Satellite Derived Trait Data Slightly Improves Tropical Forest Biomass, NPP and GPP Estimates

Abstract

Improving tropical forest current biomass estimates can help more accurately evaluate ecosystem services in tropical forests. The Global Ecosystem Dynamics Investigation (GEDI) lidar provides detailed 3D forest structure and height data, which can be used to improve above-ground biomass estimates. However, there is still debate on how best to predict tropical forest biomass using GEDI data. Here we compare stand biomass predicted by GEDI data with the observed data of 2,102 inventory plots in tropical forests and find that adding a remotely sensed (RS) trait map of leaf mass area (LMA) significantly (P < 0.001) improves field biomass predictions, but by only a small amount (r² = 0.01). However, it may also help reduce the bias of the residuals because there was a negative relationship between both LMA (r² of 0.34) and percentage of phosphorus (%P, r² = 0.31) and residuals. Leaf spectral data (400–1,075 nm) from 523 individual trees along a Peruvian tropical forest elevation gradient predicted Diameter at Breast height (DBH) (the critical measurement underlying plot biomass) with an r² = 0.01 and LMA predicts DBH with an r² = 0.04. Other data sets may offer further improvements and max temperature (T_max) predicts Amazonian biomass residuals with an r² of 0.76 (N = 66). Finally, for a network of net primary production (NPP) and gross primary production (GPP) plots (N = 21), leaf traits predicted with remote sensing are better at predicting fluxes than structure variables. Overall, trait maps, especially future improved ones produced by Surface Biology Geology, may improve biomass and carbon flux predictions by a small but significant amount.