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Remote Sensing
Volume 14
Issue 23
10.3390/rs14236063
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Article

Exploring the Potential of Lidar and Sentinel-2 Data to Model the Post-Fire Structural Characteristics of Gorse Shrublands in NW Spain

by
José María Fernández-Alonso
1,
Rafael Llorens
2,
José Antonio Sobrino
2,
Ana Daría Ruiz-González
3,
Juan Gabriel Alvarez-González
3,
José Antonio Vega
1 and
Cristina Fernández
1,*
1
Centro de Investigación Forestal de Lourizán, Xunta de Galicia, 36156 Pontevedra, Spain
2
Global Change Unit, Image Processing Laboratory, University of Valencia, 46980 Paterna, Spain
3
Unidad de Gestión Ambiental y Forestal Sostenible (UXAFORES), Departamento de Ingeniería Agroforestal, Escuela Politécnica Superior de Ingeniería, Universidad de Santiago de Compostela, 27002 Lugo, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(23), 6063; https://doi.org/10.3390/rs14236063
Submission received: 17 October 2022 / Revised: 17 November 2022 / Accepted: 28 November 2022 / Published: 30 November 2022
(This article belongs to the Special Issue Vegetation Mapping through Multiscale Remote Sensing)
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Abstract

The characterization of aboveground biomass is important in forest management planning, with various objectives ranging from prevention of forest fires to restoration of burned areas, especially in fire-prone regions such as NW Spain. Although remotely sensed data have often been used to assess the recovery of standing aboveground biomass after perturbations, the data have seldom been validated in the field, and different shrub fractions have not been modelled. The main objective of the present study was to assess different vegetation parameters (cover, height, standing AGB and their fractions) in field plots established in five areas affected by wildfires between 2009 and 2016 by using Sentinel-2 spectral indices and LiDAR metrics. For this purpose, 22 sampling plots were established in 2019, and vegetation variables were measured by a combination of non-destructive measurement (cover and height) and destructive sampling (total biomass and fine samples of live and dead fractions of biomass).The structural characterization of gorse shrublands was addressed, and models of shrub cover—height, total biomass, and biomass by fraction and physiological condition—were constructed, with adjusted coefficients of determination ranging from 0.6 to 0.9. The addition of LiDAR data to optical remote sensing images improved the models. Further research should be conducted to calibrate the models in other vegetation communities.
Keywords: wildfire; biomass; Sentinel-2; LiDAR; fuel load wildfire; biomass; Sentinel-2; LiDAR; fuel load

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MDPI and ACS Style

Fernández-Alonso, J.M.; Llorens, R.; Sobrino, J.A.; Ruiz-González, A.D.; Alvarez-González, J.G.; Vega, J.A.; Fernández, C. Exploring the Potential of Lidar and Sentinel-2 Data to Model the Post-Fire Structural Characteristics of Gorse Shrublands in NW Spain. Remote Sens. 2022, 14, 6063. https://doi.org/10.3390/rs14236063

AMA Style

Fernández-Alonso JM, Llorens R, Sobrino JA, Ruiz-González AD, Alvarez-González JG, Vega JA, Fernández C. Exploring the Potential of Lidar and Sentinel-2 Data to Model the Post-Fire Structural Characteristics of Gorse Shrublands in NW Spain. Remote Sensing. 2022; 14(23):6063. https://doi.org/10.3390/rs14236063

Chicago/Turabian Style

Fernández-Alonso, José María, Rafael Llorens, José Antonio Sobrino, Ana Daría Ruiz-González, Juan Gabriel Alvarez-González, José Antonio Vega, and Cristina Fernández. 2022. "Exploring the Potential of Lidar and Sentinel-2 Data to Model the Post-Fire Structural Characteristics of Gorse Shrublands in NW Spain" Remote Sensing 14, no. 23: 6063. https://doi.org/10.3390/rs14236063

APA Style

Fernández-Alonso, J. M., Llorens, R., Sobrino, J. A., Ruiz-González, A. D., Alvarez-González, J. G., Vega, J. A., & Fernández, C. (2022). Exploring the Potential of Lidar and Sentinel-2 Data to Model the Post-Fire Structural Characteristics of Gorse Shrublands in NW Spain. Remote Sensing, 14(23), 6063. https://doi.org/10.3390/rs14236063

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