Skip to main content

Advertisement

SpringerLink
Log in

Assessment of the alpine plant species biodiversity in the western Himalaya using Resourcesat-2 imagery and field survey

  • Published:
Journal of Earth System Science Aims and scope Submit manuscript

Abstract

In the alpine ecosystem of the Himalaya, an observation of species diversity with habitat heterogeneity predicts some important factors that govern them. Information theory-based species biodiversity at the community level and habitat heterogeneity at the landscape level were studied. Resourcesat-2 linear imaging self-scanning sensor (LISS-III and LISS-IV)-based spectral diversity indices and species diversity indices of four summits with increasing elevation gradients were estimated. The species richness decreased with an increase in elevation. The southern aspect of the sub-alpine zone has the highest biodiversity having a 3.5 Shannon’ entropy (H). Despite receiving higher insolation, the increase in elevation leading to coldness and dominance of a few species make the southern aspects less diverse at the higher elevation. Both elevational gradients and microclimatic conditions define biodiversity in the Himalaya. Resolution from coarser (LISS-III) to finer (LISS-IV) to micro (field) scale showed an increasing range of values, H = 0.1, 0.2 and 2.1, respectively. There is significantly less correlation between field and satellite measured biodiversity indices (r, −0.5 to 0.3). To go closer to the field level of biodiversity assessment, there is a need to use satellite data having a higher spatial resolution. Spectral variation hypothesis does not hold good in the alpine ecosystem of the Himalaya.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

References

  • Annece I P, Epstein H and Lerdau M 2017 Correlating species and spectral diversities using hyperspectral remote sensing in early-successional fields; Ecol. Evol. 7(10) 3475–3488, https://doi.org/10.1002/ece3.2876.

    Article  Google Scholar 

  • Arrhenius O 1921 Species and area; J. Ecol. 9(1) 95–99.

    Article  Google Scholar 

  • Behera M D, Roy P S and Panda R M 2016 Plant species richness pattern across India’s longest longitudinal extent; Curr. Sci. 111(7) 1220–1225, https://doi.org/10.18520/cs/v111/i7/1220-1225.

    Article  Google Scholar 

  • Chandrashekhar M B, Singh S and Roy P S 2003 Geospatial modelling techniques for rapid assessment of phytodiversity at landscape level in Western Himalayas, Himachal Pradesh; Curr. Sci. 84(5) 663–670.

    Google Scholar 

  • Cleveland W S 1979 Robust locally weighted regression and smoothing scatterplots; J. Am. Stat. Assoc. 74(368) 829–836.

    Article  Google Scholar 

  • Cleveland W S and Devlin S J 1988 Locally weighted regression: An approach to regression analysis by local fitting; J. Am. Stat. Assoc. 83(403) 596–610.

    Article  Google Scholar 

  • Field C B, Barros V R, Dokken D J, Mach K J, Mastrandrea M D, Bilir T E and Chatterjee M et al. 2014 Climate change 2014: Impacts, adaptation, and vulnerability. Part A. Global and sectoral aspects. Intergovernmental panel on climate change (IPCC); Cambridge University Press, Cambridge, UK.

    Google Scholar 

  • Gairola S, Proches S and Rocchini D 2013 High-resolution satellite remote sensing: A new frontier for biodiversity exploration in Indian Himalayan forests; Int. J. Remote Sens. 34(6) 2006–2022.

    Article  Google Scholar 

  • Gillespie T W, Foody G M, Rocchini D, Giorgi A P and Saatchi S 2008 Measuring and modelling biodiversity from space; Prog. Phys. Geogr. 32(2) 203–221, https://doi.org/10.1177/0309133308093606.

    Article  Google Scholar 

  • Gottfried M, Pauli H, Futschik A, Akhalkatsi M, Barancok P, Alonso J L B and Coldea G et al. 2012 Continent-wide response of mountain vegetation to climate change; Nat. Clim. Change 2 111–114.

    Article  Google Scholar 

  • Gould W 2000 Remote sensing of vegetation, plant species richness, and regional biodiversity hotspots; Ecol. Appl. 10(6) 1861–1870.

    Article  Google Scholar 

  • Grabherr G, Gottfried M and Pauli H 1994 Climate effects on mountain plants; Nature 369 448.

    Article  Google Scholar 

  • Hijmans R J 2015 raster: Geographic data analysis and modeling; R package, version 2.4-20. https://CRAN.R-project.org/package=raster.

  • Jørgensen A F and Nøhr H 1996 The use of satellite images for mapping of landscape and biological diversity in the Sahel; Int. J. Remote Sens. 17(1) 91–109.

