Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Science and Its Applications

ICCSA 2014: Computational Science and Its Applications – ICCSA 2014 pp 315–330Cite as

Predicting Land Cover Change in a Mediterranean Catchment at Different Time Scales

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8582))

Abstract

Land cover has been changing rapidly throughout the world, and this issue is important to researchers, urban planners, and ecologists for sustainable land cover planning for the future. Many modeling tools have been developed to explore and evaluate possible land cover scenarios in future and time scales vary greatly from one study to another. The main objective of this study is to test land cover change prediction at different time scales in a Mediterranean catchment in SE France. Land cover maps were created from aerial photographs (1950, 1982, 2003, 2008, and 2011) of the Giscle catchment (235 Km2) and surfaces were classified into four land cover categories: forest, vineyard, grassland, and built area. Explanatory variables were selected through Cramer’s coefficient. Different time scales were tested in the study: short (2003-2008), intermediate (1982-2003), and long (1950-1982). To test the model’s accuracy, Land Change Modeler (LCM) of IDRISI was used to predict land cover in 2011 and predicted images were compared to a real 2011 map. Kappa index and confusion matrix were used to evaluate the model’s accuracy. Altitude, slope, and distance from roads had the greatest impact on land cover changes among all variables tested. Good to perfect level of spatial and perfect level of quantitative agreement were observed in long to short time scale simulations. Kappa indices (Kquantity = 0.99and Klocation = 0.90) and confusion matrices were good for intermediate and best for short time scale. The results indicate that shorter time scales produce better predictions. Time scale effects have strong interactions with specific land cover dynamics, in which stable land covers are easier to predict than cases of rapid change and quantity is easier to predict than location for longer time periods.

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   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahmed, B., Ahmed, R.: Modeling urban land cover growth dynamics using multi-temporal satellite images: A case study of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information 1, 3–31 (2012), doi:10.3390/ijgi1010003

    Article  Google Scholar 

  2. Álvarez-Martínez, J.M., Suárez-Seoane, S., Luis Calabuig, E.D.: Modelling the risk of land cover change from environmental and socio-economic drivers in heterogeneous and changing landscapes: The role of uncertainty. Landscape and Urban Planning 101, 108–119 (2011), doi:10.1016/j.landurbplan.2011.01.009

    Article  Google Scholar 

  3. Araya, Y.H., Cabral, P.: Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, Portugal. Remote Sensing 2, 1549–1563 (2010), doi:10.3390/rs2061549

    Article  Google Scholar 

  4. Bohnet, I., Pert, P.L.: Pattern, drivers and impacts of urban growth- A study from Cairns, Queensland, Australia from 1952 to 2031. Landscape and Planning 97, 239–248 (2010), doi:10.1016/j.landurbplan.2010.06.007

    Article  Google Scholar 

  5. Chaudhuri, G., Clarke, K.C.: Temporal accuracy in urban growth forecasting: A study us-ing the SLEUTH model. Transactions in GIS 18, 302–320 (2014), doi:10.1111/tgis.12047

    Article  Google Scholar 

  6. Chen, H., Pontius Jr., R.G.: Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable. Landscape Ecology 25, 1319–1331 (2010), doi:10.1007/s10980-010-9519-5

    Article  Google Scholar 

  7. Dietzel, C., Clarke, K.: The effect of disaggregating land use categories in cellular auto-mata model calibration and forecasting. Computers, Environment and Urban Systems 30, 78–101 (2006), doi:10.1016/j.compenvurbsys.2005.04.001

    Article  Google Scholar 

  8. Eastman, J.R.: IDRISI Selva Help System. Clark Labs, Clark University, Worcester (2012)

    Google Scholar 

  9. Fox, D.M., Witz, E., Blanc, V., Soulié, C., Penalver-Navarro, M., Dervieux, A.: A case study of land cover change (1950-2003) and runoff in a Mediterranean catchment. Applied Geography 32, 810–821 (2012), doi:10.1016/j.apgeog.2011.07.007

    Article  Google Scholar 

  10. Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., Hokao, K.: Modeling urban land use change by the integration of cellular automata and Markov model. Ecological Modelling 222, 3761–3772 (2011), doi:10.1016/j.ecolmodel.2011.09.009

    Article  Google Scholar 

  11. He, C., Okada, N., Zhang, Q., Shi, P., Zhang, J.: Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China. Applied Geography 26, 323–345 (2006), doi:10.1016/j.apgeog.2006.09.006

    Article  Google Scholar 

  12. He, C., Okada, N., Zhang, Q., Shi, P., Li, J.: Modelling dynamic urban expansion processes incorporating a potential model with cellular automata. Landscape and Urban Planning 86, 79–91 (2008), doi:10.1016/j.landurbplan.2007.12.010

    Article  Google Scholar 

  13. Huang, Q., Cai, Y.: Simulation of land use change using GIS-based stochastic model: The case study of Shiqian County, Southwestern China. Stochastic Environment Research Risk Assessment 21, 419–426 (2007), doi:10.1007/s00477-006-0074-1

    Article  MATH  MathSciNet  Google Scholar 

  14. Jenerette, G.D., Wu, J.: Analysis and simulation of land -use change in the central Arizona Phonix region, USA. Landscape Ecology 16, 611–626 (2001)

