The broad overviews obtained with Landsat data can provide information concerning the location, types, and magnitudes of forest changes not easily obtainable by other means. One of the main goals of the current study is to provide spatial information regarding the more subtle forms of forest change rather than those primarily related to land cover conversion. A forest-dominated area in northern...

The water deficit index (WDI) derived from Landsat imagery was used to detect temporal and spatial changes in grassland transpiration. The WDI, which estimates relative evapotranspiration rates based on meteorological data and the relation between surface reflectance and temperature, has been successfully applied over heterogeneous terrain with little a priori information. In this study, WDI wa...

This paper presents a method for predicting areas at risk from dryland salinity using information derived from multi-temporal Landsat TM satellite images combined with landform data derived from high-quality digital elevation models. The method is applied in the south west agricultural region of Western Australia to predict areas at risk from dryland salinity. This paper presents modifications ...

Neural networks are massively parallel arrays of simple processing units that can be used for computationally complicated tasks such as image processing. This paper develops an efficient method for processing remote-sensing satellite data using complex valued artificial neurons as an approach to the problems associated with computer vision— region identification and classification—as they are a...

In this paper, we perform multi-sensor multi-resolution data fusion of Landsat-5 TM bands (at 30 m spatial resolution) and multispectral bands of World View-2 (WV-2 at 2 m spatial resolution) through linear spectral unmixing model. The advantages of fusing Landsat and WV-2 data are two fold: first, spatial resolution of the Landsat bands increases to WV-2 resolution. Second, integration of data...