ML_code_final.R

R Code to use random forests and support vector machines to classify a tallgrass prairie in Kansas, using NAIP and NEON imagery, vegetation indicies, and LiDAR. The US government has been investing in fine resolution (<2m²) remote sensing through USDA NAIP and the National Ecological Observatory Network (NEON), both of which cost multi-million dollars each year and contain different remote sensed products. We compared two methods of classification (random forests and support vector machines) with these two freely available remotely sensed aerial images to determine if and how much NEON adds to classification accuracy and determine which method of machine learning was more accurate. Necessary inputs can be found and downloaded at: DOI: 10.6073/pasta/a7b40e41080460bb1123dcc7b6d4d942