فهرست مطالب hossien aghdar

Land is the place of earthly natural ecosystem functionality that has been used by humans in multiple methods. Land-use change (LUC) simulation is the most important method for researching LUC, which leads to some environmental issues such as the decreasing supply of forestry products and increasing levels of greenhouse gas emissions. Therefore, the present study aims at (i) using the Landsat imagery to prepare land use-cover (LULC) maps for 2000 and 2014; (ii) assessing Land use changes based on land change modeler (LCM) for the period from 2000 to 2014, and (iii) predicting the plausible land cover pattern in the region of Behbahan, using an algorithm based on ANN for 2028.

A hybrid model consisting of a neural network model, Markov chain (MC), and cellular automata (CA Markov) was designed to improve the performance of the standard network model. The modeling of transfer power is done by multilayer Perceptron of an artificial neural network and six variables. The change allocated to each use and the forecasting is computed by Markov chain and CA Markov. Operation model calibration and verification of land use data at two points were conducted in 2000 and 2014.

Modeling results indicate that the model validation phase has a good ability to predict land-use change on the horizon is 14 years old (2028). The comparison between modeling map and map related to 2013 shows that residential area and agricultural land continue to their growth trend so that residential area will be increased from 3157 hectares in 2014 to 4180 hectares in 2028 and it has 2% growth that has been 2% from 2000 to 2014. The results of this study can provide a suitable perspective for planners to manage land use regarding land-use changes in the past, present, and future. They are also can be used for development assessment projects, the cumulative effects assessment, and the vulnerable and sensitive zone recognition.

At present, warmer urban environment is one of the Naagahanh unsustainable urban development is associated with a reduced vegetation. Therefore, knowledge of ground temperature for geosciences, such as changes in the global environment and especially urban weather, is necessary. temperature as one of the most significant climatic parameters can be one of the main factors is tropical in town planning That the leading type of dedicated facility in the city and even determines the structure, shape and texture of urban. Unbridled and unplanned growth of cities, especially big cities of the country are looking to increase their environmental degradation and increasing pollution has been followed. The indiscriminate construction process and reduce the space required for the development of green space as breathing lungs Dramatic changes in the micro-climate in cities, especially in large cities led. Today, because of the importance of thermal remote sensing for environmental studies, many researchers in basic research thermal remote sensing and sensors technology development and application of new thermal data necessary to know.

The studied region is between 50 degrees and 19 minutes longitude to 50 degrees and 25 minutes eastern, 30 degrees and 45 minutes latitude to north 30 degrees and 32 minutes in zone 39 located. The highest slope is 69.87 and the lowest slope 1%, the minimum annual temperature 18.1 c° and the maximum annual temperature 32.37 °c. The space of area is 615.6 square kilometers and regional climate is dry based on Domarten method. In this study, spatial distribution and variation of surface temperature were analyzed base on land use maps and vegetation index in the city of BEHBAHAN. For this purpose, ETM+ and OLI images of Landsat satellite were used. After geometric and atmospheric Corrections, images were classified using Maximum Likelihood Algorithm, and temperature changes were evaluated by LCM model. To extract the surface temperature of three steps:A: Convert the digital number to radiationNumber of ETM digital conversion of radiation to be used the following formula: L=LMIN+(LMAX-LMIN)/(QCALMAX-QCALMIN )QCAL-QCALMINWhereQCALMIN: The Digital Numeric ValueQCALMAX: Most digital valueQCAL: Digital at the pixel numberLMIN: Spectral Band 6 in the amount of zero emission value DN (W m-2 sr-2 μm-1)LMAX: radiation value of 6 band in the amount in the DN 255 (W m-2 sr-2 μm-1) Values LMIN (2.200) and LMAX (10.55) of the extracted image file and Heather in the relationship.B: Converted spectral radiance temperature blackbodyTB = K2/(Ln(K1/L+1))ETM using thermal-band data from Planck equation, the temperature blackbody radiation (TB) in which the radiation is assumed to be one, to be converted.WhereTB: Satellite effective temperature in KelvinsK1: The first calibration constant equal to 666.09 W m-2 sr-2 μm-1K2: The first calibration constant equal to 1282.71L: Spectral radiance sensor (W m-2 sr-2 μm-1)C; Be corrected radiationCorrection can restore correct radiation surface temperature as well as the quality of information obtained through thermal infrared data is effective. One of the operational and applied to obtain the radiation is (threshold method NDVI) Through the red and near infrared bands were obtainedNDVI = (float(b4)-float(b3))/(float(b4)+float(b3)) Where b4 b3 band 4 and band 3. Finally, after correcting the thermal image and calculate emissivity, land surface temperature is calculated using Equation 6.LST = TB/([1+[(λ*(TB/p))]*Lnε])WhereTB: Brightness temperature λ: Radiance emitted wavelength (μm 5/11)p= k / hcWhereh: Planck's constantc; speed of lightk: Stefan-Boltzmann constantε: Emissivity

