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Full Length Article:

Prediction of Land Use Management Scenarios Impact on Water Erosion Risk in Kashidar Watershed, Azadshahr,

Davoud AkhzariA, Samaneh Eftekhari AhandaniB, Behnaz AttaeianA, Alireza IldoromiC

AAssistant Professor, Department of Watershed and Rangeland Management, Malayer University, . (Corresponding Author). Email: [email protected] BPost Graduated Student of Desert Area Management, Department of Rangeland and Watershed Management, University of Agricultural and Natural Resources Sciences, Iran. CAssociate Professor, Department of Watershed and Rangeland Management, Malayer University, Iran.

Received on: 27/05/2013 Accepted on: 12/09/2013

Abstract. Soil erosion is a serious problem especially in northern parts of Iran. One the most important side effects on soil erosion may be the decline in qualities of soil refers to agricultural productivity. So it is very important to assess the soil erosion risk for the sustainable development of agriculture. This study outlines ways undertaken to provide a new tool to manage water erosion from physical and economical perspectives. Kashidar Watershed in north of Iran is used as a case study. The focus of this study is on exploring the economic and physical impacts of eight land use-based scenarios for water erosion management as well as conducting a trade-off analysis using the Multi-Criteria Decision Making (MCDM) technique. This involves developing a modeling system to assist decision makers in formulating scenarios, analyzing the impacts of these scenarios on water erosion, interpreting and suggesting appropriate scenarios for implementation in the area. This study was conducted with object of modeling and assessing soil erosion risk in Kashidar Watershed with the application of IMAGE\LDM. Rainfall erosivity index, relief index, soil erosivity index and land cover index were four basic factors used in IMAGE\LDM. Soil erosion risk can be divided into six groups. Furthermore, the spatial distribution characteristics were also analyzed with the application of GIS in the view of elevation, land use types. Among 8 scenarios for water erosion management, most appropriate ones that have minimum proportion of high water erosion hazard classes, maximum gross margin and minimum establishment cost were chosen as best scenarios.

Key words: Land use, Water erosion, Trade-off analysis, MCDM, Kashidar Watershed

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1. Introduction characteristics. Therefore, a scenario Economic development and human planning is required to achieve optimum welfare largely depend on optimum sustainable farming systems (Nikkami, utilization of natural resources 2009). (Karunakaran, 2012). Successive crops Severe erosion usually causes a planting cause cropland economic decrease in producing agricultural efficiency reduction. Continuing this products, which demonstrates the strong process will lead to a big reduction in impact of usage on the amount of erosion farmerʼs income (Singh, 2008). Improper (Martha, 2004). Suitable land use selection and cultivation of traditional selection reduces soil erosion (Martha, crops will exacerbate the problem 2004). Soil erosion in Kashidar (Maroyi, 2012). Appropriate land use Watershed is higher than normal amount selection in the agricultural field increase (Golestan Natural Resources Bureau, farmerʼs income (Karunakaran, 2012). 2009). Land use management scenarios Thus revision of agricultural land use is for reducing phosphorous leak to lower very useful for agricultural area unites, Green Bay in the State of Michigan using income increment, and land use the SWAT were used. This research result application improvement. Kashidar showed the best land use management watershed ecosystem has a vital role for scenarios to reduce the phosphorous leak economy of the region. Golestan Natural (Baumgart and Fermanich, 2008). The Resources bureau, (2009) recommended Unit Stream Power based an integrated management with these Erosion/Deposition model was applied to goals; 1) to increment community predict land use management scenarios awareness and skills in order to impact on water erosion. Results showed implement the conservation and that the whole erosion from urban areas rehabilitation of land in agricultural scenarios was higher than other land use systems, and 2) to establish agricultural scenarios (Leh et al., 2011). Revised land use system based on the ability of Universal Soil Loss Equation (RUSLE) land to support sustainable land use. Land model and Geographic Information use conflicts in Kashidar Watershed area Systems (GIS) with geo-statistical are associated with the preservation of techniques were adopted to study ecosystem where erosion and different land use management scenarios sedimentation rate is very high and they impact on water erosion risk. Results will improve farmersˈ welfare and showed that the RUSLE model was a income, to attain food security, poverty good method to estimate soil erosion risk spread prevention and to provide jobs in different scenarios because it was (Hengki et al., 2012). simple, fast and economical to use More than 80% of native people in (Ferreira and Panagopoulos, 2012). Kashidar Watershed live below the A model used for regional soil poverty line. Kashidar Watershed erosion evaluation is semi-quantitative farmlands are mainly rain fedcultivation. methods. The Integrated Model to Assess Income obtained from this type of the Global Environment (IMAGE) is a farming is not enough for farmers living dynamic integrated assessment modeling costs. One of the best ways to increase framework for global change. Land farmersˈ income, is land use management degraded model is one of the basic of these lands. The appropriate land use models of IMAGE (Tingting, 2008). The selection due to farmers' income aim of this study was to use the Integrated increases. Land use change requires Model to Assess the Global Environment compliance consideration with the (IMAGE)- Land Degrade Model (LDM) technical, economical and social

