We present a method to map annual landuse/land-cover (LULC) change using low-cost and consistent data from the MODIS satellite and Google Earth reference data. Our LULC maps cover Latin America and the Caribbean from years 2001 to 2009, have a 250-m pixel size, and include eight general classes. Maps were produced within 27 separate zonal biomes using the Random Forests classifier. We found tha...

The rapidly increasing volume of visual Earth Observation data calls for effective content based image retrieval solutions, specifically tailored for their high spatial resolution and heterogeneous content. In this paper, we address this issue with a novel local implementation of the well-known morphological descriptors called pattern spectra. They are computationally efficient histogram-like s...

Image classification is the important part of remote sensing, image analysis and pattern recognition. Digital Image Classification is the process of sorting all the pixels in an image into a finite number of individual classes. Landuse/Landcover classification of satellite images is an important activity for extracting geospatial information for military & civil purposes like inaccessible areas...

The Soil Water Assessment Tool (SWAT) was implemented to determine annual sediment yields and critical source areas of erosion for the Buffalo River Watershed. Model calibrations were performed by comparing simulated streamflow discharge and sediment concentrations against measured values. Monte-Carlo simulations were performed to identify the most sensitive parameters and the “best-fit” parame...

The satellite images at different spectral and spatial resolutions with the aid of image processing techniques can improve the quality of information. Especially image fusion is very helpful to extract the spatial information from two images of different resolution images of same area. An operation of image analysis such as image classification on fused images provides better results in compari...