Monitoring and forecasting of land use/land cover (LULC) in Al-Hassa Oasis, Saudi Arabia based on the integration of the Cellular Automata (CA) and the Cellular Automata-Markov Model (CA-Markov)

References

• Abdallah, S., Elmohemen, M. A., Hemdan, S., & Ibrahem, K. (2019). Assessment of land use/land cover changes induced by Jizan Dam, Saudi Arabia, and their effect on soil organic carbon. Arabian Journal of Geosciences, 12(11), 350. https://doi.org/10.1007/s12517-019-4474-1
   Web of Science ®Google Scholar
• Abdelatti, H., Elhadary, Y., & Babiker, A. A. (2017). Nature and trend of urban growth in Saudi Arabia: The case of Al-Ahsa Province – Eastern Region. Resources and Environment, 7(3), 69–80. https://doi.org/10.5923/j.re.20170703.02
   Google Scholar
• Abd El Karim, A., Alogayell, H. M., Alkadi, I. I., & Youssef, I. (2020). Mapping of GIS-Land use suitability in the rural–urban continuum between Ar Riyadh and Al Kharj Cities, KSA based on the integrating GIS multi criteria decision analysis and analytic hierarchy process. Environments, 7(10), 1–29. MDPI. https://doi.org/10.3390/environments7100075
   Web of Science ®Google Scholar
• Abdelkarim, A., Alogayell, H. M., Alkadi, I. I., & Youssef, I. (2022). Spatial–temporal prediction model for land cover of the rural–urban continuum axis between Ar-Riyadh and Al-Kharj cities in KSA in the year of 2030 using the integration of CA–Markov model, GIS-MCA, and AHP. Applied Geomatics, 14(3), 501–525. https://doi.org/10.1007/s12518-022-00448-w
   Web of Science ®Google Scholar
• Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2016). The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. International Journal of Applied Earth Observation and Geoinformation, 52, 380–389. https://doi.org/10.1016/j.jag.2016.07.007
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.jag.2017.03.006
   Web of Science ®Google Scholar
• Adhikari, S., & Southworth, J. (2012). Simulating forest cover changes of Bannerghatta National Park on a CA-Markov model. Remote Sens, 4(10), 3215–3243. https://doi.org/10.3390/rs4103215
   Google Scholar
• Al-Alola, S. S., Alogayell, H. M., Alkadi, M., II, Mohamed, S. A., & Ismail, I. Y. (2021). Recognition and prediction of land dynamics and its associated impacts in Al-Qurayyat City and along Al-Shamal train pathway in Saudi Arabia. Sustainability, 13(17), 9913. https://doi.org/10.3390/su13179913
   Web of Science ®Google Scholar
• Alharthi, A., El-Sheikh, M. A., Elhag, M., Alatar, A. A., Abbadi, G. A., Abdel-Salam, E. M., Arif, I. A., Baeshen, A. A., & Eid, E. M. (2020). Remote sensing of 10 years changes in the vegetation cover of the northwestern coastal land of Red Sea, Saudi Arabia. Saudi Journal of Biological Sciences, 27(11), 3169–3179. https://doi.org/10.1016/j.sjbs.2020.07.021
   PubMed Web of Science ®Google Scholar
• Almadini, A. M., Hassaballa, A. A., & Dewan, A. (2019). Depicting changes in land surface cover at Al-Hassa oasis of Saudi Arabia using remote sensing and GIS techniques. PLoS One, 14(11), e0221115. https://doi.org/10.1371/journal.pone.0221115
   Web of Science ®Google Scholar
• Alqarni, S., Babiker, A., & Salih, A. (2018). Detection, mapping and assessment change in urban and croplands area in Al-Hassa Oasis, eastern region in Saudi Arabia using remote sensing and geographic information system. Journal of Geographic Information System, 10(6), 659–685. https://doi.org/10.4236/jgis.2018.106034
   Google Scholar
