Classification and analysis of normalized difference vegetation index data in Dez, Karun, and Karkheh watersheds

Vegetation as a natural component plays a significant role in increasing permeability, improving soil, reducing evaporation, and reducing the runoff and thus reducing the possibility of flooding. The use of new technologies such as remote sensing and geographic information system to study plant ecosystems and prepare land cover maps is necessary to know the effectiveness of these tools and to identify the best methods of their use. The purpose of this research is to investigate the vegetation cover using the NDVI and compare the performance of three supervised classification methods, the maximum likelihood method, the minimum distance from the mean, and the parallelepiped method in a part of the Great Karun watershed. To this end, TM and ETM images of Landsat satellite were used in one interval and NDVI in a 10-year interval (May 2008 to May 2018) with the help of supervised classification and maximum likelihood algorithm. The above data were prepared and analyzed using ENVI4.2 software, and the effectiveness of each method was evaluated with the overall accuracy index and Kappa coefficient. Based on the results in the maximum likelihood method, the overall accuracy rate is 90.35% and the Kappa coefficient is 0.878, in the minimum distance method, the distance from the mean is 74.32% and its Kappa coefficient is 0.675, and in the parallelepiped method, the overall accuracy is 67.09% and the Kappa coefficient was calculated as 0.593. Based on the results, the maximum likelihood method has the highest level of accuracy in satellite data group classification. Moreover, the results showed that in the 10-year period in Dez, Karun, and Karkheh watersheds, the spectral reflectance related to vegetation has decreased by 7.4%, 10.64%, and 13.83%, respectively. The results of this research can be effective for the practical use of the analysis that was done in relation to the studies of runoff and flood. According to the process of vegetation changes due to natural or human factors, the need for proper management in this area seems necessary.