBird data to detect archaeological

Estimates of phytomass and net primary productivity in terrestrial ecosystems of the former Soviet Union identified by classified Global Vegetation Index

Energy Technology Data Exchange (ETDEWEB)

Gaston, G.G.; Kolchugina, T.P. [Oregon State Univ., Corvallis, OR (United States)

1995-12-01

Forty-two regions with similar vegetation and landcover were identified in the former Soviet Union (FSU) by classifying Global Vegetation Index (GVI) images. Image classes were described in terms of vegetation and landcover. Image classes appear to provide more accurate and precise descriptions for most ecosystems when compared to general thematic maps. The area of forest lands were estimated at 1,330 Mha and the actual area of forest ecosystems at 875 Mha. Arable lands were estimated to be 211 Mha. The area of the tundra biome was estimated at 261 Mha. The areas of the forest-tundra/dwarf forest, taiga, mixed-deciduous forest and forest-steppe biomes were estimated t 153, 882, 196, and 144 Mha, respectively. The areas of desert-semidesert biome and arable land with irrigated land and meadows, were estimated at 126 and 237 Mha, respectively. Vegetation and landcover types were associated with the Bazilevich database of phytomass and NPP for vegetation in the FSU. The phytomass in the FSU was estimated at 97.1 Gt C, with 86.8 in forest vegetation, 9.7 in natural non-forest and 0.6 Gt C in arable lands. The NPP was estimated at 8.6 Gt C/yr, with 3.2, 4.8, and 0.6 Gt C/yr of forest, natural non-forest, and arable ecosystems, respectively. The phytomass estimates for forests were greater than previous assessments which considered the age-class distribution of forest stands in the FSU. The NPP of natural ecosystems estimated in this study was 23% greater than previous estimates which used thematic maps to identify ecosystems. 47 refs., 4 figs., 2 tabs.

Evaluation of forest fires in Portugal Mainland during 2016 summer considering different satellite datasets

Science.gov (United States)

Teodoro, A. C.; Amaral, A.

2017-10-01

Portugal is one of the most affected countries in Europe by forest fires. Every year in the summer, hundreds of hectares burn, destroying goods and forests at an alarming rate. The objective of this work was to analyze the forest areas burned in Portugal in 2016 (summer) using different satellite data with different spatial resolution (Sentinel-2A MSI and Landsat 8 OLI) in two affected areas. Data from spring from 2016 and 2017 were chosen (pre-fire event and post-fire event) in order to maximize the Normalized Difference Vegetation Index (NDVI) values. The QGIS software's plugin - Semi- Automatic Classification Plugin- which allowed to obtain NDVI values for the Landsat 8 OLI and Sentinel- 2A was used. The results showed that the NDVI decreased considerably in Arouca and Vila Nova de Cerveira after de fire event, meaning a marked drop in vegetation level. In Sintra municipality this change was not verified because non forest fire was registered in this area during the study period. The results from the Sentinel-2A and Landsat 8 OLI data analysis are in agreement, however the Sentinel-2A satellite gives results more accurate than Landsat-8 OLI since it has best spatial resolution. This study could help the experts to understand both the causes and consequences of spatial variability of post-fire effects. Other vegetation spectral indices related with fire and burnt areas could also be calculated in order to discriminate burnt areas. Added to the best spatial resolution of Sentinel-2A (10 m), the temporal resolution of Sentinel- 2A (10 days) was increased with the launch of the twin Sentinel-2B (very recently) and therefore the frequency of the combined constellation revisit will be 5 days. However, for historical studies, the Landsat program remains the best option.

Hierarchical Satellite-based Approach to Global Monitoring of Crop Condition and Food Production

Science.gov (United States)

Zheng, Y.; Wu, B.; Gommes, R.; Zhang, M.; Zhang, N.; Zeng, H.; Zou, W.; Yan, N.

