Modeling Worldwide Tree Biodiversity Using Canopy Structure Metrics from Global Ecosystem Dynamics Investigation Data - Jin Xu; Kjirsten Coleman; Volker C. Radeloff; Melissa Songer; Qiongyu Huang (Remote Sensing)
Accurately quantifying global tree biodiversity is critical for enhancing forest ecosystem management and forest biodiversity conservation. With the launch of NASA’s Global Ecosystem Dynamics Investigation (GEDI), we evaluated the efficacy of space-borne lidar metrics in predicting tree species richness globally and explored whether integrating spectral vegetation metrics with space-borne lidar data could improve model performances. Using Forest Global Earth Observatory (ForestGEO) data, we developed three models using the random forest algorithm to predict global tree species richness across climate zones, including a dynamic habitat index (DHI)-only model, a GEDI-only model, and a combined GEDI-DHI model. We also developed four new canopy indices for our model and determined the optimal extent for aggregating GEDI metrics. Applying the optimal pixel size (5600 m), we found that the GEDI-only model predicted tree species richness across climate zones well (R2 = 0.55). One of our new GEDI metrics, representing canopy structure complexity, was among the top five most important features. The GEDI-DHI model performed similarly to the GEDI-only model using the ForestGEO dataset (R2 = 0.55). Our study provides an efficient and innovative method for using GEDI data to predict global tree species richness. However, the integration of GEDI metrics with DHIs did not significantly improve the model’s performance compared to the GEDI-only model. Considering the substantial variation in tree species richness across different climate zones, we recommend modeling tree species richness for each climate zone rather than using a global model. Additionally, incorporating open-source ground-measured tree species richness data can improve predictions and inform decision-making in forest conservation management.