Abstract
Land use land cover (LULC) plays a key role in earth surface processes, and it is important to understand the spatio-temporal dynamics of LULC in an area. The study is carried out in the Meenachil and Manimala basins in Kerala, India, using land change modeller (LCM) to predict future LULC. The Random Forest (RF) classifier is used to classify the LULC in Google Earth Engine (GEE) for the years 1990, 2000, 2008, 2018 and 2021. The overall accuracy obtained for the years 1990, 2000, 2008, 2018 and 2021 is 92.53%, 91.42%, 96.92%, 87.79% and 95.54%, respectively, followed by a Kappa coefficient of 90.67%, 89.27%, 96.12%, 84.55% and 94.39%. LCM is utilised for LULC change detection, the model is validated successfully in predicting the LULC distribution in 2021, and the results were compared with the actual 2021 LULC. The results revealed the expansion of the built-up area and the decline of the agriculture class in these basins. The study then utilised LCM to predict future LULC up to the year 2050 at decadal intervals. The predicted future LULC maps revealed the drastic expansion of built-up; these basins might witness in the coming decades. The built area from 1990 to 2050 is expected to increase to 100.88 km2 and 60.75 km2 in Meenachil and Manimala basins, respectively. The agriculture area showed a decrease from 861.7 to 728.29 km2 in Meenachil and 743.5–676.89 km2 in Manimala basin. The outcome of the study showed the transformation of the considered land cover classes due to developmental activities in the region. The outcomes of the study can be considered as suitable inputs to land use planners for effective land use planning and management.
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Satellite images were obtained from Google Earth Engine platform and road map from OpenStreetmap.
References
Abdu, H. A. (2019). Classification accuracy and trend assessments of land cover-land use changes from principal components of land satellite images. International Journal of Remote Sensing, 40(4), 1275–1300. https://doi.org/10.1080/01431161.2018.1524587
Abdulkareem, J. H., Pradhan, B., Sulaiman, W. N. A., & Jamil, N. R. (2019). Prediction of spatial soil loss impacted by long-term land-use/land-cover change in a tropical watershed. Geoscience Frontiers, 10, 389–403.
Abijith, D., & Saravanan, S. (2021). Assessment of land use and land cover change detection and prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu, India. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-021-15782-6
Amiri, B. J., Asgarian, A., & Sakieh, Y. (2017). Introducing landscape accuracy metric for spatial performance evaluation of land use/land cover change models. Geocarto International, 6049, 1–17. https://doi.org/10.1080/10106049.2016.1206628
Anand, V., & Oinam, B. (2020). Future land use land cover prediction with special emphasis on urbanization and wetlands. Remote Sensing Letters, 11(3), 225–234. https://doi.org/10.1080/2150704X.2019.1704304
Anderson, J. (1976). A land use and land cover classification system for use with remote sensor data. In US government printing office (Vol. 964)
Bell, E. J. (1974). Markov analysis of land use change-an application of stochastic processes to remotely sensed data. Socio-Economic Planning Sciences, 8(6), 311–316. https://doi.org/10.1016/0038-0121(74)90034-2
Burnham, B. O. (1973). Markov intertemporal land use simulation model. Southern. Journal of Agricultural Economics, 5, 253–258.
Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893–903. https://doi.org/10.1016/J.RSE.2009.01.007
Eastman, J. R. (2015). TerrSet manual. In Accessed in TerrSet version (Vol. 18)
Ellis, E., & Pontius, R. G., Jr. (2006). Land-use and land-cover change—encyclopedia of earth. Environmental Protection, 2, 142–153. https://doi.org/10.4324/9781315158402-15
Fathizad, H., Rostami, N., & Faramarzi, M. (2015). Detection and prediction of land cover changes using Markov chain model in semi-arid rangeland in western Iran. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-015-4805-y
Feng, Y., Lei, Z., Tong, X., Gao, C., Chen, S., Wang, J., & Wang, S. (2020). Spatially-explicit modeling and intensity analysis of China’s land use change 2000–2050. Journal of Environmental Management, 263, 110407. https://doi.org/10.1016/j.jenvman.2020.110407
Ganguly, K., Kumar, R., Mruthyunjaya Reddy, K., Jagadeeswara Rao, P., Raj Saxena, M., & Ravi Shankar, G. (2017). Optimization of spatial statistical approaches to identify land use/land cover change hot spots of Pune region of Maharashtra using remote sensing and GIS techniques. Geocarto International. https://doi.org/10.1080/10106049.2016.1178813
George, J. G., & Chandrashekar, H. M. (2014). Growth and trends in production and marketing of natural rubber. International Journal of Current Research and Academic Review, 2(8), 53–61.
