Monitoring for assessment of natural disasters, such as volcanic eruptions, presents a methodological challenge for the scientific community. Here, we present Unmanned Aerial Vehicles (UAVs) as a feasible, precise, rapid and safe tool for real time monitoring of the impacts of a volcanic event during the Cumbre Vieja eruption on La Palma Island, Spain (2021). UAV surveys with optical RGB (Red-Green-Blue), thermal and multispectral sensors, and a water sampling device, were carried out in different areas affected by the lava flow, including the upper volcanic edifice and the lava delta formed on the coastal fringe of the island. Our results have provided useful information for the monitoring of the advance of the lava flow and its environmental consequences during the volcanic emergency. Our data shows how La Palma island's growth, with the formation of a new lava delta of 28 ha and a total volume of lava injected into the sea of 5,138,852 m³. Moreover, our Digital Elevation Model (DEM) simulated, with a 70 % accuracy, the probabilistic simulation of the possible path followed by the lava flow in the vicinity of the fissure from which the magma emanates. In addition, significant changes of seawater physical-chemical parameters were registered in coastal surface waters by the in situ seawater samples collected with the automatic water sampling device of our UAV. The first meters of the water column, due to the instant evaporation of the seawater in contact with the hot lava, produce an increase of temperature and salinity of up to 4-5 °C and up to 5 units, respectively.

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.

Marine macrophytes constitute one of the most productive ecosystems on the planet, as well as one of the most threatened by anthropogenic activities and climate change. Their monitoring is therefore essential, which has experienced a fast methodological evolution in recent years, from traditional in situ sampling to the use of satellite remote sensing, and subsequently by sensors mounted on unmanned aerial vehicles (UAV). This study aims to advance the monitoring of these ecosystems through the use of a UAV equipped with a 10-band multispectral camera, using different algorithms [i.e., maximum likelihood classifier (MLC), minimum distance classifier (MDC), and spectral angle classifier (SAC)], and using the Bay of Cádiz Natural Park (southern Spain) as a case of study. The results obtained with MLC confirm the suitability of this technique for detecting and differentiating seagrass meadows in a range of 0–2 m depth and the efficiency of this tool for studying and monitoring marine macrophytes in coastal areas. We inferred the existence of a cover of 25452 m² of Cymodocea nodosa, and macroalgae species such as Caulerpa prolifera, covering 22172 m² of Santibañez (inner Bay of Cádiz).

Aquatic ecosystems are crucial in preserving biodiversity, regulating biogeochemical cycles, and sustaining human life; however, their resilience against climate change and anthropogenic stressors remains poorly understood. Recently, unmanned aerial vehicles (UAVs) have become a vital monitoring tool, bridging the gap between satellite imagery and ground-based observations in coastal and marine environments with high spatial resolution. The dynamic nature of water surfaces poses a challenge for photogrammetric techniques due to the absence of fixed reference points. Addressing these issues, this study introduces an innovative, efficient, and accurate workflow for georeferencing and mosaicking that overcomes previous limitations. Using open-source Python libraries, this workflow employs direct georeferencing to produce a georeferenced orthomosaic that integrates multiple UAV captures, and this has been tested in multiple locations worldwide with optical RGB, thermal, and multispectral imagery. The best case achieved a Root Mean Square Error of 4.52 m and a standard deviation of 2.51 m for georeferencing accuracy, thus preserving the UAV’s centimeter-scale spatial resolution. This open-source workflow represents a significant advancement in the monitoring of marine and coastal processes, resolving a major limitation facing UAV technology in the remote observation of local-scale phenomena over water surfaces.

Seagrasses are marine flowering plants that form extensive meadows from the inter-tidal zone up to ~50 m depth. As biological and ecological Essential Biodiversity Variables, seagrass cover and composition provide a wide range of ecosystem services. Inter-tidal seagrass meadows provide services to many ecosystems, so monitoring their occurrence, extent, condition and diversity can be used to indicate the biodiversity and health of local ecosystems. Current global estimates of seagrass extent and recent reviews either do not mention inter-tidal seagrasses and their seasonal variation, or combine them with sub-tidal seagrasses. Here, using high-spatial and high-temporal resolution satellite data (Sentinel-2), we demonstrate a method for consistently mapping inter-tidal seagrass meadows and their phenology at a continental scale. We were able to highlight varying seasonal patterns that are observable across a 23° latitudinal range. Timings of peaks in seagrass extent varied by up to 5 months, rather than the previously assumed marginal to non-existent variation in peak timing. These results will aid management by providing high-resolution spatio-temporal monitoring data to better inform seagrass conservation and restoration. They also highlight the high level of seasonal variability in inter-tidal seagrass, meaning combination with sub-tidal seagrass for global assessments will likely produce misleading or incorrect estimates.

Remote sensing has evolved as an alternative to traditional techniques in the spatio-temporal monitoring of the Antarctic ecosystem, especially with the rapid expansion of the use of Unmanned Aerial Vehicles (UAVs), providing a centimeter-scale spatial resolution. In this study, the potential of a high-spatial resolution multispectral sensor embedded in a UAV is compared to medium resolution satellite remote sensing (Sentinel-2 and Landsat 8) to monitor the characteristics of the Vapor Col Chinstrap penguin (Pygoscelis antarcticus) colony ecosystem (Deception Island, South Shetlands Islands, Antarctica). Our main objective is to generate precise thematic maps of the typical ecosystem of penguin colonies derived from the supervised analysis of the spectral information obtained with these remote sensors. For this, two parametric classification algorithms (Maximum Likelihood, MLC, and Spectral Angle, SAC) and two non-parametric machine learning classifiers (Support Vector Machine, SVM, and Random Forest, RFC) are tested with UAV imagery, obtaining the best results with the SVM classifier (93.19% OA). Our study shows that the use of UAV outperforms satellite imagery (87.26% OA with Sentinel-2 Level 2 (S2L2) and 70.77% OA with Landsat 8 Level 2 (L8L2) in SVM classification) in the characterization of the substrate due to a higher spatial resolution, although differences between UAV and S2L2 are minimal. Thus, both sensors used in tandem could provide a broader and more precise view of how the area covered by the different elements of these ecosystems can change over time in a global climate change scenario. In addition, this study represents a precise UAV monitoring that takes place in this Chinstrap penguin colony, estimating a total coverage of approximately 20,000 m² of guano areas in the study period.