Deciphering plant richness using satellite remote sensing: a study from three biodiversity hotspots

  • V. S. ChitaleEmail author
  • M. D. Behera
  • P. S. Roy
Original Paper


The ‘spectral variation hypothesis (SVH)’ assumes spectral variability as a result of variation in species richness. In the present study, we explore the potential of satellite datasets in identifying the patterns in species richness in part of three global biodiversity hotspots falling in India viz., Himalaya, Indo-Burma, and Western Ghats. We used generalized linear models to correlate remote sensing based vegetation indices (VIs) and physiographic indices (PIs) with plant richness calculated using 1264, 1114, and 1004 field plots across 21 different forest vegetation classes in Himalaya, Indo-Burma, and Western Ghats respectively. Three different vegetation indices ranked highest in explaining the variance in plant richness in the three hotspots. The variance in species richness explained by models based on only VIs was highest (69%, P < 0.01) for Bamboo vegetation in Indo Burma hotspot with Normalized Difference Vegetation Index, followed by that for dry deciduous forest in Western Ghats (57%, P < 0.001) with Normalized Difference Water Index, and for grasslands (54%, P < 0.05) in Himalaya by Modified Soil Adjusted Vegetation Index. The explained variance increased with combined models that are based on PIs and VIs to up to 85% (P < 0.05). Overall, we observed very high correlation between VIs and plant richness in open canopy vegetation classes with low species richness such as grasslands, scrubs, and dry deciduous forests, followed by vegetation classes with moderately dense canopy. Our study provides crucial insights on utility of satellite datasets as a proxy for estimating plant richness for better conservation of diverse ecosystems.


Satellite data Generalized linear models India Tropical forests Biodiversity hotspots 



The authors are thankful to Dr. PS Roy, Project Director, Biodiversity Characterization project for providing the field sampling data for this study.


The views and interpretations in this publication are those of the authors and are not necessarily attributable to ICIMOD.

Supplementary material

10531_2019_1761_MOESM1_ESM.doc (91 kb)
Supplementary material 1 (DOC 91 kb)


