Physical Sciences and Mathematics Commons^™

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 …