Satellite-derived trait data slightly improves tropical forest biomass, NPP, and GPP predictions

Improving tropical forest biomass predictions can accurately value
tropical forests for their ecosystem services. Recently, the Global
Ecosystem Dynamics Investigation (GEDI) lidar was activated on the
international space station (ISS) to improve biomass predictions by
providing detailed 3D forest structure and height data. However, there is
still debate on how best to predict tropical forest biomass using GEDI
data. Here we compare GEDI predicted biomass to 2,102 tropical forest
biomass plots and find that adding a remotely sensed (RS) trait map of LMA
(Leaf Mass per Area) 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, for instance, there was a
negative relationship between both LMA (r2 of 0.34) and %P (r2=0.31) and
residuals. This improvement in predictability corresponds with
measurements from 523 individual trees where LMA predicts Diameter at
Breast height (DBH) (the critical measurement underlying plot biomass)
with an r2=0.04, and spectroscopy (400-1075 nm) predicts DBH with an
r2=0.01. Adding environmental datasets may offer further improvements and
max temperature (Tmax) 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), RS traits are better at
predicting fluxes than structure variables like tree height or Height Of
Median Energy (HOME). Overall, trait maps, especially future improved ones
produced by surface biology geology (SBG), may improve biomass and carbon
flux predictions by a small but significant amount.

提升热带森林生物量预测精度，可精准评估热带森林的生态系统服务价值。近期，搭载于国际空间站（International Space Station, ISS）的全球生态系统动态调查（Global Ecosystem Dynamics Investigation, GEDI）激光雷达正式启用，通过提供高精度三维森林结构与冠层高度数据，助力提升生物量预测精度。然而，如何依托GEDI数据最优地预测热带森林生物量，学界仍存在争议。本研究将GEDI预测的生物量与2102块热带森林生物量样地数据进行对比，结果表明：引入叶面积干重（Leaf Mass per Area, LMA）的遥感（Remote Sensing, RS）特征图后，野外生物量预测精度得到显著提升（P<0.001），但提升幅度极小（决定系数r²=0.01）。不过，该特征图也有助于降低残差偏差：例如，LMA（r²=0.34）与磷含量百分比（%P，r²=0.31）均与残差呈负相关关系。上述预测精度的提升，与523株单木的测量结果相符：其中LMA可预测胸径（Diameter at Breast Height, DBH，样地生物量计算的核心指标），决定系数r²=0.04；而400~1075 nm波段的光谱数据对DBH的预测精度为r²=0.01。引入环境数据集或可进一步提升预测精度：其中最高气温（Tmax）对亚马逊森林生物量残差的预测精度达r²=0.76（样本量N=66）。最后，在包含21块净初级生产力（Net Primary Production, NPP）与总初级生产力（Gross Primary Production, GPP）样地的观测网络中，遥感特征相较于树高、中位能量高度（Height Of Median Energy, HOME）等结构变量，更擅长预测碳通量。总体而言，特征图（尤其是未来由地表生物地质学（Surface Biology Geology, SBG）优化生成的特征图）虽提升幅度有限，但可显著改善生物量与碳通量的预测精度。