    Article  Google Scholar 

  15. Kamusoko, C., Aniya, M., Adi, B., Manjoro, M.: Rural sustainability under threat in Zimbabwe-Simulation of future land use/cover change in the Bindura district based on the Markov-cellular automata model. Applied Geography 29, 435–447 (2009), doi:10.1016/j.apgeog.2008.10.002

    Article  Google Scholar 

  16. Khoi, D.D.: Spatial modeling of deforestation and land suitability assessment in the Tam Dao National Park region. University of Tsukuba, Vietnam. Ph. D. thesis (2011)

    Google Scholar 

  17. Lambin, E.F., et al.: The cause of land-use and land-cover change: moving beyond the myths. Global Environment Change 11, 261–269 (2001)

    Article  Google Scholar 

  18. Li, X., Yeh, A.G.O.: Neural network based cellular automata for simulating multiple land use change using GIS. International Journal of Geographical Information Science 16, 323–343 (2002), doi: 10.108 0/13658810210137004

    Google Scholar 

  19. Mas, J.F., Pérez-Vega, A., Clarke, K.C.: Assessing simulated land use/cover maps using similarity and fragmentation indices. Ecological Complexity 11, 38–45 (2012), doi:10.1016/j.ecocom.2012.01.004

    Article  Google Scholar 

  20. Mhangara, P.: Land use/ cover change modeling and land degradation assessment in the Keiskamma catchment using remote sensing and GIS. University of Nelson Mandela Met-ropolitan. Ph. D. thesis (2011)

    Google Scholar 

  21. Pérez-Vega, A., Mas, J.F., Ligmann-Zielinska, A.: Comparing two approaches to land use /cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest. Environmenta Modelling & Software 29, 11–23 (2012), doi:10.1016/j.envsoft.2011.09.011

    Article  Google Scholar 

  22. Pontius Jr., R.G., Millones, M.: Death to Kappa: birth of quantity disagreement and alloca-tion disagreement for accuracy assessment. International Journal of Remote Sensing 32, 4407–4429 (2011), doi:org/10.1080/01431161.2011.552923

    Google Scholar 

  23. Roy, H.G., Fox, D.M., Emsellem, K.: Spatial dynamics of land cover change in a Medi-terranean catchment (1950-2008). Journal of Land use Science (2014)

    Google Scholar 

  24. Sang, L., Zhang, C., Yang, J., Zhu, D., Yun, W.: Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Mathematical and Modelling 54, 938–943 (2011), doi:10.1016/j.mcm.2010.11.019

    Article  Google Scholar 

  25. Silva, T.S., Tanliani, P.R.A.: Environment planning in the medium littoral of the Rio Grand do Sul coastal plain - Southern Brazil: Elements for coastal management. Ocean and Coastal Management 59, 20–30 (2012), doi:10.1016/j.ocecoaman.2011.12.014

    Article  Google Scholar 

  26. Tewolde, M.G., Cabral, P.: Urban sprawl analysis and modeling in Asmara, Eritrea. Remote Sensing 3, 2148–2165 (2011), doi:10.3390/rs3102148

    Article  Google Scholar 

  27. Valdivieso, F.O., Sendra, J.B.: Application of GIS and remote sensing techniques in generation of land use scenarios for hydrological modeling. Journal of Hydrology 365, 256–263 (2010), doi:10.1016/j.jhydrol.2010.10.033

    Article  Google Scholar 

  28. Verburg, P.H., de Koning, G.H.J., Kok, K., Veldkamp, A., Bouma, J.: A spatial explicit allocation procedure for modellling the pattern of land use change based upon actual land use. Ecological Modelling 116, 45–61 (1999)

    Article  Google Scholar 

  29. Verburg, P.H., Schulp, C.J.E., Witte, N., Veldkamp, A.: Downscaling of land use change scenarios to assess the dynamics of European landscapes. Agriculture, Ecosystem and Environment 114, 39–56 (2006), doi:10.1016/j.agee.2005.11.024

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. UMR 7300 CNRS ESPACE, Université de Nice Sophia Antipolis, BP 3209, 06204, Nice cedex 3, France

    Hari Gobinda Roy, Dennis M. Fox & Karine Emsellem

Authors
  1. Hari Gobinda Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dennis M. Fox
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karine Emsellem
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Engineering, University of Basilicata, 85100, Potenza, Italy

    Beniamino Murgante

  2. Department of Computer and Information Sciences, Covenant University, Ota, Nigeria

    Sanjay Misra

  3. Department of Production and Systems, University of Minho, 4710-057, Braga, Portugal

    Ana Maria A. C. Rocha

  4. DICAR, Polytecnico di Bari, 70125, Bari, Italy

    Carmelo Torre

  5. University of Minho, Braga, Portugal

    Jorge Gustavo Rocha  & Maria Irene Falcão  & 

  6. Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, 813-8503, Higashi-ku, Fukuoka, Japan

    Bernady O. Apduhan

  8. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Roy, H.G., Fox, D.M., Emsellem, K. (2014). Predicting Land Cover Change in a Mediterranean Catchment at Different Time Scales. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8582. Springer, Cham. https://doi.org/10.1007/978-3-319-09147-1_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-09147-1_23

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09146-4

  • Online ISBN: 978-3-319-09147-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation