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Assessment of Uncertainties in Modelling Land Use Change with an Integrated Cellular Automata–Markov Chain Model

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Abstract

Uncertainty in future land use change modelling is crucial to study as it may result in varying spatial characteristics and features of the model. An integrated land use change model combines different modelling techniques and strengths. However, combined model uncertainties may significantly affect the accuracy of the model results. In this study, uncertainties resulting from spatial resolution and proportional errors of the input maps, as well as from iteration number of the cellular automata (CA) employing an integrated CA–Markov chain (CA-MC) model have been explored. The model uncertainty was quantified by comparing simulated maps to a classified land use map with the help of kappa statistics and confusion matrix. Further, correlation analysis was performed between kappa coefficients and land use simulations. The results show that the input data uncertainty (spatial resolution and proportional error) has a higher influence on land use simulation as compared to the CA-model parameter uncertainty (iteration number). The confusion matrix and the percent deviation from simulated land use map showed that variation in the major land use classes, namely agriculture (69.2 to 66.1%), dense forest (14.8 to 12%), degraded forest (12.6 to 14.5%), and barren land (1.31 to 5.96%), is required to be taken into account to reduce the model simulation error and hence the uncertainties. This study reveals that input datasets of fine spatial resolution and a low proportional error as well as to a lesser extent a high number of iterations can be recommended to minimize uncertainty in the CA-MC modelling.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Colvin, R. M., Witt, G. B., & Lacey, J. (2015). The social identity approach to understanding socio-political conflict in environmental and natural resources management. Global Environmental Change, 34, 237–246.

    Google Scholar 

  2. Khan, M. M. H., Bryceson, I., Kolivras, K. N., Faruque, F., Rahman, M. M., & Haque, U. (2015). Natural disasters and land-use/land-cover change in the southwest coastal areas of Bangladesh. Regional environmental change, 15(2), 241–250.

    Google Scholar 

  3. Goldewijk, K. K. (2001). Estimating global land use change over the past 300 years: The HYDE database. Global Biogeochemical Cycles, 15(2), 417–433.

    Google Scholar 

  4. Xu, Z., Mahmood, R., Yang, Z. L., Fu, C., & Su, H. (2015). Investigating diurnal and seasonal climatic response to land use and land cover change over monsoon Asia with the Community Earth System Model. Journal of Geophysical Research: Atmospheres, 120(3), 1137–1152.

    Google Scholar 

  5. Zhang, L., Nan, Z., Yu, W., & Ge, Y. (2015). Modeling land-use and land-cover change and hydrological responses under consistent climate change scenarios in the Heihe river basin China. Water Resources Management, 29(13), 4701–4717.

    CAS  Google Scholar 

  6. Verburg, P. H., Neumann, K., & Nol, L. (2011). Challenges in using land use and land cover data for global change studies. Global Change Biology, 17(2), 974–989.

    Google Scholar 

  7. Chen, Y., Cheng, S. Y., Liu, L., Guo, X. R., Wang, Z., Qin, C. H., & Gao, J. J. (2013). Assessing the effects of land use changes on non-point source pollution reduction for the Three Gorges Watershed using the SWAT model. Journal of Environmental Informatics, 22(1), 13–26.

    Google Scholar 

  8. Niehoff, D., Fritsch, U., & Bronstert, A. (2002). Land-use impacts on storm-runoff generation: Scenarios of land-use change and simulation of hydrological response in a meso-scale catchment in SW-Germany. Journal of Hydrology, 267(1), 80–93.

    Google Scholar 

  9. Wagner, P. D., Bhallamudi, S. M., Narasimhan, B., Kantakumar, L. N., Sudheer, K. P., Kumar, S., Schneider, K., & Fiener, P. (2016). Dynamic integration of land use changes in a hydrologic assessment of a rapidly developing Indian catchment. Science of the Total Environment, 539, 153–164.

