CLUE model

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CLUE (Conversion of Land Use and its Effects) model is a spatially explicit land-use change model developed to simulate future land-use and land-cover changes, including urban expansion, deforestation, land abandonment, and agricultural intensification.[1][2][3] CLUE model is a dynamic modeling framework which simulates land-use change based on quantification of biophysical and human drivers of land-use conversion.[1] The CLUE model can be applied at the national and continental scale, implemented in Central America, Ecuador, China, and Java, Indonesia.[4] CLUE model cannot be employed at regional level.[4] Different versions of CLUE model include CLUE-S,[4] CLUE-Scanner,[5] and Dyna-CLUE[6] models.

CLUE-S[edit]

The CLUE model was modified to CLUE-S (the Conversion of Land Use and its Effects at Small regional extent) model. Specifically, the CLUE-S model was developed for high-resolution spatial data and regional applications.[7][8] The model comprises two different modules, spatial (land use allocation) and non-spatial (land use demand).[4]

Dyna-CLUE[edit]

Information flow in the CLUE-S /Dyna-CLUE model (overview)[9]

The 'Dyna-CLUE (Dynamic Conversion of Land Use and its Effects) model is the adapted version of CLUE-S model, built upon the combination of the top-down approach of spatial allocation of land-use change and bottom-up approach of specification of conversions for specific land-use alterations.[6]

Framework[edit]

The difference between the CLUE-S and Dyna-CLUE models is in the allocation process. However, for both models, the allocation process is based on sets of conditions that are created by the different components including spatial policies and restrictions, land use conversion settings, land use demand, and location characteristics.

Spatial policies and restrictions[edit]

The land-use change pattern is affected by the spatial policies and restrictions as well as land tenure. So, in order to simulate the land-use change, restricted areas, such as national parks, should be defined in the model. In some cases, all land-use conversions are banned and restricted; for others, a set of specific land-use transitions are restricted and some other changes are allowed.[7]

Land-use type specific conversion settings[edit]

Land-use type conversion settings are developed to specify temporal dynamics that demonstrate the possibility of the conversion of one land-use type to another. In order to characterize the land-use categories, two distinct parameters should be considered including 1. conversion elasticity and 2. specific conversion settings and temporal characteristics (transition sequences).

The conversion elasticity represents the degree to which the specific land-use types can be converted to other types. Some land-use could not be easily converted to another type due to high capital investment alongside high demands. For instance, it is less probable for residential built-up areas and permanent crops (e.g., fruit trees) lands to convert to other land-use types. On the other hand, some land-use categories could be easily converted to other suitable land-use types, for example, arable lands are suitable candidates for urban expansion. Conversion elasticity represents the reversibility value of land-use change, ranging between 0 and 1. Zero conversion elasticity indicates the easy conversion, while one means that land-use change is irreversible. Conversion elasticity is estimated based on expert knowledge or evaluation of land-use behavior of recent years .  

Specific conversion settings and temporal characteristics, specified in the conversion matrix, represent the following aspects:

First, the matrix defines the land-use types that the current land-use type can or cannot be converted.

Second, it specifies regions that a specific conversion is allowed or not allowed to occur.

Additionally, the conversion matrix defines the amount of time or time steps needed for a particular land-use type at a specific location to stay in the same state before the possibility to convert to another land-use type.

Moreover, the maximum time interval in which a land-use category can remain unchanged, suitable for cropping within a shifting cultivation system, considering that soil capacity for providing nutrients for cropping is limited and soil productivity has time limits.

It is worth mentioning that only maximum and minimum amounts of time before conversion occurring are specified in the conversion matrix.[9][7]

Land-use demand (requirements)[edit]

Land-use demand demonstrates the required change in land-use type, calculated at the aggregate level, which means considering the case study as a whole. Defining the required change in land-use type leads to limiting the scope of the simulation. Calculation of land-use demand is independent from the model, and other approaches can be used for the calculations. Depending on the aim of the simulation, based on scenarios and case study characteristics, several methodologies are available to calculate the land-use demand including trends extrapolation and analysis of land-use change from the recent past to the near future.[9][7]

Location characteristics[edit]

Location characteristics or land-use suitability are concerned with the location preference for a specific land-use change. The goal is to find locations with a high preference for a specific land-use change. Location preference is based on the interaction between different actors as well as the process of decision-making.[7]

