| 面向Sentinel-2A影像的大理市土地利用分类方法适用性研究 |
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| 引用本文： | 贾玉洁,刘云根,杨思林,王妍,张超,徐红枫,郑淑君.面向Sentinel-2A影像的大理市土地利用分类方法适用性研究[J].浙江农林大学学报,2022,39(6):1350-1358. |
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| 作者姓名： | 贾玉洁 刘云根 杨思林 王妍 张超 徐红枫 郑淑君 |
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| 作者单位： | 1.西南林业大学 生态与环境学院，云南 昆明 6502242.西南林业大学 云南省山地农村生态环境演变与污染防治重点实验室，云南 昆明 6502243.西南林业大学 林学院，云南 昆明 650224 |
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| 基金项目： | 国家自然科学基金资助项目(32160405)；云南省高原湿地保护修复与生态服务重点实验室开放基金项目(202105AG070002) |
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| 摘 要： | 目的 获取更高效、准确的土地利用自动分类方法，为后续的高原山区土地利用分类研究提供理论支撑。 方法 选取中国典型的高原山区云南省大理市为研究区域，以Sentinel-2A影像为对象，提出一种面向对象特征的决策树分类方法，并将此方法分类结果与传统的ISODATA法和最大似然法土地利用分类结果进行对比。 结果 ①面向对象特征的决策树方法分类结果在空间分布和各地类的面积统计方面都优于ISODATA法和最大似然法，与研究区实际土地利用面积数据更为接近；②在大理市，最大似然法在水体和林地的提取上适用性较好，而面向对象特征的决策树分类方法在农田、草地、建设用地和其他这些地类的区分上适用性更强，且在冰川积雪的提取上也有更好的提取效果；③相比与传统的ISODATA法和最大似然法分类结果精度，面向对象特征的决策树分类方法可进一步提高分类精度，总体分类精度可达90.20%，Kappa系数为87.95%。 结论 相较于传统的分类方法，先粗分类再进一步细分类的分类思想，可避免区域之间的混淆问题。面向对象特征与决策树相结合的组合分类方法在高原山区有着更好的适用性，可以有效提高高原山区分类精度。图3表7参26
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| 关 键 词： | Sentinel-2A影像 大理市 土地利用 遥感分类 适用性 |
| 收稿时间： | 2022-01-21 |
Applicability of land use classification method in Dali City based on Sentinel-2A image |
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| JIA Yujie,LIU Yungen,YANG Silin,WANG Yan,ZHANG Chao,XU Hongfeng,ZHENG Shujun.Applicability of land use classification method in Dali City based on Sentinel-2A image[J].Journal of Zhejiang A&F University,2022,39(6):1350-1358. |
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| Authors: | JIA Yujie LIU Yungen YANG Silin WANG Yan ZHANG Chao XU Hongfeng ZHENG Shujun |
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| Institution: | 1.College of Ecology and Environment, Southwest Forestry University, Kunming 650224, Yunnan, China2.Yunnan Key Laboratory of Ecological Environment Evolution and Pollution Control in Mountainous and Rural Areas, Southwest Forestry University, Kunming 650224, Yunnan, China3.College of Forestry, Southwest Forestry University, Kunming 650224, Yunnan, China |
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| Abstract: | Objective The purpose of this study is to obtain more efficient and accurate automatic land use classification methods, so as to provide theoretical support for the follow-up study of land use classification in plateau mountainous areas. Method Taking Dali City, a typical plateau mountainous area of Yunnan Province in China, as the research area and Sentinel-2A image as the object, an object-oriented decision tree classification method was proposed and compared with traditional ISODATA classification method and maximum likelihood method. Result (1) The classification results of object-oriented decision tree method were better than those of ISODATA classification method and maximum likelihood classification method in terms of spatial distribution and area statistics of various classes, and were closer to the actual land use area data of the study area. (2) In Dali, the maximum likelihood classification method had better applicability in the extraction of water body and forest land, while the object-oriented decision tree classification method had stronger applicability in the extraction of farmland, grassland, construction land and other land types, and had better extraction effect in the extraction of glacier snow. (3) Compared with the traditional ISODATA method and maximum likelihood method, the object-oriented decision tree classification method could further improve the classification accuracy. The overall classification accuracy could reach 90.20% and the Kappa coefficient was 87.95%. Conclusion Compared with the traditional classification methods, the idea of coarse classification before fine classification can avoid the confusion between regions. The combination of object-oriented features and decision tree has better applicability in plateau mountainous areas, and can effectively improve the classification accuracy. Ch, 3 fig. 7 tab. 26 ref.] |
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