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This paper addresses an experimental approach to the archaeological study of Roman camps in NW Iberia using ground-penetrating radar (henceforth GPR). The main goal is to explore the capabilities of GPR to extract datasets from ephemeral features, such as temporary camps or siege works, among others. This information aims to maximise the data available before excavation, orienting it to areas that could provide good results in terms of feature detection and contrast between soil matrix and archaeological deposits. This paper explores the potential of the GPR approach and volumetric data visualisation to improve our understanding of four ephemeral sites: Alto da Raia (Montalegre, Portugal–Calvos de Randín, Spain), Sueros de Cepeda (Villamejil, Spain), Los Andinales (Villsandino, Spain), and Villa María (Sasamón, Spain). Despite the focus of this paper, other survey techniques (namely LiDAR, aerial photography, and magnetometry) were used in combination with GPR. Further excavation of the sites provided ground truthing for all data remotely gathered. [ABSTRACT FROM AUTHOR]

In this paper, we design an Anomaly Detection (AD) framework for mobile data traffic, capable of identifying different types of anomalous events generated by flash crowds in metropolitan areas. We state the problem using a semi-supervised approach and exploit the great performance of different Recurrent Neural Network (RNN) models to learn the temporal context of input sequences. Our proposal processes real traffic traces from the unencrypted LTE Physical Downlink Control Channel (PDCCH) of an operative network, gathered during an extensive measurement campaign in two major cities in Spain. The AD framework is designed to perform: i) a-posteriori analysis to understand users’ behavior and urban environment variations; ii) real-time analysis to automatically and on-the-fly alert urban anomalies; and iii) estimation of the duration of the periods identified as anomalous. Numerical results show the higher performance of our AD framework compared to classic AD algorithms and confirm that the proposed framework predicts anomalous behaviours with high accuracy and regardless of their cause. [ABSTRACT FROM AUTHOR]

In the present paper, an active landslide located to the southeast of the urban area of Alcoy (Alicante, Spain) is analysed. This landslide affects the N-340 road, as well as an area in which some industrial buildings dated from the fifteenth century and catalogued as Places of Cultural Interest are placed. This region is characterised by the existence of a steep relief composed of materials from the Miocene to the present, on which many gravitational movements are developed. The displacements have been analysed using high-resolution spotlight mode SAR images captured by the Spanish SAR satellite PAZ (which operates at X-band), which were processed by advanced differential SAR Interferometry (A-DInSAR). The behaviour of the instability has been determined using InSAR-derived time series displacement maps. Additionally, the study has been completed with a detailed geomorphological mapping at 1:2000 scale and an analysis of structural damage carried out in situ. The landslide was modelled in 3D, from a deterministic and probabilistic point of view, confirming the global instability of the area and the relationship of the landslide with rainfall and the construction of a new road. The integration of all this information obtained and its subsequent analysis has made possible to precisely define the extension of the instability, its kinematics, and to identify the main triggering factors. This information will be crucial for the proper management of this landslide by the local authorities. [ABSTRACT FROM AUTHOR]

The volcanic eruption of Cumbre Vieja (La Palma Island, Spain), started on 19 September 2021 and was declared terminated on 25 December 2021. A complete set of aerosol measurements were deployed around the volcano within the first month of the eruptive activity. This paper describes the results of the observations made at Tazacorte on the west bank of the island where a polarized micro-pulse lidar was deployed. The analyzed two-and-a-half months (16 October–31 December) reveal that the peak height of the lowermost and strongest volcanic plume did not exceed 3 km (the mean of the hourly values is 1.43 ± 0.45 km over the whole period) and was highly variable. The peak height of the lowermost volcanic plume steadily increased until week 11 after the eruption started (and 3 weeks before its end) and started decreasing afterward. The ash mass concentration was assessed with a method based on the polarization capability of the instrument. Two days with a high ash load were selected: The ash backscatter coefficient, aerosol optical depth, and the volume and particle depolarization ratios were, respectively, 3.6 (2.4) Mm−1sr−1, 0.52 (0.19), 0.13 (0.07) and 0.23 (0.13) on 18 October (15 November). Considering the limitation of current remote sensing techniques to detect large-to-giant particles, the ash mass concentration on the day with the highest ash load (18 October) was estimated to have peaked in the range of 800–3200 μg m−3 in the lowermost layer below 2.5 km. [ABSTRACT FROM AUTHOR]

When an active landslide is first identified in an artificial reservoir, a comprehensive study has to be quickly conducted to analyse the possible hazard that it may represent to such a critical infrastructure. This paper presents the case of the El Arrecife Landslide, located in a slope of the Rules Reservoir (Southern Spain), as an example of geological and motion data integration for elaborating a preliminary hazard assessment. For this purpose, a field survey was carried out to define the kinematics of the landslide: translational in favour of a specific foliation set, and rotational at the foot of the landslide. A possible failure surface has been proposed, as well as an estimation of the volume of the landslide: 14.7 million m3. At the same time, remote sensing and geophysical techniques were applied to obtain historical displacement rates. A mean subsidence rate of the landslide around 2 cm/year was obtained by means of synthetic aperture radar interferometry (InSAR) and ground-penetrating radar (GPR) data, during the last 5 and 22 years, respectively. The structure-from-motion (SfM) technique provided a rate up to 26 cm/year during the last 14 years of a slag heap located within the foot of the landslide, due to compaction of the anthropical deposits. All of this collected information will be valuable to optimise the planning of future monitoring surveys (i.e. differential global positioning systems, inclinometers, ground drilling, and InSAR) that should be applied in order to prevent further damage on the reservoir and related infrastructures. [ABSTRACT FROM AUTHOR]

Copyright of Brocar: Cuadernos de Investigación Histórica is the property of Universidad de la Rioja, Servicio de Publicaciones and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper describes the undergraduate radar course taught at the Escola Universitària Politècnica del Baix Llobregat (EUPBL) of the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain. At present, the EUPBL offers a three-year degree in Technical Telecommunications Engineering. This paper briefly describes the EUPBL, the organization of the degree program, the structure of the radar course, the evaluation methods, and two practical exercises on a sonar demonstrator and a global positioning system kit. [ABSTRACT FROM AUTHOR]

