Estimation of mean dominant height using NAIP digital aerial photogrammetry and lidar over mixed deciduous forest in the southeastern USA

In the absence of complete lidar coverage, digital surface models (DSMs) and point clouds produced from United States Department Agriculture National Imagery Program (NAIP) are increasingly being analyzed for quality application feasibility. This study compared canopy heights derived NAIP DSMs (10 m) to those data collected over Mountain Lake Biological Station Great Smoky Mountains Twin Creeks Site by Ecological Observatory Network (NEON) Airborne Observation Platform 62 mixed deciduous tree plots. Mean dominant height (MDH) was estimated using products 90th percentile in a given plot as independent variable both lidar- NAIP-derived clouds. The dependent field-measured MDH, calculated four tallest trees each 0.04-hectare based on NEON woody vegetation structure dataset. All (field remotely sensed) were 2018. Using maximum likelihood spatial error model all analyses, DSM m resolution) resulted strong relationship with MDH (coefficient determination (R2) = 0.90, standard (SE) 1.71 m). However, percentiles better at estimating than comparatively coarse resolution (NAIP clouds: R2 0.94, SE 1.40 m; lidar: 0.95, SE= 1.29 m, respectively) strongly correlated other (R2 0.99, 0.68 main limitation datasets found be where shadowing occurred due steep terrain site. These areas erroneously high heights. thus comparable these closed-canopy temperate forests is not present. utility 10 paves way statewide mapping Tennessee, Virginia, possibly beyond.