Geospatial Assessment of Landuse and Land Cover Dynamics in the Danro River Watershed Using Multi-Temporal Remote Sensing Data

By /

IJEP 45(8): 676-690 : Vol. 45 Issue. 8 (August 2025)

Full Text

Rahul Kumar Pandey1, Abhay Krishna Singh1* and Roja Eliza2

1. Dr. Shyama Prasad Mukherjee University, Department of Geography, Ranchi – 834 008, Jharkhand, India
2. Indian Institute of Technology (Indian School of Mines), Laboratory of Biogeochemistry, Department of Environmental Science and Engineering, Dhanbad – 826 004, Jharkhand, India

Abstract

The development of advanced techniques, like remote sensing has greatly aided in gathering information about landuse and land cover (LULC) changes, which have become a serious global concern due to rapid population growth and the resulting demand for land transformation, which is crucial for guiding sustainable resource management strategies. The multi-temporal study focused on detecting LULC changes using geospatial data and image processing techniques in Danro river watershed. The change detection analysis was performed for four distinct periods: 1992, 2002, 2013 and 2022, utilizing Landsat series imageries of spatial resolution of 30 m. Image preprocessing, classification and change detection were performed using ERDAS 15 and ArcGIS software. The delineation of five LULC classes: vegetation, waterbody, barren land, agricultural land and built-up areas was conducted using an unsupervised classification. A thorough image processing and classification analysis was conducted and accuracy assessments (74–79%) were performed separately for each period using overall accuracy and the kappa coefficient, with values ranging from 0.61 to 0.70 for the years 1992, 2002, 2013 and 2022. The study of the past three decades reveals a 38.26% and 102.31% increase in agrarian and built-up areas, whereas a 15.97%, 64.63% and 76.82% decrease in vegetation, waterbody and barren land, respectively. Our findings are useful for the decision-maker towards formulating holistic watershed management and conservation strategies, where active participation of local stakeholders can aid in mitigating undesirable outcomes of the LULC changes and promote sustainable land resource utilization within the Danro watershed.

