Avaliação da evolução do índice de vegetação de teledetecção usando de técnicas de processamento de imagens

Vegetation has a substantial role as an indicator of anthropic effects, specifically in cases where urban planning is required. This is especially the case in the management of coastal cities, where vegetation exerts several effects that heighten the quality of life (alleviation of unpleasant weather conditions, mitigation of erosion, aesthetics, among others). For this reason, there is an increased interest in the development of automated tools for studying the temporal and spatial evolution of the vegetation cover in wide urban areas, with an adequate spatial and temporal resolution. We present an automated image processing workflow for computing the variation of vegetation cover using any publicly available satellite imagery (ASTER, SPOT, LANDSAT, MODIS, among others) and a set of image processing algorithms specifically developed. The automatic processing methodology was developed to evaluate the spatial and temporal evolution of vegetation cover, including the Normalized Difference Vegetation Index (NDVI), the vegetation cover percentage and the vegetation variation. A prior urban area digitalization is required. The methodology was applied in Monte Hermoso city, Argentina. The vegetation cover per city block was computed and three transects over the city were outlined to evaluate the changes in NDVI values. This allows the computation of several information products, like NDVI profiles, vegetation variation assessment, and classification of city areas regarding vegetation. The information is available in GIS-readable formats, making it useful as support for urban planning decisions.A vegetação tem um papel importante como indicador de efeitos antrópicos, especificamente nos casos em que o planejamento urbano é necessário. Este é especialmente o caso na gestão de cidades costeiras, onde a vegetação exerce diversos efeitos que elevam a qualidade de vida (alívio de condições climáticas desagradáveis, mitigação da erosão, estética, entre outras). Por essa razão, há um interesse crescente no desenvolvimento de ferramentas automatizadas para o estudo da evolução temporal e espacial da cobertura vegetal em grandes áreas urbanas, com adequada resolução espacial e temporal. Apresentamos um fluxo de trabalho automatizado de processamento de imagens para calcular a variação da cobertura vegetal usando qualquer imagem de satélite publicamente disponível (ASTER, SPOT, LANDSAT, MODIS, entre outros) e um conjunto de algoritmos de processamento de imagem desenvolvidos especificamente. A metodologia de processamento automático foi desenvolvida para avaliar a evolução espacial e temporal da cobertura vegetal, incluindo o Índice de Vegetação da Diferença Normalizada (NDVI), o percentual de cobertura vegetal e a variação da vegetação. Uma digitalização prévia da área urbana foi necessária. A metodologia foi aplicada na cidade de Monte Hermoso, na Argentina. A cobertura vegetal por quarteirão foi computada e três transectos sobre a cidade foram delineados para avaliar as mudanças nos valores de NDVI. Isso permite o cálculo de vários produtos de informação, como perfis de NDVI, avaliação da variação da vegetação e classificação das áreas da cidade em relação à vegetação. A informação está disponível em formatos legíveis pelo GIS, tornando-a útil como suporte para decisões de planejamento urbano.Fil: Revollo Sarmiento, Natalia Veronica. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Revollo Sarmiento, Gisela Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Huamantinco Cisneros, María Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; ArgentinaFil: Delrieux, Claudio Augusto. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Piccolo, Maria Cintia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; Argentin

Morphological characterization of ponds and tidal courses in coastal wetlands using Google Earth imagery

Ponds and tidal courses are significant landforms that frequently arise in marshes and tidal flats environments. An understanding of their development and permanence is relevant to determine future dynamic processes that alter tidal flats and salt marshes environments, such as changes in the sea level, increase in the wave activity, and some other variations associated to the climate change. Direct access for monitoring in these regions is complex, extremely expensive and not always feasible. Remote sensing imagery represents a monitoring alternative, but requires the research of specific image processing procedures to extract the information concerning to these environmental studies. In this work, we developed a methodology for assessing the relevant morphological parameters of ponds and tidal courses using Google Earth imagery. An automatic classifier identifies these landforms as such (accuracy over 86%), producing a shape descriptors dataset. Then, ponds and tidal courses in tidal flats are morphologically characterized, and their behavior is compared to the surrounding environment. Subsequent analysis found significant differences in morphological characteristics that arise independently of the marsh environmental conditions. The evidence suggests that the evolution processes of the depressions in salt flat environments are clearly different in comparison with salt marshes environments. In salt marshes, the permanence and evolution of the depressions is related to the age of marshes, whereas in tidal flats the dynamic processes and sediment input have influence on depressions evolution.Fil: Revollo Sarmiento, Gisela Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Perillo, Gerardo Miguel E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geología; Argentin

