Multilayer perceptron and Markov Chain analysis based hybrid-approach for predicting land use land cover change dynamics with Sentinel-2 imagery

References

• Abbas F, Zhang F, Ismail M, Khan G, Iqbal J, Alrefaei AF, Albeshr MF. 2023. Optimizing machine learning algorithms for landslide susceptibility mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: a comparative study of baseline, Bayesian, and metaheuristic hyperparameter optimization techniques. Sensors (Basel). 23(15):6843. doi: 10.3390/S23156843.
   PubMed Web of Science ®Google Scholar
• Abdullahi S, Pradhan B. 2016. Sustainable brownfields land use change modeling using GIS-based weights-of-evidence approach. Appl Spat Anal. 9(1):21–38. doi: 10.1007/S12061-015-9139-1/TABLES/5.
   Web of Science ®Google Scholar
• Ahmed B, Ahmed R. 2012. Modeling urban land cover growth dynamics using multi-temporal satellite images: a case study of Dhaka, Bangladesh. IJGI. 1(1):3–31. doi: 10.3390/ijgi1010003.
   Google Scholar
• Ahmed S. 2018. Assessment of urban heat islands and impact of climate change on socioeconomic over Suez Governorate using remote sensing and GIS techniques. Egy J Remote Sens Space Sci. 21(1):15–25. doi: 10.1016/j.ejrs.2017.08.001.
   Web of Science ®Google Scholar
• Al-Hamdan MZ, Oduor P, Flores AI, Kotikot SM, Mugo R, Ababu J, Farah H. 2017. Evaluating land cover changes in Eastern and Southern Africa from 2000 to 2010 using validated Landsat and MODIS data. Int J Appl Earth Obs Geoinf. 62:8–26. doi: 10.1016/j.jag.2017.04.007.
   Web of Science ®Google Scholar
• 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. Arab J Geosci. 7(10):4291–4301. doi: 10.1007/S12517-013-1119-7/FIGURES/5.
   Web of Science ®Google Scholar
• Al-Sharif AAA, Pradhan B. 2015. A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS. Geocarto Int. 30(8):858–881. doi: 10.1080/10106049.2014.997308.
   Web of Science ®Google Scholar
• Baloch MYJ, Zhang W, Chai J, Li S, Alqurashi M, Rehman G, Tariq A, Talpur SA, Iqbal J, Munir M, et al. 2021. Shallow groundwater quality assessment and its suitability analysis for drinking and irrigation purposes. Water. 13(23):3361. doi: 10.3390/w13233361.
   Web of Science ®Google Scholar
• Bera D, Das Chatterjee N, Bera S, Ghosh S, Dinda S. 2023. Comparative performance of Sentinel-2 MSI and Landsat-8 OLI data in canopy cover prediction using Random Forest model: comparing model performance and tuning parameters. Adv Space Res. 71(11):4691–4709. doi: 10.1016/j.asr.2023.01.027.
   Web of Science ®Google Scholar
• Blank J, Clary C, Nichiporuk B. 2014. Drivers of long-term insecurity and instability in Pakistan : urbanization.
   Google Scholar
• Bloom DE, Canning D, Fink G. 2008. Urbanization and the wealth of nations. Science. 319(5864):772–775. doi: 10.1126/SCIENCE.1153057.
   PubMed Web of Science ®Google Scholar
• Bokhari SA, Saqib Z, Ali A, Haq MZu 2018. Perception of residents about urban vegetation: a comparative study of planned versus semi-planned cities of Islamabad and Rawalpindi, Pakistan. J Ecosyst Ecogr. 08(01) doi: 10.4172/2157-7625.1000251.
   Google Scholar
• Bokhari SA, Saqib Z, Ali A, Mahmud A, Akhtar N, Kanwal A, Haq MZu 2021. The impacts of land use/land cover changes on the supply-demand budget of urban ecosystem services. Arab J Geosci. 14(14):1–27. doi: 10.1007/S12517-021-07504-6/TABLES/5.
   Google Scholar
• Bradshaw CJA, Sodhi NS, Peh KSH, Brook BW. 2007. Global evidence that deforestation amplifies flood risk and severity in the developing world. Glob Change Biol. 13(11):2379–2395. doi: 10.1111/j.1365-2486.2007.01446.x.