Goldblatt, R., Deininger, K., & Hanson, G. (2018). Utilizing publicly available satellite data for urban research: Mapping built-up land cover and land use in Ho Chi Minh City, Vietnam. Development Engineering, 3(March), 83–99. https://doi.org/10.1016/j.deveng.2018.03.001
Goldblatt, R., You, W., Hanson, G., & Khandelwal, A. K. (2016). Detecting the boundaries of urban areas in India: A dataset for pixel-based image classification in Google Earth Engine. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.3390/rs8080634
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Grecchi, R. C., Gwyn, Q. H. J., Bénié, G. B., & Formaggio, A. R. (2013). Assessing the spatio-temporal rates and patterns of land-use and land-cover changes in the Cerrados of southeastern Mato Grosso, Brazil. International Journal of Remote Sensing, 34(15), 5369–5392. https://doi.org/10.1080/01431161.2013.788798
Haack, B., Mahabir, R., & Kerkering, J. (2015). Remote sensing-derived national land cover land use maps: A comparison for Malawi. Geocarto International, 30(3), 270–292. https://doi.org/10.1080/10106049.2014.952355
Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63(September 2015), 101–112. https://doi.org/10.1016/j.apgeog.2015.06.015
Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10(10), 3421. https://doi.org/10.3390/su10103421
Hu, Y., & Hu, Y. (2019). Land cover changes and their driving mechanisms in Central Asia from 2001 to 2017 supported by Google Earth Engine. Remote Sensing, 11(5), 554. https://doi.org/10.3390/rs11050554
Hussain, S., & Karuppannan, S. (2021). Land use/land cover changes and their impact on land surface temperature using remote sensing technique in district Khanewal, Punjab Pakistan. Geology, Ecology, and Landscapes. https://doi.org/10.1080/24749508.2021.1923272
John, J., Chithra, N. R., & Thampi, S. G. (2019). Prediction of land use/cover change in the Bharathapuzha river basin, India using geospatial techniques. Environmental Monitoring and Assessment, 191(6), 1–15.
Joshi, R. R., Warthe, M., Dwivedi, S., Vijay, R., & Chakrabarti, T. (2011). Monitoring changes in land use land cover of Yamuna riverbed in Delhi: A multi-temporal analysis. International Journal of Remote Sensing, 32(24), 9547–9558. https://doi.org/10.1080/01431161.2011.565377
Kale, M. P., Chavan, M., Pardeshi, S., Joshi, C., Verma, P. A., Roy, P. S., Srivastav, S. K., Srivastava, V. K., Jha, A. K., Chaudhari, S., Giri, Y., & Krishna Murthy, Y. V. N. (2016). Land-use and land-cover change in Western Ghats of India. Environmental Monitoring and Assessment, 188(7), 1–23. https://doi.org/10.1007/s10661-016-5369-1
Kogo, B. K., Kumar, L., & Koech, R. (2021). Analysis of spatio-temporal dynamics of land use and cover changes in Western Kenya. Geocarto International, 36(4), 376–391. https://doi.org/10.1080/10106049.2019.1608594
Kumar, L., & Mutanga, O. (2018). Google Earth Engine applications since inception. Remote Sensing, 10(1), 1509. https://doi.org/10.3390/rs10101509
Langat, P. K., Kumar, L., Koech, R., & Ghosh, M. K. (2019). Monitoring of land use/land-cover dynamics using remote sensing: A case of Tana River Basin, Kenya. Geocarto International. https://doi.org/10.1080/10106049.2019.1655798
Lu, Y., Wu, P., Ma, X., & Li, X. (2019). Detection and prediction of land use/land cover change using spatiotemporal data fusion and the cellular automata–Markov model. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-019-7200-2
Mahdianpari, M., Salehi, B., Mohammadimanesh, F., & Motagh, M. (2017). Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 13–31. https://doi.org/10.1016/j.isprsjprs.2017.05.010
Maviza, A., & Ahmed, F. (2020). Analysis of past and future multi-temporal land use and land cover changes in the semi-arid Upper-Mzingwane sub-catchment in the Matabeleland south province of Zimbabwe. International Journal of Remote Sensing, 41(14), 5206–5227. https://doi.org/10.1080/01431161.2020.1731001
