Strategies for monitoring environmental changes: monitoring and predicting land-use land-cover (LULC) change (Case study: South Pars special economic zone, Iran)
Subject Areas : EnvironmentSadegh Mokhtarisabet 1 , Afsaneh Shahriari 2
1 - Department of GIS and RS, Yazd Branch, Islamic Azad University, Yazd, Iran
2 - Department of Geography, Shahid Bahonar University of Kerman, Kerman, Iran
Keywords: remote sensing, CA-Markov model, Asaloyeh, Land Use Land Cover, Environmental changes,
Abstract :
Background and objective:In recent years, the importance of modeling and predicting land-use/land-cover (LULC) changes for regional planning and environmental management has grown significantly. This study aims to discover and predict LULC changes in the South Pars' special economic zone over a 20-year period.Materials and methods:In this study, geographic information system (GIS) and a remote sensing technique (RS) were used to classify satellite imagery and the land change modeler (LCM) for monitoring LULC changes. The CA-Markov model was also used to predict LULC changes. The input data of our model were satellite images from TM sensor (Thematic Mapper) for 1998, and 2008 and OLI sensor (Operation Land Imager) for 2018, and this led us to predict LULC changes for 2028.Results and conclusion:Monitoring the results indicated that the area of the built-up areas was increased by 21.2533 km2 (0.81%) during this period, and the largest reduction area was related to the Bare land with 15,298 KM2 (-1.174%). prediction of LULC changes for 2028 revealed that the area of the Built-up areas is doubled and its area will reach 48.65 KM2 (56%). Water bodies and bare land areas will decrease by 113.13 km2 (-19%) to 165.96 km2 (-12%) respectively. Vegetation cover will increase to 23.24 km2 (65%). These results showed that the study area is susceptible to changes due to environmental and human factors that should be considered in urban and environmental planning.
Aburas, M., Abdullah, S., Ramli, M., & Ash'aari, Z. (2015). Evaluating Urban Growth Phenomena in Seremban, Malaysia, Using Land-Use Change-Detection Technique. Advances in Environmental Biology, 9(27), 317-325.
Ahmed, B., & Ahmed, R. (2012). Modeling urban land cover growth dynamics using multi‑temporal satellite images: a case study of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information, 1(1), 3-31.
Aitkenhead, M. J., & Aalders, I. H. (2009). Predicting land cover using GIS, Bayesian and evolutionary algorithm methods. Journal of environmental Management, 90(1), 236-250. https://doi.org/10.1016/j.jenvman.2007.09.010
Alansi, A. W., Amin, M. S. M., Halim, G. A., Shafri, H. Z. M., Thamer, A. M., Waleed, A. R. M., ... & Ezrin, M. H. (2009). The effect of development and land use change on rainfall-runoff and runoff-sediment relationships under humid tropical condition: Case study of Bernam watershed Malaysia. European Journal of Scientific Research, 31(1), 88-105.
Amini Parsa, V., Yavari, A., & Nejadi, A. (2016). Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve: Iran. Modeling earth systems and environment, 2(4), 1-13.
An, L., Linderman, M., Qi, J., Shortridge, A., & Liu, J. (2005). Exploring complexity in a human–environment system: an agent-based spatial model for multidisciplinary and multiscale integration. Annals of the association of American geographers, 95(1), 54-79. https://doi.org/10.1111/j.1467-8306.2005.00450.x
Ashournejad, Q., Amiraslani, F., Kiavarz Moghadam, M., & Toomanian, A. (2019). Impacts of Landuse/Landcover Changes on the Ecosystem Service Values in Pars Special Economic Energy Zone Using Remote Sensing. Physical Geography Research Quarterly, 51(2), 317-333. https://dx.doi.org/10.22059/jphgr.2019.270215.1007303
Azizpour, F., & Ghasemi, S. A. (2011). The roule of south parsot economic special region in location transformation of rural settelments case: Akhand village (Khanghan area).
Baker, W. L. (1989). A review of models of landscape change. Landscape ecology, 2(2), 111-133.
Berger, T. (2001). Agent‐based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural economics, 25(2‐3), 245-260.
Bhatt, R. P., & Khanal, S. N. (2010). Environmental impact assessment system and process: A study on policy and legal instruments in Nepal. African Journal of Environmental Science and Technology, 4(9), 586-594.
