Amiri, M., Solaimani, K., Tamartash, R., Miryaghoubzadeh, M. (2013). Identification of Invasive Species Using Remote Sensing and Vegetation Indices, (Case Study: Vazroud Rangelands, Iran). Journal of Rangeland Science, 3(1), 59-70.
Mohadeseh Amiri; Karim Solaimani; Reza Tamartash; Mirhassan Miryaghoubzadeh. "Identification of Invasive Species Using Remote Sensing and Vegetation Indices, (Case Study: Vazroud Rangelands, Iran)". Journal of Rangeland Science, 3, 1, 2013, 59-70.
Amiri, M., Solaimani, K., Tamartash, R., Miryaghoubzadeh, M. (2013). 'Identification of Invasive Species Using Remote Sensing and Vegetation Indices, (Case Study: Vazroud Rangelands, Iran)', Journal of Rangeland Science, 3(1), pp. 59-70.
Amiri, M., Solaimani, K., Tamartash, R., Miryaghoubzadeh, M. Identification of Invasive Species Using Remote Sensing and Vegetation Indices, (Case Study: Vazroud Rangelands, Iran). Journal of Rangeland Science, 2013; 3(1): 59-70.
Identification of Invasive Species Using Remote Sensing and Vegetation Indices, (Case Study: Vazroud Rangelands, Iran)
1Department of Range and Watershed Management, Sari University of Agricultural and Natural Resources
2RS & GIS Center, Department of Range and Watershed Management, Sari University of Agricultural and Natural Resources,
3Academic Member of Range and Watershed Management Department, Sari University of Agricultural and Natural Resources
4Watershed Management, University of Mazandaran
Abstract
Biological invasions form a major threat to the provision of ecosystems products and 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. It has long been recognized that remote sensing and geographical information system could contribute to this capacity. This paper aims to investigate the efficiency of Landsat TM images in identifying and classifying invasive species Cirsium arvense and Stachys byzanthina in Vazroud rangelands of Iran. For optimizing results, the Cos(t) model was used for atmospheric correction on the image. Then multiple vegetation indices, by extracting the digital mean of pixels related to training samples of the corrected image, were calculated. A supervised algorithm using minimum distance of mean was used as a classification technique for evaluation against ground truth map. The results indicated that NDVI, Ratio, RVI, TVI and NRVI were the most suitable indices for the discrimination of Cirsium arvense species. The best indices for the Stachys byzanthina species were DVI, NDVI, PVI 1, PVI 2, RVI and WDVI. Of all the indices analyzed, DVI and WDVI were able to discriminate both species but with varying degrees of separation.
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