Abstract
The land is one of the most exigent natural resources and is dynamic in nature. Land use and land cover refer to the categorization and classification of human activities and natural elements on the landscape within a specific time frame. Thus, land cover is the physical material on the Earth’s surface, whereas land use describes how people utilize the land. The change in land use and land cover (LULC) is a significant issue from a global perspective. This article aims to evaluate changes in the LULC in the past (1990–2020) and to predict the future LULC (2030 and 2050) in Koch Bihar urban agglomeration (West Bengal). The present study uses Landsat 5 (TM) and Landsat 8 (OLI/TIRS) remote sensing data, ASTER DEM, open street map, and the Census of India data. The Maximum Likelihood Classifier (MLC) algorithm is used for the supervised classification of land, and an Artificial Neural Network (ANN)-based Cellular Automata-Markov Chain (CA-Markov) model has been used to predict the future LULC pattern in 2030 and 2050. The overall accuracy of the classified images (1990, 2000, 2010, and 2020) is 90%, 92%, 93%, and 91%, respectively. The Kappa coefficient is more than 0.80 (0.873 (1990), 0.899 (2000), 0.911 (2010), and 0.887 (2020). The result shows that the amount of agricultural land would decrease to a great extent, from 50.89 km2 in 1990 to 22.30 km2 in 2050. At the same time, the built-up area would increase significantly from 5.91 km2 in 1990 to 32.91 km2 in 2050. The findings of the present study guide planners and resource managers in designing a roadmap for long-term sustainable land-use and land-cover management in the Koch Bihar urban agglomeration.
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References
Abdulrahman AI, Ameen SA (2020) Predicting land use and land cover spatiotemporal changes utilizing CA-Markov model in Duhok district between 1999 and 2033. Acad J Nawroz Univ 9(4):71–80. https://doi.org/10.25007/ajnu.v9n4a892
Ahmad F, Goparaju L, Qayum A (2017) LULC analysis of urban spaces using Markov chain predictive model at Ranchi in India. Spat Inform Res 25(3):351–359. https://doi.org/10.1007/s41324-017-0102-x
Ahmed B, Ahmed R (2012) Modeling urban land cover growth dynamics using multi–temporal satellite images: a case study of Dhaka, Bangladesh. ISPRS Inter J Geo-Inform 1(1):3–31. https://doi.org/10.3390/ijgi1010003
Alamgir M, Campbell MJ, Sloan S, Engert J, Word J, Laurance WF (2020) Emerging challenges for sustainable development and forest conservation in Sarawak, Borneo. PloS one 15(3):e0229614. https://doi.org/10.1371/journal.pone.0229614
Al-shalabi M, Billa L, Pradhan B, Mansor S, Al-Sharif AA (2013) Modeling urban growth evolution and land-use changes using GIS-based cellular automata and SLEUTH models: the case of Sana’a metropolitan city, Yemen. Environ Earth Sci 70(1):425–437. https://doi.org/10.1007/s12665-012-2137-6
Alsharif AA, Pradhan B (2014a) Urban sprawl analysis of Tripoli Metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. J Indian Soci Rem Sens 42(1):149–163. https://doi.org/10.1007/s12524-013-0299-7
Al-sharif AA, 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. https://doi.org/10.1007/s12517-013-1119-7
Ansari A, Golabi MH (2019) Prediction of spatial land use changes based on LCM in a GIS environment for Desert Wetlands–A case study: Meighan Wetland, Iran. Int Soil Water Conserv Res 7(1):64–70. https://doi.org/10.1016/j.iswcr.2018.10.001
Arsanjani JJ, Helbich M, Kainz W, Boloorani AD (2013) Integration of logistic regression, Markov chain, and cellular automata models to simulate urban expansion. Int J Appl Earth Observ Geoinform 21:265–275. https://doi.org/10.1016/j.jag.2011.12.014
Atkinson PM, Tatnall AR (1997) Introduction neural networks in remote sensing. Int J Remote Sens 18(4):699–709. https://doi.org/10.1080/014311697218700