2014-12-01

The assessment of global food security goes beyond the mere estimate of crop production: It needs to take into account the spatial and temporal patterns of food availability, as well as physical and economic access. Accurate and timely information is essential to both food producers and consumers. Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, such as FY-2/3A, HJ-1 CCD, CropWatch has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The new monitoring approach adopts a hierarchical system covering four spatial levels of detail: global (sixty-five Monitoring and Reporting Units, MRU), seven major production zones (MPZ), thirty-one key countries (including China) and "sub- countries." The thirty-one countries encompass more that 80% of both global exports and production of four major crops (maize, rice, soybean and wheat). The methodology resorts to climatic and remote sensing indicators at different scales, using the integrated information to assess global, regional, and national (as well as sub-national) crop environmental condition, crop condition, drought, production, and agricultural trends. The climatic indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass are first analysed at global scale to describe overall crop growing conditions. At MPZ scale, the key indicators pay more attention to crops and include Vegetation health index (VHI), Vegetation condition index (VCI), Cropped arable land fraction (CALF) as well as Cropping intensity (CI). Together, they characterise agricultural patterns, farming intensity and stress. CropWatch carries out detailed crop condition analyses for thirty one individual countries at the national scale with a comprehensive array of variables and indicators. The Normalized difference vegetation index (NDVI), cropped areas and crop condition are

Causes of spring vegetation growth trends in the northern mid–high latitudes from 1982 to 2004

International Nuclear Information System (INIS)

Mao Jiafu; Shi Xiaoying; Thornton, Peter E; Piao Shilong; Wang Xuhui

2012-01-01

The Community Land Model version 4 (CLM4) is applied to explore the spatial–temporal patterns of spring (April–May) vegetation growth trends over the northern mid–high latitudes (NMH) (>25°N) between 1982 and 2004. During the spring season through the 23 yr period, both the satellite-derived and simulated normalized difference vegetation index (NDVI) anomalies show a statistically significant correlation and an overall greening trend within the study area. Consistently with the observed NDVI–temperature relation, the CLM4 NDVI shows a significant positive association with the spring temperature anomaly for the NMH, North America and Eurasia. Large study areas experience temperature discontinuity associated with contrasting NDVI trends. Before and after the turning point (TP) of the temperature trends, climatic variability plays a dominant role, while the other environmental factors exert minor effects on the NDVI tendencies. Simulated vegetation growth is broadly stimulated by the increasing atmospheric CO 2 . Trends show that nitrogen deposition increases NDVI mostly in southeastern China, and decreases NDVI mainly in western Russia after the temperature TP. Furthermore, land use-induced NDVI trends vary roughly with the respective changes in land management practices (crop areas and forest coverage). Our results highlight how non-climatic factors mitigate or exacerbate the impact of temperature on spring vegetation growth, particularly across regions with intensive human activity. (letter)

Causes of spring vegetation greenness trends in the northern mid-high latitudes from 1982 to 2004

Energy Technology Data Exchange (ETDEWEB)

Mao, Jiafu [ORNL; Shi, Xiaoying [ORNL; Thornton, Peter E [ORNL; Shilong, Dr. Piao [Peking University; Xuhui, Dr. Wang [Peking University

2012-01-01

The Community Land Model version 4 (CLM4) is applied to explore the spatial temporal patterns of spring (April May) vegetation growth trends over the northern mid high latitudes (NMH) (>25 N) between 1982 and 2004. During the spring season through the 23 yr period, both the satellite-derived and simulated normalized difference vegetation index (NDVI) anomalies show a statistically significant correlation and an overall greening trend within the study area. Consistently with the observed NDVI temperature relation, the CLM4 NDVI shows a significant positive association with the spring temperature anomaly for the NMH, North America and Eurasia. Large study areas experience temperature discontinuity associated with contrasting NDVI trends. Before and after the turning point (TP) of the temperature trends, climatic variability plays a dominant role, while the other environmental factors exert minor effects on the NDVI tendencies. Simulated vegetation growth is broadly stimulated by the increasing atmospheric CO2. Trends show that nitrogen deposition increases NDVI mostly in southeastern China, and decreases NDVI mainly in western Russia after the temperature TP. Furthermore, land use-induced NDVI trends vary roughly with the respective changes in land management practices (crop areas and forest coverage). Our results highlight how non-climatic factors mitigate or exacerbate the impact of temperature on spring vegetation growth, particularly across regions with intensive human activity.

