Optimal Land Use Allocation Research Articles

Optimising land use allocation to balance ecosystem services and economic benefits - A case study in Wuhan, China

The optimisation of land use allocation plays an important role in sustainable land use planning. It is crucial to realise the synergy between economic development and ecosystem conservation by optimising land use allocation. In this study, we developed a method to optimise land use allocation to balance ecosystem services and economic benefits based on the spatial difference of both ecosystem services value (ESV) and land use efficiency, based on the LAND System Cellular Automata model for Potential Effect (LANDSCAPE). In the optimisation model, spatial difference of ESV was represented by the parameter of resistance, while spatial difference of land use efficiency was expressed as the parameter of asynchronous rate of transition. Subsequently, land use allocation was optimised based on spatial difference of resistances and asynchronous rates. Taking Wuhan as the study area, the proposed optimisation model was used to conduct the optimisation of land use allocation during 2010–2020. Results showed that: economic benefits would increase by 444.77 million US$, while losses of ESV would decrease by 142.55 million US$ by optimisation of land use allocation. This indicated that the optimal allocation of land use based on spatial difference of ESV and land use efficiency can increase economic benefits at lower cost of ESV. In conclusion, it is feasible to allocate land resources to balance ecosystem services and economic benefits based on the differences of ESV and land use efficiency. This study highlights that taking the spatial difference of both ESV and land use efficiency into consideration is helpful for a sustainable land use planning.

Constraints in multi-objective optimization of land use allocation – Repair or penalize?

Combining simulation models and multi-objective optimization can help solving complex land use allocation problems by considering multiple, often competing demands on landscapes, such as agriculture, (drinking) water provision, or biodiversity conservation. The search for optimal land use allocations has to result in feasible solutions satisfying “real-world” constraints. We here introduce a generic and readily applicable tool to integrate user-specific spatial models (e.g. assessing different ecosystem services) for a Constrained Multi-objective Optimization of Land use Allocation (CoMOLA). The tool can handle basic land use conversion constraints by either a newly and specifically developed method to repair infeasible solutions or by penalizing constraint violation. CoMOLA was systematically tested for different levels of complexity using a virtual landscape and simple ecosystem service and biodiversity models. Our study shows that using repair mechanisms seems to be more effective in exploring the feasible solution space while penalizing constraint violation likely results in infeasible solutions.

The Multi-Objective Space Optimal Allocation of Urban Land Based on Spatial Genetic Algorithm

With the constant increasing scale of urban buildings, the contradiction between supply and demand of land use problems is more prominent. Therefore, the multi-objective space optimal allocation of urban land use based on spatial genetic algorithm was proposed in this paper. Firstly, the present situation of the urban land use resources was expounded; in view of the urban land use planning, a spatial genetic algorithm was proposed; then, the urban land was divided into different functional areas, and the land planning and design method was put forward; finally, taking a city's land space planning as an example, the optimal planning and design were carried out to the geological disasters, low hilly land and land overall utilization; by comparing the land use before and after the planning optimization, the advantages of land optimization design were confirmed.

A Parcel-Level Model for Ranking and Allocating Urban Land-Uses

Urban land-use allocation is a complicated problem due to the variety of land-uses, a large number of parcels, and different stakeholders with diverse and conflicting interests. Various approaches and techniques have been proposed for the optimization of urban land-use allocation. The outputs of these approaches are almost optimum plans that suggest a unique, appropriate land-use for every land unit. However, because of some restrictions, such stakeholder opposition to a specific land-use or the high cost of land-use change, it is not possible for planners to propose a desirable land-use for each parcel. As a result, planners have to identify other priorities of the land-uses. Thus, ranking land-uses for parcels along with optimal land-use allocation could be advantageous in urban land-use planning. In this paper, a parcel-level model is presented for ranking and allocating urban land-uses. The proposed model benefits from the capabilities of geographic information systems (GIS), fuzzy calculations, and Multi-Criteria Decision-Making (MCDM) methods (fuzzy TOPSIS), intends to improve the capabilities of existing urban land-use planning support systems. In this model, as a first step, using fuzzy calculations and spatial analysis capabilities of GIS, quantitative and qualitative evaluation criteria are estimated based on physical characteristics of the parcels and their neighborhoods. In the second step, through the fuzzy TOPSIS method, urban land-uses are ranked for each of the urban land units. In the third step, using the proposed land-use allocation process and genetic algorithm, the efficiency of the model is evaluated in urban land-use optimal allocation. The proposed model is tested on spatial data of region 7, district 1 of Tehran. The implementation results demonstrate that, in the study area, the land-use of 77.2% of the parcels have first priority. As such, the land-use of 22.8% of the parcels do not have first priority, and are prone to change.