Keywords

Danro watershed, Landuse land cover, Change detection, Geospatial technique

References

  1. Meyer, W.B. and B.L. Turner. 1992. Human population growth and global land-use/cover change. Annual Review Ecol. Systematics. 23: 39-61.
  2. Chen, J., et al. 2015. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogrammetry Remote Sensing. 103: 7–27. DOI: 10.1016/j.isprsjprs.2014.09.002.
  3. Hailu, A., S. Mammo and M. Kidane. 2020. Dynamics of landuse, land cover change trend and its drivers in Jimma Geneti district, Western Ethiopia. Landuse policy. 99: 105011.
  4. Mishra, P.K., A. Rai and S.C. Rai. 2020. Landuse and land cover change detection using geospatial techniques in the Sikkim Himalaya, India. Egyptian J. Remote Sensing Space Sci., 23(2): 133–143. DOI: 10.1016/j.ejrs.2019.02.001
  5. Nedd, R., et al. 2021. A synthesis of landuse/land cover studies: Definitions, classification systems, meta-studies, challenges and knowledge gaps on a global landscape. Land. 10(9): 994.
  6. Lambin, E.F., H.J. Geist and E. Lepers. 2003. Dynamics of landuse and land cover change in tropical regions. Annual review env. resour., 28(1): 205-241. DOI: 10.1146/annurev.energy.28.0503 02.105459.
  7. Sewnet, A. 2015. Landuse/cover change at Infraz watershed, northwestern Ethiopia. J. Landscape Ecol., 8(1): 69–83. DOI: 10.1515/jlecol-2015-0005.
  8. Gashaw, T., et al. 2018. Modelling the hydrological impacts of landuse/land cover changes in the Andassa watershed, Blue Nile basin, Ethiopia. Sci. Total Env., 619: 1394-1408.
  9. Twisa, S. and M.F. Buchroithner. 2019. Landuse and land cover (LULC) change detection in Wami river basin, Tanzania. Land. 8(9): 136.
  10. Herold, M., N. C. Goldstein and K.C. Clarke. 2003. The spatio-temporal form of urban growth: Measurement, analysis and modelling. Remote Sensing Env., 86(3): 286–302. DOI: 10.1016/S0034-425 7(03) 00075-0.
  11. Serra, P., X. Pons and D. Saurí. 2008. Land cover and landuse change in a mediterranean landscape: A spatial analysis of driving forces integrating biophysical and human factors. Appl. Geogr., 28 (3): 189–209. DOI: 10.1016/j.apgeog.2008. 02.001.
  12. Yuan, F., et al. 2005. Land cover classification and change analysis of the twin cities (Minnesota) metropolitan area by multi-temporal Landsat remote sensing. Remote Sensing Env., 98(2–3): 317–328. DOI: 10.1016/j.rse.2005.08.006.
  13. Jensen, J.R. 1996. Introductory digital image processing: A remote sensing perspective. Prentice-Hall Inc.
  14. Chen, X., L. Vierling and D. Deering. 2005. A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing Env., 98(1): 63–79. DOI: 10.1016/j.rse.2005.05.021.
  15. Geremew, A. A. 2013. Assessing the impacts of landuse and land cover change on hydrology of watershed: A case study on Gigel-Abbay watershed, Lake Tana basin, Ethiopia. MSc. Thesis. Universidade Nova de Lisboa’s Repository.
  16. Zeleke, G. and H. Hurni. 2001. Implications of landuse and land cover dynamics for mountain resource degradation in the northwestern Ethiopian highlands. Mountain Res. Develop., 21(2): 184–191. DOI: 10.1659/0276-4741(2001) 021[0184:IO LUA L]2.0.CO;2.
  17. Amsalu, A., L. Stroosnijder and J. De Graaff. 2007. Long-term dynamics in land resource use and the driving forces in the Beressa watershed, highlands of Ethiopia. J. Env. Manage., 83(4): 448–459. DOI: 10.1016/j.jenvman.2006.04.010.
  18. Garedew, E., et al. 2009. Land-use and land-cover dynamics in the central rift valley of Ethiopia. Env. Manage., 44(4): 683–694. DOI: 10.1007/s00267-009-9355-z.
  19. Belay, T. and D. A. Mengistu. 2019. Landuse and land cover dynamics and drivers in the Muga watershed, Upper Blue Nile basin, Ethiopia. Remote Sensing Applications: Soc. Env., 15: 100249. DOI: 10.1016/j.rsase.2019.100249.
  20. Tian, H., et al. 2014. History of landuse in India during 1880–2010: Large-scale land transformations reconstructed from satellite data and historical archives. Global Planetary Change. 121: 78–88. DOI: 10.1016/j.gloplacha.2014.07.005.
  21. Birhane, E., et al. 2019. Landuse land cover changes along topographic gradients in Hugumburda national forest priority area, northern Ethiopia. Remote Sensing Applications: Soc. Env., 13: 61–68. DOI: 10.1 016/j.rsase.2018.10.017.
  22. Ranjan, S. and V. Singh. 2023. Effect of landuse land cover changes on hydrological response of Punpun river basin. Env. Monit. Assess., 195(9): 1137. DOI: 10.1007/s10661-023-11785-7.
  23. Lu, D. and Q. Weng. 2007. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sensing. 28(5): 823–870. DOI: 10.1080/01431160600 74 6456.
  24. Gomez, C., J.C. White and M.A. Wulder. 2016. Optical remotely sensed time series data for land cover classification: A review. ISPRS J. Photogrammetry Remote Sensing. 116: 55–72. DOI: 10.101 6/j.isprsjprs.2016.03.008.
  25. Ward, D., S. R. Phinn and A. T. Murray. 2000. Monitoring growth in rapidly urbanizing areas using remotely sensed data. Professional Geographer. 52(3): 371–386. DOI: 10.1111/0033-0124.00232.
  26. Muzein, B. S. 2006. Remote sensing and GIS for land cover, landuse change detection and analysis in the seminatural ecosystems and agriculture landscapes of the central Ethiopian rift valley. Doctorate Thesis. Dresden University of Technology, Germany.
  27. Mahendra, H., B. Shivakumar and J. Praveen. 2015. Pixel-based classification of multispectral remotely sensed data using support vector machine classifier. Int. J. Innovative Res. Electrical, Electronics, Instrumentation Cont. Eng., 3(1): 94-98. doi: 10.1 7148/IJIREEICE.
