Geospatial Assessment of Land Use and Land Cover Changes in Debrigarh Wildlife Sanctuary, Odisha: A Twenty-Year Perspective

Year : 2025 | Volume : 14 | Issue : 02 | Page : 12 27
    By

    Niti Parashar,

  • Harshita Jain,

  • Maya Kumari,

  • Praveen Kumar Rai,

  1. Student, Amity Institute of Environmental Sciences, Amity University, Sector 125, Noida, Uttar Pradesh, India
  2. Assistant Professor, Amity Institute of Environmental Sciences, Amity University, Sector 125, Noida, Uttar Pradesh, India
  3. Associate Professor, Amity School of Natural Resources and Sustainable Development, Amity University, Sector 125, Noida, Uttar Pradesh, India
  4. Associate Professor, Department of Geography, Khwaja Moinuddin Chishti Language University, Lucknow, Uttar Pradesh, India

Abstract

Among the recorded dynamic processes on the surface of the earth is the change in land use and land cover (LULC) pattern as an outcome of several anthropogenic practices. Planning, development, and management of land for sustainable usage of land depend on plotting and tracing the vagaries in LULC. Anthropogenic activity, generally for forestry and food production, is altering the land surface. The steadily diminishing landscape also has tragic implications for biodiversity and critically important ecosystem services like carbon storage. The protection of areas of land provides a solution, together, these areas can work to save natural and social resources, guard human well-being, and deliver sustainable livelihoods which encourages sustainable development when effectively managed and equitably governed. Thus, this study is an attempt to track changes in LULC patterns of Debrigarh Wildlife Sanctuary (DWS) in Bargarh District of Odisha over a period of 20 years (2004 to 2024) by channels of Remote Sensing (RS) and Geographic Information System (GIS) systems. A total of 346.91 km2 , the entire territory of the DWS is being studied. The study centres on determining variations in LULC and assessing vegetation based on the well-known ration NDVI using data from satellites. Nevertheless, during the period that was examined for the study area, Landsat 4-5 TM and Landsat 8- 9 OLI/TIRS imageries sufficiently supported the detection of changes brought about by nature or human activity throughout time. LULC classification was efficiently done with substantial accuracy and it has been found that forest cover has increased while other LULC classes seem to decline.

Keywords: Land use land cover, anthropogenic practice, wildlife sanctuary, remote sensing (RS), and Geographic Information System (GIS)

[This article belongs to Research & Reviews : Journal of Space Science & Technology ]

How to cite this article:
Niti Parashar, Harshita Jain, Maya Kumari, Praveen Kumar Rai. Geospatial Assessment of Land Use and Land Cover Changes in Debrigarh Wildlife Sanctuary, Odisha: A Twenty-Year Perspective. Research & Reviews : Journal of Space Science & Technology. 2025; 14(02):12-27.
How to cite this URL:
Niti Parashar, Harshita Jain, Maya Kumari, Praveen Kumar Rai. Geospatial Assessment of Land Use and Land Cover Changes in Debrigarh Wildlife Sanctuary, Odisha: A Twenty-Year Perspective. Research & Reviews : Journal of Space Science & Technology. 2025; 14(02):12-27. Available from: https://journals.stmjournals.com/rrjosst/article=2025/view=214544


References

1. Nuissl H, Siedentop S. Urbanization and land use change. In: Sustainable land management in a European context: a co-design approach. Cham: Springer; 2021; 75–99.
2. Padon A, Iamtrakul P, Thanapirom C. The study of urbanization effect on the land use changes and urban infrastructure development in the Metropolitan Areas, in Thailand. IOP Conf Ser Earth Environ Sci. 2021; 738(1): 012077.
3. Sangeetha SKB, Kushwah VS, Sumangali K, Sangeetha R, Raja KT, Mathivanan SK. Effect of urbanization through land coverage classification. Radio Sci. 2023; 58(11): 1–13.
4. Gao Y, Li H, Song Y. Interaction relationship between urbanization and land use multifunctionality: evidence from Han River Basin, China. Land. 2021; 10(9): 938.
5. Nagendra H, Sudhira HS, Katti M, Schewenius M. Sub-regional assessment of India: effects of urbanization on land use, biodiversity and ecosystem services. In: Urbanization, biodiversity and ecosystem services:challenges and opportunities: a global assessment.Dordrecht: Springer; 2013; 65–74.
6. Mengistu DA, Salami AT. Application of remote sensing and GIS in land use/land cover mapping and change detection in a part of southwestern Nigeria. Afr J Environ Sci Technol. 2007; 1(5): 99–109.
7. Olokeogun OS, Iyiola K, Iyiola OF. Application of remote sensing and GIS in land use/land cover mapping and change detection in Shasha forest reserve, Nigeria. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2014; 40: 613–6.
8. Lubis JPG, Nakagoshi N. Land use and land cover change detection using remote sensing and geographic information system in Bodri Watershed, Central Java, Indonesia. J Int Dev Coop. 2011;18(1): 139–51.

