Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.

Remote spectral imaging of coastal areas can provide valuable information for their sustainable management and conservation of their biodiversity. Unfortunately, such areas are very sensitive to changes due to human activity, natural phenomenon, introduction of non-native species, and climate change. Thus, the main objective of this research is the implementation of a robust image processing methodology to produce accurate bathymetry maps in shallow coastal waters using high-resolution multispectral WorldView-2/3 satellite imagery for the monitoring at the maximum spatial and spectral resolutions. Two different island ecosystems have been selected for the assessment, since they stand out for their richness in endemic species and they are more vulnerable to climate change: Cabrera National Park and Maspalomas Natural Protected area, located in the Balearic and Canary Islands, Spain, respectively. In addition, a third example to show the applicability of the mapping methodology to monitor the construction of a new port in Granadilla (Canary Islands) is presented. Contributions of this work focus on improving the preprocessing methodology and, mainly, on the proposal and assessment of new satellite-derived regression and machine learning bathymetric models, which have been validated and compared with respect to measured reference bathymetry. After a thorough analysis of nine techniques, using visual and quantitative statistical parameters, ensemble learning approaches have demonstrated excellent performance, even in challenging scenarios up to 35-m depth, with mean RMSE values around 2 m.

Remote multispectral data can provide valuable information for monitoring coastal water ecosystems. Specifically, high-resolution satellite-based imaging systems, as WorldView-2 (WV-2), can generate information at spatial scales needed to implement conservation actions for protected littoral zones. However, coastal water-leaving radiance arriving at the space-based sensor is often small as compared to reflected radiance. In this work, complex approaches, which usually use an accurate radiative transfer code to correct the atmospheric effects, such as FLAASH, ATCOR and 6S, have been implemented for high-resolution imagery. They have been assessed in real scenarios using field spectroradiometer data. In this context, the three approaches have achieved excellent results and a slightly superior performance of 6S model-based algorithm has been observed. Finally, for the mapping of benthic habitats in shallow-waters marine protected environments, a relevant application of the proposed atmospheric correction combined with an automatic deglinting procedure is presented. This approach is based on the integration of a linear mixing model of benthic classes within the radiative transfer model of the water. The complete methodology has been applied to selected ecosystems in the Canary Islands (Spain) but the obtained results allow the robust mapping of the spatial distribution and density of seagrass in coastal waters and the analysis of multitemporal variations related to the human activity and climate change in littoral zones.

The precise mapping of vegetation covers in semi-arid areas is a complex task as this type of environment consists of sparse vegetation mainly composed of small shrubs. The launch of high resolution satellites, with additional spectral bands and the ability to alter the viewing angle, offers a useful technology to focus on this objective. In this context, atmospheric correction is a fundamental step in the pre-processing of such remote sensing imagery and, consequently, different algorithms have been developed for this purpose over the years. They are commonly categorized as imaged-based methods as well as in more advanced physical models based on the radiative transfer theory. Despite the relevance of this topic, a few comparative studies covering several methods have been carried out using high resolution data or which are specifically applied to vegetation covers. In this work, the performance of five representative atmospheric correction algorithms (DOS, QUAC, FLAASH, ATCOR and 6S) has been assessed, using high resolution Worldview-2 imagery and field spectroradiometer data collected simultaneously, with the goal of identifying the most appropriate techniques. The study also included a detailed analysis of the parameterization influence on the final results of the correction, the aerosol model and its optical thickness being important parameters to be properly adjusted. The effects of corrections were studied in vegetation and soil sites belonging to different protected semi-arid ecosystems (high mountain and coastal areas). In summary, the superior performance of model-based algorithms, 6S in particular, has been demonstrated, achieving reflectance estimations very close to the in-situ measurements (RMSE of between 2% and 3%). Finally, an example of the importance of the atmospheric correction in the vegetation estimation in these natural areas is presented, allowing the robust mapping of species and the analysis of multitemporal variations related to the human activity and climate change.

Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This study primarily assesses individual tree segmentation algorithms in two forest ecosystems with different levels of complexity using high-density LiDAR data captured by the Zenmuse L1 sensor on a DJI Matrice 300RTK platform. The processing methodology for LiDAR data includes preliminary preprocessing steps to create Digital Elevation Models, Digital Surface Models, and Canopy Height Models. A comprehensive evaluation of the most effective techniques for classifying ground points in the LiDAR point cloud and deriving accurate models was performed, concluding that the Triangular Irregular Network method is a suitable choice. Subsequently, the segmentation step is applied to enable the analysis of forests at the individual tree level. Segmentation is crucial for monitoring forest health, estimating biomass, and understanding species composition and diversity. However, the selection of the most appropriate segmentation technique remains a hot research topic with a lack of consensus on the optimal approach and metrics to be employed. Therefore, after the review of the state of the art, a comparative assessment of four common segmentation algorithms (Dalponte2016, Silva2016, Watershed, and Li2012) was conducted. Results demonstrated that the Li2012 algorithm, applied to the normalized 3D point cloud, achieved the best performance with an F1-score of 91% and an IoU of 83%.