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基于GF影像的区域山水林田湖草生态系统多要素综合评价 |
Multi element Comprehensive Evaluation of Regional Ecosystem of Mountains,Waters, Forests, Fields, Lakes and Grasslands Based on GF Images |
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DOI: |
中文关键词: 山水林田湖草生态系统 GF影像 熵值法 机器学习 综合评价 |
英文关键词: Ecosystem of mountains, waters, forests, fields, lakes and grasslands GF satellite imagery Entropy method Machine learning Comprehensive evaluation |
基金项目:国家自然科学基金资助项目(42172283);河南省自然资源科研基金资助项目(2021-178-5) |
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中文摘要: |
基于山水林田湖草生态系统理论,分层次构建黄河流域济源段山水林田湖草生命共同体健康评价指标体系,通过对NDVI、NDWI、TGSI、SWI等多指数和地形Slope参数合成影像,应用随机森林机器学习分类算法提取研究区主要生态参数,采用熵值法多要素综合评价研究区的生态健康状态。结果表明,2020年、2021年、2022年、2023年综合健康指数分别为0.313、0.540、0.575、0.557,研究区处于不健康、亚健康、健康、健康状态,整体呈变好趋势,指标权重贡献表现为灌木及草地指标>乔木林地指标>不透水面指标>植被覆盖度指标>种植耕地指标>裸地指标>水体指标,建议开展林草地绿化以提升城市生态系统健康水平。 |
英文摘要: |
Based on the ecosystem theory of mountains, waters, forests, fields, lakes and grasslands, a health evaluation index system was hierarchically constructed for the life community of mountains, waters, forests, fields, lakes and grasslands in Jiyuan section of the Yellow River Basin. According to the synthetic images of topographical Slope and the indexes such as NDVI, NDWI, TGSI, SWI, the main ecological parameters of the study area were extracted by random forest machine learning classification algorithm. Entropy method was applied to conduct a multi element comprehensive evaluation of the ecological health status in the study area. The results showed that the comprehensive health indexes in 2020, 2021, 2022 and 2023 were 0.313,0.540,0575 and 0.557, respectively. The study area was in a states of unhealthy, sub healthy, healthy and healthy, showing an overall improving trend. 〖GK!86mm〗The contribution of index weight was ranked in descending order as follows: shrubland and grassland index, arboreal woodland index, impervious surface index, vegetation coverage index, cultivated land index, bare land index, water quality index. It was suggested to carry out forest and grassland greening to 〖HK〗improve the health level of urban ecosystem. |
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