Accurate and up-to-date information is essential for environmental monitoring, particularly in regions characterized by complex topography and heterogeneous landscapes. This study presents a multisource remote sensing–based approach for forest vegetation classification on La Palma Island (Canary Islands, Spain), which was further used to illustrate its potential for monitoring the temporal dynamics of different forest habitat types. Very high-resolution multispectral data from the WorldView-2/3 satellites were used, complemented by multispectral and LiDAR data acquired by an unmanned aerial vehicle (UAV). Four target forest vegetation types were mapped within a six-class classification scheme that also included “Other vegetation” and “Soil/Others” as non-target/background classes. The performance of ten supervised classification algorithms was evaluated, including Minimum Distance, Mahalanobis Distance, Parallelepiped, Spectral Angle Mapper, Maximum Likelihood, Naïve Bayes, K-Nearest Neighbors, Random Forest, Support Vector Machine, and the transformer-based deep learning model SegFormer. The results indicate that Random Forest achieved the highest overall accuracy, while Support Vector Machine and SegFormer also showed competitive performance, particularly when spectral information was integrated with vegetation indices and topographic variables. The study provides practical evidence on the selection of input data and classifiers for detailed forest vegetation mapping in a large and topographically complex island.

Benthic communities, such as seagrass meadows, play a crucial environmental role in marine ecosystems and provide socio-economic benefits. Satellite remote sensing is currently used for its monitoring, and deep learning (DL) techniques offer improvements in mapping quality compared to traditional machine learning (ML). This study compares conventional ML and convolutional DL models for mapping Cymodocea nodosa meadows in El R & iacute;o, Canary Islands, using WorldView-2 satellite imagery. An in-situ measurement campaign was conducted to generate an open dataset for segmentation. Evaluated models include decision trees, Gaussian Na & iuml;ve Bayes, support vector machines, K-nearest neighbors, Subspace KNN, feedforward neural networks, U-Net, Attention U-Net, and Pix2Pix models. Results show that DL models significantly outperform conventional ML models in detecting Cymodocea nodosa. The best model (U-net) achieved an Intersection over Union (IoU) of 83% overall and 74% for Cymodocea nodosa, while the best ML model (FNN) only reached 62% and 23%, respectively. IoU was highlighted for its sensitivity to minor mapping changes. In addition, a temporal analysis revealed a dramatic 96% reduction in Cymodocea nodosa coverage over 21 years, from 245.32 ha in 2001 to 9.31 ha in 2022. This study not only compares conventional ML and convolutional DL techniques for benthic habitat mapping but also provides a valuable methodology and dataset for future marine ecosystem monitoring research.

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%.

Seagrass and seaweed meadows hold a very important role in coastal and marine ecosystems. However, anthropogenic impacts pose risks to these delicate habitats. This paper analyses the multitemporal impact of the construction of the largest industrial port in the Canary Islands, near the Special Area of Conservation Natura 2000, on Cymodocea nodosa seagrass meadows (sebadales) of the South of Tenerife, in the locality of Granadilla (Canary Islands, Spain). Very-high-resolution WorldView-2 multispectral satellite data were used for the analysis. Specifically, three images were selected before, during, and after the construction of the port (2011, 2014, and 2022, correspondingly). Initially, advanced pre-processing of the images was performed, and then seabed maps were obtained using the machine learning K-Nearest Neighbors (KNN) supervised classification model, discriminating 12 different bottom types in Case-2 complex waters. The maps achieved high-quality metrics with Precision values of 85%, 81%, and 80%, recall of 76%, 77%, and 77%, and F1 scores of 80%, 79%, and 77% for 2011, 2014, and 2022, respectively. The results mainly show that the construction directly affected the seagrass and seaweed habitats. In particular, the impact of the port on the meadows of Cymodocea nodosa, Caulerpa prolifera, and maërl was assessed. The total maërl population was reduced by 1.9 km2 throughout the study area. However, the Cymodocea nodosa population was maintained at the cost of colonizing maërl areas. Furthermore, the port sedimented a total of 0.98 km2 of seabed, especially Cymodocea nodosa and maërl. In addition, it was observed that Caulerpa prolifera was established as a meadow at the entrance of the port, replacing part of the Cymodocea nodosa and maërl areas. As additional results, bathymetric maps were generated from satellite imagery with the Sigmoid model, and the presence of a submarine outfall was, as well, presented.

