Local climate zone mapping using remote sensing:a synergetic use of daytime multi-view Ziyuan-3 stereo imageries and Luojia-1 nighttime lightdata  

作　　者：Ying Liang Shisong Cao Mingyi Du Linlin Lu Jie Jiang Jinling Quan Meizi Yang 

机构地区：[1]School of Geomatics and Urban Spatial Informatics,Beijing University of Civil Engineering and Architecture,Beijing,People’s Republic of China [2]Key Laboratory of Urban Spatial Information,Ministry of Natural Resources,Beijing,People’s Republic of China [3]Key Laboratory of Digital Earth Science,Chinese Academy of Sciences,Aerospace Information Research Institute,Beijing,People’s Republic of China [4]State Key Laboratory of Resources and Environmental Information System,Chinese Academy of Sciences,Institute of Geographic Sciences and Natural Resources Research,Beijing,People’s Republic of China [5]School of Architecture and Urban Planning,Beijing University of Civil Engineering and Architecture,Beijing,People’s Republic of China

出　　处：《International Journal of Digital Earth》2023年第1期3456-3488,共33页国际数字地球学报（英文）

基　　金：supported by the National Natural Science Foundation of China[grant number:41930650];the Scientific Research Project of Beijing Municipal Education Commission[grant number:KM202110016004];the Beijing Key Laboratory of Urban Spatial Information Engineering[grant number 20220111].

摘　　要：The local climate zone(LCZ)scheme has been widely utilized in regional climate modeling,urban planning,and thermal comfort investigations.However,existing LCz classification methods face challenges in characterizing complex urban structures and human activities involving local climatic environments.In this study,we proposed a novel LCZ mapping method that fully uses space-borne multi-view and diurnal observations,i.e.daytime Ziyuan-3 stereo imageries(2.1 m)and Luojia-1 nighttime light(NTL)data(130 m).Firstly,we performed land cover classification using multiple machine learning methods(i.e.random forest(RF)and XGBoost algorithms)and various features(i.e.spectral,textural,multi-view features,3D urban structure parameters(USPs),and NTL).In addition,we developed a set of new cumulative elevation indexes to improve building roughness assessments.The indexes can estimate building roughness directly from fused point clouds generated by both along-and across-track modes.Finally,based on the land cover and building roughness results,we extracted 2D and 3D USPs for different land covers and used multi-classifiers to perform LCZ mapping.The results for Beijing,China,show that our method yielded satisfactory accuracy for LCZ mapping,with an overall accuracy(OA)of 90.46%.The overall accuracy of land cover classification using 3D USPs generated from both along-and across-track modes increased by 4.66%,compared to that of using the single along-track mode.Additionally,the OA value of LCZ mapping using 2D and 3D USPs(88.18%)achieved a better result than using only 2D USPs(83.83%).The use of NTL data increased the classification accuracy of LCZs E(bare rock or paved)and F(bare soil or sand)by 6.54%and 3.94%,respectively.The refined LCZ classification achieved through this study will not only contribute to more accurate regional climate modeling but also provide valuable guidance for urban planning initiatives aimed at enhancing thermal comfort and overall livabillity in urban areas.Ultimately,this study paves the way for more co

关 键 词：Local climate zone land cover three-dimensional urban structure parameters nighttime light multi-classifiers 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]