Idioma:

Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data

Hernández-Stefanoni, J. Luis ; Castillo-Santiago, Miguel Ángel ; Mas, Jean Francois ; Wheeler, Charlotte E. ; Andres-Mauricio, Juan ; Tun-Dzul, Fernando ; George-Chacón, Stephanie P. ; Reyes-Palomeque, Gabriela ; Castellanos-Basto, Blanca ; Vaca, Raúl ; Dupuy, Juan Manuel

Carbon balance and management, 2020-07, Vol.15 (1), p.15-15, Article 15 [Periódico revisado por pares]

Cham: Springer International Publishing

Texto completo disponível

Citações Citado por

Enviar para

Título:
Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data
Autor: Hernández-Stefanoni, J. Luis ; Castillo-Santiago, Miguel Ángel ; Mas, Jean Francois ; Wheeler, Charlotte E. ; Andres-Mauricio, Juan ; Tun-Dzul, Fernando ; George-Chacón, Stephanie P. ; Reyes-Palomeque, Gabriela ; Castellanos-Basto, Blanca ; Vaca, Raúl ; Dupuy, Juan Manuel
Assuntos: Accuracy ; Algorithms ; Backscattering ; Biomass ; Climate change ; Climate change mitigation ; Climatic data ; Climatic water deficit ; Dry forests ; Earth and Environmental Science ; Ecological effects ; Ecosystems ; Environment ; Environmental Management ; Error analysis ; Estimates ; Forest biomass ; Forestry ; Forests ; Image acquisition ; L-band SAR ; Learning algorithms ; Lidar ; Machine learning ; Mapping ; Mitigation ; Radar ; Radar backscatter ; Radar data ; Random forest ; Remote sensing ; Satellite observation ; Satellites ; Spatial distribution ; Surface layers ; Synthetic aperture radar ; Texture ; Texture analysis ; Trees ; Tropical forests ; Water deficit ; Yucatan peninsula
É parte de: Carbon balance and management, 2020-07, Vol.15 (1), p.15-15, Article 15
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: 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. 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 (R 2 = 0.44 vs R 2 = 0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R 2 = 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 mismatch between field and remotely sensed data.
Editor: Cham: Springer International Publishing
Idioma: Inglês

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data

Hernández-Stefanoni, J. Luis ; Castillo-Santiago, Miguel Ángel ; Mas, Jean Francois ; Wheeler, Charlotte E. ; Andres-Mauricio, Juan ; Tun-Dzul, Fernando ; George-Chacón, Stephanie P. ; Reyes-Palomeque, Gabriela ; Castellanos-Basto, Blanca ; Vaca, Raúl ; Dupuy, Juan Manuel

Cham: Springer International Publishing

Buscando em bases de dados remotas. Favor aguardar.