A study was conducted to determine the capabilities of the successive numerical Landsat data for assessment and monitoring of Land Use. Kashan plain with 7230 km2 of area, which is located in an arid zone of the central part of IRAN, selected as the site of investigation. It seemed to be a region prone to desertification processes. Two sorts of Landsat data: MSS (1976), TM (1998) and the supplementary information such as the soil and topography and Land cover maps were collected.After preprocessing, the images were classified on the base of the field and subsidiary data. For Mss data, the Mss1, Mss2 and Mss3 were merged and showed the best correlation with field samples. In TM data, merging the TM3, Tm4 and Tm5 showed the best correlation. The classification performed by the minimum distance algorithm.Five Land classes were distinguished with the overall precision of 65% and 75% for Mss and TM respectively. Detection of the changes between two maps showed a decrease of the area under range, bare and salt flats as much as 7.4%, 18.3% and 1.5% respectively, and an increase in cultivated and forest Land Use by the factor of 0.5% and 20.7% respectively. some referral to reliable documents determined the good agreement between the results and the real occurrence, except for the cultivated Lands, that was significantly under estimated. It is concluded that thematic maps, which have released by image processing in a time series, could be compared and some general and Useful results will be expected. Access to the same time field data of image acquisition time can promote the precision.

Although considering a varied mix of goals in environmental planning improves its results, it simultaneously increases the complexity of the process as well. This complexity forces researchers to Use methods such as mathematical programming and Optimization algorithms. In this context, linear programming as a sub-set of mathematical programming is one of the alternative methods for decision makers. As linear programming is not basically a spatial technique, its Use in the context of spatial decision making problems such as Land Use planning is always considered in conjunction with GIS. This method has some limitations in terms of computational intensity and the time to achieve solution that is increased exponentially with increasing number of decision variables. This is a normal situation in Land Use planning. This study attempts to solve a Land Use planning problem with respect to Optimization of four Land Uses: agriculture, forest, rangeLand and development areas in Gorgan Township. In this regard, three objectives including minimizing allocating cost, Land Use conversion cost and maximizing compactness in the form of linear programming problem have been considered. Introduction: Inevitably, Land Use planning is the main clue in the process leading to sustainable development. Every unit of Land cannot be allocated to more than one Land Use simultaneously. In this regard, Land Use planning defines the proportions and locations of special Use for each spatial unit of Land. With this definition place becomes an important issue when deciding about the most appropriate Land Use. It is the reason why we call Land Use planning a place-based decision making. Usually, in this process, the study area is modeled througha raster layer in which the cells act like Land units waiting to be allocated their special Uses. The important and crucial criterion in this process is suitability maps produced from overlaying several thematic maps. However, suitability maps are not sufficient for planning and Optimization of Land Use for a region and without integration of other constraints, the result may become a fragmented Land Uses layer. In this study we attempt to solve a Land Use planning problem with respect to optimizing four categories including agriculture, forest, rangeLand and development areas in GorganTownship. Here we incorporate three objectives including minimizing allocating cost, Use conversion cost and maximizing compactness in the form of a linear programming problem. Methods: Linear programming is one of the well-known methods in decision making that in cases has been Used integrated with GIS. Decision variables, constraints and objectives are three main and critical elements of linear programming. Decision variables are the questions of problems. In this domain, problem can be defined as minimizing or maximizing a problem based on suitability or unsuitability of the considered objectives. Land Use planning or Land Use optimal allocation in the minimizing form and with respect to three objectives of this study including minimizing allocating cost, conversion cost and maximizing compactness can be considered as linear programming equations (1-8): X_ijkis binary variable that equals 1 when Land Use k is allocated to cell (i, j) and equals 0 otherwise. C_ijk refers to the cost of cell (i, j) for kth Land Use, S_ijkis the cost of converting the current Land Use into a new one (Land Use k), Y_ijkis an integer variable introduced to the model to define compactness without violating the special linearity condition in linear programming problems. It is clear that the problem is integer linear programming becaUse decision variables (X_ijk)are binary. As linear programming is an exact method that enumerate all solutions to find optimal one, adding integer variables imposes a huge burden on computational processes and intensively increases the required processing time. This burden nearly makes it impossible to find optimal solutions. Therefore, we relaxed the problem from integrity and changed it to usual and classic linear programming. So, the values obtained for X_ijkwas ranged at [0 1]. The other difficulty was that one cell could not be allocated to more than one Land Use simultaneously. Therefore, we ranked the final responses that had been obtained from implementing linear model for every cell of the study area and then selected the maximum value in every cell and equaled other values with zero. After this selection, we had a solution map of the study area that showed Land Uses with the maximum value in every cell. We found that nearly none of the values were less than 0. 5. So, we completely allocated every cell to Land Use with the maximum value. Although this result could not guarantee optimality but was very close to solution of the exact method of linear programming. Results and Discussion: In order to have an equal base for comparison, we implemented the problem with the same target area in MOLA algorithm in IDRISI. We selected MOLA becaUse it works on the base of ranking the value of cells related to distance to ideal point which is the highest value possible after standardization of the suitability values. It is necessary to say that compactness and conversion cost objectives have not been considered in MOLA. Table (1) shows the number of cells (area) of every Land Use before and after implementing the above mentioned model (target area) and Table (2) shows the number of cells for every Land Uses before and after application of MOLA. Conclusions As figure (1) shows, fragmentation of Land Use in the suggested model is less than that for MOLA. Also, it is possible to change objectives or some other criterion in the linear programming model but MOLA in IDRISI is a crisp module that cannot accept other objectives like compactness for improving the results. If we accept the fact that Land Use planning is a main clue through achieving sustainable development, its importance becomes ever more clear. What improves Land Use planning and makes it more practical and powerful is paying attention to many aspects, stakeholders and sources that are involved and affected by the process which makes even more complex. Use of Optimization algorithms is the way to address this complexity. The powerful feature of linear programming is achieving the exact solution which guarantees optimality. This feature besides its simplicity is sufficient to make this algorithm attractive and worthy of more in-depth studies. In this regard, this study attempts to introduce a way for application of linear programming in Land Use planning and Optimization.