    CAS  Google Scholar 

  10. Wagner, P. D., Bhallamudi, M. S., Narasimhan, B., Kumar, S., Fohrer, N., & Fiener, P. (2017). Comparing the effects of dynamic versus static representations of land use change in hydrologic impact assessments. Environmental Modelling & Software, 122, 103987. https://doi.org/10.1016/j.envsoft.2017.06.023

    Article  Google Scholar 

  11. Pielke, R. A. (2005). Land use and climate change. Science, 310(5754), 1625–1626.

    CAS  Google Scholar 

  12. Liu, J., Fritz, S., Van Wesenbeeck, C. F. A., Fuchs, M., You, L., Obersteiner, M., & Yang, H. (2008). A spatially explicit assessment of current and future hotspots of hunger in Sub-Saharan Africa in the context of global change. Global and Planetary Change, 64(3), 222–235.

    Google Scholar 

  13. You, L., Wood, S. R., & Wood-Sichra, U. (2007). Generating plausible crop distribution and performance maps for sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. International Food Policy Research Institute.

    Google Scholar 

  14. Fritz, S., See, L., McCallum, I., Schill, C., Obersteiner, M., Van der Velde, M., Boettcher, H., & Achard, F. (2011). Highlighting continued uncertainty in global land cover maps for the user community. Environmental Research Letters, 6(4), 044005.

  15. See, L. M., & Fritz, S. (2006). A method to compare and improve land cover datasets: Application to the GLC-2000 and MODIS land cover products. IEEE Transactions on Geoscience and Remote Sensing, 44(7), 1740–1746.

    Google Scholar 

  16. Bartholome, E., & Belward, A. S. (2005). GLC2000: A new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26, 1959–1977.

    Google Scholar 

  17. Arino, O., Bicheron, P., Achard, F., Latham, J., Witt, R., & Weber, J. L. (2008). GLOBCOVER-The most detailed portrait of Earth. ESA Bulletin-European Space Agency, 24–31.

  18. Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Schneider, A., Cooper, A., Baccini, A., Gao, F., & Schaaf, C. (2002). Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment, 83, 287–302.

    Google Scholar 

  19. Hansen, M. C., DeFries, R. S., Townshend, J. R. G., Sohlberg, R., Dimiceli, C., & Carroll, M. (2002). Towards an operational MODIS continuous field of percent tree cover algorithm: Examples using AVHRR and MODIS data. Remote Sensing of Environment, 83, 303–319.

    Google Scholar 

  20. DeFries, R. S., & Townshend, J. R. G. (1994). NDVI-derived land-cover classifications at a global-scale. International Journal of Remote Sensing, 15, 3567–3586.

    Google Scholar 

  21. Hansen, M. C., Defries, R. S., Townshend, J. R. G., & Sohlberg, R. (2000). Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21, 1331–1364.

    Google Scholar 

  22. Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L. W. M. J., & Merchant, J. W. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21(6–7), 1303–1330.

    Google Scholar 

  23. Townshend, J. R. G. (1998). Global data sets for land applications from the advanced very high resolution radiometer: An introduction. International Journal of Remote Sensing, 15(17), 3319–3332.

    Google Scholar 

  24. Herold, M., Woodcock, C. E., Loveland, T. R., Townshend, J., Brady, M., Steenmans, C., & Schmullius, C. C. (2008). Land-cover observations as part of a Global Earth Observation System of Systems (GEOSS): Progress, activities, and prospects. Systems Journal IEEE, 2(3), 414–423.

    Google Scholar 

  25. Alexander, P., Prestele, R., Verburg, P. H., Arneth, A., Baranzelli, C., Batistae Silva, F., Brown, C., Butler, A., Calvin, K., Dendoncker, N., Doelman, J.C., Dunford, R., Engström, K., Eitelberg, D., Fujimori, S., Harrison, P. A., Hasegawa, T., Halvik, P., Holzhauer, S., Humpenöder, E., Jacobs-Crisioni, C., Jain, A. K., Krisztin, T., Kyle, P., Lavalle, C., Lenton, T., Liu, J., Meiyappan, P., Popp, A., Powell, T., Sands, R. D., Schaldach, R., Stehfest, E., Steinbuks, J., Tabeau, A., van Meijl, H., Wise, M. A., & Rounsevell, M. D. A. (2017). Assessing uncertainties in land cover projections. Global Change Biology, 23(2), 767–781.

