Skip to main content
SpringerLink
Log in

Analysing the spatio-temporal patterns of vegetation dynamics and their responses to climatic parameters in Meghalaya from 2001 to 2020

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Quantification of the spatio-temporal trends in vegetation dynamics and its drivers is crucial to ensure sustainable management of ecosystems. The north-eastern state of Meghalaya possessing an idiosyncratic climatic regime has been undergoing tremendous pressure in the past decades considering the recent climate change scenario. A robust trend analysis has been performed using the MODIS NDVI (MOD13Q1) data (2001–2020) along with multi-source gridded climate data (precipitation and temperature) to detect changes in the vegetation dynamics and corresponding climatic variables by employing the Theil-Sen Median trend test and Mann–Kendall test (τ). The spatial variability of trends was gauged with respect to 7 major forest types, administrative boundaries and different elevational gradients found in the area. Results revealed a large positive inter-annual trend (85.48%) with a minimal negative trend (14.52%) in the annual mean NDVI. Mean Annual Precipitation presents a negative trend in 66.97% of the area mainly concentrated in the eastern portion of the state while the western portion displays a positive trend in about 33.03% of the area. Temperature exhibits a 98% positive trend in Meghalaya. Pettitt Change Point Detection revealed three major breakpoints viz., 2010, 2012 and 2014 in the NDVI values from 2001 to 2020 over the forested region of Meghalaya. A consistent future vegetation trend (87.78%) in Meghalaya was identified through Hurst Exponent. A positive correlation between vegetation and temperature was observed in about 82.81% of the area. The western portion of the state was seen to reflect a clear correlation between NDVI and rainfall as compared to the eastern portion where NDVI is correlated more with temperature than rainfall. A gradual deviation of rainfall towards the west was identified which might be feedback of the increasing significant greening observed in the state in the recent decades. This study, therefore, serves as a decadal archive of forest dynamics and also provides an insight into the long-term impact of climate change on vegetation which would further help in investigating and projecting the future ecosystem dynamics in Meghalaya.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Alexandersson, H. (1986). A homogeneity test applied to precipitation data. Journal of Climatology, 6(6), 661–675. https://doi.org/10.1002/JOC.3370060607

    Article  Google Scholar 

  • Atkinson, P. M., Jeganathan, C., Dash, J., & Atzberger, C. (2012). Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sensing of Environment, 123, 400–417. https://doi.org/10.1016/J.RSE.2012.04.001

    Article  Google Scholar 

  • Bartels, R. (1982). The rank version of von Neumann’s ratio test for randomness. Journal of the American Statistical Association, 77(377), 40–46. https://doi.org/10.1080/01621459.1982.10477764

    Article  Google Scholar 

  • Beck, P. S. A., Atzberger, C., Høgda, K. A., Johansen, B., & Skidmore, A. K. (2006). Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of Environment, 100(3), 321–334. https://doi.org/10.1016/J.RSE.2005.10.021

    Article  Google Scholar 

  • Borchers, H. W. (2021). Practical numerical math functions (pracma) (2.3.6). Comprehensive R Archive Network.

  • Buermann, W., Anderson, B., Tucker, C. J., Dickinson, R. E., Lucht, W., Potter, C. S., & Myneni, R. B. (2003). Interannual covariability in Northern Hemisphere air temperatures and greenness associated with El Niño-Southern Oscillation and the Arctic Oscillation. Journal of Geophysical Research: Atmospheres, 108(13). https://doi.org/10.1029/2002JD002630

  • Buermann, W., Bikash, P. R., Jung, M., Burn, D. H., & Reichstein, M. (2013). Earlier springs decrease peak summer productivity in North American boreal forests. Environmental Research Letters, 8(2). https://doi.org/10.1088/1748-9326/8/2/024027

  • Buermann, W., Parida, B., Jung, M., MacDonald, G. M., Tucker, C. J., & Reichstein, M. (2014). Recent shift in Eurasian boreal forest greening response may be associated with warmer and drier summers. Geophysical Research Letters, 41(6), 1995–2002. https://doi.org/10.1002/2014GL059450

    Article  Google Scholar 

  • Buishand, T. A. (1982). Some methods for testing the homogeneity of rainfall records. Journal of Hydrology, 58(1–2), 11–27. https://doi.org/10.1016/0022-1694(82)90066-X

    Article  Google Scholar 

  • Cao, M., & Woodward, F. I. (1998). Dynamic responses of terrestrial ecosystem carbon cycling to global climate change. Nature, 393(6682), 249–252. https://doi.org/10.1038/30460

    Article  CAS  Google Scholar 

  • Cao, R., Jiang, W., Yuan, L., Wang, W., Lv, Z., & Chen, Z. (2014). Inter-annual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010. Journal of Geographical Sciences, 24(6), 963–979. https://doi.org/10.1007/s11442-014-1131-1

  • Chakraborty, K., Mondal, P. P., Chabukdhara, M., & Sudhakar, S. (2014). Forest fire scenario and challenges of mitigation during fire season in north east India. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, XL–8(1), 27–33. https://doi.org/10.5194/ISPRSARCHIVES-XL-8-27-2014

  • Chandrasekar, K., Sai, M. V. R. S., Jeyaseelan, A. T., Dwivedi, R. S., & Roy, P. S. (2006). Vegetation response to rainfall as monitored by NOAA–AVHRR. Current Science, 91(12), 1626–1633. http://www.jstor.org/stable/24094015

  • Changkakati, T. (2019). Temporal response of NDVI to climatic attributes in North East India. International Journal of Engineering Research and Technology, 8(8). www.ijert.org

  • Chaturvedi, R. K., Gopalakrishnan, R., Jayaraman, M., Bala, G., Joshi, N. V., Sukumar, R., & Ravindranath, N. H. (2011). Impact of climate change on Indian forests: A dynamic vegetation modeling approach. Mitigation and Adaptation Strategies for Global Change, 16(2), 119–142. https://doi.org/10.1007/S11027-010-9257-7

  • Chen, A., He, B., Wang, H., Huang, L., Zhu, Y., & Lv, A. (2015). Notable shifting in the responses of vegetation activity to climate change in China. Physics and Chemistry of the Earth, Parts A/B/C, 87–88, 60–66. https://doi.org/10.1016/J.PCE.2015.08.008

    Article  Google Scholar 

  • Chen, J., & Gupta, A. K. (2007). On change point detection and estimation 30(3), 665–697. https://doi.org/10.1081/SAC-100105085

  • Choudhury, B. U., Das, A., Ngachan, S. V., Slong, A., Bordoloi, L. J., & Chowdhury, P. (2012). Trend analysis of long term weather variables in mid altitude Meghalaya, North-East India. Journal of Agricultural Physics, 12(1), 12–22.

  • Chu, H., Venevsky, S., Wu, C., & Wang, M. (2019). NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015. Science of the Total Environment, 650(September), 2051–2062. https://doi.org/10.1016/j.scitotenv.2018.09.115

    Article  CAS  Google Scholar 

  • Croitoru, A. E., Holobaca, I. H., Lazar, C., Moldovan, F., & Imbroane, A. (2012). Air temperature trend and the impact on winter wheat phenology in Romania. Climatic Change, 111(2), 393–410. https://doi.org/10.1007/S10584-011-0133-6/TABLES/5

    Article  Google Scholar 

  • Dash, J., Jeganathan, C., & Atkinson, P. M. (2010). The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India. Remote Sensing of Environment, 114(7), 1388–1402. https://doi.org/10.1016/j.rse.2010.01.021

    Article  Google Scholar 

  • de Jong, R., de Bruin, S., de Wit, A., Schaepman, M. E., & Dent, D. L. (2011). Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment, 115(2), 692–702. https://doi.org/10.1016/J.RSE.2010.10.011

    Article  Google Scholar 

  • de Jong, R., Verbesselt, J., Zeileis, A., & Schaepman, M. E. (2013). Shifts in global vegetation activity trends. Remote Sensing, 5(3), 1117–1133. https://doi.org/10.3390/rs5031117

    Article  Google Scholar 

  • Defries, R. S., & Townshend, J. R. (1994). NDVI-derived land cover classifications at a global scale. International Journal of Remote Sensing, 15(17), 3567–3586. https://doi.org/10.1080/01431169408954345