• Alqurashi, A. F., & Kumar, L. (2014). Land use and land cover change detection in the Saudi Arabian Desert cities of Makkah and Al-Taif using satellite data. Advances in Remote Sensing, 3(3), 106–119. https://doi.org/10.4236/ars.2014.33009
   Google Scholar
• Alqurashi, A. F., & Kumar, L. (2016). Spatiotemporal patterns of urban change and associated environmental impacts in five Saudi Arabian cities: A case study using remote sensing data. Habitat International, 58, 75–88. https://doi.org/10.1016/j.habitatint.2016.10.001
   Web of Science ®Google Scholar
• Alsharif, M., Alzandi, A. A., Shrahily, R., & Mobarak, B. (2022). Land use land cover change analysis for urban growth prediction using Landsat satellite data and Markov Chain Model for Al Baha Region Saudi Arabia. Forests, 13(10), 1530. https://doi.org/10.3390/f13101530
   Web of Science ®Google Scholar
• Aneesha Satya, B., Shashi, M., & Deva, P. (2020). Future land use land cover scenario simulation using open source GIS for the city of Warangal, Telangana, India. Applied Geomatics, 12(3), 281–290. https://doi.org/10.1007/s12518-020-00298-4
   Web of Science ®Google Scholar
• Appiah, D. O., Forkuo, E. K., Bugri, J. T., & Apreku, T. O. (2017). Geospatial analysis of land use and land cover transitions from 1986–2014 in a Peri-Urban Ghana. Geosciences, 7(4), 1–23. MPDI. https://doi.org/10.3390/geosciences7040125
   Google Scholar
• Balzter, H. (2000). Markov chain models for vegetation dynamics. Ecological Modelling, 126(2–3), 139–154. https://doi.org/10.1016/S0304-3800(00)00262-3
   Web of Science ®Google Scholar
• Batty, M., & Xie, Y. (1994). From cells to cities. Environment and Planning B: Planning & Design, 21(7), 31–48. https://doi.org/10.1068/b21S031
   Web of Science ®Google Scholar
• Biro Turk, K. G., & Aljughaiman, A. S. (2020). Land use/land cover assessment as related to soil and irrigation water salinity over an oasis in arid environment. Open Geosciences, 12(1), 220–231. https://doi.org/10.1515/geo-2020-0103
   Web of Science ®Google Scholar
• Chang, C. L., & Chang, J. C. (2006). Markov model and cellular automata for vegetation. Journal of Geographical Research, 45, 45–57.
   Google Scholar
• Chaplot, V., Ie Brozec, E. C., Silvera, N., & Valentin, C. (2005). Spatial and temporal assessment of linear erosion in catchments under sloping lands of northern Laos. Catena, 63(2–3), 167–184. https://doi.org/10.1016/j.catena.2005.06.003
   Web of Science ®Google Scholar
• Chen, Y., Li, X., Liu, X., Ai, B., & Li, S. (2016). Capturing the varying effects of driving forces over time for the simulation of urban growth by using survival analysis and cellular automata. Landscape and Urban Planning, 152, 59–71. https://doi.org/10.1016/j.landurbplan.2016.03.011
   Web of Science ®Google Scholar
• Chim, K., Tunnicliffe, J., Shamseldin, A., & Ota, T. (2019). Land use change detection and prediction in upper Siem Reap River, Cambodia. Hydrology, 6(3), 1–23. MPDI. https://doi.org/10.3390/hydrology6030064
   Web of Science ®Google Scholar
• Chouari, W. (2021). Wetland land cover change detection using multitemporal Landsat data: A case study of the Al-Asfar wetland, Kingdom of Saudi Arabia. Arabian Journal of Geosciences, 14(6), 523. https://doi.org/10.1007/s12517-021-06815-y
   Web of Science ®Google Scholar
• Clarke, K. C., & Gaydos, L. J. (1988). Loose-coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 12(7), 699–714. https://doi.org/10.1080/136588198241617
   Google Scholar
• Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay Area. Environment and Planning B: Planning & Design, 24(2), 247–261. https://doi.org/10.1068/b240247
   Web of Science ®Google Scholar
• Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B
   Web of Science ®Google Scholar
• Conoscenti, C., Angileri, S., Cappadonia, C., Rotigliano, E., Agnesi, V., & Märker, M. (2014). Gully erosion susceptibility assessment by means of GIS-based logistic regression: A case of Sicily (Italy). Geomorphology, 204, 399–411. https://doi.org/10.1016/j.geomorph.2013.08.021
   Web of Science ®Google Scholar
• Coppedge, B. R., Engle, D. M., & Fuhlendorf, S. D. (2007). Markov models of land cover dynamics in a southern Great Plains grassland region. Landscape Ecology, 22(9), 1383–1393. https://doi.org/10.1007/s10980-007-9116-4
   Web of Science ®Google Scholar
• Dey, N. N., Al Rakib, A., Al Kafy, A. A., & Raikwar, V. (2021). Geospatial modelling of changes in land use/land cover dynamics using multi-layer perception Markov chain model in Rajshahi City, Bangladesh. Environmental Challenges, 4, 100148. https://doi.org/10.1016/j.envc.2021.100148
   Google Scholar
• Dimitrios, G., Elias, S., Ioannis, C., & Sotirios, K. (2019). Incorporating density in spatiotemporal land use/cover change patterns: The case of Attica, Greece. Remote Sensing, 10(7), 1–18. MPDI. https://doi.org/10.3390/rs10071034
   Web of Science ®Google Scholar
• Eastman, J. R. (2015). TerrSet manual. TerrSet Vers, 18, 1–390.
   Google Scholar
• Faichia, C., Tong, Z., Zhang, J., Liu, X., Kazuva, E., Ullah, K., & Al-Shaibah, B. (2020). Using RS data-based CA–Markov Model for dynamic simulation of historical and future LUCC in Vientiane, Laos. Sustainability, 12(20), 1–20. MDPI. https://doi.org/10.3390/su12208410
   Web of Science ®Google Scholar
• Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201. https://doi.org/10.1016/S0034-4257(01)00295-4
   Web of Science ®Google Scholar
• Fung, T., & Ledrew, E. (1988). The determination of optimal threshold levels for change detection using various accuracy indices. Photogrammetric Engineering and Remote Sensing, 54, 1449–1454.
   Web of Science ®Google Scholar
• Guan, D., Zhao, Z., & Tan, J. (2019). Dynamic simulation of land use change based on logistic-CA-Markov and WLC-CA-Markov models: A case study in three Gorges reservoir area of Chongqing, China. Environmental Science and Pollution Research, 26(20), 20669–20688. https://doi.org/10.1007/s11356-019-05127-9
   PubMed Web of Science ®Google Scholar
• He, J., Huang, J., & Li, C. (2017). The evaluation for the impact of land use change on habitat quality: A joint contribution of cellular automata scenario simulation and habitat quality assessment model. Ecological Modelling, 366, 58–67. https://doi.org/10.1016/j.ecolmodel.2017.10.001
   Web of Science ®Google Scholar
• Holtgrave, A. K., Röder, N., Ackermann, A., Erasmi, S., & Kleinschmit, B. (2020). Comparing Sentinel-1 and -2 data and indices for agricultural land use monitoring. Remote Sensing, 12(18), 2919. https://doi.org/10.3390/rs12182919
   Web of Science ®Google Scholar
• 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), 60. https://doi.org/10.1007/s12665-019-8785-z
   Web of Science ®Google Scholar
• Islam, K., Jashimuddin, M., Nath, B., & Nath, T. K. (2018). Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati Wildlife Sanctuary, Bangladesh. The Egyptian Journal of Remote Sensing and Space Sciences, 21(1), 37–47. https://doi.org/10.1016/j.ejrs.2016.12.005
   Web of Science ®Google Scholar
