Unitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests

Fire severity assessment is crucial for predicting ecosystem response and prioritizing post-fire forest management strategies. Although a variety of remote sensing approaches have been developed, more research is still needed to improve the accuracy and effectiveness of fire severity mapping. This s...

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Bibliographic Details
Published in:Remote sensing of environment 2020-11, Vol.249, p.112025, Article 112025
Main Authors: Montorio, Raquel, Pérez-Cabello, Fernando, Borini Alves, Daniel, García-Martín, Alberto
Format: Article
Language:English
Subjects:
Accuracy
Adaptation
Algorithms
Ash
Canopies
Cartography
Datasets
Ecosystem management
Fire severity
Forest ecosystems
Forest fires
Forest management
Ground cover
Ground truth
Landsat
Landsat satellites
Landsat-8
Learning algorithms
Linear spectral mixing
Machine learning
Mapping
Photosynthesis
Post-fire ground covers
Prediction models
Regression analysis
Regression models
Remote sensing
Satellite imagery
Sentinel-2
Short wave radiation
Spectra
Spectral bands
Vegetation
Wildfires
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Summary:Fire severity assessment is crucial for predicting ecosystem response and prioritizing post-fire forest management strategies. Although a variety of remote sensing approaches have been developed, more research is still needed to improve the accuracy and effectiveness of fire severity mapping. This study proposes a unitemporal simulation approach based on the generation of synthetic spectral databases from linear spectral mixing. To fully exploit the potential of these training databases, the Random Forest (RF) machine learning algorithm was applied to build a classifier and regression model. The predictive models parameterized with the synthetic datasets were applied in a case study, the Sierra de Luna wildfire in Spain. Single date Landsat-8 and Sentinel-2A imagery of the immediate post-fire environment were used to develop the validation spectral datasets and a Pléiades orthoimage, providing the ground truth data. The four defined severity categories – unburned (UB), partial canopy unburned (PCU), canopy scorched (CS), and canopy consumed (CC) – demonstrated high accuracy in the bootstrapped (about 95%) and real validation sets (about 90%), with a slightly better performance observed when the Sentinel-2A dataset was used. Abundance of four ground covers (green vegetation, non-photosynthetic vegetation, soil, and ash) was also quantified with moderate (~45% for NPV) or high accuracy (higher than 75% for the remaining covers). No specific pattern in the comparison of sensors was observed. Variable importance analysis highlighted the complementary behavior of the spectral bands, although the contrast between the near and shortwave infrared regions stood out above the rest. Comparison of procedures reinforced the usefulness of the approach, as RF image-derived models and the multiple endmember spectral unmixing technique (MESMA) showed lower accuracy. The capabilities for detailed mapping are reflected in the development of different types of cartography (classification maps and fraction cover maps). The approach holds great potential for fire severity assessment, and future research needs to extend the predictive modeling to other burned areas – also in different ecosystems – and analyze its competence and the possible adaptations needed. [Display omitted] •Approach to fire severity mapping from unitemporal Landsat-8 and Sentinel-2 data•Accurate estimation of fire severity from synthetic spectral data and Random Forest•Successful application to a Mediterran
ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2020.112025