    Article  Google Scholar 

  • Deka, J., Kalita, S., & Khan, M. L. (2019). Vegetation phenological characterization of alluvial plain Shorea robusta-dominated tropical moist deciduous forest of northeast India using MODIS NDVI time series data. Journal of the Indian Society of Remote Sensing, 47(8), 1287–1293. https://doi.org/10.1007/s12524-019-00991-x

    Article  Google Scholar 

  • Deka, S. (2021). Statistical analysis of long-term rainfall trends in Cherrapunji, Meghalaya, India. Journal of Applied and Natural Science, 13(1), 170–177. https://doi.org/10.31018/jans.v13i1.2442

  • Didan, K., Barreto Munoz, A., Solano, R., & Huete, A. (2015). MODIS Vegetation Index User’s Guide (MOD13 Series). http://vip.arizona.edu

  • Fang, X., Zhu, Q., Ren, L., Xu, H., Chen, H., & Peng, C. (2014). Large-scale detection of vegetation dynamics using MODIS images and BFAST: A case study in Quebec, Canada. In 2014 IEEE Geoscience and Remote Sensing Symposium (pp. 768–771). IEEE. https://doi.org/10.1109/IGARSS.2014.6946537

  • FAO. (2013). Climate change guidelines for forest managers.

  • Fensholt, R., Langanke, T., Rasmussen, K., Reenberg, A., Prince, S. D., Tucker, C., Scholes, R. J., Le, Q. B., Bondeau, A., Eastman, R., Epstein, H., Gaughan, A. E., Hellden, U., Mbow, C., Olsson, L., Paruelo, J., Schweitzer, C., Seaquist, J., & Wessels, K. (2012). Greenness in semi-arid areas across the globe 1981–2007 — an Earth Observing Satellite based analysis of trends and drivers. Remote Sensing of Environment, 121, 144–158. https://doi.org/10.1016/J.RSE.2012.01.017

    Article  Google Scholar 

  • Fensholt, R., Rasmussen, K., Nielsen, T. T., & Mbow, C. (2009). Evaluation of earth observation based long term vegetation trends - Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sensing of Environment, 113(9), 1886–1898. https://doi.org/10.1016/j.rse.2009.04.004

    Article  Google Scholar 

  • Fensholt, R., Sandholt, I., & Rasmussen, M. S. (2004). Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sensing of Environment, 91(3–4), 490–507. https://doi.org/10.1016/j.rse.2004.04.009

    Article  Google Scholar 

  • Forest Survey of India. (2021). ISFR 2021. India State of Forest Report. https://fsi.nic.in/isfr-2021/chapter-13.pdf

  • Forkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M. D., Neigh, C. S. R., & Reichstein, M. (2013). Trend change detection in NDVI time series: Effects of inter-annual variability and methodology. Remote Sensing, 5(5), 2113–2144. https://doi.org/10.3390/rs5052113

    Article  Google Scholar 

  • 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(1), 69–96. https://doi.org/10.1007/BF00140514

    Article  CAS  Google Scholar 

  • Fuller, D. O., & Wang, Y. (2014). Recent trends in satellite vegetation index observations indicate decreasing vegetation biomass in the south eastern saline everglades wetlands. Wetlands, 34(1), 67–77. https://doi.org/10.1007/s13157-013-0483-0

    Article  Google Scholar 

  • Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2(1), 1–21. https://doi.org/10.1038/sdata.2015.66

  • Gallagher, C., Lund, R., & Robbins, M. (2013). Changepoint detection in climate time series with long-term trends. Journal of Climate, 26(14), 4994–5006. https://doi.org/10.1175/JCLI-D-12-00704.1

    Article  Google Scholar 

  • Gopalakrishnan, R., Jayaraman, M., Bala, G., & Ravindranath, N. H. (2011). Climate change and Indian forests. Current Science, 101(3), 348–355. http://www.jstor.org/stable/24078514

  • Gui-gang, W., Kefa, Z., Li, S., Yan-fang, Q., & Xue-mei, L. (2010). Study on the vegetation dynamic change and R/S analysis in the past ten years in Xinjiang. Undefined. https://doi.org/10.11873/J.ISSN.1004-0323.2010.1.84