   Web of Science ®Google Scholar
• Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32. doi: 10.1023/A:1010933404324/METRICS.
   Web of Science ®Google Scholar
• Busgeeth K, Brits A, Whisken J. 2008. Potential application of remote sensing in monitoring informal settlements in developing countries where complimentary data does not exist.
   Google Scholar
• Butt A, Shabbir R, Ahmad SS, Aziz N. 2015. Land use change mapping and analysis using Remote Sensing and GIS: a case study of Simly watershed, Islamabad, Pakistan. Egy J Remote Sens Space Sci. 18(2):251–259. doi: 10.1016/j.ejrs.2015.07.003.
   Web of Science ®Google Scholar
• Cai E, Jing Y, Liu Y, Yin C, Gao Y, Wei J. 2017. Spatial-temporal patterns and driving forces of ecological-living-production land in Hubei Province, Central China. Sustainability 2018. 10(2):66. doi: 10.3390/su10010066.
   Google Scholar
• CDA. 2022. Facts & statistics - Islamabad. Capital Development Authority. https://www.cda.gov.pk/about_islamabad/vitalstats.asp.
   Google Scholar
• Dey NN, Al Rakib A, Kafy AA, Raikwar V. 2021. Geospatial modelling of changes in land use/land cover dynamics using Multi-layer Perceptron Markov chain model in Rajshahi City, Bangladesh. Environ Chall. 4:100148. doi: 10.1016/j.envc.2021.100148.
   Google Scholar
• Eastman JR. 2006. IDRISI Andes Tutorial.
   Google Scholar
• El Ghoul I, Sellami H, Khlifi S, Vanclooster M. 2023. Impact of land use land cover changes on flow uncertainty in Siliana watershed of northwestern Tunisia. CATENA. 220:106733. doi: 10.1016/j.catena.2022.106733.
   Web of Science ®Google Scholar
• El-Hattab M, S.m A, G.e L. 2018. Monitoring and assessment of urban heat islands over the Southern region of Cairo Governorate, Egypt. Egy J Remote Sens Space Sci. 21(3):311–323. doi: 10.1016/j.ejrs.2017.08.008.
   Google Scholar
• Ewane BE, Lee HH. 2020. Assessing land use/land cover change impacts on the hydrology of Nyong River Basin, Cameroon. J Mt Sci. 17(1):50–67. doi: 10.1007/S11629-019-5611-8/METRICS.
   Web of Science ®Google Scholar
• Ewane EB. 2020. Assessing land use and landscape factors as determinants of water quality trends in Nyong River basin, Cameroon. Environ Monit Assess. 192(8) doi: 10.1007/s10661-020-08448-2.
   PubMed Web of Science ®Google Scholar
• Ewane EB. 2021. Land use land cover change and the resilience of social-ecological systems in a sub-region in South west Cameroon. Environ Monit Assess. 193(6):338. doi: 10.1007/S10661-021-09077-Z/FIGURES/8.
   PubMed Web of Science ®Google Scholar
• Fattah MA, Morshed SR, Morshed SY. 2021. Multi-layer perceptron-Markov chain-based artificial neural network for modelling future land-specific carbon emission pattern and its influences on surface temperature. SN Appl Sci. 3(3):1–22. doi: 10.1007/S42452-021-04351-8/TABLES/17.
   Web of Science ®Google Scholar
• Fortin MJ, Boots B, Csillag F, Remmel TK. 2003. On the role of spatial stochastic models in understanding landscape indices in ecology. Oikos. 102(1):203–212. doi: 10.1034/j.1600-0706.2003.12447.x.
   Web of Science ®Google Scholar
• Geneletti D. 2013. Assessing the impact of alternative land-use zoning policies on future ecosystem services. Environ Impact Assess Rev. 40(1):25–35. doi: 10.1016/j.eiar.2012.12.003.
   Google Scholar
• Ghalib A, Qadir A, Ahmad SR. 2017. Evaluation of developmental progress in some cities of Punjab, Pakistan, using urban sustainability indicators. Sustainability. 9(8):1473. doi: 10.3390/su9081473.