The MODIS Vegetation Canopy Water Content product

Science.gov (United States)

Ustin, S. L.; Riano, D.; Trombetti, M.

2008-12-01

Vegetation water stress drives wildfire behavior and risk, having important implications for biogeochemical cycling in natural ecosystems, agriculture, and forestry. Water stress limits plant transpiration and carbon gain. The regulation of photosynthesis creates close linkages between the carbon, water, and energy cycles and through metabolism to the nitrogen cycle. We generated systematic weekly CWC estimated for the USA from 2000-2006. MODIS measures the sunlit reflectance of the vegetation in the visible, near-infrared, and shortwave infrared. Radiative transfer models, such as PROSPECT-SAILH, determine how sunlight interacts with plant and soil materials. These models can be applied over a range of scales and ecosystem types. Artificial Neural Networks (ANN) were used to optimize the inversion of these models to determine vegetation water content. We carried out multi-scale validation of the product using field data, airborne and satellite cross-calibration. An Algorithm Theoretical Basis Document (ATBD) of the product is under evaluation by NASA. The CWC product inputs are 1) The MODIS Terra/Aqua surface reflectance product (MOD09A1/MYD09A1) 2) The MODIS land cover map product (MOD12Q1) reclassified to grassland, shrub-land and forest canopies; 3) An ANN trained with PROSPECT-SAILH; 4) A calibration file for each land cover type. The output is an ENVI file with the CWC values. The code is written in Matlab environment and is being adapted to read not only the 8 day MODIS composites, but also daily surface reflectance data. We plan to incorporate the cloud and snow mask and generate as output a geotiff file. Vegetation water content estimates will help predicting linkages between biogeochemical cycles, which will enable further understanding of feedbacks to atmospheric concentrations of greenhouse gases. It will also serve to estimate primary productivity of the biosphere; monitor/assess natural vegetation health related to drought, pollution or diseases

Remote sensing based mapping of leaf nitrogen and leaf area index in European landscapes using the REGularized canopy reFLECtance (REGFLEC) model

DEFF Research Database (Denmark)

Boegh, E.; Houborg, R.; Bienkowski, J.

2011-01-01

index (LAI) are important determinants of the maximum CO2 Methods/Approach uptake by plants and trees. In the EU project NitroEurope, high spatial resolution (10-20 m) remote sensing data from the HRG and HRVIR sensors onboard the SPOT satellites were acquired to derive maps of leaf N and LAI for 5...... European landscapes. The estimations of leaf N, Cab and LAI soil reflectance parameters and canopy parameters are discussed in relation to the prevailing soil types and vegetation characteristics of land cover classes across the 5 European landscapes....

Varying responses of vegetation activity to climate changes on the Tibetan Plateau grassland.

Science.gov (United States)

Cong, Nan; Shen, Miaogen; Yang, Wei; Yang, Zhiyong; Zhang, Gengxin; Piao, Shilong

2017-08-01

Vegetation activity on the Tibetan Plateau grassland has been substantially enhanced as a result of climate change, as revealed by satellite observations of vegetation greenness (i.e., the normalized difference vegetation index, NDVI). However, little is known about the temporal variations in the relationships between NDVI and temperature and precipitation, and understanding this is essential for predicting how future climate change would affect vegetation activity. Using NDVI data and meteorological records from 1982 to 2011, we found that the inter-annual partial correlation coefficient between growing season (May-September) NDVI and temperature (R NDVI-T ) in a 15-year moving window for alpine meadow showed little change, likely caused by the increasing R NDVI-T in spring (May-June) and autumn (September) and decreasing R NDVI-T in summer (July-August). Growing season R NDVI-T for alpine steppe increased slightly, mainly due to increasing R NDVI-T in spring and autumn. The partial correlation coefficient between growing season NDVI and precipitation (R NDVI-P ) for alpine meadow increased slightly, mainly in spring and summer, and R NDVI-P for alpine steppe increased, mainly in spring. Moreover, R NDVI-T for the growing season was significantly higher in those 15-year windows with more precipitation for alpine steppe. R NDVI-P for the growing season was significantly higher in those 15-year windows with higher temperature, and this tendency was stronger for alpine meadow than for alpine steppe. These results indicate that the impact of warming on vegetation activity of Tibetan Plateau grassland is more positive (or less negative) during periods with more precipitation and that the impact of increasing precipitation is more positive (or less negative) during periods with higher temperature. Such positive effects of the interactions between temperature and precipitation indicate that the projected warmer and wetter future climate will enhance vegetation activity

Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition

Directory of Open Access Journals (Sweden)

Jesslyn F. Brown

2015-12-01

Full Text Available Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite data streams in operational applications have clear benefits for monitoring vegetation, especially when information can be delivered as fast as changing surface conditions. An “expedited” processing system called “eMODIS” operated by the U.S. Geological Survey provides rapid MODIS surface reflectance data to operational applications in less than 24 h offering tailored, consistently-processed information products that complement standard MODIS products. We assessed eMODIS quality and consistency by comparing to standard MODIS data. Only land data with known high quality were analyzed in a central U.S. study area. When compared to standard MODIS (MOD/MYD09Q1, the eMODIS Normalized Difference Vegetation Index (NDVI maintained a strong, significant relationship to standard MODIS NDVI, whether from morning (Terra or afternoon (Aqua orbits. The Aqua eMODIS data were more prone to noise than the Terra data, likely due to differences in the internal cloud mask used in MOD/MYD09Q1 or compositing rules. Post-processing temporal smoothing decreased noise in eMODIS data.

Detection of Chlorophyll and Leaf Area Index Dynamics from Sub-weekly Hyperspectral Imagery

Science.gov (United States)

Houborg, Rasmus; McCabe, Matthew F.; Angel, Yoseline; Middleton, Elizabeth M.

2016-01-01

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense time series of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Detection of chlorophyll and leaf area index dynamics from sub-weekly hyperspectral imagery

KAUST Repository

Houborg, Rasmus; McCabe, Matthew; Angel, Yoseline; Middleton, Elizabeth M.

2016-01-01

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense timeseries of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Detection of chlorophyll and leaf area index dynamics from sub-weekly hyperspectral imagery

KAUST Repository

Houborg, Rasmus

2016-10-25

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense timeseries of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Correlation analysis between forest carbon stock and spectral vegetation indices in Xuan Lien Nature Reserve, Thanh Hoa, Viet Nam

Science.gov (United States)

Dung Nguyen, The; Kappas, Martin

2017-04-01

In the last several years, the interest in forest biomass and carbon stock estimation has increased due to its importance for forest management, modelling carbon cycle, and other ecosystem services. However, no estimates of biomass and carbon stocks of deferent forest cover types exist throughout in the Xuan Lien Nature Reserve, Thanh Hoa, Viet Nam. This study investigates the relationship between above ground carbon stock and different vegetation indices and to identify the most likely vegetation index that best correlate with forest carbon stock. The terrestrial inventory data come from 380 sample plots that were randomly sampled. Individual tree parameters such as DBH and tree height were collected to calculate the above ground volume, biomass and carbon for different forest types. The SPOT6 2013 satellite data was used in the study to obtain five vegetation indices NDVI, RDVI, MSR, RVI, and EVI. The relationships between the forest carbon stock and vegetation indices were investigated using a multiple linear regression analysis. R-square, RMSE values and cross-validation were used to measure the strength and validate the performance of the models. The methodology presented here demonstrates the possibility of estimating forest volume, biomass and carbon stock. It can also be further improved by addressing more spectral bands data and/or elevation.

SAT-MAP-CLIMATE project results

DEFF Research Database (Denmark)

Hasager, C.B.; Nielsen, N.W.; Soegaard, H.