  • Hou, X., Wu, T., Yu, L., & Qian, S. (2012). Characteristics of multi-temporal scale variation of vegetation coverage in the Circum Bohai Bay Region, 1999–2009. Acta Ecologica Sinica, 32(6), 297–304. https://doi.org/10.1016/J.CHNAES.2012.08.001

    Article  Google Scholar 

  • 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 

  • Hurst, H. E. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770–799. https://doi.org/10.1061/TACEAT.0006518

    Article  Google Scholar 

  • Hüttich, C., Herold, M., Schmullius, C., Egorov, V., & Bartalev, S. A. (2007). Indicators of Northern Eurasia’s land‐cover change trends from SPOT‐VEGETATION time‐series analysis 1998–2005. 28(18), 4199–4206. https://doi.org/10.1080/01431160701442054

  • IPCC. (2013). Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

  • Jaiswal, R. K., Lohani, A. K., & Tiwari, H. L. (2015). Statistical analysis for change detection and trend assessment in climatological parameters. Environmental Processes, 2(4), 729–749. https://doi.org/10.1007/S40710-015-0105-3

    Article  Google Scholar 

  • Jassby, A. D., Cloern, J. E., & Stachelek, J. (2017). Exploring water quality monitoring data (0.4.9). Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=wql

  • Jayaraj, R. S. C., Das, D. J., Kumar, A., Meena, D., Saikia, A. J., & Gogoi, G. (2021). Identification of drivers of deforestation in Meghalaya (2018 - 19). https://www.researchgate.net/publication/354401424

  • Jeganathan, C., Dash, J., & Atkinson, P. M. (2010a). Characterising the spatial pattern of phenology for the tropical vegetation of India using multi-temporal MERIS chlorophyll data. Landscape Ecology, 25(7), 1125–1141. https://doi.org/10.1007/s10980-010-9490-1

  • Jeganathan, C., Dash, J., & Atkinson, P. M. (2010b). Mapping the phenology of natural vegetation in India using a remote sensing-derived chlorophyll index. International Journal of Remote Sensing, 31(22), 5777–5796. https://doi.org/10.1080/01431161.2010.512303

  • Jeganathan, C., Dash, J., & Atkinson, P. M. (2014). Remotely sensed trends in the phenology of northern high latitude terrestrial vegetation, controlling for land cover change and vegetation type. Remote Sensing of Environment, 143, 154–170. https://doi.org/10.1016/j.rse.2013.11.020

    Article  Google Scholar 

  • Jeyaseelan, A. T., Roy, P. S., & Young, S. S. (2007). Persistent changes in NDVI between 1982 and 2003 over India using AVHRR GIMMS (Global Inventory Modeling and Mapping Studies) data. International Journal of Remote Sensing, 28(21), 4927–4946. https://doi.org/10.1080/01431160701253279

    Article  Google Scholar 

  • Jiang, W., Yuan, L., Wang, W., Cao, R., Zhang, Y., & Shen, W. (2015). Spatio-temporal analysis of vegetation variation in the Yellow River Basin. Ecological Indicators, 51, 117–126. https://doi.org/10.1016/j.ecolind.2014.07.031

    Article  Google Scholar 

  • Kong, D., Zhang, Q., Singh, V. P., & Shi, P. (2017). Seasonal vegetation response to climate change in the Northern Hemisphere (1982–2013). Global and Planetary Change, 148, 1–8. https://doi.org/10.1016/j.gloplacha.2016.10.020

    Article  Google Scholar 

  • Kuttippurath, J., Murasingh, S., Stott, P. A., Balan Sarojini, B., Jha, M. K., Kumar, P., Nair, P. J., Varikoden, H., Raj, S., Francis, P. A., & Pandey, P. C. (2021). Observed rainfall changes in the past century (1901–2019) over the wettest place on Earth. Environmental Research Letters, 16(2), 024018. https://doi.org/10.1088/1748-9326/ABCF78

    Article  Google Scholar 

  • Lairenjam, C., Hodam, S., Bandyopadhyay, A., & Bhadra, A. (2017). Historical and temporal trends of climatic parameters in North East India. Global Nest Journal, 19(4), 547–561. https://doi.org/10.30955/gnj.002384