Bagan H, Yamagata Y (2012) Landsat analysis of urban growth: how Tokyo became the world’s largest megacity during the last 40 years. Remote Sens Environ 127:210–222. https://doi.org/10.1016/j.rse.2012.09.011
Basawaraja R, Chari KB, Mise SR, Chetti SB (2011) Analysis of the impact of urban sprawl in altering the land-use, land-cover pattern of Raichur City, India, using geospatial technologies. J Geogr Reg Plann 4(8):455–462. https://doi.org/10.5897/JGRP.9000016
Basse RM, Omrani H, Charif O, Gerber P, Bódis K (2014) Land-use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Appl Geogr 53:160–171. https://doi.org/10.1016/j.apgeog.2014.06.016
Bhatta B (2009) Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata India. Int J Remote Sens 30(18):4733–4746
Bhatta B (2014) Remote sensing and GIS. Oxford University Press
Bhatta B, Saraswati S, Bandyopadhyay D (2010) Urban sprawl measurement from remote sensing data. Appl Geogr 30:731–740. https://doi.org/10.1016/j.apgeog.2010.02.002
Bonafoni S, Baldinell G, Verducci P (2017) Sustainable strategies for smart cities: analysis of the town development effect on surface urban heat island through remote sensing methodologies. Sustain Cities Soc 29:211–218. https://doi.org/10.1016/j.scs.2016.11.005
Bose A, Chowdhury IR (2020) Monitoring and modeling of spatio-temporal urban expansion and land-use/land-cover change using Markov chain model: a case study in Siliguri Metropolitan area, West Bengal, India. Model Earth Syst Environ 6(4):2235–2249. https://doi.org/10.1007/s40808-020-00842-6
Chamling M, Bera B (2020) Spatio-temporal patterns of land use/land cover change in the Bhutan-Bengal Foothill Region between 1987 and 2019: a study towards geospatial applications and policymaking. Earth Syst Envion. https://doi.org/10.1007/s41748-020-00150-0
Census of India (2011) Migration Table-D-3 appendix – 2011: Migrants by place of the last residence, duration of residence, and reason for migration and data have been computed) https://censusindia.gov.in/census.website/data/census-tables
Cihlar J (2000) Land cover mapping of large areas from satellites: status and research priorities. Int J Remote Sens 21(6–7):1093–1114. https://doi.org/10.1080/014311600210092
Civco DL (1993) Artificial neural networks for land-cover classification and mapping. Int J Geogr Inform Sci 7(2):173–186. https://doi.org/10.1080/02693799308901949
Clarke KC, Hoppen S, Gaydos L (1997) A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Envir Plann B Plann design 24(2):247–261
Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46
Cohen B (2006) Urbanization in developing countries: current trends, future projections, and key challenges for sustainability. Techn Soc 28(1–2):63–80. https://doi.org/10.1016/j.techsoc.2005.10.005
Congalton RG, Green K (1999) Assessing the accuracy of remotely sensed data: principles and practices. CRC/Lewis Press, Boca Raton
Coppedge BR, Engle DM, Fuhlendorf SD (2007) Markov models of land cover dynamics in a southern great plains grassland region. Land Ecol 22(9):1383–1393. https://doi.org/10.1007/s10980-007-9116-4
Cromley RG, Hanink DM (1999) Coupling land use allocation models with raster GIS. J Geogr Syst 1(2):137–153. https://doi.org/10.1007/s101090050009
Dasgupta B (1987) Urbanisation and rural change in West Bengal. Econ Polit Wkly 22(7):276–287
Dhinwa PS, Pathan SK, Sastry SVC, Rao M, Majumder KL, Chotani ML, Singh JP, Sinha RLP (1992) Land use change analysis of Bharatpur district using GIS. J Indian Soc Remote Sens 20(4):237–250. https://doi.org/10.1007/BF03001921
Eastman JR (2009) IDRISI Taiga Guide to GIS and Image Processing; Manual Version 16.02; Clark Labs: Worcester, MA, USA
Eastman JR (2012) IDRISI Selva Tutorial. Clark University, Worcester