Breuer, L., Huisman, J. A., & Frede, H. G. (2006). Monte Carlo assessment of uncertainty in the simulated hydrological response to land use change. Environmental Modeling & Assessment, 11(3), 209-218.
Carvalho, A., Mimoso, A. F., Mendes, A. N., & Matos, H. A. (2014). From a literature review to a framework for environmental process impact assessment index. Journal of Cleaner Production, 64, 36-62.
Costanza, R., & Ruth, M. (1998). Using dynamic modeling to scope environmental problems and build consensus. Environmental management, 22(2), 183-195. https://doi.org/10.1007/s002679900095
Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied geography, 29(3), 390-401. https://doi.org/10.1016/j.apgeog.2008.12.005
Eastman, J.R. (2014), IDRISI Selva Tutorial. Available online: http://uhulag.mendelu.cz/en
Eastman, J. R. (2003). IDRISI Kilimanjaro: guide to GIS and image processing.
Eric, K., John, S., & Aldrik, B. (2007). Modelling land-use change: progress and applications. The Netherlands: Springer.
Feng, Y., Liu, Y., & Tong, X. (2018). Spatiotemporal variation of landscape patterns and their spatial determinants in Shanghai, China. Ecological Indicators, 87, 22-32. https://doi.org/10.1016/j.ecolind.2017.12.034
Foody, G. M. (2000). Mapping land cover from remotely sensed data with a softened feedforward neural network classification. Journal of Intelligent and Robotic Systems, 29(4), 433-449. https://doi.org/10.1023/A:1008112125526
Geoghegan, J., Wainger, L. A., & Bockstael, N. E. (1997). Spatial landscape indices in a hedonic framework: an ecological economics analysis using GIS. Ecological economics, 23(3), 251-264.
Ghorbani Kalkhajeh, R., & Jamali, A. A. (2019). Analysis and predicting the trend of land use/cover changes using neural network and systematic points statistical analysis (SPSA). Journal of the Indian Society of Remote Sensing, 47(9), 1471-1485. https://doi.org/10.1007/s12524-019-00995-7
Hall, D. K., Foster, J. L., Chien, J. Y., & Riggs, G. A. (1995). Determination of actual snow-covered area using Landsat TM and digital elevation model data in Glacier National Park, Montana. Polar Record, 31(177), 191-198. https://doi.org/10.1017/S0032247400013693
Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & 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, 101-112. https://doi.org/10.1016/j.apgeog.2015.06.015
Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10(10), 3421. https://doi.org/10.3390/su10103421
Hartkamp, A. D., White, J. W., & Hoogenboom, G. (1999). Interfacing geographic information systems with agronomic modeling: a review. Agronomy journal, 91(5), 761-772. https://doi.org/10.2134/agronj1999.915761x
Hatami, A., Hafezi, M., Lashkari pour, N., & Moradi, k, (2013). Engineering geology hazards in South Pars Special Area 8th Iranian Society of Engineering Geology and Environment.
He, D., Zhou, J., Gao, W., Guo, H. Y. U. S., Yu, S., & Liu, Y. (2014). An integrated CA-markov model for dynamic simulati2on of land use change in Lake Dianchi watershed. Acta Scientiarum Naturalium Universitatis Pekinensis, 50(6), 1095-1105.
Hua, A. K. (2017). Land Use Land Cover Changes in Detection of Water Quality: A Study Based on Remote Sensing and Multivariate Statistics. Journal of environmental and public health, 2017. https://doi.org/10.1155/2017/7515130
Hyandye, C., Mandara, C. G., & Safari, J. (2015). GIS and Logit Regression Model Applications in Land Use/Land Cover Change and Distribution in Usangu Catchment. American Journal of Remote Sensing; 3(1), 6-16. https://doi.org/10.11648/j.ajrs.20150301.12
Jamali, A. A., Kalkhajeh, R. G., Randhir, T. O., & He, S. (2022). Modeling relationship between land surface temperature anomaly and environmental factors using GEE and Giovanni. Journal of Environmental Management, 302, 113970. https://doi.org/10.1016/j.jenvman.2021.113970
Jamali, A. A., Tabatabaee, R., & Randhir, T. O. (2021). Ecotourism and socioeconomic strategies for Khansar River watershed of Iran. Environment, Development and Sustainability, 23(11), 17077-17093. https://doi.org/10.1007/s10668-021-01334-y
Jamali, A. A., Zarekia, S., & Randhir, T. O. (2018). Risk assessment of sand dune disaster in relation to geomorphic properties and vulnerability in the Saduq-Yazd Erg. Applied Ecology and Environmental Research, 16(1), 579-590. https://doi.org/10.15666/aeer/1601_579590
Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic (Vol. 4, pp. 1-12). New Jersey: Prentice hall. https://doi.org/10.1109/45.468220
Koomen, E., & Borsboom-van Beurden, J. (2011). Land-use modelling in planning practice (pp. XVI-214). Springer Nature. https://doi.org/10.1007/978-94-007-1822-7
Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual review of environment and resources, 28(1), 205-241.
