Spatio-Temporal Dynamics of Land Use/Land Cover Change Using Remote Sensing and GIS Techniques in Ambegaon Taluka, Maharashtra, India
DOI:
https://doi.org/10.52151/jae2026631.1987Keywords:
change recognition, expanding agricultural land, increased settlements, maximum likelihood classifier, supervised classificationAbstract
Understanding land use/land cover (LULC) changes is a crucial step for the optimum utilization and development of land resources and to employ land usage schemes satisfactorily to meet the growing demands for human needs and welfare. This study analyzes the temporal dynamics of LULC alterations using remote sensing and geographical information system (GIS) techniques in Ambegaon taluka of Pune district in Maharashtra, India. The study employed the maximum likelihood classifier method to identify five LULC categories viz., agricultural land, settlement, water body, forest land, and barren land through supervised classification. It examined the changes in LULC over two periods, i.e., 2013-2016 and 2016-2019, influenced by both anthropogenic activities and natural factors. Satellite imageries of LANDSAT 8 for the years 2013, 2016 and 2019 were analyzed to create the LULC maps. The results indicated a significant shift in LULC patterns over the two periods. Specifically, agricultural land expanded by 22.73%, barren lands decreased by 11.34% and settlements increased by 16.10%. Additionally, there was a minor decrease of 0.09% in water bodies and a substantial reduction (23.48%) in forest lands. Population growth, rising food demand, economic pressures and incentives might have driven the conversion of barren and forest lands into agriculture, at the cost of deforestation, over-extraction of limited water resources, and the reduction of water bodies in the study area. This analysis could offer valuable insights into how land cover is distributed and composed, which is essential for making informed decisions in environmental management, urban planning, and resource allocation in the study area. This study can also aid in identifying vulnerable areas prone to landslides, such as Malin village incidence occurred in 2014, and enabling the implementation of sustainable land use management practices to mitigate risks.
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Al-Saady, Y., Merkel, B., Al-Tawash, B., & Al-Suhail, Q. (2015). Land use and land cover (LULC) mapping and change detection in the Little Zab River Basin (LZRB), Kurdistan Region, NE Iraq and NW Iran. FOG-Freiberg Online Geoscience, 43, 1-32.
Arveti, N., Etikala, B., & Dash, P. (2016). Land use/land cover analysis based on various comprehensive geospatial data sets: a case study from Tirupati area, south India. Advances in Remote Sensing, 5(2), 73-82. http://dx.doi.org/10.4236/ars.2016.52006 DOI: https://doi.org/10.4236/ars.2016.52006
Bagan, H., Wang, Q., Watanabe, M., Kameyama, S., & Bao, Y. (2008). Land-cover classification using ASTER multi-band combinations based on wavelet fusion and SOM neural network. Photogrammetric Engineering & Remote Sensing, 74(3), 333-342. http://dx.doi.org/10.14358/PERS.74.3.333 DOI: https://doi.org/10.14358/PERS.74.3.333
Berberoglu, S., & Akin, A. (2009). Assessing different remote sensing techniques to detect land use/cover changes in the eastern Mediterranean. International Journal of Applied Earth Observation and Geoinformation, 11(1), 46-53. https://doi.org/10.1016/j.jag.2008.06.002 DOI: https://doi.org/10.1016/j.jag.2008.06.002
Bopche, L., & Rege, P. P. (2022) Landslide susceptibility mapping: an integrated approach using geographic information value, remote sensing, and weight of evidence method. Geotechnical and Geological Engineering, 40, 2935–2947. https://doi.org/10.1007/S10706-022-02070-4 DOI: https://doi.org/10.1007/s10706-022-02070-4
Bosch, J. M., & Hewlett, J. D. (1982). A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration. Journal of Hydrology, 55(1-4), 3-23. https://doi.org/10.1016/0022-1694(82)90117-2 DOI: https://doi.org/10.1016/0022-1694(82)90117-2
Chughtai, A. H., Abbasi, H., & Karas, I. R. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment, 22, 100482. https://doi.org/10.1016/j.rsase.2021.100482 DOI: https://doi.org/10.1016/j.rsase.2021.100482
Cihlar, J. & Jansen, L. J. (2001). From land cover to land use: a methodology for efficient land use mapping over large areas. The Professional Geographer, 53(2), 275-289. http://dx.doi.org/10.1111/0033-0124.00285 DOI: https://doi.org/10.1080/00330124.2001.9628460