Mayaja, N. A., & Srinivasa, C. V. (2017). Land use and land cover changes and their impacts on floods in Pampa River basin in Kerala: A remote sensing based analysis. Journal of Geomatics, 11(1), 1–6. https://doi.org/10.1007/978-3-319-18663-4_120
Midekisa, A., Holl, F., Savory, D. J., Andrade-pacheco, R., Gething, W., Bennett, A., & Sturrock, H. J. W. (2017). Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing. PLoS ONE, 12(9), e0184926. https://doi.org/10.1371/journal.pone.0184926
Mishra, V. N., Prasad, R., Kumar, P., Gupta, D. K., Agarwal, S., & Gangwal, A. (2019). Assessment of spatio-temporal changes in land use/land cover over a decade (2000–2014) using earth observation datasets: A case study of Varanasi District, India. Iranian Journal of Science and Technology - Transactions of Civil Engineering, 43, 383–401. https://doi.org/10.1007/s40996-018-0172-6
Mishra, V. N., & Rai, P. K. (2016). A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian Journal of Geosciences, 9(4), 1–18. https://doi.org/10.1007/s12517-015-2138-3
Mishra, V., Rai, P., & 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
Mishra, V. N., Rai, P. K., Prasad, R., Punia, M., & Nistor, M. M. (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. Applied Geomatics, 10(3), 257–276. https://doi.org/10.1007/s12518-018-0223-5
Mondal, M. S., Sharma, N., & Kappas, M. (2013). Modeling of spatio-temporal dynamics of land use and land cover in a part of Brahmaputra River basin using geoinformatic techniques. Geocarto International, 28(7), 632–656. https://doi.org/10.1080/10106049.2013.776641
Mondal, SMd., Sharma, N., Garg, P. K., & Kappas, M. (2016). Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. Egyptian Journal of Remote Sensing and Space Science, 19(2), 259–272. https://doi.org/10.1016/j.ejrs.2016.08.001
Monserud, R. A., & Leemans, R. (1992). Comparing global vegetation maps with the Kappa statistic. Ecological Modelling, 62(4), 275–293. https://doi.org/10.1016/0304-3800(92)90003-W
Muller, M. R., & Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), 151–157. https://doi.org/10.1007/BF00124382
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A., & Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature, 403(6772), 853–858. https://doi.org/10.1038/468895a
Nath, B., Wang, Z., Ge, Y., Islam, K., & Singh, R. P. (2020). Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process. ISPRS International Journal of Geo-Information, 9(2), 134.
Parthasarathy, K. S. S., & Deka, P. C. (2019). Remote sensing and GIS application in assessment of coastal vulnerability and shoreline changes: A review. ISH Journal of Hydraulic Engineering, 27(S1), 588–600. https://doi.org/10.1080/09715010.2019.1603086
Parthasarathy, K. S. S., & Deka, P. C. (2021). Spatio-temporal classification and prediction of land use and land cover change for the Vembanad Lake system, Kerala: A machine learning approach. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-021-17257-0
Parthasarathy, K. S. S., Saravanan, S., Deka, P. C., & Devanantham, A. (2020). Assessment of potentially vulnerable zones using geospatial approach along the coast of Cuddalore district, East coast of India. ISH Journal of Hydraulic Engineering, 28(S1), 422–432. https://doi.org/10.1080/09715010.2020.1753250
Phan, T. N., Kuch, V., & Lehnert, L. W. (2020). Land cover classification using google earth engine and random forest classifier-the role of image composition. Remote Sensing, 12(15), 1–22. https://doi.org/10.3390/RS12152411
Raj, P. P. N., & Azeez, P. A. (2010). Land use and land cover changes in a tropical river basin: A case from Bharathapuzha River Basin, Southern India. Journal of Geographic Information System, 02(04), 185–193. https://doi.org/10.4236/jgis.2010.24026