Liang, B., & Weng, Q. (2010). Assessing urban environmental quality change of Indianapolis, United States, by the remote sensing and GIS integration. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(1), 43-55. https://doi.org/10.1109/JSTARS.2010.2060316
Liverman, D., Moran, E. F., Rindfuss, R., & Stern, P. C. (2000). People and Pixels: Linking Remote Sensing and Social Science (Book Review). The Geographical Bulletin, 42(1), 61.
Lo, K. F. A., & Gunasiri, C. W. (2014). Impact of coastal land use change on shoreline dynamics in Yunlin County, Taiwan. Environments, 1(2), 124-136. https://doi.org/10.3390/environments1020124
Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing, 28(5), 823-870. https://doi.org/10.1080/01431160600746456.
Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International journal of remote sensing, 25(12), 2365-2401. https://doi.org/10.1080/0143116031000139863
Macleod, R. D., & Congalton, R. G. (1998). A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric engineering and remote sensing, 64(3), 207-216.
Mertens, B., & Lambin, E. F. (1997). Spatial modelling of deforestation in southern Cameroon: spatial disaggregation of diverse deforestation processes. Applied Geography, 17(2), 143-162
Mishra, V. N., & Rai, P. K. (2016). A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian Journal of Geosciences, 9(4), 1-18. https://doi.org/10.1007/s12517-015-2138-3
Mishra, V. N., Rai, P. K., Kumar, P., & Prasad, R. (2016, June). Evaluation of land use/land cover classification accuracy using multi-resolution remote sensing images. In Forum geografic (Vol. 15, No. 1).
Mojarad, Z., Pazira, A. R., & Tabatabaie, T. (2021). Evaluation of groundwater quality in Dayyer city Bushehr using groundwater quality index (GQI). Journal of Nature and Spatial Sciences (JONASS), 1(2), 75-90. https://dx.doi.org/10.30495/jonass.2021.1922476.1006
Mondal, M. S., Sharma, N., Garg, P. K., & Kappas, M. (2016). Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. The Egyptian Journal of Remote Sensing and Space Science, 19(2), 259-272.https://doi.org/10.1016/j.ejrs.2016.08.001
Narimisa, M. R., Rezaei, M., Kamaei, H., & Zangeneh, F. K. (2013). Modeling for Environmental Impact Assessment of oil refineries in Iran. Life Science Journal, 10(7s).
Olokeogun, O. S., Iyiola, K., & Iyiola, O. F. (2014). Application of remote sensing and GIS in land use/land cover mapping and change detection in Shasha forest reserve, Nigeria. The International Archives of the Photogrammetry, Remote Sens and Spat Inf Sci, 40, 613-616. https://doi.org/10.5194/isprsarchives-XL-8-613-2014
Pandey, S. K. (2015). Environmental impact assessment and environmenta l management studies for synthetic organic chemicals. International Journal, 3(1), 60-76.
Pontius Jr, R. G., Huffaker, D., & Denman, K. (2004). Useful techniques of validation for spatially explicit land-change models. Ecological Modelling, 179(4), 445-461. https://doi.org/10.1016/j.ecolmodel.2004.05.010
Pontius Jr, R. G. (2002). Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogrammetric engineering and remote sensing, 68(10), 1041-1050.
Pontius Jr, R. G., & Schneider, L. C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, ecosystems & environment, 85(1-3), 239-248.
Pradhan, B., Lee, S., Mansor, S., Buchroithner, M., Jamaluddin, N., & Khujaimah, Z. (2008). Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model. Journal of Applied Remote Sensing, 2(1), 023542.
