Overcoming cloud obstruction: Fast forest-damage assessment in post-tropical cyclone optical remote sensing
Timely mapping of damaged forests is critical for disaster assessment. However, remote sensing data immediately after natural hazards is always scarce and susceptible to cloud contamination, hindering holistic assessment of damaged forests in a timely manner. Herein, we propose a novel method to map...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002614 |
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| author | Tianchu Wang Wentao Yang Ziteng Xu Wenwen Qi Liam Taylor Ming Wang Wei Wu |
| author_facet | Tianchu Wang Wentao Yang Ziteng Xu Wenwen Qi Liam Taylor Ming Wang Wei Wu |
| author_sort | Tianchu Wang |
| collection | DOAJ |
| description | Timely mapping of damaged forests is critical for disaster assessment. However, remote sensing data immediately after natural hazards is always scarce and susceptible to cloud contamination, hindering holistic assessment of damaged forests in a timely manner. Herein, we propose a novel method to map damaged forests obscured by clouds in post-hazard images by taking the September 2024 typhoon Yagi in Hainan Island, China as an example. Our approach uniquely integrates observed forest damage in cloud-free pixels with its influencing factors (the maximum wind speed and cumulative rainfall during the typhoon, terrain (elevation, slope and aspect), and canopy height) to interpolate the relationship into cloud-covered pixels by using three mainstream machine learning models (XGBoost, artificial neural networks and random forest). We found severe forest damage in the Northeast Hainan and the total area of the typhoon-damaged forests accounts for 12.8 %–15.5 % of the island's forest cover. This method can also be used for fast mapping of forest damage in partially available remote sensing images after other major natural hazards such as wildfires and landslides |
| format | Article |
| id | doaj-art-2d384195baf64f77a74f0814bd7d6ffd |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-2d384195baf64f77a74f0814bd7d6ffd2025-08-20T05:05:20ZengElsevierEcological Informatics1574-95412025-12-019010325210.1016/j.ecoinf.2025.103252Overcoming cloud obstruction: Fast forest-damage assessment in post-tropical cyclone optical remote sensingTianchu Wang0Wentao Yang1Ziteng Xu2Wenwen Qi3Liam Taylor4Ming Wang5Wei Wu6Three-gorges reservoir area (Jinyun Mountain, Chongqing) Forest Ecosystem National Observation and Research Station, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaThree-gorges reservoir area (Jinyun Mountain, Chongqing) Forest Ecosystem National Observation and Research Station, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China; Academy of Plateau Science and Sustainability, People's Government of Qinghai Province and Beijing Normal University, Xining 810016, China; Corresponding author at: Three-gorges reservoir area (Jinyun Mountain, Chongqing) Forest Ecosystem National Observation and Research Station, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China.Three-gorges reservoir area (Jinyun Mountain, Chongqing) Forest Ecosystem National Observation and Research Station, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, ChinaSchool of Geography, University of Leeds, Leeds LS2 9JT, UKSchool of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China; Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University at Zhuhai, Zhuhai 519087, ChinaNational Disaster Reduction Center, Ministry of Emergency Management of China, Beijing 100124, ChinaTimely mapping of damaged forests is critical for disaster assessment. However, remote sensing data immediately after natural hazards is always scarce and susceptible to cloud contamination, hindering holistic assessment of damaged forests in a timely manner. Herein, we propose a novel method to map damaged forests obscured by clouds in post-hazard images by taking the September 2024 typhoon Yagi in Hainan Island, China as an example. Our approach uniquely integrates observed forest damage in cloud-free pixels with its influencing factors (the maximum wind speed and cumulative rainfall during the typhoon, terrain (elevation, slope and aspect), and canopy height) to interpolate the relationship into cloud-covered pixels by using three mainstream machine learning models (XGBoost, artificial neural networks and random forest). We found severe forest damage in the Northeast Hainan and the total area of the typhoon-damaged forests accounts for 12.8 %–15.5 % of the island's forest cover. This method can also be used for fast mapping of forest damage in partially available remote sensing images after other major natural hazards such as wildfires and landslideshttp://www.sciencedirect.com/science/article/pii/S1574954125002614Post-hazard assessmentFast mappingMachine learningRemote sensing |
| spellingShingle | Tianchu Wang Wentao Yang Ziteng Xu Wenwen Qi Liam Taylor Ming Wang Wei Wu Overcoming cloud obstruction: Fast forest-damage assessment in post-tropical cyclone optical remote sensing Ecological Informatics Post-hazard assessment Fast mapping Machine learning Remote sensing |
| title | Overcoming cloud obstruction: Fast forest-damage assessment in post-tropical cyclone optical remote sensing |
| title_full | Overcoming cloud obstruction: Fast forest-damage assessment in post-tropical cyclone optical remote sensing |
| title_fullStr | Overcoming cloud obstruction: Fast forest-damage assessment in post-tropical cyclone optical remote sensing |
| title_full_unstemmed | Overcoming cloud obstruction: Fast forest-damage assessment in post-tropical cyclone optical remote sensing |
| title_short | Overcoming cloud obstruction: Fast forest-damage assessment in post-tropical cyclone optical remote sensing |
| title_sort | overcoming cloud obstruction fast forest damage assessment in post tropical cyclone optical remote sensing |
| topic | Post-hazard assessment Fast mapping Machine learning Remote sensing |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125002614 |
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