Salt marshes are very valuable and threatened ecosystems, and are challenging to study due to their difficulty of access and the alterable nature of their soft soil. Remote sensing methods in unmanned aerial vehicles (UAVs) offer a great opportunity to improve our knowledge in this type of complex habitat. However, further analysis of UAV technology performance is still required to standardize the application of these methods in salt marshes. This work evaluates and tunes UAV-photogrammetry and UAV-LiDAR techniques for high-resolution applications in salt marsh habitats, and also analyzes the best sensor configuration to collect reliable data and generate the best results. The performance is evaluated through the accuracy assessment of the corresponding generated products. UAV-photogrammetry yields the highest spatial resolution (1.25 cm/pixel) orthomosaics and digital models, but at the cost of large files that require long processing times, making it applicable only for small areas. On the other hand, UAV-LiDAR has proven to be a promising tool for coastal research, providing high-resolution orthomosaics (2.7 cm/pixel) and high-accuracy digital elevation models from lighter datasets, with less time required to process them. One issue with UAV-LiDAR application in salt marshes is the limited effectiveness of the autoclassification of bare ground and vegetated surfaces, since the scattering of the LiDAR point clouds for both salt marsh surfaces is similar. Fortunately, when LiDAR and multispectral data are combined, the efficiency of this step improves significantly. The correlation between LiDAR measurements and field values improves from R² values of 0.79 to 0.94 when stable reference points (i.e., a few additional GCPs in rigid infrastructures) are also included as control points. According to our results, the most reliable LiDAR sensor configuration for salt marsh applications is the nadir non-repetitive combination. This configuration has the best balance between dataset size, spatial resolution, and processing time. Nevertheless, further research is still needed to develop accurate canopy height models. The present work demonstrates that UAV-LiDAR technology offers a suitable solution for coastal research applications where high spatial and temporal resolutions are required.

Salt marshes are one of the most productive ecosystems and provide numerous ecosystem services. However, they are seriously threatened by human activities and sea level rise. One of the main characteristics of this environment is the distribution of specialized plant species. The environmental conditions governing the distribution of this vegetation, as well as its variation over time and space, still need to be better understood. In this way, these ecosystems will be managed and protected more effectively. Low-altitude remote sensing techniques are excellent for rapidly assessing salt marsh vegetation coverage. By applying a high-resolution hyperspectral imaging system onboard a UAV (UAV-HS), this study aims to differentiate between plant species and determine their distribution in salt marshes, using the salt marshes of Cadiz Bay as a case study. Hyperspectral processing techniques were used to find the purest spectral signature of each species. Continuum removal and second derivative transformations of the original spectral signatures highlight species-specific spectral absorption features. Using these methods, it is possible to differentiate salt marsh plant species with adequate precision. The elevation range occupied by these species was also estimated. Two species of Sarcocornia spp. were identified on the Cadiz Bay salt marsh, along with a class for Sporobolus maritimus. An additional class represents the transition areas from low to medium marsh with different proportions of Sarcocornia spp. and S. maritimus. S. maritimus can be successfully distinguished from soil containing microphytobenthos. The final species distribution map has up to 96% accuracy, with 43.5% of the area occupied by medium marsh species (i.e., Sarcocornia spp.) in the 2.30–2.80 m elevation range, a 29% transitional zone covering in 1.91–2.78 m, and 25% covered by S. maritims (1.22–2.35 m). Basing a method to assess the vulnerability of the marsh to SLR scenarios on the relationship between elevation and species distribution would allow prioritizing areas for rehabilitation. UAV-HS techniques have the advantage of being easily customizable and easy to execute (e.g., following extreme events or taking regular measurements). The UAV-HS data is expected to improve our understanding of coastal ecosystem responses, as well as increase our capacity to detect small changes in plant species distribution through monitoring.

