Land cover of Finnish Lapland was classified to 16 land cover classes using optical IRS LISS, Spot XS and MODIS satellite images, ancillary GIS data and decision tree classifier. The aim of this study was to test decision tree classifier for land cover classification and study the effects of its parameters to classification result. In the best case, the overall accuracy was about 68% for all 16...

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data,...

Study deals with the land cover dynamics analysis using remote sensing data over urban, suburban area in northern Belarus (Polotsk and Novopolotsk cities and surroundings) over the period 1994 – 2002. SPOT 3 and 5 images are used for the study. Land cover change detection is conducted using image differencing and post-classification comparison methods. Several classification methods are tested ...

Automatic image classification often fails at separating a large number of land cover classes that punctually may present similar spectral reflectances. To improve the classification accuracy in such situations, multi-temporal satellite data has proven to be valuable auxiliary information. In this paper, we present a study exploring the usefulness of intra-annual satellite images timeseries for...

Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of ...

The savannas of Southern Africa are an important dryland ecosystem as they cover up to 54% of the landscape and support a rich variety of biodiversity. This paper evaluates landscape change in savanna vegetation along Chobe Riverfront within Chobe National Park Botswana, from 1982 to 2011 to understand what change may be occurring in land cover. Classifying land cover in savanna environments is...

Satellite image classification is one of the most significant applications in remote sensing. Remote sensing data obtained from different optical sensors have been commonly used to characterize and quantity land information. However, conventional optical remote sensing is limited by weather conditions. Synthetic aperture radar (SAR), with the allweather and all-time advantages, is important in ...

Land cover maps are fundamental materials for resource management and change detection. Remote sensing technology is crucial for fast mapping with low cost. However, besides the inherent classification errors in the land cover products, numerous illogical transitions exist between the neighboring time points. In this study, we introduce a series of logical codes for all the land cover types acc...

Spatial Database is a collection of spatially referenced data that acts as a model of reality. Geographical Information System includes spatial data which is either in raster or vector form and data derived from remote sensors are increasingly utilized as source for GIS. Spatial data is the core part of the Geographical Information System. Data mining techniques are studied to discover knowledg...

Taking northern Xinjiang, China, as an example, this study first compares the standard MODIS Terra and Aqua snow cover classifications, and then compares the accuracy of the standard MODIS daily and 8-day snow cover products with the new daily and multi-day snow cover combination of MODIS Terra and Aqua observations using in situ measurements. Under clear sky in both products, the agreement of ...