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The goal of this study is offer a deep understanding of the landscape dynamics in the Gorgan Township, the Golestan Province, Iran. Landsat satellite imagery of two different time thresholds, i.e. the years 1992 and 2011, was acquired from the US Geological Survey database and the changes were quantified for the Gorgan area covering a 19-year time span. Furthermore, an integrated Cellular Automata-Markov Chain (CA-MC) model was applied to predict future changes up to the year 2030. We used the intensity analysis method to compare the historical dynamics of different land categories at multiple levels. The results indicated that during the 19 years, the built-up and forest areas increased by 2.33% and 0.27%, respectively, while agriculture and remnant vegetation decreased by 2.43% and 0.24%, respectively. The CA-MC model illustrated that in the following 19 years, the built-up areas could increase by 2.45%. An intensity analysis revealed that forest gains and losses were dormant while remnant vegetation gains and losses were active. The built-up area’s gains and water bodies’ losses were active and stationary during both time intervals. The transitions from water bodies and remnant vegetation to agriculture were regularly targeting and stationary, while the transition from forest to agriculture was regularly avoiding and stationary. Our findings also indicated a heavy systematic transition from agriculture to built-up areas. Regarding the increasing population growth and urbanisation in the region, the outcomes of this study can help make informed decisions for the management and protection of natural resources in the study area.

Sea-level rise (SLR) is known as a central part of the Earth’s response to human-induced global warming and is projected to continue to rise over the twenty-first century and beyond. The importance of coastal areas for both human and natural systems has led researchers to conduct extensive studies on coastal vulnerability to SLR impacts and develop adaptation options to cope with rising sea level. Investigations to date have focused mostly on developed and highly populated coasts, as well as diverse ecosystems including tidal salt marshes and mangroves. As a result, there is less information on vulnerability and adaptation of less-developed and developing coasts to sea-level rise and its associated impacts. Hence, this research aimed at outlining an appropriate coastal management framework to adapt to SLR on the coasts that are in the early stage of development. A coastal area with a low level of development, located in southern Iran along the Gulf of Oman, was selected as a case study. The types of lands exposed to the high-end estimates of SLR by 2100 were identified and used as the primary criteria in determining the practical adaptation approaches for developing coasts. The result of coastal exposure assessment showed that, of five exposed land cover types, bare land, which is potentially considered for development, has the highest percentage of exposure to future sea-level rise. In order to protect the exposed coastal lands from future development and increase adaptive capacity of coastal systems, we developed a Spatial Integrated SLR Adaptive Management Plan Framework (SISAMP) based on an exposure reduction approach. Spatial land management tools and coastal exposure assessment models along with three other key components were integrated into the proposed conceptual framework to reduce coastal vulnerability through minimizing exposure of coastal communities to SLR-induced impacts. This adaptation plan provides a comprehensive approach for sustainable coastal management in a changing climate, particularly on developing coasts.

A wide variety of mathematical and empirical models have been implemented as practical tools for land-use planning, and multilayer perceptron (MLP), logistic regression or LR (mathematical model) and multi-criteria evaluation or MCE (empirical) are among widely applied models. One of the main drawbacks of the mathematical models is that they require dependent data and the process of data collection can be so costly and time-consuming for large areas. As such, we investigated the possibility of providing dependent data set through the MCE method for tourism planning in Golestan Province, Iran. The accuracy of MCE-based algorithms was investigated using ground truth data collected during field observations from early spring up to late summer 2016. The MCE-based and ground-based outputs were investigated and compared for spatial accuracy and connectivity and compactness of the results using receiving operator characteristic (ROC) and landscape configuration metrics. ROC statistics were scored at 0.886, 0.834, 0.82 and 0.814 for ground-based MLP, ground-based LR, MCE-based MLP and MCE-based LR, respectively, showing no meaningful differences between MCE-based and ground-based methods in terms of spatial accuracy. Landscape metrics also indicated that MCE-based methods have resulted in a more connected and manageable pattern for tourism planning. According to the results of this study, MCE can serve as a preliminary approach to define field sampling spots or even as an alternative to field observation efforts in case of limited time and financial resources.

