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A novel methodological approach is presented to estimate the shoreline change rate in complex coastal settings by using multi-source/multi-temporal shoreline data extracted from both orthoimages an...

Shoreline change rate constitutes an essential parameter for coastal areas management and monitoring in order to map erosion/accretion areas and to forecast the future shoreline position. Here, shoreline rates were assessed in a heavily human influenced coastal sector of the Mediterranean coast located at Almeria province, Spain. In order to evaluate shoreline rate change assessment in Mediterranean beaches, a comparison was carried out between three methods applied throughout 2009 to 2011 period. In this sense, two kinds of sources were used in order to derive shoreline positions: (i) digitizing the high water line (HWL) through orthoimage interpretation and (ii) automatically extracting a contour level from a LiDAR-derived coastal elevation model (CEM). Shoreline extraction quality was studied by comparing HWL and two datum-based contours, one extrapolated up to 0 m and the other interpolated at 0.75 m above mean sea level (Spanish altimetric datum). It was found a significant bias between HWL and datum-based shoreline positions which had been qualified as negligible in other previous studies carried out in microtidal areas. Since HWL and 0.75 m contour-based shorelines showed a similar distribution, although presenting an added offset, and the 0 m contour was too noisy because of extrapolation errors, it was concluded that the 0.75 m contour-based shoreline was the most stable and accurate proxy datum for multitemporal shorelines comparison. Finally, a high variability of shoreline position could be tested when HWL was used as a proxy for shoreline, being HWL less accurate than CEM-derived shorelines except for the case of using poorly accurate photogrammetrically derived CEMs (e.g. based on very old aerial flights).

Greenhouse detection and mapping via remote sensing is a complex task, which has already been addressed in numerous studies. In this research, the innovative goal relies on the identification of greenhouse horticultural crops that were growing under plastic coverings on 30 September 2013. To this end, object-based image analysis (OBIA) and a decision tree classifier (DT) were applied to a set consisting of eight Landsat 8 OLI images collected from May to November 2013. Moreover, a single WorldView-2 satellite image acquired on 30 September 2013, was also used as a data source. In this approach, basic spectral information, textural features and several vegetation indices (VIs) derived from Landsat 8 and WorldView-2 multi-temporal satellite data were computed on previously segmented image objects in order to identify four of the most popular autumn crops cultivated under greenhouse in Almería, Spain (i.e., tomato, pepper, cucumber and aubergine). The best classification accuracy (81.3% overall accuracy) was achieved by using the full set of Landsat 8 time series. These results were considered good in the case of tomato and pepper crops, being significantly worse for cucumber and aubergine. These results were hardly improved by adding the information of the WorldView-2 image. The most important information for correct classification of different crops under greenhouses was related to the greenhouse management practices and not the spectral properties of the crops themselves.

Multiresolution segmentation (MRS) has been pointed out as one of the most successful image segmentation algorithms within the object-based image analysis (OBIA) framework. The performance of this algorithm depends on the selection of three tuning parameters (scale, shape and compactness) and the bands combination and weighting considered. In this work, we tested MRS on a WorldView-3 bundle imagery in order to extract plastic greenhouse polygons. A recently published command line tool created to assess the quality of segmented digital images (AssesSeg), which implements a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2), was used to select both the best aforementioned MRS parameters and the optimum image data source derived from WorldView-3 (i.e., panchromatic, multispectral and atmospherically corrected multispectral orthoimages). The best segmentation results were always attained from the atmospherically corrected multispectral WorldView-3 orthoimage.

While impressive direct geolocation accuracies better than 5.0 m CE90 (90% of circular error) can be achieved from the last DigitalGlobe’s Very High Resolution (VHR) satellites (i.e. GeoEye-1 and WorldView-1/2/3/4), it is insufficient for many precise geodetic applications. For these sensors, the best horizontal geopositioning accuracies (around 0.55 m CE90) can be attained by using third-order 3D rational functions with vendor’s rational polynomial coefficients data refined by a zero-order polynomial adjustment obtained from a small number of very accurate ground control points (GCPs). However, these high-quality GCPs are not always available. In this work, two different approaches for improving the initial direct geolocation accuracy of VHR satellite imagery are proposed. Both of them are based on the extraction of three-dimensional GCPs from freely available ancillary data at global coverage such as multi-temporal information of Google Earth and the Shuttle Radar Topography Mission 30 m digital elevation model. The application of these approaches on WorldView-2 and GeoEye-1 stereo pairs over two different study sites proved to improve the horizontal direct geolocation accuracy values around of 75%.

