J. Abdollahi, N. Baghestani, M.h. Saveqebi, M.h. Rahimian,
Volume 12, Issue 44 (7-2008)
Abstract
The present study discusses a method used to produce updated information about vegetation cover in arid and semi-arid zones, using RS data and GIS technique. In this method, Landsat ETM+ data in 2002 was collected in an area of about 60000 ha in Nodoushan basin, Yazd, Iran. To collect the necessary ground data, 50 sites of different vegetation types were selected and the percentage of vegetation cover in each one was determined. Also, different vegetation and soil indices were derived and crossed with located sampling points using ILWIS software capabilities. To get the best fitted curve, the relationship between vegetation cover, as a dependent variable, and satellite data bands, vegetation indices and environmental factors, as independent variables were assessed. Therefore, a multiple linear regression model was established for the prediction of vegetation cover percentage in the studied area. Finally, a vegetation cover map with high a precision was produced. As a conclusion, it can be said that mapping of vegetation cover via remote sensing is possible even if its vegetation cover is sparse.
M Noruzi, A Jalalian, Sh Ayoubi, H Khademi,
Volume 12, Issue 46 (1-2009)
Abstract
Crop yield, soil properties and erosion are strongly affected by terrain parameters. Therefore, knowledge about the effects of terrain parameters on strategic crops such as wheat production will help us with sustainable management of landscape. This study was conducted in 900ha, of Ardal district, Charmahal and Bakhtiari Province to develop regression models on wheat yield components vs. terrain parameters. Wheat yield and its components were measured in 100 points. Points were distributed randomly in stratified geomorphic surfaces. Yield components were measured by harvesting of 1 m2 plots. Terrain parameters were calculated by a 3×3 m spacing from digital elevation model. The result of descriptive statistics showed that all variables followed a normal distribution. The highest and lowest coefficient of variance (CV) was related to grain yield (0.36) and thousand seeds weight (0.13), respectively. Multiple regression models were established between yield components and terrain parameters attributes. The predictive models were validated using validation data set (20% of all data). The regression analysis revealed that wetness index and curvature were the most important attributes which explained about 45-78% of total yield components variability within the study area. The overall results indicated that topographic attributes may control a significant variability of rain-fed wheat yield. The result of validation analysis confirmed the above-stated conclusion with low RMSE and ME measures.
