التفاصيل البيبلوغرافية
| العنوان: |
Satellite Derived Trait Data Slightly Improves Tropical Forest Biomass, NPP and GPP Estimates. |
| المؤلفون: |
Doughty, Christopher E., Gaillard, Camille, Burns, Patrick, Malhi, Yadvinder, Shenkin, Alexander, Minor, David, Duncanson, Laura, Aguirre‐Gutiérrez, Jesús, Goetz, Scott, Tang, Hao |
| المصدر: |
Journal of Geophysical Research. Biogeosciences; Jul2024, Vol. 129 Issue 7, p1-14, 14p |
| مصطلحات موضوعية: |
TROPICAL forests, BIOLOGICAL interfaces, ECOSYSTEM dynamics, ECOLOGICAL disturbances, TROPICAL ecosystems |
| مستخلص: |
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 (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), 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. Plain Language Summary: Improving predictions of tropical forest biomass can help us to fight climate change. In this paper, we tried to improve tropical forest biomass predictions of satellite lidar (Global Ecosystem Dynamics Investigation) by adding remote sensed estimates of leaf traits. Leaf traits like leaf mass area or phosphorus slightly improved predictions of forest biomass with both a ground data set and a remotely sensed data set. Further, remotely sensed trait data could help explain the differences in the prediction of remotely sensed biomass compared with field derived biomass (residuals). Maximum temperature, but not soil fertility, also improved biomass predictions. Remotely sensed leaf traits were better than structure, like tree height, for predicting net primary production (NPP) and gross primary production (GPP). Future improved hyperspectral satellite data may be used to further improve predictions of biomass, NPP and GPP. Key Points: Both field measured and remotely sensed trait data improved biomass predictions, but only by a small amountIn the Amazon region, maximum temperature, but not soil fertility, improved biomass predictionsTrait data was better than structure data for predicting tropical forest net primary production and gross primary production [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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