Three-dimensional calibration of land use changes using the integrated model of Markov chain automatic cell in Gorgan-rud river basin

Subject Areas : Spatial data infrastructures and standardisation

Mahboobeh  Hajibigloo ¹ (PhD. Student of Watershed Management, Faculty of Range and Watershed Management, Gorgan University, Gorgan, Iran)
Vahed berdi  Sheikh ² (Assoc. Prof. Department of Watershed Management, Faculty of Range and Watershed Management, Gorgan University, Gorgan, Iran)
Hadi  Memarian ³ (Assoc. Prof. Department of Natural Resources, Faculty of Natural Resources, University of Birjand, Birjand, Iran)
Chooghi Bairam  komaki ⁴ (Assistant Professor, Department of Arid Zone Management, Faculty of Range and Watershed Management, Gorgan University, Gorgan, Iran)

Received: 2020-03-09 Accepted : 2020-05-12 Published : 2020-07-05

Keywords: Pentius-Melinos 3D analysis, CA-Markov model, Support Vector Machine Algorithm, LCM tool,

Abstract :

Background and ObjectiveLand use/cover changes (LU/LC) are considered as one of the most important issues in natural resource management, sustainable development and the environmental changes on a local, national, regional and global scale. Changing uses into each other and changing permissible uses into impermissible uses such as changing agricultural lands into residential regions or changing rangelands into eroded and low-yielding dry farming lands are always considered as importand issues in natural resources. Detection of the patterns of the land use changes and prediction of the changes in the future to carry out suitable planning for optimal utilization of uses in natural resource management reveal the need for modeling spatial and temporal changes of LU/LC. This study aims to assess the efficiency of the integrated model of Markov chain automatic cell (CA-Markov model) in simulation and prediction of spatial and temporal changes of Land use/Land cover (LU/LC) in Gorgan-rud river basin by applying three-dimensional Pentius-Melinus analysis in calibration of land use changes by using three assessment indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit as new indices in the assessment of the accuracy of CA-Markov model. Materials and Methods In this research, the Earth observing sensor images of Landsat-5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) acquired from the U.S. geographical site dependent on the U.S. Geographical Survey (USGS) were used to predict land use changes by using the integrated model of Markov chain automatic cell in Gorgan-rud river basin. Seven land use classes were separated for Gorgan-rud river basin including forest land class with the use code 1, agricultural land class with the use code 2, rangeland class (a mixture of shrubbery,langeland,agriculture) with the use code 3, water bodies class with the use code 4, barren land class (barren, rangeland, agriculture) with the use code 5, residential and industrial region class with the use code 6, streambed class with the use code 7. In this study, object-oriented classification method and  Support Vector Machine (SVM) algorithm were used to classify Landsat 5 and 8 satellite images and extract the land use classes of Gorgan-rud river basin. Segmentation scale  in this algorithm on a 50 unit scale (SL 50) was selected to classify the satellite images of 1987, 2000, 2009 and 2017. The assessment of the accuracy of Support Vector Machine algorithm in the object-based classification of satellite images was done by representing overall accuracy, Kappa cefficient, user accuracy, producer accuracy, commission error and omission error for four study periods. To understand how the changes in the region were created during the period of the study three decades and which classes had the area expansion and which classes had the area decrease, changes in the limits of the classes were revealed and percent of the changes in each class were obtained by using the classification maps and IDRISI software. CA-Markov model predicts the changes of different groups of LU/LC units based on spatial neighbourhood concept, transition probability matrix. Preparing land suitability maps is necessary to predict land use changes so that spatial changes can be controlled for each use by probability rules via filtering suitability maps. Validation of Markov model was performed by using three-dimensional Pentius-Melinus analysis with three assessment indices of Figure of Merit, Quantity Disagreement and Allocation Disagreement. Results and Discussion Support Vector Machine algorithm in the classification of the land use based on object-oriented showed that the highest rate of commission error and omission error were observed in rangelands and agricultural lands with 19.12 and 18.55 percent respectively in the land use map of the year 2009. The lowest accuracy of the producer with 71.49 percent belongs to the rangeland use class in the land use map of the year 2009 and the lowest use accuracy with 71.45 percent belongs to agricultural land use class in the land use map of the year 2017. In keeping with the obtained results, the highest positive change belongs to the agricultural land use increase and the highest negative changes belong to rangeland and forest land use decrease during the period of three decades from 1987 to 2017. The highest forest land decrease with 4.8 percent, the highest agricultural land increase with 5.3 percent, the highest rangeland decrease with 9 percent, the highest barren land increase with 4.6 percent and the highest residential and industrial land increase with 0.8 happened during the periods of 2000-2017, 1987-2017, 2009-2017, 2009-2017, and 1987-2017 respectively. After validating the predicted land use chnges in CA-Markov model, based on the analysis of the 5 existing states in three-dimensional Pentius-Melinus analysis, the CA-Markov model with the accurate prediction of simulation of 89.92 percent showed the high efficiency of CA-Markov model in simulation process. After the implementation of the CA-Markov model analysis on the obtained land use map from the classification of the satellite images, one transition probability matrix and one transitioned area matrix were created. In predictions made by using CA-Markov model in 2017 to 2033, the most changes relate to barren and forest land expansion decrease to 16966 and 6961 hectare respectively and in contrast to the use decrease, rangeland, residential and agricultural land expansion increase will be observed to 20397, 3913 and 3825 hectare respectively. Conclusion Detecting land use changes by using LCM tool for the period of three decades 1987-2017 in Gorgan-rud river basin showed that the forest, agricultural and residential use has had significant changes in this region. The obtained results of the prediction of the land use changes during the coming eighteen years by using the integrated model of Markov chain automatic cell following the detected changes by LCM tool show that we will face extreme deforestation phenomenon in this area. Investigation of the obtained results from the implementation of the future use network model by using Markov transition estimator showed that the future use changes can be predicted based on the existing environmental conditions showing that the agriculture will extremely increase in Gorgan-rud river basin during the coming eighteen years. Thus we can protect water and soil resources with comprehensive and long-term management and prevent the degradation of these valuable resources. Three indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit in three-dimensional Pentius-Melinus analysis had an important role in representation of the accuracy rate and calibration of the land use classification and the land use prediction corresponding with the obtained results from the carried out studies concerning the accuracy assessment with indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit. The results of the studied land use changes by using LCM tool and the integrated model of Markov chain automatic cell during the period of 1987 to 2035 show the degradation of more than 24309 hectare of the forest lands and agriculture increase in an area about 62421 hectare indicating human interfernces and deforestation we face in this area.

