Comparative Assessment of Hyperspectral and Multispectral Vegetation Indices for Estimating Fire Severity in Mediterranean Ecosystems
[EN] Highlights: What are the main findings? Hyperspectral imagery demonstrated superior performance compared with multispectral data for estimating fire severity across Mediterranean ecosystems. Among the hyperspectral vegetation indices, DVIRED, EVI, and CAI achieved the highest correlations acros...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad de León |
| Repositorio: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:dnet:buleria_____::155422da63ba5024e9e39186fa11f78b |
| Acceso en línea: | https://www.mdpi.com/2072-4292/18/2/244 https://hdl.handle.net/10612/28212 |
| Access Level: | acceso abierto |
| Palabra clave: | Ecología. Medio ambiente Ingeniería forestal Fire severity PRISMA Sentinel 2 Hyperspectral indices CBI 2417.13 Ecología Vegetal 3308 Ingeniería y Tecnología del Medio Ambiente 3106 Ciencia Forestal |
| Sumario: | [EN] Highlights: What are the main findings? Hyperspectral imagery demonstrated superior performance compared with multispectral data for estimating fire severity across Mediterranean ecosystems. Among the hyperspectral vegetation indices, DVIRED, EVI, and CAI achieved the highest correlations across Composite Burn Index (CBI) levels and vegetation types. What are the implications of the main findings? Hyperspectral remote sensing shows strong potential as an accurate, scalable tool for post-fire severity assessment in heterogeneous Mediterranean ecosystems. Variations in the performance of hyperspectral vegetation indices among vegetation formations reflect distinct spectral responses associated with differing vegetation structures and burn characteristics. Assessing post-fire disturbance in Mediterranean ecosystems is essential for quantifying ecological impacts and guiding restoration strategies. This study evaluates fire severity following an extreme wildfire event (~28,000 ha) in northwestern Spain using vegetation indices (VIs) derived from PRISMA hyperspectral imagery, validated against field-based Composite Burn Index (CBI) measurements at the vegetation, soil, and site levels across three vegetation formations (coniferous forests, broadleaf forests, and shrublands). Hyperspectral VIs were benchmarked against multispectral VIs derived from Sentinel-2. Hyperspectral VIs yielded stronger correlations with CBI values than multispectral VIs. Vegetation-level CBI showed the highest correlations, reflecting the sensitivity of most VIs to canopy structural and compositional changes. Indices incorporating red-edge, near-infrared (NIR), and shortwave infrared (SWIR) bands demonstrated the greatest explanatory power. Among hyperspectral indices, DVIRED, EVI, and especially CAI performed best. For multispectral data, NDRE, CIREDGE, ENDVI, and GNDVI were the most effective. These findings highlight the strong potential of hyperspectral remote sensing for accurate, scalable post-fire severity assessment in heterogeneous Mediterranean ecosystems |
|---|