Location preferences are calculated by the following formula:[9]

Rki = akX1i + bkX2i +...

where:

R: the preference to devote location i to land-use type k

ak, bk: relative impact of these characteristics on the preference for land-use type k

X1, 2,.. : biophysical or socio-economical characteristics

Examples of implementation[edit]

CLUE-Scanner[edit]

The CLUE-Scanner model is the implementation of Dyna-CLUE in DMS software of ObjectVision.[19]

References[edit]

  1. ^ a b Veldkamp, A.; Fresco, L. O. (1996-11-15). "CLUE-CR: An integrated multi-scale model to simulate land use change scenarios in Costa Rica". Ecological Modelling. 91 (1): 231–248. doi:10.1016/0304-3800(95)00158-1. ISSN 0304-3800.
  2. ^ Verburg, P. H.; de Koning, G. H. J.; Kok, K.; Veldkamp, A.; Bouma, J. (1999-03-01). "A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use". Ecological Modelling. 116 (1): 45–61. doi:10.1016/S0304-3800(98)00156-2. ISSN 0304-3800.
  3. ^ Verburg, P.H.; Overmars, K.P. (2007), Koomen, Eric; Stillwell, John; Bakema, Aldrik; Scholten, Henk J. (eds.), "Dynamic Simulation of Land-Use Change Trajectories with the Clue-S Model", Modelling Land-Use Change: Progress and Applications, The GeoJournal Library, vol. 90, Dordrecht: Springer Netherlands, pp. 321–337, doi:10.1007/978-1-4020-5648-2_18, ISBN 978-1-4020-5648-2, retrieved 2021-11-10
  4. ^ a b c d VERBURG, PETER H.; SOEPBOER, WELMOED; VELDKAMP, A.; LIMPIADA, RAMIL; ESPALDON, VICTORIA; MASTURA, SHARIFAH S.A. (2002-09-01). "Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model". Environmental Management. 30 (3): 391–405. Bibcode:2002EnMan..30..391V. doi:10.1007/s00267-002-2630-x. ISSN 1432-1009. PMID 12148073. S2CID 31913161.
  5. ^ Lavalle, Carlo; Baranzelli, Claudia; e Silva, Filipe Batista; Mubareka, Sarah; Gomes, Carla Rocha; Koomen, Eric; Hilferink, Maarten (2011). "A High Resolution Land Use/Cover Modelling Framework for Europe: Introducing the EU-ClueScanner100 Model". In Murgante, Beniamino; Gervasi, Osvaldo; Iglesias, Andrés; Taniar, David; Apduhan, Bernady O. (eds.). Computational Science and Its Applications - ICCSA 2011. Lecture Notes in Computer Science. Vol. 6782. Berlin, Heidelberg: Springer. pp. 60–75. doi:10.1007/978-3-642-21928-3_5. ISBN 978-3-642-21928-3.
  6. ^ a b Verburg, Peter H.; Overmars, Koen P. (2009-05-08). "Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model". Landscape Ecology. 24 (9): 1167. doi:10.1007/s10980-009-9355-7. ISSN 1572-9761. S2CID 31509788.
  7. ^ a b c d e Verburg, P.H.; Overmars, K.P. (2007), Koomen, Eric; Stillwell, John; Bakema, Aldrik; Scholten, Henk J. (eds.), "Dynamic Simulation of Land-Use Change Trajectories with the Clue-S Model", Modelling Land-Use Change: Progress and Applications, The GeoJournal Library, vol. 90, Dordrecht: Springer Netherlands, pp. 321–337, doi:10.1007/978-1-4020-5648-2_18, ISBN 978-1-4020-5648-2, retrieved 2021-11-23
  8. ^ Verburg, Peter H.; Veldkamp, A. (2004-01-01). "Projecting land use transitions at forest fringes in the Philippines at two spatial scales". Landscape Ecology. 19 (1): 77–98. doi:10.1023/B:LAND.0000018370.57457.58. ISSN 1572-9761. S2CID 39652303.
