DEM-based topographic corrections on Landsat-7 ETM+ imagery from rugged terrain, as an effective processing techniques to improve the accuracy of Land Use/Land Cover (LULC) classification as well as land surface parameter retrievals with remotely sensed data, has been frequently reported in the literature. However, few studies have investigated the exact effects of DEM with different resolution...

The classification of land use and cover (LULC) is a well-studied task within the domain remote sensing geographic information science. It traditionally relies on remotely sensed imagery therefore models classes with respect to their electromagnetic reflectances, aggregated in pixels. This paper introduces methodology which enables inclusion geographical object semantics (from vector data) into...

introduction land use and land cover change (lucc) is a complex issue resulted from biophysical, socio-economic, cultural, organizational and technological factors in different spatial and temporal scales. lucc considered as an important threat to biodiversity as causing the fragmentation, natural vegetation destruction and natural areas isolation the regions which managed by environmental prot...

urban growth and associated landscape transformation has been a major driver of local, regionaland global environmental change. the conversion of urban greenery to impervious landscapes has been identifiedas a key factor influencing the distinctive urban heat and associated consequences. due to the often highdemand for space in urban areas, creation and preservation of urban greenery as heat si...

Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment rules derived from a set of multiclass training samples. Consequently, classification accuracy varies with the training data set and is thus associated with uncertainty. In this study, we propose a bootstrap resampling and reclassification approach that can be applied for assessing not only the ...

BACKGROUND The estimation of forest biomass changes due to land-use change is of significant importance for estimates of the global carbon budget. The accuracy of biomass density maps depends on the availability of reliable allometric models used in combination with data derived from satellites images and forest inventory data. To reduce the uncertainty in estimates of carbon emissions resultin...

Traditional pixel-based classification methods yield poor results when applied to synthetic aperture radar (SAR) imagery because of the presence of the speckle and limited spectral information in SAR data. A novel classification method, integrating polarimetric target decomposition, object-oriented image analysis, and decision tree algorithms, is proposed for land use and land cover (LULC) clas...

Multi-scale/multi-level geographic object-based image analysis (MS-GEOBIA) methods are becoming widely-used in remote sensing because single-scale/single-level (SSGEOBIA) methods are often unable to obtain an accurate segmentation and classification of all land use/land cover (LULC) types in an image. However, there have been few comparisons between SS-GEOBIA and MS-GEOBIA approaches for the pu...