To mitigate the negative effects of land use developments, the current study focused on the hydrological connectivity within the landscape ecological network of Gharesou watershed, Iran, using Graph theory. Thus, scenarios of the future land use arrangements were used for the objective assessment of the effects of patterns on the ecological structures and functions, the main target being runoff control. Hydrological connectivity was analyzed using runoff source network, stream network and its buffer zone. Also, functions like permeability and runoff production potential were analyzed for the future scenarios. Following the ranking of the connectivity significance of the hydrological graphs elements, the ecosystem services hotspots and incompatible land uses were demonstrated. Subsequent assessments of the elements of runoff source networks using Circuit Theory helped identify the future critical areas. Analyses of the hydrological graphs and the runoff source network represented the amount and location of critical areas in each development scenario as well as the imposed hydrological costs. The hydrological and ecological land use costs were used in the process of land use optimization through Simulating Annealing algorithm (SA). Using these costs in the land use planning process resulted in detecting areas which may experience disturbance later in future. Finally, the results of the optimization of scenarios showed how land use arrangements in each scenario can be optimized to simultaneously include the ecological suitability (vertical relationships) and the ecological network relationships (horizontal relationships).

Land management and biodiversity protection are highly dependent on ecosystem classification. To identify the ecosystems, often ecologically homogenous areas are distinguished based on physical and biological features at various scales. These areas can also be considered as biodiversity surrogates for protection policies and planning. We classified the terrestrial areas of Iran into ecosystems using revised and updated layers of landform and climate as our two main criteria. Moreover, we applied a revised vegetation layer as the confirmatory criterion. At a scale of 1:1,000,000, we obtained a total of 119 homogenous ecological units, and based on the dominant vegetation types, we classified them into 21 terrestrial ecosystems at the national level. Of these ecosystems, 11 were dominated by vegetation, and the remaining 10 had sparse nondominant vegetation. Evaluation of the least and most frequent ecosystem patches and ranking of their size classes using landscape metrics provided an information basis for better land protection planning. We maintain that each ecosystem needs to be represented by a protected area and its size and distribution also helps us form a comprehensive and effective protection network in Iran. Graphical abstract.

This study dynamically analyzes the trade-off between three ecosystem services (ESs), including soil retention, habitat quality, and food supply in the Gorganrood watershed, northeastern Iran. In this regard, several analytic tools including the Integrated Valuation of Ecosystem Services and Tradeoff (InVEST) model, Cellular Automata-Markov Chain (CA-MC) model, Intensity Analysis and Trade-Off (TO) index are employed to dynamically link the process of land use change with the temporal flow of ESs. Two scenarios of business as usual (BAU) and environmentally sound planning (ESP) are developed to produce land use layers under various planning strategies. Accordingly, the BAU scenario is more successful at providing a landscape with a higher acreage of agricultural fields and rangelands. Based on tradeoff index results, the BAU scenario establishes a landscape in which food supply is intensified by declining land capability for providing soil retention and habitat quality services. In contrast, the ESP scenario generates a landscape in which the outputs of the TO index indicate that habitat quality and soil retention benefits are improved by decreasing the potential of the area to supply food. This study emphasizes that a dynamic trade-off analysis of multiple ESs can assist planners and policy makers to make informed decisions and undertake strategic planning in Iran.

Land use changes associated with urbanization often have negative impacts on scenic beauty. In this paper, we explore and compare the visual impact of two different scenarios of urban growt...

In this study, the goal was to identify critical sites for biodiversity conservation in the Gharesoo River Basin, Iran, based on integration of anthropogenic threats and biodiversity value. Firstly...

