High level education on integrated water resources management for sustainable development

Water, is in Spain a scarce commodity and although we have an age-old water culture, with an emblematic Court, such as the “Tribunal de las Aguas de Valencia”, hydraulic infrastructure, hydrological basin plans, legislation and hydraulic administration since the 20th Century, there are problems of scarcity, water quality and extreme events that often lead to conflicts between users and also among the responsible administrations for their management. Within this framework, it is of a great interest the training of technicians in matters related to planning, quality and integrated water resources management for sustainable development. In Argentina (especially in the NOA) and until a few years ago, water has not been considered as a scarce commodity. In addition to this, they do not have the history and culture of Spain on issues related to their management, planning and governance. Now, they have begun to establish laws and regulations, as well as, an Association of Consortia of Public Water Users, needing external advice. Therefore, it is necessary, to train technicians in water resources capable of working in areas related to their planning and sustainable management, with knowledge related to the quality required by users. These technicians could be integrated, both in the responsible water administrations’, as well as, in private companies. The project that is the object of this paper is based on preparing a double master's degree, in which the training needs of the students graduated of Spanish and Argentina Engineering Schools are taken into account

Multifractal and Singularity Maps of soil surface moisture distribution derived from 2D image analysis

Soil moisture distribution usually presents extreme variation at multiple spatial scales. Image analysis could be a useful tool for investigating these spatial patterns of apparent soil moisture at multiple resolutions. The objectives of the present work were (i) to describe the local scaling of apparent soil moisture distribution and (ii) to define apparent soil moisture patterns from vertical planes of Vertisol pit images

Método de segmentación basado en la estructura fractal del mapa de singularidades : aplicación a imágenes de uso agrícola

