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 the United States Department of Agriculture National Agriculture Imagery Program (NAIP) are increasingly being analyzed for quality and application feasibility. This study compared canopy heights derived from NAIP DSMs (10 m) and point clouds to those derived from lidar data collected over Mountain Lake Biological Station and the Great Smoky Mountains Twin Creeks Site by the National Ecological Observatory Network (NEON) Airborne Observation Platform for 62 mixed deciduous tree plots. Mean dominant height (MDH) was estimated using lidar and the NAIP products using the 90th percentile of heights in a given plot as the independent variable for both the lidar-and NAIP-derived point clouds. The dependent variable was field-measured MDH, calculated using the four tallest trees for each 0.04-hectare plot based on the NEON woody vegetation structure dataset. All data (field and remotely sensed) were collected in 2018. Using maximum likelihood spatial error model for all analyses, the NAIP DSM (10 m resolution) resulted in a strong relationship with MDH (coefficient of determination (R-2) = 0.90, standard error (SE) = 1.71 m). However, the 90th percentiles of heights derived from the point clouds were better at estimating MDH than was the comparatively coarse resolution DSM (NAIP point clouds: R-2 = 0.94, SE = 1.40 m; lidar: R-2 = 0.95, SE= 1.29 m, respectively) and are strongly correlated to each other (R-2 = 0.99, SE = 0.68 m). The main limitation of the NAIP datasets was found to be where shadowing occurred due to steep terrain in the Great Smoky Mountain site. These areas resulted in erroneously high vegetation heights. Mean dominant heights estimated using NAIP DSMs and point clouds are thus comparable to those estimated using lidar data in these closed-canopy temperate deciduous forests where shadowing from steep terrain is not present. The utility of both the NAIP-derived 10 m DSM and the point clouds for estimating tree heights paves the way for statewide mapping of heights over the deciduous forests in Tennessee, Virginia, and possibly beyond.

Automated summary

This study compared canopy heights derived from NAIP DSMs and point clouds to those derived from lidar data, and found that the 90th percentiles of heights derived from the point clouds were better at estimating mean dominant height (MDH) than the comparatively coarse resolution DSM. The main limitation of the NAIP datasets was found to be shadowing caused by steep terrain. However, in closed-canopy temperate deciduous forests without shadowing, the mean dominant heights estimated using NAIP DSMs and point clouds are comparable to those estimated using lidar data.