References:

Al-sharif AAA, Pradhan B. 2014. Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7(10): 4291-4301. doi:10.1007/s12517-013-1119-7.

Anand J, Gosain AK, Khosa R. 2018. Prediction of land use changes based on Land Change Modeler and attribution of changes in the water balance of Ganga basin to land use change using the SWAT model. Science of The Total Environment, 644: 503-519. doi:https://doi.org/10.1016/j.scitotenv.2018.07.017.

Araya YH, Cabral P. 2010. Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sensing, 2(6): 1549-1563. doi:https://doi.org/10.3390/rs2061549.

Arsanjani JJ, Helbich M, Kainz W, Darvishi Boloorani A. 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. doi:https://doi.org/10.1016/j.jag.2011.12.014.

Askarizadeh D, Arzani H, Jafary M, Bazrafshan J, Prentice I. 2018. Surveying of the past, present and future of vegetation changes in the central Alborz ranges in relation to climate change. Journal of RS and GIS for Natural Resources, 9(32): 1-18. (In Persian)

Beygi Heidarlou H, Banj Shafiei A, Erfanian M, Tayyebi A, Alijanpour A. 2019. Effects of preservation policy on land use changes in Iranian Northern Zagros forests. Land Use Policy, 81: 76-90. doi:https://doi.org/10.1016/j.landusepol.2018.10.036.