2002-01-01

Earth Observation (EO) data from imaging satellites are analysed with respect to albedo, land and sea surface temperatures, land cover types and vegetation parameters such as the Normalized Difference Vegetation Index (NDVI) and the leaf area index (LAI).The observed parameters are used in the DM...

TREE AGE AS ADJUSTMENT FACTOR TO NDVI

OpenAIRE

Elias Fernando Berra; Denise Cybis Fontana; Tatiana Mora Kuplich

2018-01-01

ABSTRACT This study aimed to increase satellite-derived Normalized Difference Vegetation Index (NDVI) sensitivity to biophysical parameters changes with aid of a forest age-based adjustment factor. This factor is defined as a ratio between stand age and age of rotation, which value multiplied by Landsat-5/TM-derived NDVI generated the so-called adjusted index NDVI_a. Soil Adjusted Vegetation Index (SAVI) was also calculated. The relationship between these vegetation indices (VI) with Eucalypt...

Essential climatic variables estimation with satellite imagery

Science.gov (United States)

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.

Comparability of red/near-infrared reflectance and NDVI based on the spectral response function between MODIS and 30 other satellite sensors using rice canopy spectra.

Science.gov (United States)

Huang, Weijiao; Huang, Jingfeng; Wang, Xiuzhen; Wang, Fumin; Shi, Jingjing

2013-11-26

Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from -12.67% to 36.30% for the red reflectance, -8.52% to -0.23% for the NIR reflectance, and -9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed

Calibration of transfer functions between phytolith, vegetation and climate for integration of grassland dynamics in vegetation models. Application to a 50,000 yr crater lake core in Tanzania.

Science.gov (United States)

Bremond, L.; Alexandre, A.; Hely, C.; Vincens, A.; Williamson, D.; Guiot, J.

2004-12-01

Global vegetation models provide a way to translate the outputs from climate models into maps of potential vegetation distribution for present, past and future. Validation of these models goes through the comparison between model outputs and vegetation proxies for well constrained past climatic periods. Grass-dominated biomes are widespread and numerous. This diversity is hardly mirrored by common proxies such as pollen, charcoal or carbon isotopes. Phytoliths are amorphous silica that precipitate in and/or between living plant cells. They are commonly used to trace grasslands dynamics. However, calibration between phytolith assemblages, vegetation, and climate parameters are scarce. This work introduces transfer functions between phytolith indices, inter-tropical grassland physiognomy, and bio-climatic data that will be available for model/data comparisons. The Iph phytolith index discriminates tall from short grass savannas in West Africa. A transfer function allows to estimate evapo-transpiration AET/PET. The Ic phytolith index accurately estimates the proportion of Pooideae and Panicoideae grass sub-families, and potentially the C4/C3 grass dominance on East African mountains. The D/P index appears as a good proxy of Leaf Area Index (LAI) in tropical areas. These environmental parameters are commonly used as vegetation model outputs, but have been, up to now, hardly estimated by vegetation proxies. These transfer functions are applied to a 50,000 yr phytolith sequence from a crater lake (9°S; 33°E Tanzania). The record is compared to the pollen vegetation reconstruction and confronted to simulations of the LPJ-GUESS vegetation model (Stitch et. al, 2003).

Using artificial neural network and satellite data to predict rice yield in Bangladesh

Science.gov (United States)

Akhand, Kawsar; Nizamuddin, Mohammad; Roytman, Leonid; Kogan, Felix; Goldberg, Mitch

2015-09-01

Rice production in Bangladesh is a crucial part of the national economy and providing about 70 percent of an average citizen's total calorie intake. The demand for rice is constantly rising as the new populations are added in every year in Bangladesh. Due to the increase in population, the cultivation land decreases. In addition, Bangladesh is faced with production constraints such as drought, flooding, salinity, lack of irrigation facilities and lack of modern technology. To maintain self sufficiency in rice, Bangladesh will have to continue to expand rice production by increasing yield at a rate that is at least equal to the population growth until the demand of rice has stabilized. Accurate rice yield prediction is one of the most important challenges in managing supply and demand of rice as well as decision making processes. Artificial Neural Network (ANN) is used to construct a model to predict Aus rice yield in Bangladesh. Advanced Very High Resolution Radiometer (AVHRR)-based remote sensing satellite data vegetation health (VH) indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) are used as input variables and official statistics of Aus rice yield is used as target variable for ANN prediction model. The result obtained with ANN method is encouraging and the error of prediction is less than 10%. Therefore, prediction can play an important role in planning and storing of sufficient rice to face in any future uncertainty.

Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data

Directory of Open Access Journals (Sweden)

Sandra Eckert

2012-03-01

Full Text Available Accurate estimation of aboveground biomass and carbon stock has gained importance in the context of the United Nations Framework Convention on Climate Change (UNFCCC and the Kyoto Protocol. In order to develop improved forest stratum–specific aboveground biomass and carbon estimation models for humid rainforest in northeast Madagascar, this study analyzed texture measures derived from WorldView-2 satellite data. A forest inventory was conducted to develop stratum-specific allometric equations for dry biomass. On this basis, carbon was calculated by applying a conversion factor. After satellite data preprocessing, vegetation indices, principal components, and texture measures were calculated. The strength of their relationships with the stratum-specific plot data was analyzed using Pearson’s correlation. Biomass and carbon estimation models were developed by performing stepwise multiple linear regression. Pearson’s correlation coefficients revealed that (a texture measures correlated more with biomass and carbon than spectral parameters, and (b correlations were stronger for degraded forest than for non-degraded forest. For degraded forest, the texture measures of Correlation, Angular Second Moment, and Contrast, derived from the red band, contributed to the best estimation model, which explained 84% of the variability in the field data (relative RMSE = 6.8%. For non-degraded forest, the vegetation index EVI and the texture measures of Variance, Mean, and Correlation, derived from the newly introduced coastal blue band, both NIR bands, and the red band, contributed to the best model, which explained 81% of the variability in the field data (relative RMSE = 11.8%. These results indicate that estimation of tropical rainforest biomass/carbon, based on very high resolution satellite data, can be improved by (a developing and applying forest stratum–specific models, and (b including textural information in addition to spectral information.

Satellite-based ET estimation using Landsat 8 images and SEBAL model

Directory of Open Access Journals (Sweden)

Bruno Bonemberger da Silva

Full Text Available ABSTRACT Estimation of evapotranspiration is a key factor to achieve sustainable water management in irrigated agriculture because it represents water use of crops. Satellite-based estimations provide advantages compared to direct methods as lysimeters especially when the objective is to calculate evapotranspiration at a regional scale. The present study aimed to estimate the actual evapotranspiration (ET at a regional scale, using Landsat 8 - OLI/TIRS images and complementary data collected from a weather station. SEBAL model was used in South-West Paraná, region composed of irrigated and dry agricultural areas, native vegetation and urban areas. Five Landsat 8 images, row 223 and path 78, DOY 336/2013, 19/2014, 35/2014, 131/2014 and 195/2014 were used, from which ET at daily scale was estimated as a residual of the surface energy balance to produce ET maps. The steps for obtain ET using SEBAL include radiometric calibration, calculation of the reflectance, surface albedo, vegetation indexes (NDVI, SAVI and LAI and emissivity. These parameters were obtained based on the reflective bands of the orbital sensor with temperature surface estimated from thermal band. The estimated ET values in agricultural areas, native vegetation and urban areas using SEBAL algorithm were compatible with those shown in the literature and ET errors between the ET estimates from SEBAL model and Penman Monteith FAO 56 equation were less than or equal to 1.00 mm day-1.