A comparison between GeoEye-1 (Geo format) and WorldView-2 (Basic and Ortho Ready Standard format) multispectral (MS) imagery regarding both geometric and radiometric characteristics was carried out over the same study area. Firstly, the attainable geopositioning accuracies on seven single MS images from both very high resolution sensors were compared in the same conditions, both along the sensor orientation and orthorectification phase. The orthoimages planimetric accuracy was ranging from 1.86 m to 2.22 m. The best results were achieved in the case of GeoEye-1 MS orthoimages, nearly followed by those coming from WorldView-2 ones. Secondly, and regarding the radiometric characteristics of the both MS sensors tested, a higher digital numbers’ histogram compression in the case of WorldView-2 was found in the four common bands shared by both MS sensors, especially in the blue band. A no-reference assessment of image quality by using blur ratio was also computed in order to attain image sharpness metric. The blur ratio presented slightly lower values for the GeoEye-1 images.

A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non-forested within the 2013 Landsat orthomosaic. A Random Forest classifier was applied to a set of training data based on a features vector composed of different types of object features such as vegetation indices, mean spectral values and pixel-based fractional cover derived from probabilistic spectral mixture analysis). The very high spatial resolution image data of Google Earth 2013 were employed to train/validate the Random Forest classifier, ranking the NDVI vegetation index and the corresponding pixel-based percentages of photosynthetic vegetation and bare soil as the most statistically significant object features to extract forested and non-forested areas. Regarding classification accuracy, an overall accuracy of 92.34% was achieved. The previously developed classification scheme was applied to the 1984 Landsat data to extract the forest cover change between 1984 and 2013, showing a slight net increase of 5.3% (ca. 8800 ha) in forested areas for the whole region.

Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, WorldView-2 was mainly used to segment the study area focusing on individual greenhouses. Basic spectral information, spectral and vegetation indices, textural features, seasonal statistics and a spectral metric (Moment Distance Index, MDI) derived from Landsat 8 time series and/or WorldView-2 imagery were computed on previously segmented image objects. In order to test its temporal stability, the same approach was applied for two different years, 2014 and 2015. In both years, MDI was pointed out as the most important feature to detect greenhouses. Moreover, the threshold value of this spectral metric turned to be extremely stable for both Landsat 8 and WorldView-2 imagery. A simple decision tree always using the same threshold values for features from Landsat 8 time series and WorldView-2 was finally proposed. Overall accuracies of 93.0% and 93.3% and kappa coefficients of 0.856 and 0.861 were attained for 2014 and 2015 datasets, respectively.

A family of 26 non-parametric texture descriptors based on Histograms of Equivalent Patterns (HEP) has been tested, many of them for the first time in remote sensing applications, to improve urban classification through object-based image analysis of GeoEye-1 imagery. These HEP descriptors have been compared to the widely known texture measures derived from the gray-level co-occurrence matrix (GLCM). All the five finally selected HEP descriptors (Local Binary Patterns, Improved Local Binary Patterns, Binary Gradient Contours and two different combinations of Completed Local Binary Patterns) performed faster in terms of execution time and yielded significantly better accuracy figures than GLCM features. Moreover, the HEP texture descriptors provided additional information to the basic spectral features from the GeoEye-1's bands (R, G, B, NIR, PAN) significantly improving overall accuracy values by around 3%. Conversely, and in statistic terms, strategies involving GLCM texture derivatives did not improve the classification accuracy achieved from only the spectral information. Lastly, both approaches (HEP and GLCM) showed similar behavior with regard to the training set size applied.

A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.

