Open Access. Powered by Scholars. Published by Universities.®
Physical and Environmental Geography Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Publication
- Publication Type
Articles 1 - 4 of 4
Full-Text Articles in Physical and Environmental Geography
Impact Of Drought On Land Cover Changes In Diné Bikéyah – A Study Through Remote Sensing, Anjanette A.J. Hawk
Impact Of Drought On Land Cover Changes In Diné Bikéyah – A Study Through Remote Sensing, Anjanette A.J. Hawk
Geography ETDs
This study identifies land cover changes associated with a ten-year drought period and discusses the importance of vegetation change in Diné Bikéyah, a semi-arid land located in a remote part of the southwestern United States (US). This study concludes that drought produced slight changes in vegetation within a 540 km2 study area in the Tselani-Cottonwood Chapter (TCC) in Diné Bikéyah. The data for this study consist of three Landsat images for the years 1998, 2002, and 2009. The methods used to analyze these Landsat images included image pre-processing, calculation of normalized difference vegetation index (NDVI) images, and supervised (maximum …
Using The 500 M Modis Land Cover Product To Derive A Consistent Continental Scale 30 M Landsat Land Cover Classification, Hankui Zhang, David P. Roy
Using The 500 M Modis Land Cover Product To Derive A Consistent Continental Scale 30 M Landsat Land Cover Classification, Hankui Zhang, David P. Roy
GSCE Faculty Publications
Classification is a fundamental process in remote sensing used to relate pixel values to land cover classes present on the surface. Over large areas land cover classification is challenging particularly due to the cost and difficulty of collecting representative training data that enable classifiers to be consistent and locally reliable. A novel methodology to classify large volume Landsat data using high quality training data derived from the 500 m MODIS land cover product is demonstrated and used to generate a 30 m land cover classification for all of North America between 20°N and 50°N. Publically available 30 m global monthly …
Multi-Year Modis Active Fire Type Classification Over The Brazilian Tropical Moist Forest Biome, David P. Roy, S. S. Kumar
Multi-Year Modis Active Fire Type Classification Over The Brazilian Tropical Moist Forest Biome, David P. Roy, S. S. Kumar
GSCE Faculty Publications
The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km2 and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity …
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 …