Combining remote sensing analysis with machine learning to evaluate short-term coastal evolution trend in the shoreline of Venice

Coastal Vulnerability Index sensitivity to shoreline position and coastal elevation parameters in the Niger Delta region, Nigeria

It is imperative to assess coastal vulnerability to safeguard coastal areas against extreme events and sea-level rise. In the Niger Delta region, coastal vulnerability index assessment in the past focused on open-access parameters without comparing the open-access parameters, especially coastal elevation and shoreline change. This sensitivity to the shoreline method and open-access coastal elevation limits the information for the planning of coastal adaptation. The area under investigation is the Niger Delta, which is distinguished by its low-lying coastal plains and substantial ecological and economic significance. In light of the selected parameters, Sentinel-1 GRD images from 2015 to 2022 during high tidal conditions were used to delineate the shoreline position and change rate. Also, different open-access DEMs were used to derive the coastal elevation using the Geographic Information System (GIS) approach. The study employs 5 parameters, such as shorelines obtained from Sentinel-1 SAR images and several Digital Elevation Models (DEMs), geomorphology, mean sea level rise, significant wave height, and mean tide range, in conjunction with the initial Coastal Vulnerability Index (CVI) approach. The study reveals that the type of DEM used significantly influences the coastal elevation ranking and, subsequently, the CVI. Differences in shoreline change rate estimation methods (EPR and LRR) also impact the vulnerability rankings but to a lesser extent. The findings highlight that 40.1% to 58.9% of the Niger Delta coastline is highly or very highly vulnerable to sea-level rise, depending on the shoreline change rate or DEM used. The study underscores the potential of using CVI methods with open-access data in data-poor countries for identifying vulnerable coastal areas that may need protection or adaptation. Lastly, it points out the need for higher resolution DEMs.

A novel multi-strategy hydrological feature extraction (MHFE) method to improve urban waterlogging risk prediction, a case study of Fuzhou City in China

Reliable hydrological data ensure the precision of the urban waterlogging simulation. To reduce the simulation error caused by insufficient basic data, a multi-strategy method (MHFE) for extracting hydrological features is proposed, which includes land use/land cover (LULC) extraction (LE) and digital elevation model (DEM) reconstruction (DR). First, the high-resolution remote image, satellite DEM, precipitation, flood points and depth, and planned LULC were collected. Second, the buildings, roads, and other areas of the satellite image were segmented using the U-Net model, and the LULC data with drainage features were extracted by combining the segmentation result with the planned LULC and drainage data. Then, the terrain features of the road were enhanced to construct high-precision DEM based on the fusion of multi-source data, such as elevation points, LULC, and satellite DEM. Finally, the waterlogging model was implemented under different return periods of rainfalls and typhoon rainfall to obtain the waterlogging distribution and water depth. The simulation results were compared with historical waterlogging event data and water depth observations. The results indicated that the proposed method significantly improved the accuracy of the simulation. In terms of identifying the waterlogging points, the average F1 score increased by 0.36, 0.20, and 0.07 compared to the raw model and the single LE and DR methods, respectively. In terms of water depth simulation, the average Nash-Sutcliffe efficiency (NSE) was increased from -0.24 to 0.86, with DR and LE contributing to 79.1 % and 20.9 %, respectively. The principal contribution and novelty of this paper is to explore the generic method that enhance the hydrological data, and the findings of this study improved the performance of urban waterlogging simulation.