capability assessment of ikonos images for urban vegetation mapping

high-resolution imagery such as ikonos satellite can produce useful information for many resource management applications. we investigated the capability of pan and multi spectral ikonos imagery of one region of tehran dated from 2005 in urban vegetation management and mapping of green cover. after data quality of images for radiometric noises, the images were geometrically corrected by polynomial method using gcps and dem with less than 0.2 pixel rmse. fusion of multi spectral and panchromatic images was done using pca method. some suitable vegetation indices including ndvi, vi, gndvi and tndvi were generated by rationing transformations on the main bands. based on the field checking of the study area, four classes of urban green cover types including non-forested area, broad-leaved forest, needle-leaved forest, and grass were considered. for each class, some points were sampled for training (20%) and accuracy assessment (80%) of classification using gps and aerial photos. the best band selection was done using divergence index. the classification was done by maximum likelihood algorithm on the four best bands. the accuracy assessment of classification results was accomplished using 80 percent of rest sample points. the results of accuracy assessment showed that overall accuracy and kappa coefficient were 87% and 0.80, respectively. according to the results, the non-forested area, deciduous, coniferous vegetations and grasses covered 59%, 17%, 23% and 1% of the study area, respectively. results of this research exposed that ikonos imagery has capability for mapping the urban vegetation covers and can produce useful information for managing urban vegetation covers in different time periods.