• Jensen, J. R., & Cowen, D. C. (1999). Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogrammetric Engineering and Remote Sensing, 65, 611–622. https://doi.org/10.1002/9780470979587.ch22
   Web of Science ®Google Scholar
• Kafy, A. A., Naim, M. D. N. H., Subramanyam, G., Faisal, A. A., Ahmed, N. U., Al Rakib, A., Kona, M. A., & Sattar, G. S. (2021). Cellular Automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka, Bangladesh. Environmental Challenges, 4, 100084. https://doi.org/10.1016/J.ENVC.2021.100084
   Google Scholar
• 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. https://doi.org/10.1016/j.apgeog.2008.10.002
   Web of Science ®Google Scholar
• Khawaldah, H. (2016). A prediction of future land use/land cover in Amman area using GIS-Based Markov Model and remote sensing. Journal of Geographic Information System, 8(3), 412–427. https://doi.org/10.4236/jgis.2016.83035
   Google Scholar
• Kheir, R. B., Wilson, J., & Deng, Y. (2007). Use of terrain variables for mapping gully erosion susceptibility in Lebanon. Earth Surface Processes and Landforms, 32(12), 1770–1782. https://doi.org/10.1002/esp.1501
   Web of Science ®Google Scholar
• Kuang, W. H. (2011). Simulating dynamic urban expansion at regional scale in Beijing-Tianjin-Tangshan metropolitan area. Journal of Geographical Sciences, 21(2), 317–330. https://doi.org/10.1007/s11442-011-0847-4
   Web of Science ®Google Scholar
• Li, X., Chen, Y., Liu, X., Xu, X., & Chen, G. (2017). Experiences and issues of using cellular automata for assisting urban and regional planning in China. International Journal of Geographical Information Science, 31(8), 1606–1629. https://doi.org/10.1080/13658816.2017.1301457
   Web of Science ®Google Scholar
• Mansour, S., Alahmadi, M., Atkinson, P. M., & Dewan, A. (2022). Forecasting of built-up land expansion in a desert urban environment. Remote Sensing, 14(9), 2037. https://doi.org/10.3390/rs14092037
   Web of Science ®Google Scholar
• Märker, M., Pelacani, S., & Schröder, B. (2011). A functional entity approach to predict soil erosion processes in a small plio-pleistocene Mediterranean catchment in northern Chianti, Italy. Geomorphology, 125(4), 530–540. https://doi.org/10.1016/j.geomorph.2010.10.022
   Web of Science ®Google Scholar
• Megahed, Y., Cabral, P., Silva, J., & Caetano, M. (2015). Land cover mapping analysis and urban growth modelling using remote sensing techniques in Greater Cairo region—Egypt. ISPRS International Journal of Geo-Information, 4(3), 1750–1769. https://doi.org/10.3390/ijgi4031750
   Web of Science ®Google Scholar
• Mishra, V. N., Rai, P. K., & Mohan, K. (2014). Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar), India. Journal of the Geographical Institute: Jovan Cvijic SASA, 64(1), 111–127. https://doi.org/10.2298/IJGI1401111M
   Google Scholar
• Mishra, P. K., Rai, A., & Rai, S. C. (2020). Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India. The Egyptian Journal of Remote Sensing and Space Sciences, 23(2), 133–143. https://doi.org/10.1016/j.ejrs.2019.02.001
   Web of Science ®Google Scholar
• Moghadam, H. S., & Helbich, M. (2013). Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geography, 40, 140–149. https://doi.org/10.1016/j.apgeog.2013.01.009
   Web of Science ®Google Scholar
• Mohamed, S., & El-Reay, M. E. (2018). Assessment, prediction and future simulation of land cover dynamics using remote sensing and GIS techniques. Assiut University Bulletin for Environmental Researches, 21(2), 1–13.