   Web of Science ®Google Scholar
• Gontier M, Mörtberg U, Balfors B. 2010. Comparing GIS-based habitat models for applications in EIA and SEA. Environ Impact Assess Rev. 30(1):8–18. doi: 10.1016/j.eiar.2009.05.003.
   Web of Science ®Google Scholar
• Gutman G, Radeloff V. 2017. Land-cover and land-use changes in Eastern Europe after the collapse of the Soviet Union in 1991. Land-Cover and Land-Use Changes in Eastern Europe after the Collapse of the Soviet Union in 1991. 1–247. doi: 10.1007/978-3-319-42638-9.
   Google Scholar
• Guzder-Williams B, Mackres E, Angel S, Blei AM, Lamson-Hall P. 2023. Intra-urban land use maps for a global sample of cities from Sentinel-2 satellite imagery and computer vision. Comput Environ Urban Syst. 100:101917. doi: 10.1016/j.compenvurbsys.2022.101917.
   Web of Science ®Google Scholar
• Halmy MWA, Gessler PE, Hicke JA, Salem BB. 2015. Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Appl Geogr. 63:101–112. doi: 10.1016/j.apgeog.2015.06.015.
   Web of Science ®Google Scholar
• Hassan Z, Shabbir R, Ahmad SS, Malik AH, Aziz N, Butt A, Erum S. 2016. Dynamics of land use and land cover change (LULCC) using geospatial techniques: a case study of Islamabad Pakistan. SpringerPlus. 5(1) doi: 10.1186/s40064-016-2414-z.
   Google Scholar
• Hong D, Gao L, Yao J, Zhang B, Plaza A, Chanussot J. 2021. Graph convolutional networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 59(7):5966–5978. doi: 10.1109/TGRS.2020.3015157.
   Web of Science ®Google Scholar
• Hong D, Gao L, Yokoya N, Yao J, Chanussot J, Du Q, Zhang B. 2021. More diverse means better: multimodal deep learning meets remote-sensing imagery classification. IEEE Trans Geosci Remote Sens. 59(5):4340–4354. doi: 10.1109/TGRS.2020.3016820.
   Web of Science ®Google Scholar
• Hong D, Yokoya N, Chanussot J, Zhu XX. 2018. An augmented linear mixing model to address spectral variability for hyperspectral unmixing. IEEE Trans Image Process. 28(4):1923–1938. doi: 10.1109/TIP.2018.2878958.
   Web of Science ®Google Scholar
• Hu ZJ, Sadda SR. 2019. Image analysis tools for assessment of atrophic macular diseases. Computational Retinal Image Analysis. Amsterdam, The Netherlands: Elsevier; p. 353–378. doi: 10.1016/B978-0-08-102816-2.00018-6.
   Google Scholar
• Hussain S, Karuppannan S. 2023. Land use/land cover changes and their impact on land surface temperature using remote sensing technique in district Khanewal, Punjab Pakistan. Geol Ecol Landsc. 7(1):46–58. doi: 10.1080/24749508.2021.1923272.
   Google Scholar
• Hussain S, Lu L, Mubeen M, Nasim W, Karuppannan S, Fahad S, Tariq A, Mousa BG, Mumtaz F, Aslam M. 2022. Spatiotemporal variation in land use land cover in the response to local climate change using multispectral remote sensing data. Land. 11(5):595. doi: 10.3390/land11050595.
   Web of Science ®Google Scholar
• Hussain S, Mubeen M, Karuppannan S. 2022. Land use and land cover (LULC) change analysis using TM, ETM + and OLI Landsat images in district of Okara, Punjab, Pakistan. Phys Chem Earth, Parts A/B/C. 126:103117. doi: 10.1016/j.pce.2022.103117.
   Web of Science ®Google Scholar
• Hussain S, Qin S, Nasim W, Bukhari MA, Mubeen M, Fahad S, Raza A, Abdo HG, Tariq A, Mousa BG, et al. 2022. Monitoring the dynamic changes in vegetation cover using spatio-temporal remote sensing data from 1984 to 2020. Atmosphere. 13(10):1609. doi: 10.3390/atmos13101609.