Automatic Delineation of Water Bodies in SAR Images with a Novel Stochastic Distance Approach

Coastal regions and surface waters are among the fundamental biological and social development resources worldwide. For this reason, it is essential to thoroughly monitor these regions to determine and characterize their geographical features and environmental health. These geographical regions, however, present several monitoring challenges when using remotely sensed imagery. Small water bodies tend to be surrounded by swamps, marshes, or vegetation, making accurate border detection difficult. Coastal waters, in turn, experience several phenomena due to winds, undercurrents, and waves, which also hamper the detection of environmental hazards like oil spills. In this work, we propose an automated segmentation algorithm that can be applied to these targets in airborne and spaceborne SAR images. The method is based on pointwise detection in fuzzy borders using a parameter estimation of the (Formula presented.) distribution, which has been successfully used in similar contexts. The underlying assumption is that the sought-for border separates regions with different textures, each having different distribution parameters. Then, stochastic distances can identify the most likely point where this parameter change occurs. A curve interpolation algorithm then estimates the actual contour of the body given the detected points. We assess the adequacy of eight stochastic distances that are mostly applied in the literature. We evaluate the performance of our method in terms of similarity between true and detected boundaries on simulated and actual SAR images, achieving promising results. The performance of our proposal is assessed by Hausdorff distance and Intersection over Union. In the case of synthetic data, the selection of the best stochastic distance depends on the parameters of the (Formula presented.) distribution. In contrast, the harmonic-mean and triangular distances produced the best results in detecting borders in three actual SAR images of lagoons. Finally, we present the results of our proposal applied to an image with oil spills using Bhattacharyya, Hellinger, and Jensen–Shannon distances.Fil: Rey, Andrea Alejandra. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Frery, Alejandro César. Victoria University Of Wellington; Nueva ZelandaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices

Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.Fil: Isa Jara, Ramiro Fernando. Escuela Superior Politécnica de Chimborazo; Ecuador. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pérez Sosa, Camilo José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; ArgentinaFil: Macote Yparraguirre, Erick Leonel. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Lerner, Betiana. Florida International University; Estados Unidos. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Miriuka, Santiago Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Delrieux, Claudio Augusto. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pérez, Maximiliano. Florida International University; Estados Unidos. Universidad de Buenos Aires; ArgentinaFil: Mertelsmann, Roland. Albert Ludwigs University of Freiburg; Alemani

Vessel and oil spill early detection using COSMO satellite imagery

Oil spillage is one of the most common sources of environmental damage in places where coastal wild life is found in natural reservoirs. This is especially the case in the Patagonian coast, with a littoral more than 5000 km long and a surface above a million and half square km. In addition, furtive Ïshery activities in Argentine waters are depleting the food supplies of several species, altering the ecological equilibrium. For this reason, early oil spills and vessel detection is an imperative surveillance task for environmental and governmental authorities. However, given the huge geographical extension, human assisted monitoring is unfeasible, and therefore real time remote sensing technologies are the only operative and economically feasible solution. In this work we describe the theoretical foundations and implementation details of a system speciÏcally designed to take advantage of the SAR imagery delivered by two satellite constellations (the SAOCOM mission, developed by the Argentine Space Agency, and the COSMO mission, developed by the Italian Space Agency), to provide real-time detection of vessels and oil spills. The core of the system is based on pattern recognition over a statistical characterization of the texture patterns arising in the positive and negative conditions (i.e., vessel, oil, or plain sea surfaces). Training patterns were collected from a large number of previously reported contacts tagged by experts in the National Commission on Space Activities (CONAE). The resulting system performs well above the sensitivity and speciÏcity of other avalilable systems.Fil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica ; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica ; Argentin

