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Water information cannot be accurately extracted using TM images because true information is lost in some images because of blocking clouds and missing data stripes, thereby water information cannot b...
Many global problems can be estimated and forecasted by remote sensing with its advantage of periodicity, objectivity and large scale scope, besides plant area estimation. Conventional classification ...
This research simulates the urban growth and therefore the landscape change of a mountain city of Chongqing, China through a Cellular Automata (CA) based model of SLEUTH. The fundamental data were fou...
East Oweinat district is located in the south Western Desert of Egypt. It is mainly occupied by outcrops of Archean to Early Proterozoic rocks of high to medium metamorphic grade and younger granitoi...
ABOVE-GROUND BIOMASS ESTIMATION OF SUCCESSIONAL AND MATURE FORESTS USING TM IMAGES IN THE AMAZON BASIN.
In this paper we report the results of an experimental analysis on the use of two neural models for the thematic classification of multispectral remotely sensed data. A Back Propagation network with ...
A hybrid unsupervised/supervised classification method is described and applied to Landsat TM images. This method differs from the conventional classification in the sense that the clustering algori...
MERGING LANDSAT TM IMAGES AND AIRBORNE PHOTOGRAPHS FOR MONITORING OPENCAST MINE AREA.
Evaluation of Forest Fire Risk by the Analysis of Environmental Data and TM Images
Study of Forest Vegetation Regeneration Based upon Landsat TM Images Analysis: Preliminary Results

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