9. Kindu M, Schneider T, Teketay D, Knoke T. Land use/land cover change analysis using object- based classification approach in Munessa-Shashemene landscape of the Ethiopian highlands.Remote Sens. 2013; 5(5): 2411–35.
10. Ghosh S, Sen KK, Rana U, Rao KS, Saxena KG. Application of GIS for land-use/land-cover change analysis in a mountainous terrain. J Indian Soc Remote Sens. 1996; 24: 193–202.
11. Kayet N, Pathak K. Remote sensing and GIS-based land use/land cover change detection mapping in Saranda Forest, Jharkhand, India. Int Res J Earth Sci. 2015; 3(10): 1–6.
12. Aburas MM, Ho YM, Ramli MF, Ash’aari ZH. Monitoring and assessment of urban growth patterns using spatio-temporal built-up area analysis. Environ Monit Assess. 2018; 190: 1–26.
13. Foody GM. Status of land cover classification accuracy assessment. Remote Sens Environ. 2002;80(1): 185–201.
14. Madariya G, Pandey D, Sk S. A comparative study on supervised and unsupervised techniques of land use and land cover classification. Pharma Innov J. 2022; 11(3): 517–21.
15. Rwanga SS, Ndambuki JM. Accuracy assessment of land use/land cover classification using remote sensing and GIS. Int J Geosci. 2017; 8(4): 611–622.
16. Prasad TL, Sreenivasulu G. Land use/land cover analysis using remote sensing and GIS: a case study on Pulivendula Taluk, Kadapa District, Andhra Pradesh, India. Int J Sci Res Publ. 2014; 4(6): 1–5.
17. Ranjan AK, Vallisree S, Singh RK. Role of geographic information system and remote sensing in monitoring and management of urban and watershed environment: Overview. J Remote Sens GIS.2016; 7(2): 1–14.
18. Tsegaye L. Analysis of land use and land cover change and its drivers using GIS and remote sensing: The case of West Guna Mountain, Ethiopia. Int Res J Earth Sci. 2014; 3: 53–63.
19. Bolstad P, Lillesand TM. Rapid maximum likelihood classification. Photogramm Eng Remote Sens. 1991; 57(1): 67–74.

20. Liu X. Supervised classification and unsupervised classification. ATS 670 Class Project. 2005.
21. Otukei JR, Blaschke T. Land cover change assessment using decision trees, support vector machines, and maximum likelihood classification algorithms. Int J Appl Earth Obs Geoinf. 2010;12: S27–31.
22. Sisodia PS, Tiwari V, Kumar A. Analysis of supervised maximum likelihood classification for remote sensing image. In: IEEE Int Conf Recent Adv Innov Eng (ICRAIE-2014). 2014; 1–4.
23. Sun J, Yang J, Zhang C, Yun W, Qu J. Automatic remotely sensed image classification in a grid environment based on the maximum likelihood method. Math Comput Model. 2013; 58(3–4): 573–81.
24. Udupi S, Hemamalini HC, Kumar V, Chittora V, Prabhuraj DK, Patil SS. Efficient schemes of classifiers for Remote Sensing Satellite Imageries of land use pattern classifications. Mercator, Fortaleza. 2024; 23(1): e23004.
25. Reis S. Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Sensors. 2008; 8(10): 6188–202.
26. Congalton RG. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ. 1991; 37(1): 35–46.
27. Liu C, Frazier P, Kumar L. Comparative assessment of the measures of thematic classification accuracy. Remote Sens Environ. 2007; 107(4): 606–16.
28. Norovsuren B, Renchin T, Tseveen B, Yangiv A, Altanchimeg T. Estimation of forest coverage in the northern region of Mongolia using sentinel and Landsat data. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2019; 42: 71–6.
29. Alkaradaghi K, Ali SS, Al-Ansari N, Laue J. Evaluation of land use & land cover change using multi-temporal Landsat imagery: A case study Sulaimaniyah Governorate, Iraq. J Geogr Inf Syst.2018; 10(6): 247–60.
30. Anil NC, Sankar GJ, Rao MJ, Prasad IVRK, Sailaja U. Studies on land use/land cover and change detection from parts of South West Godavari District, AP–using remote sensing and GIS techniques. J Indian Geophys Union. 2011; 15(4): 187–94.
31. Congalton RG, Plourde L. Manual of geospatial science and technology. Quality assurance and accuracy assessment of information derived from remotely sensed data. London: Taylor & Francis;2002; 349–61.
32. Manandhar R, Odeh IO, Ancev T. Improving the accuracy of land use and land cover classification of Landsat data using post-classification enhancement. Remote Sens. 2009; 1(3): 330–44.
33. Vivekananda GN, Swathi R, Sujith AVLN. Multi-temporal image analysis for LULC classification and change detection. Eur J Remote Sens. 2021; 54(sup2): 189–99.
34. Islami FA, Tarigan SD, Wahjunie ED, Dasanto BD. Accuracy assessment of land use change analysis using Google Earth in Sadar Watershed Mojokerto Regency. IOP Conf Ser Earth Environ Sci. 2022; 950(1): 012091.
35. Borana SL, Yadav SK. Accuracy assessment of land cover classification in Jodhpur City using remote sensing and GIS. Int J Adv Res Comput Commun Eng. 2017; 6(10): 220–4.
36. Tewabe D, Fentahun T. Assessing land use and land cover change detection using remote sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environ Sci. 2020; 6(1): 1778998.
37. Han J, Hayashi Y, Cao X, Imura H. Evaluating land-use change in rapidly urbanizing China: Case study of Shanghai. J Urban Plann Dev. 2009; 135(4): 166–71.
38. Forkuo EK, Frimpong. Analysis of forest cover change detection. Int J Remote Sens Appl. 2012;2: 82–92.

Regular Issue Subscription Original Research
Volume 14
Issue 02
Received 21/05/2025
Accepted 27/05/2025
Published 21/06/2025
Publication Time 31 Days
228

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