  26. Goldewijk, K. K., & Verburg, P. H. (2013). Uncertainties in global-scale reconstructions of historical land use: An illustration using the HYDE data set. Landscape Ecology, 28(5), 861–877.

    Google Scholar 

  27. Herold, M., Mayaux, P., Woodcock, C. E., Baccini, A., & Schmullius, C. (2008). Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sensing of Environment, 112(5), 2538–2556.

    Google Scholar 

  28. Omrani, H., Abdallah, F., Tayyebi, A., & Pijanowski, B. (2017). Modelling Land-Use Change with Dependence among Labels. Journal of Environmental Informatics, 30(2), 107–118.

    Google Scholar 

  29. Tayyebi, A. H., Tayyebi, A., & Khanna, N. (2014). Assessing uncertainty dimensions in land-use change models: Using swap and multiplicative error models for injecting attribute and positional errors in spatial data. International Journal of Remote Sensing, 35(1), 149–170.

    Google Scholar 

  30. Hepner, G. F., Houshmand, B., Kulikov, I., & Bryant, N. (1998). Investigation of the integration of AVIRIS and IFSAR for urban analysis. Photogrammetric Engineering and Remote Sensing, 64(8), 813–820.

    Google Scholar 

  31. Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X., Cheng,Q., Hu, L., Yao, W., Zhanga, H., Zhu, P., Zhao, Z., Zhang, H., Zheng, Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu, X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C., Clinton, N., Zhu, Z., Chen, J., & Chen, J. (2013). Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing, 34(7), 2607–2654.

  32. Yu, L., Wang, J., & Gong, P. (2013). Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: A segmentation-based approach. International Journal of Remote Sensing, 34(16), 5851–5867.

    Google Scholar 

  33. Kontgis, C., Warren, M. S., Skillman, S. W., Chartrand, R., & Moody, D. I. (2017, June). Leveraging Sentinel-1 time-series data for mapping agricultural land cover and land use in the tropics. In Analysis of Multitemporal Remote Sensing Images (MultiTemp), 2017 9th International Workshop on the (pp. 1–4). IEEE.

  34. Son, N. T., Chen, C. F., Chen, C. R., & Minh, V. Q. (2017). Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines. Geocarto International, 1–15.

  35. Verburg, P. H., Overmars, K. P., Huigen, M. G., de Groot, W. T., & Veldkamp, A. (2006). Analysis of the effects of land use change on protected areas in the Philippines. Applied Geography, 26(2), 153–173.

    Google Scholar 

  36. Kamusoko, C., Aniya, M., Adi, B., & Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), 435–447.

    Google Scholar 

  37. Han, J., Hayashi, Y., Cao, X., & Imura, H. (2009). Application of an integrated system dynamics and cellular automata model for urban growth assessment: A case study of Shanghai China. Landscape and Urban Planning, 91(3), 133–141.

    Google Scholar 

  38. Mitsova, D., Shuster, W., & Wang, X. (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and Urban Planning, 99(2), 141–153.

    Google Scholar 

  39. Hu, Z., & Lo, C. P. (2007). Modeling urban growth in Atlanta using logistic regression. Computers Environment and Urban Systems, 31(6), 667–688.

    Google Scholar 

  40. Wagner, P. D., & Waske, B. (2016). Importance of spatially distributed hydrologic variables for land use change modeling. Environmental Modelling and Software, 83, 245–254. https://doi.org/10.1016/j.envsoft.2016.06.005

    Article  Google Scholar 

  41. Huang, G. B., Ding, X., & Zhou, H. (2010). Optimization method based extreme learning machine for classification. Neurocomputing, 74(1), 155–163.

    Google Scholar 

  42. Arsanjani, J. J., Helbich, M., Kainz, W., & Boloorani, A. D. (2013). Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21, 265–275.

