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(PDF) Multi-scale standardized spectral mixture models | Cristina Milesi - blogger.com
Download Free PDF. Multi-scale standardized spectral mixture models. Cristina Milesi. Download PDF. Download Full PDF Package. This paper. A short summary of this paper. 37 Full PDFs related to this paper. READ PAPER. Multi-scale standardized spectral mixture models. Download. Multi-scale standardized spectral mixture models. Cristina Milesi. Related Papers. The Landsat ETM+ spectral 1/3/ · To our knowledge, this is the first study in which the combination of publicly available satellite imagery, spectral mixture analysis and geoprocessing cloud technologies were used to dynamically estimate high resolution FVC for grassland and rangeland management purposes. A linear spectral mixture model was developed, calibrated and implemented in Google Earth Engine using Sentinel-2 Author: Liezl Mari Vermeulen, Zahn Munch, Antony Palmer Download Free PDF. Quantification of aboveground rangeland productivity and anthropogenic degradation on the Arabian Peninsula using Landsat imagery and field inventory data. Remote Sensing of Environment, Eva Schlecht. Katja Brinkmann. Andreas Buerkert. Uta Dickhoefer. Eva Schlecht. Katja Brinkmann. Andreas Buerkert. Uta Dickhoefer. Download PDF. Download Full PDF Package.
Spectral mixture analysis and rangeland monitoring + sciencedirect+free download
The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer MODIS data. At first, various preprocessing steps were applied to a topography map.
The atmospheric and topographic corrections were applied using subtraction of the dark object method and the Lambert method. Image processing, including false-color composite, principal component spectral mixture analysis and rangeland monitoring + sciencedirect+free download, and vegetation indices were employed to produce land use and pasture production maps, spectral mixture analysis and rangeland monitoring + sciencedirect+free download.
Vegetation sampling was carried out over a period of 4 months during June—Septemberusing a stratified random sampling method. Twenty random sampling points were selected, and herbage production was estimated and verified with the double-checking method. Four MODIS data sets were used in this study. The models for image processing and integrating ground data with satellite images were processed, and the resulting images were categorized into seven classes.
Finally, the land covers were verified for accuracy. A postclassification analysis was carried out to verify the seven class change detections. The results confirmed that Normalized Difference Vegetation Index NDVI and Soil-Adjusted Vegetation Index SAVI maps had a close relationship with the field data.
The indices produced with shortwave infrared bands had a close relationship with spectral mixture analysis and rangeland monitoring + sciencedirect+free download data where the ground cover and yields were high. The R 2 value observed was 0.
During the growing season, most changes in the pastures belonged to class 5 and 2 in the NDVI and SAVI index maps, respectively. This is a preview of subscription content, access via your institution. Rent this article via DeepDyve. Akiyama T, Kawamura K Grassland degradation in China: methods of monitoring, management and restoration. Grass Sci 53 1 :1— Article Google Scholar. Amiri F, Shariff ARBM Using remote sensing data for vegetation cover assessment in semi-arid rangeland of center province of Iran.
World Appl Sci J 11 12 — Google Scholar. Anderson G, Hanson J, Haas R Evaluating Landsat Thematic Mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands.
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Remote Sens Environ 3 — Ben-Ze'ev E, Karnieli A, Agam Spectral mixture analysis and rangeland monitoring + sciencedirect+free download, Kaufman Y, Holben B Assessing vegetation condition in the presence of biomass burning smoke by applying the Aerosol-free Vegetation Index AFRI on MODIS images.
Int J Remote Sens 27 15 — Land Degrad Dev. doi: J Indian Soc Remote Sens doi. Chen D, Huang J, Jackson TJ Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near-and short-wave infrared bands.
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Int J Remote Sens 26 7 — Cottam G, Curtis JT The use of distance measures in phytosociological sampling. Ecology 37 3 — Demers MN Classification and purpose in automated vegetation maps. Geogr Rev 81 3 — Elmore AJ, Mustard JF, Manning SJ, Lobell DB Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. Remote Sens Environ 73 1 — Elvidge CD, Chen Z Comparison of broad-band and narrow-band red and near-infrared vegetation indices.
