Impact of urban sprawl on land surface temperature in the Mashhad City,Iran:A deep learning and cloudbased remote sensing analysis  

作　　者：Komeh ZINAT Hamzeh SAEID Memarian HADI Attarchi SARA LU Linlin Naboureh AMIN Alavipanah KAZEM SEYED 

机构地区：[1]Department of Remote Sensing and GIS(Geographic Information System),University of Tehran,Tehran 14178-53933,Iran [2]Department of Watershed Management,University of Birjand,Birjand 97174-34765,Iran [3]Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China [4]International Research Center of Big Data for Sustainable Development Goals,Beijing 100094,China [5]Research Center for Digital Mountain and Remote Sensing Application,Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610041,China [6]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Journal of Arid Land》2025年第3期285-303,共19页干旱区科学(英文版)

摘　　要：The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development strategies.This study aims to establish a model that maps the correlation between changes in land use and land surface temperature(LST)in the Mashhad City,northeastern Iran.Employing the Google Earth Engine(GEE)platform,we calculated the LST and extracted land use maps from 1985 to 2020.The convolutional neural network(CNN)approach was utilized to deeply explore the relationship between the LST and land use.The obtained results were compared with the standard machine learning(ML)methods such as support vector machine(SVM),random forest(RF),and linear regression.The results revealed a 1.00°C–2.00°C increase in the LST across various land use categories.This variation in temperature increases across different land use types suggested that,in addition to global warming and climatic changes,temperature rise was strongly influenced by land use changes.The LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C,while forest lands experienced the smallest increase of 1.19°C.The developed CNN demonstrated an overall prediction accuracy of 91.60%,significantly outperforming linear regression and standard ML methods,due to the ability to extract higher level features.Furthermore,the deep neural network(DNN)modeling indicated that the urban lands,comprising 69.57%and 71.34%of the studied area,were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030,respectively.In conclusion,the LST predictioin framework,combining the GEE platform and CNN method,provided an effective approach to inform urban planning and to mitigate the impacts of UHI.

关 键 词：convolutional neural network machine learning Google Earth Engine land use change random forest 

分 类 号：P463.3[天文地球—大气科学及气象学] X16[环境科学与工程—环境科学] TU984[建筑科学—城市规划与设计]