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Geo-spatial perspective of vegetation health evaluation and climate change scenario in India

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Abstract

Vegetation health of any ecosystem and changes in it are vital in global change in ecology and it is delicately linked to climate change. This study evaluated the spatial patterns of significant negative change trend using composite NOAA-AVHRR data time series (1982–2006), long term forest fire point data, invasive hotspot data and predicted climate anomalies data over the different harmonized landcover categories of India. Around 65% of Indian forest shows the trend of negative change. Significant negative change were found to be highest (203,026 km2) over ‘Tropical mixed deciduous and dry deciduous forests’ category, followed by ‘Tropical lowland forests, broadleaved, evergreen’ (81,555 km2) and ‘Evergreen shrubland & regrowth/Abandoned shifting cultivation/Extensive shifting cultivation’ (55,811 km2). Around 85% of Indian biodiversity hotspot showed the negative change. The analysis of forest fire revealed the ‘Tropical mixed deciduous and dry deciduous forests’ retained the highest forest fire percentage (40%). The prediction of temperature anomalies for the year 2030 using RCP 4.5 model showed the increase in the temperature in the range of 0.58–1.32 °C and was found highest in northern part of India. Similarly, the rainfall prediction for the year 2030 showed rainfall deficit in several states of India. The outcomes of the present study would help in prioritization of various vegetation types suffering from anthropogenic and natural disturbances and will guide the policymakers to safeguard, prioritized forest areas for effective conservation, scientific protection and climate change mitigation endeavors.

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  1. https://gisclimatechange.ucar.edu/gis-data-ar5.

References

  1. Piao, S. L., Fang, J. Y., & He, J. S. (2006). Variations in vegetation net primary production in the Qinghai-Xizang Plateau, China, from 1982 to 1999. Climate Change, 74, 253–267.

    Article  Google Scholar 

  2. Bing, G., Yi, Z., Shi-xin, W., & He-ping, T. (2014). The relationship between normalized difference vegetation index (NDVI) and climate factors in the semi-arid region: A case study in Yalu Tsangpo River Basin of Qinghai-Tibet Plateau. Journal of Mountain Science, 11(4), 926–940.

    Article  Google Scholar 

  3. Hu, C. J., Fu, B. J., Liu, G. H., Jin, T. T., & Guo, L. (2010). Vegetation patterns influence on soil microbial biomass and functional diversity in a hilly area of the Loess Plateau, China. Journal of Soils and Sediments, 10, 1082–1091.

    Article  Google Scholar 

  4. Fu, B. J., Li, S. G., Yu, X. B., Yang, P., Yu, G. R., Feng, R. G., et al. (2010). Chinese ecosystem research network: Progress and perspectives. Ecological Complexity, 7, 225–233.

    Article  Google Scholar 

  5. 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 

  6. Ning, T., Liu, W., Lin, W., & Song, X. (2015). NDVI variation and its responses to climate change on the Northern Loess Plateau of China from 1998 to 2012. Advances in Meteorology, 2015, 1–10. https://doi.org/10.1155/2015/725427.

    Article  Google Scholar 

  7. Kawabata, A., Ichii, K., & Yamaguchi, Y. (2001). Global monitoring of interannual changes in vegetation activities using NDVI and its relationships to temperature and precipitation. International Journal of Remote Sensing, 22(7), 1377–1382.

    Article  Google Scholar 

  8. Sarkar, S., & Kafatos, M. (2004). Interannual variability of vegetation over the Indian sub-continent and its relation to the different meteorological parameters. Remote Sensing of Environment, 90, 268–280.

    Article  Google Scholar 

  9. Anyamba, A., & Tucker, C. J. (2005). Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. Journal of Arid Environments, 63, 596–614.

    Article  Google Scholar 

  10. McNeely, J. A. (2001). Invasive species: A costly catastrophe for native biodiversity. Land Use and Water Resources Research, 1, 1–10.

    Google Scholar 

  11. Groom, M. J. (2006). Threats to biodiversity. In M. J. Groom, G. K. Meffe, & C. R. Caroll (Eds.), Principles of conservation biology (3rd ed., p. 63e110). Sunderland, Massachusetts: Sinauer Associates.

    Google Scholar 

  12. Reddy, C. S. (2008). Catalogue of invasive alien flora of India. Life Science Journal, 5, 84–89.

    Google Scholar 

  13. Kloster, S., Mahowald, N., Randerson, J., & Lawrence, P. (2012). The impacts of climate, land use, and demography on fires during the 21st century simulated by CLM-CN. Biogeosciences, 9, 509–525.

    Article  Google Scholar 

  14. Thonicke, K., Spessa, A., Prentice, I. C., Harrison, S. P., Dong, L., & Carmona-Moreno, C. (2010). The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: Results from a process-based model. Biogeosciences, 7, 1991–2011.

    Article  Google Scholar 

  15. Pew, K. L., & Larsen, C. P. S. (2001). GIS analysis of spatial and temporal patterns of human-caused wildfires in the temperate rain forest of Vancouver Island, Canada. Forest Ecology and Management, 140, 1–18.

    Article  Google Scholar 

  16. Fearnside, P. M. (2005). Deforestation in Brazilian Amazonia: History, rates, and consequences. Conservation Biology, 19(3), 680–688.

    Article  Google Scholar 

  17. 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 

  18. Csiszar, I., Denis, L., Giglio, L., Justice, C. O., & Hewson, J. (2005). Global fire activity from two years of MODIS data. International Journal of Wildland Fire, 14, 117–130.

    Article  Google Scholar 

  19. 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.

    Article  Google Scholar 

  20. Chuvieco, E., & Congalton, R. G. (1989). Application of remote-sensing and geographic information-systems to forest fire hazard mapping. Remote Sensing of Environment, 29, 147–159.

    Article  Google Scholar 

  21. 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 

  22. Sykes, M. T. (2009). Climate Change Impacts: Vegetation. In Encyclopedia of Life Sciences (ELS). John Wiley & Sons, Ltd: Chichester. https://doi.org/10.1002/9780470015902.a0021227

  23. 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.

    Article  Google Scholar 

  24. 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), 474–487.

    Google Scholar 

  25. Ravindranath, N., & Sukumar, R. (1998). Climate change and tropical forests in India. Climatic Change, 39, 563. https://doi.org/10.1023/A:1005394505216.

    Article  Google Scholar 

  26. 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.

    Article  Google Scholar 

  27. 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, 2003. http://forobs.jrc.ec.europa.eu/products/glc2000/products.php. Accessed on March 20, 2018

  28. 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.

  29. Geospatial world. (2012). Monitoring agricultural vulnerability using NDVI time series. https://www.geospatialworld.net/article/monitoring-agricultural-vulnerability-using-ndvi-time-series/. Accessed on March 18, 2018.

  30. Adhikari, D., Tiwary, R., & Barik, S. K. (2015). Modelling Hotspots for Invasive Alien Plants in India. PLoS ONE, 10(7), e0134665. https://doi.org/10.1371/journal.pone.0134665.

    Article  Google Scholar 

  31. 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 on March 5, 2018.

  32. Reddy, C. S., Khuroo, A. A., Harikrishna, P., Saranya, K. R. L., Jha, C. S., & Dadhwal, K. (2014). Threat evaluation for biodiversity conservation of forest ecosystems using geospatial techniques: A case study of Odisha, India. Ecological Engineering, 69, 287–303.

    Article  Google Scholar 

  33. 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 

  34. Somanathan, E. (2007). Biodiversity. In K. Basu (Ed.), The oxford companion to economics in India (pp. 32–35). New Delhi: Oxford University Press.

    Google Scholar 

  35. FAO. (2001). Global forest fire assessment 1990–2000. Rome: FAO (Forest Resources Assessment).

    Google Scholar 

  36. Xie, Y., Wang, X., & Silander, J. A. (2015). Deciduous forest responses to temperature, precipitation, and drought imply complex climate change impacts. Proceedings of the National Academy of Sciences of the USA, 112, 13585–13590. https://doi.org/10.1073/pnas.1509991112.

    Article  Google Scholar 

  37. Lal, M., Cubasch, U., Voss, R., & Waszkewitz, J. (1995). Effect of transient increases in greenhouse gases and sulphate aerosols on monsoon climate. Current Science, 69(9), 752–763.

    Google Scholar 

  38. Ramanathan, V., Chung, C., Kim, D., Bettge, T., Buja, L., Kiehl, J. T., et al. (2005). Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle. Proceedings of National Academy of Sciences of the USA, 102, 5326–5333. https://doi.org/10.1073/pnas.0500656102.

    Article  Google Scholar 

  39. Lal, M. (2003). Global climate change: India’s monsoon and its variability. Journal of Environmental Studies & Policy, 6, 1–34.

    Google Scholar 

  40. Dash, S. K., Jenamani, R. K., Kalsi, S. R., & Panda, S. K. (2007). Some evidence of climate change in twentieth-century India. Climatic Change, 85, 299–321.

    Article  Google Scholar 

  41. Trenberth, K. E., & Hoar, T. J. (1997). El Niño and climate change. Geophysical Reseach Letters, 24, 3057–3060.

    Article  Google Scholar 

  42. Aldhous, P. (2004). Borneo is burning. Nature, 432, 144–146.

    Article  Google Scholar 

  43. Zhai, P., & Pan, X. (2003). Trends in temperature extremes during 1951–1999 in China. Geophysical Research Letters, 30, 1913. https://doi.org/10.1029/2003gl018004.

    Article  Google Scholar 

  44. NAP. (2014). National Agroforestry Policy http://www.cafri.res.in/NAF_Policy.pdf. Accessed on January 12, 2018.

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Acknowledgements

The authors are grateful to the European Commission’s science and knowledge service, Forest Survey of India, National Center for Atmospheric Research and DIVA GIS for providing free download of various dataset used in the analysis. We are also greatful to the Forest survey of India (FSI) for providing free download of various dataset used in the analysis. And, also to the Department of Environment & Forests, Govt of Arunachal Pradesh Itanagar for this opportunity of carrying out the research work.

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Authors and Affiliations

  1. Vindhyan Ecology and Natural History Foundation, Mirzapur, Uttar Pradesh, India

    Firoz Ahmad & Laxmi Goparaju

  2. Department of Forests and Wildlife, UT Administration Chandigarh, Chandigarh, India

    Abdul Qayum

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Author’s contribution

FA proposed the idea and analyzed the satellite and ancillary data in GIS domain, LG supervised the analysis, and added dimensions of metrological factors and drafted the manuscript. AQ made critical evaluation regarding GIS analysis and provided continuous feedbacks. All authors read and approved the final manuscript.

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Correspondence to Abdul Qayum.

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Ahmad, F., Goparaju, L. & Qayum, A. Geo-spatial perspective of vegetation health evaluation and climate change scenario in India. Spat. Inf. Res. 27, 497–504 (2019). https://doi.org/10.1007/s41324-018-00231-3

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