Background and Objective: Environmental balance between transportation and urban LandUse components of planning is very important, becaUse they have great impact on each other. As transportation is one of the most important infrastructures of a city, its location-allocation is important for urban planning. Overall objective of this study is "Development of a GIS approach to plan appropriate locations for main urban Land Uses in the transportation network. Arak (a developing city in Iran) has been chosen as the case study.Method: This research includes the design and development of a model in the GIS environment with a view to introducing the development areas of Arak city for optimal urban Use of urban areas with the aim of increasing the efficiency of urban transport and reducing environmental impacts. The research methodology has focUsed on the calculation and mapping of fuel consumption, air and noise pollution in the urban transportation network and the proper deployment of Users.Findings: The results indicate the efficiency of the designed model for the proper location of the transportation network and urban utilities. Obtained maps show there are a lot of non-suitable zones for most of urban Land Uses. The results focus on the approaches such as road type change and location changes of the Land Uses locations to reduce environmental hazards.Discursion and Conclusion: This research has successfully managed development of a scientific approach for current and future model development. This approach can be employed in Land Uses and transportation network development. Suitability assessment can be done at both the local and national structure plan levels.

Mapping of Land Use/cover is important for many activities of planning and management, especially in arid areas. Nowadays, satellite imagery and remote sensing techniques, which provide timely and high capability data, are widely Used in producing this kind of mapping. The main objective of this study is to produce an actual Land Use map using advanced pixel-based (MLP, SVM, and SOM) approaches. The most important challenge, in this case, is to determine the optimum structure of classification methods. The Taguchi method is Used to optimize the structure of MLP, SVM, and SOM methods. Results show that the Taguchi method can be effectively Used to cope with this problem. It significantly reduces the number of classification tests. We also showed that there are significant differences between the results of the SVM method with those of the MLP and SOM methods (χ 2 more than 100) and that SVM model is more efficient than other methods. The accurate map produced by the optimized SVM approach (Overall accuracy of 0. 93) showed that this method has a better performance.

Land-Use spatial allocation is a multi-objective collaborative spatial Optimization method for rational Use of the Land Use. Based on global search capabilities and the information feedback mechanism of ant colony Optimization (ACO), a Land-Use spatial allocation model (ACO-LA) is proposed. FirstlyFirst, a construction graph is built for modeling the Land-Use spatial allocation problem. SecondlySecond, the behaviors of artificial ants are improved so that the solution can be foundobtained quickly in the searching search space. Finally, the ant colony generates optimized solutions by reconciling the conflicts between different planning objectives or by setting the relative dominance of different Land-Use types. Our study focUses on Gaoqiao Town of Fuyang City in Zhejiang Province, China. The model maximizes the Land-Use suitability and spatial compactness, and minimizes the cost of changing the Land Use, based on a variety of constraints, e.g., the optimal Land-Use structure and Land-Use policies. The results suggest that this model can obtain an optimized Land-Use spatial pattern from different sets of sub-objective weights and different development scenarios. With the constraint of the Land-Use structure, the Land-Use types can be distributed more reasonably by different sets of sub-objective weights. In different development scenarios, the model encourage encourages areas of Land-Use types in line with the development direction, adapting to meet different development needs by setting the relative dominance of the different Land-Use types, Wdominance, which is added to the component selection probability Pij.

The proper combination of different Land Use patterns is one of the important factors in maintaining and sustaining water and soil resources, especially in basin areas. Considering economic, social and environmental criteria, this study aimed at investigating the optimal and sustainable Land Uses in Hablehroud Basin in Tehran and Semnan provinces of Iran. Thus, the SWAT as a model and the PROMETHEE and ELECTRE models as two samples of the multi-attribute decision making method were employed. Data for calculating the environmental criteria such as temperature, rain, humidity and evaporation were collected during 26 years and those for the economic and social criteria such as production cost, gross income and employment were collected in 2017. The study results indicated that the horticulture Use of Land in the south areas of the basin had the first rank and the pasture Use of Land in the north had the last rank both in terms of socio-economic and environmental criteria. Also, comparing the socio-economic criteria with the environmental ones, opposite ranks were observed for horticulture Land Use in the north and pasture Land Use in the south areas. Therefore, based on the results, some recommendations were made to improve the current Land Use situations as well as to develop the Land Use patterns in the studied basin.