Clancy D, Tanner JE, McWilliam S, Spencer M. 2010. Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo. Ecological Modelling, 221(10): 1337-1347. doi:https://doi.org/10.1016/j.ecolmodel.2010.02.001.

Dezhkam S, Jabbarian Amiri B, Darvishsefat AA, Sakieh Y. 2017. Performance evaluation of land change simulation models using landscape metrics. Geocarto international, 32(6): 655-677. doi:https://doi.org/10.1080/10106049.2016.1167967.

Esch T, Asamer H, Bachofer F, Balhar J, Boettcher M, Boissier E, d'Angelo P, Gevaert CM, Hirner A, Jupova K. 2020. Digital world meets urban planet–new prospects for evidence-based urban studies arising from joint exploitation of big earth data, information technology and shared knowledge. International journal of digital earth, 13(1): 136-157. doi:https://doi.org/10.1080/17538947.2018.1548655.

Ghorbannia K, Mirsanjari M, Armin M. 2017. Forecasting of forest land changes in the Chaloosrood watershed. Journal of RS and GIS for Natural Resources, 8(2): 79-91. (In Persian)

Ghosh P, Mukhopadhyay A, Chanda A, Mondal P, Akhand A, Mukherjee S, Nayak SK, 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. doi:https://doi.org/10.1016/j.rsase.2017.01.005.

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. doi:https://doi.org/10.1016/j.ecolmodel.2011.09.009.

Hajbigloo M, Sheikh V, Memarian H, Bairam Komaki C. 2020. Determination of quantity and allocation disagreement indices in selection of appropriate algorithm for land use classification in pixel and objected base in Gorgarood river basin. Journal of RS and GIS for Natural Resources, 10(4): 1-20. (In Persian)

Kelly dOB, Alvares Soares Ribeiro CA, Marcatti GE, Lorenzon AS, Martins de Castro NL, Domingues GF, Romário de Carvalho J, Rosa dos Santos A. 2018. Markov chains and cellular automata to predict environments subject to desertification. Journal of Environmental Management, 225: 160-167. doi:https://doi.org/10.1016/j.jenvman.2018.07.064.

Khalifa MA. 2015. Evolution of informal settlements upgrading strategies in Egypt: From negligence to participatory development. Ain Shams Engineering Journal, 6(4): 1151-1159. doi:https://doi.org/10.1016/j.asej.2015.04.008.

Ku C-A. 2016. Incorporating spatial regression model into cellular automata for simulating land use change. Applied Geography, 69: 1-9. doi:https://doi.org/10.1016/j.apgeog.2016.02.005.

Kumar KS, Kumari KP, Bhaskar PU. 2016. Application of Markov Chain & Cellular Automata based model for prediction of Urban transitions. In: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, pp 4007-4012. doi:https://doi.org/4010.1109/ICEEOT.2016.7755466.

Liu Y, Feng Y, Pontius RG. 2014. Spatially-explicit simulation of urban growth through self-adaptive genetic algorithm and cellular automata modelling. Land, 3(3): 719-738. doi:https://doi.org/10.3390/land3030719.

Mansour S, Al-Belushi M, Al-Awadhi T. 2020. Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Policy, 91: 104414. doi:https://doi.org/10.1016/j.landusepol.2019.104414.

Memarian H, Balasundram SK, Talib JB, Sung CTB, Sood AM, Abbaspour K. 2012. Validation of CA-Markov for simulation of land use and cover change in the Langat Basin, Malaysia. Journal of Geographic Information System, 4(6): 542-554. doi:https://doi.org/10.4236/jgis.2012.46059.

