Incorporating Canopy Cover for Airborne-Derived Assessments of Forest Biomass in the Tropical Forests of Cambodia

This research examines the role of canopy cover in influencing above ground biomass (AGB) dynamics of an open canopied forest and evaluates the efficacy of individual-based and plot-scale height metrics in predicting AGB variation in the tropical forests of Angkor Thom, Cambodia. The AGB was modeled by including canopy cover from aerial imagery alongside with the two different canopy vertical height metrics derived from LiDAR; the plot average of maximum tree height (Max_CH) of individual trees, and the top of the canopy height (TCH). Two different statistical approaches, log-log ordinary least squares (OLS) and support vector regression (SVR), were used to model AGB variation in the study area. Ten different AGB models were developed using different combinations of airborne predictor variables. It was discovered that the inclusion of canopy cover estimates considerably improved the performance of AGB models for our study area. The most robust model was log-log OLS model comprising of canopy cover only (r = 0.87; RMSE = 42.8 Mg/ha). Other models that approximated field AGB closely included both Max_CH and canopy cover (r = 0.86, RMSE = 44.2 Mg/ha for SVR; and, r = 0.84, RMSE = 47.7 Mg/ha for log-log OLS). Hence, canopy cover should be included when modeling the AGB of open-canopied tropical forests.

本研究探讨了冠层覆盖度对开阔冠层森林地上生物量（Aboveground Biomass, AGB）动态的影响，并评估了基于个体与样地尺度的高度指标在预测柬埔寨吴哥通王城热带森林AGB变异中的效能。本研究通过结合航空影像获取的冠层覆盖度，以及两种由激光雷达（LiDAR）衍生的冠层垂直高度指标——单木最大树高的样地平均值（Max_CH）与冠层顶高度（TCH）——构建AGB预测模型。研究采用两种统计方法：双对数普通最小二乘法（OLS）与支持向量回归（SVR），对研究区域内的AGB变异进行建模。本研究通过机载预测变量的不同组合，共构建了10种AGB预测模型。研究发现，在模型中纳入冠层覆盖度估算值，可显著提升本研究区域AGB预测模型的性能。最稳健的模型为仅纳入冠层覆盖度的双对数普通最小二乘模型（决定系数r=0.87，均方根误差RMSE=42.8 吨/公顷（Mg/ha））。其余可较好拟合实地观测AGB的模型均同时纳入了Max_CH与冠层覆盖度（支持向量回归模型的决定系数r=0.86，RMSE=44.2 吨/公顷（Mg/ha）；双对数普通最小二乘模型的r=0.84，RMSE=47.7 吨/公顷（Mg/ha））。因此，在对开阔冠层热带森林的AGB进行建模时，应纳入冠层覆盖度指标。