Eastman JR (2016) IDRISI Terrset Manual; Clark Labs, Clark University: Worcester, MA, USA
Estoque RC, Murayama Y (2012) Examining the potential impact of land use/ cover changes on the ecosystem services of Baguio City, the Philippines: a scenario-based analysis. Appl Geogr 35:316–326. https://doi.org/10.1016/j.apgeog.2012.08.006
Fahad MGR, Saiful Islam AKM, Nazari R, Alfi Hasan M, Tarekul Islam GM, Bala SK (2018) Regional changes of precipitation and temperature over Bangladesh using bias-corrected multi‐model ensemble projections considering high‐emission pathways. Int J Climatol 38(4):1634–1648. https://doi.org/10.1002/joc.5284
Fenta AA, Yasuda H, Haregeweyn N, Belay AS, Hadush Z, Gebremedhin MA, Mekonnen G (2017) The dynamics of urban expansion and land use/land cover changes using remote sensing and spatial metrics: the case of Mekelle City of northern Ethiopia. Int J Remote Sens 38(14):4107–4129. https://doi.org/10.1080/01431161.2017.1317936
Foody GM (2004) Thematic map comparison. Photo Eng Remote Sens 70(5):627–633. https://doi.org/10.14358/PERS.70.5.627
Ghosh M, Ghosal S (2021) Climate change vulnerability of rural households in flood-prone areas of Himalayan foothills, West Bengal, India. Environ Dev Sustain 23:2570–2595. https://doi.org/10.1007/s10668-020-00687-0
Ghosh S, Sen KK, Rana U, Rao KS, Saxena KG (1996) Application of GIS for land-use/land-cover change analysis in mountainous terrain. J Indian Soc Remote Sens 24(3):193–202. https://doi.org/10.1007/BF03007332
Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, Briggs JM (2008) Global change and the ecology of cities. Science 319(5864):756–760. https://doi.org/10.1126/science.1150195
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. https://doi.org/10.1016/j.apgeog.2015.06.015
Hassan MM (2017) Monitoring land use/land cover change, urban growth dynamics, and landscape pattern analysis in five fastest urbanized cities in Bangladesh. Remote Sens Appl Soc Environ 7:69–83. https://doi.org/10.1016/j.rsase.2017.07.001
Hegazy IR, Kaloop MR (2015) Monitoring urban growth and land-use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. Int J Sustai Built Environ 4(1):117–124. https://doi.org/10.1016/j.ijsbe.2015.02.005
Hu Z, Lo CP (2007) Modeling urban growth in Atlanta using logistic regression. Compu. Environ Urban Syst 31(6):667–688. https://doi.org/10.1016/j.compenvurbsys.2006.11.001
Hua AK, Ping OW (2018) The influence of land-use/land-cover changes on land surface temperature: a case study of Kuala Lumpur metropolitan city. Eur J Remote Sens 51(1):1049–1069. https://doi.org/10.1080/22797254.2018.1542976
Hua L, Tang L, Cui S, Yin K (2014) Simulating urban growth using the SLEUTH model in a coastal peri-urban district in China. Sustainability 6(6):3899–3914. https://doi.org/10.3390/su6063899
Islam K, Rahman MF, Jashimuddin M (2018) Modeling land-use change using cellular automata and artificial neural network: the case of Chunati Wildlife Sanctuary, Bangladesh. Ecol Indic 88:439–453. https://doi.org/10.1016/j.ecolind.2018.01.047
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 Geogol 29(3):435–447. https://doi.org/10.1016/j.apgeog.2008.10.002
Losiri C, Nagai M, Ninsawat S, Shrestha RP (2016) Modeling urban expansion in Bangkok metropolitan region using demographic-economic data through the cellular automata-Markov chain and multi-layer perceptron-Markov chain models. Sustainability 8(7):686. https://doi.org/10.3390/su8070686
Ma Z, Redmond RL (1995) Tau coefficients for accuracy assessment of classification of remote sensing data. Photogramm Eng Remote Sens 61(4):435–439
Maithani S, Arora MK, Jain RK (2010) An artificial neural network-based approach for urban growth zonation in Dehradun city, India. Geocartol Int 25(8):663–681. https://doi.org/10.1080/10106049.2010.524313