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to evaluate the soil erosion risk in 37°5'N, the altitude of area is 950-2500 Kashidar Watershed. m above sea level with an area of 15017 ha. The study area accommodates 6 2. Materials and Methods villages (Golestan Natural Resources The study area is located in Southern East Bureau, 2009). Map of the study area in of Golestan Province, Iran. Iran and Golestan Province showed in Geographically the study area lies (Fig. 1). between 55°27' to 55°40' E and 36°56' to

Fig. 1. Map of the study area in Iran and Golestan Province

The Integrated Model to Assess the factor. According to IMAGE-LDM, the Global Environment (IMAGE)- Land monthly average intensity of rainfall Degrade Model (LDM) was used to (mm/day) was selected as the indication evaluate the soil erosion risk in the study of rainfall intensity. If the maximum area. The (IMAGE)- Land Degrade monthly average of rainfall intension of Model (LDM) input map layers include three months exceeds 2mm/day, the R- rainfall erosivity index (R-factor), relief factor is assigned 1. If the maximum index, soil erodibility index and land monthly average of rainfall intension of cover index (Tingting, 2008). three months belongs to 0 to 2mm per Among the four major factors day, the R-factor is assigned 0. If the affecting the soil erosion, rain is the main value between these two extremes a agent for erosion, which reflects the linear relation is assumed (Tingting, potential rate of soil erosion. Not all 2008). rainfalls can induce soil erosion except Based on these factors LDM those showers of high intensity. So the model provides a map that shows the erosivity of rainfall is mainly determined susceptibility and potential sensitivity to by the intensity of rainfall events. water erosion in Kashidar Watershed. Rainfall in Kashidar Watershed is very Potential susceptibility and sensitivity to unevenly distributed, which mainly water erosion is ranged from E1 to E6. concentrates in spring season, so the From E1 to E6, the potential rainfall data from March to June was susceptibility and sensitivity to water used to calculate R- erosion gradually increased (Tingting, 2008).

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These maps were prepared and considered. The feasible management superimposed using the ArcGIS software actions for the southern parts of Kashidar to estimate the water erosion severity over Watershed are enclosure, Forage the study area. The Spearman rank cultivation and orchard planting. correlation coefficient was calculated to Assuming the present condition as a base evaluate the accuracy of hazard zonation case scenario, the number of new (Mesdaghi, 2004). To develop scenarios will be 2n – 1, in which n is the management scenarios, all feasible number of management actions. The base management actions were listed and all of case scenario is regarded as scenario one the possible combinations of those actions and the other scenarios are compared with were considered. In order to determine it (Heathcote, 1998). The scenario the feasible management actions, all the development rules are shown in (Table 1). planning constraints such as time, costs, labor, efficiency, and regulations were

Table 1. Rules for land use-based scenario development for the Kashidar Watershed Suitable Areas Condition after Management Action (before Implementation of Action) Implementation of Actions

Enclosure Poor & moderate rangelands Moderate & good rangelands Forage cultivation Dry land farm Moderate agricultural land Orchard planting Irrigated farm lands Good agricultural land

