Skip to main content
SpringerLink
Log in

Exploring LULC changes in Pakhal Lake area, Telangana, India using QGIS MOLUSCE plugin

  • Published:
Spatial Information Research Aims and scope Submit manuscript

Abstract

Dynamic processes such as environmental, economic, and social factors influence land use and land cover (LULC) changes, with temporal and spatial variations. This study aims to identify changes in LULC and predict future trends in the Pakhal Lake area in Peninsular India. Satellite images for the years from 2016 to 2022 were used for LULC classification using deep learning with Sentinel − 2 imagery in Google Earth Engine (GEE). Dynamic World dataset is used to classify the LULC changes of the study area with a 10 m near-real-time dataset. Images were classified based on six different LULC classes, namely water, vegetation, flooded vegetation, agriculture, built-up area, and bare land. The Cellular Automata–Artificial Neural Network (CA − ANN) technique was used to predict LULC changes. QGIS plugin MOLUSCE with Multi-Layer Perception (MLP), was used to predict and determine potential LULC changes for 2025 and 2028. The overall Kappa coefficient value of 0.78, and an accuracy of 82% indicated good results for LULC changes and projected maps for 2025. Prediction of LULC changes using MLP − ANN for the years 2025 and 2028 showed increase in agriculture, built-up areas, and barren land. The results of the study will be useful to develop better management techniques of natural resources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Nurwanda, A., & Honjo, T. (2020). The prediction of city expansion and land surface temperature in Bogor City, Indonesia. Sustainable Cities and Society, 52, 101772.

    Article  Google Scholar 

  2. Turner, B. L. I. I., Meyer, W. B., & Skole, D. L. (1994). Global Land-Use/Land-Cover change. Towards an Integrated Study Ambio, 23(1), 91–95.

    Google Scholar 

  3. Turner, M. G., & Ruscher, C. L. (1988). Changes in landscape patterns in Georgia, USA. Landscape Ecology, 1(4), 241–251.

    Article  Google Scholar 

  4. Ruiz-Luna, A., & Berlanga-Robles, C. A. (2003). Land use, land cover changes and coastal lagoon surface reduction associated with urban growth in northwest Mexico. Landscape Ecology, 18(2), 159–171.

    Article  Google Scholar 

  5. Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84.

    Article  Google Scholar 

  6. Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2401.

    Article  Google Scholar 

  7. Reis, S. (2008). Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Sensors (Basel, Switzerland), 8(10), 6188–6202.

    Article  Google Scholar 

  8. Pervaiz, W., Uddin, V., Khan, S. A., & Khan, J. A. (2016). Satellite-based land use mapping: comparative analysis of Landsat-8, Advanced Land Imager, and big data Hyperion imagery. Journal of Applied Remote Sensing, 10(2), 026004.

    Article  Google Scholar 

  9. Srivastava, P. K., Singh, S. K., Gupta, M., Thakur, J. K., & Mukherjee, S. (2013). Modeling impact of land use change trajectories on groundwater quality using remote sensing and GIS.Environmental Engineering & Management Journal (EEMJ), 12(12).

  10. Dayamba, S. D., Djoudi, H., Zida, M., Sawadogo, L., & Verchot, L. (2016). Biodiversity and carbon stocks in different land use types in the Sudanian Zone of Burkina Faso, West Africa. Agriculture Ecosystems & Environment, 216, 61–72.

    Article  Google Scholar 

  11. Gashaw, T., Tulu, T., Argaw, M., & Worqlul, A. W. (2017). Evaluation and prediction of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia. Environmental Systems Research, 6(1), 1–15.

    Article  Google Scholar 

  12. Hyandye, C., & Martz, L. W. (2017). A Markovian and cellular automata land-use change predictive model of the Usangu Catchment. International Journal of Remote Sensing, 38(1), 64–81.

    Article  Google Scholar 

  13. 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. Annals of GIS, 27(3), 299–314.

    Article  Google Scholar 

  14. NextGIS (2017). MOLUSCE-Quick and Convenient Analysis of Land Cover Changes.

  15. 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.