Vegetation impoverishment despite greening: a case study from central Senegal

Science.gov (United States)

Herrmann, Stefanie M.; Tappan, G. Gray

2013-01-01

Recent remote sensing studies have documented a greening trend in the semi-arid Sahel and Sudan zones of West Africa since the early 1980s, which challenges the mainstream paradigm of irreversible land degradation in this region. What the greening trend means on the ground, however, has not yet been explored. This research focuses on a region in central Senegal to examine changes in woody vegetation abundance and composition in selected sites by means of a botanical inventory of woody vegetation species, repeat photography, and perceptions of local land users. Despite the greening, an impoverishment of the woody vegetation cover was observed in the studied sites, indicated by an overall reduction in woody species richness, a loss of large trees, an increasing dominance of shrubs, and a shift towards more arid-tolerant, Sahelian species since 1983. Thus, interpretation of the satellite-derived greening trend as an improvement or recovery is not always justified. The case of central Senegal represents only one of several possible pathways of greening throughout the region, all of which result in similar satellite-derived greening signals.

Greenup and evapotranspiration following the Minute 319 pulse flow to Mexico: An analysis using Landsat 8 Normalized Difference Vegetation Index (NDVI) data

Science.gov (United States)

Jarchow, Christopher J.; Nagler, Pamela L.; Glenn, Edward P.

2017-01-01

In the southwestern U.S., many riparian ecosystems have been altered by dams, water diversions, and other anthropogenic activities. This is particularly true of the Colorado River, where numerous dams and agricultural diversions have affected this water course, especially south of the U.S.–Mexico border. In the spring of 2014, 130 million cubic meters of water was released to the lower Colorado River Delta in Mexico. To understand the impact of this pulse flow release on vegetation in the delta’s riparian corridor, we analyzed a modified form of Landsat 8 Operational Land Imager (OLI) Normalized Difference Vegetation Index (NDVI*) data. We assessed greenup during the growing period and estimated actual evapotranspiration (ETa) for the period prior to (yr. 2013) and following (i.e., yr. 2014 and 2015) the pulse flow. We found a significant increase in NDVI* from 2013 to 2014 (P NDVI*. As a long term solution to the declining condition of vegetation, additional pulse releases are likely needed for restoration and survival of riparian plant communities in the Colorado River Delta.

TOMS Absorbing Aerosol Index

Data.gov (United States)

Washington University St Louis — TOMS_AI_G is an aerosol related dataset derived from the Total Ozone Monitoring Satellite (TOMS) Sensor. The TOMS aerosol index arises from absorbing aerosols such...

ALPINE VEGETATION ECOTONE DYNAMICS IN GANGOTRI CATCHMENT USING REMOTE SENSING TECHNIQUES

Directory of Open Access Journals (Sweden)

C. P. Singh

2012-09-01

Full Text Available Analysis of the satellite imagery reveals two different perspectives of the vegetation ecotone dynamics in Gangotri catchment. On one hand, there is evidence of upward shift in the alpine tree and vegetation ecotone over three decades. On the other hand, there has been densification happening at the past treeline. The time series fAPAR data of two decades from NOAA-AVHRR confirms the greening trend in the area. The density of trees in Chirbasa has gone up whereas in Bhojbasa there is no significant change in NDVI but the number of groves has increased. Near Gaumukh the vegetal activity has not shown any significant change. We found that the treeline extracted from satellite imagery has moved up about 327±80m and other vegetation line has moved up about 401±77m in three decades. The vertical rate of treeline shift is found to be 11m/yr with reference to 1976 treeline; however, this can be 5m/yr if past toposheet records (1924 – 45 are considered as reliable reference. However, the future IPCC scenario based bioclimatic fundamental niche modelling of the Betula utilis (a surrogate to alpine treeline suggests that treeline could be moving upward with an average rate of 3m/yr. This study not only confirms that there is an upward shift of vegetation in the alpine zone of Himalayas, but also indicate that old vegetation ecotones have grown denser

Climate mitigation from vegetation biophysical feedbacks during the past three decades

Energy Technology Data Exchange (ETDEWEB)