Rodrigues, A. L. M., Reis, G. B., dos Santos, M. T., da Silva, D. D., dos Santos, V. J., de Siqueira Castro, J., & Calijuri, M. L. (2019). Influence of land use and land cover’s change on the hydrological regime at a Brazilian southeast urbanized watershed. Environmental Earth Sciences, 78(20), 1–13. https://doi.org/10.1007/s12665-019-8601-9
Sewnet, A. (2016). Land use/cover change at Infraz watershed by using GIS and remote sensing techniques, northwestern Ethiopia. International Journal of River Basin Management, 14(2), 133–142. https://doi.org/10.1080/15715124.2015.1095199
Shaharum, N. S. N., Shafri, H. Z. M., Ghani, W. A. W. A. K., Samsatli, S., Prince, H. M., Yusuf, B., & Hamud, A. M. (2019). Mapping the spatial distribution and changes of oil palm land cover using an open source cloud-based mapping platform. International Journal of Remote Sensing, 40(19), 7459–7476. https://doi.org/10.1080/01431161.2019.1597311
Shalaby, A., & Tateishi, R. (2007). Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography, 27(1), 28–41. https://doi.org/10.1016/j.apgeog.2006.09.004
Shawul, A. A., & Chakma, S. (2019). Spatiotemporal detection of land use/land cover change in the large basin using integrated approaches of remote sensing and GIS in the Upper Awash basin, Ethiopia. Environmental Earth Sciences. https://doi.org/10.1007/s12665-019-8154-y
Singh, A. (1989). Review article: Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003. https://doi.org/10.1080/01431168908903939
Singh, S. K., Laari, P. B., Mustak, S., Srivastava, P. K., & Szabó, S. (2017). Modelling of land use land cover change using earth observation datasets of Tons River Basin. Geocarto International, 33(11), 1202–1222. https://doi.org/10.1080/10106049.2017.1343390
Singh, S. K., Laari, P. B., Mustak, S. K., Srivastava, P. K., & Szabó, S. (2018). Modelling of land use land cover change using earth observation datasets of Tons River Basin. Geocarto International, 33(11), 1202–1222. https://doi.org/10.1080/10106049.2017.1343390
Singh, S. K., Mustak, S., Srivastava, P. K., Szabó, S., & Islam, T. (2015). Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Environmental Processes, 2(1), 61–78. https://doi.org/10.1007/s40710-015-0062-x
Sonu, T. S., Mohammed Firoz, C., & Bhagyanathan, A. (2022). The impact of upstream land use land cover change on downstream flooding: A case of Kuttanad and Meenachil River Basin, Kerala, India. Urban Climate, 41(2021), 1–24. https://doi.org/10.1016/j.uclim.2022.101089
Souza-Filho, P. W. M., de Souza, E. B., Silva Júnior, R. O., Nascimento, W. R., Versiani de Mendonça, B. R., Guimarães, J. T. F., Dall’Agnol, R., & Siqueira, J. O. (2016). Four decades of land-cover, land-use and hydroclimatology changes in the Itacaiúnas River watershed, southeastern Amazon. Journal of Environmental Management, 167, 175–184. https://doi.org/10.1016/j.jenvman.2015.11.039
Srivastava, P. K., Han, D., Rico-Ramirez, M. A., Bray, M., & Islam, T. (2012). Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50(9), 1250–1265. https://doi.org/10.1016/j.asr.2012.06.032
Surabuddin Mondal, M., Sharma, N., Kappas, M., & Garg, P. K. (2013). Modeling of spatio-temporal dynamics of land use and land cover in a part of Brahmaputra River basin using Geoinformatic techniques. Geocarto International, 28(7), 632–656. https://doi.org/10.1080/10106049.2013.776641
Tadese, S., Soromessa, T., & Bekele, T. (2021). Analysis of the current and future prediction of land use/land cover change using remote sensing and the CA-Markov model in Majang Forest Biosphere Reserves of Gambella, Southwestern Ethiopia. The Scientific World Journal. https://doi.org/10.1155/2021/6685045
Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., & Adeli, S. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review ISPRS Journal of Photogrammetry and Remote Sensing Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164(May), 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001
