Abstract
Human population growth, movement, and demand have a substantial impact on land use and land cover dynamics. Thematic maps of land use and land cover (LULC) serve as a reference for scrutinizing, source administration, and forecasting, making it easier to establish plans that balance preservation, competing uses, and growth compressions. This study aims to identify the changeover of land-use changes in the Bhavani basin for the two periods 2005 and 2015 and to forecast and establish potential land-use changes in the years 2025 and 2030 by using QGIS 2.18.24 version MOLUSCE plugin (MLP-ANN) model. The five criteria, such as DEM, slope, aspect, distance from the road, and distance from builtup, are used as spatial variable maps in the processes of learning in MLP-ANN to predict their influences on LULC between 2005 and 2010. It was found that DEM, distance from the road, and distance from the builtup have significant effects. The projected and accurate LULC maps for 2015 indicate a good level of accuracy, with an overall Kappa value of 0.69 and a percentage of the correctness of 76.28%. MLP-ANN is then used to forecast changes in LULC for the years 2025 and 2030, which shows a significant rise in cropland and builtup areas, by 20 km2 and 10 km2, respectively. The findings assist farmers and policymakers in developing optimal land use plans and better management techniques for the long-term development of natural resources.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Availability of data and materials
The Land use and Land cover (LULC) maps used for the current study are obtained from the National Remote sensing Centre (NRC), Hyderabad, (https://bhuvan.nrsc.gov.in/bhuvan_links.php), and the road maps are obtained from the open street map (https://www.openstreetmap.org). The CARTO DEM for this study is obtained from Bhuvan Indian Geo-platform of ISRO (https://bhuvan-app3.nrsc.gov.in/data/download/index.php).
References
Abdul Rahaman S, Aruchamy S, Balasubramani K, Jegankumar R (2017) Land use/land cover changes in a semi-arid mountain landscape in Southern India: a geoinformatics based Markov chain approach. Int Arch Photogramm Remote Sens Spat Inf Sci ISPRS Arch 42:231–237. https://doi.org/10.5194/isprs-archives-XLII-1-W1-231-2017
Alam N, Saha S, Gupta S, Chakraborty S (2021) Prediction modelling of riverine landscape dynamics in the context of sustainable management of floodplain: a Geospatial approach. Ann GIS. https://doi.org/10.1080/19475683.2020.1870558
Alawamy JS, Balasundram SK, Hanif AHM, Sung CTB (2020) Detecting and analyzing land use and land cover changes in the Region of Al-Jabal Al-Akhdar, Libya using time-series Landsat data from 1985 to 2017. Sustainability (Switzerland) 12. https://doi.org/10.3390/su12114490
Anand B, Karunanidhi D, Subramani T (n.d.) Promoting artificial recharge to enhance groundwater potential in the lower Bhavani River basin of South India using geospatial techniques. https://doi.org/10.1007/s11356-020-09019-1/Published
Aneesha Satya B, Shashi M, Deva P (2020) Future land uses land cover scenario simulation using open source GIS for Warangal, Telangana, India. Appl Geomat 12:281–290. https://doi.org/10.1007/s12518-020-00298-4
Ashaolu ED, Olorunfemi JF, Ifabiyi IP (2019) Assessing the spatio-temporal pattern of land use and land cover changes in Osun Drainage Basin, Nigeria. J Environ Geogr 12:41–50. https://doi.org/10.2478/jengeo-2019-0005
Balasubramanian A, Duraisamy K, Thirumalaisamy S, Krishnaraj S, Yatheendradasan RK (2017) Prioritization of subwatersheds based on quantitative morphometric analysis in lower Bhavani basin, Tamil Nadu, India using DEM and GIS techniques. Arab J Geosci 10(24). https://doi.org/10.1007/s12517-017-3312-6
Bhattacharya RK, das Chatterjee N, Das K (2020) Land use and Land cover change and its resultant erosion susceptible level: an appraisal using RUSLE and Logistic Regression in a tropical plateau basin of West Bengal, India, Environment, Development, and Sustainability. Springer, Netherlands. https://doi.org/10.1007/s10668-020-00628-x
Bhuvan | NRSC Open EO Data Archive | NOEDA | Ortho | DEM | Elevation | AWiFS | LISS III | HySI | TCHP | OHC | Free GIS Data | Download
Buğday E, Erkan Buğday S (2019) Modelling and simulating land use/cover change using the artificial neural network from remote sensing data. Cerne 25:246–254. https://doi.org/10.1590/01047760201925022634
Census of India Website : Office of the Registrar General & Census Commissioner, India (censusindia.gov.in)
Das S, Sarkar R (2019) Predicting the land use and land cover change using Markov model: a catchment level analysis of the Bhagirathi-Hugli River. Spat Inf Res 27:439–452. https://doi.org/10.1007/s41324-019-00251-7
