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Physical and Environmental Geography Commons™
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Full-Text Articles in Physical and Environmental Geography
Characterizing Spatiotemporal Patterns Of White Mold In Soybean Across South Dakota Using Remote Sensing, Confiance L. Mfuka
Characterizing Spatiotemporal Patterns Of White Mold In Soybean Across South Dakota Using Remote Sensing, Confiance L. Mfuka
Electronic Theses and Dissertations
Soybean is among the most important crops, cultivated primarily for beans, which are used for food, feed, and biofuel. According to FAO, the United States was the biggest soybeans producer in 2016. The main soybean producing regions in the United States are the Corn Belt and the lower Mississippi Valley. Despite its importance, soybean production is reduced by several diseases, among which Sclerotinia stem rot, also known as white mold, a fungal disease that is caused by the fungus Sclerotinia sclerotiorum is among the top 10 soybean diseases. The disease may attack several plants and considerably reduce yield. According to …
Rapid Land Cover Map Updates Using Change Detection And Robust Random Forest Classifiersrapid Land Cover Map Updates Using Change Detection And Robust Random Forest Classifiers, Konrad J. Wassels, Frans Van Den Bergh, David P. Roy, Brian P. Salmon, Karen C. Steenkemp, Bryan Macalister, Derick Swanepoel, Debbie Jewitt
Rapid Land Cover Map Updates Using Change Detection And Robust Random Forest Classifiersrapid Land Cover Map Updates Using Change Detection And Robust Random Forest Classifiers, Konrad J. Wassels, Frans Van Den Bergh, David P. Roy, Brian P. Salmon, Karen C. Steenkemp, Bryan Macalister, Derick Swanepoel, Debbie Jewitt
GSCE Faculty Publications
The paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM) system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) to identify areas of change and no change. The system then automatically generates large amounts of training samples (n > 1 million) in the no-change areas as input to an optimized Random Forest classifier. Experiments were conducted in the KwaZulu-Natal Province of South Africa using a reference land cover map from 2008, a change mask between 2008 and …
Conterminous United States Crop Field Size Quantification From Multi-Temporal Landsat Data, Lin Yan Dr., David P. Roy
Conterminous United States Crop Field Size Quantification From Multi-Temporal Landsat Data, Lin Yan Dr., David P. Roy
GSCE Faculty Publications
Agricultural field size is indicative of the degree of agricultural capital investment, mechanization and labor intensity, and it is ecologically important. A recently published automated computational methodology to extract agricultural crop fields from weekly 30 m Web Enabled Landsat data (WELD) time series was refined and applied to a year of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhance Thematic Mapper Plus (ETM +) acquisitions for all of the conterminous United States (CONUS). For the first time, spatially explicit CONUS field size maps and derived information are presented. A total of 4,182,777 fields were extracted with mean and median …