   Web of Science ®Google Scholar
• IDRISI. IDRISI. 2012. IDRISI Selva GIS Image Processing Brochure - [PDF Document]. Software. https://vdocument.in/idrisi-selva-gis-image-processing-brochure.html.
   Google Scholar
• Iqbal J, Su C, Rashid A, Yang N, Baloch MYJ, Talpur SA, Ullah Z, Rahman G, Rahman NU, Earjh E, et al. 2021. Hydrogeochemical assessment of groundwater and suitability analysis for domestic and agricultural utility in Southern Punjab, Pakistan. Water. 13(24):3589. doi: 10.3390/w13243589.
   Web of Science ®Google Scholar
• Iqbal J, Su C, Wang M, Abbas H, Baloch MYJ, Ghani J, Ullah Z, Huq ME. 2023. Groundwater fluoride and nitrate contamination and associated human health risk assessment in South Punjab, Pakistan. Environ Sci Pollut Res Int. 30(22):61606–61625. doi: 10.1007/S11356-023-25958-X/METRICS.
   PubMed Web of Science ®Google Scholar
• Jat Baloch MY, Zhang W, Shoumik BAA, Nigar A, Elhassan AAM, Elshekh AEA, Bashir MO, Mohamed Salih Ebrahim AF, Adam Mohamed KA, Iqbal J. 2022. Hydrogeochemical mechanism associated with land use land cover indices using geospatial, Remote sensing techniques, and health risks model. Sustainability. 14(24):16768. doi: 10.3390/su142416768.
   Web of Science ®Google Scholar
• Jenness J, Wynne J. 2005. Cohen’s Kappa and Classification Table Metrics 2.0 : an ArcView 3x Extension for Accuracy Assessment of Spatially Explicit Models. USGS Publications Warehouse. doi: 10.3133/OFR20051363.
   Google Scholar
• Kafy A-A, Islam M, Sikdar S, Ashrafi TJ, Al-Faisal A, Islam MA, Al Rakib A, Hasan Khan MH, Sarker MHS, Ali MY. 2021. Remote sensing-based approach to identify the influence of land use/land cover change on the urban thermal environment : a case study in Chattogram City, Bangladesh. Re-Envisioning Remote Sensing Applications, London: Taylor & Francis Group; p. 217–240. doi: 10.1201/9781003049210-16.
   Google Scholar
• Kafy, A. Al, Abdullah-Al-Faisal, Raikwar, V., Rakib, A. Al, Kona, M. A., & Ferdousi, J. (2021). Geospatial approach for developing an integrated water resource management plan in Rajshahi, Bangladesh. Environ Chall, 4, 100139. doi: 10.1016/j.envc.2021.100139.
   Google Scholar
• Kafy AA, Rahman MS, Faisal AA, Hasan MM, Islam M. 2020. Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sens Appl: Soc Environ. 18:100314. doi: 10.1016/j.rsase.2020.100314.
   Google Scholar
• Khan, JA, Khayyam U, Waheed A, Khokhar MF, Kamran. 2023. Exploring the nexus between land use land cover (LULC) changes and population growth in a planned city of islamabad and unplanned city of Rawalpindi, Pakistan. Heliyon, 9(2), e13297. doi: 10.1016/J.HELIYON.2023.E13297.
   PubMed Web of Science ®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. Appl Geogr. 29(3):435–447. doi: 10.1016/j.apgeog.2008.10.002.
   Web of Science ®Google Scholar
• Khalifa MA. 2015. Evolution of informal settlements upgrading strategies in Egypt: from negligence to participatory development. Ain Shams Eng J. 6(4):1151–1159. doi: 10.1016/j.asej.2015.04.008.
   Web of Science ®Google Scholar
• Khan A, Sudheer M. 2022. Machine learning-based monitoring and modeling for spatio-temporal urban growth of Islamabad. Egy J Remote Sens Space Sci. 25(2):541–550. doi: 10.1016/j.ejrs.2022.03.012.