The Galician coast (NW Spain) is a region that is strongly influenced by the presence of low pressure systems in the mid-Atlantic Ocean and the periodic passage of storms that give rise to severe sea states. Since its wave climate is one of the most energetic in Europe, the objectives of this paper were twofold. The first objective was to characterize the most extreme wave height events in Galicia over the wintertime of a two-year period (2015-2016) by using reliable high-frequency radar wave parameters in concert with predictions from a regional wave (WAV) forecasting system running operationally in the Iberia-Biscay-Ireland (IBI) area, denominated IBI-WAV. The second objective was to showcase the application of satellite wave altimetry (in particular, remote-sensed three-hourly wave height estimations) for the daily skill assessment of the IBI-WAV model product. Special attention was focused on monitoring Ophelia-one of the major hurricanes on record in the easternmost Atlantic-during its 3-day track over Ireland and the UK (15-17 October 2017). Overall, the results reveal the significant accuracy of IBI-WAV forecasts and prove that a combined observational and modeling approach can provide a comprehensive characterization of severe wave conditions in coastal areas and shows the benefits from the complementary nature of both systems. [ABSTRACT FROM AUTHOR]

Over the last 50 years there have been significant changes in land cover and land use, mainly those classified as artificial. This process, and its generalisation on a global scale, affect directly the basic functions of the soil, increasing other problems such as the loss of biodiversity, pollution, soil degradation, flooding and the effects of climate change. In the study area (Mazarrón, Region of Murcia) this problem is exemplary: the binomial urban development associated with the increase of beach tourism and intensive agriculture (under greenhouses) alter drastically the nature of the soil. The aim of this paper is twofold: to establish a supervised classification model that distinguishes, with an assumable error, the different classes established, highlighting those considered as sealed surfaces and, in addition, to make a comparison with the latest Corine Land Cover available information (2018). For this purpose, Sentinel 2A satellite images were selected and a maximum likelihood classification was performed. To validate the results, a confusion matrix was developed and an overall accuracy of 89% was obtained. Finally, a significantly underestimation by the Corine Land Cover of 75% of the sealed surfaces was observed, mainly due to its resolution. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents an overview of the research activities undertaken by La Salle and the Ebro Observatory in the field of remote sensing. On 2003 we started a research project with two main objectives: implement a long-haul oblique ionospheric sounder and transmit the data from remote sensors located at the Spanish Antarctic station Juan Carlos I to Spain. The paper focuses on a study of feasibility of two possible physical layer candidates for the skywave link between both points. A DS-SS based solution and an OFDM based solution are considered to achieve a reliable low-power low-rate communication system between Antarctica and Spain. [ABSTRACT FROM AUTHOR]

This paper includes the mapping of cultivated areas (vineyards) in the experimental zone of the EFEDA project, where vineyard crop is the dominant one. The study area is located in central Spain with a surface area of about 10km × 10 km and showing important desertification hazards. The objective of the paper is to establish the mapping of vineyard areas by remote sensing techniques and to establish relationships between the spectral signature and agronomical parameters (biomass, percent of cover, etc.). For these objectives, several vegetation classes have been defined (vineyards, cereals, fallow, olives and forest) and multi-temporal Landsat-5 Thematic Mapper images were used with the maximum likelihood classification method and masking techniques. The results were checked by ground truth and by existing vegetation cover maps (1525000) and show in general a good correspondence between both information sources. Moreover, these results have permitted to define new vineyard categories like abandoned vineyards and young vineyards that explain the difference between the classification results for vineyards and the ground truth. The later stage of the study pays special attention to the correlation of the agronomical parameters, such as the height of the crop and biomass with the spectral signature of the vineyards represented by the normalized index NDVI, using multi-temporal Landsat images between May and July that comprehend the phenological evolution of the vineyards. Good correlation coefficients (r =0.86 and r =0.90) were obtained successively for the biomass, height and (r =0.82) for the vegetation cover surface. Finally, maps representing the spatial distribution of these parameters were generated for 14 July (period of time when the vineyard is most developed) using the adjustment previously obtained between the NDVI of the vineyard and these parameters. [ABSTRACT FROM AUTHOR]

One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment. [ABSTRACT FROM AUTHOR]

Efficient water management strategies are of utmost importance in drought-prone regions, given the fundamental role irrigation plays in avoiding yield losses and food shortages. Traditional methodologies for estimating irrigation amounts face limitations in terms of overall precision and operational scalability. This study proposes to estimate irrigation amounts from soil moisture (SM) data by adapting the PrISM (Precipitation Inferred from Soil Moisture) methodology. The PrISM assimilates SM into a simple Antecedent Precipitation Index (API) model using a particle filter approach, which allows the creation and estimation of irrigation events. The methodology is applied in a semi-arid region in the Ebro basin, located in the north-east of Spain (Catalonia), from 2016 to 2023. Multi-year drought, which started in 2020, particularly affected the region starting from the spring of 2023, which led to significant reductions in irrigation district water allocations in some of the areas of the region. This study demonstrates that the PrISM approach can correctly identify areas where water restrictions were adopted in 2023, and monitor the water usage with good performances and reliable results. When compared with in situ data for 8 consecutive years, PrISM showed a significant person's correlation between 0.58 and 0.76 and a cumulative weekly root mean squared error (rmse) between 7 and 11 mm. Additionally, PrISM was applied to three irrigation districts with different levels of modernization, due to the different predominant irrigation systems: flood, sprinkler, and drip. This analysis underlined the strengths and limitations of PrISM depending on the irrigation techniques monitored. PrISM has good performances in areas irrigated by sprinkler and flood systems, while difficulties are present over drip irrigated areas, where the very localized and limited irrigation amounts could not be detected from S M observations. [ABSTRACT FROM AUTHOR]

The exposure of Mediterranean forests to large wildfires requires mechanisms to prevent and mitigate their negative effects on the territory and ecosystems. Fuel models synthesize the complexity and heterogeneity of forest fuels and allow for the understanding and modeling of fire behavior. However, it is sometimes challenging to define the fuel type in a structurally heterogeneous forest stand due to the mixture of characteristics from the different types and limitations of qualitative field observations and passive and active airborne remote sensing. This can impact the performance of classification models that rely on the in situ identification of fuel types as the ground truth, which can lead to a mistaken prediction of fuel types over larger areas in fire prediction models. In this study, a handheld mobile laser scanner (HMLS) system was used to assess its capability to define Prometheus fuel types in 43 forest plots in Aragón (NE Spain). The HMLS system captured the vertical and horizontal distribution of fuel at an extremely high resolution to derive high-density three-dimensional point clouds (average: 63,148 points/m2), which were discretized into voxels of 0.05 m3. The total number of voxels in each 5 cm height stratum was calculated to quantify the fuel volume in each stratum, providing the vertical distribution of fuels (m3/m2) for each plot at a centimetric scale. Additionally, the fuel volume was computed for each Prometheus height stratum (0.60, 2, and 4 m) in each plot. The Prometheus fuel types were satisfactorily identified in each plot and were compared with the fuel types estimated in the field. This led to the modification of the ground truth in 10 out of the 43 plots, resulting in errors being found in the field estimation between types FT2–FT3, FT5–FT6, and FT6–FT7. These results demonstrate the ability of the HMLS systems to capture fuel heterogeneity at centimetric scales for the definition of fuel types in the field in Mediterranean forests, making them powerful tools for fuel mapping, fire modeling, and ultimately for improving wildfire prevention and forest management. [ABSTRACT FROM AUTHOR]

