Mapping forest landscape fragmentations under different fire severity using Sentinel-2 data with SNIC-MVI RF

• SNIC-MVI RF can accurately get forest landscape fragmentation in mountain areas. • Indices segmented from pixels to objects can relieve “salt and pepper” noises. • Significant differences exist in post-fire forest landscape fragmentation indices. • Pre-fire meteorological conditions and vegetation coverage are two main factors. Mapping forest landscape fragmentation (FLF) after fires is essential for maintaining ecosystem balance, reducing fire-related losses, and minimizing environmental damage. However, the “salt-and-pepper” phenomenon complicates accurate FLF mapping at fine resolutions (≤ 10 m) in mountainous regions. To relieve the phenomenon, this study used the multi-vegetation index (MVI) to represent FLF and applied the Simple Non-Iterative Clustering (SNIC) algorithm to convert pixels into objects before classification, and then SNIC-MVI RF was introduced to map FLF using Sentinel-2 imagery and land use data. In combination with topographical and meteorological data, the Geodetector was employed to analyze the influencing factors on FLF patterns across different fire severity ranges. The results indicate that: (1) The proposed SNIC-MVI RF can accurately retrieve FLF across a range of fire severity using 10-m Sentinel-2 imagery while mitigating the “salt-and-pepper” phenomenon, with an average precision of 96.2 % and an average F1-score of 97.4 %. (2) The FLF spatial patterns exhibit significant differences across fire severity. Low fire severity (LFS) leads to an increase in the number of small patches, moderate fire severity (MFS) results in more complex patch edges, and high fire severity (HFS) promotes greater patch aggregation. (3) Pre-fire meteorological conditions and vegetation cover are major influences on the spatial patterns of FLF under different fire severities. This study will provide a new method for exploring the impact of different fire severities on FLF in central Yunnan. As an important indicator for assessing ecological disturbance and habitat degradation, analyzing the response of fragmentation to different fire severities can help administrators evaluate the impacts of fire on FLF more scientifically and develop more targeted and sustainable forest management and ecological restoration strategies.

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Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2025-11-20
DOI
https://doi.org/10.1016/j.jag.2025.104962
Primary Topic
Fire effects on ecosystems
Type
article
Field-Weighted Citation Impact
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article

Mapping forest landscape fragmentations under different fire severity using Sentinel-2 data with SNIC-MVI RF

Ruixiong Duan, Jiayue Gao, Qiuhua Wang, Ran Meng et al.
International Journal of Applied Earth Observation and Geoinformation
Fire effects on ecosystems
article

Mapping forest landscape fragmentations under different fire severity using Sentinel-2 data with SNIC-MVI RF

Ruixiong Duan, Jiayue Gao, Qiuhua Wang, Ran Meng, Weili Kou, Yue Chen, Yi Yang, Yang He, Ning Lu, Chao Duan, Lingling Guo
article en

Abstract

• SNIC-MVI RF can accurately get forest landscape fragmentation in mountain areas. • Indices segmented from pixels to objects can relieve “salt and pepper” noises. • Significant differences exist in post-fire forest landscape fragmentation indices. • Pre-fire meteorological conditions and vegetation coverage are two main factors. Mapping forest landscape fragmentation (FLF) after fires is essential for maintaining ecosystem balance, reducing fire-related losses, and minimizing environmental damage. However, the “salt-and-pepper” phenomenon complicates accurate FLF mapping at fine resolutions (≤ 10 m) in mountainous regions. To relieve the phenomenon, this study used the multi-vegetation index (MVI) to represent FLF and applied the Simple Non-Iterative Clustering (SNIC) algorithm to convert pixels into objects before classification, and then SNIC-MVI RF was introduced to map FLF using Sentinel-2 imagery and land use data. In combination with topographical and meteorological data, the Geodetector was employed to analyze the influencing factors on FLF patterns across different fire severity ranges. The results indicate that: (1) The proposed SNIC-MVI RF can accurately retrieve FLF across a range of fire severity using 10-m Sentinel-2 imagery while mitigating the “salt-and-pepper” phenomenon, with an average precision of 96.2 % and an average F1-score of 97.4 %. (2) The FLF spatial patterns exhibit significant differences across fire severity. Low fire severity (LFS) leads to an increase in the number of small patches, moderate fire severity (MFS) results in more complex patch edges, and high fire severity (HFS) promotes greater patch aggregation. (3) Pre-fire meteorological conditions and vegetation cover are major influences on the spatial patterns of FLF under different fire severities. This study will provide a new method for exploring the impact of different fire severities on FLF in central Yunnan. As an important indicator for assessing ecological disturbance and habitat degradation, analyzing the response of fragmentation to different fire severities can help administrators evaluate the impacts of fire on FLF more scientifically and develop more targeted and sustainable forest management and ecological restoration strategies.

International Journal of Applied Earth Observation and GeoinformationVol. 145
Harbin Institute of Technology (CN), Southwest Forestry University (CN), Lishui University (CN), Yunnan Academy of Forestry (CN)
Openalex Percentile: Top 67%
Fire effects on ecosystems
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