Simulación prospectiva del crecimiento urbano en la Comunidad Autónoma de Madrid a partir de modelos basados en autómatas celulares y modelos basados en EMC

En el presente trabajo se analizan los resultados de varias simulaciones prospectivas de crecimiento urbano, entre los años 2000 y 2020, realizadas en la Comunidad de Madrid, aplicando dos modelos diferentes: uno basado en Autómatas Celulares (AC) y otro en técnicas de Evaluación MultiCriterio (EMC). El objetivo es comparar los resultados de las simulaciones de ambos modelos para detectar si se aproximan o están fuertemente influidos por la técnica empleada. Esto también permitiría, en cierta forma, evaluar los modelos desarrollados y su utilidad, pudiendo generar una cartografía de los resultados más robustos, es decir, de localizaciones que reiteradamente aparecen en las distintas simulaciones como las más apropiadas para desarrollar una futura ocupación urbana. En este caso concreto, se ha podido comprobar la escasa coincidencia entre resultados, evidenciando la necesidad, por un lado, de utilizar metodologías de comparación de mapas más flexibles, que permitan valorar mejor las semejanzas/diferencias encontradas, y, por otro, de detectar particularidades de los modelos que estén condicionando los resultados e, inclusive, deficiencias en su desarrollo.Proyecto SIMURBAN2: Instrumentos de Geosimulación y planificación ambiental en la ordenación territorial de ámbitos metropolitanos. Aplicación a escalas intermedias (Ref.: CSO2012-38158-C02-01)

Dynamique De L’occupation Du Sol Dans Les Zones Humides De La Commune D’allada Au Sud-Benin (Sites Ramsar 1017 Et 1018)

The wetlands are the integral element of the natural resource of Benin Republic. However, anthropic pressure on those “fragil” environments, contribute to the reducing of their surface and accordingly, to a loss their biodiversity. The target objective is to make cartography of land units from 1990 to 2014 in order to identify the various pressures upon the wet ecosystems. A 2014 Landsat 8 OLI-TIRS image and a 1990 map of Benin land cover were used to establish the cartography. We used the Maximum likelihood algorithm to execute the supervised classification of the landsat image in ERDAS. The mapping of the land’s units in the wetlands was then carried out in ArcGIS. The results revealed that the tree savana have completely disappeared. It represents 11.47 % of the landscape in 1990 against 0 % in 2014. The mosaics of fields and fallows under palm plantations have reduced to -30.42 % in 2014. They represent 66.63 % of the landscape. The land units which progressed are the mosaic of fields and fallow (12.06 %), the swamps (10.47 %), the plantations (5.26 %) and the agglomerations (2.71 %). This shows strong human pressure exerted on the natural vegetation of the wetlands in the Allada district. These results will provide the local authorities with a tool for decision support, for an efficient use and a sustainable management of these natural wet ecosystems

Species distribution models and empirical test: Comparing predictions with well-understood geographical distribution of Bothrops alternatus in Argentina

Species distribution models (SDMs) estimate the geographical distribution of species although with several limitations due to sources of inaccuracy and biases. Empirical tests arose as the most important steps in scientific knowledge to assess the efficiency of model predictions, which are poorly rigorous in SDMs. A good approach to the empirical distribution (ED) of a species can be obtained from comprehensive empirical knowledge, that is, well-understood distributions gathered from large amount of data generated with appropriate spatial and temporal samples coverage. The aims of this study were to (a) compare different SDMs predictions with an ED; and (b) evaluate if fuzzy global matching (FGM) could be used as an index to compare SDMs predictions and ED. Six algorithms with 5 and 20 variables were used to assess their accuracy in predicting the ED of the venomous snake Bothrops alternatus (Viperidae). Its entire distribution is known, thanks to thorough field surveys across Argentina, with 1,767 records. ED was compared with SDMs predictions using Map Comparison Kit. SDMs predictions showed important biases in all methods used, from 70% sub-estimation to 40% over-estimation of ED. BIOCLIM predicted ≈31% of B. alternatus ED. DOMAIN predicted 99% of ED, but over-estimated 40% of the area. GLM with five variables calculated 75% of ED, while Genetic Algorithm for Rule-set Prediction showed ≈60% of ED; the last two presenting overpredictions in areas with favorable climatic conditions but not inhabited by the species. MaxEnt and RF were the only methods to detect isolated populations in the southern distribution of B. alternatus. Although SDMs proved useful in making predictions about species distribution, predictions need validation with expert maps knowledge and ED. Moreover, FGM showed a good performance as an index with values similar to True Skill Statistic, so that it could be used to relate ED and SDMs predictions.Fil: Sarquis, Juan Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto Nacional de Limnología. Universidad Nacional del Litoral. Instituto Nacional de Limnología; ArgentinaFil: Cristaldi, Maximiliano Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto Nacional de Limnología. Universidad Nacional del Litoral. Instituto Nacional de Limnología; ArgentinaFil: Arzamendia, Vanesa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto Nacional de Limnología. Universidad Nacional del Litoral. Instituto Nacional de Limnología; ArgentinaFil: Bellini, Gisela Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto Nacional de Limnología. Universidad Nacional del Litoral. Instituto Nacional de Limnología; ArgentinaFil: Giraudo, Alejandro Raul. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto Nacional de Limnología. Universidad Nacional del Litoral. Instituto Nacional de Limnología; Argentin