Earth Observation (EO) imagery is difficult to find and access for the intermediate user, requiring advanced skills and tools to transform it into useful information. Currently, remote sensing data is increasingly freely and openly available from different satellite platforms. However, the variety of images in terms of different types of sensors, spatial and spectral resolutions generates limitations due to the heterogeneity and complexity of the data, making it difficult to exploit the full potential of satellite imagery. Addressing this issue requires new approaches to organize, manage, and analyse remote-sensing imagery. This paper focuses on the growing trend based on satellite EO and the analysis-ready data (ARD) to integrate two public optical satellite missions: Landsat 8 (L8) and Sentinel 2 (S2). This paper proposes a new way to combine S2 and L8 imagery based on a Local Nested Grid (LNG). The LNG designed plays a key role in the development of new products within the European EO downstream sector, which must incorporate assimilation techniques and interoperability best practices, automatization, systemization, and integrated web-based services that will potentially lead to pre-operational downstream services. The approach was tested in the Duero river basin (78,859 km2) and in the groundwater Mancha Oriental (7279 km2) in the Jucar river basin, Spain. In addition, a viewer based on Geoserver was prepared for visualizing the LNG of S2 and L8, and the Normalized Difference Vegetation Index (NDVI) values in points. Thanks to the LNG presented in this paper, the processing, storage, and publication tasks are optimal for the combined use of images from two different satellite sensors when the relationship between spatial resolutions is an integer (3 in the case of L8 and S2). [ABSTRACT FROM AUTHOR]

Copyright of Spatial Economic Analysis is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deformation monitoring is a common practice in most of dams to ensure their structural health and safety status. Systematic monitoring is frequently carried out by means of geotechnical sensors and geodetic techniques that, although very precise an accurate, can be time-consuming and economically costly. Remote sensing techniques are proved to be very effective in assessing deformation. Changes in the structure, shell or associated infrastructures of dams, including adjacent slopes, can be efficiently recorded by using satellite Synthetic Aperture Radar Inteferometry (InSAR) techniques, in particular, Muti-Temporal InSAR time-series analyses. This is a mature technology nowadays but not very common as a routine procedure for dam monitoring. Today, thanks to the availability of spaceborne satellites with high spatial resolution SAR images and short revisit times, this technology is a powerful cost-effective way to monitor millimeter-level displacements of the dam structure and its surroundings. What is more, the potential of the technique is increased since the Copernicus C-band SAR Sentinel-1 satellites are in orbit, due to the high revisit time of 6 days and the free data availability. ReMoDams is a Spanish research project devoted to provide the deformation monitoring of several embankments dams using advances time-series InSAR techniques. One of these dams is The Arenoso dam, located in the province of Cordova (southern Spain). This dam has been monitored using Sentinel-1 SAR data since the beginning of the mission in 2014. In this paper, we show the processing of 382 SLC SAR images both in ascending and descending tracks until March 2019. The results indicate that the main displacement of the dam in this period is in the vertical direction with a rate in the order of -1 cm/year in the central part of the dam body. [ABSTRACT FROM AUTHOR]

[Display omitted] • Inclusion of temporal data can improve detection F 1 -Score by 8% • Infrared bands provide about 4% more F 1 -Score than visible bands despite lower resolution • Using over 80 features provides an increase of about 12% F 1 -Score over using less than 10. • The proposed method successfully detects all test plots with nearly 90% F 1 -Score • A dataset of recent manure application, verified through on-site validation, is made public. The use of manure in agricultural fields during the wet season can lead to environmental pollution by releasing nitrates into nearby water sources. To address this issue, authorities may impose closed periods during which manure application is prohibited. However, ensuring compliance with these regulations can be challenging, as it is difficult to monitor all fields in a country. To tackle this problem, a solution has been proposed that involves employing machine learning techniques in conjunction with satellite imagery to automatically identify freshly manured fields. This paper investigates the relationship and effectiveness of the Sentinel-2 satellite bands and 51 frequently utilized multispectral indices in the context of precision agriculture, by exploring different feature selection methods. The proposed method achieves nearly 90% F 1 -Score and detects all test plots of the northern Spanish region, showing its potential for large-scale use in precision agriculture and environmental monitoring. This method incorporates temporal data, resulting in an 8% improvement in the detection F 1 -Score. Despite their lower spatial resolution, infrared bands have proven to be more effective than visible bands, enhancing the F 1 -Score by 4%. Furthermore, the use of over 80 features contributes to a 12% increase in the F 1 -Score compared to using fewer than 10 features. For further research and future studies, a dataset of recently manured plots, verified on-site, has been developed and made publicly available. [ABSTRACT FROM AUTHOR]

In this study, we used Spanish National Forest Inventory (SNFI) data, Sentinel-2 imagery and ancillary data to develop models that estimate forest variables for major commercial timber plantations in northern Spain. We carried out the analysis in two stages. In the first stage, we considered plots with and without sub-meter geolocation, three pre-processing levels for the Sentinel-2 images and two machine learning algorithms. In most cases, geometrically, radiometrically, atmospherically and topographically (L2A-ATC) corrected images and the random forest algorithm provided the best results, with topographic correction producing a greater gain in model accuracy as the average slope of the plots increased. Our results did not show any clear impact of the geolocation accuracy of SNFI plots on results, suggesting that the usual geolocation accuracy of SNFI plots is adequate for developing forest models with data obtained from passive sensors. In the second stage, we used all plots together with L2A-ATC-corrected images to select five different groups of predictor variables in a cumulative process to determine the influence of each group of variables in the final RF model predictions. Yield variables produced the best fits, with R2 ranging from 0.39 to 0.46 (RMSE% ranged from 44.6% to 61.9%). Although the Sentinel-2-based estimates obtained in this research are less precise than those previously obtained with Airborne Laser Scanning (ALS) data for the same species and region, they are unbiased (Bias% was always below 1%). Therefore, accurate estimates for one hectare are expected, as they are obtained by averaging the values of 100 pixels (model resolution of 10 m pixel−1) with an expected error compensation. Moreover, the use of these models will overcome the temporal resolution problem associated with the previous ALS-based models and will enable annual updates of forest timber resource estimates to be obtained. [ABSTRACT FROM AUTHOR]

Spain is the second country in Europe with the most swimming pools. However, the legal literature estimates that 20% of swimming pools are not declared or irregular.The administration has a corps of people who manually analyze satellite or drone images to detect illegal or irregular structures. This method is costly in terms of effort and time, and it is also a method based on the subjectivity of the person carrying it out. This proposal aims to design a platform that allows the automatic detection of irregular pools. Using geographic information tools (GIS) based on orthophotography, combined with advanced machine learning techniques for object detection, allows this work. Furthermore, using a multi-agent architecture allows the system to be modular, with the possibility of the different parts of the system working together, balancing the workload. The proposed system has been validated by testing it in different towns in Spain. The system has shown promising results in performing this task, with an F1-Score of 97.1%. [ABSTRACT FROM AUTHOR]

This editorial introduces the first selection of papers in iJOE from the REV annual conference held in 2016. It publishes an overview of the REV coverage of issues and topics inside their papers published. [ABSTRACT FROM AUTHOR]

This paper deals with the assessment of heterogeneity in water status in a commercial orchard, as a prerequisite for precision irrigation management. Remote sensing-derived indicators could be suitable for mapping water stress over large areas, and recent studies have demonstrated that high resolution airborne thermal imagery enables the assessment of discontinuous canopies as pure tree crowns can be targeted, thus eliminating the background effects. Airborne campaigns were conducted over a drip-irrigated commercial orchard in Southwestern Spain composed of five different orchard tree crops. An unmanned aerial vehicle with a thermal camera onboard was flown three times during the day on 8 July 2010, at 9, 11 and 13 h (local time). Stem water potential was measured at the same time of the flights. In some irrigation units, irrigation was stopped prior to the measurement date to induce water deficits for comparative purposes. Several approaches for using the thermal data were proposed. Daily evolution of the differential between canopy and air temperature ( T − T) was compared to tree water status. The slope of the evolution of T − T with time was well correlated with water status and is proposed as a novel indicator linked with the stomatal behavior. The Crop Water Stress Index (CWSI) was calculated with the temperature data from the 13.00 h flight using an empirical approach for defining the upper and lower limits of T − T. The assessment of variability in water status was also performed using differences in relative canopy temperatures. Ample variability was detected among and within irrigation units, demonstrating that the approach proposed was viable for precision irrigation management. The assessment led to the identification of water-stressed areas, and to the definition of threshold CWSI values and associated risks. Such thresholds may be used by growers for irrigation management based on crop developmental stages and economic considerations. [ABSTRACT FROM AUTHOR]

World agriculture is facing a great challenge since it is necessary to find a sustainable way to increase food production. Current trends in advancing the agriculture sector are based on leveraging remote sensing technology and the use of biostimulants. However, the efficient implementation of both of these on a commercial scale for the purposes of productivity improvement remains a challenge. Thus, by proposing a crop monitoring strategy based on remote sensing data, this paper aims to verify and anticipate the impact of applying a Glycinebetaine biostimulant (GB) on the final yield. The study was carried out in a rice-producing area in Eastern Spain (Valencia) in 2021. GB was applied by drone 33 days after sowing (tillering phase). Phenology was monitored and crop production parameters were determined. Regarding satellite data, Sentinel-2 cloud-free images were obtained from sowing to harvest, using the bands at 10 m. Planet data were used to evaluate the results from Sentinel-2. The results show that GB applied 33 days after sowing improves both crop productive parameters and commercial yield (13.06% increase). The design of the proposed monitoring strategy was based on the dynamics and correlations between the visible (green and red) and NIR bands. The analysis showed differences when comparing the GB and control areas, and permitted the determination of the moment in which the effect of GB on yield (tillering and maturity) may be greater. In addition, an index was constructed to verify the crop monitoring strategy, its mathematical expression being: NCMI = (NIR − (red + green))/(NIR + red + green). Compared with the other VIs (NDVI, GNDVI and EVI2), the NCMI presents a greater sensitivity to changes in the green, red and NIR bands, a lower saturation phenomenon than NDVI and a better monitoring of rice phenology and management than GNDVI and EVI2. These results were evaluated with Planet images, obtaining similar results. In conclusion, in this study, we confirm the improvement in rice crop productivity by improving sustainable plant nutrition with the use of biostimulants and by increasing the components that define crop yield (productive tillers, spikelets and grains). Additionally, crop monitoring using remote sensing technology permits the anticipation and understanding of the productive behavior and the evolution of the phenological stages of the crop, in accordance with crop management. [ABSTRACT FROM AUTHOR]

Ground subsidence of detrital deposits in the Almería basin (SE Spain) was studied using the remote sensing technique of Differential Interferometry SAR (DInSAR). This basin is one of the most arid in Europe, receiving an average rainfall of 250 mm per year. Over the last 60 years the region has experienced an enormous agricultural and urban expansion, whose water demand has been largely supplied from groundwater, leading to the current situation of overexploitation of water resources. This paper outlines the likely relationship between groundwater abstraction and subsidence. To this end, 34 ERS and Envisat images, taken between 2003 and 2009, were analysed to estimate ground surface deformations, and hence, compared with water table variations measured in a number of piezometers in the basin. The analysis shows a clear parallelism between the variations in piezometric level and deformation of the ground surface. In addition, the zones of greatest subsidence coincide with those areas where groundwater abstractions are concentrated. Subsidence over the examined period varies from 10 to 30 mm, with extreme values as high as 50 mm, which translates to a rate of between 1·7 and 5 mm/year, reaching maximum rates of 8 mm/year at some points. Given such subsidence rates, damage to urban infrastructures are, for the moment, incipient. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Granitic boulders are widespread geomorphological elements, particularly found in humid granitic areas. Although they seldom represent a hazard for people or infrastructures, sometimes their location in steep or natural slopes may jeopardise their stability and potentially affect people or infrastructures. In addition, their complex geometry makes the instability mechanisms difficult to identify, so it is even more difficult to compute factors of safety regarding their stability. In this paper, the authors analyse potential failure mechanisms of granite boulders based on analytical mechanic calculations and physical tilt tests, permitting understanding the phenomena under scrutiny. Then, they study the stability of one of these boulders: the Pena do Equilibrio or equilibrium rock. To do that they resort to standard geotechnical characterization and advanced geometrical characterization derived from UAV photogrammetric and 3D Laser Scanning of the boulder. The presented results exemplify how the application of these recently available topographic technologies, in combination with rock mechanics approaches, enable a rigorous analysis of the stability of granite boulders. • Failure mechanisms of granite boulders have been analysed through analytical calculations and tilt tests in laboratory. • Pena do Equilibrio boulder, located in NW Spain, has been selected for the presented analysis. • UAS and Terrestrial Laser Scanner have been resorted for geometrical calculations like volume and position of the centroid. • The stability of the granitic boulder has been assessed through rock mechanics approaches combined with remote sensing techniques. • A 3D-printed 1:50-scale plastic replica of the boulder has been utilized for analyzing results in laboratory. [ABSTRACT FROM AUTHOR]

