In the presented methodology, multitemporal Landsat images were used to develop enhanced information about complex assemblages of vegetation and patterns of seasonal land cover variability, thereby facilitating improved land use and land cover (LULC) classification of urbanized areas among sensitive environments along the Mississippi Gulf Coast. For Landsat-5 and Landsat-7 images acquired for l...

The paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM) system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) to identify areas of change and no change. The system then automatically generates large amounts of training sa...

Mapping of urban land cover using remote sensing technology has been widely explored, especially with the recent availability of high resolution images and object-based processing techniques. This study uses the InterIMAGE system and WorldView-2 orbital sensor imagery, two technologies which are new and still little explored in urban studies, to classify land cover in five test-sites near to th...

This study uses the InterIMAGE system and imagery from the QuickBird II optical sensor for the classification of the land cover of two test-sites in the metropolis of São Paulo, Brazil. InterIMAGE is an open source and free access framework for knowledge-based image classification. Within InterIMAGE human knowledge is represented as a semantic net and by user-defined rules based on the paradigm...

Although geographic object based image analysis (GEOBIA) has been successfully applied to derive local maps (1-10s km) from very high spatial resolution (VHR) image data (pixels < 1.0 x 1.0 m), its potential for automatically mapping large areas remains unknown. The aim of this study was to create and apply a GEOBIA method to automatically map land cover classes in subsets with different enviro...

Global land cover data are fundamental for applications, especially ecological environmental assessment and climate change research. Currently available global land cover data products show some deficiencies in data accuracy and spatial and temporal resolution. So we discuss fast automatic classification methods for the study area in Antarctica. A classification method based on a Support vector...

This study uses the InterIMAGE system and imagery from the QuickBird II optical sensor for the land use classification at two testsites in the metropolis of São Paulo, Brazil. InterIMAGE is an open source and free access system for knowledge-based image classification. Within InterIMAGE human knowledge is represented as a semantic net and by user-defined rules that can emulate human reasoning b...

SUMMARY One of the main parameter for urban heat island mapping is the land cover information. Satellite data were used to map the land cover over the study area, CyberJaya. Remotely sensed data were processed using pixel based and object based image processing techniques. Most traditional classification approaches are based exclusively on the digital number of the pixel itself. Thereby only th...

In this study, using the common classification systems of IGBP-17, IGBP-9, IPCC-5 and TC (vegetation, wetlands and others only), we studied spatial and areal inconsistencies in the three most recent multi-resource land cover products in a complex mountain-oasis-desert system and quantitatively discussed the uncertainties in classification system conversion. This is the first study to compare th...