  • le Quéré, C., Raupach, M. R., Canadell, J. G., Marland, G., Bopp, L., Ciais, P., Conway, T. J., Doney, S. C., Feely, R. A., Foster, P., Friedlingstein, P., Gurney, K., Houghton, R. A., House, J. I., Huntingford, C., Levy, P. E., Lomas, M. R., Majkut, J., Metzl, N., & Woodward, F. I. (2009). Trends in the sources and sinks of carbon dioxide. Nature Geoscience, 2(12), 831–836. https://doi.org/10.1038/ngeo689

    Article  CAS  Google Scholar 

  • Liu, Y., & Lei, H. (2015). Responses of natural vegetation dynamics to climate drivers in China from 1982 to 2011. Remote Sensing, 7(8), 10243–10268. https://doi.org/10.3390/RS70810243

  • Liu, Y., Wang, X., Guo, M., Tani, H., Matsuoka, N., & Matsumura, S. (2011). Spatial and temporal relationships among NDVI, climate factors, and land cover changes in Northeast Asia from 1982 to 2009. GIScience and Remote Sensing, 48(3), 371–393. https://doi.org/10.2747/1548-1603.48.3.371

    Article  Google Scholar 

  • Lobell, D. B., & Field, C. B. (2008). Estimation of the carbon dioxide (CO2) fertilization effect using growth rate anomalies of CO2 and crop yields since 1961. Global Change Biology, 14(1), 39–45. https://doi.org/10.1111/J.1365-2486.2007.01476.X

    Article  Google Scholar 

  • Lucht, W., Prentice, I. C., Myneni, R. B., Sitch, S., Friedlingstein, P., Cramer, W., Bousquet, P., Buermann, W., & Smith, B. (2002). Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science, 296(5573), 1687–1689. https://doi.org/10.1126/SCIENCE.1071828

    Article  CAS  Google Scholar 

  • Ma, B., Wang, S., Mupenzi, C., Li, H., Ma, J., & Li, Z. (2021). Quantitative contributions of climate change and human activities to vegetation changes in the upper white Nile river. Remote Sensing, 13(18). https://doi.org/10.3390/rs13183648

  • Mandelbrot, B. B., & Wallis, J. R. (1969). Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence. Water Resources Research, 5(5), 967–988. https://doi.org/10.1029/WR005I005P00967

    Article  Google Scholar 

  • Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245. https://doi.org/10.2307/1907187

    Article  Google Scholar 

  • McLeod, A. I. (2011). Kendall rank correlation and Mann-Kendall trend test (Kendall) (2.2). Comprehensive R Archive Network (CRAN). http://www.stats.uwo.ca/faculty/aim

  • Meghalaya Basin Development Authority. (2014). In Conversation with People of Meghalaya.

  • Milich, L., & Weiss, E. (2000). GAC NDVI interannual coefficient of variation (CoV) images: Ground truth sampling of the Sahel along north-south transects. International Journal of Remote Sensing, 21(2), 235–260. https://doi.org/10.1080/014311600210812

    Article  Google Scholar 

  • Mishra, N. B., & Mainali, K. P. (2017). Greening and browning of the Himalaya: Spatial patterns and the role of climatic change and human drivers. Science of the Total Environment, 587–588, 326–339. https://doi.org/10.1016/j.scitotenv.2017.02.156

    Article  CAS  Google Scholar 

  • Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G., & Nemani, R. R. (1997). Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386(6626), 698–702. https://doi.org/10.1038/386698A0

    Article  CAS  Google Scholar 

  • Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C., Tucker, C. J., Myneni, R. B., & Running, S. W. (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300(5625), 1560–1563. https://doi.org/10.1126/SCIENCE.1082750

    Article  CAS  Google Scholar 

  • Nicholson, S. E., & Farrar, T. J. (1994). The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. I. NDVI response to rainfall. Remote Sensing of Environment, 50, 107–120.

