Detecting Burned Vegetation Areas by Merging Spectral and Texture Features in a ResNet Deep Learning Architecture

Authors

Jiahui Fan, Beijing Normal University
Yunjun Yao, Beijing Normal University
Yajie Li, Huairou Meteorological Bureau
Xueyi Zhang, Beijing Normal University
Jiquan Chen, Michigan State University
Joshua B. Fisher, Chapman UniversityFollow
Xiaotong Zhang, Beijing Normal University
Bo Jiang, Beijing Normal University
Lu Liu, Beijing Normal University
Zijing Xie, Beijing Normal University
Luna Zhang, Beijing Normal University
Fei Qiu, Beijing Normal University

Document Type

Article

Publication Date

11-7-2025

Abstract

Timely and accurate detection of burned areas is crucial for assessing fire damage and contributing to ecosystem recovery efforts. In this study, we propose a framework for detecting fire-affected vegetation anomalies on the basis of a ResNet deep learning (DL) algorithm by merging spectral and textural features (ResNet-IST) and the vegetation abnormal spectral texture index (VASTI). To train the ResNet-IST, a vegetation anomaly dataset was constructed on high-resolution 30 m fire-affected remote sensing images selected from the Global Fire Atlas (GFA) to extract the spectral and textural features. We tested the model to detect fire-affected vegetation in ten study areas across four continents. The experimental results demonstrated that the ResNet-IST outperformed the VASTI by approximately 3% in terms of anomaly detection accuracy and achieved a 5–15% improvement in the detection of the normalized burn ratio (NBR). Furthermore, the accuracy of the VASTI was significantly greater than that of NBR for burn detection, indicating that the merging of spectral and textural features provides complementary advantages, leading to stronger classification performance than the use of SFs alone. Our results suggest that deep learning outperforms traditional mathematical models in burned vegetation anomaly detection tasks. Nevertheless, the scope and applicability of this study are somewhat limited, which also provides directions for future research.

Comments

This article was originally published in Remote Sensing, volume 17, issue 22, in 2025. https://doi.org/10.3390/rs17223665

Recommended Citation

Fan, J.; Yao, Y.; Li, Y.; Zhang, X.; Chen, J.; Fisher, J.B.; Zhang, X.; Jiang, B.; Liu, L.; Xie, Z.; et al. Detecting Burned Vegetation Areas by Merging Spectral and Texture Features in a ResNet Deep Learning Architecture. Remote Sens. 2025, 17, 3665. https://doi.org/10.3390/rs17223665

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This work is licensed under a Creative Commons Attribution 4.0 License.