Biomass Assessment and Carbon Sequestration in Post-Fire Shrublands by Means of Sentinel-2 and Gaussian Processes

0558233 - ÚVGZ 2023 RIV CH eng J - Článek v odborném periodiku
Vinue-Visus, D. - Ruiz-Peinado, R. - Fuente Herraiz, David - Oliver-Villanueva, J. V. - Coll-Aliaga, E. - Lerma-Arce, V.
Biomass Assessment and Carbon Sequestration in Post-Fire Shrublands by Means of Sentinel-2 and Gaussian Processes.
Forests. Roč. 13, č. 5 (2022), č. článku 771. E-ISSN 1999-4907
Institucionální podpora: RVO:86652079
Klíčová slova: forest * retrieval * boreal * machine learning * remote sensing * Gaussian process regression * forest inventory
Obor OECD: Forestry
Impakt faktor: 2.9, rok: 2022
Způsob publikování: Open access
https://www.mdpi.com/1999-4907/13/5/771

In this contribution, we assessed the biomass and carbon stock of a post-fire area covered by a young oak coppice of Quercus pyrenaica Willd. associated with shrubs, mainly of Cistus laurifolius L. This area was burned during the fire event of Chequilla (Guadalajara, Spain) in 2012. Sentinel-2 imagery was used together with our own forest inventories in 2020 and machine learning methods to assess the total biomass of the area. The inventory includes plots of total dry weight ranging between 6 and 14 Mg center dot ha(-1) with individuals up to 8 years old. Nonlinear, nonparametric Gaussian process regression methods were applied to link reflectance values from Sentinel-2 imagery with total shrub biomass. With a reduced inventory of only 32 plots covering 136 ha, the total biomass could be assessed with a root-mean-square error of 1.36 Mg center dot ha(-1) and a bias of0.04 Mg center dot ha(-1), getting a relative error between 9.8% and 20.4% for the gathered biomass. This is a rather good estimation considering the little effort and time invested, thus, the suggested methodology is very suitable for forest monitoring and management.
Trvalý link: http://hdl.handle.net/11104/0331975