Zeng, Zhenzhong [Peking Univ., Beijing (China); Piao, Shilong [Peking Univ., Beijing (China); Chinese Academy of Sciences (CAS), Beijing (China); Li, Laurent Z. X. [Sorbonne Univ. Paris (France); Zhou, Liming [State Univ. of New York (SUNY), Albany, NY (United States); Ciais, Philippe [Alternative Energies and Atomic Energy Commission (CEA), Gif-sur-Yvette (France); Wang, Tao [Chinese Academy of Sciences (CAS), Beijing (China); Li, Yue [Peking Univ., Beijing (China); Lian, Xu [Peking Univ., Beijing (China); Wood, Eric F. [Princeton Univ., NJ (United States); Friedlingstein, Pierre [Univ. of Exeter (United Kingdom); Mao, Jiafu [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Estes, Lyndon D. [Princeton Univ., NJ (United States); Clark Univ., Worcester, MA (United States); Myneni, Ranga B. [Boston Univ., MA (United States); Peng, Shushi [Peking Univ., Beijing (China); Shi, Xiaoying [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Seneviratne, Sonia I. [ETH Zurich (Switzerland); Wang, Yingping [Commonwealth Scientific and Industrial Research Organization (CSIRO), Aspendale, VIC (Australia)

2017-05-22

The surface air temperature response to vegetation changes has been studied for the extreme case of land-cover change; yet, it has never been quantified for the slow but persistent increase in leaf area index (LAI) observed over the past 30 years (Earth greening). We isolate the fingerprint of increasing LAI on surface air temperature using a coupled land–atmosphere global climate model prescribed with satellite LAI observations. Furthermore, we found that the global greening has slowed down the rise in global land-surface air temperature by 0.09 ± 0.02 °C since 1982. This net cooling effect is the sum of cooling from increased evapotranspiration (70%), changed atmospheric circulation (44%), decreased shortwave transmissivity (21%), and warming from increased longwave air emissivity (-29%) and decreased albedo (-6%). The global cooling originated from the regions where LAI has increased, including boreal Eurasia, Europe, India, northwest Amazonia, and the Sahel. Increasing LAI did not, but, significantly change surface air temperature in eastern North America and East Asia, where the effects of large-scale atmospheric circulation changes mask local vegetation feedbacks. Overall, the sum of biophysical feedbacks related to the greening of the Earth mitigated 12% of global land-surface warming for the past 30 years.

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

Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters

DEFF Research Database (Denmark)

Tagesson, T.; Horion, S.; Nieto, H.

2018-01-01

the Temperature and Vegetation Dryness Index (TVDI) that served as SM proxy within the disaggregation process. West Africa (3 N, 26 W; 28 N, 26 E) was selected as a case study as it presents both an important North-South climate gradient and a diverse range of ecosystem types. The main challenge was to set up...... resolution of SMOS SM, with potential application for local drought/flood monitoring of importance for the livelihood of the population of West Africa....

NDVI indicated characteristics of vegetation cover change in China's metropolises over the last three decades.

Science.gov (United States)

Sun, Jinyu; Wang, Xuhui; Chen, Anping; Ma, Yuecun; Cui, Mengdi; Piao, Shilong

2011-08-01

How urban vegetation was influenced by three decades of intensive urbanization in China is of great interest but rarely studied. In this paper, we used satellite derived Normalized Difference Vegetation Index (NDVI) and socioeconomic data to evaluate effects of urbanization on vegetation cover in China's 117 metropolises over the last three decades. Our results suggest that current urbanization has caused deterioration of urban vegetation across most cities in China, particularly in East China. At the national scale, average urban area NDVI (NDVI(u)) significantly decreased during the last three decades (P NDVI(u) did not show statistically significant trend before 1990 but decrease remarkably after 1990 (P NDVI(u) turning point. The year when NDVI(u) started to decline significantly for Central China and East China was 1987 and 1990, respectively, while NDVI(u) in West China remained relatively constant until 1998. NDVI(u) changes in the Yangtze River Delta and the Pearl River Delta, two regions which has been undergoing the most rapid urbanization in China, also show different characteristics. The Pearl River Delta experienced a rapid decline in NDVI(u) from the early 1980s to the mid-1990s; while in the Yangtze River Delta, NDVI(u) did not decline significantly until the early 1990s. Such different patterns of NDVI(u) changes are closely linked with policy-oriented difference in urbanization dynamics of these regions, which highlights the importance of implementing a sustainable urban development policy.