  28. Aasen, H., et al. 2018. Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: A review of sensor technology, measurement procedures and data correction workflows. Remote Sensing. 10(7): 1091. DOI: 10.3390/rs10071091.
  29. Lu, D., et al. 2004. Change detection techniques. Int. J. Remote Sensing. 25(12): 2365–2401. DOI: 10.1080/0143116031000139863.
  30. Prakash, A. and R.P. Gupta. 1998. Landuse mapping and change detection in a coal mining area – A case study in the Jharia coalfield, India. Int. J. Remote Sensing. 19: 391–410. DOI: 10.1080/014311 698216053.
  31. Foody, G.M. 2002. Status of land cover classification accuracy assessment. Remote Sensing Env., 80(1): 185–201. DOI: 10.1016/S0034-4257 (01)0 0295-4.
  32. Stehman, S. V. and G.M. Foody. 2019. Key issues in rigorous accuracy assessment of land cover products. Remote Sensing Env., 231: 111199. DOI: 10.1016/j.rse.2019.05.018.
  33. Han, J., et al. 2009. Evaluating landuse change in rapidly urbanizing China: Case study of Shanghai. J. Urban Planning Develop., 135(4): 166–171. DOI: 10. 1061/(ASCE)0733-9488(2009)135:4 (166).
  34. Wang, X., et al. 2023. Terrain gradient response of landscape ecological environment to landuse and land cover change in the hilly watershed in South China. Ecol. Indicators. 146: 109797. DOI: 10.101 6/j.ecolind.2022.109797.
  35. Forkuor, G. and O. Cofie. 2011. Dynamics of land-use and land cover change in Freetown, Sierra Leone and its effects on urban and peri-urban agriculture- A remote sensing approach. Int. J. Remote Sensing. 32(4): 1017–1037. DOI: 10.1080/0143116 0903505302.
  36. Moriasi, D. N., et al. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions ASABE. 50 (3): 885–900. DOI: 10.13031/2013.23153.
  37. Anderson, J. R., et al. 1976. A landuse and land cover classification system for use with remote sensor data. Professional paper 964. US Government Printing Office.
  38. Anshumali, et al. 2023. Morphometric delineation of administrative boundaries and classification of threatened categories of small watersheds in transboundary rivers. Sci. Reports. 13(1): 1652. DOI: 10.1038/s41598-023-28913-5.
  39. Venkatesh, M. and Anshumali. 2019. A GIS-based assessment of recent changes in drainage and morphometry of Betwa river basin and sub-basins, Central India. Appl. Water Sci., 9(7): 157. DOI: 10.1007/s13201-019-1033-6.
  40. Chowdhury, M., M. E. Hasan and M.M. Abdullah-Al-Mamun. 2020. Landuse/land cover change assessment of Halda watershed using remote sensing and GIS. Egyptian J. Remote Sensing Space Sci., 23(1): 63-75. DOI: 10.1016/j.ejrs.2018. 11.003.
  41. Potapov, P., et al. 2022. The global 2000-2020 land cover and landuse change dataset derived from the Landsat archive: First results. Frontiers Remote Sensing. 3: 856903. doi: 10.3389/frsen.2022. 856903.
  42. MohanRajan, S. N., et al. 2024. Fuzzy Swin transformer for landuse/ land cover change detection using LISS-III satellite data. Earth Sci. Informatics. 17(2): 1745–1764. DOI: 10.1007/s12145-023-01208-z.
  43. Belay, H., A.M. Melesse and G. Tegegne 2024. Scenario-based landuse and land cover change detection and prediction using the cellular Automata–Markov model in the Gumara watershed, Upper Blue Nile basin, Ethiopia. Land. 13(3): 3. DOI: 10.3390/land13030396.
  44. Neog, R. 2023. Monitoring landuse dynamics, urban sprawl and land surface temperature in Dimapur urban area, Nagaland, India. Int. J. Env. Sci. Tech., 20(7): 7519–7532. DOI: 10.1007/s13762-022-04378-3.
  45. Mitra, S., S. Roy and S. Hore. 2023. Assessment and forecasting of the urban dynamics through LULC based mixed model: Evidence from Agartala, India. GeoJ., 88(2): 2399–2422. DOI: 10.1007/s10708-022-10730-4.
  46. Sarif, Md. O. and R.D. Gupta. 2022. Spatiotemporal mapping of landuse/land cover dynamics using remote sensing and GIS approach: A case study of Prayagraj city, India (1988–2018). Env. Develop. Sustain., 24(1): 888–920. DOI: 10.1007/s10668-021-01475-0.
  47. Krishnaveni, K.S. and P.P.A. Kumar. 2022. Spatio-temporal dynamics of urban sprawl in a rapidly urbanizing city using machine learning classification. Geocarto Int., 37(27): 17403–17434. DOI: 10.1 080/10106049.2022.2129817.
  48. Parihar, S.M., et al. 2022. Landuse dynamics and impact on regional climate post-Tehri dam in the Bhilangana basin, Garhwal Himalaya. Sustain., 14 (16): 10221. DOI: 10.3390/su141610221.
  49. Tewabe, D. and T. Fentahun. 2020. Assessing land-use and land cover change detection using remote sensing in the lake Tana basin, northwest Ethiopia. Cogent Env. Sci., 6(1): 1778998. DOI: 10.1080/23311843.2020.1778998.
  50. Viana, C.M., I. Girão and J. Rocha. 2019. Long-term satellite image time-series for landuse/land cover change detection using refined open source data in a rural region. Remote Sensing. 11(9): 1104. DOI: 10.3390/rs11091104.
  51. Abd El-Kawy, O.R., et al. 2011. Landuse and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography. 31(2): 483–494. DOI: 10.1016/j.apgeog.2010. 10.012.
IJEP Logo

Quick Link

Menu

Query Form

    Contact Us

    Indian Journal of Environmental Protection,
    Kalpana Corporation, Kalpana Bhawan,
    N 10/60 H-12, Shyama Nagar
    P.O. Bajardiha, Varanasi – 221106

    Phone Number : +91 8130034876

    Email: kalpanacorp81@gmail.com

    Websites : www.e-ijep.co.in

    Copyright © 2025 Indian Journal of Environmental Protection | Kalpana Corporation