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Wednesday, April 11 • 10:15am - 10:30am
SYMPOSIA-13: Detecting Land Change Through Land Surface Phenology: An Application to the Dynamic Northern Great Plains

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AUTHORS: Lan H. Nguyen, Geospatial Sciences Center of Excellence, South Dakota State University; Geoffrey M. Henebry, Geospatial Sciences Center of Excellence and Department of Natural Resource Management, South Dakota State University

ABSTRACT: Despite its low population, the Northern Great Plains (NGP) region of the United States of America has been undergoing substantial land cover/land use change (LCLUC) over the past two decades, including conversion of grassland to cropland, urbanization, fossil fuel mining, and expansion of irrigation. Better detection of rapid land change is critical for management and conservation of diminishing prairie habitats and ecosystem services and for projecting crop and biofuels production and their impacts on rural transportation infrastructure. Most change detection strategies that have been commonly used in remote sensing studies were developed in an era of image scarcity and limited computation power, and thus focus on comparing just a few scenes. In the current era of abundant earth observations, the better approach takes advantage of all available data. Here, we demonstrate an approach to detect LCLUC using land surface phenology. Our study area spans multiple regions of interest across the NGP where land use transitions have been documented: near ethanol bio-refineries, major towns and cities, and fossil fuel mining sites. First, we compute accumulated growing degree-days (AGDD) from MODIS 8-day land surface temperature. Using EVI derived from Landsat surface reflectance, we then fit at each pixel a downward convex quadratic model to each year’s progression of AGDD (EVI = a+ß×AGDD-?×AGDD2). We then use information from the fitted model as input to two different classification techniques, e.g., linear mixture analysis (LMA) and random forest classifiers (RFC), to generate a land use/land cover map of the study area. We use the USDA Cropland Data Layer (CDL) as a reference for LMA (choose endmembers) and RFC (select training and validation dataset), while acknowledging the CDL’s limitations. By tracking the proportions (LMA output) or land cover map (RFC output) over the 2001-2011 period, we characterize and quantify various land change transitions across the NGP.

Wednesday April 11, 2018 10:15am - 10:30am CDT
Water Tower Parlor