    Google Scholar 

  43. Veldkamp, A., & Fresco, L. O. (1996). CLUE: A conceptual model to study the conversion of land use and its effects. Ecological modelling, 85(2–3), 253–270.

    Google Scholar 

  44. Verburg, P. H., De Koning, G. H. J., Kok, K., Veldkamp, A., & Bouma, J. (1999). A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use. Ecological modelling, 116(1), 45–61.

    Google Scholar 

  45. Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222(20), 3761–3772.

    Google Scholar 

  46. Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54(3), 938–943.

    Google Scholar 

  47. Pontius, R. G., Jr., & Cheuk, M. L. (2006). A generalized cross-tabulation matrix to compare soft-classified maps at multiple resolutions. International Journal of Geographical Information Science, 20(1), 1–30.

    Google Scholar 

  48. Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2017). Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio. International Journal of Applied Earth Observation and Geoinformation, 59, 65–78.

    Google Scholar 

  49. Gemitzi, A. (2021). Predicting land cover changes using a CA Markov model under different shared socioeconomic pathways in Greece. GIScience & Remote Sensing, 1–17.

  50. Ghosh, P., Mukhopadhyay, A., Chanda, A., Mondal, P., Akhand, A., Mukherjee, S., Nayak, S. K., Ghosh, S., Mitra, D., Ghosh, T., & Hazra, S. (2017). Application of cellular automata and Markov-chain model in geospatial environmental modeling-A review. Remote Sensing Applications: Society and Environment, 5, 64–77.

    Google Scholar 

  51. Mondal, P., & Southworth, J. (2010). Evaluation of conservation interventions using a cellular automata-Markov model. Forest Ecology and Management, 260(10), 1716–1725.

    Google Scholar 

  52. Motlagh, Z. K., Lotfi, A., Pourmanafi, S., Ahmadizadeh, S., & Soffianian, A. (2020). Spatial modeling of land-use change in a rapidly urbanizing landscape in central Iran: Integration of remote sensing, CA-Markov, and landscape metrics. Environmental Monitoring and Assessment, 192(11), 1–19.

    Google Scholar 

  53. Tavangar, S., Moradi, H., Massah Bavani, A., & Gholamalifard, M. (2019). A futuristic survey of the effects of LU/LC change on stream flow by CA–Markov model: A case of the Nekarood watershed Iran. Geocarto International, 1–17.

  54. Wu, H., Li, Z., Clarke, K. C., Shi, W., Fang, L., Lin, A., & Zhou, J. (2019). Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change. International Journal of Geographical Information Science, 33(5), 1040–1061.

    Google Scholar 

  55. Huang, Y., Yang, B., Wang, M., Liu, B., & Yang, X. (2020). Analysis of the future land cover change in Beijing using CA–Markov chain model. Environmental Earth Sciences, 79(2), 1–12.

    Google Scholar 

  56. Zhou, L., Dang, X., Sun, Q., & Wang, S. (2020). Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Sustainable Cities and Society, 55, 102045.

  57. Kocabas, V., & Dragicevic, S. (2006). Assessing cellular automata model behaviour using a sensitivity analysis approach. Computers Environment and Urban Systems, 30(6), 921–953.

    Google Scholar 

  58. Zhang, J., Zhou, Y., Li, R., Zhou, Z., Zhang, L., Shi, Q., & Pan, X. (2010). Accuracy assessments and uncertainty analysis of spatially explicit modeling for land use/cover change and urbanization: A case in Beijing metropolitan area. Science China Earth Sciences, 53(2), 173–180.

    Google Scholar 

  59. Palmate, S. S., Pandey, A., & Mishra, S. K. (2017). Modelling spatiotemporal land dynamics for a trans-boundary river basin using integrated Cellular Automata and Markov Chain approach. Applied Geography, 82, 11–23. https://doi.org/10.1016/j.apgeog.2017.03.001

    Article  Google Scholar 

  60. Kumar, D., Gautam, A. K., Palmate, S. S., Pandey, A., Suryavanshi, S., Rathore, N., & Sharma, N. (2017). Evaluation of TRMM multi-satellite precipitation analysis (TMPA) against terrestrial measurement over a humid sub-tropical basin India. Theoretical and Applied Climatology, 129(3), 783–799.