Monitoring dense forest ecosystems, such as the laurel forest in Garajonay National Park, is vital for biodiversity conservation, carbon storage, and ecological balance. This study employs satellite remote sensing technologies to introduce a novel methodology, based on vegetation indices, aiming to assess and protect the health of the forest. Utilizing the Jeffries-Matusita distance and a histogram-based method, optimal indices to map forest degradation, like Wide Dynamic Range Vegetation Index (WDRVI) and Modified Simple Ratio (MSR), were identified among 19 generated indices. The study processed imagery from three satellite sensors (WorldView-2, PlanetScope and Sentinel-2), producing maps distinguishing healthy and degraded areas. The study's practical significance lies in offering a method to assess the suitability of sensors and indices for effectively mapping forest degradation. This approach aids conservation efforts and provides valuable insights for environmental managers and policymakers, facilitating the implementation of targeted strategies to safeguard Garajonay National Park's unique laurel forest ecosystem. Emphasizing the role of remote sensing in practical vegetation protection endeavors, the study contributes to on-the-ground initiatives, ensuring the preservation and sustainability of the park's rich biodiversity.

The shallow Tagoro submarine volcano monitoring represents a unique opportunity not only for improving our sparse understanding of submarine volcanic processes in specific scientific fields as physical and chemical oceanography or marine geology but also its interactions over the marine biology in one of the richest marine ecosystems in Europe. This chapter aims to summarize the most relevant physical–chemical, geological and biological changes that occurred in the marine ecosystem of El Hierro island, at the Marine Reserve Punta de La Restinga—El Mar de Las Calmas, due to the genesis of the new underwater volcano Tagoro (27º37′07″N–017º59′28″W) in October 2011. During the first six months of the eruption, extreme physical–chemical perturbations caused by this event, comprising thermal increase from up to + 18.8 °C, water acidification with a pH decrease of 2.9 units, deoxygenation to anoxic levels and extremely high metal enrichment among others, resulted in significant and dramatic alterations of the marine ecosystem. After March 2012, once the eruptive phase was finished, the new submarine volcano entered an active hydrothermal phase involving the release of heat with smaller but still significant and important thermal anomalies of up to + 2.55 °C around the craters, density decrease of − 1.43 kg m−3, pH decrease of − 1.25 units, and high concentrations of metals and inorganic nutrients similar to upwelling zones. These enrichments are still active up to date, producing clear signs of marine recovery not only in the benthonic strata but also in the whole water column compared with pre-eruptive data. Since its eruption ten years ago, an unprecedented monitoring effort has turned into the longest and most complete multidisciplinary time-series for the study of a shallow submarine volcano, with the realization of 31 oceanographic multidisciplinary expeditions that systematically measure more than 40 different physical–chemical and biological variables. All this information and the results obtained during the evolution of the process could serve as a baseline for better understanding future or similar submarine eruptions worldwide.

The reliable detection of vegetation disease and plant stress are challenges in forest ecosystems. To address this problem, remote sensing existing methods of detection mostly rely on vegetation indices, however, in dense forest, the spectral saturation must be considered to select the most appropriate index. In this work, after a revision of the state of the art, a total of 20 vegetation indices were preliminary selected to perform a thorough statistical analysis with the aim to identify the disease and devitalization phenomena in a complex laurel forest. Multisensor very high resolution imagery, from the same month, with a time difference of a decade have been used. A robust methodology has been implemented to generate accurate vigor maps and to identify the forest areas that have experienced a degradation in plant health after 10 years.

Coastal island ecosystems are unique and fragile environments and very sensitive to climate change and direct anthropogenic impact. The use of remote sensing offers the advantage of monitoring these valuable areas in an accessible and cost-effective manner. The main objective of this research, linked to the sustainable management of littoral areas, is the generation of knowledge that is materialized in the implementation of a robust image processing methodology to generate accurate bathymetry and benthic high-resolution maps in coastal shallow waters using remote sensing satellite multispectral sensors (WorldView-2/3). So, this paper presents a methodology for the monitoring of two protected ecosystems in Canary Islands (Spain): Las Canteras beach (Gran Canaria Island) and the channel of La Graciosa-Lanzarote Island. In addition, a multitemporal study is presented where the usefulness of the technology in the monitoring of marine ecosystems is presented.