Costa, H., Almeida, D., Vala, F., Marcelino, F., & Caetano, M. (2018). Land cover mapping from remotely sensed and auxiliary data for harmonized official statistics. ISPRS International Journal of Geo-Information, 7(4), 157. https://doi.org/10.3390/ijgi7040157 DOI: https://doi.org/10.3390/ijgi7040157
Dadarao, W. S., Yadav, V. K., Patro, S. K., Meharoof, M., & Sharma, A. (2023). Temporal dynamics of LULC with environmental and climatic parameter assessment using remote sensing and GIS: A case study in Dimbe Reservoir, Ambegaon Taluk, Maharashtra, India. Journal of the Inland Fisheries Society of India, 55(1), 75-93. http://dx.doi.org/10.47780/jifsi.55.1.2023.141459
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., & Snyder, P. K. (2005). Global consequences of land use. Science, 309(5734), 570-574. https://doi.org/10.1126/science.1111772 DOI: https://doi.org/10.1126/science.1111772
Goddard, F. G. B., Ban, R., Barr, D. B., Brown, J., Cannon, J., Colford Jr. J. M., & Clasen, T. (2020). Measuring environmental exposure to enteric pathogens in low-income settings: review and recommendations of an interdisciplinary working group. Environmental Science & Technology, 54(19), 11673-11691. https://doi.org/10.1021/acs.est.0c02421 DOI: https://doi.org/10.1021/acs.est.0c02421
Gondwe, J. F., Lin, S., & Munthali, R. M. (2021). Analysis of land use and land cover changes in urban areas using remote sensing: Case of Blantyre city. Discrete Dynamics in Nature and Society, 2021(1), 8011565. https://doi.org/10.1155/2021/8011565 DOI: https://doi.org/10.1155/2021/8011565
Goodchild, M. F. (2003). Geographic information science and systems for environmental management. Annual Review of Environment and Resources, 28(1), 493-519. https://doi.org/10.1146/annurev.energy.28.050302.105521 DOI: https://doi.org/10.1146/annurev.energy.28.050302.105521
Gyssels, G., Poesen, J., Bochet, E., & Li, Y. (2005). Impact of plant roots on the resistance of soils to erosion by water: A review. Progress in Physical Geography, 29(2), 189-217. https://doi.org/10.1191/0309133305pp443ra DOI: https://doi.org/10.1191/0309133305pp443ra
Haque, M. I., & Basak, R. (2017). Land cover change detection using GIS and remote sensing techniques: A spatio-temporal study on TanguarHaor, Sunamganj, Bangladesh. The Egyptian Journal of Remote Sensing and Space Science, 20(2), 251-263. http://dx.doi.org/10.1016/j.ejrs.2016.12.003 DOI: https://doi.org/10.1016/j.ejrs.2016.12.003
Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91-1016. https://doi.org/10.1016/j.isprsjprs.2013.03.006 DOI: https://doi.org/10.1016/j.isprsjprs.2013.03.006
Kadival, H., Wadkar, R., Gunjal, S., Khinchi, R., Kothekar, P., & Dhore, M. (2023). Analysis of micronutrients and correlations of Zinc and Iron of soil in Pune region. In: Iglesias, A., Shin, J., Patel, B., Joshi, A. (editors), Proceedings of World Conference on Information Systems for Business Management (vol. 834, pp 491–502). Lecture Notes in Networks and Systems, , Springer, Singapore. https://doi.org/10.1007/978-981-99-8349-0_39 DOI: https://doi.org/10.1007/978-981-99-8349-0_39
Kennedy, R. E., Townsend, P. A., Gross, J. E., Cohen, W. B., Bolstad, P., Wang, Y. Q., & Adams, P. (2009). Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote sensing of environment, 113(7), 1382-1396. https://doi.org/10.1016/j.rse.2008.07.018 DOI: https://doi.org/10.1016/j.rse.2008.07.018
Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28, 205-241. https://doi.org/10.1146/annurev.energy.28.050302.105459 DOI: https://doi.org/10.1146/annurev.energy.28.050302.105459
Makuti, S., Nex, F., & Yang, M. Y. (2018). Multi-temporal classification and change detection using UAV images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 651-658. https://doi.org/10.5194/isprs-archives-XLII-2-651-2018 DOI: https://doi.org/10.5194/isprs-archives-XLII-2-651-2018
Mondal, S., 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 DOI: https://doi.org/10.1080/10106049.2013.776641
National Remote Sensing Agency (NRSA). (2007). Integrated Land Use/Land Cover Mapping Using Multi-Temporal AWiFS Data (2005-06). Project Report- NRSA/RSGIS-AA/NRC/NLULC-AWiFS/PROJREP/R01/JUN07. National Remote Sensing Agency, Department of Space, Government of India. Available at: https://bhuvan-app1.nrsc.gov.in/2dresources/thematic/LULC250/0506.pdf (accessed on 24 February 2026).
