Improving the predictive performance of CLUE-S by extending demand to land transitions: The trans-CLUE-S model

Kiziridis, Diogenis A.; Mastrogianni, Anna; Pleniou, Magdalini; Tsiftsis, Spyros; Xystrakis, Fotios; Tsiripidis, Ioannis

doi:10.1016/j.ecolmodel.2023.110307

Cited by 15 publications

(8 citation statements)

References 36 publications

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“…The region’s dominant woody taxa classify it to the “thermophilous deciduous oaks” vegetation formation (Bohn et al, 2007). We have mapped the area’s LUC for years 1945, 1970, 1996 and 2015 (Kiziridis et al, 2022). The mapping focused on five land types which are broad steps of progressive vegetation succession: farmland, grassland, open-scrub, closed-scrub and forest.…”

Section: Methodsmentioning

confidence: 99%

“…The study area has an altitude ranging from 390 to 1203 m, and a slope ranging from 0–48°. The wider region had a history of low-intensity agriculture and transhumant livestock grazing until the 1940s, but abandonment of farmlands and grasslands thenceforth has commonly led to vegetation succession and afforestation (Kiziridis et al, 2022; Zomeni et al, 2008). During 1996–2015, at the study area in particular, the population density median decreased from 17.3 to 12.1 inhabitants km −2 , and the livestock density median decreased from 135.9 to 51.9 small grazing livestock units km −2 .…”

Section: Methodsmentioning

confidence: 99%

“…During 1996–2015, at the study area in particular, the population density median decreased from 17.3 to 12.1 inhabitants km −2 , and the livestock density median decreased from 135.9 to 51.9 small grazing livestock units km −2 . More details about the empirical reference landscape can be found in Kiziridis et al (2022).…”

Section: Methodsmentioning

confidence: 99%

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Improving the predictive performance of CLUE-S by extending demand to land transitions: the trans-CLUE-S model

Kiziridis

Mastrogianni

Pleniou

et al. 2023

Preprint

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The CLUE-S model is a popular choice for modelling land use and land cover change from local to regional scales, but it spatially allocates the demand for only the total cover of each land class in the predicted map. In the present work, we introduce a CLUE-S variant that allocates demand at the more detailed level of land type transitions, the trans-CLUE-S model. We implemented this extension algorithmically in R, without the need of new parameters. By processing each row of the land transition matrix separately, the model allocates the demand of each land category's transitions via the CLUE-S allocation routine for only the cells which were of that category in the map of the previous time step. We found that the trans-CLUE-S model had half the total and configuration disagreement of the CLUE-S predictions in an empirical landscape, and in simulated landscapes of different characteristics. Moreover, the trans-CLUE-S performance was less sensitive to the number of environmental predictors of land type suitability for allocating demand. Although trans-CLUE-S is computationally more demanding due to running a CLUE-S allocation for each land class, we appended the solution of a land-use assignment optimisation problem that facilitates the convergence and acceleration of allocation. We additionally provide R functions for: CLUE-S variants at other levels of demand resolution; random instead of environment-based allocation; and for simulating landscapes of desired characteristics. Our R code for the models and functions can contribute to more reproducible, transparent and accurate modelling, analysis and interpretation of land cover change.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Improving the predictive performance of CLUE-S by extending demand to land transitions: the trans-CLUE-S model

Kiziridis

Mastrogianni

Pleniou

et al. 2023

Preprint

Self Cite

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“…However, further deliberations are necessary to ascertain the suitability of these models for simulating land use changes in small-scale regions. The CLUE-S model emerges as an advantageous tool for simulating land use changes in small-scale regions [29]. This model, an improvement upon the CLUE model, has been refined to overcome limitations regarding the number of land use types and other factors.…”

Section: Introductionmentioning

confidence: 99%

Evolving Urban Expansion Patterns and Multi-Scenario Simulation Analysis from a Composite Perspective of “Social–Economic–Ecological”: A Case Study of the Hilly and Gully Regions of Northern Loess Plateau in Shaanxi Province

