Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data
Hernández Stefanoni, José Luis [autor]
| Castillo Santiago, Miguel Ángel [autor] | Francois Mas, Jean [autor] | Wheeler, Charlotte E [autora] | Andres Mauricio, Juan [autor] | Tun Dzul, Fernando Jesús [autor] | George Chacón, Stephanie P [autora] | Reyes Palomeque, Gabriela [autora] | Castellanos Basto, Blanca [autora] | Vaca, Raúl [autor] | Dupuy Rada, Juan Manuel [autor].
Tipo de material: 
Artículo
en línea Tema(s): Bosques tropicales | Biomasa aérea | Distribución espacial | Conocimiento tradicional | Inventarios forestalesTema(s) en inglés: Tropical forests | Aboveground biomass | Spatial distribution | Indigenous knowledge | Forest inventoriesDescriptor(es) geográficos: Yucatán (Península) (México) Nota de acceso: Acceso en línea sin restricciones
En: Carbon Balance and Management. Volumen 15, artículo número 15 (July 2020), páginas 1-17. --ISSN: 1750-0680Número de sistema: 9550Resumen:| Tipo de ítem | Biblioteca actual | Colección | Signatura | Estado | Fecha de vencimiento | Código de barras |
|---|---|---|---|---|---|---|
| Artículos | Biblioteca Electrónica Recursos en línea (RE) | ECOSUR | Recurso digital | ECO400095508196 |
Acceso en línea sin restricciones
Background: Reliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates. eng
Results: Correcting NFI plot data for both small trees and temporal differences between field and remotely sensed measurements reduced the relative error of biomass estimates by 12.2%. Using a machine learning algorithm, Random Forest, with corrected field plot data, backscatter and surface texture from the L‑band synthetic aperture radar (PALSAR) installed on the on the Advanced Land Observing Satellite‑1 (ALOS), and climatic water deficit data improved the accuracy of the maps obtained in this study as compared to previous studies (R²=0.44 vs R²= 0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R²= 0.26). Conclusions: This study reveals that the suggested approach has the potential to improve AGB maps of tropical dry forests and shows predictors of AGB that should be considered in future studies. Our results highlight the importance of using ecological knowledge to correct errors associated with both the plot‑level biomass estimates and the mis‑match between field and remotely sensed data. eng
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