Patidar, S., & Sankhla, V. (2015). Change detection of land-use and land-cover of Dehradun city: a spatio-temporal analysis. International Journal of Advanced Remote Sensing and GIS, 4(1), 1170-1180. http://dx.doi.org/10.23953/cloud.ijarsg.105 DOI: https://doi.org/10.23953/cloud.ijarsg.105
Pimm, S. L., Jenkins, C. N., Abell, R., Brooks, T. M., Gittleman, J. L., Joppa, L. N., Raven, P. H., Roberts, C. M., & Sexton J. O. (2014). The biodiversity of species and their rates of extinction, distribution, and protection. Science, 344(6187), 1246752. https://doi.org/10.1126/science.1246752 DOI: https://doi.org/10.1126/science.1246752
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. https://doi.org/10.1016/j.ejrs.2015.02.002 DOI: https://doi.org/10.1016/j.ejrs.2015.02.002
Rimal, B. (2011). Application of remote sensing and GIS, land use/land cover change in Kathmandu Metropolitan city, Nepal. Journal of Theoretical & Applied Information Technology, 23(2), 80-86.
Sarvade, S. M., Sarvade, M. M., Khadatare, P. S., & Kolekar, M. R. (2014). 30/7 Malin landslide: a case study. Proceedings of National Conference on GEPSID held at Ludhiana. pp. 691-698.
Seto, K. C., Guneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. PNAS, 109(40), 16083-16088. https://doi.org/10.1073/pnas.1211658109 DOI: https://doi.org/10.1073/pnas.1211658109
Seyam, M. M. H., Haque, M. R., &Rahman, M. M. (2023). Identifying the land use land cover (LULC) changes using remote sensing and GIS approach: A case study at Bhaluka in Mymensingh, Bangladesh. Case Studies in Chemical and Environmental Engineering, 7, 100293. https://doi.org/10.1016/j.cscee.2022.100293 DOI: https://doi.org/10.1016/j.cscee.2022.100293
Shah, C. R., Sathe, S. S., Bhagawati, P. B., & Mohite, S. S. (2019): A hill slope failure analysis: A case study of Malingoan village, Maharashtra, India, Geology, Ecology, and Landscapes, 5(1), 1-6. https://doi.org/10.1080/24749508.2019.1695714 DOI: https://doi.org/10.1080/24749508.2019.1695714
Su, T., Zhang, S., & Liu, T. (2020). Multi-spectral image classification based on an object-based active learning approach. Remote Sensing, 12(3), 504. http://dx.doi.org/10.3390/rs12030504 DOI: https://doi.org/10.3390/rs12030504
Tak, P. P., Haque, T., Guha, A., Rao, A. B., Shah, N., & Khanolkar, R. S. (2019). Study of supply chain, production potential of Hirda and design of Hirda decortication machine for livelihood generation for tribal people. In: Saha, S., Ravi, M. (eds), Rural Technology Development and Delivery. Design Science and Innovation (pp. 249-261). Springer Singapore. https://doi.org/10.1007/978-981-13-6435-8_18 DOI: https://doi.org/10.1007/978-981-13-6435-8_18
Taloor, A. K., Kumar, V., Singh, V. K., Singh, A. K., Kale, R. V., Sharma, R., & Chowdhary, N. H. (2020). Land use land cover dynamics using remote sensing and GIS Techniques in Western Doon Valley, Uttarakhand, India. In: Sahdev, S., Singh, R., Kumar, M. (eds), Geoecology of Landscape Dynamics. Advances in Geographical and Environmental Sciences (pp. 37-51). Springer, Singapore. https://doi.org/10.1007/978-981-15-2097-6_4 DOI: https://doi.org/10.1007/978-981-15-2097-6_4
Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y. A. & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations-A review. Remote Sensing, 12(7), 1135. http://dx.doi.org/10.3390/rs12071135 DOI: https://doi.org/10.3390/rs12071135
Tikke, B. B., Patil, A. K., Chavan, R. R., & Desai, S.S. (2014). Review of landslides: A special attention to Western Ghats of Maharashtra. International Journal of Research in Advent Technology, 2(3), 128-134.
Vorosmarty, C. J., McIntyre, P., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., & Davies, P. M. (2010). Global threats to human water security and river biodiversity. Nature, 467, 555-561. https://doi.org/10.1038/nature09440 DOI: https://doi.org/10.1038/nature09440
Zhang, C., & Li, X. (2022). Land use and land cover mapping in the era of big data. Land, 11(10), 1692. https://doi.org/10.3390/land11101692 DOI: https://doi.org/10.3390/land11101692
Zhang, L., Hao, T., Wu, J., & Wang, J. (2005).Application of image enhancement techniques to potential field data. Applied Geophysics, 2(3), 145-152. https://doi.org/10.1007/s11770-005-0017-5 DOI: https://doi.org/10.1007/s11770-005-0017-5