Remote Sens Environ 54 1 — Farzadmehr J, Arzani H, Darvish sefat AA, spectral mixture analysis and rangeland monitoring + sciencedirect+free download, Jafari M Investigation in estimating vegetation cover and phytomass production, using enhanced landsat data in a semi arid region. Iranian J Nat Resour 57 2 — Feng X, Zhao Y Grazing intensity monitoring in Northern China steppe: integrating CENTURY model and MODIS data.
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Remote Sens Environ 5 — Hobbs T The use of NOAA-AVHRR NDVI data to assess herbage production in the arid rangelands of Central Australia. Int J Remote Sens 16 7 — Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83 1 — Huete A, Justice C, spectral mixture analysis and rangeland monitoring + sciencedirect+free download, Liu H Development of vegetation and soil indices for MODIS-EOS.
Remote Sens Environ 49 3 — Huete AR A soil-adjusted vegetation index SAVI. Remote Sens Environ 25 3 — Jacob F, Petitcolin F, Schmugge T, Vermote E, French A, Ogawa K Comparison of land surface emissivity and radiometric temperature derived from MODIS and ASTER sensors.
Remote Sens Environ 90 2 — Jafari R, Lewis MM, Ostendorf B Evaluation of vegetation indices for assessing vegetation cover in southern arid lands in South Australia. Rangeland J 29 1 — Jensen JR Remote sensing of the environment: an earth resource perspective. Pearson Prentice Hall, Upper Saddle River, University of Puerto Rico at Mayaguez, Chapter Remote sensing of vegetation.
Jianlong L, Tiangang L, Quangong C Estimating grassland yields using remote sensing and GIS technologies in China. New Zeal J Agr Res 41 1 — Justice C, Townshend J, Vermote E, Masuoka E, Wolfe R, Saleous N, Roy D, Morisette J An overview of MODIS land data processing and product status. Remote Sens Environ 83 1—2 :3— Karnieli A, Kaufman YJ, Remer L, Wald A AFRI-aerosol free vegetation index. Remote Sens Environ 77 1 — Kaufman YJ, Tanre D a Atmospherically resistant vegetation index ARVI for EOS-MODIS.
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IEEE T Geosci Remote 35 5 — Kawamura K, Akiyama T, Yokota H, Tsutsumi M, Watanabe O, Wang S Quantification of grazing intensities on plant biomass in Xilingol steppe, China using Terra MODIS image. pp 21— Kawamura K, Akiyama T, Yokota H, Tsutsumi M, Yasuda T, Watanabe O, Wang S a Comparing MODIS vegetation indices with AVHRR NDVI for monitoring the forage quantity and quality in Inner Mongolia grassland, China. Grass Sci 51 1 — Kawamura K, Akiyama T, Yokota H, Tsutsumi M, Yasuda T, Watanabe O, Wang S b Quantifying grazing intensities using geographic information systems and satellite remote sensing in the Xilingol steppe region, Inner Mongolia, China.
Agr Ecosyst Environ 1 — Arab J Geo 4 3 — Langley SK, Cheshire HM, Humes KS A comparison of single date and multitemporal satellite image classifications in a semi-arid grassland.
Range Management Part 1
, time: 27:13Spectral mixture analysis and rangeland monitoring + sciencedirect+free download
Download Free PDF. Multi-scale standardized spectral mixture models. Cristina Milesi. Download PDF. Download Full PDF Package. This paper. A short summary of this paper. 37 Full PDFs related to this paper. READ PAPER. Multi-scale standardized spectral mixture models. Download. Multi-scale standardized spectral mixture models. Cristina Milesi. Related Papers. The Landsat ETM+ spectral 16/5/ · Spectral mixture analysis (SMA) generally involves the following steps: (1) determine how many spectral constituents are present in an image or data set; (2) identify the physical nature of each of the constituents or “endmembers” within a pixel; and (3) derive the fractional amounts of each component in each pixel. The first step is generally accomplished with principal components 1/3/ · To our knowledge, this is the first study in which the combination of publicly available satellite imagery, spectral mixture analysis and geoprocessing cloud technologies were used to dynamically estimate high resolution FVC for grassland and rangeland management purposes. A linear spectral mixture model was developed, calibrated and implemented in Google Earth Engine using Sentinel-2 Author: Liezl Mari Vermeulen, Zahn Munch, Antony Palmer
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