   Web of Science ®Google Scholar
• Khan MS, Ullah S, Sun T, Rehman AU, Chen L. 2020. Land-use/land-cover changes and its contribution to urban heat island: a case study of Islamabad, Pakistan. Sustainability. 12(9):3861. doi: 10.3390/su12093861.
   Web of Science ®Google Scholar
• Khoi DD, Murayama Y. 2010. Forecasting areas vulnerable to forest conversion in the tam Dao National Park region, Vietnam. Remote Sens. 2(5):1249–1272. doi: 10.3390/rs2051249.
   Google Scholar
• Kombe WJ. 2005. Land use dynamics in peri-urban areas and their implications on the urban growth and form: the case of Dar es Salaam, Tanzania. Habitat Int. 29(1):113–135. doi: 10.1016/S0197-3975(03)00076-6.
   Web of Science ®Google Scholar
• Kuffer M, Barros J. 2011. Urban morphology of unplanned settlements: the use of spatial metrics in VHR remotely sensed images. Procedia Environ Sci. 7:152–157. doi: 10.1016/j.proenv.2011.07.027.
   Google Scholar
• Lambin EF, Meyfroidt P. 2011. Global land use change, economic globalization, and the looming land scarcity. Proc Natl Acad Sci U S A. 108(9):3465–3472. doi: 10.1073/PNAS.1100480108/SUPPL_FILE/PNAS.201100480SI.PDF.
   PubMed Web of Science ®Google Scholar
• Leta MK, Demissie TA, Tränckner J. 2021. Modeling and prediction of land use land cover change dynamics based on land change modeler (LCM) in Nashe watershed, Upper Blue Nile Basin, Ethiopia. Sustainability. 13(7):3740. doi: 10.3390/su13073740.
   Web of Science ®Google Scholar
• Li C, Zhang B, Hong D, Yao J, Chanussot J. 2023. LRR-Net: an interpretable deep unfolding network for hyperspectral anomaly detection. IEEE Trans Geosci Remote Sens. 61:1–12. doi: 10.1109/TGRS.2023.3279834.
   Web of Science ®Google Scholar
• Li Y, Cao Z, Long H, Liu Y, Li W. 2017. Dynamic analysis of ecological environment combined with land cover and NDVI changes and implications for sustainable urban–rural development: the case of Mu Us Sandy Land, China. J Cleaner Prod. 142:697–715. doi: 10.1016/j.jclepro.2016.09.011.
   Web of Science ®Google Scholar
• Liverman DM, Cuesta RMR. 2008. Human interactions with the Earth system: people and pixels revisited. Earth Surf Process Landforms. 33(9):1458–1471. doi: 10.1002/esp.1715.
   Web of Science ®Google Scholar
• Maithani S. 2014. Neural networks-based simulation of land cover scenarios in Doon valley, India. Geocarto Int. 30(2):1–23. doi: 10.1080/10106049.2014.927535.
   Web of Science ®Google Scholar
• Makinde OO. 2012. Urbanization, housing and environment: megacities of Africa. Int J Dev Sustain. 1(3). www.isdsnet.com/ijds.
   Google Scholar
• Malik N, Asmi F, Ali M, Rahman MM. 2017. Major factors leading rapid urbanization in China and Pakistan: a comparative study. JSSS. 5(1):148. doi: 10.5296/jsss.v5i1.11710.
   Google Scholar
• Malik S, Wahid J. 2014. Rapid urbanization: problems and challenges for adequate housing in Pakistan. JSSW. 2(2) doi: 10.15640/jssw.v2n2a6.
   Google Scholar
• Manandhar R, Odeh I, Ancev T. 2009. Improving the accuracy of land use and land cover classification of landsat data using post-classification enhancement. remote. Sens. 1(3):330–344. doi: 10.3390/rs1030330.
   Web of Science ®Google Scholar
• Mas JF, Kolb M, Paegelow M, Camacho Olmedo MT, Houet T. 2014. Inductive pattern-based land use/cover change models: a comparison of four software packages. Environ Modell Softw. 51:94–111. doi: 10.1016/j.envsoft.2013.09.010.