The results showed that class 1 of temperature (Temperatures lower than 13°c), which indicates the coolest areas, has the most expansion in northern and southern mountains. In the urban core, this class of temperature was expanded as scattered spots according to urban parks in 2000, but on the contrary in 2014, urban areas were located in the first class of temperature because of significant temperature reduction compared to previous years. Overlaying the map of each Temperature class with land use maps showed that downtown and some parts of bare and agricultural lands in the west areas were located in the first temperature class, most bare and agricultural lands and some parts of pastures were located in second class, most of pasture lands in third class, and a little part of them were located in the fourth class. After the preparation of the temperature map, it attempted to detect the changes and review the changes during the studied period with the model LCM, which is extensively used in IDRISI Tiga software. These changes include reductions, increments and net changes for each class, and transition from one floor to another. The highest temperature drop was observed in the third floor with an average temperature of 16 ° C and the highest increase in the second floor with an average temperature of 14 ° C. This means that in 2013, in the western regions of the area (third floor), the air temperature was reduced and, naturally, the area of this class was lower than in 2000, and reduced area as shown in Fig. 7 and the results are added to the second floor temperature. In fact, the displacement of the area between the second and third temperature classes has occurred, and in general, the temperature has cooled down to 2000.

The severity of the impact of human activity on the environment of cities, as concentrated areas of human use of the environment, depends heavily on the distance from urban centers. The farther away from urban centers and rural areas closer to the effects of human activities on the environment and reduced for certain changes. NDVI increase is the increase in the prevalence of vegetation and land cover is more homogeneous and homogeneous. but NDVI decrease introduce more varied (water, outdoor, bare soil, construction, etc.) and more heterogeneous mosaic of land.

Natural habitats such as rangelands and forests as one of the most sensitive elements of the environment and an important role in living organisms, including humans. In the decades before the present era of urban development has been such that an imbalance in the use of urban land led and pristine converted to urban land in others. In the present study to understand the changes in urban land, pasture and forest in Behbahan, images Landsat ETM + sensor OLI 2000 and 2014 were analyzed. CROSSTAB was used to assess the changes occurred. As well as to predict the trends of the year 2028 from the transmission potential maps logistic regression and Markov chain method was used. Results of the survey area in the first period (2000-2014) shows that urban land area of 1605 hectares in 2000 to 3157 ha in 2014 has increased. The most degradation of rangelands (6233 ha) and forests has occurred. The second period (2014-2028) compared to the 2014forest area remains unchanged, but increased urban development and reducing the area of pastures in Outlook 2028 will continue. In the period studied, the destruction of agricultural land for conversion into residential areas has increased significantly so that during 2000 to 2014, 291 hectares and in 2028, 626 hectares of agricultural land for construction purposes have been demolished.

In this research, chemical properties of groundwater of Mehran and Dehloran areas was considered by using of geostatistical analysis. For this purpose, 29 groundwater samples were studied from 2001 to 2014 and the water conductivity and sodium adsorption ratio were chemically analyzed. Semi variogram of parameters was computed by GS software and were fitted by Goussian, sphrical, Expotential and Goussian models. The results showed that the spatial structure of qualitative data of groundwater in these region is reconciled the Gaussian model. Then, using of Arc GIS 10.3 software the Kriging interpolation, inverse distance weighted and Radial basis function methods were done. to select the appropriate model to fit the experimental variogram of the RMSE were used less and more stronger spatial structure. The results of kriging with Inverse distance weighted and radial basis functions using the RMSE and ME RDF Criteria which measure the accuracy rate, were evaluated. Eventually the results of calculating the RMSE and ME showed that kriging method is superior than two other methods and has high precision and low error.so Interpolation was done using this method. In the end, using fuzzy logic, WILCOX classification and WHO classification, zoning map of the study area, for agriculture were prepared. Then Hot spot analysis were used to illustrate the spatial concentration of elements. According to the final map, 38% groundwater of the region is appropriate and sort of appropriate for agriculture and 62% is inappropriate. So can be said that water quality of the study area is not appropriate for agriculture.