Comparisons of Land use classification and Land cover mapping results for developing guildline for Land use/ cover mapping using RS imagery.

In each research it is needed spectral and map information. But, due to poor quality in vegetation and land use map, we want to provide map with image and available software. In our country due to exist various climatological and natural characteristics, different methods may be used for providing map. This research can be used as a base for other studies and it can reduce cost. The goal of this study is to produce vegetation and land use map paying attention to natural characteristics of each region, to provide information that is used in other research projects and to introduce the best method for providing maps. This research was studied in three climatologic zones such as Gilan province as humid area, Hamedan Province as semi arid and Qum province as arid zone. To accomplish this, first the data are collected and the references are reviewed. Then to work on satelite images, the image processing in ILWIS, providing actual and virtual color images and processings such as geometric, radiometric, atmospheric and other corrections was done.To check boundary of land use units and provide present land use map, field checking is done. The next stage is to process, classify and compare the classified images with sampling field map and finally the best method for providing vegetation and land use map is selected. The results showed that using the vegetation indices method (NDVI, SAVI, and PVI), the highest overall accuracy related to PVI indices of ETM+ sensor, for land cover map and PVI indice of Liss III sensor. In a band supervised classification with density slicing method for landcover map, the highest overall accuracy is related to the band 7 of ETM+data .The highest overall accuracy for landuse is band 7 of ETM+ with. In the multispectral supervised classification for landcover map, the highest overall accuracy related to ETM+ data and Minimum Distance that is the highest accuracy and the best of 4 method classifications for providing of land cover map. In land use map, the highest overall accuracy related to Liss III data and fusion of Pan and Liss III data. In the hybrid method, for land cover map, the highest overall accuracy is 75.77% and 0.60 for kappa coefficient related to Liss III data and 79.38% Overall accuracy and 0.72 for kappa coefficient related to Liss III data that is the highest accuracy and the best of 4 method classification for providing of landuse map.