Satellite Derived Trait Data Slightly Improves Tropical Forest Biomass, NPP and GPP Estimates
Corresponding Author
Christopher E. Doughty
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Correspondence to:
C. E. Doughty,
Contribution: Conceptualization, Methodology, Formal analysis, Writing - original draft
Search for more papers by this authorCamille Gaillard
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Contribution: Writing - review & editing
Search for more papers by this authorPatrick Burns
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Contribution: Methodology
Search for more papers by this authorYadvinder Malhi
Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
Search for more papers by this authorAlexander Shenkin
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Contribution: Methodology
Search for more papers by this authorDavid Minor
Geographical Sciences, University of Maryland, College Park, MD, USA
Contribution: Methodology
Search for more papers by this authorLaura Duncanson
Geographical Sciences, University of Maryland, College Park, MD, USA
Contribution: Writing - review & editing, Methodology
Search for more papers by this authorJesús Aguirre-Gutiérrez
Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
Contribution: Writing - review & editing, Methodology
Search for more papers by this authorScott Goetz
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Contribution: Writing - review & editing, Conceptualization
Search for more papers by this authorHao Tang
Department of Geography, National University of Singapore, Singapore, Singapore
Contribution: Writing - review & editing, Conceptualization
Search for more papers by this authorCorresponding Author
Christopher E. Doughty
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Correspondence to:
C. E. Doughty,
Contribution: Conceptualization, Methodology, Formal analysis, Writing - original draft
Search for more papers by this authorCamille Gaillard
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Contribution: Writing - review & editing
Search for more papers by this authorPatrick Burns
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Contribution: Methodology
Search for more papers by this authorYadvinder Malhi
Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
Search for more papers by this authorAlexander Shenkin
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Contribution: Methodology
Search for more papers by this authorDavid Minor
Geographical Sciences, University of Maryland, College Park, MD, USA
Contribution: Methodology
Search for more papers by this authorLaura Duncanson
Geographical Sciences, University of Maryland, College Park, MD, USA
Contribution: Writing - review & editing, Methodology
Search for more papers by this authorJesús Aguirre-Gutiérrez
Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
Contribution: Writing - review & editing, Methodology
Search for more papers by this authorScott Goetz
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Contribution: Writing - review & editing, Conceptualization
Search for more papers by this authorHao Tang
Department of Geography, National University of Singapore, Singapore, Singapore
Contribution: Writing - review & editing, Conceptualization
Search for more papers by this authorAbstract
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
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Both field measured and remotely sensed trait data improved biomass predictions, but only by a small amount
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In the Amazon region, maximum temperature, but not soil fertility, improved biomass predictions
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Trait data was better than structure data for predicting tropical forest net primary production and gross primary production
Open Research
Data Availability Statement
Description of the Type(s) of data and/or software. Data—Data and its descriptions to create all figures and tables in this paper are available (Doughty, 2024). Software—All code and its descriptions to create all figures and tables in this paper are available (Doughty, 2024).
Supporting Information
| Filename | Description |
|---|---|
| 2024JG008108-sup-0001-Supporting Information SI-S01.docx511.8 KB | Supporting Information S1 |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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