   Web of Science ®Google Scholar
• McCarthy MJ, Radabaugh KR, Moyer RP, Muller-Karger FE. 2018. Enabling efficient, large-scale high-spatial resolution wetland mapping using satellites. Remote Sens Environ. 208:189–201. doi: 10.1016/j.rse.2018.02.021.
   Web of Science ®Google Scholar
• McHugh ML. 2012. Interrater reliability: the kappa statistic. Biochem Med. 22(3):276–282. doi: 10.11613/BM.2012.031.
   PubMed Web of Science ®Google Scholar
• Metropolitan A, Rahman Rakad Alshabeeb A, Alshawabkeh Y, Rezgallah Al-Amoush H, Al-Fugara R, Kif A-SA, Rahman Al-Shabeeb A, Rania Q, Al-Amoush H, Al-Adamat R. 2018. Simulation and prediction of urban spatial expansion in highly vibrant cities using the sleuth model: a case study of Amman metropolitan, Jordan. https://www.researchgate.net/publication/323238203.
   Google Scholar
• Mishra VN, Rai 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. J Geographical I JC. 64(1):111–127. doi: 10.2298/IJGI1401111M.
   Google Scholar
• Mishra VN, Rai PK. 2016. A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arab J Geosci. 9(4):1–18. doi: 10.1007/S12517-015-2138-3/FIGURES/10.
   Web of Science ®Google Scholar
• Mishra VN, Rai PK, Prasad R, Punia M, Nistor MM. 2018. Prediction of spatio-temporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India, using geospatial approach: a comparison of hybrid models. Appl Geomat. 10(3):257–276. doi: 10.1007/S12518-018-0223-5/TABLES/12.
   Web of Science ®Google Scholar
• Mitsuda Y, Ito S. 2011. A review of spatial-explicit factors determining spatial distribution of land use/land-use change. Landscape Ecol Eng. 7(1):117–125. doi: 10.1007/S11355-010-0113-4/TABLES/1.
   Web of Science ®Google Scholar
• Morales-Barquero L, Lyons MB, Phinn SR, Roelfsema CM. 2019. Trends in remote sensing accuracy assessment approaches in the context of natural resources. Remote Sens. 11(19):2305. doi: 10.5281/ZENODO.3464691.
   Google Scholar
• Mozumder C, Tripathi NK. 2014. Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network. Int J Appl Earth Obs Geoinf. 32(1):92–104. doi: 10.1016/j.jag.2014.03.002.
   Google Scholar
• Naeem S, Cao C, Fatima K, Najmuddin O, Acharya BK. 2018. Landscape greening policies-based land use/land cover simulation for Beijing and Islamabad—an implication of sustainable urban ecosystems. Sustainability 2018. 10(4):10, 1049. doi: 10.3390/su10041049.
   Google Scholar
• Nath B, Niu Z, Singh RP. 2018. Land use and land cover changes, and environment and risk evaluation of Dujiangyan City (SW China) using remote sensing and GIS techniques. Sustainability. 10(12):4631. doi: 10.3390/su10124631.
   Web of Science ®Google Scholar
• Nguyen HTT, Doan TM, Radeloff V. 2018. Applying random forest classification to map land use/land cover using Landsat 8 OLI. Int Arch Photogramm Remote Sens Spatial Inf Sci. XLII-3/W4(3W4):363–367. doi: 10.5194/isprs-archives-XLII-3-W4-363-2018.
   Google Scholar
• Nguyen T. 2015. Optimal ground control points for geometric correction using genetic algorithm with global accuracy. Eur J Remote Sens. 48(1):101–120. doi: 10.5721/EuJRS20154807.
   Web of Science ®Google Scholar
• Niquisse S, Cabral P, Rodrigues Â, Augusto G. 2017. Ecosystem services and biodiversity trends in Mozambique as a consequence of land cover change. Int J Biodivers Sci Ecosyst Serv Manage. 13(1):297–311. doi: 10.1080/21513732.2017.1349836/SUPPL_FILE/TBSM_A_1349836_SM1650.ZIP.
   Google Scholar
• Omurakunova G, Bao A, Jiapaer G, Khan G, Jiang L, Jolochieva E. 2021. Urban growth dynamics during the period 1992–2013 in Kyrgyzstan based on DMSP-OLS nightlight satellite data. Arab J Geosci. 14(19) doi: 10.1007/s12517-021-08291-w.
