فهرست مطالب majid kiavarz moghaddam

Introduction Ecosystem services are the benefits people get from ecosystems and human always needs ecosystem services and products for survival (MEA, 2005). These services are divided into the four categories of productive, regulatory, cultural and life support services; the first three categories are directly affecting people, and the fourth one is critical for the continuation of other services provided by ecosystems (Ayanu et al, 2012). Each of these four categories comprises a wide range of services. Since ecosystem services are not fully tradable in commercial markets or are not quantifiable in a way comparable to economic services and capital production, they are often being ignored in policy making and spatial planning (Costanza et al, 1997; Gowan, 2006). Assessing the impacts of landuse/landcover changes on the ecosystem service values is required for the special economic zones that are experiencing rapid changes in land use/land cover. Pars Special Economic Energy Zone (PSEEZ) located in southern Iran is considered as an example of these areas which has become a national and transnational zone within a short period of time due to activities related to the extraction, exploitation, refining and exporting of gas resources. This zone is one of the areas where extensive industrial investment has been made in less than 10 years (Talebian et al, 2008). This area was established in 1998 to extract oil and gas resources from the South Pars oil field with economic activities carried out in the city of Assaluyeh, Bushehr province. The Nayband national marine park is located in the PSEEZ (Davoodi et al., 2017). Nayband Bay along with a part of Nayband's headland with an area of 19,500 hectares was designated as Nayband Protected Area in Iran. This area, along with parts of Persian Gulf waters, with an approximate area of 49,815 hectares, were registered as the first Iranian marine national park in 2003. The presence of coral reefs, mangrove forests, rocky beaches, and real estuaries make this area one of the most diverse and beautiful coastal ecosystems in the world. There are also various aquatic species, marine mammals such as dolphins and whales, endangered reptiles, such as the green and eagle tip turtle and various kinds of aquatic birds in the area. The construction of PSEEZ has led to the development of petrochemical refineries, which in turn resulted in an increase of population (from 2000 to 60,000) and the expansion of human settlements. On the other hand, the development has led to the expansion of land, sea, and air transport infrastructure. Due to the rapid changes in this region, assessing the impacts of landuse/landcover changes on the ecosystem service values is required. In this regard, remote sensing, as the most important way of obtaining the spatial data, allows the quantification and mapping of these services. This technology brings practical benefits to biodiversity conservation and promotes the sustainable utilization of natural resources within the arena of ecosystem services (Cord et al, 2017; Araujo-Barbosa et al, 2015). Methodology This research emphasises on landuse/landcover changes as the basis of the evaluation of ecosystem services. In assessing the status of changes in the PSEEZ, a surface area of 300,000 hectares (3000 square kilometres) were reviewed over a period of 32 years. Those images of 1986 and 1998 were considered to show the trend of regional changes before the establishment of the PSEEZ, and the image of 2018 was used to show the current changes. In selecting Landsat images, we considered climate conditions as well as the characteristics of the Persian Gulf tide and its effects on mangrove forests. The weather condition of the region is suitable for farming during the winter season, so crop fields in the months of January, February and March, had better resolution than other months of the year captured by remote sensing images. To classify the images of area changes, Random Forests technique was used due to the higher classification accuracy and processing speed compared to the other two methods (Inglada et al, 2016). In assessing the economic value of the ecosystem services in the PSEEZ, landuse/landcover changes and global estimations of ecosystem services are integrated (Costanza et al., 2014). Results and discussion The results indicate that agricultural lands and palm groves had a significant increase of 308.79 and 852.48 hectares before the establishment of the PSEEZ and the mangroves forests decreased by 67.68 hectares. With the establishment of the PSEEZ, human built-ups and mangrove forests were increased by 2756.61 and 113.4 hectares, while Barren lands, agricultural lands and palm groves decreased by 4651.92, 397.53 and 579.33 hectares. The drying of wetlands for the construction of roads and airports has greatly reduced the size of the wetland area with an estimated loss of economic value of ecosystem services of 100 million dollars. The results also indicate that the economic value of the ecosystem services of the the PSEEZ for the years 1986, 1998 and 2018 is equal to 570.02, 393.92 and 463.52 million dollars, respectively. The service function and its changes indicate that erosion control, recreation, nutrient cycling, waste treatment and food production are of the highest value in the study area. In contrast, pollination, gas regulation, soil formation, water supply and biological control have shown the lowest value. It was revealed that during these years, water regulation, habitat/refugia, disturbance regulation, erosion control and recreation had the most changes at 22.772, 12.445, 11.89, 10.791 and 9.192 million dollars in the study area. Conclusion Based on the findings of this research, we conclude: - The status of the Nayband wetland is in a state of warning, and human built-ups in its remaining space should be avoided. - The potential of the recreation ecosystem service of this region should be given more attention and planned to be preserved. - Since it is not possible to classify coral reefs in the region (Haleh and Asaloyeh) with these images, studies are recommended to review their ecosystem services' value and services.