  1. Austin GE, Thomas CJ, Houston DC, Thompson DB (1996) Predicting the spatial distribution of buzzard Buteo buteo nesting areas using a Geographical Information System and remote sensing. J Appl Ecol 33:1541–1550CrossRefGoogle Scholar
  2. Carlson KM, Asner GP, Hughes RF, Ostertag R, Martin RE (2007) Hyperspectral remote sensing of canopy biodiversity in Hawaiian lowland rainforests. Ecosystems 10(4):536–549CrossRefGoogle Scholar
  3. Cayuela L, Golicher DJ, Benayas JMR, González-Espinosa M, Ramírez N (2006) Fragmentation, disturbance and tree diversity conservation in tropical montane forests. J Appl Ecol 43(6):1172–1181CrossRefGoogle Scholar
  4. Currie DJ (1991) Energy and large-scale patterns of animal-and plant-species richness. Am Nat 137(1):27–49CrossRefGoogle Scholar
  5. Duro DC, Coops NC, Wulder MA, Han T (2007) Development of a large area biodiversity monitoring system driven by remote sensing. Prog Phys Geogr 31(3):235–260CrossRefGoogle Scholar
  6. Fairbanks DH, McGwire KC (2004) Patterns of floristic richness in vegetation communities of California: regional scale analysis with multi-temporal NDVI. Glob Ecol Biogeogr 13(3):221–235CrossRefGoogle Scholar
  7. Foody GM (2004) Sub-pixel methods in remote sensing Remote sensing image analysis: including the spatial domain. Springer, New York, pp 37–49CrossRefGoogle Scholar
  8. Gaitán JJ, Bran D, Oliva G, Ciari G, Nakamatsu V, Salomone J (2013) Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes. Ecol Ind 34:181–191CrossRefGoogle Scholar
  9. Gao B (1996) NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58(3):257–266CrossRefGoogle Scholar
  10. Gillespie TW (2005) Predicting woody-plant species richness in tropical dry forests: a case study from south Florida, USA. Ecol Appl 15(1):27–37CrossRefGoogle Scholar
  11. Gillespie TW, Foody GM, Rocchini D, Giorgi AP, Saatchi S (2008) Measuring and modelling biodiversity from space. Prog Phys Geogr 32(2):203–221CrossRefGoogle Scholar
  12. Gould W (2000) Remote sensing of vegetation, plant species richness, and regional biodiversity hotspots. Ecol Appl 10(6):1861–1870CrossRefGoogle Scholar
  13. Levin N, Shmida A, Levanoni O, Tamari H, Kark S (2007) Predicting mountain plant richness and rarity from space using satellite-derived vegetation indices. Divers Distrib 13(6):692–703CrossRefGoogle Scholar
  14. Myers N, Mittermeier RA, Mittermeier CG, Da Fonseca GA, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403(6772):853CrossRefGoogle Scholar
  15. Nagendra H (2001) Using remote sensing to assess biodiversity. Int J Remote Sens 22(12):2377–2400CrossRefGoogle Scholar
  16. Nagendra H, Rocchini D, Ghate R, Sharma B, Pareeth S (2010) Assessing plant diversity in a dry tropical forest: comparing the utility of Landsat and IKONOS satellite images. Remote Sens 2(2):478–496CrossRefGoogle Scholar
  17. O’brien EM, Field R, Whittaker RJ (2000) Climatic gradients in woody plant (tree and shrub) diversity: water-energy dynamics, residual variation, and topography. Oikos 89(3):588–600CrossRefGoogle Scholar
  18. Oindo BO, Skidmore AK (2002) Interannual variability of NDVI and species richness in Kenya. Int J Remote Sens 23(2):285–298CrossRefGoogle Scholar
  19. Öster M, Cousins SA, Eriksson O (2007) Size and heterogeneity rather than landscape context determine plant species richness in semi-natural grasslands. J Veg Sci 18(6):859–868CrossRefGoogle Scholar
  20. Pau S, Gillespie TW, Wolkovich EM (2012) Dissecting NDVI–species richness relationships in Hawaiian dry forests. J Biogeogr 39(9):1678–1686CrossRefGoogle Scholar
  21. Pausas JG (1994) Species richness patterns in the understorey of Pyrenean Pinus sylvestris forest. J Veg Sci 5(4):517–524CrossRefGoogle Scholar
  22. Pouteau R, Gillespie TW, Birnbaum P (2018) Predicting tropical tree species richness from Normalized Difference Vegetation Index time series: the devil is perhaps not in the detail. Remote Sens 10(5):698CrossRefGoogle Scholar
  23. Qi J, Chehbouni A, Huete A, Kerr Y, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48(2):119–126CrossRefGoogle Scholar
  24. R Development Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  25. Rocchini D (2007) Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery. Remote Sens Environ 111(4):423–434CrossRefGoogle Scholar
  26. Rocchini D, Ricotta C, Chiarucci A (2007) Using satellite imagery to assess plant species richness: the role of multispectral systems. Appl Veg Sci 10(3):325–331CrossRefGoogle Scholar
  27. Rocchini D, Petras V, Petrasova A, Horning N, Furtkevicova L, Neteler M (2017) Open data and open source for remote sensing training in ecology. Ecol Inform 40:57–61CrossRefGoogle Scholar
  28. Roy P, Karnatak H, Kushwaha S, Roy A, Saran S (2012) India’s plant diversity database at landscape level on geospatial platform: prospects and utility in today’s changing climate. Curr Sci (Bangalore) 102(8):1136–1142Google Scholar
  29. Roy PS, Behera MD, Murthy M, Roy A, Singh S, Kushwaha S (2015) New vegetation type map of India prepared using satellite remote sensing: comparison with global vegetation maps and utilities. Int J Appl Earth Obs Geoinf 39:142–159CrossRefGoogle Scholar
  30. Schwarz M, Zimmermann NE (2005) A new GLM-based method for mapping tree cover continuous fields using regional MODIS reflectance data. Remote Sens Environ 95(4):428–443CrossRefGoogle Scholar
  31. Simpson EH (1949) Measurement of diversity. Nature 163:688CrossRefGoogle Scholar
  32. Tripathi P, Behera MD, Roy PS (2017) Optimized grid representation of plant species richness in India—Utility of an existing national database in integrated ecological analysis. PloS one 12(3):e0173774CrossRefGoogle Scholar
  33. Viedma O, Torres I, Pérez B, Moreno JM (2012) Modeling plant species richness using reflectance and texture data derived from QuickBird in a recently burned area of Central Spain. Remote Sens Environ 119:208–221CrossRefGoogle Scholar
  34. Willis KJ, Whittaker RJ (2002) Species diversity-scale matters. Science 295(5558):1245–1248CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Centre for Oceans, Rivers, Atmosphere and Land SciencesIndian Institute of TechnologyKharagpurIndia
  2. 2.International Centre for Integrated Mountain Development (ICIMOD)KathmanduNepal
  3. 3.School of Water ResourcesIndian Institute of TechnologyKharagpurIndia
  4. 4.International Crops Research Institute for Semi-Arid Tropics (ICRISAT)HyderabadIndia

Personalised recommendations