Mas JF, Flores JJ (2008) The application of artificial neural networks to the analysis of remotely sensed data. Int J Remote Sens 29(3):617–663. https://doi.org/10.1080/01431160701352154
Mas JF, Kolb M, Paegelow M, Olmedo MTC, Houet T (2014) Inductive pattern-based land use/cover change models: a comparison of four software packages. Environ Modell Soft 51:94–111. https://doi.org/10.1016/j.envsoft.2013.09.010
Matlhodi B, Kenabatho PK, Parida BP, Maphanyane JG (2019) Evaluating land use and land cover change in the Gaborone dam catchment, Botswana, from 1984–2015 using GIS and remote sensing. Sustainability 11:5174. https://doi.org/10.3390/su11195174
Mendiratta P, Gedam S (2018) Assessment of urban growth dynamics in Mumbai Metropolitan Region, India using object-based image analysis for medium-resolution data. Appl Geogra 98:110–120
Meyer-Baese A, Schmid V, Schmid VBTPR (2014) Foundations of neural networks. Pattern Recognition and Signal Analysis in Medical Imaging; Elsevier: Amsterdam, The Netherlands. https://doi.org/10.1016/B978-0-12-409545-8.00007-8
Mishra VN, Rai PK (2016) 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. https://doi.org/10.1007/s12517-015-2138-3
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 the geospatial approach: a comparison of hybrid models. Appl Geoma 10(3):257–276. https://doi.org/10.1007/s12518-018-0223-5
Mitsova D, Shuster W, Wang X (2011) A cellular automata model of land cover change integrates urban growth with open space conservation. Lands Urban Plann 99(2):141–153. https://doi.org/10.1016/j.landurbplan.2010.10.001
Mohanty MD, Mohanty MN (2022) Verbal sentiment analysis and detection using recurrent neural network. Advanced Data Mining Tools and Methods for Social Computing (pp. 85–106). Academic Press. https://doi.org/10.1016/B978-0-32-385708-6.00012-6
National Research Council (2014) Advancing Land Change Modeling: Opportunities and Research Requirements; The National Academies Press: Washington, DC, USA, ISBN 978-0-309-28833-0
Nkeki FN (2016) Spatio-temporal analysis of land-use transition and urban growth characterization in Benin metropolitan region, Nigeria. Remote Sens Appl Soc Environ 4:119–137. https://doi.org/10.1016/j.rsase.2016.08.002
Nzoiwu CP, Agulue EI, Mbah S, Igboanugo CP (2017) Impact of land use/land cover change on surface temperature condition of Awka Town, Nigeria. J Geogra Inform Syst 9(06):763. https://doi.org/10.4236/jgis.2017.96047
Oñate-Valdivieso F, Sendra JB (2010) Application of GIS and remote sensing techniques in generation of land-use scenarios for hydrological modeling. J Hydro 395(3–4):256–263. https://doi.org/10.1016/j.jhydrol.2010.10.033
Pal S, Ziaul SK (2017) Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egypt J Remote Sens Space Sci 20(1):125–145. https://doi.org/10.1016/j.ejrs.2016.11.003
Patino JE, Duque JC (2013) A review of regional science applications of satellite remote sensing in urban settings. Comput Environ Urban Syst 37:1–17. https://doi.org/10.1016/j.compenvurbsys.2012.06.003
Petit C, Scudder T, Lambin E (2001) Quantifying processes of land-cover change by remote sensing: resettlement and rapid land-cover changes in south-eastern Zambia. Int J Remote Sens 22(17):3435–3456. https://doi.org/10.1080/01431160010006881
Roy B, Kasemi N (2021) Monitoring urban growth dynamics using remote sensing and GIS techniques of Raiganj Urban Agglomeration, India. Egypt J Remo Sens Space Sci 24(2):221–230. https://doi.org/10.1016/j.ejrs.2021.02.001
Sadidy J, Firouzabadi PZ, Entezari A (2009) The use of Radarsat and Landsat image fusion algorithms and different supervised classification methods to use map accuracy-case study: SariPlain-Iran. http://www.isprs.org/proceedings/XXXVI/5-C55/papers/sadidy_javad.pdf
Sahebgharani A (2016) Multi-objective land-use optimization through parallel particle swarm algorithm: case study Baboldasht district of Isfahan, Iran. J Urban Environ Engin 10(1):42–49. https://www.jstor.org/stable/26240810