Conversion of forest land to farmland in the Hyrcanian forest of northern Iran increases the nutrient input, especially the phosphorus (P) nutrient, thus impacting the water quality. Modeling the effect of forest loss on surface water quality provides valuable information for forest management. This study predicts the future impacts of forest loss between 2010 and 2040 on P loading in the Tajan River watershed at the sub-watershed level. To understand drivers of the land cover, we used Land Change Modeler (LCM) combining with the Soil Water Assessment Tool (SWAT) model to simulate the impacts of land use change on P loading. We characterized priority management areas for locating comprehensive and cost-effective management practices at the sub-watershed level. Results show that agricultural expansion has led to an intense deforestation. During the future period 2010–2040, forest area is expected to decrease by 34,739 hm2. And the areas of pasture and agriculture are expected to increase by 7668 and 27,071 hm2, respectively. In most sub-watersheds, P pollution will be intensified with the increase in deforestation by the year 2040. And the P concentration is expected to increase from 0.08 to 2.30 mg/L in all of sub-watersheds by the year 2040. It should be noted that the phosphorous concentration exceeds the American Public Health Association′s water quality standard of 0.2 mg/L for P in drinking water in both current and future scenarios in the Tajan River watershed. Only 30% of sub-watersheds will comply with the water quality standards by the year 2040. The finding of the present study highlights the importance of conserving forest area to maintain a stable water quality.

Setting a quantitative conservation target comprises an important step in systematic conservation planning. This paper outlines a first attempt to calculate quantitative conservation targets for selection of national parks, wildlife refuges, and protected areas in 21 terrestrial ecosystems at the national level. To formulate and calculate targets, we further developed Pressey and Taffs’s formula (2001) and included baseline target, natural rarity and potential ecological vulnerability (PEV) in our formulation. The PEV was defined as the potential impacts resulting from exposure to main threats plus ecological sensitivity of any land tract without considering adaptation. Our results showed that the minimum and maximum conservation targets for selection of national parks, wildlife refuges and protected areas as percentages of the area of each terrestrial ecosystem were between 1.44%–3.17%, 3.87%–8.08%, and 6.36%–13.27%, respectively. We showed how to design an effective network of three management categories including the full range of terrestrial ecosystem types within the country. Nationally and under current conditions, we found that the Moderate-Desert Woodland & Shrublands and the Cold-Desert Steppe Scrubland ecosystems required the most and least attention, respectively. This finding provided additional support for the establishment of a representative, comprehensive and balanced network of protected areas in the country and finally a fundamental guide for effective ecological conservation and environmental management.

This study seeks to benefit from a medical science-based implication for urban and landscape planning in the Gorgan Township area, a highly developing region in northeastern Iran. Based on growth stages and patterns of a malignant cancer tumor inside a human's body, a sustainable, realistic and practical urban growth pattern is introduced applying land suitability analysis, SLEUTH urban growth modeling, landscape metrics and statistical analyses. This pattern, in this study called cancer-treating , is implemented through three predictive scenarios including afforest protection (AP), rangeland protection (RP) and historical growth (HG). Accordingly, the effects of each scenario on land suitability parameters for afforest, rangeland and urbanization activities are evaluated to compare their efficiency. In addition, Spearman correlation analysis is also undertaken to measure the bivariate associations between landscape metrics and land suitability values to specifically address the consistency of the cancer-treating growth pattern with the potential of the study area. According to the results, the HG scenario yielded the highest amount of urban growth without substantially modifying the historical growth trajectory while no negative impact on land potential for targeted land-uses were detected. Correlations between landscape metrics and land suitability parameters also provided evidence for the functionality of the cancer-treating growth pattern in terms of area and connectivity metrics. Findings of the present study highlight that cancer-treating growth strategy can be considered as a realistic, practical and sustainable planning alternative for Iranian developing urban environments that are at times undergoing uncontrolled expansion processes.