  9. ^ a b c d Verburg, Peter H.; Malek, Žiga; Goodwin, Sean P.; Zagaria, Cecilia (2021). "The Integrated Economic-Environmental Modeling (IEEM) Platform: IEEM Platform Technical Guides: User Guide for the IEEM-enhanced Land Use Land Cover Change Model Dyna-CLUE". doi:10.18235/0003625. S2CID 244193552. {{cite journal}}: Cite journal requires |journal= (help)
  10. ^ Tizora, Petronella; Le Roux, Alize; Mans, Gerbrand; Cooper, Antony K. (2018-09-18). "Adapting the Dyna-CLUE model for simulating land use and land cover change in the Western Cape Province". South African Journal of Geomatics. 7 (2): 190. doi:10.4314/sajg.v7i2.7. hdl:2263/71522. ISSN 2225-8531. S2CID 64364322.
  11. ^ Hu, Mengmeng; Wang, Yafei; Xia, Beicheng; Jiao, Mengyu; Huang, Guohe (2020-10-01). "How to balance ecosystem services and economic benefits? – A case study in the Pearl River Delta, China". Journal of Environmental Management. 271: 110917. doi:10.1016/j.jenvman.2020.110917. ISSN 0301-4797. PMID 32583803. S2CID 220058455.
  12. ^ Adhikari, Riwaz Kumar; Mohanasundaram, S.; Shrestha, Sangam (2020-06-01). "Impacts of land-use changes on the groundwater recharge in the Ho Chi Minh city, Vietnam". Environmental Research. 185: 109440. Bibcode:2020ER....185j9440A. doi:10.1016/j.envres.2020.109440. ISSN 0013-9351. PMID 32247909. S2CID 214808026.
  13. ^ Yang, Yuanyuan; Bao, Wenkai; Liu, Yansui (2020-07-01). "Scenario simulation of land system change in the Beijing-Tianjin-Hebei region". Land Use Policy. 96: 104677. doi:10.1016/j.landusepol.2020.104677. ISSN 0264-8377. S2CID 219404134.
  14. ^ Ghimire, Usha; Shrestha, Sangam; Neupane, Sanjiv; Mohanasundaram, S.; Lorphensri, Oranuj (2021-10-20). "Climate and land-use change impacts on spatiotemporal variations in groundwater recharge: A case study of the Bangkok Area, Thailand". Science of the Total Environment. 792: 148370. Bibcode:2021ScTEn.792n8370G. doi:10.1016/j.scitotenv.2021.148370. ISSN 0048-9697. PMID 34465055.
  15. ^ Shrestha, Sangam; Binod Bhatta; Talchabhadel, Rocky; Virdis, Salvatore Gonario Pasquale (2022-02-01). "Integrated assessment of the landuse change and climate change impacts on the sediment yield in the Songkhram River Basin, Thailand". CATENA. 209: 105859. doi:10.1016/j.catena.2021.105859. ISSN 0341-8162. S2CID 244002151.
  16. ^ Gong, Jianzhou; Hu, Zhiren; Chen, Wenli; Liu, Yansui; Wang, Jieyong (2018-03-01). "Urban expansion dynamics and modes in metropolitan Guangzhou, China". Land Use Policy. 72: 100–109. doi:10.1016/j.landusepol.2017.12.025. ISSN 0264-8377.
  17. ^ Shirmohammadi, Bagher; Malekian, Arash; Salajegheh, Ali; Taheri, Bahram; Azarnivand, Hossein; Malek, Ziga; Verburg, Peter H. (2020-01-01). "Scenario analysis for integrated water resources management under future land use change in the Urmia Lake region, Iran". Land Use Policy. 90: 104299. doi:10.1016/j.landusepol.2019.104299. hdl:1871.1/49530830-e939-4b0e-a3ec-f7633afc3c7e. ISSN 0264-8377. S2CID 207976894.
  18. ^ Das, P.; Behera, M. D.; Pal, S.; Chowdary, V. M.; Behera, P. R.; Singh, T. P. (2020-01-27). "Studying land use dynamics using decadal satellite images and Dyna-CLUE model in the Mahanadi River basin, India". Environmental Monitoring and Assessment. 191 (3): 804. doi:10.1007/s10661-019-7698-3. ISSN 1573-2959. PMID 31989334. S2CID 210914483.
  19. ^ Institute for Environmental Studies (IVM). "CLUE model". Retrieved 2021-11-23.
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