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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Land-Surface Parameters For Spatial Predictive Mapping And Modeling, Aaron E. Maxwell, Charles Shobe
Land-Surface Parameters For Spatial Predictive Mapping And Modeling, Aaron E. Maxwell, Charles Shobe
Faculty & Staff Scholarship
Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and …
Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, Geobia, And Naip Orthophotography: Findings And Recommendations, Aaron E. Maxwell, Michael P. Strager, Timothy A. Warner, Christopher A. Ramezan, Alice N. Morgan, Cameron E. Pauley
Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, Geobia, And Naip Orthophotography: Findings And Recommendations, Aaron E. Maxwell, Michael P. Strager, Timothy A. Warner, Christopher A. Ramezan, Alice N. Morgan, Cameron E. Pauley
Faculty & Staff Scholarship
Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture Imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km2. We obtained an overall accuracy of 96.7% and a Kappa statistic of …