فصلنامه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی
سال ششم شماره 2 (تابستان 1394)

The present research was performed with the aim of microzonation risk of earthquake using fuzzy inference system model and fuzzy analytic hierarchy process in district 1 of Ahvaz Municipality. In this study 19 criteria (population, land use, building density, occupancy level, historical background, floors, structures, distance from health centers, outdoor fireplaces and military facilities, proximity to faults, electrical, subway, high-pressure gas and petrol stations) were selected and converted into raster format in ArcGIS®10.1 environment to generate the standard layers. Linguistic variables limitation and fuzzy membership functions were then written using MATLAB program. The results indicated that, the fuzzy inference system has better performance than fuzzy analytic hierarch process model. In addition, based on the results of the fuzzy inference system, existence of areas with a very high, high, and a medium rate of risk showed 60% (4.48 Km2) of total area (​7.6 Km2). This indicated the existence of the medium up to a high rate of risk in mentioned district. Furthermore the area with low and very low rate of risk was 40% of area (3.12 Km2) which is mostly consisted of arid lands and farms in the west and southwest of the district.

Land surface temperature is a key indicator of energy balance. Besides, it serves as input data for models of climate change, agriculture, meteorology, urban heat islands, choosing the best time to agricultural activities, study of volcanic and geothermal activity, and fire detection. In this study land surface temperature has been extracted by available methods using 4 images of TM and ETM+ sensors of Landsat in span years of 1985 to 2003. The methods of land lurface temperature extraction included landsat project science office, mono window, SEBAL, Stefan-Boltzmann and single channel. Because of the multiplicity of methods and the number of images used in this study using a statistical method is required. It is required to determine the most efficient extraction method of land surface temperature, which is close to the existing field data. The statistical indicator used in this study was a mean absolute error (MAE). The results indicated that Stefan-Boltzmann method was the best method for both TM and ETM+ sensors. The MAE values for TM and ETM+ were 4.3 and 6.8 respectively, which showed a minimum value among other results.

The acquisition of knowledge about the vegetation plays an important role in soil management. However, vegetation estimating in the usual way, including an overall assessment of the vegetation is time consuming and does not also provide accurate enough information. Therefore, remote sensing technology is a desirable way for reducing time and cost compared to other usual methods. In this study, forest cover maps were prepared using remote sensing techniques and LandSat ETM+ imagery of year 2000 and LandSat 8 of year 2013. The classification of the study area digital images was performed to prepare land use map classification using maximum likelihood and neural network with participation of different bands. The results showed that the best overall accuracy of image classification using neural networks ETM+ in 2000 and LandSat 8 in 2013 was 0.95 and 0.95 respectively. It was also indicated that the kappa coefficient was estimated 0.91 and 0.91 respectively. The overall accuracy of maximum likelihood method of the collected images of 2000 and 2013 was 0.95 and 0.85, but it was 0.86 and 0.84 for Kappa statistics method. The results also showed a 1054.507 and 635.319 hectares decreasing of forest cover using neural network classification and maximum likelihood classification methods respectively. According to classification accuracy and Kappa statistics, it was observed that the accuracy and kappa coefficient of neural network classification was higher than accuracy and the Kappa coefficient of maximum likelihood method.

The aims of this study were to find the distribution of sound speed under the influence of water's physical parameters; to predict spatial analysis in oceanography using geostatistical methods; to forecast value parameters for the Persian Gulf and zoning the sound speed. Sound Speed was calculated using Chen-Millero formula and pressure, salinity, and temperature data. The data extracted from World Ocean Atlas 2013 with regular mesh grid 0.25 degree. Sound speed was calculated using the Chen-Millero formula. Spatial analysis of the sound speed comparison based on three methods Kriging, Co-Kriging and Inverse Distance Weighted. These methods were performed using GS+ software in both warm and cold season. The best method finally used to forcast and prepare the plans of zoning sound speed. The Pearson’s correlation test was performed between independent variables and sound speed showed that the maximum correlation occurs between temperature and sound speed. Therefore, the temperature was considered as the auxiliary variable in Co-Kriging method for spatial analysis of sound speed. Cross-validation results showed that model's forecasting in cold season was better compared to warm season in this region. Results of spatial analysis showed that the sound speed decreased about 20m/s in all layers from the Hormuz Strait toward the northwestern part of the Persian Gulf. Because of the increased salinity the maximum of sound speed was always in the south shallow area. In all investigated stations, sound speed reduced with increasing depth, due to temperature reduction and the sound channel is not also observed.