    Google Scholar 

  61. Suryavanshi, S., Pandey, A., Chaube, U. C., & Joshi, N. (2014). Long-term historic changes in climatic variables of Betwa Basin India. Theoretical and Applied climatology, 117(3), 403–418.

    Google Scholar 

  62. Chandramouli, C., & Sinha, S. (2014). Census of India 2011: District Census Handbook Bhopal. Directorate of Census Operations, Madhya Pradesh, Government of India, Series-24, Part XII-B.

  63. Pandey, R. P., Mishra, S. K., Singh, R., & Ramasastri, K. S. (2008). Streamflow drought severity analysis of Betwa river system (India). Water Resources Management, 22(8), 1127–1141.

    Google Scholar 

  64. Latifovic, R., & Olthof, I. (2004). Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data. Remote Sensing of Environment, 90(2), 153–165.

    Google Scholar 

  65. Wang, S. Q., Zheng, X. Q., & Zang, X. B. (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13, 1238–1245.

    Google Scholar 

  66. Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201.

    Google Scholar 

  67. Stehman, S. V., & Czaplewski, R. L. (1998). Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sensing of Environment, 64(3), 331–344.

    Google Scholar 

  68. Yang, Q. S., & Li, X. (2007). Integration of multi-agent systems with cellular automata for simulating urban land expansion. Scientia Geographica Sinica, 27(4), 542.

    Google Scholar 

  69. Balmann, A. (1997). Farm-based modelling of regional structural change: A cellular automata approach. European Review of Agricultural Economics, 24(1), 85–108.

    Google Scholar 

  70. White, R., & Engelen, G. (1997). Cellular automata as the basis of integrated dynamic regional modelling. Environment and Planning B: Planning and design, 24(2), 235–246.

    Google Scholar 

  71. Batty, M., Xie, Y., & Sun, Z. (1999). Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23(3), 205–233.

    Google Scholar 

  72. Wu, F., & Webster, C. J. (1998). Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environment and Planning B: Planning and design, 25(1), 103–126.

    Google Scholar 

  73. Xiyong, H., Bin, C., & Xinfang, Y. (2004). Land use change in Hexi corridor based on CA-Markov methods [J]. Transactions of The Chinese Society of Agricultural Engineering, 5, 065.

    Google Scholar 

  74. Paul, S. S., Li, J., Wheate, R., & Li, Y. (2018). Application of object oriented image classification and Markov chain modeling for land use and land cover change analysis. Journal of Environmental Informatics, 31(1), 30–40.

    Google Scholar 

  75. Mousivand, A. J., Alimohammadi Sarab, A., & Shayan, S. (2007). A new approach of predicting land use and land cover changes by satellite imagery and Markov chain model (Case study: Tehran). Tarbiat Modares University.

    Google Scholar 

  76. Guan, D., Gao, W., Watari, K., & Fukahori, H. (2008). Land use change of Kitakyushu based on landscape ecology and Markov model. Journal of Geographical Sciences, 18(4), 455–468.

    Google Scholar 

  77. Cabral, P., & Zamyatin, A. (2009). Markov processes in modeling land use and land cover changes in Sintra-Cascais Portugal. Dyna, 76(158), 191–198.

    Google Scholar 

  78. Ruiz-Benito, P., Cuevas, J. A., Bravo, D. L. P. R., Prieto, F., Garcia, D. B. J. M., & Zavala, M. A. (2010). Land use change in a Mediterranean metropolitan region and its periphery: Assessment of conservation policies through CORINE Land Cover data and Markov models. Forest Systems, 19, 315–328.

    Google Scholar 

  79. Wickramasuriya, R. C., Bregt, A. K., Van Delden, H., & Hagen-Zanker, A. (2009). The dynamics of shifting cultivation captured in an extended Constrained Cellular Automata land use model. Ecological Modelling, 220(18), 2302–2309.

