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

Abstract

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 10m. Spatial-temporal evolution analysis revealed that the ISA of the YRDHEEZ decreased from 5211.39 km2 in 2018 to 5147.02 km2 in 2022 with an average rate of −16.09 km2/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.

Automated Method for Artificial Impervious Surface Area Mapping in Temperate, Tropical, and Arid Environments Using Hyperlocal Training Data With Sentinel-2 Imagery

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Analysis of Spatiotemporal Evolution Patterns and Driving Forces of Reservoirs on the Northern Slope of the Tianshan Mountains in Xinjiang

“…The remote sensing image datasets were preprocessed via geometric correction and radiation correction. Strip restoration was performed using the focus statistics and fusion function for 2005 data alone, and then all images meeting the temporal and spatial requirements in the study area were stitched according to their median values using the image mosaic function, and finally, the remote sensing image data of the study area were cropped using the cropping function [39]. The workflow diagram is shown in Figure 2 [40], and photographs of some reservoirs in the study area are shown in Figure 3 [41].…”

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