An Integrative Modelling Approach to Analyse Landscape Dynamics Through Intensity Analysis and Cellular Automata-Markov Chain Model

Authors

  • Mohammad Hasani Gorgan University of Agricultural Sciences and Natural Resources, College of the Environmental Sciences, Basij square, Gorgan, Golestan Provice, Iran https://orcid.org/0000-0002-8731-1666
  • Abdolrassoul Salmanmahiny Gorgan University of Agricultural Sciences and Natural Resources, College of the Environmental Sciences, Basij square, Gorgan, Golestan Provice, Iran https://orcid.org/0000-0002-5188-7356
  • Alireza Mikaeili Tabrizi Gorgan University of Agricultural Sciences and Natural Resources, College of the Environmental Sciences, Basij square, Gorgan, Golestan Provice, Iran

DOI:

https://doi.org/10.18778/1231-1952.27.1.11

Keywords:

landscape dynamics, satellite imagery, Cellular Automata-Markov Chain, Intensity Analysis, Iran

Abstract

The goal of this study is offer a deep understanding of the landscape dynamics in the Gorgan Township, the Golestan Province, Iran. Landsat satellite imagery of two different time thresholds, i.e. the years 1992 and 2011, was acquired from the US Geological Survey database and the changes were quantified for the Gorgan area covering a 19-year time span. Furthermore, an integrated Cellular Automata-Markov Chain (CA-MC) model was applied to predict future changes up to the year 2030. We used the intensity analysis method to compare the historical dynamics of different land categories at multiple levels. The results indicated that during the 19 years, the built-up and forest areas increased by 2.33% and 0.27%, respectively, while agriculture and remnant vegetation decreased by 2.43% and 0.24%, respectively. The CA-MC model illustrated that in the following 19 years, the built-up areas could increase by 2.45%. An intensity analysis revealed that forest gains and losses were dormant while remnant vegetation gains and losses were active. The built-up area’s gains and water bodies’ losses were active and stationary during both time intervals. The transitions from water bodies and remnant vegetation to agriculture were regularly targeting and stationary, while the transition from forest to agriculture was regularly avoiding and stationary. Our findings also indicated a heavy systematic transition from agriculture to built-up areas. Regarding the increasing population growth and urbanisation in the region, the outcomes of this study can help make informed decisions for the management and protection of natural resources in the study area.

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References

ABD EL-KAWY, O.R., RØD, J.K., ISMAIL, H.A. and SULIMAN, A.S., (2011) ‘Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data’, Appl Geogr, 31, pp. 483–494. https://doi.org/10.1016/j.apgeog.2010.10.012
Google Scholar DOI: https://doi.org/10.1016/j.apgeog.2010.10.012

ALDWAIK, S.Z. and PONTIUS, R.G.Jr., (2012), ‘Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition’, Landscape and Urban Planning, 106, pp. 103–114. https://doi.org/10.1016/j.landurbplan.2012.02.010
Google Scholar DOI: https://doi.org/10.1016/j.landurbplan.2012.02.010

ALDWAIK, S.Z. and PONTIUS, R.G.Jr. (2013), ’Map errors that could account for deviations from a uniform intensity of land change’, International Journal of Geographical Information Science, 27 (9), pp. 1717–1379. https://doi.org/10.1080/13658816.2013.787618
Google Scholar DOI: https://doi.org/10.1080/13658816.2013.787618

BHAGAWAT, R. (2011), ‘Application of remote sensing and GIS, land use/land cover change in Kathmandu metropolitan city’, Nepal J. Theor. Appl. Inform. Technol, 23 (2), pp. 80–86.
Google Scholar

CHANDER, G., MARKHAM, B.L. and HELDER, D.L. (2009), ‘Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors’, Rem. Sen. Envi, 113 (5), pp. 893–903. https://doi.org/10.1016/j.rse.2009.01.007
Google Scholar DOI: https://doi.org/10.1016/j.rse.2009.01.007

CLARK, W. (1965), ‘Markov chain analysis in geography: an application to the movement of rental housing areas’, Ann. Assoc. Am. Geogr., 55 (2), pp. 351–359. https://doi.org/10.1111/j.1467-8306.1965.tb00523.x
Google Scholar DOI: https://doi.org/10.1111/j.1467-8306.1965.tb00523.x

