Authors: Matthew Marsik*, Department of Geography, University of Florida, Caroline Staub, International Programs, Institute of Food and Agricultural Sciences, University of Florida, William Kleindl, Department of Land Resources and Environmental Sciences, Montana State University, Jaclyn Hall, Health Outcomes and Biomedical Informatics, University of Florida, Chiung-Shiuan Fu, Department of Geography, University of Florida, Di Yang, Department of Geography, University of Florida, Forrest Stevens, Department of Geography and Geosciences, University of Louisville, Michael Binford, Department of Geography, University of Florida & U.S. National Science Foundation
Topics: Remote Sensing, Spatial Analysis & Modeling, Land Use
Keywords: forest management, time-series analysis, MODIS, random forest
Session Type: Paper
Start / End Time: 3:05 PM / 4:45 PM
Room: Buchanan, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Forests in the United States are managed by multiple public and private entities making harmonization of available data and subsequent mapping of management challenging. We mapped four important types of forest management, production, ecological, passive, and preservation, at 250-meter spatial resolution in the Southeastern (SEUS) and Pacific Northwest (PNW) USA. Both ecologically and socio-economically dynamic regions, the SEUS and PNW forests represent, respectively, 22.0% and 10.4% of forests in the coterminous US. We built a random forest classifier using seasonal time-series analysis of 16 years of MODIS 16-day composite Enhanced Vegetation Index, and ancillary data containing forest ownership, roads, US Forest Service wilderness and forestry areas, proportion conifer and proportion riparian. The map accuracies for SEUS are 89% (10-fold cross-validation) and 67% (external validation) and PNW are 91% and 70% respectively with the same validation. The now publicly available forest management maps, probability surfaces for each management class and uncertainty layer for each region can be viewed and analyzed in commercial and open-source GIS and remote sensing software. We are working to extend this methodology to additional forested regions of the United States.