{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T15:22:37Z","timestamp":1775575357165,"version":"3.50.1"},"reference-count":97,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42205195"],"award-info":[{"award-number":["42205195"]}]},{"name":"National Natural Science Foundation of China","award":["PAEKL-2022-618 K07"],"award-info":[{"award-number":["PAEKL-2022-618 K07"]}]},{"name":"National Natural Science Foundation of China","award":["SCQXKJQN202111"],"award-info":[{"award-number":["SCQXKJQN202111"]}]},{"name":"Development Project of Plateau Atmosphere and Environment Key Laboratory of Sichuan Province","award":["42205195"],"award-info":[{"award-number":["42205195"]}]},{"name":"Development Project of Plateau Atmosphere and Environment Key Laboratory of Sichuan Province","award":["PAEKL-2022-618 K07"],"award-info":[{"award-number":["PAEKL-2022-618 K07"]}]},{"name":"Development Project of Plateau Atmosphere and Environment Key Laboratory of Sichuan Province","award":["SCQXKJQN202111"],"award-info":[{"award-number":["SCQXKJQN202111"]}]},{"name":"Sichuan Province Key Laboratory Science and Technology Development Fund Project","award":["42205195"],"award-info":[{"award-number":["42205195"]}]},{"name":"Sichuan Province Key Laboratory Science and Technology Development Fund Project","award":["PAEKL-2022-618 K07"],"award-info":[{"award-number":["PAEKL-2022-618 K07"]}]},{"name":"Sichuan Province Key Laboratory Science and Technology Development Fund Project","award":["SCQXKJQN202111"],"award-info":[{"award-number":["SCQXKJQN202111"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004\u20132020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region.<\/jats:p>","DOI":"10.3390\/rs16193602","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T06:10:27Z","timestamp":1727417427000},"page":"3602","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Application of Remote Sensing and Explainable Artificial Intelligence (XAI) for Wildfire Occurrence Mapping in the Mountainous Region of Southwest China"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7575-100X","authenticated-orcid":false,"given":"Jia","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences and Ministry of Water Resources, Chengdu 610041, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"Sichuan Provincial Climate Centre, Sichuan Provincial Meteorological Service, Chengdu 610072, China"},{"name":"Wenjiang National Climatic Observatory, Wenjiang District Meteorological Service, Chengdu 611100, China"}]},{"given":"Yukuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences and Ministry of Water Resources, Chengdu 610041, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7146-5724","authenticated-orcid":false,"given":"Yafeng","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences and Ministry of Water Resources, Chengdu 610041, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Pengguo","family":"Zhao","sequence":"additional","affiliation":[{"name":"Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5036-939X","authenticated-orcid":false,"given":"Shunjiu","family":"Wang","sequence":"additional","affiliation":[{"name":"Sichuan Provincial Climate Centre, Sichuan Provincial Meteorological Service, Chengdu 610072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5599-1003","authenticated-orcid":false,"given":"Yu","family":"Sun","sequence":"additional","affiliation":[{"name":"Sichuan Province Meteorological Disaster Defense Technology Center, Sichuan Provincial Meteorological Service, Chengdu 610072, China"}]},{"given":"Yu","family":"Luo","sequence":"additional","affiliation":[{"name":"Sichuan Provincial Climate Centre, Sichuan Provincial Meteorological Service, Chengdu 610072, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1023\/B:BIOC.0000011728.46362.3c","article-title":"Biodiversity and biodiversity conservation in Yunnan, China","volume":"13","author":"Yang","year":"2004","journal-title":"Biodivers. 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