Skip to main content
SpringerLink
Log in
Book cover

Geomatic Approaches for Modeling Land Change Scenarios pp 11–25Cite as

LUCC Modeling Approaches to Calibration

  • Chapter
  • First Online:

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

Abstract

In land change modeling, calibration enables the modeler to establish the parameters for the model in order to produce expected outcomes, similar to those observed for the study area over a period in the past or consistent with a given scenario. Depending on the modeling approach, the parameters are set using maps which describe past change or information obtained from experts or stakeholders. These parameters will control the behavior of the model during the simulation with regard to aspects such as the quantity and the spatiotemporal patterns of modeled change. This chapter focuses on different aspects of calibration, such as the selection and transformation of input variables and the different approaches for estimating the parameters of the most common pattern-based models (PBM) and constraint cellular automata-based models (CCAM).

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    See the short presentations in Part V of this book about (in alphabetical order) APoLUS, CA_MARKOV, CLUMondo, Dinamica EGO, Land Change Modeler (LCM), LucSim, Metronamica and SLEUTH. The authors are also grateful to all contributors who helped us understand the different software packages.

References

  • Aldwaik S, Pontius R (2012) Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition. Landsc Urban Plann 106:103–114

    Article  Google Scholar 

  • Almeida CM, Monteiro AMV, Soares-Filho BS, Cerqueira GC, Pennachin CL, Batty M (2005) GIS and remote sensing as tools for the simulation of urban land-use change. Int J Remote Sens 26(4):759–774

    Article  Google Scholar 

  • Barni PE, Fearnside PM, de Alencastro Lima, Graça PM (2015) simulating deforestation and carbon loss in Amazonia: impacts in Brazil’s Roraima State from reconstructing highway BR-319 (Manaus-Porto Velho). Environ Manage 55:259–278

    Article  Google Scholar 

  • Bonham-Carter GF (1994) Geographic information systems for geoscientists: modelling with GIS. Pergamon, Ontario

    Google Scholar 

  • Brown DG, Walker R, Manson S, Seto K (2004) Modeling land use and land cover change in land change science. Remote Sens Digit Image Process 6:395–409 (Springer)

    Google Scholar 

  • Chang LA, Mas JF (2017) Modelación espacio temporal de un sistema roza-tuba-quema. In: Mas JF (ed) Análisis y modelación de patrones y procesos de cambio. CIGA-UNAM, Mexico

    Google Scholar 

  • Chen H, Pontius RG (2011) Sensitivity of a land change model to pixel resolution and precision of the independent variable. Environ Model Assess 16:37–52

    Article  Google Scholar 

  • Collins L, Drewett R, Ferguson R (1974) Markov models in geography. The. Statistician 23:179–209

    Article  Google Scholar 

  • Foody GM, See L, Fritz S, Van der Velde M, Perger C, Schill C, Boyd DS (2013) Assessing the accuracy of volunteered geographic information arising from multiple contributors to an internet based collaborative project. Trans GIS 17:847–860. doi:10.1111/tgis.12033

    Article  Google Scholar 

  • Goodchild MF (2007) Citizens as sensors: the world of volunteered geography. GeoJournal 69(4):211–221. doi:10.1007/s10708-007-9111-y

    Article  Google Scholar 

  • Hewitt R (2015) APoLUS model full system documentation. Project Report for EU FP7 Programme COMPLEX Project (deliverable 3.5) https://www.researchgate.net/publication/292047909_APoLUS_model_full_system_documentation

  • Houet T, Aguejdad R, Doukari O, Battaia G, Clarke K (2016) Description and validation of a “non path-dependent” model for projecting contrasting urban growth futures. Cybergeo: European Journal of Geography 759 doi:10.4000/cybergeo.27397

  • Houet T, Schaller N, Castets M, Gaucherel C (2014) Improving the simulation of fine-resolution landscape changes by coupling top-down and bottom-up land use and cover changes rules. Int J Geogr Inf Sci 28(9):1848–1876. doi:10.1080/13658816.2014.900775

    Article  Google Scholar 

  • Jokar Arsanjani J, Helbich M, Bakillah M, Hagenauer J, Zipf A (2013) Toward mapping land-use patterns from volunteered geographic information. Int J Geogr Inf Sci 27(12):2264–2278. doi:10.1080/13658816.2013.800871

