The Experts below are selected from a list of 36570 Experts worldwide ranked by ideXlab platform
Kamini Yadav - One of the best experts on this subject based on the ideXlab platform.
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mapping cropland extent of southeast and northeast asia using multi year time series landsat 30 m data using a random forest classifier on the google earth engine cloud
International Journal of Applied Earth Observation and Geoinformation, 2019Co-Authors: A Oliphant, Prasad S Thenkabail, P Teluguntla, Jun Xiong, Murali Krishna Gumma, Russell G Congalton, Kamini YadavAbstract:Abstract Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide Agricultural Statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate Agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small ( www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .
P Teluguntla - One of the best experts on this subject based on the ideXlab platform.
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mapping cropland extent of southeast and northeast asia using multi year time series landsat 30 m data using a random forest classifier on the google earth engine cloud
International Journal of Applied Earth Observation and Geoinformation, 2019Co-Authors: A Oliphant, Prasad S Thenkabail, P Teluguntla, Jun Xiong, Murali Krishna Gumma, Russell G Congalton, Kamini YadavAbstract:Abstract Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide Agricultural Statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate Agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small ( www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .
Jun Xiong - One of the best experts on this subject based on the ideXlab platform.
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mapping cropland extent of southeast and northeast asia using multi year time series landsat 30 m data using a random forest classifier on the google earth engine cloud
International Journal of Applied Earth Observation and Geoinformation, 2019Co-Authors: A Oliphant, Prasad S Thenkabail, P Teluguntla, Jun Xiong, Murali Krishna Gumma, Russell G Congalton, Kamini YadavAbstract:Abstract Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide Agricultural Statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate Agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small ( www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .
A Oliphant - One of the best experts on this subject based on the ideXlab platform.
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mapping cropland extent of southeast and northeast asia using multi year time series landsat 30 m data using a random forest classifier on the google earth engine cloud
International Journal of Applied Earth Observation and Geoinformation, 2019Co-Authors: A Oliphant, Prasad S Thenkabail, P Teluguntla, Jun Xiong, Murali Krishna Gumma, Russell G Congalton, Kamini YadavAbstract:Abstract Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide Agricultural Statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate Agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small ( www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .
F Workneh - One of the best experts on this subject based on the ideXlab platform.
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satellite remote sensing of wheat infected by wheat streak mosaic virus
Plant Disease, 2011Co-Authors: Mustafa Mirik, F Workneh, D C Jones, Jacob A Price, R J Ansley, C M RushAbstract:Abstract The prevalence of wheat streak mosaic, caused by Wheat streak mosaic virus, was assessed using Landsat 5 Thematic Mapper (TM) images in two counties of the Texas Panhandle during the 2005–2006 and 2007–2008 crop years. In both crop years, wheat streak mosaic was widely distributed in the counties studied. Healthy and diseased wheat were separated on the images using the maximum likelihood classifier. The overall classification accuracies were between 89.47 and 99.07% for disease detection when compared to “ground truth” field observations. Omission errors (i.e., pixels incorrectly excluded from a particular class and assigned to other classes) varied between 0 and 12.50%. Commission errors (i.e., pixels incorrectly assigned to a particular class that actually belong to other classes) ranged from 0 to 23.81%. There were substantial differences between planted wheat acreage reported by the United States Department of Agriculture-National Agricultural Statistics Service (USDA-NASS) and that detected...
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soybean brown stem rot phytophthora sojae and heterodera glycines affected by soil texture and tillage relations
Phytopathology, 1999Co-Authors: F Workneh, X B Yang, Gregory L TylkaAbstract:ABSTRACT Investigations were conducted to determine whether the effects of tillage practices on the prevalence of brown stem rot of soybean (caused by Phialophora gregata), Heterodera glycines, and Phytophthora sojae were confounded by soil texture in samples collected in the fall of 1995 and 1996. Soil and soybean stem samples, along with tillage information, were collected from 1,462 randomly selected fields in Illinois, Iowa, Minnesota, Missouri, and Ohio in collaboration with the National Agricultural Statistics Service. The incidence of brown stem rot was determined from 20 soybean stem pieces collected from each field in a zigzag pattern. The detection frequency of P. sojae (expressed as percent leaf disks colonized) and population densities of H. glycines were determined from soil cores also collected in a zigzag pattern. The soil samples were grouped into various textural classes, and the effect of soil texture and tillage relations on the activities of each pathogen were determined. Both tillage ...
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regional assessment of soybean brown stem rot phytophthora sojae and heterodera glycines using area frame sampling prevalence and effects of tillage
Phytopathology, 1999Co-Authors: F Workneh, X B Yang, Gregory L Tylka, J Faghihi, J M FerrisAbstract:ABSTRACT The prevalence of brown stem rot (caused by Phialophora gregata), Heterodera glycines, and Phytophthora sojae in the north central United States was investigated during the fall of 1995 and 1996. Soybean fields were randomly selected using an area-frame sampling design in collaboration with the National Agricultural Statistics Service. Soil and soybean stem samples, along with tillage information, were collected from 1,462 fields in Illinois, Iowa, Minnesota, Missouri, and Ohio. An additional 275 soil samples collected from Indiana were assessed for H. glycines. For each field, the incidence and prevalence of brown stem rot was assessed in 20 soybean stem pieces. The prevalence and recovery (expressed as the percentage of leaf disks colonized) of P. sojae and the prevalence and population densities of H. glycines were determined from the soil samples. The prevalence of brown stem rot ranged from 28% in Missouri to 73% in Illinois; 68 and 72% of the fields in Minnesota and Iowa, respectively, show...
