Spatial planning with regard to military defense
Mahshad Bagheri; Amir Ansari; Azadeh Kazemi; Mahmoud Bayat; Sahar Heidari Masteali
Abstract
Extended Abstract
Introduction
Proper distribution of urban green space is one of the most important issues in urban planning and especially in management of urban green space. In other words, the physical expansion of cities destroys surrounding natural environments and arable lands. It also results ...
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Extended Abstract
Introduction
Proper distribution of urban green space is one of the most important issues in urban planning and especially in management of urban green space. In other words, the physical expansion of cities destroys surrounding natural environments and arable lands. It also results in fundamental changes in ecological structure and functionof urban landscape, along with gradual changesin spatial structure and patterns of this landscape (Wang et al., 2008). Since ecosystem processes depends on its structure, landscape metrics have been accepted as a very useful tool for expressing the structure of urban green space and its human-causedchanges (Hessburg et al., 2013).There has always been discussion onacceptable per capita green space or changes in green space over time and place. Iranian cities are no exception in this regard, thougheven a city enjoying a high ratio of green space per capita may still lack enough green space per capita in some districts. This suggests the necessity of investigating various measures and avoiding studies limited to per capita green space and urban forestry. (Botequilha and Ahren, 2002). If as an ecological structure,green space is proportional to populationcomposition and distribution, ecological performance and land use type of an urban area, it can have important ecological functions. Since most studies on urban green space have primarily focused onfinding a proper location, calculating appropriate per capita green space and introducing suitable species for green space, investigatingthe spatial distribution of urban green spaceseems to be of great importance. Therefore, the present study seeks to investigate the spatial pattern and distribution of public green space in Khomein using a landscape approach.
Materials and methods
Study area
The study area, Khomein, is bounded by agricultural lands and gardens in its northeast, west, and partly in its south. Only the main area of urban texture is located on barren lands (Abbasi et al., 1986). The study area includes four districts of Khomeinin which the pattern of green space distribution isinvestigated.
Methods
Sentinel-2 images were used in the present study. Satellite images were processed and then, their geographical effects were extracted inthe first step of classification. Different indices were defined for each patch of the image and using supervised method, images were classified into four classes of agricultural lands, barren lands, urban parks and residential areas in accordance with the training data. Visual method was used to improve classification results. In this method, classification results are matched with the imagesand possible errors are rectified. Google Earth was used to evaluate the accuracy of results obtained from classification of satellite images. In the next step,the base map of the present study was produced and then, the layer containing urban parkswas integrated with the layers prepared for four districts of Khomein. It should be noted that the present study focuses on urban parks prepared by the municipality for public use and does not include other urban green spaceareas such as the green belt or private gardens, etc.
To study the spatial distribution of green space, measures of land cover were calculated and analyzed in each of the four districts. Geographic Information System (GIS) and Landscape Measurement Analysis Program (FRAGSTATS) were among the tools used to calculate and measure landusein the present study. Landscape metrics used in the present study included:
Landscape Shape Index (LSI) which measures the area of the largest patch in a class divided by the total area of that landscape (multiplied by 100 to convert to percent)
Euclidean Nearest Neighbor distance (ENN) which is the average distance between patches in a class. Meter is used as the standard unit of measurement for this index.
Perimeter /Area Ratio (PARA) which is the ratio of the perimeter of the patch (m) to its area (m2). This measure lacks a specific unit and for PARA> 0 it is without a limit.
Number of Patches (NP) equals the number of patchesof the corresponding patch type (class).
Shape Index:sum of patches’ perimeter divided by the square root of the area of the patch (ha) for each class (class surface) or the entire patch (land surface). This index iscalculated for circle standard (polygon), or square standard (grid) and divided by the number of patches.
Largest Patch Index (LPI) which measures the area of the largest patch in a class divided by the total area of the landscape (multiplied by 100 to convert to percent)
Mean Patch Size (MPS) which measures the average size of a patchin the landscape.
Results and Discussion
District 3 ranked highest and district 1 ranked lowest in ENN indexindicating that urban green space patches in this district were closer together, while green space patches in the third district were limited and far apart from each other. Regarding LPI index, the second district ranked thehighest and the third district ranked the lowest indicating that the largest urban parks in this districtwere much smaller than other districts. Other district had a relatively acceptable statusin this respect. In MPS index, district 2 with 697 patches ranked highest and district 1 with 564 patches ranked lowest indicating that average green space patches in district 1 were smaller. This was also confirmed by maps prepared based on other metrics.Regarding the LSI index, district 1 ranked highest and district 2 ranked lowest, while districts 3 and 4 had a similar status in this measure. The first district had the highest number of patches (NP), while the third district had the lowest NP. The highestPARA ratio was observed in District 1, and the lowestin District 4, while districts 3 and 2 ranked near the middle. In Landscape shape index which increases with the heterogeneity of patches,district 1 (with 13.12) ranked highest and District 3(6.64) ranked lowestwhiledistricts 2 and 4 ranked near the middle.This indicates the heterogeneous shape of green space patches in district 1, while showing that patches of green space in district 3 are very simple and homogeneous.Finally it should be noted that calculating landscape metrics for the four districts ofKhomein indicated a very low per capita green space in this city and also absence of a proper and equitable spatial distribution.
