Hasan Fathi Zad; Rashid Fallah Shamsi; Ali Mahdavi; Saleh Arekhi
Volume 22, Issue 1 , June 2015, , Pages 59-72
Abstract
Rangelands are one of the most important renewable resources and because of their extent and economic, social and distinctive environmental impacts are of very special importance. Unfortunately, in our country, like most developing countries, rangelands have been exposed to degradation for various reasons ...
Read More
Rangelands are one of the most important renewable resources and because of their extent and economic, social and distinctive environmental impacts are of very special importance. Unfortunately, in our country, like most developing countries, rangelands have been exposed to degradation for various reasons including the non-systematic management of these resources. Remote sensing technology and satellite data are useful tools in the studies of rangeland and vegetation sciences. One of the applications of satellite data is mapping range land use. The aim of this study was to compare two methods of maximum probability and fuzzy for rangeland zonation. For this purpose, Landsat ETM+ was used; then, after final geometric and radiometric corrections, the final classification map was prepared. According to the results of accuracy of these two methods using the kappa coefficient, the artificial neural network algorithm of fuzzy Artmap with a coefficient of 0.9614 was more accurate than the maximum probability algorithm with a coefficient of 0.8058. Results of this study also indicated that the traditional algorithms of classification such as statistical methods due to their low flexibility, and parametric types such as maximum probability method because of the dependence on the Gaussian statistics model, could not provide optimal results, when the samples were not normal. In this study, ENVI 4.5, Idrisi Andes 15 and Arc GIS9.3 software were used
Hossein Arzani; Zeinolabedin Hosseini; Khosro Mirakhorlou
Volume 21, Issue 1 , June 2014, , Pages 24-31
Abstract
This study was aimed to assess the applicability of LANDSAT ETM+ satellite images for estimating vegetation production and cover. The images were digitized using topographic maps and geometrized in 1:25000 scales. Required processes such as spectral ratio measurement and vegetation indices were applied ...
Read More
This study was aimed to assess the applicability of LANDSAT ETM+ satellite images for estimating vegetation production and cover. The images were digitized using topographic maps and geometrized in 1:25000 scales. Required processes such as spectral ratio measurement and vegetation indices were applied on the images. Collection was carried out for vegetation cover and production in various vegetation types in homogeneous units. Sampling points' locations were recorded with GPS. Sampling method was random-systematic in such a way that in each unit, a circle with 20 meters radius was considered. One and 9 sampling plots were placed on the centre and on the perimeter, respectively. The plot size was 1m * 1m. In each plot, vegetation percentage was estimated and the production was calculated using double sampling method. Then, DN values for each sampling unit (9 pixels for one unit) were elicited in respect to primary bands' images, vegetation indices and spectral ratios. Correlation and regression analyses between geo-information and satellite information (Digital numbers) were carried out. Results revealed that 7th and 5th Bands and IR1, MIRV2 and VNIR2 indices had a significant correlation with production and given parameter could be estimated through regression models. Likewise, RA, IR1 and TVI indices had a significant correlation with vegetation percentage and this parameter could be estimated through regression models.