Integrating Physical-Based Models and Structure-from-Motion Photogrammetry to Retrieve Fire Severity by Ecosystem Strata from Very High Resolution UAV Imagery

Author:

Fernández-Guisuraga José Manuel12ORCID,Calvo Leonor1ORCID,Pérez-Rodríguez Luis Alfonso3,Suárez-Seoane Susana45ORCID

Affiliation:

1. Department of Biodiversity and Environmental Management, Faculty of Biological and Environmental Sciences, University of León, 24071 León, Spain

2. Centro de Investigação e de Tecnologias Agroambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal

3. Agrarian Science and Engineering Department, School of Agricultural and Forestry Engineering, University of León, 24400 Ponferrada, Spain

4. Department of Organisms and Systems Biology, University of Oviedo, 33071 Oviedo, Spain

5. Research Institute of Biodiversity, IMIB, University of Oviedo—CSIC—Principality of Asturias, 33600 Mieres, Spain

Abstract

We propose a novel mono-temporal framework with a physical basis and ecological consistency to retrieve fire severity at very high spatial resolution. First, we sampled the Composite Burn Index (CBI) in 108 field plots that were subsequently surveyed through unmanned aerial vehicle (UAV) flights. Then, we mimicked the field methodology for CBI assessment in the remote sensing framework. CBI strata were identified through individual tree segmentation and geographic object-based image analysis (GEOBIA). In each stratum, wildfire ecological effects were estimated through the following methods: (i) the vertical structural complexity of vegetation legacies was computed from 3D-point clouds, as a proxy for biomass consumption; and (ii) the vegetation biophysical variables were retrieved from multispectral data by the inversion of the PROSAIL radiative transfer model, with a direct physical link with the vegetation legacies remaining after canopy scorch and torch. The CBI scores predicted from UAV ecologically related metrics at the strata level featured high fit with respect to the field-measured CBI scores (R2 > 0.81 and RMSE < 0.26). Conversely, the conventional retrieval of fire effects using a battery of UAV structural and spectral predictors (point height distribution metrics and spectral indices) computed at the plot level provided a much worse performance (R2 = 0.677 and RMSE = 0.349).

Funder

Spanish Ministry of Science and Innovation

the State Research Agency

European Regional Development Fund

Regional Government of the Principality of Asturias

Regional Government of Castile and León

Publisher

MDPI AG

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