{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:43:39Z","timestamp":1760150619312,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project of Technology Innovation Plan Project of Hunan Provincial Forestry and Grassland Department","award":["XLK202108-8","32071682","31901311"],"award-info":[{"award-number":["XLK202108-8","32071682","31901311"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XLK202108-8","32071682","31901311"],"award-info":[{"award-number":["XLK202108-8","32071682","31901311"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fine fuel load (FFL) is a crucial variable influencing the occurrence of wildfire. Accurate knowledge of the distribution of FFL in mountainous forests is essential for ongoing wildfire risk management and the stability of mountain ecosystems. Traditional methods of estimating forest fuel load typically involve ground surveys combined with remote sensing, which can be costly and inefficient. Therefore, low-cost, large-scale FFL estimation remains challenging. In this study, Sentinel-2A satellite imagery from the Changsha forest region was used as the data source. Firstly, different feature variables were constructed based on false-color (B843), true-color (B432), four-band (B8432) combinations, and the Normalized Difference Water Index (NDWI). Subsequently, a machine learning approach based on random convolution was employed to estimate FFL. This study also included accuracy assessments of the estimation results and the creation of FFL maps for the study area. The results showed that the FFL estimation based on the B8432 band combination achieved the highest accuracy, with RMSE and R2 values of 5.847 t\u00b7hm\u22122 and 0.656, respectively. FFL estimation results based on false-color imagery followed, with true-color imagery and NDWI index-based estimation results exhibiting lower accuracy. This study offers critical FFL insights using random convolution techniques applied to Sentinel-2A imagery, enhancing the ability to monitor and manage forest fuel conditions effectively, thereby facilitating more informed regional wildfire risk management strategies.<\/jats:p>","DOI":"10.3390\/rs15245721","type":"journal-article","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T03:58:02Z","timestamp":1702526282000},"page":"5721","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0397-7154","authenticated-orcid":false,"given":"Lei","family":"Deng","sequence":"first","affiliation":[{"name":"Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, China"},{"name":"Hunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Changsha 410004, China"},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern China, Changsha 410004, China"}]},{"given":"Enping","family":"Yan","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, China"},{"name":"Hunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Changsha 410004, China"},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern China, Changsha 410004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8454-5440","authenticated-orcid":false,"given":"Jiawei","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Sun Yat-Sen University, Zhuhai 519082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7894-0359","authenticated-orcid":false,"given":"Dengkui","family":"Mo","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, China"},{"name":"Hunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Changsha 410004, China"},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern China, Changsha 410004, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1038\/nature13946","article-title":"Learning to coexist with wildfire","volume":"515","author":"Moritz","year":"2014","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"112282","DOI":"10.1016\/j.rse.2020.112282","article-title":"Forest fire fuel through the lens of remote sensing: Review of approaches, challenges and future directions in the remote sensing of biotic determinants of fire behaviour","volume":"255","author":"Gale","year":"2021","journal-title":"Remote Sens. 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