   Google Scholar
• Mondal, M. S., Sharma, N., Kappas, M., & Garg, P. K. (2019). CA-Markov modeling of land use land cover dynamics and sensitivity analysis to identify sensitive parameter(s). International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 13, 723–729. https://doi.org/10.5194/isprs-archives-XLII-2-W13-723-2019
   Google Scholar
• Nadoushan, M. A., Soffianian, A., & Alebrahim, A. (2015). Modeling land use/cover changes by the combination of Markov chain and cellular automata Markov (CA-Markov) models. Journal of Earth, Environment and Health Sciences, 1(1), 16–21. https://www.ijeehs.org/text.asp?2015/1/1/16/159922
   Google Scholar
• Omar, Q., Ahamad, M., Hussin, W., Samat, N., & Ahmad, Z., & Markov, CA. (2013). Multi regression, and multiple decision making for modeling historical changes in Kirkuk City. Journal of the Indian Society of Remote Sensing, 42(1), 165–178. https://doi.org/10.1007/s12524-013-0311-2
   Web of Science ®Google Scholar
• Ozturk, D. (2015). Urban growth simulation of Atakum (Samsun, Turkey) using cellular automata-Markov chain and multi-layer perceptron-Markov Chain Models. Remote Sensing, 7(5), 5918–5950. https://doi.org/10.3390/rs70505918
   Web of Science ®Google Scholar
• Pakawan, C., & Saowanee, W. (2019). Predicting urban expansion and urban land use changes in Nakhon Ratchasima City using a CA-Markov Model under two different scenarios. Land, 8(9), 1–16. MPDI. https://doi.org/10.3390/land8090140
   Web of Science ®Google Scholar
• Patino, J. E., & Duque, J. C. (2013). A review of regional science applications of satellite remote sensing in urban settings, computers. Environment and Urban Systems, 37, 1–17. https://doi.org/10.1016/j.compenvurbsys.2012.06.003
   Web of Science ®Google Scholar
• Piekarczyk, J. (2014). Application of remote sensing in agriculture. Geoinformatica Polonica, 13(1), 1. https://doi.org/10.2478/gein-2014-0007
   Google Scholar
• Pinto, C., Jing, X., & Leigh, L. (2020). Evaluation analysis of Landsat Level-1 and Level-2 data products using in situ measurements. Remote Sensing, 12(6), 1–27. MDPI. https://doi.org/10.3390/rs12162597
   Google Scholar
• Pourghasemi, H. R., Yousefi, S., Kornejady, A., & Cerdà, A. (2017). Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. The Science of the Total Environment, 31(609), 764–775. https://doi.org/10.1016/j.scitotenv.2017.07.198
   Google Scholar
• Rahman, M. T. (2016). Detection of land use/land cover changes and urban sprawl in Al-Khobar, Saudi Arabia: An analysis of multi-temporal remote sensing data. ISPRS International Journal of Geo-Information, 5(2), 15. https://doi.org/10.3390/ijgi5020015
   Web of Science ®Google Scholar
• Rahman, M. T., Aldosary, A. S., & Mortoja, M. G. (2017). Modeling future land cover changes and their effects on the land surface temperatures in the Saudi Arabian eastern coastal city of Dammam. Land, 6(2), 36. https://doi.org/10.3390/land6020036
   Web of Science ®Google Scholar
• Rezazadeh, R., & Mirahmadi, M. (2009). A cellular automata is a new method to simulate urban growth. Journal of Educational Technology, 3(4), 9. https://doi.org/10.22061/tej.2009.1335
   Google Scholar
• Roy, H. G., Dennis, M. F., & Emsellem, K. (2014). Predicting land cover change in a Mediterranean catchment at different time scales. In Computational Science and Its Applications—ICCSA (Vol. 8582, pp. 315–330). Springer International Publishing. https://doi.org/10.1007/978-3-319-09147-1_23
   Google Scholar
• Ruben, B., Zhang, K., Dong, Z., & Jun, X. (2020). Analysis and projection of land-use/land-cover dynamics through scenario-based simulations using the CA-Markov Model: A case study in Guanting Reservoir Basin, China. Sustainability, 12(9), 1–20. MDPI. https://doi.org/10.3390/su12093747
   Web of Science ®Google Scholar
