Para la cartografía y monitoreo de los arrecifes de coral, un mapa batimétrico es útil como un mapa base. Diferentes métodos han sido desarrollados para cartografiar la batimetría usando sensores remotos. Dos grandes grupos se pueden distinguir. El primero utiliza datos de teledetección de sensores activos y el segundo se basa en el empleo de sensores pasivos para generar información multiespectral. Este artículo se centra en el método de datos provenientes de sensores pasivos. Una modificación al método DOP “Profundidad de penetración” mediante espectrometría que se llevó a cabo en imágenes Landsat ETM+ sobre Archipiélago de San Andrés y Providen- cia (Colombia). Las mediciones de profundidad utilizadas en la validación del mo- delo fueron derivadas de lecturas de ecosonda en campo e interpolación de mapas batimétricos. La exactitud de la prueba reveló que el modelo batimétrico resultante es útil para el mapeo en zonas de arrecifes de coral del mar Caribe hasta 25m de profundidad aproximadamente. Los datos no conformes a la realidad fueron gene- rados por suposiciones inherentes a la teoría utilizada, la interpolación de datos, las características de la imagen de satélite y los errores durante la ejecución del método.

Abstract-In this paper we present two algorithms that automatically calculate linear expressions for Time Series. To estimate the maximum number of terms of the linear expression and the intervals in which the series coefficients vary, the algorithms are based in the Box-Jenkins methodology. With this information and establishing beforehand the number of terms that are required, the Self Adaptive Genetic Algorithms are applied in several stages to obtain the series model. The proposed algorithms were tested in the Box-Jenkins classical examples, obtaining satisfactory results. It is worth it to mention that these algorithms allow treating series with time-dependent trends and variances. The methodology based on Self Adaptive Genetic Algorithms is used to estimate linear models for every example of NN3 2007, although in this paper we are presenting only the results of NN3-REDUCED.

The use of remote sensing images for the characterization of the coastal marine ecosystems requires the prior removal of the atmospheric effects, which can be done in a semi-automated manner, by the use of algorithms based on alternative assumptions contained in the processing tools for different software packages. The main objective of this study was to statistically compare the spectral behavior of the coverages contained in a high-resolution WorldView-2 image atmospherically corrected according to the ATCOR and empirical linear models (ELM), using field spectroradiometry conducted in the insular areas of the archipelago of San Andres and Providence. The ATCOR correction model was applied through the PCI 2015 Geomatics software; regarding the ELM model, the ENVI 5.2 software was used. For the spectral comparison four (4) types of coverage were selected (vegetation, reef formations, beach sand and submerged sandbank), with twenty (20) replicas each, for a total of eighty (80) sampling points distributed in a stratified way in the image. The statistical results showed a linear correlation greater than 0.9 between the reflectance values for each of the bands (Blue, Green, Red and NIR-1) and indicate that both models of the atmospheric correction have a high capacity to eliminate the atmospheric effects present in this type of images. However, there are minor significant differences between the middle quadratic errors in the reflectance values for each band of the corrected images.