EL BASTAWESY, M. (2014), ‘Hydrological Scenarios of the Renaissance Dam in Ethiopia and Its Hydro-Environmental Impact on the Nile Downstream’, J. Hydro. Engin., http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0001112
Google Scholar DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0001112

ENARUVBE, G. and PONTIUS, R.G.Jr. (2015), ‘Influence of classification errors on Intensity Analysis of land changes in southern Nigeria’, International Journal of Remote Sensing, 31 (1), pp. 244–261. https://doi.org/10.1080/01431161.2014.994721
Google Scholar DOI: https://doi.org/10.1080/01431161.2014.994721

GONG, W., YUAN, L., FAN, W. and STOTT, P., (2015), ‘Analysis and simulation of land use spatial pattern in Harbin prefecture based on trajectories and cellular automata Markov modelling’, International Journal of Applied Earth Observation and Geoinformation, 34 (3), pp. 207–216. https://doi.org/10.1016/j.jag.2014.07.005
Google Scholar DOI: https://doi.org/10.1016/j.jag.2014.07.005

GUAN, D., LI, H., INOHAE, T., SU, W., NAGAIE, T. and HOKAO, K. (2011), ‘Modeling urban’, Ecological Modelling, 222, pp. 3761–3772. https://doi.org/10.1016/j.ecolmodel.2011.09.009
Google Scholar DOI: https://doi.org/10.1016/j.ecolmodel.2011.09.009

HALMY, M.W.A., GESSLER P.E., HICKE, J.A. and SALEM, B.B. (2015), ‘Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA’, Applied Geography, 63, pp. 101–112. https://doi.org/10.1016/j.apgeog.2015.06.015
Google Scholar DOI: https://doi.org/10.1016/j.apgeog.2015.06.015

HASANI, M., SAKIEH, Y., DEZHKAM, S., ARDAKANI, T. and SALMANMAHINY, A. (2017), ‘Environmental monitoring and assessment of landscape dynamics in southern coast of the Caspian Sea through intensity analysis and imprecise land-use data’, Environ Monit Assess. https://doi.org/10.1007/s10661-017-5883-9
Google Scholar DOI: https://doi.org/10.1007/s10661-017-5883-9

HEGAZY, I.R. and KALOOP, M.R. (2015), ‘Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt’, International Journal of Sustainable Built Environment, 4, pp. 117–124. https://doi.org/10.1016/j.ijsbe.2015.02.005
Google Scholar DOI: https://doi.org/10.1016/j.ijsbe.2015.02.005

KAMUSOKO, C., ANIYA, M., ADI, B. and MANJORO, M. (2009), ‘Rural sustainability under threat in Zimbabwe – simulation of futurs land use/cover changes in the Bindura district based on the Markov-cellular automata model’, Applied Geography, 29, pp. 435–447. https://doi.org/10.1016/j.apgeog.2008.10.002
Google Scholar DOI: https://doi.org/10.1016/j.apgeog.2008.10.002

LIU, J.Y. and DENG, X.Z. (2010), ‘Progress of the research methodologies on the temporal and spatial process of LUCC’, Chin. Sci. Bull., 55, pp. 1354–1362. https://doi.org/10.1007/s11434-009-0733-y
Google Scholar DOI: https://doi.org/10.1007/s11434-009-0733-y

LIU, Y., NISHIYAMA, S. and YANO, T. (2004), ‘Analysis of four change detection algorithms in bi-temporal space with a case study’, International Journal of Remote Sensing, 25 (11), pp. 2121–2139. https://doi.org/10.1080/01431160310001606647
Google Scholar DOI: https://doi.org/10.1080/01431160310001606647

LO, C.P. and CHOI, J. (2004), ‘A hybrid approach to urban land use/cover mapping using Landsat 7 enhanced thematic mapper plus (ETM+) images’, Inter. J. Rem. Sen., 25 (14), pp. 2687–2700. https://doi.org/10.1080/01431160310001618428
Google Scholar DOI: https://doi.org/10.1080/01431160310001618428

MULLER, M.R. and MIDDLETON, J. (1994), ‘A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada’, Landscape Ecology, 9, pp. 151–157.
Google Scholar