    Article  Google Scholar 

  • Li X, Lin J, Chen Y, Liu X, Ai B (2013) Calibrating cellular automata based on landscape metrics by using genetic algorithms. Int J Geogr Inf Sci 27(3):594–613. doi:10.1080/13658816.2012.698391

    Article  Google Scholar 

  • Liu XL, Ma X, Li B, Ai S, Li He Z (2014) Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata. Int J Geogr Inf Sci 28(1):148–163. doi:10.1080/13658816.2013.831097

    Article  Google Scholar 

  • Mas JF (2016) Combining Geographically Weighted and pattern-based models to simulate deforestation processes. In: Sauvage S, Sánchez-Pérez JM, Rizzoli AE (eds) Proceedings of the 8th international congress on environmental modelling and software July 10–14, Toulouse, France, pp 1321–1327. ISBN: 978-88-9035-745-9

    Google Scholar 

  • Mas JF, Kolb M, Paegelow M, Camacho Olmedo MT, Houet T (2014) Inductive pattern-based land use/cover change models: a comparison of four software packages. Environ Model Softw 51(1):94–111. doi:10.1016/j.envsoft.2013.09.010

    Article  Google Scholar 

  • Mas JF, Puig H, Palacio JL, Sosa AA (2004) Modelling deforestation using GIS and artificial neural networks. Environ Model Softw 19(5):461–471

    Article  Google Scholar 

  • Mas JF, Soares-Filho B, Rodrigues H (2015) Calibrating cellular automata of land use/cover change models using a genetic algorithm, Int. ISPRS Geospatial Week 2015, La Grande Motte, France, 28th September - 2nd October 2015. International Archives of the Photogrammetry- Remote Sensing and Spatial Information Sciences XL-3/W3 67–70

    Google Scholar 

  • Mendoza Ponce AV, Galicia Sarmiento L, Corona Núñez RO (2017) Cambios de usos y cobertura del suelo bajo diferentes escenarios socioeconómicos y climáticos en México. In Mas JF (ed) Análisis y modelación de patrones y procesos de cambio. CIGA-UNAM, Mexico

    Google Scholar 

  • National Research Council (2014) Advancing land change modeling: opportunities and research requirements. The National Academies Press, Washington DC

    Google Scholar 

  • Openshaw S (1984) The modifiable areal unit problem concepts and techniques in modern geography, vol 28. Geo Books, Norwich

    Google Scholar 

  • Overmars KP, Verburg PH, Veldkamp TA (2007) Comparison of a deductive and an inductive approach to specify land suitability in a spatially explicit land use model. Land Use Policy 24:584–599

    Article  Google Scholar 

  • Paegelow M, Camacho Olmedo MT, Mas JF, Houet T (2014) Benchmarking of LUCC modelling tools by various validation techniques and error analysis. Cybergeo document 701 http://cybergeo.revues.org

  • Petit C, Scudder T, Lambin E (2001) Quantifying processes of land-cover change by remote sensing: resettlement and rapid land-cover changes in south-eastern Zambia. Int J Remote Sens 22(17):3435–3456

    Article  Google Scholar 

  • Pontius RG, Malanson J (2005) Comparison of the structure and accuracy of two land change models. Int J Geogr Inf Sci 19(2):243–265

    Article  Google Scholar 

  • Pontius RG, Shusas E, McEachern M (2004) Detecting important categorical land changes while accounting for persistence. Agr Ecosyst Environ 101:251–268

    Article  Google Scholar 

  • Runfola DM, Pontius RG (2013) Measuring the Temporal Instability of Land Change using the Flow Matrix. Int J Geogr Inf Sci 27(9):1696–1716

    Article  Google Scholar 

  • Sangermano F, Toledano J, Eastman J (2012) Land cover change in the Bolivian Amazon and its implications for REDD+ and endemic biodiversity. Landsc Ecol 27(4):571–584 https://doi.org/10.1007/s10980-012-9710-y

  • Shryock HS, Siegel JS (1976) The methods and materials of demography, studies in population. Academic Press, San Diego. ISBN 9780126411508 http://dx.doi.org/10.1016/B978-0-12-641150-8.50001-9

  • Silva EA, Clarke KC (2002) Calibration of the SLEUTH urban growth model for Lisbon and Porto. Portugal Comput Environ Urban Syst 26(6):525–552. doi:10.1016/S0198-9715(01)00014

    Article  Google Scholar 

  • Soares-Filho BS, Coutinho Cerqueira G, Lopes Pennachin C (2002) DINAMICA – a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecol Model 154:217–235