Highlights • Satellite images have been utilized to estimate crop coefficients. • The estimated and the theoretical crop coefficients have been compared. • The comparison was made among field plots, municipalities and irrigation districts. • A set of water use efficiency indicators have been calculated. Abstract Irrigation in the Duero river basin accounts for 75% of total water resources. Nevertheless, irrigation in this area is paramount to maintain agricultural production. Within this context, this paper is aimed at assessing the water use efficiency (WUE), for the major crop (corn), among four irrigation districts (IDs) from 2014 to 2017. Since the WUE indicators base upon crop water requirements, these were calculated by estimating the reference evapotranspiration and the local crop coefficients Kc. First, they were estimated by satellite images and then, compared with the recommendation from the Regional Irrigation Advisory Service. Finally, the gross irrigation requirements were compared with the irrigation supply. WUE indicators varied among IDs depending upon the hydrologic year, the water available for irrigation (superficial and subsurface water), and the corn sowing date. Usually, the IDs performed deficit irrigation in the dry years, and either full irrigation or over irrigation in the wet years. The rainfall only fulfilled less than 10% of corn requirements. Small differences in Kc were observed within municipalities (in each ID) and/or among IDs. The pattern of the Kc fitted curves varied among IDs and was affected by sowing dates. Although, the local Kc values were close to the general coefficients recommended by the Irrigation Advisory Service, they affected the determination of gross irrigation requirements which were significant different among IDs. Moreover, it was observed that the FAO recommendation for the duration of the corn growth stages fitted better the estimated Kc than the one from the Advisory Center. The gross irrigation requirements were similar among IDs and years, varying between 6476 and 7646 m3/ha. The local Kc estimation could help irrigation managers to adapt the irrigation supply to the actual corn needs. [ABSTRACT FROM AUTHOR]

This paper shows the combined use of remotely sensed data and hydraulic geometry methods as an alternative to rainfall-runoff models. Hydraulic geometric data and boolean images of water sheets obtained from satellite images after storm events were integrated in a Geographical Information System. Channel cross-sections were extracted from a high resolution Digital Terrain Model (DTM) and superimposed on the image cover to estimate the peak flow using HEC-RAS. The proposed methodology has been tested in ephemeral channels (ramblas) on the coastal zone in south-eastern Spain. These fluvial systems constitute an important natural hazard due to their high discharges and sediment loads. In particular, different areas affected by floods during the period 1997 to 2009 were delimited through HEC-GeoRAs from hydraulic geometry data and Landsat images of these floods (Landsat-TM5 and Landsat-ETM+7). Such an approach has been validated against rainfall-surface runoff models (SCS Dimensionless Unit Hydrograph, SCSD, Témez gamma HU Tγ and the Modified Rational method, MRM) comparing their results with flood hydrographs of the Automatic Hydrologic Information System (AHIS) in several ephemeral channels in the Murcia Region. The results obtained from the method providing a better fit were used to calculate different hydraulic geometry parameters, especially in residual flood areas. [ABSTRACT FROM AUTHOR]

This paper is focused on the potential and limits of Persistent Scatterer Interferometry (psi), a powerful remote sensing technique used to measure deformation phenomena. It only refers to satellite-based PSI techniques, focusing on the most important sources of C-band SAR data: ERS and Envisat. In addition, it compares Cand X-band results, considering data from the high-resolution TerraSAR-X sensor. The paper begins with a description of the main characteristics of Psi. It then discusses the most important PSI products and their performances, analyzing their spatial sampling, the so-called residual topographic error and PSi geocoding, the average displacement rates, and the deformation time series. As C-band products are concerned, the paper reports some relevant psi validation results, which come from the ESAfunded Terrafirma Validation Project. Regarding the X-band, it describes the results obtained over the City of Barcelona by processing 13 TerraSAR-X images. The last part discusses the main limits of PSI. [ABSTRACT FROM AUTHOR]

The main goal of the SMOS (Soil Moisture and Ocean Salinity) mission is to deliver global fields of surface soil moisture and sea surface salinity using L-band (1.4 GHz) radiometry. Within the context of the Science preparation for SMOS, the Valencia Anchor Station (VAS) experimental site, in Spain, was chosen to be one of the main test sites in Europe for Calibration/Validation (Cal/Val) activities. In this framework, the paper presents an approach consisting in accurately simulating a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over the wide VAS surface (50×50 km2). Ground and meteorological measurements over the area are used as the input of a Soil-Vegetation-Atmosphere-Transfer (SVAT) model, SURFEX (Externalized Surface) - module ISBA (Interactions between Soil-Biosphere-Atmosphere) to simulate the spatial and temporal distribution of surface soil moisture. The calibration as well as the validation of the ISBA model are performed using in situ soil moisture measurements. It is shown that a good consistency is reached when point comparisons between simulated and in situ soil moisture measurements are made. Actually, an important challenge in remote sensing approaches concerns product validation. In order to obtain an representative soil moisture mapping over the Valencia Anchor Station (50×50 km2 area), a spatialization method is applied. For verification, a comparison between the simulated spatialized soil moisture and remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E) and from the European Remote Sensing Satellites (ERS-SCAT) is performed. Despite the fact that AMSR-E surface soil moisture product is not reproducing accurately the absolute values, it provides trustworthy information on surface soil moisture temporal variability. However, during the vegetation growing season the signal is perturbed. By using the polarization ratio a better agreement is obtained. ERS-SCAT soil moisture products are also used to be compared with the simulated spatialized soil moisture. However, the lack of soil moisture data from the ERS-SCAT sensor over the area (45 observations for one year) prevented capturing the soil moisture variability. [ABSTRACT FROM AUTHOR]

Forest fires play a critical role in landscape transformation, vegetation succession, soil degradation and air quality. Improvements in fire risk estimation are vital to reduce the negative impacts of fire, either by lessen burn severity or intensity through fuel management, or by aiding the natural vegetation recovery using post-fire treatments. This paper presents the methods to generate the input variables and the risk integration developed within the Firemap project (funded under the Spanish Ministry of Science and Technology) to map wildland fire risk for several regions of Spain. After defining the conceptual scheme for fire risk assessment, the paper describes the methods used to generate the risk parameters, and presents proposals for their integration into synthetic risk indices. The generation of the input variables was based on an extensive use of geographic information system and remote sensing technologies, since the project was intended to provide a spatial and temporal assessment of risk conditions. All variables were mapped at 1km2 spatial resolution, and were integrated into a web-mapping service system. This service was active in the summer of 2007 for semi-operational testing of end-users. The paper also presents the first validation results of the danger index, by comparing temporal trends of different danger components and fire occurrence in the different study regions. [Copyright &y& Elsevier]

