Identification of Typical Ecosystem Types by Integrating Active and Passive Time Series Data of the Guangdong–Hong Kong–Macao Greater Bay Area, China

Changlong Li, Yan Wang, Zhihai Gao, Bin Sun (), He Xing and Yu Zang
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Changlong Li: School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou 511363, China
Yan Wang: Shandong Geographical Institute of Land Spatial Data and Remote Sensing Technology, Jinan 250002, China
Zhihai Gao: Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Bin Sun: Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
He Xing: School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou 511363, China
Yu Zang: School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou 511363, China

IJERPH, 2022, vol. 19, issue 22, 1-19

Abstract: 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.

Keywords: Guangdong–Hong Kong–Macao greater bay area (GBA); typical ecosystem types; integrating active and passive data; time series data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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