The WRASTIC model is one of the most practical methods to assess the risk and an advanced method to evaluate the potential for the pollution in a catchment basin. In order to assess the risk of pollution in the Gorgan Bay catchment, six sub-basins of the study area were extracted using the elevation map in QGIS and TerrSet software. The risks have been detected by field visits, library studies, and asking experts and related organizations. Finally, the WRASTIC index was provided as seven informational layers and the index number for each sub-basin was calculated by weighting, ranking, and integrating the layers. The results showed that three sub-basins have a high risk and three others have a medium risk. According to the level of risk, this catchment is exposed to pollution and has vulnerability mainly due to human activities including a high volume of sewage discharged into the catchment, a high amount of agricultural and entertainment activities in the region, transport pathways and railways, as well as the vastness of the catchment. Therefore, supervising and controlling human activities in the catchment is necessary to improve the ecological conditions of water resources in the area.

The primary objective of this study was to use ecosystem services in land allocation across Gharehsoo watershed in Golestan Province, north of Iran. Hence, land suitability was assessed using multi-criteria evaluation (MCE) methods to develop forestry, agriculture, range management, construction, and conservation land uses. Ecosystem services including water yield, habitat quality, and aesthetic quality were also quantified using integrated valuation of environmental services and tradeoffs (InVEST) and MCE methods. Next, the land allocation was investigated under five scenarios developed based on the land suitability and ecosystem services, namely land suitability, water yield, habitat quality, aesthetic quality and integration scenarios. The scenarios were then implemented using particle swarm optimization algorithm and compared to each other, based on the spatial land use variability and land use fragmentation. The results revealed that there is an obvious difference between the northern and southern parts of the region in terms of their land suitability and ecosystem services. An almost increasing trend was also observed for the allocated area to forestry, range management, and conservation land uses in the following order of scenarios: land suitability, aesthetic quality, water yield, integration, and habitat quality. However, an almost decreasing trend was obtained for agriculture and construction land uses in the same order of scenarios. Besides, range management and agriculture land uses had the highest and lowest spatial variability in all the scenarios, respectively. The results also clarified that the land use fragmentation decreased more in the water yield scenario than the other scenarios. This study indicated that using ecosystem services in land allocation can improve land use pattern and arrangement, consequently, helping take into account the ecosystem conditions in land use planning. In conclusion, this approach can help land managers to make appropriate decisions for maintaining ecosystem services in any region.

This study emphasizes the utility of decentralized urban planning in a developing region. The multi criteria evaluation (MCE) method was applied to produce suitability surfaces for agriculture, environmental protection and urbanization. The SLEUTH urban growth model was calibrated using two of the suitability maps: agriculture and environmental protection whereas the urbanization suitability layer was used for evaluating the results. These layers were used as weighted excluded inputs under two scenarios of agricultural land protection (AP) and environmental protection (EP). In this way ranges of resistance values against urbanization were provided for the SLEUTH model calibration. Hence, the model was forced to allocate urban clusters in locations with a minimum negative impact on farmlands and natural areas. The MCE-informed SLEUTH calibration assisted in performing decentralized urban growth allocation. Predicted under both scenarios (up to 2040) in the Gorgan township area, Iran; the major urban cores with large sizes were strictly constrained from further sprawl, instead, small scale and decentralized urban centers were established and started their own growth cycles. While substantial increase in the amount of impervious surface was allowed to occur, no meaningful negative impact was brought about on the land suitability of targeted land-uses. For both scenarios, the connectivity and compactness of urban patches were also investigated. The EP storyline yielded urban patches with more connectivity, while the AP scenario was more successful in retaining higher suitability values. The decentralized strategy can serve as a planning paradigm, which simultaneously considers several environmental parameters.