To quantify the amount of carbon sequestered by salt marshes, it is essential to estimate their aboveground biomass (AGB). In this study, we propose utilizing low-altitude remote sensing techniques to collect high-resolution LiDAR and multispectral (MS) data for biomass assessment. We characterized salt marsh vegetation habitats by examining vegetation indices (VIs), and the high-resolution topographic information from LiDAR helped assess habitat distribution. Specifically, the Anthocyanin Reflectance Index 2 (ARI2), combined with the Digital Surface Model (DSM), allows for the identification and separation of the two habitats with distinct dominant species (Sarcocornia spp. and Sporobolus maritimus). The VIs for the two vegetation classes exhibit different seasonal changes throughout the annual cycle, suggesting distinct growth mechanisms for each. Biomass models for the specific seasons are created, showing higher precision (up to 99%) from habitat-specific models compared to those treating species uniformly. Differences are observed in biomass estimation patterns depending on whether the marsh is assessed as a whole or separated into dominating habitats, indicating that the two dominant species exhibit different behaviours that influence biomass production differently throughout the year. Seasonal variations in AGB indicate a peak in summer and a decline in spring, with annual variation accounting for just 9% of the total output, possibly influenced by increased soil salinity and stress in spring. Using LiDAR and MS data from an unmanned aerial vehicle (UAV) is essential for precise identification of primary marsh habitats, facilitating the creation of highly accurate biomass models. This user-friendly, repeatable, and cost-effective method enables the study of salt marshes, evolutionary trends, and climate change response requiring less fieldwork.

One of the major environmental problems caused by mining operations is water pollution. In sulfide mining, pyrite is exposed to atmospheric conditions causing oxidize and release acidity and metals. The water transports these contaminants, producing a leachate known as Acid Mine Drainage (AMD). The Iberian Pyritic Belt (IBP), which belongs the province of Huelva, is very rich in massive sulfide deposits that are mainly composed of pyrite. Mining activity has left numerous abandoned mines with enormous amounts of waste rich in sulfides, including Tharsis mines. Numerous acid leachates emerge from the waste from the Tharsis mines, which have not been active since 2001, and drain to rivers of the area. In the dry season, pollutants precipitate in the form of soluble evaporitic salts that are redissolved with the first important rains of autumn. This redissolution of salts generates a pH decrease and the release of high amounts of contaminants such as Fe, Al, Zn, Mn, As, SO4, Cu, Ni, etc. Remote sensors provide a cost-effective, consistent and accurate approach to monitoring mining pollution. Multispectral and hyperspectral sensors have been widely used due to the distinctive spectral absorption characteristics of minerals. The emerging use of unmanned aerial systems (UAS), such as multicopters coupled with hyperspectral sensors, has become a tool for collecting data at a higher spatial resolution than most aircraft and satellites, resulting in greater accuracy.To obtain the aerial images, a DJI Matrice 600 Pro octacopter UAS was used with a Headwall Hyperspectral (HS) Coaligned VNIR-SWIR sensor with visible and near infrared range (VNIR; 400-1000 nm) and shortwave infrared range (SWIR; 900-2500 nm) and equipped with a LiDAR to quickly obtain an DSM for georeferencing of the hypercubes. The data obtained have been preprocessed to obtain an orthomosaic with the VNIR and SWIR spectra. The data are processed with ENVI v 5.3.6 and QGIS v 3.26.3 software. Wavelengths from 2001 nm to 2450 nm are selected to identify the minerals that are present in the study area, resulting in an orthomosaic with 76 exploitable bands. MNF and PPI techniques are applied to this orthomosaic to obtain the spectral signatures of the study area. These spectral signatures are compared to the USGS mineral library to identify the minerals present. In this way, 8 minerals are identified: hematite, goethite, jarosite, epsomite, copiapite, illite, clinochlore and chlorite + muscovite. Afterwards, the pixels in the study area are classified and the location of the minerals is obtained. Jarosite, epsomite and copiapite are the most abundant minerals and mainly present in the surroundings of waste leachates. The first rains after summer, evaporitic salts and Fe oxides, hydroxides and hydrosulfates are redissolve and cause a considerable polluting load increase. The surface area occupied by these minerals is 3,861 m2, which represents a high load of pollutants that reach the rivers in the area.