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.
ResumenEl 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. Palabras-clave: Zona Intermareal; Procesamiento de imágenes; Métodos de Interpolación; Estuario de Bahía Blanca AbstractThe 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 betwe...
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