Satellite remote sensing is an efficient and economical technique for studying coastal bottoms in clear and shallow waters. Accordingly, the main objective of this study is the generation of benthic maps using high spatial resolution multispectral images from the WorldView-2/3 satellites. In this context, one of the main challenges consists of eliminating the disturbances caused in the signal by the atmosphere, the sea surface, and the water column. Regarding the water column correction, there is controversy about its effectiveness to improve the results achieved. To assess the impact of the water column correction in seagrass mapping, two coastal areas with different characteristics have been selected. Specifically, an analysis has been carried out consisting of the assessment of the Lyzenga and Sagawa water column correction models to identify the algorithm that provides the best mapping precision and, additionally, to seek if this pre-processing stage is helpful when classifying the seabed. The classification models selected for the study were: Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Subspace KNN (S-KNN). Machine learning techniques have proven to achieve better results and, in particular, SVM and KNN models provide the best overall accuracy. The results after benthic mapping have demonstrated, that image classification without water column corrections provides better accuracy (95.36% and 99.20%) than using Lyzenga (73.49% and 97.80%) or Sagawa (82.04% and 99.10%), for Case 2 and 1 waters, respectively.

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.

Coastal areas are key to sustaining biodiversity, but their complexity and variability makes their analysis challenging. On the other hand, mountain ecosystems include a large percentage of the global biodiversity and their monitoring is essential, as they are especially vulnerable to climate change. In this context, remote sensing offers a cost-effective technology for the conservation of both kinds of natural areas. In this work, multispectral and hyperspectral data recorded by sensors, onboard satellites, aircrafts and remotely piloted aircraft systems (RPAS), have been used for the sustainable management of natural resources. Specifically, a multiplatform methodology has been developed to process multisensor high spatial resolution imagery and the main benefits and drawbacks of each technology have been identified. Advanced processing techniques in each stage of the methodology have been selected to provide accurate and validated benthic and vegetation maps. Two challenging ecosystems, located in Cabrera and Teide National Parks, have been selected for this study. They correspond with a coastal and a mountain island ecosystem, respectively. To address the associated challenges, the use of imagery with the maximum spatial and spectral resolution, provided by Sentinel-2, WorldView-2, CASI and Pika-L, has been considered. Results have been validated with in-situ data and by the National Parks’ managers and they have shown the ability of remote sensing to accurately map both Parks when the appropriate imagery and techniques are selected. The best performance was achieved with the Support Vector Machine classifier and, in general, WorldView can be considered the most appropriate platform when factoring in cost, coverage and accuracy.

The accurate monitoring of water quality indicators, bathymetry and distribution of benthic habitats in vulnerable ecosystems is key to assessing the effects of climate change, the quality of natural areas and to guide appropriate biodiversity, tourism or fisheries policies. Coastal and inland water ecosystems are very complex but crucial due to their richness and primary production. In this context, remote sensing can be a reliable way to monitor these areas, mainly thanks to satellite sensors' improved spatial and spectral capabilities and airborne or drone instruments. In general, mapping bodies of water is challenging due to low signal-to-noise (SNR) at sensor level, due to the very low reflectance of water surfaces as well as atmospheric effects. Therefore, the main objective of this work is to provide a robust processing framework to estimate water quality parameters in inland shallow waters using multiplatform data. More specifically, we measured chlorophyll concentrations (Chl-a) from multispectral and hyperspectral sensors on board satellites, aircrafts and drones. The Natural Reserve of Maspalomas, Canary Island (Spain), was chosen for the study because of its complexity as well as being an inner lagoon with considerable organic and inorganic matter and chlorophyll concentration. This area can also be considered a well-known coastal-dune ecosystem attracting a large amount of tourists. The water quality parameter estimated by the remote sensing platforms has been validated using co-temporal in situ measurements collected during field campaigns, and quite satisfactory results have been achieved for this complex ecosystem. In particular, for the drone hyperspectral instrument, the root mean square error, computed to quantify the differences between the estimated and in situ chlorophyll-a concentrations, was 3.45 with a bias of 2.96.

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing.

Coastal and inner water lagoon ecosystems are essential due to their high biodiversity and primary production. However, they are extremely complex. Remote sensing can be very useful due to the spatial and spectral improvement of satellites and the availability of airborne or drone hyperspectral sensors. Unfortunately, the mapping of coastal and inner lakes areas is challenging due to the low SNR received at the sensor, as a consequence of the minimum reflectivity of the water surface and the atmospheric disturbances. In this context, the goal of this work is to obtain a robust image processing methodology to generate accurate water quality maps in shallow waters using multiplatform imagery: WorldView-2 (High Resolution Satellite Multispectral Sensor), AHS (Airborne Hyperspectral Scanner) and Pika-L (Drone Hyperspectral Scanner) images. Maspalomas (Gran Canaria, Spain) inner water lagoon ecosystems was studied due to its complexity and the presence of important chlorophyll concentration.