Sensors provide robotic systems with the information required to perceive the changes that happen in unstructured environments and modify their actions accordingly. The robotic controllers which process and analyze this sensory information are usually based on three types of sensors (visual, force/torque and tactile) which identify the most widespread robotic control strategies: visual servoing control, force control and tactile control. This paper presents a detailed review on the sensor architectures, algorithmic techniques and applications which have been developed by Spanish researchers in order to implement these mono-sensor and multi-sensor controllers which combine several sensors. [ABSTRACT FROM AUTHOR]

Magnetic data consists of a sequence of collected points with spatial coordinates and magnetic information. The spatial location of these points needs to be as exact as possible in order to develop a precise interpretation of magnetic anomalies. GPS is a valuable tool for accomplishing this objective, especially if the RTK approach is used. In this paper the VRS (Virtual Reference Station) technique is introduced as a new approach for real-time positioning of magnetic sensors. The main advantages of the VRS approach are, firstly, that only a single GPS receiver is needed (no base station is necessary), reducing field work and equipment costs. Secondly, VRS can operate at distances separated 50-70 km from the reference stations without degrading accuracy. A compact integration of a GSM-19 magnetometer sensor with a geodetic GPS antenna is presented; this integration does not diminish the operational flexibility of the original magnetometer and can work with the VRS approach. The coupled devices were tested in marshlands around Gandia, a city located approximately 100 km South of Valencia (Spain), thought to be the site of a Roman cemetery. The results obtained show adequate geometry and high-precision positioning for the structures to be studied (a comparison with the original low precision GPS of the magnetometer is presented). Finally, the results of the magnetic survey are of great interest for archaeological purposes. [ABSTRACT FROM AUTHOR]

This paper presents an assessment of the performance of a hybrid method that allows a simultaneous retrieval of land-surface temperature (LST) and emissivity (LSE) from remotely-sensed data. The proposed method is based on a synergistic usage of the split-window (SW) algorithm and the two-temperature method (TTM) and combines the advantages of both procedures while mitigating their drawbacks. The method was implemented for thermal channels 76 (10.56 µm) and 78 (11.72 µm) of the Airborne Hyperspectral Scanner (AHS), which was flown over the Barrax test site (Albacete, Spain) in the second week of July 2005, within the framework of the Sentinel-2 and Fluorescence Experiment (SEN2FLEX) field campaign. A set of radiometric measurements was performed in the thermal infrared region in coincidence with aircraft overpasses for different surface types, e.g. bare soil, water body, corn, wheat, grass. The hybrid method was tested and compared with a standard SW algorithm and the results obtained show that the hybrid method is able to provide better estimates of LST, with values of bias (RMSE) of the order of 0.8 K (1.9 K), i.e. about one third (one half) of the corresponding values of 2.7 K (3.4 K) that were obtained for bias (RMSE) when using the SW algorithm. These figures provide a sound indication that the developed hybrid method is particularly useful for surface and atmospheric conditions where SW algorithms cannot be accurately applied. [ABSTRACT FROM AUTHOR]

The magnitude of the environmental and social consequences of soil erosion and land degradation in semiarid areas of the Mediterranean region has long been recognized and studied. This paper investigates the interrelationship between land use/cover (LULC) changes and land degradation using remotely sensed and ancillary data for southeastern Spain. The area of study, the Xaló River catchment situated in the north of the Alicante Province, has been subjected to a number of LULC changes during the second half of the 20th century such as agricultural abandonment, forest fires, and tourist development. Aerial photographs dating back to 1956 were used for the delineation of historic LULC types; Landsat ETM+ data were used for the analysis and mapping of current conditions. Two important indicators of land degradation, namely, susceptibility to surface runoff and soil erosion, were estimated for the two dates using easily parametrizable models. The comparison of 1956 to 2000 conditions shows an overall “recuperating” trend over the catchment and increased susceptibility to soil erosion only in 3% of the catchment area. The results also identify potential degradation hot-spots where mitigation measures should be taken to prevent further degradation. The readily implemented methodology, based on modest data requirements demonstrated by this study, is a useful tool for catchment to regional scale land use change and land degradation studies and strategic planning for environmental management. [ABSTRACT FROM AUTHOR]

The great urban and industrial development of the towns surrounding Madrid has caused an irreversible and rapid soil loss. In this paper, soil sealed evolution in Guadalajara province during the past 15 years, mainly in the capital, industrial district and small municipalities near Madrid, has been studied. This province has experimented a sharp reduction of the most fertile soils, located on the Henares valley, due to the action of new models of local and regional development. The main goal of this paper is the mapping of the covered soils in a pilot area of Spain. Supervised classification of Landsat images from 1989 to 2002 has been carried out. Geo-referred image analysis allows us to detect precisely the amount of covered soils and where this phenomenon has taken place. [Copyright &y& Elsevier]

Classification accuracy depends on a number of factors, of which the nature of the training samples, the number of bands used, the number of classes to be identified relative to the spatial resolution of the image and the properties of the classifier are the most important. This paper evaluates the effects of these factors on classification accuracy using a test area in La Mancha, Spain. High spectral and spatial resolution DAIS data were used to compare the performance of four classification procedures (maximum likelihood, neural network, support vector machines and decision tree). There was no evidence to support the view that classification accuracy inevitably declines as the data dimensionality increases. The support vector machine classifier performed well with all test data sets. The use of the orthogonal MNF transform resulted in a decline in classification accuracy. However, the decision‐tree approach to feature selection worked well. Small increases in classifier accuracy may be obtained using more sophisticated techniques, but it is suggested here that greater attention should be given to the collection of training and test data that represent the range of land surface variability at the spatial scale of the image. [ABSTRACT FROM AUTHOR]

Space‐borne synthetic aperture radar has been proven to be a useful tool for ocean oil spill monitoring due to its large coverage, independence of the day–night cycle and all‐weather capability. In this paper, a method for oil spill detection based on a visual interpretation was applied to two consecutive Advanced Synthetic Aperture Radar (ASAR) images acquired during the Prestige oil spill off the Spanish coast. The obtained oil spill information was integrated into a Geographical Information System (GIS) database in order to study the spatial distribution and the evolution of the slicks between both days, in addition to carrying out a comparison with field observations. The results show the great capability of monitoring and forecasting marine oil spills caused by large oil tanker accidents by means of the use of radar imagery jointly with other information, such as wind data or in situ observations. [ABSTRACT FROM AUTHOR]

