Skip to main content
Log in

Analysis of forest health and socioeconomic dimension in climate change scenario and its future impacts: remote sensing and GIS approach

  • Published:
Spatial Information Research Aims and scope Submit manuscript

Abstract

The present study examined the relationship among various diversified datasets using remote sensing and GIS. About 72% of the total forest area of Chhattisgarh state (59,935 km2) has shown a trend of negative change between the periods (1982 and 2006). Around 50% of the total forest fires of the state were found in the two tehsils of Narayanpur and Bijapur with two major forest fire hotspots. Approximately 86% of the total forest fire event of the state occurred in the category of “tropical mixed deciduous and dry deciduous forests” whereas the intensity of forest fire events was found 2.2 times in the category “tropical lowland forests, broadleaved, evergreen, < 1000 m” when it was compared with the category of “tropical mixed deciduous and dry deciduous forests.” The highest poverty percent was found in the tehsil of Bijapur (65.9%) which retains a significantly high percentage of the tribal population (73.1%). The adaptive capacity of Raipur tehsil (state capital) is high whereas it reduces significantly towards north and south from the state capital. The climate anomaly data evaluation for the year 2030 showed variation such as reduction in rainfall and increase in temperature will significantly maneuver the forest fire regime in future is a matter of serious concern. The outcomes of the present study would certainly guide the policymakers of the state of Chhattisgarh to prepare a meaningful, transparent and robust plan for the betterment of people keeping in mind of future climate change impact.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. http://www.fao.org/docrep/ARTICLE/WFC/XII/0586-C1.HTM.

  2. http://www.census2011.co.in/.

References

  1. Bothale, R. V., & Katpatal, Y. B. (2014). Response of rainfall and vegetation to ENSO events during 2001–2011 in upper Wardha Watershed, Maharashtra, India. Journal of Hydrologic Engineering, 19(3), 583–592.

    Article  Google Scholar 

  2. Sykes, M. T. (2009). Climate change impacts: vegetation. Encyclopedia Life Science (ELS). https://doi.org/10.1002/9780470015902.a0021227.

    Google Scholar 

  3. Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanam, M. S., & Xavier, P. K. (2006). Increasing trends of extreme rain events over India in a warming environment. Science, 314(5804), 1442–1445. https://doi.org/10.1126/science.1132027.

    Article  Google Scholar 

  4. Birthal, P. S., Negi, D. S., Kumar, S., Aggarwal, S., Suresh, A., & Khan, M. T. (2014). How sensitive is Indian agriculture to climate change? Indian Journal of Agricultural Economics, 69(4), 474–484.

    Google Scholar 

  5. Fussel, H. M., & Klein, R. J. T. (2006). Climate change 2007: Working group II: impacts, adaptation and vulnerability. https://www.ipcc.ch/publications_and_data/ar4/wg2/en/ch17.html. Accessed March 18, 2018.

  6. Chaturvedi, R. K., Gopalakrishnan, R., Jayaraman, M., et al. (2011). Impact of climate change on Indian forests: A dynamic vegetation modeling approach. Mitigation and Adaptation Strategies for Global Change, 16, 119. https://doi.org/10.1007/s11027-010-9257-7.

    Article  Google Scholar 

  7. Sinha, S., Sharma, L. K., & Nathawat, M. S. (2015). Improved land-use/land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing. The Egyptian Journal of Remote Sensing and Space Sciences, 18(2), 217–233.

    Article  Google Scholar 

  8. Guo, L., Wu, S., Zhao, D., Yin, Y., Leng, G., & Zhang, Q. (2014). NDVI-based vegetation change in inner Mongolia from 1982 to 2006 and its relationship to climate at the biome scale. Advances in Meteorology, 2014, 1–12. https://doi.org/10.1155/2014/692068.

    Article  Google Scholar 

  9. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2.

    Article  Google Scholar 

  10. Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20(9), 503–510. https://doi.org/10.1016/j.tree.2005.05.011.

    Article  Google Scholar 

  11. Anyamba, A., & Tucker, C. J. (2005). Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. Journal of Arid Environments, 63(3), 596–614. https://doi.org/10.1016/j.jaridenv.2005.03.007.

    Article  Google Scholar 

  12. Julien, Y., & Sobrino, J. A. (2009). Global land surface phenology trends from GIMMS database. International Journal of Remote Sensing, 30(13), 3495–3513. https://doi.org/10.1080/01431160802562255.

    Article  Google Scholar 

  13. Chakraborty, A., Seshasai, M. V. R., Reddy, C. S., & Dadhwal, V. K. (2018). Persistent negative changes in seasonal greenness over different forest types of India using MODIS time series NDVI data (2001–2014). Ecological Indicators, 85, 887–903. https://doi.org/10.1016/j.ecolind.2017.11.032.

    Article  Google Scholar 

  14. Ager, A. A., Evers, C. R., Day, M. A., Preisler, H. K., Barros, A. M. G., & Nielsen-Pincus, M. (2017). Network analysis of wildfire transmission and implications for risk governance. PLoS ONE, 12(3), e0172867. https://doi.org/10.1371/journal.pone.0172867.

    Article  Google Scholar 

  15. Ahmad, F., & Goparaju, L. (2017). Assessment of threats to forest ecosystems using geospatial technology in Jharkhand state of India. Current World Environment, 12(2), 355–365. https://doi.org/10.12944/CWE.12.2.19.

    Article  Google Scholar 

  16. Eva, H., & Lambin, E. F. (2000). Fires and land-cover change in the tropics: A remote sensing analysis at the landscape scale. Journal of Biogeography, 27, 765–776. https://doi.org/10.1046/j.1365-2699.2000.00441.x.

    Article  Google Scholar 

  17. Ahmad, F., Goparaju, L., Qayum, A., & Quli, S. M. S. (2017). Forest fire trend analysis & effect of environmental parameters: A study in Jharkhand State of India using geospatial technology. World Scientific News, 90, 31–50.

    Google Scholar 

  18. Ahmad, F., Goparaju, L., & Qayum, A. (2018). Himalayan forest fire characterization in relation to topography, socio-economy and meteorology parameters in Arunachal Pradesh, India. Spatial Information Research. https://doi.org/10.1007/s41324-018-0175-1.

    Google Scholar 

  19. Harrison, S., Marlon, J., & Bartlein, P. (2010). Fire in the earth system. In J. Dodson (Ed.), Changing climates, earth systems and society. International year of planet earth. Dordrecht: Springer. https://doi.org/10.1007/978-90-481-8716-4_3.

    Google Scholar 

  20. Kirschbaum, M. U. F., Cannell, M. G. R., Cruz, R. V. O., Galinski, W., & Cramer, W. P. (1996). Climate change impacts onforests. In R. T. Watson, M. C. Zinyowera, R. H. Moss, & D. J. Dokken (Eds.), Climate change 1995. Impacts, adaptation and mitigation of climate change: Scientific-technical analyses. Cambridge: Cambridge University Press.

    Google Scholar 

  21. PIA Report. (2011). Poverty impact assessment report, Chhattisgarh. https://www.giz.de/de/downloads/giz2011-en-poverty-impact-assessment-report.pdf. Accessed May 2, 2018.

  22. Agrawal, P. K., & Agrawal, S. (2010). To what extent are the indigenous women of Jharkhand, India living in disadvantageous conditions: Findings from India’s National Family Health Survey. Asian Ethnicity, 11(1), 61–80. https://doi.org/10.1080/14631360903506778.

    Article  Google Scholar 

  23. Roy, P. S., Agrawal, S., Joshi, P., & Shukla, Y. (2003). The land cover map for Southern Asia for the year 2000. GLC2000 database, European Commision Joint Research Centre. http://forobs.jrc.ec.europa.eu/products/glc2000/products.php.

  24. Bartholome, E., Belward, A. S., Achard, F., et al. (2002). GLC 2000, global land cover mapping for the year 2000. European Commission Joint Research Centre, Institute for Environment and Sustainability I—21020 Ispra-VA, Italy.

  25. Xu, C., Li, Y., Hu, J., Yang, X., Sheng, S., & Liu, M. (2012). Evaluating the difference between the normalized difference vegetation index and net primary productivity as the indicators of vegetation vigor assessment at landscape scale. Environmental Monitoring and Assessment, 184, 1275–1286.

    Article  Google Scholar 

  26. Geospatial World. (2012). https://www.geospatialworld.net/article/monitoring-agricultural-vulnerability-using-ndvi-time-series/. Accessed March 18, 2018.

  27. Bhandari, L., & Chakraborty, M. (2014). Spatial poverty in Jharkhand. https://www.livemint.com/Opinion/TelfQDSiZ1IPufDbwZ526K/Spatial-poverty-in-Chhattisgarh.html. Accessed March 18, 2018.

  28. Chakraborty, A., & Joshi, P. (2014). Mapping disaster vulnerability in India using analytical hierarchy process. Geomatics, Natural Hazards and Risk. https://doi.org/10.1080/19475705.2014.897656.

    Google Scholar 

  29. NCAR GIS Program. (2012). Climate change scenarios, version 2.0. Community climate system model, June 2004 version 3.0. http://www.cesm.ucar.edu/models/ccsm3.0/ was used to derive data products. NCAR/UCAR. http://www.gisclimatechange.org. Accessed March 5, 2018.

  30. Sharma, R. C. (2007). Re-inventing Forestry Agencies: Establishment of the Chhattisgarh Forest Department Processes and key issues. http://www.fao.org/docrep/010/ai412e/AI412E12.htm. Accessed April 2, 2018.

  31. Ahmad, F., & Goparaju, L. (2017). Geospatial assessment of forest fires in Jharkhand (India). Indian Journal of Science and Technology, 10(21), 7. https://doi.org/10.17485/ijst/2017/v10i21/113215.

    Article  Google Scholar 

  32. Kumar, N., et al. (2000). Alleviating poverty through participatory forestry development: An evaluation of India’s forest development and World Bank assistance. World Bank: Washington, DC. http://lnweb90.worldbank.org/oed/oeddoclib.nsf/24cc3bb1f94ae11c85256808006a0046/c14758e94c1f79e985256970007b95b1/$FILE/India.pdf. Accessed April 30, 2018.

  33. Minj, H. P. (2013). Social dimension of climate change on tribal societies of Jharkhand. International Journal of Social Science & Interdisciplinary Research, 2(3), 34–41.

    Google Scholar 

  34. Wotton, B. M., Nock, C. A., & Flannigan, M. D. (2010). Forest fire occurrence and climate change in Canada. International Journal of Wildland Fire, 19, 253–271. https://doi.org/10.1071/WF09002.

    Article  Google Scholar 

  35. Tian, X., Shu, L., Zhao, F., & Wang, M. (2012). Forest fire danger changes for southwest China under future scenarios. Scientia Silvae Sinicae, 48, 121–125. https://doi.org/10.11707/j.1001-7488.20120120.

    Google Scholar 

  36. Flannigan, M. D., Stocks, B. J., & Wotton, B. M. (2000). Climate change and forest fires. Science of the Total Environment, 262(3), 221–229. https://doi.org/10.1016/S0048-9697(00)00524-6.

    Article  Google Scholar 

  37. Fried, J. S., Torn, M. S., & Mills, E. (2004). The impact of climate change on wildfire severity: A regional forecast for northern California. Climatic Change, 64(1–2), 169–191. https://doi.org/10.1023/B:CLIM.0000024667.89579.ed.

    Article  Google Scholar 

  38. Stephens, S. L. (2005). Forest fire causes and extent on United States Forest Service lands. International Journal of Wildland Fire, 14(3), 213–222. https://doi.org/10.1071/WF04006.

    Article  Google Scholar 

  39. Westerling, A. L., Gershunov, A., Brown, T. J., Cayan, D. R., & Dettinger, M. D. (2003). Climate and wildfire in the western United States. Bulletin of the American Meteorological Society, 84(5), 595–604. https://doi.org/10.1175/BAMS-84-5-595.

    Article  Google Scholar 

  40. Ravindranath, N. H., & Sukumar, R. (1998). Climate change and tropical forests in India. Climate Change, 39, 563–581.

    Article  Google Scholar 

  41. Vulnerability Report. (2015). http://www.ignfa.gov.in/photogallery/documents/REDD-plus%20Cell/Modules%20for%20forest%20&%20Climate%20Change/Presentations/Group%20Presentations/Group%20II.pdf. Accessed April 30, 2018.

  42. Ahmad, F., & Goparaju, L. (2018). A geospatial analysis of climate variability and its impact on forest fire: A case study in Orissa state of India. Spatial Information Research, 26, 587. https://doi.org/10.1007/s41324-018-0197-8.

    Article  Google Scholar 

  43. Ahmad, F., Goparaju, L., & Qayum, A. (2017). LULC analysis of urban spaces using Markov chain predictive model at Ranchi in India. Spatial Information Research, 25, 351. https://doi.org/10.1007/s41324-017-0102-x.

    Article  Google Scholar 

  44. Ahmad, F., Uddin, M. M., & Goparaju, L. (2018). Geospatial application for agroforestry suitability mapping based on FAO guideline: Case study of Lohardaga, Jharkhand State of India. Spatial Information Research, 26, 517. https://doi.org/10.1007/s41324-018-0194-y.

    Article  Google Scholar 

  45. Jana, A., Maiti, S., & Biswas, A. (2017). Appraisal of long-term shoreline oscillations from a part of coastal zones of Sundarban delta, Eastern India: A study based on geospatial technology. Spatial Information Research, 25, 713. https://doi.org/10.1007/s41324-017-0139-x.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the NASA Fire Information for Resource Management System, European Commission’s science and knowledge service, National Center for Atmospheric Research, and DIVA GIS for providing free download of various dataset used in the analysis.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Firoz Ahmad.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 1218 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmad, F., Uddin, M.M. & Goparaju, L. Analysis of forest health and socioeconomic dimension in climate change scenario and its future impacts: remote sensing and GIS approach. Spat. Inf. Res. 27, 385–397 (2019). https://doi.org/10.1007/s41324-019-00245-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41324-019-00245-5

Keywords

Navigation