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Article

A Spectral–Spatial Method for Mapping Fire Severity Using Morphological Attribute Profiles

by 1, 1,2,* and 1,2
1
School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
2
Hubei Subsurface Multi-Scale Imaging Key Laboratory, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(3), 699; https://doi.org/10.3390/rs15030699
Received: 15 December 2022 / Revised: 20 January 2023 / Accepted: 22 January 2023 / Published: 24 January 2023
(This article belongs to the Section Earth Observation for Emergency Management)

Abstract

Fast and accurate fire severity mapping can provide an essential resource for fire management and studying fire-related ecological and climate change. Currently, mainstream fire severity mapping approaches are based only on pixel-wise spectral features. However, the landscape pattern of fire severity originates from variations in spatial dependence, which should be described by spatial features combined with spectral features. In this paper, we propose a morphological attribute profiles-based spectral–spatial approach, named Burn Attribute Profiles (BAP), to improve fire severity classification and mapping accuracy. Specifically, the BAP method uses principal component transformation and attributes with automatically determined thresholds to extract spatial features, which are integrated with spectral features to form spectral–spatial features for fire severity. We systematically tested and compared the BAP-based spectral–spatial features and spectral index features in the extremely randomized trees machine learning framework. Sentinel-2 imagery was used for seven fires in the Mediterranean region, while Landsat-8 imagery was used for another seven fires in the northwestern continental United States region. The results showed that, except for 2 fires (overall accuracy (OA) for EMSR213_P: 59.6%, EL: 59.5%), BAP performed well for the other 12 fires (OA for the 2 fires: 60%–70%, 6 fires: 70%–80%, 4 fires: >80%). Furthermore, compared with the spectral indices-based method, the BAP method showed OA improvement in all 14 fires (OA improvement in Mediterranean: 0.2%–14.3%, US: 4.7%–12.9%). Recall and Precision were also improved for each fire severity level in most fire events. Moreover, the BAP method improved the "salt-and-pepper" phenomenon in the homogeneous area, where the results are visually closest to the reference data. The above results suggest that the spectral–spatial method based on morphological attribute profiles can map fire severity more accurately.
Keywords: fire severity; spectral–spatial feature; morphological attribute profiles; spectral indices; extremely randomized trees fire severity; spectral–spatial feature; morphological attribute profiles; spectral indices; extremely randomized trees

Share and Cite

MDPI and ACS Style

Ren, X.; Yu, X.; Wang, Y. A Spectral–Spatial Method for Mapping Fire Severity Using Morphological Attribute Profiles. Remote Sens. 2023, 15, 699. https://doi.org/10.3390/rs15030699

AMA Style

Ren X, Yu X, Wang Y. A Spectral–Spatial Method for Mapping Fire Severity Using Morphological Attribute Profiles. Remote Sensing. 2023; 15(3):699. https://doi.org/10.3390/rs15030699

Chicago/Turabian Style

Ren, Xiaoyang, Xin Yu, and Yi Wang. 2023. "A Spectral–Spatial Method for Mapping Fire Severity Using Morphological Attribute Profiles" Remote Sensing 15, no. 3: 699. https://doi.org/10.3390/rs15030699

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