NAHUELHUAL, L., CARMONA, A., LARA, A., ECHEVERRA, C. and GONZلLEZ, M.E. (2012), ‘Landcover change to forest plantations: proximate causes and implications for the landscape in south-central Chile’, Landscape and Urban Planning, 107 (1), pp. 12–20. https://doi.org/10.1016/j.landurbplan.2012.04.006
Google Scholar DOI: https://doi.org/10.1016/j.landurbplan.2012.04.006

NITSCH, H., OSTERBURG, B., ROGGENDORF, W. and LAGGNER, B. (2012), ‘Cross compliance and the protection of grassland – illustrative analyses of land use transitions between permanent grassland and arable land in German regions’, Land Use Policy, 29, pp. 440–448. https://doi.org/10.1016/j.landusepol.2011.09.001
Google Scholar DOI: https://doi.org/10.1016/j.landusepol.2011.09.001

OTUKEI, J.R. and BLASCHKE, T. (2010), ‘Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms’, International Journal of Applied Earth Observation and Geoinformation, 12, pp. S27–S31. https://doi.org/10.1016/j.jag.2009.11.002
Google Scholar DOI: https://doi.org/10.1016/j.jag.2009.11.002

OVERMARS, K.P., DE KONING, G.H.J. and VELDKAMP, A. (2003), ‘Spatial autocorrelation in multi-scale land use models’, Ecological Modelling, 164, pp. 257–270. https://doi.org/10.1016/S0304-3800(03)00070-X
Google Scholar DOI: https://doi.org/10.1016/S0304-3800(03)00070-X

PALIWAL, M.C. and KATIYAR, S.K. (2015), ‘Accuracy Assessment of Land Cover /Land Use Mapping Using Medium Resolution Satellite Imagery’, International Journal of Scientific & Engineering Research, 6 (7), pp. 1428–1432.
Google Scholar

PONTIUS, G.R. and MALANSON, J. (2005), ‘Comparison of the structure and accuracy of two land change models’, International Journal of Geographical Information Science, 19, pp. 243–265. https://doi.org/10.1080/13658810410001713434
Google Scholar DOI: https://doi.org/10.1080/13658810410001713434

PONTIUS JR., R.G, SHUSAS, E. and MCEACHERN, M. (2004), ‘Detecting important categorical land changes while accounting for persistence’, Agriculture, Ecosystems & Environment, 101 (2–3), pp. 251–268. https://doi.org/10.1016/j.agee.2003.09.008
Google Scholar DOI: https://doi.org/10.1016/j.agee.2003.09.008

PONTIUS JR., R.G., GAO, Y., NICHOLAS, M.G., KOHYAMA, T., OSAKI, M. and HIROSE, K. (2013), ‘Design and interpretation of intensity analysis illustrated by land change in Central Kalimantan, Indonesia’, Land, 2 (3), pp. 351–369. https://doi.org/10.3390/land2030351
Google Scholar DOI: https://doi.org/10.3390/land2030351

PRAKASAM, C. (2010), ‘Land use and land cover change detection through remote sensing approach: A case study of Kodaikanal taluk, Tamil nadu’, International Journal of Geomatics and Geosciences, 1 (2), pp. 150–158.
Google Scholar

RAWAT, J.S. and KUMAR, M. (2015), ‘Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India’, Remote Sensing and Space Sciences, 18, pp. 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002
Google Scholar DOI: https://doi.org/10.1016/j.ejrs.2015.02.002

SAKIEH, Y., SALMANMAHINY, A., JAFARNEZHAD, J., MEHRI, A., KAMYAB, H. and GALDAVI, S. (2015), ‘Evaluating the strategy of decentralized urban land-use planning in a developing region’, Land Use Policy, 48, pp. 534–551. https://doi.org/10.1016/j.landusepol.2015.07.004
Google Scholar DOI: https://doi.org/10.1016/j.landusepol.2015.07.004

SAKIEH, Y., SALMANMAHINY, A., MIRKARIMI, S.H. and SAEIDI, S. (2016), ‘Measuring the relationships between landscape aesthetics suitability and spatial patterns of urbanized lands: An informed modeling framework for developing urban growth scenarios’, Geocarto International. https://doi.org/10.1080/10106049.2016.1178817
Google Scholar DOI: https://doi.org/10.1080/10106049.2016.1178817