Supervised machine learning classification of human sperm head based on morphological features

We developed an automatic framework to classify sperm heads as normal or abnormal using image processing and machine learning techniques. The framework segments each sperm head using a color-space-based classification method. A novel set of morphological features is proposed to better describe the sperm head morphology. Finally, a supervised learning model is trained and tested to analyze the feature data for classification. To train and test the model, a publicly available dataset of human sperm heads was used. All sperm samples were manually labeled as normal or abnormal according to the strict criteria of the World Health Organization laboratory manual for the examination and processing of human semen. The segmentation method preserves shape without losing key morphological aspects. The classification model based on morphological descriptors produces better discrimination as compared with the traditional shape descriptors, achieving a 92% accuracy in the discrimination of normal or abnormal spermatozoa.Fil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Revollo Sarmiento, Gisela Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Herrera, Marcela. VITA Medicina Reproductiva; ArgentinaFil: Gonzalez-Jose, Rolando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentin

Automatic methodology for mapping of coastal zones in video sequences

Gathering precise and detailed geomorphological and dynamic information of coastal processes is increasingly required for environmental studies and coastal management policies as well. Traditional methods for in situ measurements, or remote sensing monitoring by satellites or airbone imagery, impose limitations and tradeoffs between image quality, operational costs, availability, and negative environmental effects. These limitations and tradeoffs restrict the kind of environmental studies that can be undertaken, specifically when a high spatial and temporal resolution is required over wide geographical areas. In the last decades, video monitoring systems have demonstrated to be a cost-effective alternative for this and other similar purposes. Notwithstanding that, video processing is not fully mature in the context of environmental monitoring in general, and, thus, most of the past and current efforts have been developed in an ad hoc basis. This has the drawback that most available solutions are hardly useful in contexts different from their original setup. In this work we develop an autonomous application for geographic feature extraction and recognition in coastal videos. Specifically, we address the classification and feature measurement of multiple beach zones, a topic addressed to a lesser extent by other projects. The system is designed to be deployed in inexpensive, off-the-shelf hardware, and open source software development frameworks, in a way such that the results can be easily replicated by other research groups. The initial setup and calibration requires very simple supervision, thus allowing the system to be used in a variety of coastal environments.Fil: Revollo Sarmiento, Natalia Veronica. Universidad Nacional de Jujuy. Facultad de Ingeniería; Argentina. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Delrieux, Claudio Augusto. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Perillo, Gerardo Miguel E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales. Departamento de Geología; Argentin

DEM generation using image processing techniques for waterline extraction on satellite imagery

El método de extracción de la línea de agua es una de las herramientas más efectivas para el estudio de cambios en zonas de planicies de marea y líneas de costa. Entre otras aplicaciones, puede utilizarse para la elaboración de modelos digitales de elevación (DEM) de la zona sumergida si se cuenta con información suficiente y precisa. En este trabajo se presenta la aplicación de nuevos algoritmos de segmentación de líneas de agua para la construcción de un DEM de la zona intermareal interna del Estuario de Bahía Blanca. Se utilizaron imágenes Landsat-8 del año 2014, eligiéndose un conjunto representativo de imágenes en condiciones de marea suficientemente variadas. Se aplicaron estos nuevos algoritmos a cada imagen para segmentar la zona inundada. Posteriormente, se asoció cada línea de agua al dato disponible correspondiente a la altura de marea en la hora de toma de la imagen. Usando un sistema de información geográfico (SIG) se fusionaron las diferentes líneas de agua junto con sus alturas respectivas, y aplicando diferentes interpolaciones es posible generar el DEM. Con el fin de evaluar la precisión del modelo generado y validar los resultados, se cotejaron los valores obtenidos en sitios puntuales con datos batimétricos de gran precisión de una zona del Canal Principal del estuario, tomados durante una campaña realizada en 2014. Utilizando solo los puntos de la batimetría correspondientes a la zona intermareal para evaluar la precisión del DEM generado, se encontró una correlación con buen coeficiente de determinación entre las alturas estimadas por el DEM y la batimetría medida in situ .The waterline extraction method is an effective tool to study changes in tidal flats or shorelines. Among other applications, when adequate information is available, it can be taken advantage in the construction of Digital Elevation Models (DEMs) of the intertidal area. In this paper we present new waterline extraction algorithms, which are applied to generate a DEM of the internal intertidal zone in the Bahía Blanca Estuary. For this, waterlines from satellite images were extracted. We selected a set of Landsat-8 images generated in 2014 with representative tidal conditions. Initially, to identify the waterline, we developed and applied to each image a novel segmentation algorithm based on minimum distance to multiple prototypes, each pixel selected is representative either of water or land. With the segmented image, the contour of the waterline was extracted using a specific marching algorithm. Then, the tide height was associated to each waterline. The resulting geographic information, included in a GIS, was employed to generate the DEM using an interpolation method. To validate the accuracy of the model, we compared the height of the DEM at several point sites with bathymetric information gathered in an oceanographic cruise carried out in the same year. The results show a good correlation between the computed DEM and this in situ bathymetric information.Fil: Blanco Monroy, Katy Lorena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; ArgentinaFil: Revollo Sarmiento, Gisela Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica ; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica ; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Perillo, Gerardo Miguel E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geología; Argentin

