Journal of RS and GIS for Natural Resources
Year:2018 | Volume:9 | Issue:3 (32) |Papers Count:9

Acceleration of climate trend change is caused by the swift shift of rangeland conditions that using modern methods of evaluation to them are counted to sustainable management of the rangelands. In order for an investigation of trend change of rangeland vegetation due to climate change, central Alborz rangelands were selected. Normalized Difference Vegetation Index (NDVI) for the period of 30-year (1987-2016) was extracted by Landsat satellite, TM, ETM+, and OLI series. Drought periods were determined using the Standardized Precipitation Index (SPI). The Markov Chain model was used to anticipate the future changes of rangeland vegetation. The results showed that the vegetation cover index’ s changes have risen and fallen for three decades in which, despite of increasing for some years 1986 (0. 86), 2002 (0. 87), 2005 (0. 87), and 2015 (0. 86); the changes trend was decreasingly for 1995 (0. 53), 1998 (0. 65), 2000 (0. 62), and 2008 (0. 61) years, especially for fair to very poor classes. The highest correlation (91. 5%) between the SPI and NDVI was shown that severe to moderate drought has taken place along with decreased vegetation periods. Moreover, the Markov Chain model has anticipated a forcible declined change of vegetation cover for 2031 and 2046 periods. Therefore, range management approaches have to prepare itself in order to the gradual increase of temperature, which has destructive effects on vegetation cover, via regulating of grazing capacity and replacing of highly performance livestock in the future.

The state of groundwater pollution in Torghabeh-Shandiz watershed of Khorasan County has become a major problem, particularly in respect of the increasing population, and the agricultural, industrial and commercial development. The present work attempts to interpret the groundwater vulnerability of this watershed using the DRASTIC model together with the geographic information system (GIS). The data which correspond to the seven parameters (depth to water level, net recharge, aquifer media, soil media, topography, the impact of vadose zone and hydraulic conductivity) of the DRASTIC model were collected and converted into thematic maps with GIS. The study showed that the DRASTIC index shows lies in the range of 68 to 225 which were classified into five zones, very low, low, moderate, high, and very high risk zone of groundwater contamination. The DRASTIC map illustrates that an area of 15. 9% is a very low vulnerability, 20. 29% low vulnerability, 26. 33% moderate vulnerability, 24. 34% high vulnerability and 13. 14% very high vulnerability in the study area. The river flows from the northeast region of the watershed allows more recharge of water, which may drain the fertilizers from the surrounding agricultural lands along with it to the groundwater and hence leads to groundwater vulnerability of this region. To check the reliability of the DRASTIC index map in the field condition, groundwater samples were collected for the analysis of nitrate (NO3) which is found as one of the pollutants in groundwater resulting due to use of fertilizers during agriculture. The presence of high nitrate concentration over high to moderate risk pollution zone in the study area validated satisfactorily.

Determining the suitability of rangeland using the constraints of the FAO method, due to the multiplicity of factors and in some cases the need for laboratory operations and mapping, is time-consuming and the possibility of its application by all experts, the existence does not have. Therefore, the range classification guidelines for grazing livestock (Manual of Suitability for Animal Grazing; MSAG) in various climatic zones were proposed by the rangeland technical office. According to the proposed guidelines, rangeland suitability for trapping a variety of livestock is achieved by combining the three criteria of vegetation (20 points), water (15 points) and soil erosion (15 points). In order to assess the capability of the mentioned in the semi-arid region, the present study was carried out in the mountain ranges of Imam Kandi Urmia and the results of determining the suitability of plant breed types based on the proposed instruction with the results of the FAO limiting conditions method, by nonparametric test Wilcoxon, was compared. The results showed that in the FAO restrictive conditions, in the 83% of cases, the forage production rate was 46%, soil sensitivity to erosion and in 11%, the water resources criterion, as the criteria for reducing the suitability of plant types. But in the proposed instruction, only soil sensitivity to erosion has reduced the score of the indices and, consequently, the classes of merit. The results indicate that there is no significant difference between the two methods in terms of final suitable classes (P≤ 0. 05), and more than 85% of the rangelands of the region are classified in the same way in two methods. Therefore, the proposed directive is capable of identifying and categorizing susceptible and non-susceptible species in sheep breeding, and its efficiency is significant compared to the FAO restrictive conditions, due to cost savings and simplicity. This instruction will enable the experts to adapt to the use of grazing rangelands when they prepare multi-purpose portfolio projects in a simple and practical way.

In order to increase the spectral and spatial resolution of satellite images, the fusion of multi-resolution images can be used, however, the image fusion usually introduces some spectral and spatial distortions in the resulting fused image that the amount of these distortions highly depends on the pan sharpening technique as well as the type of data. The aim of this study is the fusion of Landsat 8 Operational Land Imager (OLI) with the date of 8 January 2018, and Sentinel-2 Multispectral Imager (MSI) with the date of 11 January 2018, using the Gram Schmidt, CN, Brovey, DWT, SFIM, Multiplication methods in the Ahvaz city. In order to evaluate the image quality that fussed, CC, SAM, PSNR, SIMM, and Laplacian indices were used. The results showed among the used methods, DWT and Brovey, have the best results from the spectral and spatial aspect, respectively. Also, the highest spatial distortion with the amount of 1. 18 in the SAM index was in the Multiplication method and the maximum spatial deviation in the DWT index with a correlation of 0. 47 with the reference image. Multiplication and DWT methods have the most spatially and spectrally distortion. Therefore, the results showed that SFIM is the optimum method of spectral and spatial aspects.

