一种不透水面精细制图新方法及其在城市SDGs指标评估上的应用  

Effective and Novel Impervious Surface Fine Mapping Method and Its Application on Monitoring Urban Sustainable Development Goals

作　　者：符冰雪 张继超[1] 杜文杰 王鹏龙 孙中昶 Fu Bingxue;Zhang Jichao;Du Wenjie;Wang Penglong;Sun Zhongchang(School of Geomatics,Liaoning Technical University,Fuxin 123000,China;Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;Laboratory Earth Observation Hainan Province,Sanya Institute of Remote Sensing,Sanya 572029,China;Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China)

机构地区：[1]辽宁工程技术大学测绘与地理科学学院,辽宁阜新123000 [2]中国科学院空天信息创新研究院,北京100094 [3]海南省地球观测重点实验室,海南三亚572029 [4]中国科学院西北生态环境资源研究院,甘肃兰州730000

出　　处：《遥感技术与应用》2021年第6期1339-1349,共11页Remote Sensing Technology and Application

基　　金：海南省重点研发计划项目(ZDYF2019008);中国科学院战略性先导科技专项(XDA19030104);国家重点研发计划项目(2016YFA0600302-04)。

摘　　要：城市不透水面是城市化程度的重要指示器,也是城市环境的重要敏感因子。联合国提出的城市可持续发展SDG11.3.1指标——城市土地使用率与人口增长率之比(LCRPGR)需要有效监测土地城镇化与人口城镇化关系。针对其监测与评估中高分辨率和高精度城市用地产品缺失,以及低纬度地区城市可持续发展研究较少的问题。基于Google Earth Engine平台,提出一种多时相升降轨SAR与光学影像等多源数据融合的不透水面提取方法,提取了2015年和2018年10 m分辨率印度不透水面。根据人口格网界定城市范围,将范围内不透水面面积与城市人口进行耦合,用于指标计算。研究结果表明:(1)精度验证结果显示,两期产品总体精度(OA)高于91%,Kappa系数高于0.82,R^(2)值分别为0.85和0.86,并与其他产品细节对比,证明了方法的有效性;(2)印度总体不透水面面积由2015年的47499.35 km^(2)增加到2018年的49944.69 km^(2),城市平均LCRPGR为0.76,表明其城市人口城镇化大于土地城镇化,城市可持续发展面临挑战。结合空间分析,印度城市可持续发展水平存在南北差异、东西差异以及沿海与内陆的差异。The percent cover of impervious surfaces has been widely used as an indicator to quantify the urbanization level and urban environmental quality,and is essential to understand the interactions between human and the environment.The indicator 11.3.1 proposed by the United Nations-The ratio of land consumption rate to population growth rate(LCRPGR)requires effective monitoring of the relationship between land urbanization and population urbanization.In the light of the existing problems at present,including the lack of high-resolution and high-precision urban land products,as well as few researches on urban sustainable development in low latitude areas.Based on the Google Earth Engine platform,a method of multi-source(SAR and optical)data fusion was proposed to extract India impervious surface information with 10-m resolution in 2015 and 2018.In addition,the city scope was determined according to the population grid,and the urban impervious surface area was coupled with urban population to calculate the index.The results show that:(1)The overall accuracy of impervious surface mapping in this paper is higher than 91%,and the average Kappa coefficient is higher than 0.82,and values of R^(2) are 0.85 and 0.86,respectively,the overall accuracy is high.Comparised with the details of other products,the effectiveness of the method was further proved.(2)The average LCRPGR of cities is 0.76,indicating that the population growth rate of cities is higher than that of land expansion in India,and urban sustainable development faces challenges.Combined with spatial analysis,there are differences in the level of sustainable development of Indian cities from north to south,east to west,and coastal and inland.

关 键 词：城镇化 SDG11.3.1指标 Google Earth Engine 多源数据融合 不透水面 

分 类 号：P237[天文地球—摄影测量与遥感]