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Spatial Distribution Pattern of Forests in Yunnan Province in 2022: Analysis Based on Multi-Source Remote Sensing Data and Machine Learning

文献类型： 外文期刊

作者： Li, Guangyang 1 ; Lai, Hongyan 1 ; Chen, Bangqian 1 ; Yin, Xiong 1 ; Kou, Weili 3 ; Wu, Zhixiang 1 ; Chen, Zongzhu 2 ; Wang, Guizhen 1 ;

作者机构： 1.Chinese Acad Trop Agr Sci, Rubber Res Inst, Haikou 571101, Peoples R China

2.Hainan Acad Mangrove, Hainan Acad Forestry, Haikou 571100, Peoples R China

3.Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Peoples R China

4.Nanjing Forestry Univ, Coll Forestry, Nanjing 210037, Peoples R China

关键词： sentinle-2; random forest; forest cover; terrain; spatial distribution

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2025 年 17 卷 7 期

页码：

收录情况： SCI

摘要： Forest mapping using remote sensing has made considerable progress over the past decade, but substantial uncertainties remain in complex regions, particularly where terrain and climate vary dramatically. Yunnan Province, China, represents such a challenging case, with its diverse climatic zones ranging from tropical to temperate and its topography spanning over 6500 m in elevation. These factors contribute to substantial variation in vegetation types, complicating the accurate identification of forest cover through remote sensing. This study aims to enhance forest mapping in Yunnan by leveraging multi-temporal remote sensing data from Sentinel-2 and Landsat 8/9 imagery, incorporating key phenological stages-such as the leaf greening (GRN) period, as well as the senescence, defoliation, and foliation (SDF) stages of deciduous forests-along with kNDVI and terrain factors. A random forest (RF) classifier was applied on the Google Earth Engine (GEE) platform to create a 10 m resolution forest map (LS2-RF). This map achieved an overall accuracy of 96.35% when validated with 1572 ground samples, significantly outperforming existing global datasets, such as Dynamic World (73.88%) and WorldCover (87.66%). These maps agreed well in extensive forested areas; discrepancies were noted in mixed land types, including farmland, urban areas, and regions with fragmented landscapes. In 2022, Yunnan's forest cover was 60.40%, with higher coverage in the southwestern region and lower in the northeast. The largest forested area was found in Pu'er City, while the smallest was in Yuxi City. Forests were most abundant at elevations between 1500 and 2500 m (occupying 52.29% of the total forest area) and slopes of 15 degrees to 25 degrees (occupying 39.19% of the total forest area). Conversely, forest cover was lowest in areas below 500 m elevation (occupying 0.64% of the total forest area) and on slopes less than 5 degrees (occupying 2.40% of the total forest area). The analysis also revealed a general trend of increasing forest cover with decreasing latitude and longitude, with peak forest coverage at mid-elevations and slopes, followed by a decline at higher elevations. The resultant forest map provides valuable data for ecological assessments, forest conservation initiatives, and informed policy decision-making.

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