Integrating Physical-Based Models and Structure-from-Motion Photogrammetry to Retrieve Fire Severity by Ecosystem Strata from Very High Resolution UAV Imagery DOI Creative Commons
José Manuel Fernández‐Guisuraga, Leonor Calvo,

Luis A. Pérez-Rodríguez

et al.

Fire, Journal Year: 2024, Volume and Issue: 7(9), P. 304 - 304

Published: Aug. 27, 2024

We propose a novel mono-temporal framework with 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, mimicked methodology for CBI assessment remote sensing framework. strata identified individual tree segmentation geographic object-based image analysis (GEOBIA). In each stratum, wildfire effects estimated following methods: (i) vertical structural complexity of vegetation legacies was computed from 3D-point clouds, as proxy biomass consumption; (ii) biophysical variables retrieved multispectral data by inversion PROSAIL radiative transfer model, direct link remaining after canopy scorch torch. The scores predicted UAV ecologically related metrics level featured fit respect field-measured (R2 > 0.81 RMSE < 0.26). Conversely, conventional retrieval using battery spectral predictors (point height distribution indices) plot provided much worse performance = 0.677 0.349).

Language: Английский

Improved Fire Severity Prediction Using Pre-Fire Remote Sensing and Meteorological Time Series: Application to the French Mediterranean Area DOI
Victor Pénot, Thomas Opitz, François Pimont

et al.

Published: Jan. 1, 2024

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Language: Английский

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0
Integrating Physical-Based Models and Structure-from-Motion Photogrammetry to Retrieve Fire Severity by Ecosystem Strata from Very High Resolution UAV Imagery DOI Creative Commons
José Manuel Fernández‐Guisuraga, Leonor Calvo,

Luis A. Pérez-Rodríguez

et al.

Fire, Journal Year: 2024, Volume and Issue: 7(9), P. 304 - 304

Published: Aug. 27, 2024

We propose a novel mono-temporal framework with 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, mimicked methodology for CBI assessment remote sensing framework. strata identified individual tree segmentation geographic object-based image analysis (GEOBIA). In each stratum, wildfire effects estimated following methods: (i) vertical structural complexity of vegetation legacies was computed from 3D-point clouds, as proxy biomass consumption; (ii) biophysical variables retrieved multispectral data by inversion PROSAIL radiative transfer model, direct link remaining after canopy scorch torch. The scores predicted UAV ecologically related metrics level featured fit respect field-measured (R2 > 0.81 RMSE < 0.26). Conversely, conventional retrieval using battery spectral predictors (point height distribution indices) plot provided much worse performance = 0.677 0.349).

Language: Английский

Citations

0