• Saadat, H., Adamowski, J., Bonnell, R., Sharifi, F., Namdar, M., & Ale-Ebrahim, S. (2011). Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 66(5), 608–616. https://doi.org/10.1016/j.isprsjprs.2011.04.001
   Web of Science ®Google Scholar
• Salih, A. (2018). Classification and mapping of land cover types and attributes in Al-Ahsaa Oasis, eastern region, Saudi Arabia using Landsat-7 data. Journal of Remote Sensing & GIS, 7(01), 228. https://doi.org/10.4172/2469-4134.1000228
   Google Scholar
• 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. https://doi.org/10.1016/j.mcm.2010.11.019
   Web of Science ®Google Scholar
• Santé, I., García, A. M., Miranda, D., & Crecente, R. (2010). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96(2), 108–220. https://doi.org/10.1016/j.landurbplan.2010.03.001
   Web of Science ®Google Scholar
• Saudi General Authority for Statistics. (2020). Atlas of population and housing: Maps distributions of preliminary results 2010. https://www.stats.gov.sa/sites/default/files/en-dmaps2010_0_0.pdf
   Google Scholar
• Shooshtari, S. J., & Gholamalifard, M. (2015). Scenario-based land cover change modeling and its implications for landscape pattern analysis in the Neka Watershed, Iran. RemoteSensing Applications Society and Environment, 1, 1–19. https://doi.org/10.1016/j.rsase.2015.05.001
   Google Scholar
• Shrahily, R. Y., Alsharif, M. A., Mobarak, B. A., & Alzandi, A. A. (2022). Land cover mapping using GIS and remote sensing databases for Al Baha Region Saudi Arabia. Applied Science, 12(16), 8115. https://doi.org/10.3390/app12168115
   Google Scholar
• 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
   Google Scholar
• Sohl, T. L., Wimberly, M. C., Radeloff, V. C., Theobald, D. M., & Sleeter, B. M. (2016). Divergent projections of future land use in the United States arising from different models and scenarios. Ecological Modelling, 337, 281–297. https://doi.org/10.1016/j.ecolmodel.2016.07.016
   Web of Science ®Google Scholar
• Solomon, H., Woldeamlak, B., Jan, N., & James, L. (2019). Analysing past land use land cover change and CA-Markov-based future modelling in the Middle Suluh Valley, Northern Ethiopia. Geocarto International, 35, 1–32. https://doi.org/10.1080/10106049.2018.1516241
   Web of Science ®Google Scholar
• 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
   Google Scholar
• Svoray, T., Michailov, E., Cohen, A., Rokah, L., & Sturm, A. (2012). Predicting gully initiation: Comparing data mining techniques, analytical hierarchy processes and the topographic threshold. Earth Surface Processes and Landforms, 37(6), 607–619. https://doi.org/10.1002/esp.2273
   Web of Science ®Google Scholar
• Taubenböck, H., Wegmann, M., Roth, A., Mehl, H., & Dech, S. (2009). Urbanization in India - Spatiotemporal analysis using remote sensing data. Computers, Environment and Urban Systems, 33(3), 179–188. https://doi.org/10.1016/j.compenvurbsys.2008.09.003
   Web of Science ®Google Scholar
• Taufik, A., Ahmad, S. S. S., & Ahmad, A. (2016). Classification of Landsat 8 Satellite data using NDVI thresholds. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8, 4. http://journal.utem.edu.my/index.php/jtec/article/download/1168/671
   Google Scholar
• Tewolde, M. G., & Cabral, P. (2011). Urban sprawl analysis and modelling in Asmara, Eritrea. Remote Sensing, 3(10), 2148–2165. MDPI. https://doi.org/10.3390/rs3102148
   Web of Science ®Google Scholar
• Tingting, X., Jay, G., & Giovanni, C. (2019). Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata. International Journal of Geographical Information Science, 33(10), 1960–1983. https://doi.org/10.1080/13658816.2019.1600701
   Web of Science ®Google Scholar
• Vanwalleghem, T., Van Den Eeckhaut, M., Poesen, J., Govers, G., & Deckers, J. (2008). Spatial analysis of factors controlling the presence of closed depressions and gullies under forest: Application of rare event logistic regression. Geomorphology, 95(3–4), 504–517. https://doi.org/10.1016/j.geomorph.2007.07.003