For each scenario, the land cover pattern chance that water erosion will occur map was synthesized using the query accounting for the actual land use and command of the ArcGIS software. By land cover. According to LDM, soil assuming that the other four input maps erosion susceptibility and sensitivity of the LDM model are not changing by index were calculated. On the basis of the management actions, the water water erosion-sensitivity index, soil erosion hazard map for each scenario was erosion risk grade can be determined created. The LDM is based on the concept (Tingting, 2008). The eight land use- of soil susceptibility and sensitivity to based scenarios developed for the study water erosion. Susceptibility to water area by combining all different erosion is based on the current terrain management actions is shown in (Table erodibility and rainfall erosivity. 2). Sensitivity to water erosion describes the

Table 2. Land use-based scenarios developed to manage the water erosion in the Kashidar Watershed Management Action S1 S2 S3 S4 S5 S6 S7 S8 Enclosure - + - - + - + + Forage cultivation - - + - + + - + Orchard planting - - - + - + + +

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The extent of water erosion hazard For land use-based scenarios the classes for each scenario was compared establishment costs are identified as labor with classes of the present condition (base cost and seed price. The establishment case scenario). The Kappa index of costs of each management scenario were agreement was used for comparison calculated by Equation 2. purposes. Several criteria and indices can n be used to select the best scenario among E  di Ai  Ai  Equation 2 various scenarios. Usually a set of criteria i1 Where, which include the public attitude and E is establishment costs; values are suggested (Heathcote, 1998). d is the cost of the management activity i; However, in this study, the physical and i A is the area of activity i; economical criteria were used. i Ā is the area of activity i for base case Differences between water erosion hazard i scenario; and n is the number of maps at the present condition after management actions. implementation of each scenario were Therefore, the costs of each used as the physical index. To sum up, the management scenario are the sum of all ordinal values of water erosion hazard actions costs. classes had been multiplied by their The linear scale transformation extent and gathered to obtain the value of had been used to convert the original the physical index. Since the index values into standardized index implementation of each scenario results values. There are various methods of into changes in the dry mass production, linear scale transformation. In this study, total gross margin and establishment costs the method of maximum standardization were used as two indices of economic had been applied. In this method, to criteria. Total gross margin is described standardize a benefit effect, the value of as the gross income minus the variable each index was divided by the highest costs associated with an value of the index across different enterprise/activity (Heathcote et al., 002). scenarios. For instance, to standardize the The total gross margin generated gross margin index, its value for each from a given set of management activities scenario was divided by the highest value is calculated by Equation 1. m of the index across different scenarios. Equation 1 For a cost effect, such as water erosion G  PjYj C j .Aj j1 (the physical index) and establishment Where: costs (an economic index) Equation 3 had G is total gross margin; been used: Pj is price of crop j (Iranian Rials score  score per production unit, kg); score 1 i min standardized score Yjis yield of crop j per unit area max (ha); (Equation 3) Cj running cost of crop j (Iranian The Delphi method was used to Rials per unit area); assign weights to the indices. For this m is the number of crops, and purpose, a panel of six experts in natural Aj is the area under crop j. resources management had been addressed and requested to weight the The values of input parameters indices on a given scale of 0 to 1. After used in the economic calculations were gathering the responses, they had been obtained from the previous rangeland collated and returned back to the management studies conducted in the contributors and requested to revisit the study area (Golestan Natural Resources weights in case of inconsistency. This Bureau, 2009). process was repeated until a consensus

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was reached on the weights assigned to 3. Results the criteria. Multiple Criteria Decision 3.1. Model analysis Making (MCDM) technique had then The input parameters of the LDM model been applied to evaluate the scenarios. were estimated and summed up to predict For each scenario, the standardized score the water erosion severity of the study of indices had been multiplied by their area across the management scenarios and corresponding weights and summed up to their respective water erosion hazard provide a criterion for evaluation purpose. maps were then synthesized. For instance, The scenarios with higher total sum of (Fig. 2 and Table 3), show the water weighted scores were identified as the erosion hazard map and the extent of best ones. For visual comparison of the water erosion hazard classes of the study index values associated with each area for the present condition, scenario, segment diagram presentation respectively. was utilized. A sensitivity analysis was carried out to determine the dependency of results to the weights of the indices (Knack, 1996).

