Mapping Forest Structure and Composition from Low-Density LiDAR for Informed Forest, Fuel, and Fire Management at Eglin Air Force Base, Florida, USA

Abstract

Eglin Air Force Base (AFB) in Florida, in the United States, conserves a large reservoir of native longleaf pine (Pinus palustris Mill.) stands that land managers maintain by using frequent fires. We predicted tree density, basal area, and dominant tree species from 195 forest inventory plots, low-density airborne LiDAR, and Landsat data available across the entirety of Eglin AFB. We used the Random Forests (RF) machine learning algorithm to predict the 3 overstory responses via univariate regression or classification, or multivariate k-NN imputation. Ten predictor variables explained ∼ 50% of variation and were used in all models. Model accuracy and precision statistics were similar among the various RF approaches, so we chose the imputation approach for its advantage of allowing prediction of the ancillary plot attributes of surface fuels and ground cover plant species richness. Maps of the 3 overstory response variables and ancillary attributes were imputed at 30-m resolution and then aggregated to the management block level, where they were significantly correlated with each other and with fire history variables summarized from independent data. We conclude that functional relationships among overstory structure, surface fuels, species richness, and fire history emerge and become more apparent at the block level where management decisions are made.