Hydrological connectivity assessment of landscape ecological network to mitigate development impacts.

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 use change modeling through scenario-based cellular automata Markov: improving spatial forecasting.

Efficient land use management requires awareness of past changes, present actions, and plans for future developments. Part of these requirements is achieved using scenarios that describe a future situation and the course of changes. This research aims to link scenario results with spatially explicit and quantitative forecasting of land use development. To develop land use scenarios, SMIC PROB-EXPERT and MORPHOL methods were used. It revealed eight scenarios as the most probable. To apply the scenarios, we considered population growth rate and used a cellular automata-Markov chain (CA-MC) model to implement the quantified changes described by each scenario. For each scenario, a set of landscape metrics was used to assess the ecological integrity of land use classes in terms of fragmentation and structural connectivity. The approach enabled us to develop spatial scenarios of land use change and detect their differences for choosing the most integrated landscape pattern in terms of landscape metrics. Finally, the comparison between paired forecasted scenarios based on landscape metrics indicates that scenarios 1-1, 2-2, 3-2, and 4-1 have a more suitable integrity. The proposed methodology for developing spatial scenarios helps executive managers to create scenarios with many repetitions and customize spatial patterns in real world applications and policies.

Evaluation of the relationship between soil erosion and landscape metrics across Gorgan Watershed in northern Iran.

Soil erosion is one of the most serious environmental threats strongly influenced by the spatial pattern of land uses. This study was designed to evaluate the relevance of land use pattern and soil erosion using landscape metrics across Gorgan Watershed in northern Iran. Therefore, the revised universal soil loss equation was applied to evaluate and model soil loss and sedimentation in the region. Then, soil erosion relationship to land use pattern was analyzed using a variety of metrics including percentage of landscape, number of patches, largest patch index, and landscape shape index. The results revealed that potential of soil loss, sediment retention, and sediment yield for the whole watershed were 6.6, 2.4, and 1.5 t ha-1 year-1, respectively. The quantity of sediment retention was estimated at 4.3, 3.2, 1.0, and 1.2 t ha-1 year-1 in forest, rangelands, agriculture, and built-up areas, respectively. Similarly, sediment yield was 0.6, 1.6, 1.5, and 2.1 t ha-1 year-1, respectively. The results revealed that the soil loss increased with decreasing metrics of forest and rangelands while increasing metrics of built-up regions and agricultural lands accelerated the process. Moreover, we showed that land use type of patches was an important factor on soil erosion, and soil loss was also affected by area, number, shape, and density of landscape patches. Result of this study can facilitate monitoring of erosion-sensitive areas in the watershed which can help managers and decision makers to design more suitable measures for soil conservation.

Tailoring a non-path-dependent model for environmental risk management and polycentric urban land-use planning.

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.

Rules versus layers: which side wins the battle of model calibration?

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.