Caracterización de cambios estructurales en la vegetación y su relación con la severidad del fuego mediante datos LiDAR multi-temporales

  1. Dario Domingo
  2. María Teresa Lamelas
  3. María Begoña García
Journal:
Ecosistemas: Revista científica y técnica de ecología y medio ambiente

ISSN: 1697-2473

Year of publication: 2021

Issue Title: Ultimas tendencias en investigacion ecológica

Volume: 30

Issue: 2

Type: Article

DOI: 10.7818/ECOS.2103 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Ecosistemas: Revista científica y técnica de ecología y medio ambiente

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Abstract

The assessment of canopy changes and gap presence after fire provides relevant information to better understand the ecological effect of fire in Mediterranean forests. The study characterizes those changes in Pinus and Quercus forested areas, and their relationship with fire severity in Calcena wildfire. The wildfire burned 4,573 ha in 2012 partially affecting the protected “Parque Natural de la Dehesa del Moncayo” located in Aragón region (Spain). Multi-temporal Light Detection and Ranging (LiDAR) data from two coverages (2011 and 2016) captured within the National Plan from Aerial Ortophotography (PNOA) were used. Landsat 7 images were used to estimate fire severity using the differenced Normalized Burn Ratio (dNBR). Structural changes were characterized using pre-fire and post-fire LiDAR metrics. Gap frequency distribution, size and number of gaps were also analysed. The relationship between severity and the structural changes was analysed through the correlation coefficient. Most of the surface was burned with low (43.32%) or moderate low severity (30.38%) levels reducing forest height, canopy density, and structural diversity. In general, gap size increased after fire. The number of small gaps decreased while medium size gaps (0.2 ha) increased. LiDAR metrics related to the height, variability of height in vertical profile, and density of the forest canopy showed the strongest correlations with fire severity, showing the strongest changes. Results show the interest to use LiDAR data for characterizing structural changes and supporting forest management.