Fig. 2. Water erosion hazard map of the Kashidar Watershed for the present conditions

Table 3. Distribution of water erosion hazard classes for the present condition (base case scenario) From E1 to E6 in the Kashidar Watershed Hazard Class E1 E2 E3 E4 E5 E6 Sum

Area (ha) 982 0 1446 3303 3754 5531 15017 Area (%) 6.5 0 9.5 22 25 37 100

There was no area with E2 water erosion scenarios are due to the changes in two hazard class in Kashidar Watershed input indices of land cover and relief (Table 3). Also the water erosion hazard indices. maps corresponding to scenarios containing single actions were displayed in (Fig. 3). According to the LDM model, the differences observed in the water erosion hazard maps of the management

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Fig. 3. Water erosion hazard maps corresponding to the single action management The water erosion hazard map of the Scenario 8 all the management actions present condition was compared with were implemented over the whole study those of the other management scenarios area while in Scenario 4 only a limited pairwise. (Table 4), presents the Kappa- proportion of the study area, suitable for index agreement of water erosion hazard the action, was allocated to orchard for scenario1 against the other scenarios. planting. As shown in the table, the degree of agreement varies from 0.01 to 0.4. The low degree of agreement indicates the significant impact of the management scenarios. The minimum and maximum degrees of agreement correspond to the S8 and S7, respectively. This is mostly due to the extent of the areas allocated to the management actions. For instance, in

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Table 4. The Kappa-index of agreement of water erosion hazard for scenario1 against the other scenarios Scenario S2 S3 S4 S5 S6 S7 S8 Kappa index 0.09 0.03 0.07 0.11 0.08 0.11 0.16

The Spearman correlation addition, for some actions there were coefficient indicated the conformity some running costs (variable costs) which between the hazard classes of water should be figured out. They include erosion map predicted by the LDM preparation, re-plantation, enclosure, model and ground evidences. It varies maintenance, and harvesting costs. For between -1 (a perfect negative fifteen-year decision horizon, the total correlation) and +1 (a perfect positive costs of forage cultivation and orchard correlation). This indicates the planting were estimated 300 million and appropriate performance of the LDM 3,000, million IRI Rls per unit area. (Fig. model to assess water erosion hazard 4), illustrates the change in total gross classes in the Kashidar Watershed. margin (Terms of ten million Rials) for each scenario and (Fig. 5), shows the 3.2. Indices analysis establishment costs (Terms of ten million The following assumptions were made to Rials) corresponding to each scenario. quantify the economic indices. The price To quantify the physical index, the of unit of dry mass production is 4000 IRI water erosion hazard maps corresponding Rls. The enclosure and forage cultivation to various scenarios were used. For each will increase the dry mass production by -1 scenario, the rank of each water erosion 100 and 7000 kg.ha , respectively. The hazard class was multiplied by its extent implementation of each scenario incurs and summed up to obtain the quantitative some establishment costs which are about value of the physical index. (Fig. 6), 20 and 200 million IRI Rls per hectare for displays the quantitative value of the forage cultivation and Orchard planting physical index for various management actions, respectively. There was no scenarios. establishment cost for enclosure. In

Fig. 4. The change in total gross margin across eight management scenarios

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Fig. 5. The establishment costs across eight management scenarios