   Web of Science ®Google Scholar
• Verburg, P. H., Schot, P. P., Dijst, M. J., & Veldkamp, A. (2004). Land use change modelling: Current practice and research priorities. GeoJournal, 61(4), 309–324. https://doi.org/10.1007/s10708-004-4946-y
   Google Scholar
• Wang, C., Huang, S., & Wang, J. (2022). Spatio-Temporal dynamic evolution and simulation of Dike-Pond landscape and ecosystem service value based on MCE-CA-Markov: A case study of Shunde, Foshan. Forests, 13(8), 1241. https://doi.org/10.3390/f13081241
   Web of Science ®Google Scholar
• Wang, S. W., Munkhnasan, L., & Lee, W. K. (2021). Land use and land cover change detection and prediction in Bhutan’s high altitude city of Thimphu, using cellular automata and Markov chain. Environmental Challenges, 2, 100017. https://doi.org/10.1016/j.envc.2020.100017
   Google Scholar
• Wang, Y., & Zhang, X. (2001). A dynamic modeling approach to simulating socioeconomic effects on landscape changes. Ecological Modelling, 140(1–2), 141–162. https://doi.org/10.1016/S0304-3800(01)00262-9
   Web of Science ®Google Scholar
• Ward, D. P., Murray, A. T., & Phinn, S. R. (2000). A stochastically constrained cellular model of urban growth. Computers, Environment and Urban Systems, 24(6), 539–558. https://doi.org/10.1016/S0198-9715(00)00008-9
   Google Scholar
• Weng, Q. (2002). Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management, 64(3), 273–284. https://doi.org/10.1006/jema.2001.0509
   PubMed Web of Science ®Google Scholar
• White, R., & Engelen, G. (2000). High-Resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers, Environment and Urban Systems, 24(5), 383–400. https://doi.org/10.1016/S0198-9715(00)00012-0
   Google Scholar
• Wu, F. (1998). SimLand: A prototype to simulate land conversion through the integrated GIS and CA with AHP-Derived transition rules. International Journal of Geographical Information Science, 12(1), 63–82. https://doi.org/10.1080/136588198242012
   Web of Science ®Google Scholar
• Wu, F. (2002). Calibration of stochastic cellular automata: The application to rural-urban land conversions. International Journal of Geographical Information Science, 16(8), 795–818. https://doi.org/10.1080/13658810210157769
   Web of Science ®Google Scholar
• Yang, H. F., Zhong, X. N., Deng, S. Q., Nie, S. N., & Godron, M. (2022). Impact of LUCC on landscape pattern in the Yangtze River Basin during 2001–2019. Ecological Information, 69, 101631. https://doi.org/10.1016/j.ecoinf.2022.101631
   Web of Science ®Google Scholar
• Ye, B., & Bai, Z. (2008). Simulating land use/cover changes of Nenjiang County based on CA-Markov model. Computer and Computing Technologies in Agriculture, 1, 321–329. https://doi.org/10.1007/978-0-387-77251-6_35
   Google Scholar
• Yu, N., Du, Q., Qingyun, D. (2011). Urban growth pattern modelling using logistic regression. Geospatial Information Science, 14(1), 62–67. https://doi.org/10.1007/s11806-011-0427-x
   Google Scholar
• Zadbagher, E., Becek, K., & Berberoglu, S. (2018). Modeling land use/land cover change using remote sensing and geographic information systems: Case study of the Seyhan. Environmental Monitoring and Assessment, 190(8), 494. https://doi.org/10.1007/s10661-018-6877-y
   PubMed Web of Science ®Google Scholar
• Zakerinejad, R., & Marker, M. (2014). Prediction of gully erosion susceptibilities using detailed terrain analysis and maximum entropy modeling: A case study in the mazayejan plain, southwest Iran. GeografiaFisica e Dinamica Quaternaria, 37, 67–76.
   Web of Science ®Google Scholar
• Zheng, H. W., Shen, G. Q., Wang, H., & Hong, J. (2015). Simulating land-use change in urban renewal areas: A case study in Hong Kong. Habitat International, 46, 23–34. https://doi.org/10.1016/j.habitatint.2014.10.008
   Web of Science ®Google Scholar