شیرکو جعفری

Green space has an important role in the quality of urban environments. The purpose of this study was to investigate the relationships between urban green space, air and noise pollution and temperature in Tehran using landscape metrics approach. For this purpose, the green space map was prepared and updated and then landscape metrics were calculated. Then, through interpolated of annual mean data, the air and noise pollution maps were extracted, while the temperature map was prepared by the regression method. Finally, the required information from the prepared maps was extracted for 52 study units and was analyzed using structural equations modelling. The results indicated green space has a decreasing effect on air and noise pollution and temperature. As per unit increase in standard deviation of green space, 0.509 units in the standard deviation of temperature, 0.462 units in the standard deviation of noise pollution and also 0.831 units in the standard deviation of air pollution decrease were observed. For green space construct, the indices of cohesion and patch density had the highest role and the area index had the lowest role in decreasing air and noise pollution and temperature. The highest and lowest amounts of green space construct effect on the air pollution were related to particles less than 2.5 microns and sulfur dioxide, respectively. The Cronbach's Alpha value was 0.807 and the Composite Reliability coefficient of 0.808 indicates high reliability and Average Variance Extracted of 0.523 represents a high convergence validity in the air pollution constructs. In the green space construct, the variance inflation factor value indicates a noncollinearity error.

Degradation model is a method for environmental impact assessment and it has been widely applied for accumulative impact assessment of human activities. In addition, landscape indicators have been widely used in landscape planning as quantitative tools. One important application of landscape metrics is their contribution to landscape degradation model. The goal of this research was to assess landscape degradation in Jajrood protected area. In doing so, landscape indicators were calculated using a land use/cover map of Jajrood protected area for 16 sub basins. For land use/cover mapping, Landsat satellite image was firstly pre-processed, and then, hybrid classification approach was undertaken to categorize the image into different land use/cover categories. According to landscape degradation model, ecological vulnerability and the severity of activities as well as the degradation degree were calculated for the targeted landscape. The results of this research indicated that sub basin 5 with a value of 31 had the highest level of landscape degradation and sub basins 1, 13 and 15 with values 25, 25 and 22, respectively, were on the next orders in Jajrood protected area. In addition, sub basins 16, 7 and 4 with values 3.5, 3.25 and 3.25, respectively, were least degraded and these sub basins had the least level of fragmentation. The results show that 68% of the area was outlined to be potential for development or further development, only 17% of the area is suitable for protection. Approximately 13% of the area should be considered for rehabilitation that demonstrated natural land cover change and degradation in the protected area.

Urbanization is one of the most significant global changes. The rapid urban growth has been imposing high pressure to land and their resources. Neural networks are a powerful tool for understanding the spatial processes and patterns. Hence, the neural network model called multi-layer perceptron was applied as a tool for simulating the urban growth in Hashtpar township. The Root Mean Square Error (RMSE) was used as an index in design and stopping the training process of the network in this study. After normalization and removal of the covariate variables, distance to city center, main transportation and hydrographical networks, agriculture, grassland, barren land and slope were chosen as effective variables on the urban growth for study area. Architecture of the network has been designed as 7-14-1, which stands for number of input, hidden and output nodes, respectively. The training process was conducted by implementation of the sigmoid function and extracting the training samples of the urban change (1989-2000) and then simulating the urban growth for 2007. Investigating the performance of the model and analyzing the pattern of the simulated landscape was carried out using the relative operating characteristic and the landscape ecological metrics. The values of the ROC and landscape Ecological metrics indicate an acceptable spatial agreement between the simulated and classified maps. Accordingly, the neural network model has a good reliability in simulation of the class area, Euclidean nearest distance and fractal dimension index of the urban patches, as well. Finally, sensitivity of the model was examined using stepwise independent variable elimination and comparing the results with the full model. The results revealed that the distance to city center and the main transport network can be considered as the most effective variables in simulating the urban growth in the study area.

This study presents the predicted amount of precipitation, temperature and the climate simulation periods 2099-2070, 2049-2020 and 2008-1979 observation period under A2 and B2 SRES scenarios using the statistical downscaling model (SDSM) in Tehran, Zanjan, Qazvin and Rasht stations on the southern side and Ramsar, Babol and Gorgan in Northern side of the Alborz. Also, future climate was determined using the Domarten method. The main objective was to compare the climate change on two fronts in the north and south of the Alborz. The performance of model to predict the climatic parameters was evaluated based on the coefficient of determination (R2 ) and root mean squared estimation error (RMSE). The results of predicting the climate parameters indicate that the model simulated these parameters adequately. The results of the precipitation in all stations in period 2020- 2049 compared with the period 1979-2008 show an increasing trend in all stations and the period 2070-2099 compared to observation periods show an increasing trend in precipitation; while it has a decreasing trend compared to the periods 2020-2049. In 2070-2099 and 2020–2049 periods, the average, minimum and maximum temperature was increased relative to the observation period 1979-2008. The results of the climate determination by the Domarten method show that the climate in the Babolsar, Qazvin, Ramsar and Rasht stations will be change comparing with the observed climate in 1979-2008 in the future periods. In the Gorgan station at period 2070-2099 under the A2 scenario, the climate will change from semi-arid to the arid climate. In the Zanjan station at period 2020-2049 under the A2 scenario, the climate will change from semi-arid to Mediterranean climate and at period 2070-2099 climate from Mediterranean to semi-arid climate. This data can be simulated with high accuracy to better foresight the climatic conditions in future periods to help the future macro-management in providing better productivity of resources, particularly the water resources management.