Esta tesis se centra en el estudio de dos procesos complejos con origen en la Naturaleza, a saber, el fenómeno de la sequía y la estructura interna del suelo. Este estudio se realiza mediante imágenes digitales de los mismos: los mapas de índices de vegetación satelitales y las tomografías axiales computarizadas (TAC) de suelos, respectivamente. El análisis de imágenes digitales constituye un campo de investigación en continuo crecimiento. Una de las herramientas más utilizadas en el análisis de imágenes es la segmentación, es decir, la identificación de regiones de interés que comparten ciertas propiedades morfológicas, estadísticas, etc. Las técnicas de segmentación ya han sido aplicadas a ambos tipos de imágenes con distintos propósitos. En los mapas de índices de vegetación normalmente se requiere realizar una zonificación del área de estudio con el propósito de encontrar regiones que compartan propiedades estadísticas. La delimitación de estas regiones, también denominadas zonas homogéneas, es muy útil para una mejor cuantificación de los daños por sequía en el contexto de los seguros agrarios indexados. Este tipo de seguros establece indemnizaciones a los agricultores cuando se producen episodios de sequía en sus cultivos (normalmente pastos). Estos daños son cuantificados mediante un índice de vegetación. En el caso de las TAC de suelos se ha aplicado mayoritariamente la binarización por métodos de umbralización tanto global como local con el objetivo de delimitar del espacio de poros. El conocimiento del espacio de poros de un tipo de suelo resulta de mucha utilidad para el estudio de sus propiedades físicas, químicas y microbiológicas. La autosimilitud fractal o multifractal es una propiedad que comparten muchas imágenes digitales de procesos con origen en la Naturaleza, y no es una excepción para el tipo de imágenes que se están analizando en esta tesis a pesar de las escalas tan diferentes que representan ambos tipos de imágenes (cientos de metros para los mapas satelitales y micras para suelos). Numerosos estudios han demostrado que los mapas de índices de vegetación y las imágenes digitales de suelos tienen propiedades multifractales en un rango de escalas determinado. Trabajos precedentes descritos en Cheng et al. (1994) han utilizado las propiedades fractales que aparecen en la distribución espacial de la concentración de un elemento químico para la detección de yacimientos minerales. En estos trabajos se han utilizado las propiedades fractales de un mapa de concentraciones para realizar una binarización, donde la región de interés la constituía el yacimiento mineral, es decir, una anomalía en la concentración del elemento químico. La adaptación de esta metodología nos ha permitido introducir un método alternativo a la zonificación de regiones estadísticamente homogéneas en los mapas de índices de vegetación, al que hemos denominado método “Singularidad-Concentración-Área” (S-CA) (Martín-Sotoca et al., 2017c). En el caso de las imágenes de suelos hemos podido aplicar el mismo método S-CA para la delimitación del espacio de poros (Martín-Sotoca et al., 2017a). En la versión tridimensional hemos denominado al nuevo método como “Singularidad-Concentración-Volumen” (S-CV) (Martín-Sotoca et al., 2016). Ambos métodos, S-CA y S-CV, se fundamentan en el hallazgo de tramos lineales en los gráficos log-log de las distribuciones acumuladas de la variable espacial “exponente de singularidad”, poniendo de manifiesto las propiedades autosimilares de la misma. Estos tramos lineales nos han permitido establecer umbrales de segmentación en ambos tipos de imágenes, tal como sucedía en los mapas de concentración en el trabajo de Cheng et al. (1994). Para la evaluación de los métodos S-CA y S-CV en la delimitación del espacio de poros en TAC se ha realizado la comparación con los siguientes métodos tradicionales de binarización: Otsu, Iterativo y Máxima Entropía. Para ello se ha utilizado una imagen sintética de suelo con un espacio de poros previamente definido. Esta imagen sintética ha sido obtenida mediante un nuevo método al que hemos denominado como método de los Multifractales Truncados (MT). Este método replica de forma satisfactoria las características de las TAC de suelos, a saber, histogramas unimodales y distribuciones espaciales autosimilares (Martín-Sotoca et al., 2016; 2017a). Los métodos S-CA y S-CV han demostrado ser más eficaces en la delimitación de los poros de tamaño mediano y grande obteniendo porosidades y distribuciones de tamaños de poros más cercanas a las reales. El principal inconveniente de los métodos S-CA y S-CV es la introducción de pequeños poros de forma incorrecta debido a la amplificación que hacen estos métodos de las anomalías de intensidad en la TAC. Es por ello que también se presenta en esta tesis una mejora del método S-CA al que denominamos “S-CA Combinado” (Martín-Sotoca et al., 2017b). La combinación del método S-CA con un método de umbralización global (el método de Máxima Entropía) permite mejorar los parámetros de porosidad y clasificación errónea de poros, al eliminar la mayoría de los poros pequeños incorrectamente detectados por el método original. ----------ABSTRACT---------- This thesis focuses in the study of two complex processes originating in Nature, namely, the drought event and the internal soil structure. This study is performed by the following digital images: maps of satellite Vegetation Indexes (VI) and soil Computed Tomographies (CT), respectively. The analysis of digital images is a research field in continuous growth. One of the most useful tools in this analysis is the segmentation process. Segmentation identifies regions of interest (ROI) in images which share some morphological or statistical properties. Segmentation techniques have already been applied to both types of images with different purposes. In the case of maps of VI, a zoning of the study area is usually required with the aim to find regions which share statistical properties. The delimitation of these regions, also known as homogeneous zones, is very useful to better quantify the damage by drought in the context of the agricultural index-based insures. This damage is quantified by vegetation indexes. This type of insure establishes compensations to farmers when a drought event occurs and crops (normally pastures) result damaged. In the case of soil CT, a binarization of the image is required with the aim of delimiting the pore space. Binarization is usually achieved by global or local thresholding methods. The knowledge of the soil pore space is very important to understand its physical, chemical and microbiological properties. Many digital images of processes originating in Nature share the fractal or multifractal self-similarity property. The digital images analysed in this thesis, despite such different scales representing (hundreds of metres for satellite maps and microns for CT images), also own this special property and numerous studies demonstrate the multifractal behaviour in a range of scales. Previous studies described in Cheng et al. (1994) have taken advantage of fractal properties appearing in the spatial distribution of a chemical concentration map to detect mineral deposits. In these studies, binarization is based on the fractal properties of the concentration map. The ROI (mineral deposit) consists of an anomaly in the concentration map. The adaptation of this methodology has allowed us to introduce an alternative method, named as the “Singularity-Concentration-Area” (S-CA) method, with the aim of: 1) Detecting statistically homogeneous regions in maps of VI (Martín-Sotoca et al., 2017c). 2) Delimiting the pore space in soil CT images. In this case we have dealt with 2D images (Martín-Sotoca et al., 2017a) and 3D images (Martín-Sotoca et al., 2016). The 3D version of this method is named as the “Singularity-Concentration-Volume” (S-CV) method. Both methods, S-CA and S-CV, are based on the existence of linear segments in accumulated distributions of singularity maps, revealing the self-similar properties of the analysed images. These linear segments have allowed us to establish segmentation thresholds in both types of images, as it happened in the concentration maps (Cheng et al., 1994). To assess the S-CA and S-CV methods in delimiting the pore space of soil CT images, a comparison has been performed among the following traditional segmentation methods: Otsu, Iterative and Maximum Entropy. To do so, we have used a synthetic soil image with a well-defined pore space. This synthetic soil image has been obtained by the new Truncated Multifractal (TM) method (Martín-Sotoca et al., 2016; 2017a). This method replicates successfully the soil CT characteristics, namely, non-bimodal histograms and self-similar spatial distributions. S-CA and S-CV methods have demonstrated to be more efficient in delimiting medium and large-size pores, obtaining porosities and pore size distributions closer to the real ones. The main drawback of S-CA and S-CV methods is the incorrect detection of small-size pores due to high sensitivity to small intensity anomalies in soil CT images. To solve this issue, an improved S-CA method is introduced in this thesis, named as the “Combining S-CA method” (Martín-Sotoca et al., 2017b). The combination of the S-CA method with a global thresholding method (the Maximum Entropy method) improves the porosity and the Misclassification Error, by eliminating most of the small-size pores incorrectly detected by the original method