Study on Community’s Land Allocation in Long Pahangai District

<p>Land use allocation for community has been a crucial process for supporting the spatial allocation either at the regency or provincial level. This study was emphasized on the analysis of land allocation at the district level. The study applied a linear programming approach to optimize the land use in Long Pahangai District then linked with the spatial information. The optimization considered several factors, i.e., land productivity, the degree of erosion and the preference of the community living in the study area. To support the optimization, the availability of land use was determined by considering the land capability using the query tools in the Geographic Information System. The level of land capability applied five constraints, namely, slope, drainage, soil texture, effective depth and erosion. The study found that the optimal allocation of land use in the study area are primary forest of 6,635.11 ha (25.19%), secondary forest of 19,025.7 ha (71.9%), mixed plantation area of 289.61 ha (1.1%), settlement area of 8.3 ha (0.03%) and rice field of 487.35 ha (1.844%). This optimal allocation might increase the community income per capita by approximately 80% from 9,602,000.- to 17,275,171.-/capita/ha/year. </p>

MULTI-AGENT BASED SIMULATION OF OPTIMAL URBAN LAND USE ALLOCATION IN THE MIDDLE REACHES OF THE YANGTZE RIVER, CHINA

Abstract. The optimization of land-use allocation is one of important approaches to achieve regional sustainable development. This study selects Chang-Zhu-Tan agglomeration as study area and proposed a new land use optimization allocation model. Using multi-agent based simulation model, the future urban land use optimization allocation was simulated in 2020 and 2030 under three different scenarios. This kind of quantitative information about urban land use optimization allocation and urban expansions in future would be of great interest to urban planning, water and land resource management, and climate change research.

MULTI-AGENT BASED SIMULATION OF OPTIMAL URBAN LAND USE ALLOCATION IN THE MIDDLE REACHES OF THE YANGTZE RIVER, CHINA

The optimization of land-use allocation is one of important approaches to achieve regional sustainable development. This study selects Chang-Zhu-Tan agglomeration as study area and proposed a new land use optimization allocation model. Using multi-agent based simulation model, the future urban land use optimization allocation was simulated in 2020 and 2030 under three different scenarios. This kind of quantitative information about urban land use optimization allocation and urban expansions in future would be of great interest to urban planning, water and land resource management, and climate change research.

An improved artificial bee colony algorithm for optimal land-use allocation

Land-use allocation is of great importance for rapid urban planning and natural resource management. This article presents an improved artificial bee colony (ABC) algorithm to solve the spatial optimization problem. The new approach consists of a heuristic information-based pseudorandom initialization (HIPI) method for initial solutions and pseudorandom search strategy based on a long-chain (LC) mechanism for neighborhood searches; together, these methods substantially improve the search efficiency and quality when handling spatial data in large areas. We evaluated the approach via a series of land-use allocation experiments and compared it with particle swarm optimization (PSO) and genetic algorithm (GA) methods. The experimental results show that the new approach outperforms the current methods in both computing efficiency and optimization quality.

Regional land-use allocation using a coupled MAS and GA model: from local simulation to global optimization, a case study in Caidian District, Wuhan, China

China is facing the pressures of both rapid economic development and environmental protection, and land-use allocation optimization is an important way to manage the conflicts between these pressures and to achieve sustainable development. Optimization of land-use allocation is a nonlinear multiobjective spatial optimization problem, and a purely local simulation model or global optimization model is insufficient to solve it. It is essential to bridge the gap between the two models through the combination of top-down and bottom-up approaches. This study integrates a multiagent system (MAS) that simulates the behaviors of land-use stakeholders with regard to their choices of specific locations, with a genetic algorithm (GA) that simultaneously evaluates and optimizes land-use configurations to meet various regional development objectives. The model is expected to achieve the optimization of land use in terms of the composition and spatial configuration. Caidian District, Wuhan, China, was chosen as the study area to test the model in this paper. The results show that the performance of the coupled model is superior to a pure GA model or MAS model. The optimal configuration improves on the economic output, spatial compactness, and carbon storage of the current configuration and promotes sustainable regional land-use development from the local scale to the regional scale.