Zhang,

Liu,

Sheng

et al. 2024

Sustainability

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Over recent decades, the hilly and gully regions of the northern Loess Plateau in Shaanxi province have grappled with severe soil erosion and a precarious ecological milieu. Shaped by urbanization policies, this locale has encountered a gamut of issues, including an imbalance in human–environment dynamics and the degradation of ecological integrity. Consequently, the comprehension of how urban expansion impacts the optimization of regional landscape configurations, the alignment of human–environment interactions in the Loess Plateau’s hilly and gully domains, and the mitigation of urban ecological challenges assumes paramount importance. Leveraging data from land use remote sensing monitoring, alongside inputs from natural geography and socio-economic spheres, and employing methodologies such as landscape pattern indices, we conduct an exhaustive analysis of Zichang City’s urban fabric from 1980 to 2020. Furthermore, employing the CLUE-S model, we undertake multifaceted scenario simulations to forecast urban expansion in Zichang City through to 2035. Our findings delineate two distinct phases in Zichang City’s urban expansion trajectory over the past four decades. From 1980 to 2000, urban construction land in Zichang City experienced a phase of methodical and steady growth, augmenting by 64.98 hectares, alongside a marginal decrease in the landscape shape index (LSI) by 0.02 and a commensurate increase in the aggregation index (AI) by 1.17. Conversely, from 2000 to 2020, urban construction land in Zichang City witnessed an epoch of rapid and haphazard expansion, doubling in expanse, marked by a notable escalation in LSI (2.45) and a corresponding descent in the AI (2.85). The precision of CLUE-S model simulations for Zichang City’s land use alterations registers at 0.88, fulfilling the exigent demand for further urban expansion and land use change prognostication. Under the aegis of the natural development scenario, the augmentation of urban construction land in Zichang City primarily encroaches upon grassland, farmland, and woodland, effectuating an increase of 159.81 hectares. Conversely, under the ambit of urbanization development, urban construction land contends predominantly with farmland, grassland, and woodland, heralding an augmentation of 520.42 hectares. Lastly, under the mantle of ecological protection, urban construction land expansion predominantly encroaches upon grassland, farmland, and woodland, resulting in an augmentation of 4.27 hectares. Through a nuanced analysis of the spatiotemporal evolution of urban expansion and scenario-based simulations, this study endeavors to furnish multi-faceted, scenario-driven, and policy-centric insights for regional planning, urban spatial delineation, and regional ecological safeguarding.

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“…It is currently widely used in various fields such as population expansion, land use, land use assessment, and urban expansion [11][12][13][14][15]. The CA model also forms the basis for many composite models, such as the CLUE-S model [16,17], ANN-CA model [18,19], and Logistic-CA model [20,21]. The CA-Markov model is a more mature simulation approach, combining the CA model's capability to simulate spatial changes in complex systems with the predictive advantages of the Markov model over time, effectively overcoming the limitations of single landscape-type dynamic simulation models [22,23].…”

Section: Introductionmentioning

confidence: 99%

Predicting Sub-Forest Type Transition Characteristics Using Canopy Density: An Analysis of the Ganjiang River Basin Case Study

Zhou,

Hu,

Liu

et al. 2024

Forests

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In the process of societal development, forest land categories often conflict with other land use types, leading to impacts on the ecological environment. Therefore, research on changes in forest land categories has increasingly become a globally focused topic. To anticipate potential forest ecological security issues under urbanization trends, studies on regional land use simulation become more important. This paper, based on land use data from the Ganjiang River basin, analyzes the distribution characteristics and changing trends of land use types from 2000 to 2020. Using the CA-Markov model, it predicts the land use pattern of the basin in 2040 and analyzes the transfer characteristics of forest land categories. The conclusions indicate that, between 2000 and 2020, the most significant trend in land use evolution was the transfer between various subcategories of forest land, especially frequent in the high-altitude mountainous areas in the southern and western parts of the basin. The land use pattern prediction model constructed in this paper has a kappa index of 0.92, indicating high accuracy and reliability of the predictions. In 2040, the most significant land evolution phenomenon would be from forest land to arable land to construction land, particularly pronounced around large cities. Over the next 20 years, the focus of land use evolution may shift from the southern part of the basin to the central and northern parts, with urban expansion possibly becoming the main driving force of land use changes during this period. Forest land restoration work is an effective method to compensate for the loss of forest land area in the Ganjiang River basin, with key areas for such work including Longnan, Yudu, Xingguo, Ningdu, Lianhua, and Yongxin counties.

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Improving the predictive performance of CLUE-S by extending demand to land transitions: The trans-CLUE-S model

Cited by 15 publications

References 36 publications

Improving the predictive performance of CLUE-S by extending demand to land transitions: the trans-CLUE-S model

Improving the predictive performance of CLUE-S by extending demand to land transitions: the trans-CLUE-S model

Evolving Urban Expansion Patterns and Multi-Scenario Simulation Analysis from a Composite Perspective of “Social–Economic–Ecological”: A Case Study of the Hilly and Gully Regions of Northern Loess Plateau in Shaanxi Province

Predicting Sub-Forest Type Transition Characteristics Using Canopy Density: An Analysis of the Ganjiang River Basin Case Study

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