    Article  Google Scholar 

  • Pan, N., Feng, X., Fu, B., Wang, S., Ji, F., & Pan, S. (2018). Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends. Remote Sensing of Environment, 214, 59–72. https://doi.org/10.1016/j.rse.2018.05.018

    Article  Google Scholar 

  • Parida, B. R., Pandey, A. C., & Patel, N. R. (2020). Greening and browning trends of vegetation in India and their responses to climatic and non-climatic drivers. Climate, 8(8). https://doi.org/10.3390/CLI8080092

  • Parmesan, C. (2006). Ecological and evolutionary responses to recent climate change. In Annual Review of Ecology, Evolution, and Systematics (Vol. 37, pp. 637–669). https://doi.org/10.1146/annurev.ecolsys.37.091305.110100

  • Pettitt, A. N. (1979). A non-parametric approach to the change-point problem. Applied Statistics, 28(2), 126. https://doi.org/10.2307/2346729

    Article  Google Scholar 

  • 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 

  • Piao, S., Ciais, P., Friedlingstein, P., de Noblet-Ducoudré, N., Cadule, P., Viovy, N., & Wang, T. (2009). Spatiotemporal patterns of terrestrial carbon cycle during the 20th century. Global Biogeochemical Cycles, 23(4). https://doi.org/10.1029/2008GB003339

  • Piao, S., Friedlingstein, P., Ciais, P., Zhou, L., & Chen, A. (2006). Effect of climate and CO2 changes on the greening of the Northern Hemisphere over the past two decades. Geophysical Research Letters, 33(23). https://doi.org/10.1029/2006GL028205

  • Piao, S., Nan, H., Huntingford, C., Ciais, P., Friedlingstein, P., Sitch, S., Peng, S., Ahlström, A., Canadell, J. G., Cong, N., Levis, S., Levy, P. E., Liu, L., Lomas, M. R., Mao, J., Myneni, R. B., Peylin, P., Poulter, B., Shi, X., & Chen, A. (2014). Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nature Communications, 5(1), 1–7. https://doi.org/10.1038/ncomms6018

  • Piao, S., Yin, G., Tan, J., Cheng, L., Huang, M., Li, Y., Liu, R., Mao, J., Myneni, R. B., Peng, S., Poulter, B., Shi, X., Xiao, Z., Zeng, N., Zeng, Z., & Wang, Y. (2015). Detection and attribution of vegetation greening trend in China over the last 30 years. Global Change Biology, 21(4), 1601–1609. https://doi.org/10.1111/gcb.12795

    Article  Google Scholar 

  • Pohlert, T. (2020). Non-parametric trend tests and change-point detection (trend) (1.1.4). Comprehensive R Archive Network (CRAN). https://orcid.org/0000-0003-3855-3025

  • Prasad, V. K., Badarinath, K. V. S., & Eaturu, A. (2008). Effects of precipitation, temperature and topographic parameters on evergreen vegetation greenery in the Western Ghats. India. International Journal of Climatology, 28(13), 1807–1819. https://doi.org/10.1002/JOC.1662

    Article  Google Scholar 

  • Praveen, B., Talukdar, S., Shahfahad, Mahato, S., Mondal, J., Sharma, P., Islam, A. R. M. T., & Rahman, A. (2020). Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches. Scientific Reports, 10(1), 1–21. https://doi.org/10.1038/s41598-020-67228-7

  • Prokop, P. (2020). Remote sensing of severely degraded land: Detection of long-term land-use changes using high-resolution satellite images on the Meghalaya Plateau, northeast India. Remote Sensing Applications: Society and Environment, 20, 100432. https://doi.org/10.1016/j.rsase.2020.100432

  • Propastin, P. A., Kappas, M., Erasmi, S., & Muratova, N. R. (2007). Remote sensing based study on intra-annual dynamics of vegetation and climate in drylands of Kazakhstan. Basic and Applied Dryland Research, 1(2), 138-154. https://doi.org/10.1127/badr/1/2007/138

  • Rajan, H., & Jeganathan, C. (2019). Understanding spatio-temporal pattern of grassland phenology in the western Indian Himalayan state. Journal of the Indian Society of Remote Sensing, 47(7), 1137–1151. https://doi.org/10.1007/s12524-019-00976-w