Conclusions
Calculatinglandscape metrics in the four districts of Khomeinindicated thatcompared to other districts, district 1, located in the southern part of the city, has a more desirable status in indices such as PARA, LSI, NP, and ENN. At the same time, district 3, located in the southeastern part of the city, has the least appropriate status regarding these metrics indicating the necessity of a comprehensive analysis of green space areas in this district in near future. Urban managers and planners need to focus on this district and its green space, and if possible find appropriate sites for future green space areas in this district.Although the status of districts 2 and 4, located in the west and north of the cityrespectively, were not very desirable, theyranked higher than districts 3in NP, LPI, and MPS. Using GIS in combination with satellite imagery, and land use metrics provided an innovative way to study the gradual spatial changes in urban green space. Results of landscape metrics analysis indicated an unbalanced distribution of land use in the four urban districts in this study.
Sharareh Pourebrahim; Mehrdad Hadipour; Mehdi Mardian; Amir Ansari
Abstract
Extended Abstract Introduction Strategic and important industries are established in areas with possible access to water. Industrial development requires abundant water. Analysis of environmental resources and their pollution is the first consequence of industrial and human activities. Therefore ...
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Extended Abstract Introduction Strategic and important industries are established in areas with possible access to water. Industrial development requires abundant water. Analysis of environmental resources and their pollution is the first consequence of industrial and human activities. Therefore today, due to the large volume of discharge and pollution in the environment, direct use of water is neither reasonable nor possible. Discharging industrial wastewater in land could severely contaminate the groundwater. In oil pollution monitoring researches, it is noteworthy that pollution detection and renovation operations require time and economic costs. Contamination of Soil and groundwater with pollutants such as hydrocarbons and chemical solvents has various environmental impacts. In Iran, the concentration of pollutants in some groundwater resources has been reported to be up to three times more than the standard value. This indicates the effect of a large amount of waste in the area which decrease soil quality in a way that soil layers are not able to compensate for it. Therefore, wastewater changes the drainage of underground water resources. In Iran and many other countries, causes such as leakage from contaminated petroleum storage tanks, leakage from transferring lines due to worn pipes, transportation of oil products, etc. in oil extraction mines, and refineries results in groundwater and surrounding areas facing oil leakage. Materials & Methods The purpose of this research is to produce the water quality map of Shazand plain in Markazi province using Geographic Information System (GIS) technique and to investigate the effects of oil industries on the quality of underground waters. The first step is to identify areas affected by these oil industries and identified factors. Appropriate agricultural areas with water supply in the qualitative range were also identified. The location of existing wells in the plain, particularly wells located around the refinery and petrochemical complexes were investigated for the first time. Then, considering the direction of the water land in the plain and the paths of wells located at upstream mountains to downstream ordinary rivers, wells located in the refinery and petrochemical complexes were selected. Accordingly, 14 wells were sampled in the first stage and their coordinates were obtained using GPS. The samples were classified in the laboratory into four groups including physical parameters, chemical parameters, oil and water aromatic parameters and water volatile organic compounds parameters. In the next stage, the maps of water quality parameters zoning were prepared using the "Geostatistical Analyst" developer with the use of the interpolation method of "Inverse Distance Weighting (IDW)". Finally, spatial variations and the groundwater quality changes were investigated. Results & Discussion Oil and aromatic parameters of water are presented along with the results of laboratory analysis in table 2. Results indicate that the numerical value of many parameters were less than 0.1 mg/L. Just two parameters (Anthracene and Pyrene) in well no.10 had a value of more than 0.1 mg/L. Yet, the total value of oil pollutant was quite different. In wells no. 3, 8, 9, and especially well no.10 the value was more than 0.1 mg/L. The zoning map and spatial variation trend, along with statistical-descriptive indexes of total petroleum hydrocarbon of wells were also produced. The spatial variation of oil pollutants in Dashte- Shazand wells in south-north direction showed an increasing trend, which gradually changed into a decreasing trend. A decreasing trend was also observed in west-east direction. Comparing descriptive-statistical indexes with the standard level, we concluded that the total oil pollutant parameter near well no.10, which is located in petrochemical complex faces contamination. Conclusion The present study sought to measure some important indexes of oil contamination in groundwater and surface water near Dasht-e Shazand refinery and petrochemical complex. Therefore, data were collected from 14 wells in the study area. Then, oil and aromatic products were analyzed in laboratory. Using geostatistical technique, spatial variations of quality parameters concentration were investigated and compared with the desired and standard level. Results indicate that most of the wells near Dashte- Shazand refinery and petrochemical complex do not show any sign of contamination. Yet, the concentration of Anthracene and Pyrene parameters in well no.10 is several times more than the standard level. This can increase the potential of contamination in Dashte- Shazand ground water resources. In wells no. 3, 8, 9, and especially in well no. 10, total petroleum hydrocarbon (TPH) was more than other wells. According to the TPH and PAH results, the contamination potential of well no.10 was quite large. Due to the development of Shazand refinery, ground water resources of the area face an increasing danger of contamination. Moreover, the area has a high potential of population increase in residential areas. Thus, water contamination can also endanger the local environment. This shows the necessity of an appropriate management plan and regular monitoring of ground water, surface water, soil and air in the area.