Richards, J. A., & Richards, J. A. (1999). Remote sensing digital image analysis (Vol. 3, pp. 10-38). Berlin: springer. https://doi.org/10.1007/978-3-662-03978-6
Roy, H. G., Fox, D. M., & Emsellem, K. (2014, June). Predicting land cover change in a Mediterranean catchment at different time scales. In International Conference on Computational Science and Its Applications (pp. 315-330). Springer, Cham. https://doi.org/10.1007/978-3-319-09147-1_23
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(3-4), 938-943.
Scepan, J., Menz, G., & Hansen, M. C. (1999). The DlsGover Validation lmage lnterpretation. Photogrammetric Engineering & Remote Sensing, 65(9), 1075-1081.
Shamsi, S. F. (2010). Integrating Linear Programming and Analytical Hierarchical Processing in Raster-GIS to optimize land use pattern at watershed level. Journal of Applied Sciences and Environmental Management, 14(2). https://doi.org/10.4314/jasem.v14i2.57868
Shao, G. U. O. F. A. N., Liu, D. E. G. A. N. G., & Zhao, G. U. A. N. G. (2001). Relationships of image classification accuracy and variation of landscape statistics. Canadian Journal of Remote Sensing, 27(1), 33-43. https://doi.org/10.1080/07038992.2001.10854917
Singh, S. K., Laari, P. B., Mustak, S. K., Srivastava, P. K., & Szabó, S. (2018). Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India. Geocarto international, 33(11), 1202-1222. https://doi.org/10.1080/10106049.2017.1343390
Singh, S. K., Mustak, S., Srivastava, P. K., Szabó, S., & Islam, T. (2015). Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Environmental Processes, 2(1), 61-78. https://doi.org/10.1007/s40710-015-0062-x
Singh RK, Murty HR, Gupta SK, Dikshit AK (2012) An overview of sustainability assessment methodologies. Ecological Indicators 15: 281-299. https://doi.org/10.1016/j.ecolind.2011.01.007.
Sisodia, P. S., Tiwari, V., & Kumar, A. (2014, September). A comparative analysis of remote sensing image classification techniques. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1418-1421). IEEE. https://doi.org/10.1109/ICACCI.2014.6968245
Stefanov, W. L., Ramsey, M. S., & Christensen, P. R. (2001). Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers. Remote sensing of Environment, 77(2), 173-185. https://doi.org/10.1016/S0034-4257(01)00204-8
Stephenne, N., & Lambin, E. F. (2001). A dynamic simulation model of land-use changes in Sudano-sahelian countries of Africa (SALU). Agriculture, ecosystems & environment, 85(1-3), 145-161.
Subedi, P., Subedi, K., & Thapa, B. (2013). Application of a hybrid cellular automaton–Markov (CA-Markov) model in land-use change prediction: a case study of Saddle Creek Drainage Basin, Florida. Applied Ecology and Environmental Sciences, 1(6), 126-132. https://doi.org/10.12691/aees-1-6-5
Teodoro Carlón Allende, T., López Granados, E. M., & Mendoza, M. E. (2021). Identifying future climatic change patterns at basin level in Baja California, México. Journal of Nature and Spatial Sciences (JONASS), 1(2), 56-74. https://dx.doi.org/10.30495/jonass.2021.1939621.1017
Turner, B. L. (1995). Land-use and land-cover change Science. International Geosphere-Biosphere Programme.
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. https://doi.org/10.1016/0304-3800(94)00151-0
Vishwakarma, C. A., Thakur, S., Rai, P. K., Kamajal, M. S., & Mukherjee, S. (2016). Changing land trajectories: a case study from India using a remote sensing-based approach. Eur J Geogr, 7(2), 61-71.
Wu, C. D., Cheng, C. C., Lo, H. C., & Chen, Y. K. (2010). Application of SEBAL and Markov models for future stream flow simulation through remote sensing. Water resources management, 24(14), 3773-3797.
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.
Yang, X., Zheng, X. Q., & Chen, R. (2014). A land use change model: Integrating landscape pattern indexes and Markov-CA. Ecological Modelling, 283, 1-7. https://doi.org/10.1016/j.ecolmodel.2014.03.011
Yang, X., Zheng, X. Q., & Lv, L. N. (2012). A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological Modelling, 233, 11-19.
Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S., & Mi, J. (2021). GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth System Science Data, 13(6), 2753-2776.