Precipitation variable is the key parameter of agricultural and hydrometeorology studies. Therefore, measurement and estimation of rainfall at the regional level help to understand the manner of water and energy cycles. In spite of the high variability of temporal and spatial precipitation quantity, precipitation monitoring using ground stations at regional scales is considerably difficult. The use of satellite images with high spatial resolution is therefore recommended. However, it is first necessary to evaluate temporal and spatial accuracy of data. The aim of this study was to evaluate the accuracy of Tropical Rainfall Measuring Mission (TRMM) satellite data for estimating daily, monthly, and annual rainfall on a regular spatial scale (0.25° ×0.25°) during 2000-2012, and to compare these estimated data with precipitation data at 30 stations (synoptic and climatological) in the west boundary basin of Iran. The Pearson correlation coefficient was calculated daily, monthly, and annually for the mentioned rainfall period changes. The results indicated a significant correlation (p<0.05) among the monthly and annual collected data of terrestrial and satellite precipitation. The relationship between monthly precipitation data from satellite images and observed data from stations were also determined using a linear and nonlinear regression for each year. The model reliability was also subsequently determined using statistical validation.

Monitoring and optimal management of natural resources is requiring an update and accurate information. In this context, land use/cover maps is considered as a one of the most important sources of information on natural resources management. Optimal management of resources requires assessment and understanding of the changes and degradation of resources in the past. It also needs to have an accurate plan in order to control and inhibition of the happened destruction potential in future. The Markov chain model is one of the most efficient methods for predicting changes in land use and land cover. In this research, land cover changes in previous years and the possibility of predicting in the future are investigated in Bostagh plain using the Markov chain model. Therefore, using MSS (1987), ETM+ (2002) and OLI (2014) images sensors and region ancillary information, land use map is provided and 2024 land use map is predicted too. Land use maps were performed using kappa coefficient after correcting satellite images, determining training samples, and evaluating classification accuracy. According to the results, bare/barren and rangeland classes are the most dynamic existing usage in the region. The area percentage of these lands during 1987 to 2014 was 21.64% subtractive and 31.14% additive respectively. This represents a total degradation and replacement of the weaker use in the region. The results of predicting changes in the time interval 2014-2024, showed that 98% of residential lands, 88% of bare land, 77% of saline land, 45% of rangeland, and 37% of agriculture will remain unchanged. Moreover, the conversion of rangeland to bare land (41.94%) are the highest, and the conversion of bare lands to residential lands (0.02%) and rangeland to residential lands (0.03%) are the lowest possibility of conversion. Predicting maps derived from the Markov chain model are very important to provide an overview for better natural resources management. Landuse changes Satellite images Predict of changes Markov chain model Bostagh plain

This study aims to investigate the effect of physiographical factors on land use/cover changes in the Yasouj Forest Park during 1965 to 2011. It was evaluated and monitored using aerial photographs 1:20000 scale, panchromatic satellite imageries from IRS-P5 (2011), and online Google Earth images (2011). Two series of images in two adjacent monitors were interpreted and compared using a simultaneously comparison and interpretation method. For this purpose some of land use/cover characteristics within sample plots taken from similar location were used in a digital systematic grid. The results showed that about 60% of sample plots located in different classes of slopes and elevations were not changed during this period of time. However the most land use/cover changes have been occurring in low slope areas and the least changes have been occurring in high slope areas. It is concluded that topographical factors, particularly slope are important factors for the protection of the forest cover in such areas. Park area protection could also be as an important prohibiting factor for major changes in the study area. The distribution pattern study of woody spices’s maturer stems is not good enough for about a 50 year period. Increasing dirt road in the park during the recent decades leads a social need for better planning of this decentralized recreational zone.