This research modeling of the Urban Environmental Quality (UEQ) of districts of 3, 6 and 11 of Tehran using an integrated Geographic Information System and remote sensing approach. The identified indicators in this study contain both natural and artificial aspects in order to model the quality of urban environments. The current study aims to develop a method for spatiotemporal modelling of UEQ. For this purpose fuzzy logic method is used. The results show the existence of a fairly regular pattern as an increase in desirability of UEQ from south to north of the area. The seasonal changes of UEQ show the improvement of environmental condition in spring and summer compared to autumn and winter. Fast growth of the urban population and increasing demands for high living standards, have intensified the pressure on natural resources and made it more difficult to answer every need. Regardless to the fact of the environmental capacity, population and economy would effect on the fundamental functions of the environment. So, analysis of the environmental quality can help us to understand the exact need for natural resources in any urban areas along with considering its economy and social development scale. The quality of urban environment is recognized as an indicator for assessing and measuring the degree of suitability in urban settlements. It is also a rate for meeting the needs of individuals and society which can be affected by several factors such as air, noise and etc. All these factors would vary by any changes in time and space. Previous studies have mainly focused on spatial changes, but in this paper we decided to consider seasonal changes in addition to spatial ones. Also, we tried to use more complete set of indicators. So, the main purpose of this study is modeling the quality of urban environment based on a set of spatio-temporal factors. We used satellite imagery and some geospatial data such as NDVI index maps, land surface temperature, Land Surface moisture, Land Surface Albedo, Solar radiation, air pollution, Urban Heat Island, Building height, population density, Enhanced Built-Up and Bareness Index and also noise pollution. Landsat 8 (OLI) is used to calculate NDVI indices, land surface temperature, land surface moisture, Land Surface Albedo, Urban Heat Island, and Enhanced Built-Up and Bareness Index. A digital elevation model (DEM) used to extract solar radiation. Finally, we used based location field data to enhance Air pollution, Building height, population density and noise pollution. Because of the uncertain nature of quality measurements, we used Fuzzy logical approach to model the quality of urban environments. One of the most important fuzzy operators for overlapping indices is the GAMMA. Gamma operator is the general mode of multiplication and addition. In other words, the gamma fuzzy function is the product of the algebraic multiplication of two functions of collect and multiply fuzzy. This function is the result of the compatibility between the incremental effect of the fuzzy sum function and the decreasing effect of the fuzzy multiplication function. Therefore, districts of 3, 6 and 11 of Tehran municipality have been selected to be measured for the quality of urban environment in Tehran along with northern-southern line. The results show a northern-southern trend in the quality of urban environment which is reducing from north to south. The environmental quality conditions of the three defined urban areas are categorized into five classes, moderate, ‘very good’, ‘good’, ‘very low’ and ‘low’. As the results show, region number three has a better environmental condition than the regions of six and eleven. We can also realize that the most of the selected indicators have shown seasonal changes within a year in our study area. This is due to the existance of more parks and less air pollution in the northern regions. Also, time intervals show a better quality situation in spring and summer than in autumn and winter. To investigate seasonal changes, the total area of each class was compared in a different season and the urban environmental qualities were devised into five categories: very good, good, medium, low and very low. In the spring, a large partial of the region has a modest and good quality, and a small part of it has a very good situation. In the summer, most of the area has a middle class situation and a small part with a very low level, which indicates the region's good status on this season. In the fall, we have maximum of the area with the lowest quality and the minimum of it with a very good level that indicates the worst condition for the urban environmental quality. In the winter, the situation is a little better. most parts of