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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Montorio, Raquel [1, 2] ; Perez-Cabello, Fernando [1, 2] ; Alves, Daniel Borini [3] ; Garcia-Martin, Alberto [2, 4]
Total Authors: 4
[1] Univ Zaragoza, Dept Geog & Spatial Management, C Pedro Cerbuna 12, Zaragoza 50009 - Spain
[2] Univ Zaragoza, Environm Sci Inst IUCA, GEOFOREST IUCA Res Grp, C Pedro Cerbuna 12, Zaragoza 50009 - Spain
[3] Univ Estadual Paulista UNESP, Lab Vegetat Ecol, Inst Biociencias, Ave 24-A 1515, BR-13506900 Rio Claro - Brazil
[4] Ctr Univ Def Zaragoza, Acad Gen Mil, Ctra Huesca S-N, Zaragoza 50090 - Spain
Total Affiliations: 4
Document type: Journal article
Web of Science Citations: 1

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 Pleiades 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 (similar to 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. (AU)

FAPESP's process: 19/07357-8 - Fire behavior and vegetation responses in experimental fires on Amazonian Savanna using remote sensing and in situ measurements
Grantee:Daniel Borini Alves
Support Opportunities: Scholarships in Brazil - Post-Doctoral