摘要
居民地是人类的基本生活场所,其宜居性对人类的生活体验有着十分重要的影响。从自然环境与社会环境2个方面出发,考虑了自然环境、环境污染、人文环境、生活便利、社会安全等因素,构建区域宜居性评价方法,以长治市为研究区,基于2018年Landsat OLI影像、数字高程模型、在线地图POI点等数据源,采用层次分析法构建区域宜居性评价指标体系并计算其宜居指数,采用均值标准差法对其进行等级划分。以此为基础,分析长治市的区域宜居情况,旨在为城市的规划管理与可持续发展提供数据支持。研究结果表明,研究区宜居程度较高的地区主要集中在中南部、西部以及中部偏西北部;宜居程度较低的地区集中在西北部、西南部以及东北部;潞州区和上党区的高宜居性区域较多,其余各区县的中等宜居性区域较多。
Residents are the basic living places of human beings,and their livability is significant for human life experience.Based on the two aspects of natural environment and social environment and considering the factors of natural environment,environmental pollution,cultural environment,living convenience and social security,the authors constructed the method of regional livability evaluation.With Changzhi City as the study area,the authors established regional livability evaluation index system using analytic hierarchy process method and calculated the livability index based on Landsat OLI image,DEM,online map POI point and other data in 2018.In addition,the authors classified it by the mean standard deviation method.The livable situation in Changzhi City was analyzed,and the result can provide data support for urban planning management and sustainable development.The results show that the areas with high livability in the study area are mainly concentrated in the south-central as well as western and northwestern of central,whereas the areas with lower livability are concentrated in the northwest,southwest and northeast.There are more high-living areas and higher livable areas in Luzhou District and Shangdang District,and more moderately livable areas in other districts and counties.
作者
桑潇
国巧真
乔悦
吴欢欢
臧金龙
SANG Xiao;GUO Qiaozhen;QIAO Yue;WU Huanhuan;ZANG Jinlong(School of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, China)
出处
《国土资源遥感》
CSCD
北大核心
2020年第3期200-207,共8页
Remote Sensing for Land & Resources
基金
天津市自然科学基金项目“天津滨海新区地表水环境遥感监测与生态风险评价”(编号:18JCYBJC90900)
天津市教委科研计划项目“遥感技术视角下的天津市地表温度研究”(编号:2018KJ164)共同资助。
关键词
多源数据
宜居性
层次分析法
均值标准差法
multi-source data
livability
analytic hierarchy process
mean standard deviation method