Ebenezer B, Geophery KA, Jonathan AQ-B, Emmanuel AD (2018) Land-use change and sediment yield studies in Ghana: review. J Geogr Reg Plan 11:122–133. https://doi.org/10.5897/jgrp2018.0707
El-Tantawi AM, Bao A, Chang C, Liu Y (2019) Monitoring and predicting land use/cover changes in the Aksu-Tarim River Basin, Xinjiang-China (1990–2030). Environ Monit Assess 191:1–18. https://doi.org/10.1007/s10661-019-7478-0
Guan DJ, Li HF, Inohae T, Su W, Nagaie T, Hokao K (2011) Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol Model 222(20–22):37613772. https://doi.org/10.1016/j.ecolmodel.2011.09.009
Guidingan MLG, Sanou CL, Ragatoa DS, Fafa CO, Mishra VN (2019) Assessing land use/land cover dynamic and its impact in Benin Republic using land change model and CCI-LC products. Earth Syst Environ 3:127–137. https://doi.org/10.1007/s41748-018-0083-5
Hakim AMY, Baja S, Rampisela DA, Arif S (2019) Spatial dynamic prediction of land use/landcover change (case study: Tamalanrea sub-district, Makassar city), in IOP Conference Series: Earth and Environmental Science. Institute of Physics Publishing. https://doi.org/10.1088/1755-1315/280/1/012023
India-WRIS (indiawris.gov.in)
Ministry of India water resources (2017) Central Ground Water Board Ministry of Water Resources, River Development and Ganga Rejuvenation Government of India AQUIFER MAPPING AND GROUNDWATER
Msovu UE, Mulungu DM, Nobert JK, Mahoo H (2019) Land Use/Cover Change and their Impacts on Streamflow in Kikuletwa Catchment of Pangani River Basin, Tanzania. Tanz J Eng Technol
Muthusamy S, Krishnamurthy RR, Jayaprakash M, Mohana Perumal P (2013) Geo-environmental analysis using multitemporal satellite data and GIS techniques—a case study for Bhavani River Basin, Tamil Nadu, India. Cloud Publications International Journal of Advanced Remote Sensing and GIS
Narayanamurthi V (2020) Impact of climate change in sediment yield from the catchment of Bhavani Sagar reservoir using SWAT model
NextGIS (2017) MOLUSCE-quick and convenient aanalysis of LandCoverChanges. https://nextgis.com/blog/molusce/ (accessed 01 May 2019)
Nugroho AB, Hasyim AW, Usman F (2018) Urban growth modelling of Malang city using artificial neural network based on multi-temporal remote sensing. Civ Environ Sci J I(02)
Perović V, Jakšić D, Jaramaz D, Koković N, Čakmak D, Mitrović M, Pavlović P (2018) Spatio-temporal analysis of land use/land cover change and its effects on soil erosion (Case study in the Oplenac wine-producing area, Serbia). Environ Monit Assess 190. https://doi.org/10.1007/s10661-018-7025-4
Rahman MTU, Tabassum F, Rasheduzzaman M, Saba H, Sarkar L, Ferdous J, Uddin SZ, Zahedul Islam AZM (2017) Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh. Environ Monit Assess 189. https://doi.org/10.1007/s10661-017-6272-0
Saputra MH, Lee HS (2019) Prediction of land use and land cover changes for North Sumatra, Indonesia, using an artificial-neural-network-based cellular automaton. Sustainability (Switzerland) 11. https://doi.org/10.3390/su11113024
Saravanan K, Kasiviswanathan KS, Saravanan S (2015) Morphometric analysis of Bhavani Drainage Basin in Tamilnadu State. In India Article in International Journal of Applied Engineering Research.https://www.researchgate.net/publication/280028731
Srivastava R, Singh S, Oran A (2020) Changes in vegetation cover using GIS and remote sensing: a case study of South Campus BHU, Mirzapur, India. J Sci Res 64:135–141. https://doi.org/10.37398/jsr.2020.640219
Ullah S, Tahir AA, Akbar TA, Hassan QK, Dewan A, Khan AJ, Khan M (2019) Remote sensing-based quantification of the relationships between land use land cover changes and surface temperature over the lower Himalayan region. Sustainability (Switzerland) 11. https://doi.org/10.3390/su11195492
Wang SW, 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. Environ Chall 2:100017. https://doi.org/10.1016/j.envc.2020.1000
Author information
Authors and Affiliations
Contributions
MK wrote the manuscript in consultation with SR. MK carried out all technical details, prepared the map, and performed GIS analysis. SR contributed to the verification of the analysis and the results. Both authors contributed to shaping the work by discussing the results and contributed to the final manuscript.
Corresponding author
Ethics declarations
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Philippe Garrigues
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kamaraj, M., Rangarajan, S. Predicting the future land use and land cover changes for Bhavani basin, Tamil Nadu, India, using QGIS MOLUSCE plugin. Environ Sci Pollut Res 29, 86337–86348 (2022). https://doi.org/10.1007/s11356-021-17904-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-021-17904-6