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林草资源研究 ›› 2024›› Issue (2): 141-148.doi: 10.13466/j.cnki.lczyyj.2024.02.017

• 研究简报 • 上一篇    下一篇

基于Sentinel-2A的云南省城市绿地分类研究

胥晓1,2(), 张加龙3()   

  1. 1.云南财经大学 物流与管理工程学院,昆明 650221
    2.中南林业科技大学 风景园林学院,长沙 410004
    3.西南林业大学 林学院,昆明 650224
  • 收稿日期:2024-02-28 修回日期:2024-04-14 出版日期:2024-04-28 发布日期:2024-09-02
  • 通讯作者: 张加龙,教授,博士,主要研究方向为林业遥感。Email:jialongzhang@swfu.edu.cn
  • 作者简介:胥晓,讲师,博士研究生,主要研究方向为风景园林规划与设计、城乡规划。Email:xuxiao0630@126.com
  • 基金资助:
    云南省教育厅科学研究基金项目“基于GEE的城区绿地提取及空间格局比较分析”(2023J0652)

Classification of Urban Green Space in Yunnan Province Based on Sentinel-2A

XU Xiao1,2(), ZHANG Jialong3()   

  1. 1. School of Logistics and Management Engineering,Yunnan University of Finance and Economics,Kunming 650221,China
    2. College of Landscape Architecture,Central South University of Forestry and Technology,Changsha 410004,China
    3. College of Forestry,Southwest Forestry University,Kunming 650224,China
  • Received:2024-02-28 Revised:2024-04-14 Online:2024-04-28 Published:2024-09-02

摘要:

为了解云南省主要城市绿地的空间分布和构成情况,以文山、景洪、芒市、香格里拉、玉溪等5个代表性的主城区为研究区,基于Sentinel-2A卫星遥感影像提取光谱、纹理、植被指数和地形特征,采用随机森林算法对公园绿地、防护绿地、附属绿地、生产绿地4类绿地进行分类。结果表明:1)参与4种城市绿地分类的因子重要性排名中,海拔(Elevation)的贡献度最高;2)5个主城区绿地分类的制图精度(PA)与用户精度(UA)均高于非绿地的精度,且总体精度(OA)和调和平均值F1精度都在84%以上,Kappa系数为0.75;3)景洪市主城区和香格里拉主城区的4种绿地分类精度优于芒市主城区、文山主城区和玉溪主城区;4)文山、景洪、芒市、香格里拉、玉溪5个主城区的绿地总面积分别达到6.22、20.81、2.52、6.65、8.58 km2。采用随机森林算法进行城市绿地分类,具有较高的准确率,为云南省绿地资源的规划、生态环境保护管理等提供了有力的技术支撑。

关键词: Sentinel-2A, 城市绿地, 分类, 随机森林

Abstract:

In order to understand the spatial distribution and composition of green spaces in major cities of Yunnan Province,the Wenshan,Jinghong,Mangshi,Shangri-La,and Yuxi are selected as the study areas.Based on Sentinel-2A satellite remote sensing images,features including spectrum,texture,vegetation index and terrain characteristics are extracted and.The random forest algorithm is used to classify the four types of green spaces:park green space,protective green space,affiliated green space,and production green space.The results show:1)Among the factors participating in the classification of four urban green spaces types,elevation exhibits the highest contribution.2)The mapping accuracy(PA)and user accuracy(UA)of green space classification in the five main urban areas surpass the accuracy of non-green space,with an overall accuracy(OA)and harmonic average F1 accuracy both exceeding 84%,and a Kappa value of 0.75.3)The classification accuracy of the four green spaces types in Jinghong City main urban area and Shangri-La main urban area is superior to that of Mangshi Main urban area,Wenshan main urban area and Yuxi main urban area.4)The total green space area in the five main urban areas of Wenshan,Jinghong,Mangshi,Shangri-La and Yuxi amounts to 6.22,20.81,2.52,6.65 and 8.58 km2,respectively.The random forest algorithm effectively classifies urban green spaces with high accuracy,thereby providing substantial technical support for green space resources planning and ecological environment protection management in Yunnan Province.

Key words: Sentinel-2A, urban green space, classification, random forest

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