| 云南香格里拉区域尺度森林类型遥感分类评价 |
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| 引用本文： | 李瑾, 王雷光, 郑晨, 等. 云南香格里拉区域尺度森林类型遥感分类评价[J]. 西南林业大学学报（自然科学）, 2022, 42(1): 124–132.doi:10.11929/j.swfu.202101063 |
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| 作者姓名： | 李瑾 王雷光 郑晨 徐伟恒 代沁伶 |
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| 作者单位： | 1. 西南林业大学林学院，云南 昆明 650233;2. 西南林业大学大数据与人工智能研究院，云南 昆明 650233;3. 西南林业大学森林生态大数据国家林业与草原局重点实验室，云南 昆明 650233;4. 河南大学数学与统计学院，河南 开封 475004;5. 西南林业大学艺术与设计学院，云南 昆明 650233 |
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| 摘 要： | 基于Google Earth Engine云平台和Sentinel–2影像，通过多时相影像和地形特征的不同组合，利用随机森林算法对云南省香格里拉地区的森林类型进行3个层次上的识别和分类制图。结果表明：多时相特征结合地形信息在3个层次上分类精度最高；森林和非森林类型，总体精度为98.15%，Kappa系数为0.9624；针叶林和阔叶林，总体精度为89.74%，Kappa系数为0.7926；8种针叶林类型，总体精度为92.87%，Kappa系数为0.9180。地形信息有利于森林类型信息的提取，多时相的Sentinel–2数据对于大范围精确识别森林类型具有较大的潜力。
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| 关 键 词： | 多时相 Sentinel–2 区域尺度 森林类型 针叶林 |
| 收稿时间： | 2021-01-26 |
Remote Sensing Classification and Evaluation of Regional Scale Forest Types in Shangri-La,Yunnan |
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| Li Jin, Wang Leiguang, Zheng Chen, Xu Weiheng and Dai Qinling. Remote Sensing Classification and Evaluation of Regional Scale Forest Types in Shangri-La, Yunnan[J]. Journal of Southwest Forestry University, 2022, 42(1): 124-132.doi:10.11929/j.swfu.202101063 |
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| Authors: | Li Jin Wang Leiguang Zheng Chen Xu Weiheng Dai Qinling |
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| Affiliation: | 1. College of Forestry, Southwest Forestry University, Kunming Yunnan 650233, China;2. Institutes of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming Yunnan 650233, China;3. Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Southwest Forestry University, Kunming Yunnan 650233, China;4. School of Mathematics and Statistics, Henan University, Kaifeng Henan 475004, China;5. College of Art and Design, Southwest Forestry University, Kunming Yunnan 650233, China |
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| Abstract: | Based on the Google Earth Engine cloud platform and Sentinel–2 image, the random forest algorithm was used to identify and classify the forest types in Shangri-La region of Yunnan Province at 3 levels by different combinations of multi-temporal images and topographic features. The results showed that the classification accuracy of multi-temporal features combined with topographic information was the highest at 3 levels. The overall accuracy of forest and non-forest types was 98.15%, and the Kappa coefficient was 0.9624. In coniferous forest and broad-leaved forest, the overall accuracy was 89.74% and the Kappa coefficient was 0.7926. For 8 coniferous forest types, the overall accuracy was 92.87%, and the Kappa coefficient was 0.9180. The results concluded that the terrain information is beneficial to the extraction of forest type information, and the multi-temporal Sentinel–2 data has great potential for the accurate identification of forest type in a large range. |
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| Keywords: | multi-temporal Sentinel–2 regional-scale forest type coniferous forest |
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