Lush Hyrcanian mixed temperate forests are globally important ecosystems with considerable ecological and economic values and high variety of ecosystem services (ES). In this study, an ES-based approach is adopted to develop a spatial conservation framework for Gorganrood Watershed, northeastern Iran. In doing so, the integrated valuation of ecosystem services and trade-offs modeling tool was implemented to spatially quantify a collection of five ES including soil retention, habitat quality (as a proxy for biodiversity), water yield, food supply and carbon storage. These services were integrated into a single layer based on the Total Ecosystem Services (TES) index. By performing correlation analyses, the type and the strength of relationships between ES, TES index values and different land features were analyzed to reveal which land-use categories at what locations are more capable to provide bundles of ES. Accordingly, Hyrcanian mixed temperate forests in the southern sub-watersheds of the area were detected to have higher potential for simultaneous provisioning of multiple ES. In addition, we show that biodiversity hotspots and provision of other ES are highly correlated and thus that conservation of one group can be beneficial for the other. Our findings are particularly applicable in areas where complex network of land-uses and limited resources are major barriers against effective conservation of Hyrcanian mixed temperate forests.

A systematic approach for prioritization of protected areas is the use of artificial intelligence. This approach employs computer algorithms based on an objective function to identify the best network of areas to be protected. Site selection algorithms are commonly used to identify areas of high conservation value. This study used three types of heuristic algorithms (simulated annealing, greedy, rarity) to prioritize areas for protection in Mazandaran Province of Iran using Marxan software. The goal was to select a conservation network with the smallest possible area in which maximum protection targets are achievable. The effects of spatial scale, algorithm, and zone compactness were also examined. We found that the existing network of protected areas is inadequate to achieve conservation targets. The algorithm results provided the best areas for supplementation of the current network. The simulated annealing algorithm provided the most plausible results for all scenarios. These results can be used to modify the existing boundaries of the protected areas network and introduce new sites for protection of plant and animal species.

Nowadays, human societies encounter the challenges to provide food and livelihood of the people. The aquaculture industry is one of the essential solutions dealing with these problems. Site selection is a beneficial way to reduce pressure on the ecosystem and to understand the environment's potential. In this study, suitable sites for sturgeon cultivation in the southern coast of the Caspian Sea, Golestan Province, Iran were optimized using multi-criteria evaluation (MCE) method. First, the criteria into three groups were categorized: environmental, infrastructural, and water resources. Then, the maps were Fuzzy based on the sturgeon's characteristics and expert opinions. Afterward, to determine the effect of each criterion, criteria were weighted by the Analytic Hierarchy Process (AHP). Finally, based on the weighted linear combination (WLC) approach, the layers and created the final map were combined. Results represented three sites which were most desirable for sturgeon farming in the Golestan Province coastal area and there are 24,638 ha in this region which has the highest desirability for sturgeon farming. Factors such as distance to water resource and distance to roads, industrial and residential areas have had the highest effect on the process of site selection for sturgeon farming. Using MCE method, site selection makes a reasonable decision with little data and creates an understanding of the conflicts among other users in the coastal lands.

This study attempts to develop a non-path-dependent model for environmental risk management and polycentric urban land-use planning in Gorgan Township area, Iran. Applying three suitability layers of environmental risk (soil erosion, flood risk, fire risk, and land susceptibility), urbanization potential, and integrated surface (environmental risk plus urbanization potential layers), a non-path-dependent Cellular Automata-Markov Chain (CA-MC) model was configured to execute three scenarios of polycentric urban growth allocation. Specifically, the modeling approach improved the traditional functionality of the CA-MC model from a prediction algorithm into an innovative land allocation tool. Besides, due to its flexibility, the non-path-dependent model was able to explicitly include different characteristics of the landscape structure ranging from physical land attributes to landscape functions and processes (natural hazards). Accordingly, three polycentric urban growth allocation efforts were undertaken and compared in terms of connectivity and compactness of the resultant patterns and consumption of other land resources. Based on results, the polycentric allocation procedure based on integrated suitability layer produced a more manageable pattern of urban landscape, while the growth option based on environmental risk layer was more successful for protecting farmlands against excessive urbanization. This study suggests that polycentric urban land-use planning under the strategy of rural land development programs is an available option for designing an urban landscape with lower exposure to natural hazards and more economic benefits to rural residents. Finally, the non-path-dependent modeling is a recommended approach, when highly flexible and interactive decision-support systems as well as trend-breaking scenarios are desired.