SALMANMAHINY, A. (2013), Golestan Province Land Use Planning Report, Gorgan University of Agriculture and Natural Resources.
Google Scholar

SELCUK, R., NISANCI, R., UZUN, B., YALCIN, A., INAN, H. and YOMRALIOGLU, T. (2003), ‘Monitoring land-use changes by GIS and remote sensing techniques: case study of Trabzon’, http://www.fig.net/pub/morocco/proceedings/TS18/TS18_6_reis_el_al.pdf 5.
Google Scholar

SONG, C., WOODCOCK, C.E., SETO, K.C., LENNEY, M.P. and MACOMBER, S.A. (2001), ‘Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?’, Remote Sensing of Environment, 75, pp. 230–244. https://doi.org/10.1016/S0034-4257(00)00169-3
Google Scholar DOI: https://doi.org/10.1016/S0034-4257(00)00169-3

SRIVASTAVA, P.K., HAN, D., RICO-RAMIREZ, M.A., BRAY, M. and ISLAM, T. (2012), ‘Selection of classification techniques for land use/land cover change investigation’, Advances in Space Research, 50, pp. 1250–1265. https://doi.org/10.1016/j.asr.2012.06.032
Google Scholar DOI: https://doi.org/10.1016/j.asr.2012.06.032

TEIXEIRA, Z., MARQUES, J.C. and PONTIUS Jr., R.G. (2016), ‘Evidence for deviations from uniform changes in a Portuguese watershed illustrated by CORINE maps: an intensity analysis approach’, Ecological Indicators, 66, pp. 382–390. https://doi.org/10.1016/j.ecolind.2016.01.018
Google Scholar DOI: https://doi.org/10.1016/j.ecolind.2016.01.018

VERBURG, P.H. and VELDKAMP, A. (2005), ‘Introduction to the Special Issue on Spatial modeling to explore land use dynamics’, International Journal of Geographical Information Science, 19 (2), pp. 99–102. https://doi.org/10.1080/13658810410001713362
Google Scholar DOI: https://doi.org/10.1080/13658810410001713362

VERBURG, P.H., KOK, K., PONTIUS, R.G. and VELDKAMP, A., (2006), ‘Modeling Land-Use and Land-Cover Change’, [in:] LAMBIN E.F. and GEIST H. (eds.) Land-Use and Land-Cover Change, Global Change – The IGBP Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32202-7_5
Google Scholar DOI: https://doi.org/10.1007/3-540-32202-7_5

WARWADE, P., HARDAHA, M.K., CHANDNIHA, S.K. and KUMAR, D. (2013), ‘Land use land cover change detection of Patani micro- watershed of Madhya Pradesh using remote sensing data’, Academicjournals, 8 (40), pp. 1983–1990.
Google Scholar

WENG, Q. (2001), ‘A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, southern China’, Inter. J. Rem. Sens., 22 (10), pp. 1999–2014. https://doi.org/10.1080/01431160152043676
Google Scholar DOI: https://doi.org/10.1080/01431160152043676

YUAN, F., SAWAYA, K.E., LOEFFELHOLZ, B.C. and BAUER, M.E. (2005), ‘Land cover classification and change analysis of the twin cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing’, Rem. Sens. Envi., 98, pp. 317–328. https://doi.org/10.1016/j.rse.2005.08.006
Google Scholar DOI: https://doi.org/10.1016/j.rse.2005.08.006

ZHOU, P., HUANG, J., PONTIUS, R.G. and HONG, H., (2014), ‘Land Classification and Change Intensity Analysis in a Coastal Watershed of Southeast China’, Sensors, 14, pp. 11640–11658. https://doi.org/10.3390/s140711640
Google Scholar DOI: https://doi.org/10.3390/s140711640

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Published

2020-06-30

How to Cite

Hasani, M., Salmanmahiny, A., & Tabrizi, A. M. (2020). An Integrative Modelling Approach to Analyse Landscape Dynamics Through Intensity Analysis and Cellular Automata-Markov Chain Model . European Spatial Research and Policy, 27(1), 243–261. https://doi.org/10.18778/1231-1952.27.1.11

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