    Article  Google Scholar 

  • Rashid, I., Romshoo, S. A., Chaturvedi, R. K., Ravindranath, N. H., Sukumar, R., Jayaraman, M., Lakshmi, T. V., & Sharma, J. (2015). Projected climate change impacts on vegetation distribution over Kashmir Himalayas. Climatic Change, 132(4), 601–613. https://doi.org/10.1007/S10584-015-1456-5

  • Ravindranath, N. H., Sharma, N., Sagadevan, A., Jayaraman, M., & Munsi, M. (2012). Climate change and its impacts on forests of Sikkim. Climate Change in Sikkim - Patterns, Impacts and Initiatives, 1, 125–140. https://www.researchgate.net/publication/269249290_Climate_Change_and_its_Impacts_on_Forests_of_Sikkim

  • Roy, P. S., Behera, M. D., Murthy, M. S. R., Roy, A., Singh, S., Kushwaha, S. P. S., Jha, C. S., Sudhakar, S., Joshi, P. K., Reddy, C. S., Gupta, S., Pujar, G., Dutt, C. B. S., Srivastava, V. K., Porwal, M. C., Tripathi, P., Singh, J. S., Chitale, V., Skidmore, A. K., & Ramachandran, R. M. (2015). New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities. International Journal of Applied Earth Observation and Geoinformation, 39, 142–159. https://doi.org/10.1016/j.jag.2015.03.003

    Article  Google Scholar 

  • Saikia, A. (2009). NDVI variability in North East India. Scottish Geographical Journal, 125(2), 195–213. https://doi.org/10.1080/14702540903071113

    Article  Google Scholar 

  • Sarmah, S., Jia, G., & Zhang, A. (2018). Satellite view of seasonal greenness trends and controls in South Asia. Environmental Research Letters, 13(3). https://doi.org/10.1088/1748-9326/aaa866

  • Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s Tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934

    Article  Google Scholar 

  • Singh, B., Jeganathan, C., & Rathore, V. S. (2020). Improved NDVI based proxy leaf-fall indicator to assess rainfall sensitivity of deciduousness in the central Indian forests through remote sensing. Scientific Reports, 10(1), 17638. https://doi.org/10.1038/s41598-020-74563-2

  • Singh, B., Jeganathan, C., Rathore, V. S., Behera, M. D., Singh, C. P., Roy, P. S., & Atkinson, P. M. (2021). Resilience of the central Indian forest ecosystem to rainfall variability in the context of a changing climate. Remote Sensing, 13(21). https://doi.org/10.3390/rs13214474

  • Theil, H. (1992). A rank-invariant method of linear and polynomial regression analysis. 345–381. https://doi.org/10.1007/978-94-011-2546-8_20

  • Tottrup, C., & Rasmussen, M. S. (2004). Mapping long-term changes in savannah crop productivity in Senegal through trend analysis of time series of remote sensing data. Agriculture, Ecosystems & Environment, 103(3), 545–560. https://doi.org/10.1016/J.AGEE.2003.11.009

    Article  Google Scholar 

  • Tucker, C. J., Justice, C. O., & Prince, S. D. (1986). Monitoring the grasslands of the Sahel 1984–1985. International Journal of Remote Sensing, 7(11), 1984–1985. https://doi.org/10.1080/01431168608948954

    Article  Google Scholar 

  • Tucker, C. J., Slayback, D. A., Pinzon, J. E., Los, S. O., Myneni, R. B., & Taylor, M. G. (2001). Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. International Journal of Biometeorology, 45(4), 184–190. https://doi.org/10.1007/S00484-001-0109-8

    Article  CAS  Google Scholar 

  • Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S., & Briggs, J. M. (1999). Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment, 70(1), 52–68. https://doi.org/10.1016/S0034-4257(99)00057-7

    Article  Google Scholar 

  • Upgupta, S., Sharma, J., Jayaraman, M., Kumar, V., & Ravindranath, N. H. (2015). Climate change impact and vulnerability assessment of forests in the Indian Western Himalayan region: a case study of Himachal Pradesh, India. Climate Risk Management, 10, 63–76. https://doi.org/10.1016/J.CRM.2015.08.002

    Article  Google Scholar 

  • Verbesselt, J., Hyndman, R., Zeileis, A., & Culvenor, D. (2010). Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sensing of Environment, 114(12), 2970–2980. https://doi.org/10.1016/j.rse.2010.08.003

    Article  Google Scholar 

  • Wani, A., Raj, A., & Kanwar, M. (2013). Impact of climate change on forests of Eastern Himalayas and adaptation strategies for combating it. Undefined.