Tangud, T., Nasahara, K., Pingchun, H., & Bagan, H. (2018). Land-cover change in the Wulagai grassland, Inner Mongolia of China between 1986 and 2014 analysed using multi-temporal Landsat images. Geocarto International, 34(11), 1237–1251. https://doi.org/10.1080/10106049.2018.1478457
Tarawally, M., Wenbo, X., Weiming, H., Mushore, T. D., & Kursah, M. B. (2019). Land use/land cover change evaluation using land change modeller: A comparative analysis between two main cities in Sierra Leone. Remote Sensing Applications: Society and Environment, 16(September), 100262. https://doi.org/10.1016/j.rsase.2019.100262
Tavangar, S., Moradi, H., Massah Bavani, A., & Gholamalifard, M. (2019). A futuristic survey of the effects of LU/LC change on stream flow by CA–Markov model: A case of the Nekarood watershed, Iran. Geocarto International. https://doi.org/10.1080/10106049.2019.1633419
Tendaupenyu, P., Hilary, C., Magadza, D., & Murwira, A. (2017). Changes in landuse/landcover patterns and human population growth in the Lake Chivero catchment, Zimbabwe. Geocarto International, 32(7), 797–811. https://doi.org/10.1080/10106049.2016.1178815
Torres-Vera, M. A., Prol-Ledesma, R. M., & Garcia-Lopez, D. (2008). Three decades of land use variations in Mexico City. International Journal of Remote Sensing, 30(1), 117–138. https://doi.org/10.1080/01431160802261163
Tsarouchi, G. M., Mijic, A., Moulds, S., & Buytaert, W. (2014). Historical and future land-cover changes in the Upper Ganges basin of India. International Journal of Remote Sensing, 35(9), 3150–3176. https://doi.org/10.1080/01431161.2014.903352
Ullah, S., Ahmad, K., Sajjad, R. U., Abbasi, A. M., Nazeer, A., & Tahir, A. A. (2019). Analysis and simulation of land cover changes and their impacts on land surface temperature in a lower Himalayan region. Journal of Environmental Management, 245, 348–357. https://doi.org/10.1016/j.jenvman.2019.05.063
Vanderpost, C., Ringrose, S., Kgathi, D., & Matheson, W. (2007). The nature and possible causes of land cover change (1984–1996) along a rainfall gradient in southeastern Botswana. Geocarto International, 22(3), 161–183. https://doi.org/10.1080/10106040701202002
Veldkamp, A., & Lambin, E. F. (2001). Predicting land-use change. Agriculture, Ecosystems and Environment, 85, 1–6.
Wagle, N., Acharya, T. D., Kolluru, V., Huang, H., & Lee, D. H. (2020). Multi-temporal land cover change mapping using google earth engine and ensemble learning methods. Applied Sciences (switzerland), 10(22), 1–20. https://doi.org/10.3390/app10228083
Wahap, N. A., & Shafri, H. Z. M. (2020). Utilization of Google Earth Engine (GEE) for land cover monitoring over Klang Valley, Malaysia. IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/540/1/012003
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
Waseem, M., Halmy, A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63, 101–112. https://doi.org/10.1016/j.apgeog.2015.06.015
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
Yu, W., Zang, S., Wu, C., Liu, W., & Na, X. (2011). Analyzing and modeling land use land cover change (LUCC) in the Daqing. Applied Geography, 31(2), 600–608. https://doi.org/10.1016/j.apgeog.2010.11.019
Zadbagher, E., & Becek, K. (2018). Modeling land use/land cover change using remote sensing and geographic information systems: Case study of the Seyhan Basin, Turkey. Environmental Monitoring and Assessment, 190(8), 1–15.
Zurqani, H. A., Post, C. J., Mikhailova, E. A., Schlautman, M. A., & Sharp, J. L. (2018). Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 69, 175–185. https://doi.org/10.1016/j.jag.2017.12.006
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Abraham, A., Kundapura, S. Spatio-temporal Dynamics of Land Use Land Cover Changes and Future Prediction Using Geospatial Techniques. J Indian Soc Remote Sens 50, 2175–2191 (2022). https://doi.org/10.1007/s12524-022-01588-7
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DOI: https://doi.org/10.1007/s12524-022-01588-7