Improving silvicultural practices for Mediterranean forests through fire behaviour modelling using LiDAR-derived canopy fuel characteristics
Brigite Botequim A D * , Paulo M. Fernandes B , José G. Borges A , Eduardo González-Ferreiro C and Juan Guerra-Hernández A D *
+ Author Affiliations
- Author Affiliations
A Forest Research Centre, School of Agriculture, University of Lisbon, Instituto Superior de Agronomía (ISA), Tapada da Ajuda, P-1349-017, Lisbon, Portugal.
B Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal.
C Departamento de Tecnología Minera, Topografía y de Infraestructuras, Grupo de Investigación en Geomática e Ingeniería Cartográfica, GI-202-GEOINCA, Escuela Superior y Técnica de Ingenieros de Minas, Universidad de León, Avenida de Astorga s/n, Campus de Ponferrada, 24401 Ponferrada, Spain.
D Corresponding authors. Emails: juanguerra@isa.ulisboa.pt; bbotequim@isa.ulisboa.pt
International Journal of Wildland Fire 28(11) 823-839 https://doi.org/10.1071/WF19001
Submitted: 8 January 2019 Accepted: 2 August 2019 Published: 15 October 2019
Journal Compilation © IAWF 2019 Open Access CC BY-NC-ND
Abstract
Wildfires cause substantial environmental and socioeconomic impacts and threaten many Spanish forested landscapes. We describe how LiDAR-derived canopy fuel characteristics and spatial fire simulation can be integrated with stand metrics to derive models describing fire behaviour. We assessed the potential use of very-low-density airborne LiDAR (light detection and ranging) data to estimate canopy fuel characteristics in south-western Spain Mediterranean forests. Forest type-specific equations were used to estimate canopy fuel attributes, namely stand height, canopy base height, fuel load, bulk density and cover. Regressions explained 61–85, 70–85, 38–96 and 75–95% of the variability in field estimated stand height, canopy fuel load, crown bulk density and canopy base height, respectively. The weakest relationships were found for mixed forests, where fuel loading variability was highest. Potential fire behaviour for typical wildfire conditions was predicted with FlamMap using LiDAR-derived canopy fuel characteristics and custom fuel models. Classification tree analysis was used to identify stand structures in relation to crown fire likelihood and fire suppression difficulty levels. The results of the research are useful for integrating multi-objective fire management decisions and effective fire prevention strategies within forest ecosystem management planning.
Additional keywords: airborne laser scanning (ALS), fire management, remote sensing, Spanish PNOA project (Plan Nacional de Ortofotografía Aérea de España).
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