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Aboveground biomass mapping and fire potential severity assessment: a case study for eucalypts and shrubland areas in the Central Inland Region of Portugal
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Abstract(s)
Shrubland and forestland covers are highly prone to fire. The Normalized Difference Vegetation Index (NDVI) has been widely used for biomass quantitative assessment. The objectives of this study were as follows: (1) to compute the NDVI annual curve for two types of land cover eucalypts and shrubland areas; (2) to collect field data in these two types of land cover to estimate aboveground biomass (AGB); and (3) to produce AGB maps for eucalypts and shrubland areas by modelling AGB with NDVI, validate them with other data sources, and to compare fuel loads with fire severity levels. A study area in the central inland region of Portugal was considered. The wildfire on 4 August 2023 was considered for burn severity levels assessment using the Normalized Burn Index (NRB). The Sentinel-2 MSI imagery was used to compute the NDVI for the years of 2022 and 2023 and the NBR for the pre-fire and post-fire dates. The NDVI annual curve for 2022 showed a minimum observed between July and August, in accordance with the climatological data, and allowed differentiating eucalypts from shrubland areas. Spectral signatures also confirmed this differentiation. The fitted linear models for AGB prediction using the NDVI imagery showed good fitting performances (R2 of 0.76 and 0.77). The AGB maps provided a relevant decision support tool for forest management and for fire hazard and fire severity mitigation. Further research is needed using more robust datasets for an independent validation of the model.
Description
Keywords
Biomass components Carbon sequestration NDVI-CV method Spectral signatures Fire hazard Burn severity levels
Citation
ALEGRIA, C.M.M. (2023) - Aboveground biomass mapping and fire potential severity assessment: a case study for eucalypts and shrubland areas in the Central Inland Region of Portugal. Forests. Vol. 14, 1795. DOI: 10.3390/f14091795
Publisher
MDPI