Fig. 6. The physical index across the management scenarios 3.3. Trade off analysis values of variables were scaled The Delphi approach was applied to independently so that the maximum value assign the weights to the indices. Based (or „best‟) in each variable was 1 and the on this approach the weights of water minimum (or „worst‟) was 0.0 Segment erosion (physical index), gross margin, diagrams facilitate comparison between and establishment costs (economic cases. To facilitate comparison among the indices) was determined as 0.4, 0.4, and management scenarios in segment 0.2, respectively. After standardization of diagrams, for those variables with adverse the indices, their values were multiplied impacts, their inverted values were by their weights and summed up to obtain represented in the diagrams. This was the the final score for each scenario. The case for „establishment costs‟ and scenarios S8, S5, S7, and S2 ranked from „physical index‟. That is, an „increase‟ in 1 to 4, respectively. all variables corresponds to a good A suitable visual technique assists outcome. Hence, the radii of the diagrams in representing and interpreting show the level of achievement of multivariate data sets. Thus, segment management objectives considering all diagram presentation was utilized to impact indices. represent the outcome variables corresponding to each management scenario (Fig. 7). In segment diagrams the

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Fig. 7. Values of impact indices for the 8 management scenarios in the Kashidar Watershed

Trade-off analysis indicates that the been considered. It was also assumed that scenarios S8, S5, S7 and S2 were the best there were no serious ecological and scenarios to control water erosion hazard social limitations for implementation of in the Kashidar Watershed. To investigate the management actions. In other words, the robustness of the results, a sensitivity all of the scenarios were considered to be analysis was carried out. To this end, we feasible. used three different perspectives, in each Considering the physical index, a specific index was emphasized on. the best scenario was the one that corresponds to an erosion map with a 4. Discussion minimum proportion of high water Based on the LDM model, land cover and erosion hazard classes. While considering relief indices are the two important the economic indices, the scenarios which parameters controlling the water erosion result in minimum establishment costs rate and hazard. Therefore, selection and and maximum total gross income are implementation of best land use types and identified as best scenarios. The scenario management practices are necessary to S7, S8, S5 and S6 were appropriate control water erosion in a region. Using a scenarios when only the physical index is scenario-based approach is a straight considered (Fig. 6). Considering the total forward and efficient way to choose the gross income index, the scenarios S8, S5, best land use type over an area. Since S7 and S2 were among best group of each management scenario may have scenarios. Regarding the establishment some positive and negative physical costs, the best group of scenarios was and/or economical impacts, a MCDM identified as S1, S7, S5 and S8. However, approach was applied to trade off the when the physical and economic indices impacts and chooses best scenario/s. were collectively considered the order of The Spearman correlation best scenarios differs markedly. To do coefficient indicated a high conformity this, a MCDM approach had been used. between the hazard classes of water Based on this approach, the scenarios S8, erosion map predicted by the LDM S5, S7 and S2 had been ranked as best model and ground evidences. To develop ones to control water erosion in the study the scenarios, the technical limitations area. To evaluate the different related to the management actions had management scenarios, they had been