Supervised learning for semantic segmentation of human spermatozoa

Image-based diagnosis is able to spot several diseases and clinical conditions faster and more accurately than traditional manual ones, becoming also an alternative in monitoring and predicting patients responses to specific health treatments. In this work, we present a supervised learning approach to segment pixel-wise parts of spermatozoa using a random forest (RF) classifier. The framework created a multi-channel image combining intensity RGB bands with three neighborhood based bands. The last neighborhood based bands were Sobel’s magnitude and orientation and Shannon’s entropy. A RF was trained using labeled pixels provided by expert andrologists, biochemists and specialists in reproductive health. We compared results with a simple model on the RGB only. The whole automatic process (segmentation and classification) achieved an average precision of 98%, recall of 98% and F-Score of 98%. Highest improvement in comparison to the RGB model was shown on the segmentation of the tail. We provided a fully automatic spermatozoa semantic segmentation based on local and non-local information. The results are aimed to develop a CASA (Computer Assisted Sperm Analysis) system that can provide results over the Internet. The experiment was conducted on normalized images of a specific microscope. We are planning to extend the experiment in future work to more realistic conditions including different stainings, microscopes and resolutions.Fil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Thomsen, Felix Sebastian Leo. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: González José, Rolando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina15th International Symposium on Medical Information Processing and AnalysisMedellínColombiaThe International Society of Optics and Photonics Searc

Beach carrying capacity assessment through image processing tools for coastal management

Coastal environments are spaces where people may develop varied economic and recreational activities, such as tourism, which usually damage beaches and other natural resources commonly placed in these settings. The aim of this paper is to present a methodology to estimate and evaluate the Beach Carrying Capacity (BCC) and the actual beach usage level in coastal cities, using on-site information and video processing to provide significant real-time data. To test our methodology, we chose the coastal city of Monte Hermoso, Argentina, as a pilot site because it is by far the prime choice for a large population during summer vacation in this country. Initially, to estimate BCC, cartographic information about facilities and beach zones was collected and combined with surveys requested to tourists, to better understand their habits and preferences. This allowed an accurate estimation of other beach capacities related to BCC. Secondly, beach video sequences were processed with an algorithm that identified, located and counted people on the beach with an adequate accuracy. The actual occupancy factor was computed and used to asses whether the BCC had been exceeded. Also, people were tracked and their preferred relaxing areas were registered (e.g, closer to seaside during the morning). Finally, all the information was stored and visualized using a Geographic Information System (GIS) which allows both to analyze the different information layers and to produce interactive thematic maps. In this way, the resulting methodology may help to identify zones under risk of deterioration and to define suitable places for the development of varied activities (specially those related to tourism). It may also serve as a dashboard for decision and policy making and contribute to coastal management planning as well.Fil: Huamantinco Cisneros, María Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; ArgentinaFil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional de Jujuy. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Piccolo, Maria Cintia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; ArgentinaFil: Perillo, Gerardo Miguel E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentin