GIS Based Study of Reclamation of Degraded Semi-Arid Soil: A Case Study from Rajasthan, India

IJEP 42(3): 302-315 : Vol. 42 Issue. 3 (March 2022)

Brototi Biswas1, Ashutosh Singh2, Praveen Kumar Rai3*, Jai Kumar4 and Sakshi Walker5

1. Mizoram University, Department of Geography and RM, Aizawl, Mizoram – 796 004, India
2. Mizoram University, Pachhunga University College, Department of Geography, Aizawl, Mizoram – 796 001, India
3. K.M.C. Language University, Department of Geography, Lucknow, Uttar Pradesh – 226 013, India
4. SHUATS, Centre for Geospatial Technologies, Prayagraj, Uttar Pradesh – 211 007, India
5. St. John’s College, Department of Botany, Agra, Uttar Pradesh – 282 002, India


Soil degradation takes place not only as a result of interaction between physico-chemical and biological factors comprising topography, soil properties and climatic features but also includes human factors and landuse management practices. Web GIS can be used in rural development administration for planning, monitoring and exchange of data. A study was conducted in Dungargarh tehsil and Churu district of Rajasthan, India to demonstrate the integration of village-level spatial and non-spatial data alongwith the preparation of thematic maps which would be linked to a biodiversity information system database. The region suffers from the acute environmental problems of soil erosion, shifting sand dunes and excessive soil salinity alongwith the economic problems of lack of livelihood options. In this study, the integration of village level spatial and non-spatial data was done, into a useful tool called biodiversity/plant diversity information system (BIS), for environmental resource identification for sustainable environment and livelihood options. Various environmental parameters, like landuse/land cover (LULC), distribution of natural vegetation, soil, water and climate were examined and suitable thematic maps were prepared with an aim of making a present environmental database and probable environmental management and conservation techniques according to the strength and lacuna of the region in a sustainable way for the economic and environmental resurgence of this backward region. The various thematic maps were utilized for demarcation of the mentioned problems and prospects. Further weighted index overlay method was adopted to identify the suitability of the target species for dealing with each of the environmental problems and medicinal gardening. The thematic maps and attribute data were integrated through ArcGIS 10.2 and then published on Web GIS platform through Arc GIS online in a dynamic interactive Web application for ready reference and use by others.


Biodiversity information system, Web GIS, weighted index overlay, remote sensing, soil salinity, Landuse land cover