Six decades of changes in the riparian corridor of a Mediterranean river: A synthetic analysis based on historical data sources

Riparian corridors in semi-arid Mediterranean environments are ecosystems of high biodiversity and complexity. However, they are threatened because of high levels of human intervention. River damming and related flow manipulation is considered as one of the most prominent human impacts on riparian corridors. This study combines historical time series information on river flows and their human manipulation, historical aerial images depicting changes in riparian land cover and ground observations of the species - age composition and morphology of the riparian corridor of a Mediterranean river (the Mijares River, Eastern Spain) over the last 60years. In this sense, we explored how to integrate information from a wide variety of data sources, and we extracted a variety of indices and undertook analyses that identified and summarized spatio-temporal changes in riparian structure and in the driving flow processes. Results revealed an increase in the cover and density of woody vegetation and a decrease in bare sediment areas (essential for recruitment of riparian pioneer species), with a synchronous reduction in the complexity of the riparian corridor of the middle reaches of the Mijares River. These vegetation changes have accompanied a decrease in the magnitude and variability of river flows over the last six decades, with higher severity since dam closure. This study illustrates the effectiveness of combining disparate historical data sources and the effectiveness of processing these sources to extract informative metrics that can improve the understanding and management of riparian systems. © 2012 John Wiley & Sons, Ltd.The authors are grateful to Paula De Lamo (who worked in an early version of this study), Carlos Gonzalez-Hidalgo (who gave us access to the MOPREDAS database) and Alicia Garcia-Arias and Oscar Belmar (for their support in the calculation of confusion matrices and in the flow regime analysis, respectively). We also thank Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Food and Environment) and the professors Juan Marco Segura and Javier Paredes for the hydrological data provided to develop this study. TECNOMA S. A. provided logistic support. Finally, we acknowledge the Universitat Politecnica de Valencia for the two grants of the Support Programme for Research and Development 'Programa de Apoyo a la Investigacion y Desarrollo' (PAID 00-10 and 00-11). This study was partially funded by the Spanish Ministry of Economy and Competitiveness with the projects 'Recent environmental changes in fluvial systems: morphological and sedimentological consequences' (CGL2009-14220-C02-02-BTE) and SCARCE (Consolider-Ingenio 2010 CSD2009-00065). The feedback of two anonymous reviewers has been very helpful and is greatly appreciated.Garófano-Gómez, V.; Martinez-Capel, F.; Bertoldi, W.; Gurnell, Á.; Estornell Cremades, J.; Segura-Beltrán, F. (2012). Six decades of changes in the riparian corridor of a Mediterranean river: A synthetic analysis based on historical data sources. Ecohydrology. 0:0-0. https://doi.org/10.1002/eco.1330S00

Land Cover Change under Future Scenarios of Zero-deforestation Commitment Implementation in South America

M.S

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

Hierarchical fuzzy pattern matching for the regional comparison of land use maps

The evaluation of the spatial similarities between two raster maps is traditionally based on pixel-by-pixel comparison techniques. These procedures determine the number of cells in agreement for each landuse category and express the overall agreement with a boolean global similarity value. The problem with a pixel-by-pixel comparison is that a small displacement in pixels will be registered as disagreement even though the land use patterns between the maps maybe essentially the same. The issues of unique polygons mapping and hierarchical fuzzy pattern matching, where the maps are compared on both a local and global level emerge as viable and robust alternatives. The local matchings determine the degree of containment of each unique polygon to its spatial counterparts on the original maps in terms of fuzzy areal intersections. Local agreement values for the unique polygons based on their polygon area containments are calculated from fuzzy logical Max-Min compositional algorithms. A global agreement value is derived by the fuzzy summation of the local matchings. The uses of these basic methods are discussed and further refinements and modeling possibilities are outlined

Change detection and landscape similarity comparison using computer vision methods

Human-induced disturbances of terrestrial and aquatic ecosystems continue at alarming rates. With the advent of both raw sensor and analysis-ready datasets, the need to monitor ecosystem disturbances is now more imperative than ever; yet the task is becoming increasingly complex with increasing sources and varieties of earth observation data. In this research, computer vision methods and tools are interrogated to understand their capability for comparing spatial patterns. A critical survey of literature provides evidence that computer vision methods are relatively robust to scale and highlights issues involved in parameterization of computer vision models for characterizing significant pattern information in a geographic context. Utilizing two widely used pattern indices to compare spatial patterns in simulated and real-world datasets revealed their potential to detect subtle changes in spatial patterns which would not otherwise be feasible using traditional pixel-level techniques. A texture-based CNN model was developed to extract spatially relevant information for landscape similarity comparison; the CNN feature maps proved to be effective in distinguishing agriculture landscapes from other landscape types (e.g., forest and mountainous landscapes). For real-world human disturbance monitoring, a U-Net CNN was developed and compared with a random forest model. Both modeling frameworks exhibit promising potential to map placer mining disturbance; however, random forests proved simple to train and deploy for placer mapping, while the U-Net may be used to augment RF as it is capable of reducing misclassification errors and will benefit from increasing availability of detailed training data