Remote sensing of coastal areas requires multispectral satellite images with high spatial resolution. In this sense, WorldView-2 is a very high resolution satellite, which provides an advanced multispectral sensor with eight narrow bands, allowing the proliferation of new environmental monitoring and mapping applications in shallow coastal ecosystems. The problem of estimating water depths using a radiative model has yielded good results as it considers the physical phenomena of water absorption-backscattering and the relationship between the albedo of the seafloor and the reflectivity of the shallow waters. The sophisticated model developed and evaluated in this study expands the ratio algorithm model allowing for the increased amount of information provided in WorldView-2 imagery to be included in the retrieval of water depth of shallow coastal waters.

Vegetation mapping is a priority when managing natural protected areas. In this context, very high resolution satellite remote sensing data can be fundamental in providing accurate vegetation cartography at species level. In this work, a complete processing methodology has been developed and validated in a complex vulnerable coastal-dune ecosystem. Specifically, the analysis has been carried out using WorldView-2 imagery, which offers spatial and spectral resolutions. A thorough assessment of 5 atmospheric correction models has been performed using real reflectance measures from a field radiometry campaign. To select the classification methodology, different strategies have been evaluated, including additional spectral (23 vegetation indices) and spatial (4 texture parameters) information to the multispectral bands. Likewise, the application of linear unmixing techniques has been tested and abundance maps of each plant species have been generated using the library of spectral signatures recorded during the campaign. After the analysis conducted, a new methodology has been proposed based on the use of the 6S atmospheric model and the Support Vector Machine classification algorithm applied to a combination of different spectral and spatial input data. Specifically, an overall accuracy of 88,03% was achieved combining the corrected multispectral bands plus a vegetation index (MSAVI2) and texture information (variance of the first principal component). Furthermore, the methodology has been validated by photointerpretation and 3 plant species achieve significant accuracy: Tamarix canariensis (94,9%), Juncus acutus (85,7%) and Launaea arborescens (62,4%). Finally, the classified procedure comparing maps for different seasons has also shown robustness to changes in the phenological state of the vegetation.

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.

Image fusion (pan-sharpening) plays an important role in remote sensing applications. Mainly, this process allows to obtain images of high spatial and spectral resolution. However, pan-sharpened images usually present spectral and spatial distortion when comparing with the source images. Because of this, the evaluation of the spectral quality of pan-sharpened images is a fundamental subject to optimize and compare the results of different algorithms. Several assessments of spectral quality have been described in the scientific literature. However, no consensus has been reached on which one describes optimally the spectral distortion in the image. In addition, its performance from the point of view of perceived spectral quality has not been addressed. The aim of this paper is to explore the use of CIEDE2000 distance to evaluate the spectral quality of the fused images. To do this, a database containing remote sensing imagery and its fusion products was created. The spectral quality of the imagery on the database was evaluated using both common quantitative indices and CIEDE2000. With the purpose of determining the relationship between the quantitative indices of spectral quality and the subjective perception of the spectral quality of the merged image, these results were compared to the qualitative assessment provided by a mean opinion score test.

Satellite sensors usually provide two types of data: panchromatic and multispectral images which are characterized by their high spatial resolution and high spectral resolution respectively. In this context, the fusion techniques or pansharpening consist of merging these different aspects to obtain a fused (or pan-sharpened) image with high spatial and spectral resolutions.In this paper, a new quality assessment scheme for pan-sharpened remote sensing imagery is proposed. The methodology described extracts the segments of the images to constitute the basic elements of the measuring quality methodology. This new strategy overcomes traditional pixel-based perspectives, approaching an evaluation by human observers. The results of its application to a set of fused images show that an object-based assessment is consistent in terms of quality determination of both the spectral and spatial properties of remote sensing images. (C) 2017 The Authors. Published by Elsevier B.V.

Se desarrolla el modelo matematico para un sistema de control que se aplica a un robot movil monociclo, en el cual, debido a sus caracteristicas, no se pueden exceder los limites de velocidad tanto lineal como angular, mientras se aproxima a una trayectoria de referencia. Para este proposito se procura que los errores entre la posicion del robot y la posicion de referencia sean lo menor posibles, actuando sobre los parametros de control kv y k ω, que a su vez estan delimitados en un intervalo acotado por un valor maximos y un valor minimo. En el modelado se aplica el metodo interpolacion lineal y se lo implementa por medio de un algoritmo de control que se basa en el denominado metodo delta. La validacion del controlador se la realiza aplicando el sistema en diferentes trayectorias no lineales.