مجله کاربرد سیستم اطلاعات جغرافیایی و سنجش از دور در برنامه ریزی
سال دهم شماره 4 (پیاپی 38، زمستان 1398)

Land use changes due to the physical expansion of the city in most cities in Iran are so rapid, that urban planners and managers are facing a dynamic and complex development as they integrate the planning process in these areas. The purpose of this study is to investigate land use changes and physical development of Hashtgerd city during the past 19 years and to predict land use change trends for the future. In this study, Landsat multi-time images were used. Using the support vector classification machine algorithm and the algorithm for Cross-Tab change, land use change trends over the past 19 years was evaluated. Also, using the Cellular Automata Markov prediction model, the process of land use change and physical expansion of the city is predicted for the future. The results of this study indicate the unnecessary expansion of the city over the past 19 years. So that the built-up with 736.56% growth have caused excessive destruction of agricultural and bare lands on the outskirts of the city. Investigations show that with increasing distance from land use changes have significantly reduced the amount of land use. Investigation of changes in land uses showed that 564/166 hectares of waste land has become residential land use. Predicting land use changes for 2028 and 2038 showed that residential land use will continue to increase. This highlights the need for special attention of urban planners and managers to the issue of urban development and its consequences in the region. Finally, the evaluation of the accuracy of the automated cell model showed that the percentage of classes area difference was less than 8%.

So far, many studies have been conducted to identify and detect land cover changes and land use changes. Optimizing land cover monitoring is an inevitable necessity in natural resource management. Given the evidence and effects of climate change in the region and the declining and increasing trend of some coverages and land uses can be effective in prioritizing conservation and zoning strategies. Factors such as land resources, environment, population, economy and society must be taken into account for sustainable development. Any type of development, regardless of quantity or quality, has specific environmental effects (1). Over time, dam construction has made some changes in the land cover - use of dams, in this study we aim to detect these changes. Therefore, for detecting cover changes - upstream and downstream of the Khordad 15 Khordad Dam during the period 201–990 1990, first visualize the changes using Googel Earth, then use Landsat 5 (1990 image) before. Dam Construction and Landsat 8 (Picture 2019) After dam construction and flooding using ENVI software, visual and automated changes detection are performed and the results of automatic image changes and classification in two classes Modified and unchanged Accuracy was 86.38% and was unchanged and modified by classification into three classes ( Decreased) reached 85.51% accuracy.Key words: Sustainable development,RemoteSensing,Dams,Change Detection,Classification.

The concept of human development is a holistic approach to a better life that emphasizes the cultivation of the talents and mental abilities along with the consumption of goods and services and places the dynamic horizon of human choices ahead of the goal of a better life.On the horizon of Iran's twenty-year vision is:A developed Iran, with advanced knowledge, equal opportunities, adequate income distribution and health.This descriptive-analytical study aims to evaluate the current status of provinces in terms of human development indicators based on the censuses of 2011 and 2016. According to the research findings, the average education index in the country is 0.503. 0.673 Health Index. And standard of living 0.336 And the combined index of human development is 0.749. Health index has the highest rate and standard of living has the lowest figure in the country. The most favorable situation of human development index was related to Tehran provinces with 0.936 respectively. Alborz with 0.926 and Yazd with 0.862. The most unfavorable situation of this index is related to Sistan and Baluchestan provinces with 0.446 and Kurdistan with 0.561 respectively. Sistan and Baluchestan province has the most unfavorable situation in all indicators of human development. Although the ranking of provinces based on the Human Development Index indicates that the most disadvantaged category corresponds to the border provinces and the most desirable ones belong to the regions most often located in the center of the country, but based on the results in most provinces of the country between the two The growth census observed that deprived provinces had the highest growth.

In changes detection process, the choice of information extraction method plays an important role in the quality of final changes detecting. In this study, Landsat 8 multi-temporal data fusion method based on spectral distortion was used to detect changes and to determine the range of floods. For this reason, both pre and post flood images were fused using the Gram Schmitt algorithm to increase spatial resolution of images. In the following, three algorithms, Gram Schmitt, IHS, PCA, were used to detect changes and determine the extent of flood. In this study, input of each algorithm was pre-flooded as a multicolor image and post-flood infrared image as a panchromatic image selected to determine the extent of flood using the spectral distortion generated in each algorithm. The results showed that the capability of data fusion method based on spectral distortion is very high in detecting of changes. The spectral distortion generated in IHS is the most accurate distortion and the output of this algorithm is highly consistent with the reference data. Also, the output of the Gram Schmitt algorithm has spectral distortions in the unchanged regions. The PCA algorithm, which is highly sensitive to inputs, distorts most image regions, which is not recommended for detecting changes based on spectral distortion.

In today’s society solid waste management is one of the most important concerns of mankind. Increasing waste volume and diversification of waste has added to the complexity of waste collection and disposal.Landfill if not handled property will cause contamination of water, soil and air…According to the latest census of the year, the fixed population of the city of Rhine is 982 people. The population reaches about 3000 to 4000 people in the four monthes of the year. On average, the amount of waste product in in the city of Rhine is between 3 and 3.5 tonnes per day. In the city of Rhine, landfill and incineration methods are used to dispose of municipal solid wasre. Therefore, choosing the right location for landfill is important.In the present study, it has been attempted to identify the best location for landfill sanitation considering ecological factors in order to minimize environmental damage. For this purpose, the location of municipal solid waste landfill was determined by using spatial analysis in GIS environment. The required digital data were collected for this purpose and using the hierarchical analytical method of AHP, the criteria were evaluated and then taken to the GIS environment. Due to limitations in the initial data collection, several factors were not taken into account, including the distance between walls and groundwater. In future research, by carefully examining all the factors, one can increase the accuracy and specify a more accurate burial site.

It is important to have new land use plans in many areas, including natural resource management and land planning. Remote sensing data has a high potential for preparing up-to-date land use maps and land cover. The purpose of this study is to evaluate the methods of preparing land use maps of Kamyaran city using satellite images. In this study, OLI Landsat 8 satellite sensor data for June 2018 were used. Initially, preliminary processing, including radiometric, atmospheric, and geometric corrections, was performed on raw data. ground control points were used for training, accreditation, and land use mapping. The Landuse class was identified at each point by field survey and using Google Earth images in 9 user classes of agricultural lands, forest, garden, rich pastures, medium pastures, residential areas, water area, barren lands and rocky outcrops. In the following, maximum probability, minimum distance, support vector machine and Mahanalubi distance were used for the supervised classification in ENVI 5.3 software. To evaluate the accuracy of classification methods, two criteria of general accuracy and capa coefficient were used with ground control data. The results showed that the support vector machine method was 91.4% more accurate and the Kappa coefficient was 0.88% more accurate than other methods.