Support vector machines (SVM) represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of two experiments in which multi-class SVMs are compared with maximum likelihood (ML) and artificial neural network (ANN) methods in terms of classification accuracy. The two land cover classification experiments use multispectral (Landsat-7 ETM+) and hyperspectral (DAIS) data, respectively, for test areas in eastern England and central Spain. Our results show that the SVM achieves a higher level of classification accuracy than either the ML or the ANN classifier, and that the SVM can be used with small training datasets and high-dimensional data. [ABSTRACT FROM AUTHOR]

The high spectral resolution of Airborne Visible Infrared Imaging Spectrometer (AVIRIS; 224 channels from 400 to 2455 nm) and HyMap (127 channels between 437 and 2485 nm) images is necessary to conduct geological analysis with remote petrological determinations of rock types or soils, or to determine vegetation groups. When airborne images and field spectra are well adjusted between each other, and when the vegetation does not interfere in the analysis, the spectral shape analysis (SSA) method represents an easy treatment to reveal a large amount of geological information. The method presented in the paper takes into account both wells and peaks of spectra resulting from a combination of absorption features and continuum shapes. It was conducted on the Ronda peridotite, in the south-west of Spain, which was imaged by AVIRIS in 1991 and by HyMap in 2000, and which was partially sampled in the field using a GER 3700 spectrometer in 1997, 2000 and 2001. In this study, the AVIRIS and HyMap images are processed to infer geological features using first photo-interpretation of colour composite images and then using the SSA method. This allows us to distinguish easily the peridotite massif from its surrounding rocks, to identify petrological variations inside the peridotite, including different varieties of serpentines, but also inside the surrounding rocks, particularly the carbonate-bearing ones which comprise marbles and dolomites. [ABSTRACT FROM AUTHOR]

Approaches linking biodiversity assessment with landscape structure are necessary in the framework of sustainable rural development. The present paper describes a methodology to estimate plant diversity involving landscape structure as a proportional weight associated with different plant communities found in the landscape mosaic. The area occupied by a plant community, its patch number or its spatial distribution of patches are variables that could be expressed in gamma plant diversity of a territory. The methodology applies (1) remote sensing information, to identify land cover and land use types; (2) aspect, to discriminate composition of plant communities in each land cover type; (3) multi-scale field techniques, to asses plant diversity; (4) affinity analysis of plant community composition, to validate the stratified random sampling design and (5) the additive model that partitions gamma diversity into its alpha and beta components. The method was applied to three Spanish rural areas and was able to record 150–260 species per ha. Species richness, Shannon information index and Simpson concentration index were used to measure diversity in each area. The estimation using Shannon diversity index and the product of patch number and patch interspersion as weighting of plant community diversity was found to be the most appropriate method of measuring plant diversity at the landscape level. [ABSTRACT FROM AUTHOR]

In this paper three methods for updating inventories of burned areas have been presented and examined. They include Multitemporal Principal Component Analysis (MPCA), Change Vector Analysis (CVA) and Multitemporal NDVI Classification (MNC). First, 11 Landsat-5 Thematic Mapper (TM) images of a forest area were radiometrically corrected to derive a multitemporal series of intercomparable images for each spring from 1984 to 1994. Then, in order to check the feasibility of the three approaches, they were used for mapping fire burns that occurred during 1992. The various procedures yielded different maps of burned areas; the MNC method seemed to be more reliable than the others, because it merges spectral data corresponding not only to 1992 (pre-fire) and 1993 (post-fire) but also to 1994 (the second year after the fires), which is key in the vegetation regeneration. Finally, this methodology was automated to yield an inventory of burned areas for each year during the period of study. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a variable of the climatic system and hydrological cycle that plays an important role in biosphere–atmosphere–hydrosphere interactions. In this paper, remote sensing-based ET estimates with the simplified surface energy balance index (S-SEBI) model using Landsat 8 data were compared with in situ lysimeter measurements for different land covers (Grass, Wheat, Barley, and Vineyard) at the Barrax site, Spain, for the period 2014–2018. Daily estimates produced superior performance than hourly estimates in all the land covers, with an average difference of 12% and 15% for daily and hourly ET estimates, respectively. Grass and Vineyard showed the best performance, with an RMSE of 0.10 mm/h and 0.09 mm/h and 1.11 mm/day and 0.63 mm/day, respectively. Thus, the S-SEBI model is able to retrieve ET from Landsat 8 data with an average RMSE for daily ET of 0.86 mm/day. Some model uncertainties were also analyzed, and we concluded that the overpass of the Landsat missions represents neither the maximum daily ET nor the average daily ET, which contributes to an increase in errors in the estimated ET. However, the S-SEBI model can be used to operationally retrieve ET from agriculture sites with good accuracy and sufficient variation between pixels, thus being a suitable option to be adopted into operational ET remote sensing programs for irrigation scheduling or other purposes. [ABSTRACT FROM AUTHOR]

Plastic covered greenhouse (PCG) mapping via remote sensing has received a great deal of attention over the past decades. The WorldView-3 (WV3) satellite is a very high resolution (VHR) sensor with eight multispectral bands in the visible and near-infrared (VNIR) spectral range, and eight additional bands in the short-wave infrared (SWIR) region. A few studies have already established the importance of indices based on some of these SWIR bands to detect urban plastic materials and hydrocarbons which are also related to plastics. This paper aims to investigate the capability of WV3 (VNIR and SWIR) for direct PCG detection following an object-based image analysis (OBIA) approach. Three strategies were carried out: (i) using object features only derived from VNIR bands (VNIR); (ii) object features only derived from SWIR bands (SWIR), and (iii) object features derived from both VNIR and SWIR bands (All Features). The results showed that the majority of predictive power was attributed to SWIR indices, especially to the Normalized Difference Plastic Index (NDPI). Overall, accuracy values of 90.85%, 96.79% and 97.38% were attained for VNIR, SWIR and All Features strategies, respectively. The main PCG misclassification problem was related to the agricultural practice of greenhouse whitewash (greenhouse shading) that temporally masked the spectral signature of the plastic film. [ABSTRACT FROM AUTHOR]