    Article  Google Scholar 

  • Kalkhan M A, Stafford E J and Stohlgren T J 2007 Rapid plant diversity assessment using a pixel nested plot design: A case study in Beaver Meadows, Rocky Mountain National Park, Colorado, USA; Divers. Distrib. 13 379–388.

    Article  Google Scholar 

  • Kindt R and Coe R 2005 Tree diversity analysis: A manual and software for common statistical methods for ecological and biodiversity studies; World Agroforestry Centre (ICRAF), Nairobi.

    Google Scholar 

  • Koh L P, Lee T M, Sodhi N S and Ghazoul J 2010 An overhaul of the species-area approach for predicting biodiversity loss: Incorporating matrix and edge effects; J. Appl. Ecol. 47 1063–1070.

    Article  Google Scholar 

  • Körner C 2012 Alpine treelines: Functional ecology of the global high elevation tree limits; Springer, Basel.

    Book  Google Scholar 

  • Lauver C L 1997 Mapping species diversity patterns in the Kansas shortgrass region by integrating remote sensing and vegetation analysis; J. Veg. Sci. 8 387–394.

    Article  Google Scholar 

  • Levin N, Shmida A, Levanoni O, Hagit T and Kark S 2007 Predicting mountain plant richness and rarity from space using satellite-derived vegetation indices; Divers. Distrib. 13 692–703.

    Article  Google Scholar 

  • MacArthur R H and MacArthur J W 1961 On the bird species diversity; Ecology 42(3) 594–598.

    Article  Google Scholar 

  • Mairota P, Cafarelli B, Didham R K, Lovergine F P, Lucas R M, Nagendra H, Rocchini D and Tarantino C 2015 Challenges and opportunities in harnessing satellite remote-sensing for biodiversity monitoring; Ecol. Inf. 30 207–214.

    Article  Google Scholar 

  • Matthew M W, Adler-Golden M S M, Berk A, Richtsmeier S C, Levine R Y, Bernstein L S and Acharya P K et al. 2000 Status of atmospheric correction using a MODTRAN4-based algorithm; In: Algorithms for multispectral, hyperspectral, and ultraspectral imagery VI, 199, Proc. SPIE 4049, https://doi.org/10.1117/12.410341.

  • Mittermeier R A, Turner W R, Larsen F W, Brooks T M and Gascon C 2011 Global biodiversity conservation: The critical role of hotspots; In: Biodiversity hotspots: Distribution and protection of conservation priority areas (eds) Zachos F E and Habel J C, Springer, Heidelberg, pp. 3–22.

    Chapter  Google Scholar 

  • Mohapatra J 2015a Impacts of climate change on the alpine ecosystems: A review; Advances in Remote Sensing of Environment, Internal Report, EHD/BPSG/EPSA, SAC, ISRO.

  • Mohapatra J 2015b The changing face of the alpine ecosystem in the Himalaya; ENVIS Newsl. Himalayan Ecol. 12(2) 9, ISSN: 2277-9000.

  • Muralikrishnan S, Narender B, Reddy S and Pillai A 2011 Evaluation of Indian National DEM from Cartosat-1 Data; Aerial services and digital mapping area, National Remote Sensing Centre, Hyderabad, India.

    Google Scholar 

  • Nagendra H 2001 Using remote sensing to assess biodiversity; Int. J. Remote Sens. 22(12) 2377–2400, https://doi.org/10.1080/01431160117096.

    Article  Google Scholar 

  • Nagendra H, Rocchini D, Ghate R, Sharma B and Pareeth S 2010 Assessing plant diversity in a dry tropical forest: Comparing the utility of landsat and Ikonos satellite images; Remote Sens. 2 478–496, https://doi.org/10.3390/rs2020478.

    Article  Google Scholar 

  • Neteler M, Bowman M H, Landa M and Metz M 2012 GRASS GIS: A multi-purpose open source GIS; Environ. Model. Softw. 31 124–130.

    Article  Google Scholar 

  • Oksanen J, Blanchet F G, Kindt R, Legendre P, Minchin P R, O’Hara R B, Simpson G L, Solymos P, Stevens M H H and Wagner H 2015 vegan: Community ecology package; R package, version 2.3-1. https://CRAN.R-project.org/package=vegan.

  • Oldeland J, Wesuls D, Rocchini D, Schmidt M and Jürgens N 2010 Does using species abundance data improve estimates of species diversity from remotely sensed spectral heterogeneity? Ecol. Indic. 10 390–396.

    Article  Google Scholar 

  • Palmer M W 1990 The estimation of species richness by extrapolation; Ecology 71(3) 1195–1198.

