An evaluation of vegetation health and the socioeconomic dimension of the vulnerability of Jharkhand state of India in climate change scenarios and their likely impact: a geospatial approach

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Abstract

Geospatial evaluation of various datasets is extremely important because it gives a better comprehension of the past, present and future and can therefore be significantly utilized in effective decision making strategies. This study examined the relationships, using geospatial tools, between various diversified datasets such as land use/land cover (LULC), long term Normalized Difference Vegetation Index (NDVI) based changes, long term forest fire points, poverty percentage, tribal percentage, forest fire hotspots, climate change vulnerability, agricultural vulnerability and future (2030) climate change anomalies (RCP-6) of Jharkhand state, India, for a better understanding and knowledge of its vegetation health, LULC, poverty, tribal population and future climate change impact. The long term NDVI (1982-2006) evaluation revealed negative change trends in seven northwest districts of Jharkhand state, these were: Hazaribag, Ramgarh, Palamu, Lohardaga, Chatra, Garhwa and Latehar. The forests as well as the agriculture of these districts have lost their greenness during this period. The forest fire frequency events were found to be more pronounced in the land use/land cover of “tropical lowland forests, broadleaved, evergreen, <1000 m” category, and were roughly twice the intensity of the “tropical mixed deciduous and dry deciduous forests” category. In the nine districts of Jharkhand it was found that 40 % of the population was living below the poverty line which is around twice the national average. The highest poverty districts, in percentage, were: Garwah (53.93), Palamu (49.24), Latehar (47.99) and Chatra (46.2). The southwest and south of Jharkhand state shows a tribal population density of more than 40%. The climate change vulnerability was found to be highest in the district of Saraikela followed by Pashchim Singhbhum, whereas agricultural vulnerability was found to be highest in the district of Pashchim Singhbhum followed by Saraikela, Garhwa, Simdega, Latehar, Palamu and Lohardaga. The temperature anomalies prediction for the year 2030 shows an increasing trend in temperature with values of 0.8°C to 1°C in the state of Jharkhand. The highest increases were observed in the districts of Pashchim Singhbhum, Simdega and Saraikela. Based on these evaluations we can conclude that a few of the districts of Jharkhand, such as Pashchim Singhbhum, Garhwa, Palamu and Latehar need to be prioritized for development on an urgent basis. The outcomes of this study would certainly guide the policymakers to prepare more robust plans when keeping in mind the future climate change impacts for the prioritization of various districts of Jharkhand which suffer from extreme poverty, diminished livelihood and insignificant agricultural productivity for the betterment of the people of Jharkhand based on their adaptive capacity.

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.

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.

Ahmad F., Goparaju L. 2017a. Geospatial Assessment of Forest Fires in Jharkhand (India). Indian Journal of Science and Technology, 10, 21: 1–7.

Ahmad F., Goparaju L. 2017b. Assessment of Threats to Forest Ecosystems Using Geospatial Technology in Jharkhand State of India. Current World Environment, 12, 2: 11.

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, 26, 3: 305–315.

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.

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.

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.

Bhandari L., Chakraborty M. 2014. Spatial poverty in Jharkhand https://www.livemint.com/Specials/bqVly6xj4usB3DiTibS3DK/Spatial-poverty-in-Jharkhand.html (Accessed on 18th March 2018).

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.

Birthal P.S., Negi D.S., Kumar S., Agarwal S., Suresh A., Khan M.T. 2014. How sensitive is Indian Agriculture to Climate Change? Indian Journal of Agricultural Economics, 69, 4: 474–487.

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.

Campbell B.M., Vermeulen S.J., Aggarwal P.K., Corner-Dolloff C., Girvetz E., Loboguerrero A.M., Ramirez-Villegas J., Rosenstock T., Sebastian L., Thornton P., Wollenberg E. 2016. Reducing risks to food security from climate change. Global Food Security, 11: 34–43.

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.

Dwyer E., Pinnock S., Gregoire J.M., Pereira J.M.C. 2000. Global spatial and temporal distribution of vegetation fire as determined from satellite observations. International Journal of Remote Sensing, 21:1289−1302.

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.

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 on 18 March 2018).

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.

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, Article ID 692068.

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. Springer, Dordrecht: 21–48.

Jana B.K., Majumder M. (eds) 2010. Impact of Climate Change on Natural Resource Management. Publisher: Springer Netherlands, Springer Science & Business Media, Raj.

JAPCC 2014. Jharkhand Action Plan on Climate Change http://www.moef.nic.in/sites/default/files/sapcc/Jharkhand.pdf (Accessed on 2nd April 2017)

Kirschbaum M.U.F., Cannell M.G.R., Cruz R.V.O., Galinski W., Cramer W.P. 1996. Climate change impacts on forests. [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 University Press, Cambridge: 95–129.

McNeeley S.M., Even T.L., Gioia J.B., Knapp C.N., Beeton T.A. 2017. Expanding vulnerability assessment for public lands: the social complement to ecological approaches. Climate Risk Management. 16: 106–119.

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.

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/ wasused to derive data products. NCAR/UCAR. URL: http://www.gisclimatechange.org. (Accessed on 5th March 2018).

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, Article ID 725427.

Rao C.S. 2018. Assessment of Risk and Vulnerability of Agricultural Systems. https://unfccc.int/files/land_use_and_climate_change/agriculture/application/pdf/india_(risk_&_vulnarability_agril_systems)_new.pdf (Accessed on 10th April 2018).

Rawat J.S. Kumar M. 2015. Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Sciences, 18: 77–84.

Roy P.S., Agrawal S., Joshi P., Shukla Y. 2003. The Land Cover Map for Southern Asia for the Year 2000. GLC2000 database, European Commission Joint Research Centre. http://forobs.jrc.ec.europa.eu/products/glc2000/products.php.

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.

Sykes M.T. 2009. Climate Change Impacts: Vegetation. Encyclopedia Life Sciences.

Tao S., Xu Y., Liu K., Pan J., Gou S. 2011. Research progress in agriculture vulnerability to climate change. Advances in Climate Change Research, 2, 4: 203–210.

Tirkey A.S., Gosh M., Pandey A.C., Shekhar S. 2017. Assessment of climate extremes and its long term spatial variability over the Jharkhand state of India. The Egyptian Journal of Remote Sensing and Space Science. 21, 1: 49–63.

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