Coastal dams along the Yellow River Delta are built to prevent seawater intrusion. However, land subsidence caused by significant oil, gas and brine extraction, as well as sediment compaction, could exacerbate the flooding effects of sea-level rise and storm surge. In order to evaluate the coastal dam vulnerability, we combined unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) with small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) results to generate an accurate coastal dam digital elevation model (DEM) over the next 10, 30 and 80 years. Sea-level simulation was derived from the relative sea-level rise scenarios published by the Intergovernmental Panel on Climate Change (IPCC) and local long-term tide gauge records. Assuming that the current rate of dam vertical deformation and sea-level rise are linear, we then generated different inundation scenarios by the superposition of DEMs and sea-levels at different periods by way of a bathtub model. We found that the overtopping event would likely occur around Year 2050, and the northern part of the dam would lose its protective capability almost entirely by the end of this century. This article provides an alternative cost-effective method for the detection, extraction and monitoring of coastal artificial infrastructure.
The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances.
Coastal subsidence exacerbates relative sea level rise (SLR) and increases the risk of coastal flooding. However, the contribution of local land subsidence (LLS) in the Yellow River Delta (YRD) to the relative SLR remains unclear, leading to a gap in the understanding of future inundation scenarios. In this study, we firstly used five years of Sentinel-1 data to generate the high-accuracy coastal subsidence of the YRD. Radar interferometry (InSAR) results show that fast subsiding funnels larger than 50 mm/yr are mainly distributed in the brine mining clusters, and the maximum subsidence rate exceeds 300 mm/yr. We then proposed an inundation estimation method by combining extended seeded region growing model, InSAR-derived LLS and SLR. This method can effectively output the coastal inundation time series, quantify and characterize the changes of inundation area and depth without detailed hydrodynamic conditions. Moreover, we presented high spatiotemporal resolution inundation scenarios for the entire YRD, revealing that in the absence of control measures, annual subsidence of 19 mm/yr contributes at least three times more than that SLR to the increased flood risk in 2050 under the low greenhouse gas emissions scenario (SSP1-2.6). However, under the scenario of SSP5-8.5, 4611 km 2 of land would be inundated by 2100 and coastal dams are extremely likely to be damaged. This article is expected to provide a practical and cost-effective alternative to understanding the contribution of coastal subsidence to the relative SLR, and for choosing when and how to mitigate land subsidence to prevent future coastal flooding in the delta.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.