High level education on integrated water resources management for sustainable development

Water, is in Spain a scarce commodity and although we have an age-old water culture, with an emblematic Court, such as the “Tribunal de las Aguas de Valencia”, hydraulic infrastructure, hydrological basin plans, legislation and hydraulic administration since the 20th Century, there are problems of scarcity, water quality and extreme events that often lead to conflicts between users and also among the responsible administrations for their management. Within this framework, it is of a great interest the training of technicians in matters related to planning, quality and integrated water resources management for sustainable development. In Argentina (especially in the NOA) and until a few years ago, water has not been considered as a scarce commodity. In addition to this, they do not have the history and culture of Spain on issues related to their management, planning and governance. Now, they have begun to establish laws and regulations, as well as, an Association of Consortia of Public Water Users, needing external advice. Therefore, it is necessary, to train technicians in water resources capable of working in areas related to their planning and sustainable management, with knowledge related to the quality required by users. These technicians could be integrated, both in the responsible water administrations’, as well as, in private companies. The project that is the object of this paper is based on preparing a double master's degree, in which the training needs of the students graduated of Spanish and Argentina Engineering Schools are taken into account

Scaling properties of binary and greyscale images in the context of X-ray soil tomography

Characterization of the complex soil structure is one the cornerstones of soil science and pore space detection is a crucial step in this process. Synthetic soil image construction has been proved to be an efficient resource for validating different binarization methods given that, unlike in real world, ground truth information is known. In this work, we introduce an improved Truncated Multifractal Method (TMM), to better simulate synthetic computed tomography (CT) soil images and then we generate 150 synthetic images with three different porosities (7%, 12% and 17%), both in greyscale and in binary scale (pore spaces). Synthetic images are then compared with two sets of 260 slides of real CT soil images, in order to validate the goodness of the method. All images are subjected to multifractal analysis where we show a detailed comparative analysis of parameters such as lacunarity, characteristic length and multifractal spectrum, that are calculated both for the whole set of synthetic (greyscale and binary) and for the sets of real CT soil images. With respect to lacunarity, a not previously reported inverse relationship between binary and grey lacunarity is found for this range of porosities. Moreover, we have also reported a new relationship between lacunarity and characteristic length. Similar multifractal results, that we obtain for real CT and synthetic soil images, prove that TMM is a reasonable solution to create simulated CT soil images. Finally, a segmentation test was carried out, using TMM synthetic greyscale soil images and its binary counterpart as ground-truth information, evaluating global (Otsu) and local (Combining Singularity-CA) binarization methods, where we report better performance for the last.Programa Propio (Universidad Politecnica de Madrid)Ministerio de Economia, Industria y Competitividad, Gobierno de España (FIS2017-84151-P)Comunidad de Madrid6.114 JCR (2020) Q1, 3/37 Soil Science1.846 SJR (2020) Q1, 6/140 Soil ScienceNo data IDR 2020UE

Discrete multi-criteria methods for election of lands use alternatives in the Toro river hydro-basin (Province of Salta, Argentina)

The Salta Province (Img. 1) has 155.000 km2. It is at NW of Argentina (NOA) having latitudes around 25ºS. Some winds from South or SE made climate less hot and bring rain from 400 to 800 mm/year (with peaks of 1200mm in high altitude places in SO), and rainfall is concentrated in the summer time. Altitude has great ranges (at NE are areas at 200m and at NW a PUNA region with summits higher than 6000m). With 1.200.000 inhabitants it has a low density of population, and the city of Salta concentrates more or less fifty per cent of the total

Pore detection in 3-D CT soil samples through an improved sub-segmentation method