    Article  Google Scholar 

  • Pandit M K 2013 The Himalayas must be protected; Nature 501 283.

    Article  Google Scholar 

  • Pauli H, Gottfried M, Lamprecht A, Nießner S, Rumpf S, Winkler M, Steinbauer K and Grabherr G 2015 The GLORIA field manual: Standard multi-summit approach, supplementary methods and extra approaches (5th edn); GLORIA – Coordination, Austrian Academy of Sciences and University of Natural Resources and Life Sciences, Vienna.

    Google Scholar 

  • Pebesma E J and Bivand R S 2005 Classes and methods for spatial data in R; R News 5(2), http://cran.r-project.org/doc/Rnews/.

  • Pielou E 1966 The measurement of diversity in different types of biological collections; J. Theor. Biol. 13 131–144.

    Article  Google Scholar 

  • R Core Team 2017 R: A language and environment for statistical computing; R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/.

  • Rashid I, Romshoo S A and Vijayalakshmi T 2013 Geospatial modelling approach for identifying disturbance regimes and biodiversity rich areas in North Western Himalayas, India; Biodivers. Conserv., https://doi.org/10.1007/s10531-013-0538-9.

    Article  Google Scholar 

  • Rényi A 1961 On measures of entropy and information; In: Proceedings of the fourth Berkeley symposium on mathematical statistics and probability (ed.) Neyman J, 1960(v1), University of California Press, Berkeley and Los Angeles, pp. 547–561.

  • Ricotta C 2005 Additive partitioning of Rao’s quadratic diversity: A hierarchical approach; Ecol. Model. 183(4) 365–371.

    Article  Google Scholar 

  • Rocchini D 2007 Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery; Remote Sens. Environ. 111 423–434.

    Article  Google Scholar 

  • Rocchini D, Chiarucci A and Loiselle S A 2004 Testing the spectral variation hypothesis by using satellite multispectral images; Acta Oecol. 26 117–120.

    Article  Google Scholar 

  • Rocchini D, McGlinn D, Ricotta C, Neteler M and Wohlgemuth T 2011 Landscape complexity and spatial scale influence the relationship between remotely sensed spectral diversity and survey-based plant species richness; J. Veg. Sci. 22 688–698.

    Article  Google Scholar 

  • Rocchini D, Delucchi L, Bacaro G, Cavallini P, Feilhauer H, Foody G M and He K S et al. 2013 Calculating landscape diversity with information-theory based indices: A GRASS GIS solution; Ecol. Inf. 17 82–93.

    Article  Google Scholar 

  • Rocchini D, Hernández-Stefanoni J L and He K S 2015 Advancing species diversity estimate by remotely sensed proxies: A conceptual review; Ecol. Inf. 25 22–28.

    Article  Google Scholar 

  • Rocchini D, Petras V, Petrasova A, Chemin Y, Ricotta C, Frigeri A and Landa M et al. 2016 Spatio-ecological complexity measures in GRASS GIS; Comput. Geosci., https://doi.org/10.1016/j.cageo.2016.05.006.

    Article  Google Scholar 

  • Rosenzweig M L 1995 Species diversity in space and time; Cambridge University Press, Cambridge.

    Book  Google Scholar 

  • Roy P S and Behera M D 2005 Assessment of biological richness in different altitudinal zones in the Eastern Himalayas, Arunachal Pradesh, India; Curr. Sci. 88(2) 250–257.

    Google Scholar 

  • Saini V, Tiwari R K and Gupta R P 2016 Comparison of FLAASH and QUAC atmospheric correction methods for Resourcesat-2 LISS-IV data; In: Earth observing missions and sensors: Development, implementation, and characterization IV (eds) Xiong X J, Kuriakose S A and Kimura T, Proc. SPIE 9881(98811V), https://doi.org/10.1117/12.2228097.

  • Schmidtlein S and Fassnacht F E 2017 The spectral variability hypothesis does not hold across landscapes; Remote Sens. Environ. 192 114–125.

    Article  Google Scholar 

  • SFR (State of Forest Report) 2015 India state of forest report; Forest Survey of India, Ministry of Environment, Forest and Climate Change, Government of India, ISBN: 97881929285-2-4.

  • Shannon C 1948 A mathematical theory of communication; Bell Syst. Tech. J. 27 379–423, 623–656.

    Article  Google Scholar 

  • Simpson E 1949 Measurement of diversity; Nature 163 688.

    Article  Google Scholar 

  • Singh J S, Roy P S, Murthy M S R and Jha C S 2010 Application of landscape ecology and remote sensing for assessment, monitoring and conservation of biodiversity; J. Indian Soc. Remote Sens. 38 365–385.

