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Social and Behavioral Sciences Commons

Open Access. Powered by Scholars. Published by Universities.®

Geography

South Dakota State University

Land cover

2017

Articles 1 - 2 of 2

Full-Text Articles in Social and Behavioral Sciences

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 Aug 2017

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 Jan 2017

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 …