An Integrated Approach between Multispectral Satellite Images and Geophysical and Morpho-Topographic Surveys for the Detection of Water Stress Associated with Coastal Dune Erosion

Abstract

Coastal erosion occurs due to different processes involving physical and ecological systems. One of these factors is the degree of water stress experienced by dune vegetation. While healthy dune vegetation can help to stabilize the dune systems, water-stressed vegetation can instead enhance dune erosion. In this study, remote sensing techniques were used to monitor the water stress affecting the dune vegetation in dune systems along the alluvial plain of the Chiatona coast (Apulia, Southern Italy) located on the Ionian Arc. Multispectral satellite data from Landsat 8/9 and Sentinel-2 were used to assess the water stress at different spatial scales over a 4-year monitoring period from 2019 to 2023. The normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were used to identify dune surfaces that were experiencing water stress. Furthermore, a terrestrial laser scanner and LiDAR data were acquired at different temporal ranges in areas affected by water stress to highlight coastal changes in areas associated with unhealthy dune vegetation. A large drop in NDVI values was observed in May 2020 due to the occurrence of coastal fires in some parts of the Chiatona coast. Geoelectrical surveys were conducted to investigate if coastal fires were capable of saline groundwater contamination, potentially enhancing dune erosion in these areas. The joint analysis of remote sensing, topographical, and geoelectric data showed that water stress reduced the amount of healthy dune vegetation, triggering dune deflation processes that resulted in increased coastal erosion rates, while also leading to the saline contamination of groundwater.

Study on the Comprehensive Health Effects of Coastal Green Areas in Qingdao City, China

“…In this study, we delineated the coastline of the experimental plot using the normalized difference water index (NDWI) approach [21], leveraging spectral differences to detect abrupt pixel value transitions, which indicated the coastline [22]. Three cloudless images captured near local high tide levels in 2022 were chosen as data sources [23].…”