Featured Application: Pistachio orchard management using remote sensing with UAVs. Pistachio (Pistacia vera L.) has earned recognition as a significant crop due to its unique nutrient composition and its adaptability to the growing threat of climate change. Consequently, the utilization of remote sensing techniques for non-invasive pistachio monitoring has become critically important. This research was conducted in two pistachio orchards located in Spain, aiming to assess the effectiveness of vegetation indices (VIs) in estimating nut yield and quality under various irrigation conditions. To this end, high-resolution multispectral and thermal imagery were gathered using a Micasense ALTUM sensor carried by a DJI Inspire 2 drone in order to calculate the NDRE (normalized difference red edge index), GNDVI (green normalized difference vegetation index), NDVI (normalized difference vegetation index), and CWSI (crop water stress index). Each orchard underwent two flights at distinct growth stages, totaling four flights. In June, NDRE-carbohydrates (r = 0.78) and CWSI-oleic (r = 0.77) showed the highest correlations, while in September, CWSI-carbohydrates (r = 0.62) and NDVI-iron (r = 0.54) Despite NDVI's limitations due to saturation effects, all VIs had significant yield and quality correlations, with GNDVI proving most effective in both flights. CWSI correlated considerably on both dates in terms of several quality parameters (carbohydrate percentage, magnesium, iron, and fatty acids, namely palmitoyl, stearic, oleic, and linoleic), surpassing non-thermal indices. Finally, it is important to consider the impact of environmental factors, such as the location of the sun, when interpreting the CWSI, as it modifies the temperature distribution pattern within the canopy. This study supports the viability of remote sensing and vegetation indices as potential tools for enhancing the management of pistachio orchards. [ABSTRACT FROM AUTHOR]

Walkability is a modern concept that has become important in recent years due to the doubtless effects it has on aspects such as health and wellbeing, sustainable development, climate change, and tourism. It is necessary, therefore, that urban development strategies aim to achieve walkable cities. The main objective of this study is to define a methodology to calculate the walkability index in tourist cities and to predict the effects of climate change on this index, which is applied to three World Heritage cities in central Spain: Salamanca, Ávila, and Segovia. The methodology is developed in three phases. Phase I focus on the calculation of walkability, considering the following factors: facilities and services, accessibility, sidewalk width, population density, green areas, and urban trees. In Phase II, walkability in 2020, climate-related variables were added to the previous result: temperatures, solar radiation, and shadows. Finally, the third phase, walkability under climate change pressure in 2030, 2050, and 2100, establish predictions for different climate scenarios. The results show excellent walkability indices (higher) in city centers and newly built neighborhoods and low values in the rest of the peripheral areas, industrial estates, and neighborhoods. Climate predictions showed a generalized decrease in walkability over time, even higher in the scenario with high greenhouse gas emissions. Likewise, the models can be an excellent tool for the tourist management of cities since they show the most walkable areas and, therefore, the most suitable for tourist routes. [ABSTRACT FROM AUTHOR]

Agricultural land abandonment is an increasing problem in Europe. The Comunitat Valenciana Region (Spain) is one of the most important citrus producers in Europe suffering this problem. This region characterizes by small sized citrus plots and high spatial fragmentation which makes necessary to use Very High-Resolution images to detect abandoned plots. In this paper spectral and Gray Level Co-Occurrence Matrix (GLCM)-based textural information derived from the Normalized Difference Vegetation Index (NDVI) are used to map abandoned citrus plots in Oliva municipality (eastern Spain). The proposed methodology is based on three general steps: (a) extraction of spectral and textural features from the image, (b) pixel-based classification of the image using the Random Forest algorithm, and (c) assignment of a single value per plot by majority voting. The best results were obtained when extracting the texture features with a 9 × 9 window size and the Random Forest model showed convergence around 100 decision trees. Cross-validation of the model showed an overall accuracy of the pixel-based classification of 87% and an overall accuracy of the plot-based classification of 95%. All the variables used are statistically significant for the classification, however the most important were contrast, dissimilarity, NIR band (720 nm), and blue band (620 nm). According to our results, 31% of the plots classified as citrus in Oliva by current methodology are abandoned. This is very important to avoid overestimating crop yield calculations by public administrations. The model was applied successfully outside the main study area (Oliva municipality); with a slightly lower accuracy (92%). This research provides a new approach to map small agricultural plots, especially to detect land abandonment in woody evergreen crops that have been little studied until now. [ABSTRACT FROM AUTHOR]

This paper discusses the combined use of tools from dynamical systems theory and remote sensing techniques and shows how they are effective instruments which may greatly contribute to the decision making protocols of the emergency services for the real-time management of oil spills. This work presents the successful interplay of these techniques for a recent situation, the sinking of the Oleg Naydenov fishing ship that took place in Spain, close to the Canary Islands, in April 2015. [ABSTRACT FROM AUTHOR]

Biogeography is a key concept associated with microbial terroir, which is responsible for the differentiation and uniqueness of wines. One of the factors influencing this microbial terroir is the vegetation, which in turn is influenced by climate, soil, and cultural practices. Remote sensing instruments can provide useful information about vegetation. This study analyses the relationship between NDVI, calculated using Sentinel-2 and Landsat-8 satellite images of different veraison dates, and microbial data obtained in 2015 from 14 commercial (organic and conventional) vineyards belonging to four Designations of Origin (DOs) from Galicia (northwest Spain). Microbial populations in grapes and musts were identified using PCR techniques and confirmed by sequencing. Statistical analyses were made using PCA, CCA, TB-PLS, and correlation analyses. This study confirms that the NDVI is positively correlated with the diversity of yeasts, both in grapes' surface and must samples. Moreover, the results of this study show: (i) Sentinel-2 images, as well as Landsat-8 images, can establish differences in NDVI related to yeast terroir in grapes and musts, as it is the most relevant DO factor, (ii) Sentinel-2 NDVI and yeast biogeography are moderately to strongly correlated, (iii) Sentinel-2 achieved a better delimitation of the DOs than Landsat-8 and can establish more accurate differences in NDVI–yeast terroir correlations, and (iv) a higher NDVI was associated with the yeast biogeographical patterns of the DOs with higher species richness (S) consisting of weakly fermenting yeasts (Hanseniaspora uvarum, Pichia spp., Starmerella bacillaris, and Zygosaccharomyces spp). However, NDVI values did not correlate well with biogeographic patterns of yeasts previously studied at frequency level (proportion or percentage of each species) in each particular DO. This study suggests that satellite imagery has the potential to be a valuable tool for wine quality management and a decision-making instrument for DO regulators and winegrowers. [ABSTRACT FROM AUTHOR]

The application of intensive plasticulture farming has resulted in an increased yield of horticulture crops during the last few decades in the world, particularly in China, Spain, and Italy. Proper monitoring of plastic‐covered greenhouse (PCG) farming areas and plastic‐mulched farmlands (PMF) is necessary for the sustainable development of horticulture, including high‐quality agricultural production in limited areas with cost‐effectiveness, and reduced pollution. The development of sophisticated image processing algorithms, such as pixel‐based, object‐based, and machine learning approaches, for mapping landscape and land use changes have revolutionized monitoring plasticulture areas in recent decades. Even though a complex task, remote sensing offers cost‐effective, large‐scale monitoring of plasticulture areas. Advancements in UAV technology are expected to improve the mapping and monitoring accuracy of PCGs and PMFs in the future in addition to high resolution optical data combined with digital surface models (DSM) with high vertical accuracy. [ABSTRACT FROM AUTHOR]