A review of multi-criteria optimization techniques for agricultural land use allocation

Kaim, Andrea; Cord, Anna F.; Völk, Martin

doi:10.1016/j.envsoft.2018.03.031

Cited by 138 publications

(88 citation statements)

References 77 publications

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“…Constraints are an important part of the mathematical optimization for land use allocation. Defining the constraints in areas of specific land use helps to ensure that the optimization outputs are feasible solutions by reflecting environmental, social, and political limits (Kaim, Cord, & Volk, ). In our case study, two types of constraints were defined: separate constraints set up for separate cities in the WHA, and overall constraints set up for the entire WHA rather than for each city.…”

Section: Case Studymentioning

confidence: 99%

Spatio‐temporal land use multi‐objective optimization: A case study in Central China

Cao

Zhang

Wang

2019

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Much effort has been applied to the study of land use multi‐objective optimization. However, most of these studies have focused on the final land use scenarios in the projected year, without considering how to reach the final optimized land use scenario. To fill this gap, a spatio‐temporal land use multi‐objective optimization (STLU‐MOO) model is innovatively proposed in this research to determine possible spatial land use solutions over time. The STLU‐MOO is an extension of a genetic land use multi‐objective optimization model (LU‐MOO) in which the LU‐MOO is generally carried out in different years, and the solutions at year T will affect the solutions at year T + 1. We used the Wuhan agglomeration (WHA) as our case study area. The STLU‐MOO model was employed separately for the nine cities in the WHA, and social, economic, and environmental objectives have been considered. The success of the experiments in the case study demonstrated the value and novelty of our proposed STLU‐MOO model. In addition, the results also indicated that the objectives considered in the case study were in conflict. According to the results, the optimal land use plan in 2050 can be traced back to 2040, 2030, and 2020, providing a series of Pareto solutions over the years which can provide spatio‐temporal land use multi‐objective optimization solutions to support the land use planning process.

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Section: Case Studymentioning

confidence: 99%

Spatio‐temporal land use multi‐objective optimization: A case study in Central China

Cao

Zhang

Wang

2019

Transactions in GIS

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“…Various studies have used optimization approaches to integrate a variety of ES in land-use decision-making (Bagdon, Huang, & Dewhurst, 2016;Estrella, Cattrysse, & van Orshoven, 2014;Kaim, Cord, & Volk, 2018). Results of such studies have shown that incorporating ES can substantially change the allocation of land to different LULC types (Bateman et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Accounting for multiple ecosystem services in a simulation of land‐use decisions: Does it reduce tropical deforestation?

Knoke

Paul

Rammig

et al. 2020

Global Change Biology

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Conversion of tropical forests is among the primary causes of global environmental change. The loss of their important environmental services has prompted calls to integrate ecosystem services (ES) in addition to socio‐economic objectives in decision‐making. To test the effect of accounting for both ES and socio‐economic objectives in land‐use decisions, we develop a new dynamic approach to model deforestation scenarios for tropical mountain forests. We integrate multi‐objective optimization of land allocation with an innovative approach to consider uncertainty spaces for each objective. These uncertainty spaces account for potential variability among decision‐makers, who may have different expectations about the future. When optimizing only socio‐economic objectives, the model continues the past trend in deforestation (1975–2015) in the projected land‐use allocation (2015–2070). Based on indicators for biomass production, carbon storage, climate and water regulation, and soil quality, we show that considering multiple ES in addition to the socio‐economic objectives has heterogeneous effects on land‐use allocation. It saves some natural forest if the natural forest share is below 38%, and can stop deforestation once the natural forest share drops below 10%. For landscapes with high shares of forest (38%–80% in our study), accounting for multiple ES under high uncertainty of their indicators may, however, accelerate deforestation. For such multifunctional landscapes, two main effects prevail: (a) accelerated expansion of diversified non‐natural areas to elevate the levels of the indicators and (b) increased landscape diversification to maintain multiple ES, reducing the proportion of natural forest. Only when accounting for vascular plant species richness as an explicit objective in the optimization, deforestation was consistently reduced. Aiming for multifunctional landscapes may therefore conflict with the aim of reducing deforestation, which we can quantify here for the first time. Our findings are relevant for identifying types of landscapes where this conflict may arise and to better align respective policies.

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“…Methods of scalarization integrate multiple targets into a single target and provide one optimal solution [summary of drawbacks by Duh and Brown (2007)]. Recent reviews of spatial optimization techniques for land use allocation by Yao et al (2017) and Kaim et al (2018) provide an overview on such studies and the individual methods available for the two types.…”

Section: Introductionmentioning

confidence: 99%

“…For trade-off analyses with up to four targets, Kaim et al (2018) suggest Pareto-based algorithms; out of these, genetic algorithms are frequently used. Also, Yao et al (2017) see a lot of potential for heuristics such as genetic algorithms for optimization studies.…”

Section: Introductionmentioning

confidence: 99%

Synergies or Trade-Offs? Optimizing a Virtual Urban Region to Foster Plant Species Richness, Climate Regulation, and Compactness Under Varying Landscape Composition

Schwarz

Hoffmann

Knapp

et al. 2020

Front. Environ. Sci.

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Ongoing urbanization forces us to reflect on how we can cater for multiple targets when building cities. In this study, we investigate whether compactness as a common urban development strategy, climate regulation as an example for an ecosystem service, and vascular plant species richness as a measure of biodiversity form a synergistic relationship or whether trade-offs exist. We use a genetic algorithm to optimize the spatial allocation of three types of land cover blocks in a stylized urban region. These blocks are categorized as high-, low-density, or park blocks, depending on the proportion of green and built-up cells within each block. We systematically vary landscape composition at the block level, but keep city size constant. Our most important finding is that the relationships of target functions can shift between tradeoff and synergy, and this is shaped by landscape composition. For example, we found a trade-off between species richness and urban compactness in landscapes with large proportions of high-density areas and parks, while they have a synergistic relationship in low-density landscapes. Such dependencies of trade-offs and synergies on landscape composition need to be explored further, which may help address the wickedness of urban planning problems.

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A review of multi-criteria optimization techniques for agricultural land use allocation

Cited by 138 publications

References 77 publications

Spatio‐temporal land use multi‐objective optimization: A case study in Central China

Spatio‐temporal land use multi‐objective optimization: A case study in Central China

Accounting for multiple ecosystem services in a simulation of land‐use decisions: Does it reduce tropical deforestation?

Synergies or Trade-Offs? Optimizing a Virtual Urban Region to Foster Plant Species Richness, Climate Regulation, and Compactness Under Varying Landscape Composition

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