   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 Sens. 7(5):5918–5950. doi: 10.3390/rs70505918.
   Google Scholar
• PBS. 2017. Census. Pakistan Bureau of Statistics. https://www.pbs.gov.pk/content/final-results-census-2017.
   Google Scholar
• Pontius RG, Millones M. 2011. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens. 32(15):4407–4429. doi: 10.1080/01431161.2011.552923.
   Web of Science ®Google Scholar
• Rashid A, Ayub M, Ullah Z, Ali A, Sardar T, Iqbal J, Gao X, Bundschuh J, Li C, Khattak SA, et al. 2023. Groundwater quality, health risk assessment, and source distribution of heavy metals contamination around chromite mines: application of GIS, sustainable groundwater management, geostatistics, PCAMLR, and PMF receptor model. Int J Environ Res Public Health. 20(3):2113. doi: 10.3390/IJERPH20032113/S1.
   PubMedGoogle Scholar
• Saha P, Mitra R, Chakraborty K, Roy M. 2022. Application of multilayer perceptron neural network Markov Chain model for LULC change detection in the sub-Himalayan North Bengal. Remote Sens Appl: Soc Environ. 26:100730. doi: 10.1016/j.rsase.2022.100730.
   Google Scholar
• Sakieh Y, Salmanmahiny A, Jafarnezhad J, Mehri A, Kamyab H, Galdavi S. 2015. Evaluating the strategy of decentralized urban land-use planning in a developing region. Land Use Policy. 48:534–551. doi: 10.1016/j.landusepol.2015.07.004.
   Web of Science ®Google Scholar
• Salazar A, Baldi G, Hirota M, Syktus J, McAlpine C. 2015. Land use and land cover change impacts on the regional climate of non-Amazonian South America: a review. Glob Planet Change. 128:103–119. doi: 10.1016/j.gloplacha.2015.02.009.
   Web of Science ®Google Scholar
• Sánchez-Espinosa A, Schröder C. 2019. Land use and land cover mapping in wetlands one step closer to the ground: sentinel-2 versus landsat 8. J Environ Manage. 247:484–498. doi: 10.1016/J.JENVMAN.2019.06.084.
   PubMed Web of Science ®Google Scholar
• Shah A, Ali K, Nizami SM. 2021. Four decadal urban land degradation in Pakistan a case study of capital city islamabad during 1979–2019. Environ Sustain Indic. 10:100108. doi: 10.1016/j.indic.2021.100108.
   Google Scholar
• Shahbaz M, Chaudhary AR, Ozturk I. 2017. Does urbanization cause increasing energy demand in Pakistan? Empirical evidence from STIRPAT model. Energy. 122:83–93. doi: 10.1016/j.energy.2017.01.080.
   Web of Science ®Google Scholar
• Tanguay GA, Rajaonson J, Lefebvre JF, Lanoie P. 2010. Measuring the sustainability of cities: an analysis of the use of local indicators. Ecol Indic. 10(2):407–418. doi: 10.1016/j.ecolind.2009.07.013.
   Web of Science ®Google Scholar
• Tendaupenyu P, Magadza CHD, Murwira A. 2017. Changes in landuse/landcover patterns and human population growth in the Lake Chivero catchment, Zimbabwe. Geocarto Int. 32(7):797–811. doi: 10.1080/10106049.2016.1178815.
   Web of Science ®Google Scholar
• Tokar O, Vovk O, Kolyasa L, Havryliuk S, Korol M. 2018. Using the random forest classification for land cover interpretation of landsat images in the prykarpattya region of Ukraine. Int Sci Tech Conf Comput Sci Inf Technol. 1, 241–244. doi: 10.1109/STC-CSIT.2018.8526646.
   Google Scholar
• Tolessa T, Senbeta F, Kidane M. 2017. The impact of land use/land cover change on ecosystem services in the central highlands of Ethiopia. Ecosyst Serv. 23:47–54. doi: 10.1016/j.ecoser.2016.11.010.
