Effect of Temperature and Precipitation on the Vegetation Dynamics of High and Moderate Altitude Natural Forests in India

  • Odai Ibrahim Al Balasmeh
  • Tapas KarmakerEmail author
Research Article


In the present study, the high-altitude vegetation dynamics of natural forest cover were analyzed to find the effect of local hydrology for about 10 years. Vegetation dynamics in four different topographical and climatic conditions in India were studied using normalized difference vegetation index (NDVI) data from vegetation sensor of SPOT satellite, and daily temperature and precipitation data from Asian Precipitation–Highly Resolved Observational Data Integration Towards Evaluation project. The main objective was to understand how and to what extent the natural vegetation reciprocates in various climatic conditions. First, the vegetation data was denoised by using empirical mode decomposition technique. The relation between the vegetation growth and hydrological parameters was studied to find the anomalies. Then, the wavelet analysis of the hydrological data was carried out to find the frequency and extent of the anomalies. Finally, nonparametric Mann–Kendall trend analysis and Sen’s slope analysis were applied to find the trend of the vegetation, temperature, and precipitation dynamics. Results indicate that the growth of the vegetation starts when the average temperature is 10 °C or higher. Beyond that the increase in temperature may have a negligible effect on growth of vegetation. The vegetation also shows positive change in monthly NDVI with the increase in precipitation depending forest type and local climate. However, with excessive rainfall, a declining trend in vegetation growth was observed. The NDVI data show positive trend in all four sites. In northern region, the temperature showed positive trend, while precipitation had negative trend. In eastern and western regions, the temperature had negative trend and precipitation had positive trend.


Natural vegetation dynamics Forest cover Wavelet analysis Trend analysis 



Authors would like to express their sincere thanks to the reviewer for the suggestions. Authors also like to acknowledge the contribution by Ms. Parul Tandon for downloading and help in analyzing the data.


  1. Bellone, T., Boccardo, P., & Perez, F. (2009a). Investigation of vegetation dynamics using long-term normalized difference vegetation index time-series. American Journal of Environmental Sciences, 5(4), 461. Scholar
  2. Bellone, G., Novarino, A., Vizio, B., & Ciuffreda, L. (2009b). Impact of surgery and chemotherapy on cellular immunity in pancreatic carcinoma patients in view of an integration of standard cancer treatment with immunotherapy. International Journal of Oncology, 34(6), 1701–1715.CrossRefGoogle Scholar
  3. Braswell, B. H., Schimel, D. S., Linder, E., & Moore, B., III. (1997). The response of global terrestrial ecosystems to inter-annual temperature variability. Science, 278, 870–873.CrossRefGoogle Scholar
  4. Chang, C. T., Lin, T. C., Wang, S. F., & Vadeboncoeur, M. A. (2011). Assessing growing season beginning and end dates and their relation to climate in taiwan using satellite data. International Journal of Remote Sensing, 32(18), 5035–5058.CrossRefGoogle Scholar
  5. Chang, C. T., Wang, S. F., Vadeboncoeur, M. A., & Lin, T. C. (2014). Relating vegetation dynamics to temperature and precipitation at monthly and annual timescales in Taiwan using MODIS vegetation indices. International Journal of Remote Sensing, 35(2), 598–620.CrossRefGoogle Scholar
  6. Chiu, C. A., Lin, P. H., & Lu, K. C. (2009). GIS-based tests for quality control of meteorological data and spatial interpolation of climate data. Mountain Research and Development, 29(4), 339–349.CrossRefGoogle Scholar
  7. Chmielewski, F. M., & Rotzer, T. (2001). Response of tree phenology to climate change across Europe. Agricultural and Forest Meteorology, 108(2), 101–112.CrossRefGoogle Scholar
  8. Cramer, W., Bondeau, A., Woodward, I., & Young-Molling, C. (2001). Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biology, 7(4), 357–373.CrossRefGoogle Scholar
  9. Davenport, M. L., & Nicholson, S. (1993). On the relation between rainfall and the Normalized Difference Vegetation Index for diverse vegetation types in East Africa. International Journal of Remote Sensing, 14(12), 2369–2389.CrossRefGoogle Scholar
  10. Deng, Y., Chen, X., Chuvieco, E., Warner, T., & Wilson, J. P. (2007). Multi-scale linkages between topographic attributes and vegetation indices in a mountainous landscape. Remote Sensing of Environment, 111, 122–134.CrossRefGoogle Scholar
  11. Fuller, D. O., & Prince, S. D. (1996). Rainfall and foliar dynamics in tropical Southern Africa: potential impacts of global climatic change on savanna vegetation. Climatic Change, 33, 69–96.CrossRefGoogle Scholar
  12. Guo, N., Zhu, Y. J., Wang, J. M., & Deng, C. P. (2008). The relationship betweenNDVI and climate elements for 22 years in different vegetationareas of Northeast China. Chinese Journal of Plant Ecology, 32(2), 319–327. (in Chinese).Google Scholar
  13. Holben, B. (1986a). Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7(11), 1417–1434.CrossRefGoogle Scholar
  14. Holben, B. N. (1986b). Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7, 1417–1434.CrossRefGoogle Scholar
  15. Huang, N. E., Shen, Z., Long, S. R., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Series A, 454, 903–995.CrossRefGoogle Scholar
  16. Karmaker, T., Medhi, H., & Dutta, S. (2017). Study of channel instability in the braided Brahmaputra river using satellite imagery. Current Science, 112(7), 1533–1543.CrossRefGoogle Scholar
  17. Li, X., Zhu, J., Xiao, Y., & Wang, R. (2010). A model-based observation-thinning scheme for the assimilation of high-resolution SST in the shelf and coastal seas around China. Journal of Atmospheric and Oceanic Technology, 27, 1044–1058.CrossRefGoogle Scholar
  18. Méndez-Barroso, L. A., Vivoni, E. R., Watts, C. J., & Rodrguezc, J. C. (2009). Seasonal and interannual relations between precipitation, surface soil moisture and vegetation dynamics in the North American monsoon region. Journal of Hydrology, 377, 59–70.CrossRefGoogle Scholar
  19. Meshram, S. G., Singh, V. P., & Meshram, C. (2017). Long-term trend and variability of precipitation in Chhattisgarh State, India. Theoretical and Applied Climatology, 129(3–4), 729–744.CrossRefGoogle Scholar
  20. National Council of Education Research and Training (NCERT). (2006). India physical environment. NCERT Campus, Sri Aurobindo Marg, New Delhi. ISBN 81-7450-538-5.Google Scholar
  21. Prasad, V. K., Anuradha, E., & Badarinath, K. V. S. (2005). Climatic controls of vegetation vigor in four contrasting forest typesof India—evaluation from National Oceanic and AtmosphericAdministration’s Advanced Very High Resolution Radiometerdatasets (1990–2000). International Journal of Biometeorology, 50, 6–16.CrossRefGoogle Scholar
  22. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324), 1379–1389.CrossRefGoogle Scholar
  23. Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78.CrossRefGoogle Scholar
  24. Verma, R., & Dutta, S. (2013). Vegetation dynamics from denoised NDVI using empirical mode decomposition. Journal of the Indian Society of Remote Sensing, 41, 555–566.CrossRefGoogle Scholar
  25. Wolfe, J. M., Horowitz, T. S., & Kenner, N. M. (2005). Rare items often missed in visual searches. Nature, 435, 439–440.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringThapar Institute of Engineering and TechnologyPatialaIndia

Personalised recommendations