Evapotranspiration is one of the important components of water balance which is measured and estimated by several methods. Since these methods mainly involve pointby-point measuring and requiring a large amount of grounded data, so they have limitations. In this study, potential evapotranspiration for 8 days in 2014, 2015 and 2016 was estimated using the Priestley-Taylor method and remote sensing technique in Fariman area in Khorasan Razavi province using Landsat 8 Operational Land Imager (OLI). To determine the accuracy of the estimates, the results of this study were compared with the FAO Penman-Monteith method (the reference method for estimating potential evapotranspiration). Comparison of the obtained results by the Priestley-Taylor method with the FAO Penman-Monteith method showed that the R2 and Root Mean Square Error (RMSE) are 0. 91 and 0. 78 mm/d, respectively. This result indicates that the high accuracy of this method in estimating potential evapotranspiration in a semi-arid climate.

In this research, the capability of Landsat-8 OLI data for generating a soil organic carbon (SOC) map is investigated in a semi-steppe rangeland of Chaharmahal-va-Bakhtiari province. To do so, in the June 2013 ground sampling was performed based on a systematic-random scheme in 24 sampling sites within each site 3 transects was established and along each transect 5 soil samples were chosen and collected from 0 to 20 cm depth and SOC content of the samples was measured. In order to compare, on ground sampled values of SOC with the corresponding and Landsat-8 OLI data (June 2013), vegetation indices and tasseled cap transformation bands were calculated and extracted from the study area. The values of vegetation indices and tasseled cap transformation bands (dependent variable) were regressed against organic carbon values (independent variable) at site level in SPSS software. Finally, the SOC map was drawn based on the best-fitted model between the independent and dependent variable. The results showed that amongst vegetation indices, PVI and Brightness band have the most significant correlation with SOC. Finally, the SOC maps of the study area were drawn by the quadratic linear regression after finding the best regression fit between SOC and vegetation index as well as the tasseled cap. The results of the validation test show that between vegetation indices the PVI index (R=0. 53) and tasseled cap transformation bands (R=0. 63) showed the highest correlation with soil organic carbon (SOC). Finally, by calculating the fitting of binary linear regression, organic carbon maps were prepared. The validation results of the model indicate that there is no significant difference between ground sampled SOC and extracted values of vegetation indices and tasseled cap. Therefore, the spectral data of the Landsat-8 satellite images (OLI) are a valuable source for determining the soil organic carbon changes in such areas.

Identifying susceptible areas for erosion can be considered as one of the most important soil conservation measures. In this study, the capability of Dempster-Shafer (DS) model for mapping potential areas for erosion was investigated in Kakareza watershed in Lorestan province. First thematic layers of influential factors in soil erosion, including altitude, slope, slope, aspect, plan curvature, lithology, land use, distance from the river, soil and topographic wetness index were prepared. In addition, 29 eroded positions in the study area that their positions were obtained from GPS and Google earth on 10 July 20016 were mapped and then were divided into a training (70%) and testing (30%) points. The layers of environmental variables were classified into different classes according to and then based on the density of eroded points in the study area and DS analysis, the weight of each class was determined and the potential map of vulnerable areas to erosion was obtained according to the DS model. The accuracy of a generated map was also investigated using testing points and receiver operating characteristic (ROC) curve. The result showed that the produced map has the success rate of 21%, that means the poor capability of the DS model for mapping susceptible areas of erosion. In addition, according to the DS map, areas with the highest potential to surface erosion are located in the central and eastern part of the study area. Therefore, it can be indicated that this model has a poor ability in identifying potential and vulnerable areas to surface erosion compared to other phenomena such as flood and gully erosion.

Soil salinization is a predominant process in the degradation of arid and semi-arid soils that causes reduced the yield of crops, increases erosion and intensifies desertification. The Zahed Shahr studied area is located in the south and east of Fasa city, Fars province and in recent years the soil salinity has affected large areas of the farms and gardens in this area. The aim of this study was to evaluate the salinity changes of the soils during a 17 year period using Landsat 7 and 8 satellite images using the spectral angle mapping method. To do research, the salty soil spectra were extracted from satellite data and then using supervised classification the areas with salty soil were identified changes in salinity soils in ArcGIS software were investigated. Field studies, soil surface sampling, chemical analysis and spectral analysis of soil were performed by Spectroradiometer, and X-ray diffraction analysis for mineral identification was done. The results obtained from visible and shortwave infrared Spectroradiometer, and X-ray diffraction analysis showed the presence of evaporites minerals (calcite, halite, gypsum). By comparison with real ground data, the spectral angle mapper method was identified to be efficient in the classification of soil salinity. The results of the SAM method for a period of 17 years show four times spatial increase in soil salinity in this area. The area of saline soils has increased from 1600 hectares in 2000 to 6500 hectares in 2017.