Combining system dynamics and hybrid particle swarm optimization for land use allocation

Urban land use spatial allocation is crucial to lots of countries that are usually under severe environmental and demographic pressures, because it can be used to alleviate some land use problems. A number of models have been proposed for the optimal allocation of land use. However, most of these models only address the suitability of individual land use types and spatial competition between different land uses at micro-scales, but ignore macro-level socio-economic variables and driving forces. This article proposes a novel model (SDHPSO-LA) that integrates system dynamics (SD) and hybrid particle swarm optimization (HPSO) for solving land use allocation problems in a large area. The SD module is used to project land use demands influenced by economy, technology, population, policy, and their interactions at macro-scales. Furthermore, particle swarm optimization (PSO) is modified by incorporating genetic operators to allocate land use in discrete geographic space. The SDHPSO-LA model was then applied to a case study in Panyu, Guangdong, China. The experiments demonstrated the proposed model had the ability to reflect the complex behavior of land use system at different scales, and can be used to generate alternative land use patterns based on various scenarios.

The Optimal Allocation of Global Land Use in the Food-Energy-Environment Trilemma

This study analyzes the optimal allocation of the world's land resources over the course of the next century in the dynamic forward-looking framework, which brings together distinct strands of economic, agronomic, and biophysical literature and incorporates key drivers affecting global landuse. We show that, while some deforestation is optimal in the near term, the desirability of further deforestation is elimated by mid-century under the baseline scenario. While the adverse productivity shocks from climate change have a modest effect on global land use, when combined with high growth in energy prices they lead to significant deforestation and higher GHG emissions than in the baseline. Imposition of GHG emissions constraint further heightens the competition for land, as fertilizer use declines and land-based mitigation strategies expand. However, the effectiveness of such a pre-announced constraint is completely diluted by intertemporal substitution of deforestation which accelerates prior to imposition of the target.

Watershed land use and aquatic ecosystem response: Ecohydrologic approach to conservation policy

Summary Land use activities change the natural functions of a watershed impacting the flow of water and water quality, and impair aquatic ecosystems. Optimal allocation of land use depends on attributes related to terrestrial and aquatic environments. A dynamic model that links land use, overland flow, suspended sediment, and an aquatic species is used to evaluate alternate land use policies. The dwarf wedge mussel that is classified as endangered in the region is used as an indicator species of aquatic health in a watershed in Massachusetts. The simulation model is used to evaluate spatial nature of processes and land use policies. Spatial and temporal changes in runoff, sediment loading, and mussel population are modeled over a period of 4 years. Ten policy scenarios represent combinations of best management practices and development of agriculture and urban land at spatial locations of headwaters, main stem regions, riparian, and entire watershed. Increasing the proportion of agriculture and high density residential land use increased runoff, while increasing the frequency and magnitude of peak flows in the watershed. Sediment loading increased with an increased proportion of agriculture area and decreased with an expansion of high density residential area. Scenarios with an increase in sediment loading above the baseline mean exhibited an irregular recovery of the mussel population from high loading events. Policy implications include the need for best management practices to decrease runoff and sediment loading in the watershed, through education and incentive programs.

Optimal land-use allocation in central Sudan

Abstract The optimal land-use in the gerf land of central Sudan was determined from the social welfare point of view. The alternative land-use options considered were Acacia nilotica plantations, eucalyptus plantations and bananas. The evaluation technique adopted was cost–benefit analysis using the Little–Mirrlees–Squire–van der Tak (LMST) approach. The results indicate that eucalyptus plantations are the optimum land-use option on the basis of economic growth maximization (economic analysis) as well as maximization of economic growth with equity as a constraint (social analysis). The A. nilotica option came second to eucalyptus, while banana performed poorly with negative contribution in most cases. The financial analysis which used distorted market prices revealed that all three land-use alternatives were profitable, with eucalyptus being the most profitable and A. nilotica the least. Compared to the financial profitability, the economic profitability of the two forest options was considerably higher. In contrast, the profitability of the banana option was lowest among the three options. It is concluded that distorting agricultural policy had the impact of misallocating land to the optimal use.

Structural patterns of land types and optimal allocation of land use in Qinling Mountains

s: The case study based on Qinling Mountains in Shaanxi Province of China, in virtue of the information from TM image, classifies the land types and analyzes their spatial and temporal differential law, and puts forward three structural patterns of land types in mountainous areas, namely, spatial, quantitative and qualitative structures of mountainous land types. Furthermore, it has been noticed that the analysis of structural patterns can disclose the heterogeneity and orderliness of combination of land types, which can lay the theoretic foundation for comprehensively recognizing ecological characteristics and succession law of structure and function of land types. After the all-around comparative analysis, an optimal allocation of land use in Qinling Mountains has been put forward according to the principle of sustainable development and landscape ecology, which can lay the scientific foundation in practice for the structural adjustment and distribution optimization from the macro level to micro level.