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Radar and multispectral remote sensing data accurately estimate vegetation vertical structure diversity as a fire resilience indicator [Dataset]
Autor
Facultad/Centro
Área de conocimiento
Es parte de
http://hdl.handle.net/10612/15062
Asociado a la publicación
Fernández-Guisuraga, J. M., Suárez-Seoane, S., & Calvo, L. (2023). Radar and multispectral remote sensing data accurately estimate vegetation vertical structure diversity as a fire resilience indicator. Remote Sensing in Ecology and Conservation, 9(1), 117-132. https://doi.org/10.1002/RSE2.299
Cita Bibliográfica
Fernández-Guisuraga, J. M., Suárez-Seoane, S., & Calvo, L. (2022). Radar and multispectral remote sensing data accurately estimate vegetation vertical structure diversity as a fire resilience indicator. 10.18002/10612/16244 [Dataset]
Fecha
2023-04-27
Resumen
[EN] The structural complexity of plant communities contributes to maintaining the ecosystem functioning in fire-prone landscapes and plays a crucial role in driving ecological resilience to fire. The objective of this study was to evaluate the resilience to fire off several plant communities with reference to the temporal evolution of their vertical structural diversity (VSD) estimated from the data fusion of C-band synthetic aperture radar (SAR) backscatter (Sentinel-1) and multispectral remote sensing reflectance (Sentinel-2) in a burned landscape of the western Mediterranean Basin. We estimated VSD in the field 1 and 2 years after fire using Shannon's index as a measure of vertical heterogeneity in vegetation structure from the vegetation cover in several strata, both in burned and unburned control plots. Random forest (RF) was used to model VSD in the control (analogous to prefire scenario) and burned plots (1 year after fire) using as predictors (i) Sentinel-1 VV and VH backscatter coefficients and (ii) surface reflectance of Sentinel-2 bands. The transferability of the RF model from 1 to 2 years after wildfire was also evaluated. We generated VSD prediction maps across the study site for the prefire scenario and 1 to 4 years postfire. RF models accurately explained VSD in unburned control plots (R2 = 87.68; RMSE = 0.16) and burned plots 1 year after fire (R2 = 80.48; RMSE = 0.13). RF model transferability only involved a reduction in the VSD predictive capacity from 0.13 to 0.20 in terms of RMSE. The VSD of each plant community 4 years after the fire disturbance was significantly lower than in the prefire scenario. Plant communities dominated by resprouter species featured significantly higher VSD recovery values than communities dominated by facultative or obligate seeders. Our results support the applicability of SAR and multispectral data fusion for monitoring VSD as a generalizable resilience indicator in fire-prone landscapes.
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- info: eu-repo/grantAgreement/AEI/Programa Estatal de I+D+i Orientada a los Retos de la Sociedad/AGL2017-86075-C2-1-R/SEVERIDAD DE GRANDES INCENDIOS EN SISTEMAS FORESTALES PROPENSOS AL FUEGO: CONDICIONANTES, EFECTOS EN LA PROVISION DE SERVICIOS Y SOLUCIONES DE GESTION PRE- Y POST-INCENDIO
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