  • Weron, R. L. (2002). Estimating long-range dependence: ÿnite sample properties and conÿdence intervals. In Physica A (Vol. 312). www.elsevier.com/locate/physa

  • Yang, Y., Wang, S., Bai, X., Tan, Q., Li, Q., Wu, L., Tian, S., Hu, Z., Li, C., & Deng, Y. (2019). Factors affecting long-term trends in global NDVI. Forests, 10(5), 1–17. https://doi.org/10.3390/f10050372

    Article  Google Scholar 

  • Zhang, Y., Xu, G., Li, P., Li, Z., Wang, Y., Wang, B., Jia, L., Cheng, Y., Zhang, J., Zhuang, S., & Chen, Y. (2019). Vegetation change and its relationship with climate factors and elevation on the Tibetan plateau. International Journal of Environmental Research and Public Health, 16(23). https://doi.org/10.3390/ijerph16234709

  • Zhang, Y., Zhang, C., Wang, Z., Chen, Y., Gang, C., An, R., & Li, J. (2016). Vegetation dynamics and its driving forces from climate change and human activities in the Three-River Source Region, China from 1982 to 2012. Science of the Total Environment, 563–564, 210–220. https://doi.org/10.1016/j.scitotenv.2016.03.223

    Article  CAS  Google Scholar 

  • Zhao, J., Zhang, Y., Song, F., Xu, Z., & Xiao, L. (2013). Phenological response of tropical plants to regional climate change in Xishuangbanna, south-western China. Journal of Tropical Ecology, 29(2), 161–172. http://www.jstor.org/stable/23464267

  • Zhao, M., & Running, S. W. (2010). Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science, 329(5994), 940–943. https://doi.org/10.1126/science.1192666

    Article  CAS  Google Scholar 

  • Zhou, Y., Fan, J., & Wang, X. (2020). Assessment of varying changes of vegetation and the response to climatic factors using GIMMS NDVI3g on the Tibetan Plateau. PLoS One, 15(6 June). https://doi.org/10.1371/journal.pone.0234848

  • Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L., Kato, E., Koven, C., Li, Y., Lian, X., Liu, Y., Liu, R., Mao, J., & Zeng, N. (2016). Greening of the Earth and its drivers. Nature Climate Change, 6(8), 791–795. https://doi.org/10.1038/nclimate3004

Download references

Acknowledgements

The authors thank NASA for providing the freely available MODIS data. We are also thankful to Climate Hazards Group for allowing access to CHIRPS Rainfall data. We also extend our gratitude towards Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) for providing the required Surface Air Temperature Data. The first author thanks the INSPIRE program (Inspire Fellowship Registration number – IF190591) of the Department of Science and Technology, India, for providing the required funding for conducting the research through the Sanction Order number (DST/INSPIRE Fellowship/2019/IF190591). The authors would also like to thank the support provided by ISRO-RESPOND for this work.

Author information

Authors and Affiliations

  1. Department of Remote Sensing, BIT, Mesra, 835215, Ranchi, India

    Mallika Bhuyan, Beependra Singh, Swayam Vid & C. Jeganathan

Authors
  1. Mallika Bhuyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Beependra Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Swayam Vid
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. Jeganathan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.B and C.J. designed the study. M.B., B.S., and C.J. participated in data analysis. M.B. created figures with the guidance of C.J., and B.S.; M.B. wrote the paper and others reviewed the manuscript, discussed results, and approved the final version. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Mallika Bhuyan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhuyan, M., Singh, B., Vid, S. et al. Analysing the spatio-temporal patterns of vegetation dynamics and their responses to climatic parameters in Meghalaya from 2001 to 2020. Environ Monit Assess 195, 94 (2023). https://doi.org/10.1007/s10661-022-10685-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-022-10685-6

Keywords

Search

Navigation