    Article  Google Scholar 

  • Soares-Filho B, Rodrigues H, Follador M (2013) A hybrid analytical-heuristic method for calibrating land-use change models. Environ Model Softw 43:80–87. doi:10.1016/j.envsoft.2013.01.010

    Article  Google Scholar 

  • Takada T, Miyamoto A, Hasegawa SF (2010) Derivation of a yearly transition probability matrix for land-use dynamics and its applications. Landsc Ecol 25(4):561–572

    Article  Google Scholar 

  • Torrens PM, O’Sullivan D (2001) Cellular automata and urban simulation: where do we go from here? Environ Plan 28:163–168

    Article  Google Scholar 

  • van Asselen S, Verburg PH (2013) Land cover change or land-use intensification: simulating land system change with a global-scale land change model. Glob Chang Biol 19:3648–3667. doi:10.1111/gcb.12331

    Article  Google Scholar 

  • van Vliet J, Bregt AK, Brown DG, van Delden H, Heckbert S, Verburg PH (2016) A review of current calibration and validation practices in land-change modeling. Environ Model Softw 82:174–182. doi:10.1016/j.envsoft.2016.04.017

    Article  Google Scholar 

  • van Vliet J, Naus N, van Lammeren RJ, Bregt AK, Hurkens J, van Delden H (2013) Measuring the neighbourhood effect to calibrate land use models. Comput Environ Urban Syst 41:55–64

    Article  Google Scholar 

  • van Vliet J, Bregt AK, Hagen-Zanker A (2011) Revisiting Kappa to account for change in the accuracy assessment of land-use change models. Ecol Model 222(8):1367–1375

    Article  Google Scholar 

  • Vieilledent G, Grinand C, Vaudry R (2013) Forecasting deforestation and carbon emissions in tropical developing countries facing demographic expansion: a case study in Madagascar. Ecol Evol 3(6):1702–1716

    Article  Google Scholar 

  • Voinov A, Kolagani N, McCall MK, Glynn PD, Kragt ME, Ostermann FO, Pierce SA, Ramu P (2016) Modelling with stakeholders—Next generation. Environ Modell Softw 77:196–220. ISSN 1364-8152 http://dx.doi.org/10.1016/j.envsoft.2015.11.016

  • Walker RT, Moran E, Anselin L (2000) Deforestation and cattle ranching in the Brazilian Amazon: External capital and household processes. World Dev 28(4):683–699

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the Consejo Nacional de Ciencia y Tecnología (CONACYT) and the Secretaría de Educación Pública through the project entitled “¿Puede la modelación espacial ayudarnos a entender los procesos de cambio de cobertura/uso del suelo y de degradación ambiental?—Fondos SEP-CONACyT 178816”. This work was also supported by the BIA2013-43462-P project funded by the Spanish Ministry of Economy and Competitiveness and by the FEDER European Regional Fund.

Author information

Authors and Affiliations

  1. Centro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México (UNAM), Morelia, Michoacán, Mexico

    J. F. Mas

  2. GEODE UMR 5602 CNRS, Université de Toulouse Jean Jaurès, Toulouse, France

    M. Paegelow

  3. Departamento de Análisis Geográfico Regional y Geografía Física, Universidad de Granada, Granada, Spain

    M. T. Camacho Olmedo

Authors
  1. J. F. Mas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Paegelow
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. T. Camacho Olmedo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. F. Mas .

Editor information

Editors and Affiliations

  1. Departamento de Análisis Geográfico Regional y Geografía Física, Universidad de Granada, Granada, Spain

    María Teresa Camacho Olmedo

  2. Département de Géographie, Aménagement, Environnement, Université Toulouse Jean Jaurès, Toulouse, Garonne (Haute), France

    Martin Paegelow

  3. Centro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México (UNAM), Morelia, Michoacán, Mexico

    Jean-François Mas

  4. Department of Geology, Geography and Environmental Sciences, University of Alcalá, Alcalá de Henares, Madrid, Spain

    Francisco Escobar

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Mas, J.F., Paegelow, M., Camacho Olmedo, M.T. (2018). LUCC Modeling Approaches to Calibration. In: Camacho Olmedo, M., Paegelow, M., Mas, JF., Escobar, F. (eds) Geomatic Approaches for Modeling Land Change Scenarios. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-60801-3_2

Download citation

Publish with us

Policies and ethics

Search

Navigation