Allocating urban Land-Uses to Land-units with regard to different criteria and constraints is considered as a spatial multiobjective problem. Generating various urban Land-Use layouts with respect to defined objectives for urban Land-Use allocation can support urban planners in confirming appropriate layouts. Hence, in this research, a multi-objective Optimization algorithm based on grid is proposed to generate well-distributed solutions in objective space. In order to preserve diversity in Pareto front approximation, a grid is defined in objective space. The cells of this grid cluster the solutions and determine the suitable solutions for next generation in Optimization process. The Land-Uses of region 1 of the district 7 of Tehran is Used to assess the efficiency of the algorithm in optimizing urban Land-Use allocation. The results of the proposed algorithm are compared with the results of Non-dominated Sorting Genetic Algorithm II and III (NSGA-II and NSGA-III). Comparing the results indicate that the proposed algorithm acts better than NSGA-II and NSGA-III in preserving diversity and improving the convergence of the solutions in Pareto front.

Over the past few decades, several models such as the LP model have been Used for managing watersheds and determining optimal cultivation pattern for agricultural planning. Management and planning methods are the most important applied tools of management science for optimal allocating resources in order to gain the most profits in the different fields including natural resources, water resources. In this research, using Optimization methods, a single objective fuzzy linear programming model was Used to Land Use Optimization and a multi objective Optimization model for optimal allocation of resources. The results showed that the proposed combined model can be the base for correct management of resources and decrease the amount of soil erosion to 12%. Also, the results indicated that under optimized condition, the area allocated to dry farming Lands will decrease about 59% and the area of rangeLands, irrigated agricultures, gardens and forest plantation will increase about 1%, 9%, 94% and 229%, respectively. It is mentioned that the new produced model is a model which is compatible with social and economic condition of watershed and can apply for better Use, conservation and rehabilitation of existent natural resources and can be introduce as model which is on the basis of environmental approach.

The rapid rate of development of cities has caUsed extensive changes in the Land Use pattern around cities, and these effects of city development and urbanization can be seen in developing countries as well as developed countries. In Iran, as a developing country, the growth rate has been very high, and following that, there have been extensive changes in the field of Land changes. Change of Users is inevitable but it can be managed. The process of Land Use changes and the recognition of the effective factors and its results can help to identify obstacles, problems, analyze and understand urban issues as best as possible and become a tool for managers and urban planners to predict the future growth and development of cities and be able to manage effectively and Look for opportunities for cities. One of the biggest and most challenging issues in recent years in Iran is the change of urban Land Use, which has many effects in cities, and if these changes are carried out in an uncontrolled and unsupervised manner, they will have irreparable effects. . The purpose of this research is to investigate the effects of Land Use change on Land Use planning. Due to the spread of these changes, urban design and urban planning, using error tests and investigations, should always seek to adapt to the conditions that have arisen and are ahead, and they need changes and modernity of opinions in the previous standards. Therefore, he must always adapt to new conditions. It is for this reason that the discussion of flexible planning has been brought up and paid attention to in order to achieve better results than in the past. The most change of Land Use in cities is related to the change of Use of agricultural Lands and their conversion to urban Uses, which caUses more instability in cities, which is caUsed by the added value of the change of Use in urban spaces. With control tools, mayors have a very effective role in preserving and maintaining existing natural Lands, and by using their control tools, they can prevent Land trading and changes in the Use of natural Lands to other Uses.

The proper Use of natural resources can preserve these valuable assets. In line with the management of natural resources, Land Use Optimization can be highly Useful. The aim of the present study is to propose an appropriate integrative model for optimized allocation of Lands for surface runoff and sediment load minimization and net income maximization in Bayg watershed, Iran. In this study, five categories of Land Uses, i.e. irrigated orchard, rangeLand, irrigated farming, rainfed farming and almond orchard were spatially optimized to minimize surface runoff and sediment yield and to increase net income by integrating three approaches: weighted goal programming, analytic hierarchy process and multi-objective Land allocation algorithm. To achieve the target levels in this work, the acreages of almond orchard and rainfed farming should be reduced by 100% and 37.32% respectively, and irrigated farming acreage should be increased by 138.53%. Through these alterations in the Land Use acreage, the sediment load will be reduced by 16.78% and net income will be improved by 72.52%. However, runoff volume will be increased by 0.22%. Results indicated that weighted goal programming satisfied 96% and 46% of the target levels of sediment load and net income respectively, but failed to reduce runoff volume. Therefore, it is necessary for managers to control runoff using the strategies related to runoff harvesting, especially on steep slopes. Generally, it can be concluded that a combination of the techniques weighted goal programming, analytic hierarchy process and multi-objective Land allocation is highly capable to optimize Land Use and Land covers based on the conflicting objectives.