Estimation of sedimentation and erosion without sediment statistics serves as one of the main issues of basins and requires the application of empirical approaches to utilize data for managing plans. EPM model is one of the empirical methods widely used in the study of watersheds all over country. The main objective of the present study is to evaluate the effect of geological formations on annual erosion and sedimentation using EPM model and GIS and the investigation of its efficiency in erosion and sediment studies. Rock formation erosion and entering of huge sediments to Taleghan reservoir clarify importance of investigation on how sediments are produced and transported. In respect to the above results, it is worthy to note that Zidasht basin is moderate in terms of erosions and sedimentation and its erosion coefficient is 0.69. In addition, classification of erosion intensity showed that this basin has two intense and moderate classes implying that considering sediment and erosion is essential in soil and water conservation projects. The highest and lowest erosion intensity coefficients are observed in Dint2 and D1 respectively mainly due to presence of formations Ngm, Q1g, and orchards in sub-basin Dint2 and resistant formations of Ekta and Ekv and suitable rangeland land use in D1.

Internationally، one of the most important purposes to establish a national park is to protect a unique ecosystem with all its inclusive biota. Therefore، this will facilitate the management and protection of the neighboring lands. Unfortunately، this trend was reversed in Iranian national parks. During recent decades، the land cover changes in protected areas have led to the decrease of the remnant biodiversity in these areas. In this article، land use/cover change and development trend was studied by using satellite images in Khojeer and Sorkhe Hesar national parks between 1986، 2000 and 2010. The satellite images were corrected and they were classified in five classes. The surface areas of the classes have been determined separately and they were compared between each year. The results show that during the study period، both national parks appeared with land use change toward degradation. Khojeer national park faced more with decrease in range lands and Sorkhe Hesar National park expressed with human development، which was superior compared to the Khojeer.

Increasing the impacts of human activities has resulted in great pressure even on quite natural areas. Miankaleh wetland as one of the most important wildlife refuges and aqua ecosystems of Iran has witnessed significant impacts on its structure and functions during last two decades. In this regard, it is crucially important to adopt appropriate policies by the aim of sustainable resource management and wetland protection. In this survey, Miankalaeh wetland is investigated from weaknesses and strengths in addition to opportunities and threats points of view. These different aspects of Miankaleh wetland were identified and explored in both present and future conditions and efficient strategically management measures are proposed. In this concern, weaknesses, strengths, opportunities and threats factors of wetland are grouped, weighted and analyzed through SWOT matrix leading to appropriate strategical measures. Finally, using QSPM matrix strategies were scored. According to the results, in order to meet the need of sustainable resource allocation for tourism, conservation, agriculture and development activities, the strategy of zoning has gained the highest attraction score. By way of contrast, development of integrated pestmanagement activities and controlled fertilizing plans has attracted the lowest score. With regard to importance of sustainable management, wetland conservation, main weaknesses and threats of Miankaleh wetland as well as derived results from strategic analysis, zoning as an optimized resource allocation tool for conservation, tourism, agricultural and development activities is suggested.

The echoes of human activities are felt everywhere، even in regions usually regarded as wilderness، such as national parks. Presence of adverse activities inside these areas، which is inconsistent with conservation of their primary goal of protection، will end in structural damages and prevent the fulfillment of the function of these sensitive and important areas. protected areas are amongst the most valuable ecological reserves، thus، knowing the way they change through time will help us in their management quality. Having their change trend at reach will assist us to know about the cause of their changes and will end up in the best decision making and policies. In this study Khojeer and Sorkheh-Hesar national parks that are parts from a single protected area are investigated as case studies، located at Eastern Tehran، Iran. During the recent decades، built-up areas were expanded throughout the region which is threatening the natural environment. To find out the level of degradation of the region caused by this kind of land use change during years 1996، 2000 and 2010 we used image processing using the remote sensing techniques for change detection assessment of study area. Then for this purpose we classified the land use maps in five classes for each period and change detection Was done. The results of this study showed that the greatest destruction caused by human development، such as industrial and residential town development of and dam occurred in the years 2000-2010. Also the highest increase in land use classes is relate to residential in the study area..