InSAR and machine learning reveal new understanding of coastal subsidence risk in the Yellow River Delta, China

Guoyang Wang; Peng Li; Zhenhong Li; Jie Liu; Yi Zhang; Houjie Wang

doi:10.1016/j.scitotenv.2024.170203

InSAR and machine learning reveal new understanding of coastal subsidence risk in the Yellow River Delta, China

Sci Total Environ. 2024 Mar 10:915:170203. doi: 10.1016/j.scitotenv.2024.170203. Epub 2024 Jan 19.

Authors

Guoyang Wang¹, Peng Li², Zhenhong Li³, Jie Liu⁴, Yi Zhang⁵, Houjie Wang¹

Affiliations

¹ Institute of Estuarine and Coastal Zone, College of Marine Geosciences, Key Lab of Submarine Geosciences and Prospecting Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China; Laboratory of Marine Geology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266061, China.
² Institute of Estuarine and Coastal Zone, College of Marine Geosciences, Key Lab of Submarine Geosciences and Prospecting Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China; Laboratory of Marine Geology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266061, China. Electronic address: pengli@ouc.edu.cn.
³ College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China.
⁴ China Academy of Aerospace Science and Innovation, Advanced Detection and Information Center, Beijing 100000, China.
⁵ College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

PMID: 38246383
DOI: 10.1016/j.scitotenv.2024.170203

Abstract

Coastal subsidence is a geological disaster that has devastating consequences. However, an accurate understanding of its risks involves more than simply assessing the amount or rate of land subsidence. The existing methods used to evaluate geological disaster risks depend on extensive data collection, entail substantial workloads, suffer from error estimation challenges, and lack regional adaptability. These limitations prevent us from fully understanding coastal subsidence risks in estuarine deltas. Therefore, in this study, we propose a new subsidence risk assessment method that addresses the challenges of traditional geological risk assessments in terms of spatial coverage, spatiotemporal resolution, and data collection difficulty. First, Sentinel-1 multitemporal interferometric synthetic aperture radar (MT-InSAR) and cluster analysis were used to estimate the subsidence hazards. Subsequently, Landsat-8 imagery and a random forest (RF) classifier were used to obtain land use and land cover (LULC), and the analytic hierarchy process (AHP) was used to obtain settlement vulnerability. Thereafter, subsidence susceptibility was derived from the sediment layer thickness. By combining subsidence hazard, vulnerability, and susceptibility, the first subsidence risk map with a 30 m resolution was generated. The results showed that 4.54 % of the Yellow River Delta (YRD) area was high-risk, 8.75 % was medium-risk, and 10.14 % was low-risk. Notably, the risk map shows a clear overlap between high-risk and saltwater mining areas in the YRD. The proposed method is expected to improve our understanding of the coastal subsidence risk in estuarine deltas. Considering that the risk in high-value economic areas in the YRD is increasing, whereas the risk in low-value economic areas may change owing to human activity, early preventive measures are required.

Keywords: Analytic hierarchy process; Cluster analysis; Coastal subsidence; Risk assessment; Susceptibility; Vulnerability.