identification of invasive species using remote sensing and vegetation indices, (case study: vazroud rangelands, iran)

biological invasions form a major threat to the provision of ecosystems productsand services and can affect ecosystems across a wide spectrum of bioclimatic conditions.therefore, it is important to systematically monitor the spread of species over broad regions. ithas long been recognized that remote sensing and geographical information system couldcontribute to this capacity. this paper aims to investigate the efficiency of landsat tm imagesin identifying and classifying invasive species cirsium arvense and stachys byzanthina invazroud rangelands of iran. for optimizing results, the cos(t) model was used for atmosphericcorrection on the image. then multiple vegetation indices, by extracting the digital mean ofpixels related to training samples of the corrected image, were calculated. a supervisedalgorithm using minimum distance of mean was used as a classification technique forevaluation against ground truth map. the results indicated that ndvi, ratio, rvi, tvi andnrvi were the most suitable indices for the discrimination of cirsium arvense species. thebest indices for the stachys byzanthina species were dvi, ndvi, pvi 1, pvi 2, rvi andwdvi. of all the indices analyzed, dvi and wdvi were able to discriminate both species butwith varying degrees of separation.