    Google Scholar 

  80. Tong, S. T. Y., Sun, Y., & Yang, Y. J. (2012). Generating a future land use change scenario with a modified population-coupled Markov cellular automata model. Journal of Environmental Informatics, 19(2), 108–119. https://doi.org/10.3808/jei.201200213

    Article  Google Scholar 

  81. Pontius, R. G. (2000). Quantification error versus location error in comparison of categorical maps. Photogrammetric Engineering and Remote Sensing, 66(8), 1011–1016.

    Google Scholar 

  82. Schneider, L. C., & Pontius, R. G. (2001). Modeling land-use change in the Ipswich watershed Massachusetts USA. Agriculture Ecosystems & Environment, 85(1), 83–94.

    Google Scholar 

  83. Hay, A. M. (1988). The derivation of global estimates from a confusion matrix. International Journal of Remote Sensing, 9(8), 1395–1398.

    Google Scholar 

  84. Lewis, H. G., & Brown, M. (2001). A generalized confusion matrix for assessing area estimates from remotely sensed data. International Journal of Remote Sensing, 22(16), 3223–3235.

    Google Scholar 

  85. Moody, A., & Woodcock, C. (1994). Scale-dependent errors in the estimation of land-cover proportions: Implications for global land-cover datasets. Photogrammetric engineering and remote sensing, 60(5), 585–594.

    Google Scholar 

  86. Flamenco-Sandoval, A., Ramos, M. M., & Masera, O. R. (2007). Assessing implications of land-use and land-cover change dynamics for conservation of a highly diverse tropical rain forest. Biological conservation, 138(1), 131–145.

    Google Scholar 

  87. Mertens, B., & Lambin, E. F. (2000). Land-cover-change trajectories in southern Cameroon. Annals of the Association of American Geographers, 90(3), 467–494.

    Google Scholar 

  88. Ferchichi, A., Boulila, W., & Farah, I. R. (2018). Reducing uncertainties in land cover change models using sensitivity analysis. Knowledge and Information Systems, 55(3), 719–740.

    Google Scholar 

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Acknowledgements

The authors sincerely thank the Department of Water Resources Development and Management, Indian Institute of Technology (IIT) Roorkee, India, for providing necessary research facilities, and the Ministry of Water Resources, Government of India, New Delhi, for providing financial assistance to purchase satellite imagery from National Remote Sensing Centre (NRSC) Hyderabad. Furthermore, the first author would like to acknowledge the Ministry of Human Resource Development (MHRD), Government of India, to provide financial support in the form of scholarship during the stay at IIT Roorkee. Also, the first author sincerely acknowledges support by the World Bank Robert S. McNamara (RSM) Fellowship to take research guidance in 2017 at the Department of Hydrology and Water Resources Management, Institute for Natural Resources Conservation, Kiel University, Germany.

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This research was supported by the World Bank Robert S. McNamara Fellowship.

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Authors and Affiliations

  1. Department of Water Resources Development & Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India

    Santosh S. Palmate & Ashish Pandey

  2. Texas A&M AgriLife Research and Extension Center at El Paso, Texas A&M University System, 1380 A&M Circle, El Paso, TX, 79927, USA

    Santosh S. Palmate

  3. Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, Kiel University, Olshausenstr. 75, 24118, Kiel, Germany

    Paul D. Wagner & Nicola Fohrer

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Contributions

S.S. Palmate conceptualized the initial idea, collected the data, designed the study, conducted the model simulation, and wrote the manuscript; P.D. Wagner contributed to developing the concept, supervised the research, helped in the result interpretation, and revised the manuscript; N. Fohrer and A. Pandey provided guidance for the modelling and reviewed the manuscript. All authors discussed the results, reviewed, and approved the final content of the manuscript.

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Correspondence to Santosh S. Palmate.

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Palmate, S.S., Wagner, P.D., Fohrer, N. et al. Assessment of Uncertainties in Modelling Land Use Change with an Integrated Cellular Automata–Markov Chain Model. Environ Model Assess 27, 275–293 (2022). https://doi.org/10.1007/s10666-021-09804-3

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