Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters

Abstract

Remote-sensing ocean colour studies have already been used to determine coastal water quality, coastal biodiversity, and nutrient availability. In recent years, Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors, originally designed for agriculture applications, have also enabled water-quality studies of coastal waters. However, since the sea surface is constantly changing, commonly used photogrammetric methods fail when applied to UAV images captured over water areas. In this work, we evaluate the applicability of a five-band multispectral sensor mounted on a UAV to derive scientifically valuable water parameters such as chlorophyll-a (Chl-a) concentration and total suspended solids (TSS), including a new Python workflow for the manual generation of an orthomosaic in aquatic areas exclusively based on the sensor’s metadata. We show water-quality details in two different sites along the Maltese coastline on the centimetre-scale, improving the existing approximations that are available for the region through Sentinel-3 OLCI imagery at a much lower spatial resolution of 300 m. The Chl-a and TSS values derived for the studied regions were within the expected ranges and varied between 0 to 3 mg/m3 and 10 to 20 mg/m3, respectively. Spectral comparisons were also carried out along with some statistics calculations such as RMSE, MAE, or bias in order to validate the obtained results.

Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal Zones

“…The extraction of bathymetry from satellite images acquired over time can be used to comprehend temporal changes in sedimentation patterns that shape the seafloor in coastal systems. This information is particularly valuable for research on coastal erosion and sedimentation and can result in morphodynamic models that provide managers with a tool capable of predicting the dynamic evolution of coastal sediment and make informed management decisions [52][53][54].…”

Long-Term Monitoring of Inland Water Quality Parameters Using Landsat Time-Series and Back-Propagated ANN: Assessment and Usability in a Real-Case Scenario

“…For chl-a, the accuracy attained in this paper (R 2 : 0.99, NRMSE: 0.79%) was higher than the ones reported in previous studies. Barraza-Moraga et al [62] achieved an NRMSE of 3.6% (R 2 : 0.97, RMSE: 2.58) using Sentinel 2 images to develop an MLE model for chl-a monitoring, while [63] used UAV images and MLR resulting in a R 2 of 0.91 and RMSE of 0.07. Along the same lines, Ref.…”

Spatiotemporal pattern of coastal water pollution and its driving factors: implications for improving water environment along Hainan Island, China

“…Coastal waters as a complex, sensitive, and highly dynamic ecosystem are strongly influenced by land-sea interactions, natural change and usage characteristics of the adjacent land ( Wells et al, 2015 ; Chen et al, 2019 ; Román et al, 2023 ). Coastal waters have ecological importance because they can support 25% of primary production and 80% of carbon production for global scale ( United Nations Environment Programme, 1992 ).…”