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The knowledge representation model plays an important role in the dynamic extracting and web service for Land Cover Change. While former researches were mainly focus on the
In the land cover layer, the nodes correspond to superpixels extracted from the image data, whereas in the land use layer the nodes correspond to objects of a geospatial land use
In addition to land cover/use maps, other remote sensing products, such as NDVI (Normalized Difference Vegetation Index) images calculated from satellite data with medium
Multi – Temporal and multi spectral data, Delineated the land use/land cover changes Research in the upper gulf of Thailand would increasingly important. These
The study area was classified into six main classes like forest, water bodies, bush grasses, barren land snow and agricultural land in supervised classification
The manual methods of interpretation of satellite images and classification of land cover are generally performed based on the observation using the data acquired
Non-parametric classifier Support Vector Machine (SVM) is used to classify RISAT-1 data with optimized polarization combination into five land use land cover classes
As the Land Use/Land Cover map, Geomorphology map, Soil map and Slope map are obtained at various scales and resolutions; Multi-Scale Integration using Majority Rule