    Article  Google Scholar 

  • Singh C P, Panigrahy S, Thapliyal A, Kimothi M M, Soni P and Parihar J S 2012 Monitoring the Alpine treeline shift in parts of the Indian Himalayas using remote sensing; Curr. Sci. 102(4) 559–562.

    Google Scholar 

  • Singh C P, Panigrahy S, Parihar J S and Dharaiya N 2013 Modeling environmental Niche of Himalayan Birch and remote sensing based vicarious validation; Trop. Ecol. 54(3) 321–329.

    Google Scholar 

  • Singh C P, Mohapatra J and Dharaiya N 2015 Remote sensing of Alpine treeline dynamics; ISG Newsl. 21(4) 3–8. ISSN: 0972-642X.

  • Stohlgren T J, Chong G W, Kalkhan M A and Schell L D 1997 Multiscale sampling of plant diversity: Effects on minimum mapping unit size; Ecol. Appl. 7(3) 1064–1074.

    Article  Google Scholar 

  • Turner W, Spector S, Gardiner N, Fladeland M, Sterling E and Steininger M 2003 Remote sensing for biodiversity science and conservation; Trends Ecol. Evol. 18(6) 306–314.

    Article  Google Scholar 

  • Van Rossum G 1995 Python library reference; CWI Report CS-R9524.

  • Wani R A 2001 Historical temporal trends of climatic variables over Kashmir valley and discharge response to climate variability in upper Jhelum Catchment; In: Climate change and biodiversity (eds) Singh M, Singh R B and Hassan M I, Springer, Heidelberg, pp. 103–112.

    Google Scholar 

  • Wei T and Simko V 2016 corrplot: Visualization of a correlation matrix; R package, version 0.77. https://CRAN.R-project.org/package=corrplot.

  • Winkler M, Lamprecht A, Steinbauer K, Hülber K, Theurillat J-P, Breiner F and Choler P et al. 2016 The rich sides of mountain summits – A Pan-European view on aspect preferences of Alpine plants; J. Biogeogr. 43 2261–2273.

    Article  Google Scholar 

  • Wright D H 1983 Species-energy theory: An extension of species-area theory; Oikos 41 496–506.

    Article  Google Scholar 

Download references

Acknowledgements

The authors duly acknowledge the support extended by Shri Tapan Misra, Director, Space Applications Centre (SAC), Indian Space Research Organisation (ISRO), Ahmedabad and Dr Raj Kumar, Deputy Director, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area (EPSA), SAC, ISRO, for their support and encouragement. The authors are also thankful to Dr B K Bhattacharya, Head, Agriculture and Land Ecosystem Division (AED); Dr R P Singh, Project Director (PRACRITI-II) and Head, Land Hydrology Division (LHD) and Dr Prakash Chauhan, Group Director, Biological and Planetary Sciences and Applications Group (BPSG), EPSA, SAC, ISRO, for their suggestions. The project has been carried out under ‘Alpine Ecosystem Dynamics and Impact of Climate Change in Indian Himalaya’ under PRACRITI-II programme of ISRO. The authors would also like to thank the anonymous reviewers for their constructive suggestions that greatly improved the quality of this paper.

Author information

Authors and Affiliations

  1. Agriculture and Land Ecosystem Division (AED), Biological and Planetary Sciences and Applications Group (BPSG), Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area (EPSA), Space Applications Centre (SAC), Indian Space Research Organisation (ISRO), Ahmedabad, Gujarat, 380 015, India

    Jakesh Mohapatra & Chandra Prakash Singh

  2. Department of Botany, Bioinformatics and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, Gujarat, 380 009, India

    Jakesh Mohapatra & Himanshu A Pandya

  3. Centre for Biodiversity and Taxonomy (CBT), Department of Botany, University of Kashmir, Srinagar, Jammu and Kashmir, 190 006, India

    Maroof Hamid, Anzar A Khuroo & Akhtar H Malik

Authors
  1. Jakesh Mohapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chandra Prakash Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maroof Hamid
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anzar A Khuroo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Akhtar H Malik
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Himanshu A Pandya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jakesh Mohapatra.

Additional information

Communicated by Prashant K Srivastava

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohapatra, J., Singh, C.P., Hamid, M. et al. Assessment of the alpine plant species biodiversity in the western Himalaya using Resourcesat-2 imagery and field survey. J Earth Syst Sci 128, 189 (2019). https://doi.org/10.1007/s12040-019-1219-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12040-019-1219-1

Keywords

Search

Navigation