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From the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover information from satellite imagery.
Ensemble classification is an emerging approach to land cover mapping whereby the final classification output is a result of a consensus of classifiers. Intuitively, an ensemble system should consist of base classifiers which are diverse i.e.
There is a trend to acquire high accuracy land-cover maps using multi-source classification methods, most of which are based on data fusion, especially pixel- or feature-level fusions.
Declines in habitat and wildlife in semiarid African savannas arefrequently attributed to factors such as agropastoral population growth, livestock impacts, and subsistence cultivation.
The aim of the research is to analyse the landscape structure changes from the end of the Soviet times in 1974–1986 until 2005 when market economy existed in Lithuania.
This study established the possibility of rapid evaluation of land cover structure and situation using as an example the Temelin Nuclear Power Plant (Czech Republic) emergency zone.
Remotely-sensed images and Geographical Information Systems (GIS) data were integrated into the SLEUTH cellular automata (CA) model to analyze land-use/land-cover dynamics in Chiang Mai city and its surrounds.
The present paper gives an overview of the survey of LUCAS points in Slovakia. The Land Use/Cover Area frame Statistical survey was designed to collect agricultural, environmental data and photographs by field observation of a specific geographically referenced point.
To meet growing population demands for food and other agricultural commodities, agricultural land-use intensification and extensification seems to be increasing in the Abbay (Upper Blue Nile) basin in Ethiopia.
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