Mohammadi S, Habashi K, Pourmanafi S. 2018. Monitoring and prediction land use/land cover changes and its relation to drought (Case study: sub-basin Parsel B2, Zayandeh Rood watershed). Journal of RS and GIS for Natural Resources, 9(1): 24-39. (In Persian)

Munthali MG, Mustak S, Adeola A, Botai J, Singh SK, Davis N. 2020. Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov model. Remote Sensing Applications: Society and Environment, 17: 100276. doi:https://doi.org/10.1016/j.rsase.2019.100276.

Pontius Jr RG, Millones M. 2011. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15): 4407-4429. doi:https://doi.org/10.1080/01431161.2011.552923.

Pontius Jr RG, Peethambaram S, Castella J-C. 2011. Comparison of three maps at multiple resolutions: a case study of land change simulation in Cho Don District, Vietnam. Annals of the Association of American Geographers, 101(1): 45-62. doi:https://doi.org/10.1080/00045608.2010.517742.

Prestele R, Alexander P, Rounsevell MD, Arneth A, Calvin K, Doelman J, Eitelberg DA, Engström K, Fujimori S, Hasegawa T. 2016. Hotspots of uncertainty in land‐use and land‐cover change projections: a global‐scale model comparison. Global change biology, 22(12): 3967-3983. doi:https://doi.org/10.1111/gcb.13337.

Ralha CG, Abreu CG, Coelho CGC, Zaghetto A, Macchiavello B, Machado RB. 2013. A multi-agent model system for land-use change simulation. Environmental Modelling & Software, 42: 30-46. doi:https://doi.org/10.1016/j.envsoft.2012.12.003.

Rimal B, Zhang L, Keshtkar H, Haack BN, Rijal S, Zhang P. 2018. Land use/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and markov chain. ISPRS International Journal of Geo-Information, 7(4): 154. doi:https://doi.org/10.3390/ijgi7040154.

Saaty TL. 1990. How to make a decision: the analytic hierarchy process. European journal of operational research, 48(1): 9-26.

Salehi N, Ekhtesasi M, Talebi A. 2019. Predicting locational trend of land use changes using CA-Markov model (Case study: Safarod Ramsar watershed). Journal of RS and GIS for Natural Resources, 10(1): 106-120. (In Persian)

Singh SK, Mustak S, Srivastava PK, 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. doi:https://doi.org/10.1007/s40710-015-0062-x.

Solomon N, Hishe H, Annang T, Pabi O, Asante IK, Birhane E. 2018. Forest cover change, key drivers and community perception in Wujig Mahgo Waren forest of northern Ethiopia. Land, 7(1): 32. doi:https://doi.org/10.3390/land7010032.

Stefanov WL, Ramsey MS, Christensen PR. 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. doi:https://doi.org/10.1016/S0034-4257(01)00204-8.

Tajbakhsh M, Memarian H, Shahrokhi Y. 2016. Analyzing and modeling urban sprawl and land use changes in a developing city using a CA-Markovian approach. Global Journal of Environmental Science and Management, 2(4): 397-410. doi:https://doi.org/10.22034/GJESM.2016.02.04.009.

Varga OG, Pontius RG, Singh SK, Szabó S. 2019. Intensity Analysis and the Figure of Merit’s components for assessment of a Cellular Automata – Markov simulation model. Ecological Indicators, 101: 933-942. doi:https://doi.org/10.1016/j.ecolind.2019.01.057.

Verburg PH, Soepboer W, Veldkamp A, Limpiada R, Espaldon V, Mastura SSA. 2002. Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model. Environmental Management, 30(3): 391-405. doi:10.1007/s00267-002-2630-x.

Yu H, Jia H. 2017. Simulating the spatial dynamics of urban growth with an integrated modeling approach: A case study of Foshan, China. Ecological Modelling, 353: 107-116. doi:https://doi.org/10.1016/j.ecolmodel.2016.04.005.

Zang S, Huang X. 2006. An aggregated multivariate regression land-use model and its application to land-use change processes in the Daqing region (northeast China). Ecological Modelling, 193(3): 503-516. doi:https://doi.org/10.1016/j.ecolmodel.2005.08.026.