    Article  Google Scholar 

  16. Rahman, M., Tabassum, F., Rasheduzzaman, M., Saba, H., Sarkar, L., Ferdous, J., & Zahedul Islam, A. Z. M. (2017). Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh. Environmental Monitoring and Assessment, 189(11), 1–18.

    Article  Google Scholar 

  17. Saputra, M. H., & Lee, H. S. (2019). Prediction of land use and land cover changes for north sumatra, indonesia, using an artificial-neural-network-based cellular automaton. Sustainability, 11(11), 3024.

    Article  Google Scholar 

  18. Achu, A. L., Thomas, J., Aju, C. D., Gopinath, G., Kumar, S., & Reghunath, R. (2021). Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India. Ecological Informatics, 64, 101348.

    Article  Google Scholar 

  19. Biswas, H., Raizada, A., Mandal, D., Kumar, S., Srinivas, S., & Mishra, P. K. (2015). Identification of areas vulnerable to soil erosion risk in India using GIS methods. Solid Earth, 6(4), 1247–1257. https://doi.org/10.5194/se-6-1247-2015.

    Article  Google Scholar 

  20. Ashaolu, E. D., Olorunfemi, J. F., & Ifabiyi, I. P. (2019). Assessing the spatio-temporal pattern of land use and land cover changes in Osun drainage basin, Nigeria. Journal of Environmental Geography, 12(1–2), 41–50.

    Article  Google Scholar 

  21. Hakim, A. M. Y., Baja, S., Rampisela, D. A., & Arif, S. (2019, June). Spatial dynamic prediction of landuse/landcover change (case study: tamalanrea sub-district, makassar city). In IOP Conference Series: Earth and Environmental Science (Vol. 280, No. 1, p. 012023). IOP Publishing.

  22. Guidigan, M. L. G., Sanou, C. L., Ragatoa, D. S., Fafa, C. O., & Mishra, V. N. (2019). Assessing land use/land cover dynamic and its impact in Benin Republic using land change model and CCI-LC products. Earth Systems and Environment, 3(1), 127–137.

    Article  Google Scholar 

  23. El-Tantawi, A. M., Bao, A., Chang, C., & Liu, Y. (2019). Monitoring and predicting land use/cover changes in the Aksu-Tarim River Basin, Xinjiang-China (1990–2030). Environmental Monitoring and Assessment, 191(8), 1–18.

    Article  Google Scholar 

  24. Bhattacharya, R. K., Chatterjee, D., N., & Das, K. (2021). 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, 23(2), 1411–1446.

    Article  Google Scholar 

  25. 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. Spatial Information Research, 27(4), 439–452.

    Article  Google Scholar 

  26. 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). Environmental Monitoring and Assessment, 190(11), 1–18.

    Article  Google Scholar 

  27. Kamaraj, M., & Rangarajan, S. (2022). Predicting the future land use and land cover changes for Bhavani basin, Tamil Nadu, India, using QGIS MOLUSCE plugin. Environmental Science and Pollution Research, 1–12.

Download references

Funding

No fund was received from any source for this research, which is part of PhD research.

Author information

Authors and Affiliations

  1. Research Scholar, Department of Civil Engineering, National Institute of Technology, Warangal, India

    Ashok Amgoth

  2. Department of Mathematics, National Institute of Technology, Warangal, India

    Hari Ponnamma Rani

  3. Department of Civil Engineering, National Institute of Technology, Warangal, India

    K. V. Jayakumar

Authors
  1. Ashok Amgoth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hari Ponnamma Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. V. Jayakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashok Amgoth.

Ethics declarations

Compliance with ethical standards

There is no conflict of interest. On behalf of all the authors, the corresponding author claims that there is no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amgoth, A., Rani, H.P. & Jayakumar, K.V. Exploring LULC changes in Pakhal Lake area, Telangana, India using QGIS MOLUSCE plugin. Spat. Inf. Res. 31, 429–438 (2023). https://doi.org/10.1007/s41324-023-00509-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41324-023-00509-1

Keywords

Search

Navigation