X-ray computer tomography (CT) is a non-invasive technique for image acquisition. Recent technologicaladvances have enabled reliable and high-resolution images to be obtained. In soil samples, for example, thissubserves the identication of pores and their structure and the analysis of their geometric characteristics.However, the lack of contrast between pores and solids in soil samples makes it difcult to identify the pores, andit poses problems for their connectivity when a three-dimensional (3-D) reconstruction is made from a group ofconsecutive 2-D images obtained with a scanner. To solve this problem, an improved sub-segmentation method,which had been developed and tested previously, was applied in this research to achieve a better identication ofthe pore space and consequently the solid space in the 2-D slices of the image, followed by a 3-D reconstructionof the soil sample. In this study, two soil samples were used, one real soil sample with 255 2-D CT consecutiveimages and a synthetic image with 215 2-D images. The latter sample was used only to evaluate the robustnessof the improved sub-segmentation method and the results from analysis of the pore connectivity in a knownstructure. The results obtained with the improved sub-segmentation were compared with those of traditionalclustering algorithms for image segmentation by k-means, fuzzy c-means and Otsu’s methods. The results werepromising, and the 3-D reconstruction presents a realistic structure for the continuity and coincidence of theshapes of the pores in the consecutive images. In addition, the pore regions detected have a small non-uniformity(NU) value, which indicates both good pore detection and homogeneity, which facilitates pore connectivitybetween the different 2-D images.Sin financiación3.742 JCR (2019) Q1, 8/38 Soil Science1.267 SJR (2019) Q1, 16/145 Soil ScienceNo data IDR 2019UE

Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

Vegetation indices time series analysis is increasingly improved for characterizing agricultural land processes. However, this is challenging because of the multeity of factors affecting vegetation growth. In semiarid regions the rainfall, the soil properties and climate are strongly correlated with crop growth. These relationships are commonly analyzed using the normalized difference vegetation index (NDVI). NDVI series from two sites, belonging to different agroclimatic zones, were examined, decomposing them into the overall average pattern, residuals, and anomalies series. All of them were studied by applying the concept of the generalized Hurst exponent. This is derived from the generalized structure function, which characterizes the series’ scaling properties. The cycle pattern of NDVI series from both zones presented differences that could be explained by the differences in the climatic precipitation pattern and soil characteristics. The significant differences found in the soil reflectance bands confirm the differences in both sites. The scaling properties of NDVI original series were confirmed with Hurst exponents higher than 0.5 showing a persistent structure. The opposite was found when analyzing the residual and the anomaly series with a stronger anti-persistent character. These findings reveal the influences of soil–climate interactions in the dynamic of NDVI series of rainfed cereals in the semiarid

Local Fractal Connections to Characterize the Spatial Processes of Deforestation in the Ecuadorian Amazon

Deforestation by human activities is a common issue in Amazonian countries. This occurs at different spatial and temporal scales causing primary forest loss and land fragmentation issues. During the deforestation process as the forest loses connectivity, the deforested patches create new intricate connections, which in turn create complex networks. In this study, we analyzed the local connected fractal dimension (LCFD) of the deforestation process in the Sumaco Biosphere Reserve (SBR) with two segmentation methods, —CA-wavelet and K-means—to categorize the complexity of deforested patches’ connections and then relate these with the spatial processes. The results showed an agreement with both methods, in which LCFD values below 1 corresponded to isolated patches with simple shapes and those above 1 signified more complex and connected patches. From CA-wavelet a threshold of 1.57 was detected allowing us to identify and discern low and high land transformation, while the threshold for K-means was 1.61. Both values represent the region from which deforestation performs local aggressive expansion networks. The thresholds were used to map the LCFD in which all spatial processes were visually detected. However, the threshold of 1.6 ± 0.03 was more effective in discerning high land transformation. such as shrinkage and attrition, in the deforestation process in the SBR

Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds

Vegetation indices based on satellite images, such as the normalized difference vegetation index (NDVI), have been used in countries like the USA, Canada and Spain for damaged pasture and forage insurance over the last few years. This type of agricultural insurance is called satellite-index-based insurance (SIBI). In SIBI, the occurrence of damage is defined as normal distributions. In this work a pasture area at the north of the Community of Madrid (Spain) has been delimited by means of Moderate Resolution Imaging Spectroradiometer (MODIS) images. A statistical analysis of NDVI histograms was applied to seek for alternative distributions using the maximum likelihood method and χ2 test. The results show that the normal distribution is not the optimal representation and the generalized extreme value (GEV) distribution presents a better fit through the year based on a quality estimator. A comparison between normal and GEV is shown with respect to the probability under a NDVI threshold value throughout the year. This suggests that an a priori distribution should not be selected and a percentile methodology should be used to define a NDVI damage threshold rather than the average and standard deviation, typically of normal distributions.Sin financiación3.102 JCR (2019) Q1, 50/200 Geosciences Multidisciplinary1.005 SJR (2019) Q1, 43/328 Earth and Planetary Sciences (miscellaneous)No data IDR 2019UE