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compared with the present condition. This Heathcote, J., Perri, F. and Violante, G. L., 2002. was similar to the methodology Unequal we stand: An empirical analysis of economic inequality in the United States, 1967- implemented by Cerck (1996), Armanino 2006. Jour. Review of Economic Dynamics, et al. (2000), and Sadoddin (2006). 13(1), 15_51. The sensitivity analysis indicated Hengki, D. B., Walangitan, A. P., Setiawan, B. that the results of the MCDM were not B., Bambang, T. R., Harjo, S. A. and Bobby, significantly affected by the different P., 2012. Optimization of land use and perspectives. The result of the sensitivity allocation to ensure sustainable agriculture in analysis indicated that four scenarios of the Catchment Area of Lake Tondano, S8, S5, S7 and S2 were among best Minahasa, North Sulawesi, Indonesia. International Journal of Civil & Environmental scenarios regardless of the weighting Engineering Ijcee-Ijens, 12: 68-75. perspectives. These four scenarios are identical with the scenarios which were Karunakaran, K., 2012. Is the current land use pattern in crop agriculture is sustainable in the chosen by the Delphi approach as best Bhavani Basin of Southern India?: Application scenarios. This indicates the robustness of of a Bio-economic model. Coimbatore the approach implemented in this study. University Publicaction Press, India, 17 pp. Knack, S., 1996. Sensitivity analysis for provide Acknowledgements the best selection in empirical researchers. Jour. The authors would like to thank Doctor Soil and Water Conservation, 11: 53 – 68. Amir Sadoddin and Vahedberdi Sheikh. Leh, M., Bajwa, S and Chaubey, I., 2011. Impact of land use change on erosion risk: an integrated References remote sensing, geographical Information system and Modeling Methodology. Jour. Land Degradation and Development, 10: 1-13. Armanino, D. L., Clemens, A. G., Coburn, C. H., Molotch, P. N., Oakes, S. A., and Richardson, Maroyi, V. 2012. Enhancing food security K., 2000. Analysis of alternative watershed through cultivation of traditional food crops in management strategies for the Lauro Canyon Nhema communal area, Midlands Province, Watershed, Santa Barbara County, California. Zimbabwe. African Jour. Agricultural School of Environmental Science and Research, 39: 5412-5420. Management, University of California, Santa Martha, M., 2004. Soil erosion as a driver of land- Barbara. A group project submitted in partial use change. Agriculture, Ecosystems and satisfaction of the requirements of the Degree of Environment, 105: 467–481. Master of Environmental Science and Management. Mesdaghi, M., 2004. Regression methods in agricultural and natural resources, Imam Baumgart, P. and Fermanich, K., 2008. Lower Hossien Publication, 290 pp. (In Persian). Fox River suspended sediment and phosphorous load allocation and reduction strategies to Green Nikkami, D., 2009. Land Use Scenarios and Bay using the SWAT. 5th International SWAT Optimization in a Watershed. Applied Science, Conference. Beijing, China. 9: 287-295. Cerck, S., 1996. Sensitivity analysis for providing Sadoddin, A., 2006. Bayesian network models for the best selection in empirical researches. Jour. integrated catchment - scale management of Soil and Water Conservation,11: 53-68 salinity. Center for Resource and Environmental . Studies. The Australian National University. Ferreira, V., and Panagopoulos, T., 2012. Ph.D thesis, 227 pp. Predicting Soil Erosion Risk at the Alqueva Dam Watershed. Jour. Spatial and Singh, L. P., 2008. Changing profile of farm- Organizational Dynamics, 9: 60-80. production marketing - A post globalization perspective. Mithila University Publicaction Golestan Natural Resources Bureau, 2009. Press, India, 33 pp. Watershed management studies of Kashidar Watershed, 248 pp. Tingting, V., 2008. Assessment of soil erosion risk in Northern Thailand. The International Heathcote, I. W., 1998. Integrated watershed Archives of the Photogrammetry, Remote management, John Wiley and Sons Publication, Sensing and Spatial Information Sciences, 37: 414 pp. 703-708.

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پ٘فثٌٖ٘ اثطات ؾٌبضَّٗبٕ هسٗطٗت وبضثطٕ اضاضٖ ثط ذطط فطؾبٗف آثٖ )هطبلؿِ هَضزٕ: حَظُ آثر٘ع وبق٘ساض، قْطؾتبى آظاز قْط، اؾتبى گلؿتبى(

زاٍز اذضطٕ، اؾتبزٗبض گطٍُ هطتؽ ٍ آثر٘عزاضٕ زاًكگبُ هالٗط، زاًكگبُ هالٗط، اٗطاى )ؿًٌَٗسُ هؿئَل(