   Web of Science ®Google Scholar
• Ullah S, Ahmad K, Sajjad RU, Abbasi AM, Nazeer A, Tahir AA. 2019. Analysis and simulation of land cover changes and their impacts on land surface temperature in a lower Himalayan region. J Environ Manage. 245:348–357. doi: 10.1016/J.JENVMAN.2019.05.063.
   PubMed Web of Science ®Google Scholar
• Ullah Z, Rashid A, Ghani J, Nawab J, Zeng XC, Shah M, Alrefaei AF, Kamel M, Aleya L, Abdel-Daim MM, et al. 2022. Groundwater contamination through potentially harmful metals and its implications in groundwater management. Front Environ Sci. 10:1021596. doi: 10.3389/FENVS.2022.1021596/BIBTEX.
   Web of Science ®Google Scholar
• Ullah Z, Xu Y, Zeng XC, Rashid A, Ali A, Iqbal J, Almutairi MH, Aleya L, Abdel-Daim MM, Shah M. 2022. Non-carcinogenic health risk evaluation of elevated fluoride in groundwater and its suitability assessment for drinking purposes based on water quality index. IJERPH. 19(15):9071. doi: 10.3390/ijerph19159071.
   Web of Science ®Google Scholar
• Umair A. 2022. March 14. Islamabad fast losing its charm due to rapid urbanisation. The News International. https://www.thenews.com.pk/print/941223-islamabad-fast-losing-its-charm-due-to-rapid-urbanisation.
   Google Scholar
• Umar A. 2022. March 14. Islamabad fast losing its charm due to rapid urbanisation. International The News. https://www.thenews.com.pk/print/941223-islamabad-fast-losing-its-charm-due-to-rapid-urbanisation.
   Google Scholar
• Václavík T, Rogan J. 2009. Identifying trends in land use/land cover changes in the context of post-socialist transformation in Central Europe: a case study of the greater Olomouc region, Czech Republic. GISci Remote Sens. 46(1):54–76. doi: 10.2747/1548-1603.46.1.54.
   Web of Science ®Google Scholar
• Verburg PH, Kok K, Pontius RG, Veldkamp A. 2006. Model Land-Use Land-Cover Change. 117–135. doi: 10.1007/3-540-32202-7_5.
   Google Scholar
• Wang SQ, Zheng XQ, Zang XB. 2012. Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environ Sci. 13:1238–1245. doi: 10.1016/j.proenv.2012.01.117.
   Google Scholar
• Wu J, Jenerette GD, Buyantuyev A, Redman CL. 2011. Quantifying spatiotemporal patterns of urbanization: the case of the two fastest growing metropolitan regions in the United States. Ecol Complex. 8(1):1–8. doi: 10.1016/j.ecocom.2010.03.002.
   Web of Science ®Google Scholar
• Wu X, Hong D, Chanussot J. 2022. Convolutional neural networks for multimodal remote sensing data classification. IEEE Trans Geosci Remote Sen. 60:1–10. doi: 10.1109/TGRS.2021.3124913.
   Google Scholar
• Yang C, He X, Yan F, Yu L, Bu K, Yang J, Chang L, Zhang S. 2017. Mapping the influence of land use/land cover changes on the urban heat island effect—a case study of Changchun, China. Sustainability. 9(2):312. doi: 10.3390/su9020312.
   Web of Science ®Google Scholar
• Ying C, Ling H, Kai H. 2017. Change and optimization of landscape patterns in a basin based on remote sensing images: a case study in China. Pol J Environ Stud. 26(5):2343–2353. doi: 10.15244/pjoes/70007.
   Web of Science ®Google Scholar
• Yirsaw E, Wu W, Shi X, Temesgen H, Bekele B. 2017. Land use/land cover change modeling and the prediction of subsequent changes in ecosystem service values in a coastal area of China, the Su-Xi-Chang region. Sustainability. 9(7):1204. doi: 10.3390/su9071204.
   Web of Science ®Google Scholar
• Zhang XQ. 2016. The trends, promises and challenges of urbanisation in the world. Habitat Int. 54:241–252. doi: 10.1016/j.habitatint.2015.11.018.
   Web of Science ®Google Scholar