文献类型：期刊文献

英文题名：Identification of Typical Ecosystem Types by Integrating Active and Passive Time Series Data of the Guangdong-Hong Kong-Macao Greater Bay Area, China

作者：Li, Changlong[1,2,3] Wang, Yan[4] Gao, Zhihai[2,3] Sun, Bin[2,3] Xing, He[1] Zang, Yu[1]

第一作者：Li, Changlong

通信作者：Sun, B[1];Sun, B[2]

机构：[1]Guangzhou Coll Commerce, Sch Informat Technol & Engn, Guangzhou 511363, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]NFGA, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[4]Shandong Geog Inst Land Spatial Data & Remote Sen, Jinan 250002, Peoples R China

年份：2022

卷号：19

期号：22

外文期刊名：INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH

收录：;Scopus(收录号：2-s2.0-85142503955);WOS:【SSCI(收录号:WOS:000887301000001)，SCI-EXPANDED(收录号:WOS:000887301000001)】；

基金：This work was supported by (Civil Aerospace Pre-research Project) (Grant numbers (D040104)), (Major Special Project of High-Resolution Earth Observation System) (Grant numbers (21-Y30B02-9001-19/22)), and (Guangdong Provincial General University Young Innovative Talents Project) (Grant numbers (2022KQNCX123)).

语种：英文

外文关键词：Guangdong-Hong Kong-Macao greater bay area (GBA); typical ecosystem types; integrating active and passive data; time series data

摘要：The identification of ecosystem types is important in ecological environmental assessment. However, due to cloud and rain and complex land cover characteristics, commonly used ecosystem identification methods have always lacked accuracy in subtropical urban agglomerations. In this study, China's Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was taken as a study area, and the Sentinel-1 and Sentinel-2 data were used as the fusion of active and passive remote sensing data with time series data to distinguish typical ecosystem types in subtropical urban agglomerations. Our results showed the following: (1) The importance of different features varies widely in different types of ecosystems. For grassland and arable land, two specific texture features (VV_dvar and VH_diss) are most important; in forest and mangrove areas, synthetic-aperture radar (SAR) data for the months of October and September are most important. (2) The use of active time series remote sensing data can significantly improve the classification accuracy by 3.33%, while passive time series remote sensing data improves by 4.76%. When they are integrated, accuracy is further improved, reaching a level of 84.29%. (3) Time series passive data (NDVI) serve best to distinguish grassland from arable land, while time series active data (SAR data) are best able to distinguish mangrove from forest. The integration of active and passive time series data also improves precision in distinguishing vegetation ecosystem types, such as forest, mangrove, arable land, and, especially, grassland, where the accuracy increased by 21.88%. By obtaining real-time and more accurate land cover type change information, this study could better serve regional change detection and ecosystem service function assessment at different scales, thereby supporting decision makers in urban agglomerations.