Sainlez M, Heyen G (2011) Recurrent neural network prediction of steam production in a Kraft recovery boiler. In Computer Aided Chemical Engineering (Vol. 29, pp. 1784–1788). Elsevier. https://doi.org/10.1016/B978-0-444-54298-4.50135-5
Shafizadeh-Moghadam H, Asghari A, Tayyebi A, Taleai M (2017) Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth. Comput Environ Urban Syst 64:297–308. https://doi.org/10.1016/j.compenvurbsys.2017.04.002
Shao Z, Sumari NS, Portnov A, Ujoh F, Musakwa W, Mandela PJ (2021) Urban sprawl and its impact on sustainable urban development: a combination of remote sensing and social media data. Geo Spat Inf Sci 24(2):241–255. https://doi.org/10.1080/10095020.2020.1787800
Shaw A (2013) Emerging perspective on small cities and towns. In: Sharma RN, Sandhu RS (eds) Small cities and towns in the global era: emerging changes and perspectives. Rawat Publications, Jaipur, pp 36–53
Shi G, Jiang N, Yao L (2018) Land use and cover change during the rapid economic growth period from 1990 to 2010: a case study of Shanghai. Sustainability 10:426. https://doi.org/10.3390/su10020426
Tang J, Wang L, Yao Z (2007) Spatio-temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm. Int J Remote Sens 28(15):3255–3271. https://doi.org/10.1080/01431160600962749
Taubenböck H, Wegmann M, Roth A, Mehl H, Dech S (2009) Urbanization in India–Spatiotemporal analysis using remote sensing data. Comput Environ Urban Syst 33(3):179–188. https://doi.org/10.1016/j.compenvurbsys.2008.09.003
Tayyeb A, Pijanowski BC, Tayyebi AH (2011) An urban growth boundary model using neural networks, GIS and radial parameterization: an application to Tehran, Iran. Lands Urban Plann 100(1–2):35–44. https://doi.org/10.1016/j.landurbplan.2010.10.007
Turner MG, Gardner RH, O’neill RV, O’Neill RV (2001) Landscape ecology in theory and practice, vol 401. Springer, New York
United Nations (2012) World urbanization prospects. The 2011 Revision. New York
United Nations, Population Division (2015) Department of Economic and Social Affairs, (2015). World Urbanization Prospects: The 2014 Revision, (ST/ESA/SER.A/366)
United Nations (2018) World urbanization prospects 2018: United Nations. Retrieved 23 Nov 2022, from https://digitallibrary.un.org/record/3828520
Veldkamp A, Fresco LO (1996) CLUE: a conceptual model to study the conversion of land use and its effects. Ecol Modell 85(2–3):253–270. https://doi.org/10.1016/0304-3800(94)00151-0
Vinayak B, Lee HS, Gede S (2021) Prediction of land use and land cover changes in Mumbai City, India, using remote sensing data and a multi-layer perceptron neural network-based Markov chain model. Sustainability 13(2):471. https://doi.org/10.3390/su13020471
Vitousek PM, Mooney HA, Lubchenco J, Melillo JM (1997) Human domination of Earth’s ecosystems. Science 277:494–499. https://doi.org/10.1126/science.277.5325.494
Weng YC (2007) Spatiotemporal changes of landscape pattern in response to urbanization. Lands Urban Plan 81(4):341–353. https://doi.org/10.1016/j.landurbplan.2007.01.009
Youssef AM, Pradhan B, Tarabees E (2011) Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: contribution from the analytic hierarchy process. Arab J Geosci 4(3):463–473. https://doi.org/10.1007/s12517-009-0118-1
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Debnath, M., Islam, N., Gayen, S.K. et al. Prediction of spatio-temporal (2030 and 2050) land-use and land-cover changes in Koch Bihar urban agglomeration (West Bengal), India, using artificial neural network-based Markov chain model. Model. Earth Syst. Environ. 9, 3621–3642 (2023). https://doi.org/10.1007/s40808-023-01713-6
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DOI: https://doi.org/10.1007/s40808-023-01713-6