the area are in middle levels and small parts of it is in the lowest class. therefore, the quality of urban environments changes dramatically within a year. At the next step, we studied the Pearson correlation coefficient of indicators and the results showed that the greenness is the most effective indicator of quality in urban environments. One-At-A-Time (OAT) Sensitivity Analysis were used to analyze the sensitivity of the model. Results show that the effect of 30% increasement on all inputs is between 2% and 17%. By considering the fact that all the changes in model outcome is less than the total percentage of input change (30% increase) for all the variables, it can be concluded that the results of the gamma fuzzy model are reliable and not affected by one or more specific variables. According to an extensive review of the literature, this study selects a wide range of factors in both natural and artificial environments to assess the urban environmental quality (UEQ) of Tehran. It is hoped that this study provides a useful basis for a more researches in the field of UEQ, combining both natural and built-up parts of urban zones. Further work will focus on validation and verification of the UEQ indices.

this study examines and compares the performance of seven spectral indices in the classification and change detection of built-up lands from Landsat-7 ETM (Enhanced Thematic Mapper Plus) and Landsat-8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) imageries. The Study site with an area of 68,995 hectares in this study in the Tehran It includes three mid-infrared (MIR)-based indices, i.e. the urban index (UI), the normalized difference built-up index (NDBI), and the index-based built-up index (IBI), two proposed visible (Vis)-based indices, i.e. the VrNIR-BI and VgNIR-BI or the visible red/green-based built-up indices, ,one thermal infrared (TIR)-based index, i.e. the normalized difference impervious surface index (NDISI) and visible blue/ mid-infrared (SWIR1) based built-up indices (VbSWIR1). In addition, a water index, i.e. the modified normalized difference water index (MNDWI), was also derived. Otsu’s method was used to separate water from the non-water areas on the MNDWI map. Subsequently, a water mask was produced and used to mask all the built-up index maps, leaving only the non-water areas. Using the same thresholding method, the non-water areas of all the built-up index maps were classified into built-up and non-built-up classes. The classification accuracy was assessed using 3500 reference points for each image The results show that the VbSWIR1-BI, with an overall accuracy of 92.88% (Landsat-7) and 91.68% (Landsat-8), were more robust and superior. The results also show indications that the detected spatiotemporal urban LULC changes based on the VbSWIR1-BI were also the most accurate.

Recently, due to the predominance in terms of the time and the cost, the drones are dramatically developing and their applications getting more widespread in various fields such as earth science, natural resources, environmental studies and urban planning. However, applying such drones suffers from several disadvantages, inter alia, the imaging unstable geometry, not using metric cameras as well as images’ variable parameters. In this study, computer vision science techniques for processing prepared images of the drone were precisely investigated and the performance of these images and techniques for producing a surface Elevation model with a high spatial resolution was investigated. Due to mentioned problems for the imaging and the absence of internal and external justifying parameters, the SIFT operator was used in order to extract nodal points in the shared areas of images. The relative and absolute justification of images are performed by using nodal points and land control points, respectively. The cloud of dense points was created through a dense three-dimensional matching algorithm and by using triangulated irregular interpolation methods area digital surfaces are formed. Finally, by using land checkpoints, the accuracy of the georeferencing and the final surface digital model were assessed. 5853 nodal points were automatically extracted by the SIFT operator and images were georeferenced with a surface accuracy of 21/12 cm. Then, by creating millions of points and triangulated irregular network, the height digital model with a height accuracy of 19/25 cm was created. Results of the current study indicate that due to the imaging unstable geometry and variable parameters for images, the combination of the SIFT algorithm with computer vision techniques in processing the UAV images in order to extract nodal points and to precisely justify images, are necessary.