文献类型：期刊文献

英文题名：Large-Scale Impervious Surface Area Mapping and Pattern Evolution of the Yellow River Delta Using Sentinel-1/2 on the GEE

作者：Liu, Jiantao[1];Li, Yexiang[1];Zhang, Yan[1];Liu, Xiaoqian[2]

第一作者：Liu, Jiantao

通讯作者：Liu, XQ[1]

机构：[1]Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250101, Peoples R China;[2]Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China

第一机构：Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250101, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China.|[114172]北京联合大学应用文理学院;[11417]北京联合大学;

年份：2023

卷号：15

期号：1

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20230213350284);Scopus(收录号：2-s2.0-85145592666);WOS:【SCI-EXPANDED(收录号:WOS:000909890900001)】；

基金：This research was funded by the National Natural Science Foundation of China, grant number 42171113, 42271112 and Shandong Natural Science Foundation, grant number ZR2020QD017, ZR2020QD049.

语种：英文

外文关键词：Yellow River Delta; impervious surface area; Sentinel-1; 2; GEE; random forest

摘要：The ecological environment of Yellow River Delta High-efficiency Ecological Economic Zone (YRDHEEZ) is adjacent to the Bohai Sea. The unique geographical location makes it highly sensitive to anthropogenic disturbances. As an important land surface biophysical parameter, the impervious surface area (ISA) can characterize the level of urbanization and measure the intensity of human activities, and hence, the timely understanding of ISA dynamic changes is of great significance to protect the ecological safety of the YRDHEEZ. Based on the multi-source and multi-modal Sentinel-1/2 remotely sensed data provided by Google Earth Engine (GEE) cloud computing platform, this study developed a novel approach for the extraction of time-series ISA in the YRDHEEZ through a combination of random forest algorithm and numerous representative features extracted from Sentinel-1/2. Subsequently, we revealed the pattern of the ISA spatial-temporal evolution in this region over the past five years. The results demonstrated that the proposed method has good performance with an average overall accuracy of 94.84% and an average kappa coefficient of 0.9393, which verified the feasibility of the proposed method for large-scale ISA mapping with 10 m. Spatial-temporal evolution analysis revealed that the ISA of the YRDHEEZ decreased from 5211.39 km(2) in 2018 to 5147.02 km(2) in 2022 with an average rate of -16.09 km(2)/year in the last 5 years, suggesting that the ISA of YRDHEEZ has decreased while its overall pattern was not significantly changed over time. The presented workflow can provide a reference for large-scale ISA mapping and its evolution analysis, especially in regions on estuarine deltas.