ORIGINAL RESEARCH article
Front. For. Glob. Change
Sec. Forest Management
This article is part of the Research TopicForest Hazard Mitigation and Ecosystem Function Restoration in the Era of Climate Crisis: Safeguarding Ecological Integrity for Sustainable Forest ManagementView all 7 articles
Enhanced forest monitoring through mapping with integrated seasonal canopy and spectral reflectance features
Provisionally accepted
- 1Kookmin University, Seoul, Republic of Korea
- 2National Institute of Ecology, Seocheon-gun, Republic of Korea
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Forest mapping is essential for sustainable forest management and climate adaptation, enabling the assessment of forest composition and condition to prevent degradation. This study developed a U-Net-based deep learning framework for forest type classification using Sentinel-2 MSI satellite imagery and vegetation indices that capture seasonal canopy properties. A two-step approach was adopted, first delineating forested areas and then classifying forest types into needleleaf, broadleaf, and mixed forests. The forest area classification model achieved an overall accuracy of 0.958 (Kappa = 0.916), confirming reliable separation of forest and non-forest areas. For forest type classification, incorporating multi-seasonal imagery consistently enhanced performance, with the NDVI-based model achieving the highest overall accuracy of 0.831 (Kappa = 0.698). These results highlight the importance of integrating multi-seasonal spectral information to capture canopy variability and improve classification accuracy. The resulting reproducible framework thus supports ecosystem monitoring, hazard assessment, and adaptive forest management, offering foundational data for near real-time resource management under changing climatic conditions.
Keywords: forest management, Multi-temporal analysis, Open-source satellite data, Remote sensing classification, U-Net segmentation
Received: 16 Dec 2025; Accepted: 09 Feb 2026.
Copyright: © 2026 CHANG, Lim, Ko, Kang and Cheon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Wanmo Kang
Kwangil Cheon
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