فصلنامه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی
سال ششم شماره 4 (زمستان 1394)

In this research, land use changes in previous years and the possibility of predicting in the future using Markov chain model were investigated in the Maraveh Tappeh region of Golestan province. Therefore, using images of MSS, ETM+ and OLI sensors of LandSat satellite and using ancillary information, land use maps of 1986, 2000 and 2014 was provided and land use map of 2024 was predicted. According to the results, dense forest area decreased during the study period and with passing time but the area of agricultural land increased with the passage of time while the dense rangeland area decreased during the period 1984-2000. The annual growth rate of agricultural land has achieved 113.45 ha during the period 1984-2000 and this change value was obtained 91.27 ha for the period 2000-2014. The results of predicting changes in the time interval 2014-2028, showed it is possible that will be decreased semi-dense forest and dense rangelands and will be increased other land-use areas according to results of model predictions. The highest increase will be belonging to agricultural land use that will be increased to 25.89 ha per year. According to research findings, land-use changes are causing degradation of natural resource areas. However, in recent years, have taken effective actions to protect these areas, but more attention and protection of natural resources and environment in the Marave Tappeh region is essentially still.

This study was carried out to determine the relative hazard zonation of the slope instabilities and landslide occurrence in Sepiddasht, Iran. The method of Artificial Neural Network with the multiple-layer percepteron structure and the back propagation learning algorithm were used. In order to study the stability of the slopes, the landslides of the region were initially identified and recorded using satellite images of TM and ETM+, aerial images of 1:50,000, and field surveys (year, 2014). The impact of each factor including slope, aspect, land use, elevation, lithology, precipitation, the distance from the fault road and drainage on the slope instabilities was estimated using the ArcGIS®10.1 software via combining the map of the factors influencing the landslide with the landslide distribution map. Then a proper structure (1-13-9) for the landslide hazard zonation of Sepiddasht region was obtained through training the artificial neural network by MATLAB software. Based on the results of the landslide hazard zonation, 0.18, 12.41, 14.09, 29.85, and 43.52 percent of the region were located in very low, low, medium, high, and very high risk classes respectively.

Dust phenomenon is one of the climatic hazards in arid and semi-arid regions of the world that usually brings damages to human life and properties. In recent decades due to long droughts in Iran, the number of dust event increased and affected many cities and villages. The aim of this study was to investigate the effect of vegetation and relationship with dust events in Khorasan Razavi province during 2004-2013 using Geographic information system (GIS) and Normalized difference vegetation index (NDVI). The data of dusty days in the region synoptic stations were provided from Meteorological Organization and the frequency of dusty days in different seasons were determined. Using the method of Normal Kriging in GIS, dusty days were zoned in the province. Vegetation map area, were provided using MODIS image in ENVI®4.8 software. The results showed closed relations between the number of dust event and vegetation distribution. In addition, decreasing of vegetations caused the increasing of the number of dust event. Results showed the number of dust event increased during the recent years and in 2008 and 2005 were recorded as the most and the least number of dust event respectively.

Isfahan is one of the major cities in Iran in terms of population, economic and industrial growth, history, and tourism. Thus, seismicity and active fault identity are essential in this region. The current study examined the seismotectonics framework and earthquake hazard in 1:250000 scale map of Isfahan.The study area included the Northeast to the Southwest region, the Central Iran zone, the Sanandaj-Sirjan zone and the high Zagros zone. Using satellite images, historical and instrumental earthquake data, geological and tectonic maps and aeromagnetic map, the fundamental faults of the area and active seismic area were evaluated. Based on the available information, the seismotectonics map of the area was prepared and four seismic zones were distinguished. The results showed a significant intensification of seismicity from the Northeast to the Southwest direction. Seismic risk analysis in the study area using the Gutenberg-Richter law predicted the probability for an earthquake with a magnitude of 5.5 up to 6 on the Richter scale in the area during the next 100 years, 100 percent. The risk analysis of earthquakes with a magnitude of 6.5 on the Richter scale was 84 percent and the risk analysis of earthquakes with a magnitude of 7 on the Richter scale was 46 percent.

Evaporation waste of water is one of the most important factors. Because evapotranspiration is a complex phenomenon that depends on many factors and data, accurate estimation of evaporation and transpiration, is very difficult and costly. Therefore, the purpose of this study was to estimate evapotranspiration using the surface energy balance algorithm for land (SEBAL) and also evaluate the performance of artificial neural networks. To estimates the Evapotranspiration rate the method of SEBAL Algorithmby using satellite images was applied. For this purpose, four images of Landsat 8 in this study were used that by comparing the results from the two methods, Remote Sensing and Penman-Monteith- FAO Equation presented MSE and MAE as respectively 1.54 and 1.04 per day. To solve the complexity of the evaporation process, Artificial Neural Networks was used for forecasting evaporation pan based on meteorological data. Perceptron with Back-propagation algorithm was applied for training it in this study. It used daily climate data that collected during 13 years from a Safi Abad station in Dezful city for network training. The results showed that the best network was the network with all inputs along with a hidden layer and 28 Neurons in the middle layer. The implementation results of this network presented that statistical Indicators were as MSE (0.0032), MAE (0.0445), R2 (0.9609). Comparing the results from Artificial Neural Networks and Penman-Monteith- FAO as reference method showed that MSE and MAE were 1.11 and 0.52 mm per day, respectively. These results presents that the performance of Artificial Neural Networks was better than the remote sensing method in the estimation of evapotranspiration rate.

Simulation of runoff resulted from any significant rainfall events can help to estimate the severity of floods. To protect watersheds, application of a model which is able to predict runoff at certain times and places is essential. Therefore, this study aimed to simulate runoff generation in upstream catchments of the Shahrake Behzisti as well as assessing the effect of implementation of vegetation management scenarios, using three relatively intense rainfall events occurred in recent years. The model of physically based Limburg soil erosion model (LISEM) within PCRaster GIS and programming language was applied in this study. In order to run the LISEM model, four basic raster maps of digital elevation model, landuse, soil texture and roads (impervious areas) were prepared. Digital elevation model map of the catchment was extracted from the Ultracam image of 1 m resolution within the ArcGIS environment. During this study, only two events on 2/11/2014 and 16/9/2015 led to runoff generation. Thus, the first event was used for calibration and the second one for validation of the model. Statistical comparison of the simulated and observed hydrographs of the rain events on 2/11/2014 and 16/9/2015 showed the correlation coefficients of 0.80 and 0.76 and Nash-Sutcliffe criteria of 0.8 and 0.5 respectively. The results of the simulation showed that the model was able to predict total runoff, peak discharge, runoff initiation time and time to peak discharge with relatively good performance. The results of the application of the LISEM model showed that runoff volume and peak discharge significantly decreased by implementation of vegetation management scenarios.

In this study, to determine appropriate locations for the construction of wind power plants in the East Azerbaijan, several criteria, including wind speed, prevailing wind speed, pressure, temperature, elevation, slope, road, city, village, protected areas, land use, river, fault and earthquakes were considered. All criteria were implemented by using an analytical hierarchy process (AHP) weight of layers in EC2000 software. For fuzzification the parameters were coded by 0, 1 into the IDRISI software and based on the expert opinions, the control points and fuzzy function of each layer was determined. Then the software Arc®GIS9.3 was used to overlay layers, and finally, the East Azerbaijan potential wind power plant was constructed in four levels: excellent (217294.36 ha), good (972887.64 ha), fair (1650932.35 ha) and poor (1709038.40 ha).