The present work of investigation tries to design, with a historical perspective, a model of tourist Planning for Protected Natural Spaces. In the first place, the conception of the PNS, which has matured remarkably, is studied as well as its historical evolution. Later, the evolution of the legal planning of the PNS in Spain, and the present planning structure are also studied. Finally, the technical models of processing of sectorial plans are analysed and, in spite of the relatively recent character of the PNS planning, these models have been very varied and heterogenous and allow to define the methodologic aspects for the final obtaining of a Plan of compatible Tourist Development. This plan will be constructed from the proposed ideas by the local population and will have to be integrated in the planning structure of the PNS., [es] El presente trabajo de investigación pretende diseñar, con perspectiva histórica, un modelo de Planificación turística para Espacios Naturales Protegidos. En primer lugar, se estudia la concepción de los ENP, que ha madurado notablemente, y su evolución histórica. Posteriormente, se estudia la evolución de la planificación legal de los ENP en España, así como la actual estructura planificadora. Finalmente, se analizan los modelos técnicos de elaboración de planes sectoriales que, a pesar del carácter relativamente reciente de la planificación de ENP, han sido muy variados y heterogéneos y permiten definir los aspectos metodológicos para la obtención final de un Plan de Desarrollo Turístico compatible. Este se construirá a partir de las ideas propuestas por la población local, y deberá integrarse en la estructura planificadora del ENP. [fr] Le travail actuel de recherche a l´intention de concevoir, dans une perspective historique, un modèle de la planification du tourisme pour les Espaces Naturels Protégés. Premièrement, on étudie la conception de l'ENP, qui a notablement mûri, ainsi que son évolution historique. On procède ensuite a une étude de l'évolution de la planification légale des ENP en Espagne et de la structure actuelle de planification. Finalement, on analyse les modèles techniques du traitement des plans sectoriels qui, malgré le caractère relativement récent de la planification des ENP, ont été très divers et hétérogènes, et permettent de définir les aspects méthodologiques pour l'obtention finale d'un Plan du Développement Touristique compatible. Ce plan sera construit a partir des idées proposées par la population locale, et devra être intégré dans la structure de planification de l'ENP.

[ES] La vigilancia de la costa para la protección de las poblaciones establecidas en el interior ha sido uno de los principales problemas en la provincia de Almería durante una parte importante de nuestra Edad Moderna. Debido a esto, se planificó y desarrolló toda una infraestructura defensiva y de observación de la llegada de corsarios y piratas a las costas almerienses. Con el presente trabajo se pretende, en primer lugar, dar importancia a estas estructuras desde un punto de vista paisajístico, turístico y cultural, mediante técnicas GIS de creación de cuencas visuales. Por otro lado, del solapamiento entre cuencas visuales, o su ausencia, se pueden extraer conclusiones desde el punto de vista arqueológico, como parte de un proyecto cuyo objetivo es la investigación de la conexión visual entre las defensas costeras y las poblaciones del interior., [EN] Monitoring of the coast for the protection of the in-land populations has been one of the main problems in the province of Almería during a significant part of Modern Age. Due to this was planned and developed an entire infrastructure defensive and observational in view of the arrival of pirates and corsairs to the coast of Almería. With the present work it is tried firstly to give importance to these structures from a landscape point of view, tourism and culture through GIS techniques to create view sheds. On the other, from overlapping each different view shed, their presence or absence, can give useful conclusions for the archaeology science, as a part of a research project on the visual connection between the coastal defenses and in-land populations.

The fundamental basis for security and peace in a territory is linked to the absence of internal and external conflicts. Situations of internal conflicts in modern societies normally have their immediate origin in social inequalities and are expressed in an unjust distribution of wealth. The causes that determine these inequalities may be derived from environmental situations, as is the case with the exhaustion of natural resources which assured the existence of a community; they may also be due to the abandonment or isolation of social groups who are then deprived of the possibility of developing their own capacities to access resources, as a result of administrative or political irredentism. They may even derive from the installation of external models of socio-economic exploitation, as is the case of 19th century colonialism, which drained the resources of territories without promoting the socio-economic development of their inhabitants.