Continuous surface of urbanization suitability, as an input to many urban growth models (UGM), has a significant role on a proper calibration process. The present study evaluates and compares the simulation success of the Cellular Automata-Markov Chain (CA-MC) model through multiple methods. For this, a series of mapping algorithms are applied ranging from empirical methods such as multi-criteria evaluation (MCE) to statistical algorithms without spatially explicit suitability mapping rules such as logistic regression (LR) and multi-layer perceptron (MLP) neural network and finally statistical and spatially explicit rule-based methods such as SLEUTH-Genetic Algorithm (SLEUTH-GA) model. The CA-MC model was calibrated in three study locations including Azadshahr, Gonbad, and Gorgan cities in northeastern Iran. Applying Kappa-based indices (Kappa, K location, K Simulation, and K Transloc) and computing relative error (RE) values of landscape metrics, performance of the model was quantified and compared across the three study sites. The MCE and SLEUTH-GA methods, as the most data-demanding and the most computationally complex methods, respectively, yielded approximately similar results (especially in case of Kappa-based indices) and these methods were less successful compared to LR and MLP models. LR and MLP models were less data-demanding, while they produced approximately equal results. This study concludes that, when historical growth patterns feed an urbanization suitability mapping process, neither rules (SLEUTH-GA) nor layers (MCE) are effectively efficient when applied in a separated manner. Instead, methods with statistical rules and least-correlated input layers (LR and MLP) provide better simulation outputs. In contrast, methods such as MCE are more applicable when a non-path-dependent mapping procedure is desired since this method does not require training data (dependent variable) and the provided flexibilities in urbanization suitability mapping under various scenarios can improve the functionality of land-use change prediction algorithms into innovative land allocation tools.

The combination of degrading natural conditions and resources, climate change, growing population, urban development, and competition in a global market complicate optimization of land for agricultural products. The use of pesticides and fertilizers for crop production in the agricultural fields has become excessive in the recent years and Golestan Province of Iran is no exception in this regard. For this, effective management with an efficient and cost-effective practice should be undertaken, maintaining public service at a high level and preserving the environment. Improving the production efficiency of agriculture, efficient use of water resources, decreasing the use of pesticides and fertilizers, improving farmer revenue, and conservation of natural resources are the main objectives of the allocation, ranking, and optimization of agricultural products. The goal of this paper is to use an optimization procedure to lower the negative effects of agriculture while maintaining a high production rate, which is currently a gap in the study area. We collected information about fertilizer and pesticide consumption and other data in croplands of eastern Golestan Province through face-to-face interviews with farmers to optimize cultivation of the agricultural products. The toxicity of pesticides according to LD50 was also included in the optimization model. A decision-support software system called multiple criteria analysis tool was used to simultaneously minimize consumption of water, chemical fertilizers, and pesticides and maximize socio-economic returns. Three scenarios for optimization of agricultural products were generated that alternatively emphasized on environmental and socio-economic goals. Comparing socio-economic and environmental performance of the optimized agricultural products under the three scenarios illustrated the conflict between social, economic, and environmental objectives. Of the six crops studied (wheat, barley, rice, soybeans, oilseed rape, and maize), rice ranked second in the social and fifth in the economic scenarios. Soybeans had the lowest rank for economic and social scenarios and its cultivation in the study area, in terms of economic and social goals, was rejected by the model. However, cultivation of soybeans continues in the area as a responsibility to cater for the major need of the country. Because of subsidized prices of water, fertilizers, and pesticides, the use of these items are far from optimized in the current agricultural practices in the area.