ؾوبًِ افتربضٕ اٌّساًٖ، زاًف آهَذتِ وبضقٌبؾٖ اضقس هسٗطٗت هٌبطك ث٘بثبًٖ، زاًكگبُ ؾلَم وكبٍضظٕ ٍ هٌبثؽ طجؿٖ٘ گطگبى، اٗطاى ثٌْبظ ؾطبئ٘بى، اؾتبزٗبض گطٍُ هطتؽ ٍ آثر٘عزاضٕ زاًكگبُ هالٗط، زاًكگبُ هالٗط، اٗطاى ؾل٘طضب اٗلسضهٖ، زاًك٘بض گطٍُ هطتؽ ٍ آثر٘عزاضٕ زاًكگبُ هالٗط، زاًكگبُ هالٗط، اٗطاى چکیده فطؾبٗف آثٖ زض اٗطاى ٍ ثرهَل ثرفّبٕ قوبلٖ اٗي وكَض قسٗس اؾتت. ٗىتٖ اظ هْوتتطٗي اثتطات خبًجٖ فطؾبٗف آثٖ وبّف و٘ف٘ت ذبن ثطإ تَل٘س هحهَالت وكبٍضظٕ اؾتت. ثٌتبثطاٗي اضظٗتبثٖ ذطتط فطؾبٗف آثٖ ثطإ تؿؾَِ پبٗساض زض ثرف وكبٍضظٕ ضطٍضٕ اؾت. اٗي تحم٘ك خْت زؾتت٘بثٖ ثتِ اثتعاضٕ خسٗس ثطإ هسٗطٗت فطؾبٗف آثٖ ثب تَخِ ثِ ؾَاهل ف٘عٗىٖ ٍ التهبزٕ اًدبم قس. حَظُ آثر٘ع وبق٘ساض زض قوبل اٗطاى ثِ ؾٌَاى هطبلؿِ هَضزٕ اًتربة قس. تدعِٗ ٍ تحل٘تل اثتطات ف٘عٗىتٖ ٍ التهتبزٕ ؾ 8تٌبضَٗ هسٗطٗت وبضثطٕ اضاضٖ ثب اؾتفبزُ اظ ضٍـ تهو٘نگ٘طٕ چٌس هؿ٘بضُ اًدبم قس. ضٍـ تهتو٘ن گ٘تطٕ چٌتس هؿ٘بضُ ٗه ؿ٘ؾتن هسل ؾبظٕ ثطإ ووه ثِ تهتو٘ن گ٘تط اى زض فطهَلتِ وتطزى ؾتٌبضَّٗبٕ پ٘كتٌْبزٕ، تدعِٗ ٍ تحل٘ل اثطات اٗي ؾٌبضَّٗب زض فطؾبٗف آثٖ، تفؿ٘ط ٍ پ٘كٌْبز ؾتٌبضَّٗبٕ هٌبؾتت ثتطإ پ٘تبزُ ؾبظٕ زض هٌطمِ اؾت. ا ٗ ي ه ط ب ل ؿ ِ ث ب ّ س ف ه س ل ؾ ت ب ظ ٕ ٍ ا ض ظ ٗ ت ب ث ٖ ذ ط ت ط ف ط ؾ ت ب ٗ ف آ ث ت ٖ ز ض ح ت َ ظ ُ آ ث ر ٘ ت ع وبق٘ساض ثب اؾتفبزُ اظ هسل IMAGE\LDM اًدبم قس. قبذمّبٕ تَاى فطؾبٗكٖ ثبضـ، پؿتٖ ٍ ثلٌتسٕ، فطؾبٗف پصٗطٕ ذبن ٍ پَقف ؾتطحٖ چْتبض ؾبهتل اؾبؾتٖ هتَضز اؾتتفبزُ زض هتسل IMAGE\LDM

ؿّتٌس. ذطط فطؾبٗف آثٖ زض قف والؼ طجمِثٌسٕ قسُ اؾت. ؾالٍُ ثط اٗي، تَظؽٗ فضبٖٗ اضتفبؼ ٍ اًتَاؼ

اؾتفبزُ اظ ظه٘ي ً٘ع هَضز اضظٗبثٖ لطاض گطفت. تدعِٗ ٍ تح٘تل ّتبٕ اًدتبم قتسُ ًكتبى زاز وتِ اظ ه٘تبى 8

ؾٌبضَٕٗ هرتلف هسٗطٗت فطؾبٗف آثٖ ؾٌبضَّٗبٖٗ وِ هؿتبحت وتالؼ ّتبٕ ذطتط فطؾتبٗف آثتٖ آًْتب حسالل، زضآهس ًبذبلم حساوثط ٍ ّعٌِٗ اؾتمطاض حسالل اؾت، ؾٌبضَّٗبٕ ثطتط ؿّتٌس.

کلمات کلیدی: وبضثطٕ اضاضٖ، فطؾبٗف آثٖ، تدعِٗ ٍ تحل٘ل، MCDM، حَظُ آثر٘ع وبق٘ساض