Estimation of secondary forest parameters by integrating image and point cloud-based metrics acquired from unmanned aerial vehicle

To better assess spatial variabilities in subtropical secondary forest biomass, the goal of the present study was to employ small-footprint, discrete-return light detection and ranging and unmanned aerial vehicles, integrated with structure from motion (UAV-SFM) data, to accurately estimate the stand characteristic parameters of subtropical forests. The Lorey's height, density, basal area, volume, and aboveground biomass (AGB) of 30 plots (circle divide 10 m) were modeled using an array of SFM point cloud metrics and image color metrics (forestry vegetation coverage and color index set). Further, the individual models developed using stepwise multiregression analysis and a branch-and-bound algorithm were employed to examine the best models. Overall, the results indicated that the coefficient of determination (R-2) for Lorey's height (R-2 = 0.58 to 0.95), volume (R-2 = 0.29 to 0.71), and AGB (R-2 = 0.27 to 0.64) were significantly enhanced compared with the density (R-2 = 0.18 to 0.48), and basal area (R-2 = 0.24 to 0.52) Utilizing independent stand-level data from ground inventory, our results revealed that, overall, the model fit was significant for most stand characteristic parameters, with relationships close to a 1:1 line, thereby indicating no significant bias [relative root mean square error (rRMSE) = 5.32% to 27.04%]. Our image metrics had a statistically significant association with Lorey's height (rRMSE decreased by 0.08%), volume (rRMSE decreased by 0.58%), and AGB (rRMSE decreased by 3.11%), which provided additional explanatory power in the regression analysis. This research demonstrated the potential of UAV-SFM as a technology to accurately assess subtropical forest carbon while providing an improved elucidation of the positive effects of image color metrics. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.