  1. Barbero-Sierra, C., et al. 2015. How is deseri-fication research addressed in Spain? Land versus soil approaches. Land Degrad. Develop., 26:423-432. DOI: 10.1002/ldr.2344.
  2. Brevik, E.C., et al. 2005. The interdisciplinary nature of soil. Soil. 1:117-129. DOI: 10.10.5194/soil-1117-2015.
  3. Taguas, E.V., et al. 2015. Exploring the linkage between spontaneous grass cover biodiversity and soil degradation in two olive orchard micro-catchments with contrasting environmental and management conditions. Soil. 1:651-664. DOI: 10.5194/soil-1-651-2015.
  4. Khaledian, Y., et al. 2017. Assessment and monitoring of soil degradation during landuse change using multivariate analysis. Land Degrad. Develop., 28:128-141. DOI: 10.1002/ldr.2541.
  5. Camprubi, A., et al. 2015. Field performance and essential oil production of mycorrhizal rosemary in restoration low-nutrient soils. Land Degrad. Develop., 26:793-799. DOI: 10.1002/ldr.2229.
  6. Cerda, A., et al. 2016. The use of barley straw residues to avoid high erosion and runoff rates on persimmon plantations in eastern spain under low frequency-high magnitude simulated rainfall events. Soil Res., 54:154-165. DOI: 10.1071/SR15092.
  7. Kiran, et al. 2009. Reclaiming degraded land in India : Through cultivation of medicinal plants. Botany Res. Int., 2(3):174-181.
  8. Kumar, V.T. and C. Venkatesan. 2012. The cultivation of medicinal plants through wasteland in Tamil Nadu, India. Indian Streams Res. J., 2 (9):1-6.
  9. Biswas, B. and R.D. Kaplay. 2012. Environmental impact assessment of Agra products in Nanded district of Maharashtra. Arch. Appl. Sci. Res., 4(1):323-329.
  10. Batugal, P.A., et al. 2004. Medicinal plants research in Asia : I : The framework and project work plans. International Plant Genetic Resources Institute-Regional Office for Asia. The Pacific and Oceania (IPGRI-APO), Serdang, Malaysia.
  11. Maiti. 2004. Inventory, documentation and status of medicinal plants research in India. Medicinal Plants Research in Asia. International Plant Genetic Resources Institute.
  12. Kipgen, S. 2013. Bioprospecting and identification of anti-malarial compounds in plants from biodiversity rich areas in India. CSIR-OSDD Natural Plant Products Project.
  13. Al Bakri, J.T., et al. 2011. GIS-based analysis of spatial distribution of medicinal and herbal plants in arid and semi-arid zones in north-west of Jordon. Annals Arid Zone. 50(2):99-115.
  14. Qayum, A., A.M. Lynn and R. Arya. 2014. Traditional knowledge system based GIS mapping of antimalarial plants : Statistical distribution analysis. J. Geographical Information System. 6:478-491.
  15. Roy, P.S. and M.D. Behera. 2005. Assessment of biological richness in different altitudinal zones in the eastern Himalayas, Arunachal Pradesh, India. Curr. Sci., 88:250-257.
  16. Khan, N.M., et al. 2005. Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agric. water Manage., 77:96-109.
  17. Roy, P.S. and M.D. Behera. 2005. Assessment of biological richness in different altitudinal zones in the Eastern Himalayas, Arunachal Pradesh, India. Curr. Sci., 88: 250-257.
  18. Mustalish, R.W., et al. 1996. Development of a phytohabitat index for medicinal plants in the Peruvian Amazon. Acta Horticulturae. 426:123-131.
  19. Schumaker, N.H. 1996. Using landscape indices to predict habitat connectivity. Ecol., 77:1210-1225.
  20. Sperduto, M.B. and R.G. Congalton. 1996. Predicting rare orchid (small whorled pogonia) habitat using GIS. Photogrammetric Eng. Remote Sensing. 62:1269-1279.
  21. Menon, S. and K. S. Bawa. 1997. Applications of geographic information systems, remote-sensing and a landscape ecology approach to biodiversity conservation in the Western Ghats. Curr. Sci., 73:134-145.
  22. Debinski, D.M., M.E. Jakubauskas and K. Kindscher. 1999. A remote sensing and GIS-based model of habitats and biodiversity in the Greater yellowstone ecosystem. Int. J. Remote Sensing. 20:3281-3292.
  23. Roy, P.S. and S. Tomar. 2000. Biodiversity characterization at landscape level using geospatial modeling technique. Biodiversity Conser., 95:95-109.
  24. Porwal, M.C., L. Sharma and P.S. Roy. 2003. Stratification and mapping of Ephedra gerardiana wall in Poh/Lahul and Spiti using remote sensing and GIS. Curr. Sci., 84:208-212.
  25. Anderson, D.M., et al. 2005. Conserving the sacred medicine mountains : A vegetation analysis of Tibetan sacred sites in northwest Yunnan. Biodiversity Conser., 14:3065-3091.
  26. Yang, X., et al. 2006. Mapping non-wood forest product (Matsatake mushrooms) using logistic regression and a GIS expert system. Ecol. Modeling. 198:208-218.
  27. Minhas, P.S., et al. 1996. Saline-water irrigation for the establishment of furrow-planted trees in northwestern India. Agroforestry Systems. 35:177-186.
  28. Walke, N., et al. 2012. GIS-based multicriteria overlay analysis in soil-suitability evaluation for cotton (Gossypium spp.) : A case study in the black soil region of Central India. Computers Geosci., Available at : COPUTGEOSCI.41.10.1016/j.cageo. 2011.08.020.
  29. Garge, S., et al. 2015. Site suitability analysis for JFM plantation sites using geo-spatial techniques. Int. J. Adv. Remote Sensing GIS. 4(1):920-930.
  30. Maguire, D., M. Batty and M. Goodchild. 2005. GIS : Special analysis and modelling. ESRI Press, U.S.A.
  31. Walker, B.J., et al. 2016. The costs of photorespiration to food production now and in the future. Annual Review Platn Biol., 67: 107-129.