Abstract
Hyperspectral leaf reflectance of a plant provides unique information that is characteristic of that plant. The present investigation is a preliminary attempt to assess whether spectra of leaves of mangrove species recorded under field conditions contain adequate spectral information for discerning mangroves at species rank. The paper highlights the hyperspectral characteristics of leaf surfaces of four prominent tropical mangrove species, viz., Avicennia alba, Avicennia marina, Rhizophora mucronata and Sonneratia caseolaris, found in the tidal forests of India. Hyperspectral observations were recorded using a field spectroradiometer, and pre-processed and averaged reflectance values of samples for three types of arrangements, viz., (1) randomly arranged leaves, (2) dorsal leaf surfaces and (3) ventral leaf surfaces of the species were statistically tested using one-way ANOVA to see whether the values significantly differed at every spectral location. All the four species were statistically different at all the spectral locations with majority of the bands exhibiting 99% confidence level. Finally, discriminant analysis was performed to identify the bands for maximum separability for the three types of arrangement of the leaves of the species taken separately and in different combinations. The optimal Wilks’ Lambda (L) were achieved with: six, three, eleven, six, five, thirteen and eleven wavelengths for discriminating random leaves of the four species, dorsal and ventral surfaces of A. alba, A. marina, R. mucronata, S. caseolaris, upper leaf surfaces of all the species, lower leaf surfaces, respectively. Factor analysis was used as an added tool to identify the wavelengths that were uncorrelated and contained maximum information in the combination of selected wavelengths. The results confirmed the unique spectral signatures of the four species, which could be explained in terms of leaf properties unique to the species. Cellular structure and pigmentation of the isolateral leaves of S. caseolaris are very different from the dorsiventral ones of the other three, which significantly changed the reflectance characteristics of the species.



Similar content being viewed by others
References
ASD (2008) RS3™ user manual ASD Document 600545 Rev. E © 2008. Analytical Spectral Device Inc., Boulder. www.asdi.com
Broge NH, Leblanc E (2000) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ 76:156–172
Cochrane MA (2000) Using vegetation reflectance variability for species level classification of hyperspectral data. Int J Remote Sens 21:2075–2087
Curran PJ (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30:271–278
Das S (2001) An adaptive feature of some mangroves of Sundarbans, West Bengal. http://duduf2.free.fr/discussion/adapt_feature.htm
Das S, Ghose M (1996) Anatomy of leaves of some mangroves and their associates from Sundarbans (West Bengal). Phytomorphology 46:139–150
Das AB, Parida A, Basak UC, Das P (2002) Studies on pigments, proteins and photosynthetic rates in some mangroves and mangrove associates from Bhitarkanika, Orissa. Mar Biol 141:415–422
Elvidge CD (1990) Visible and near infrared reflectance characteristics of dry plant materials. Int J Remote Sens 11:1775–1795
FieldSpec® 3 (2008) User manual ASD Document 600540 Rev. H © 2008. Analytical Spectral Device Inc., Boulder. www.asdi.com
Green PE, Caroll JD (1978) Mathematical tools for applied multivariate analysis. Academic Press, New York
Hergbert HL (1973) Infrared spectra. In: Sarkanen KV, Ludwig CH (eds) Lignins: occurrences, formation, structure and reactions. Wiley Interscience, New York
Hsu JC (1996) Multiple comparisons: theory and methods. Chapman and Hall, London
Kumar L, Schmidt K, Durry S, Skidmore AK (2001) Imaging spectrometry and vegetation science. In: van der Meer FD, de Jong SM (eds) Imaging spectrometry. Kluwer, Dordrecht, pp 111–155
Liang S (2004) Quantitative remote sensing of land surfaces. Wiley, New Jersey
Norusis M (2004) SPSS 13.0 statistical procedures companion. Prentice Hall Inc., Upper Saddle River
Ross J (1981) The radiation regime and architecture of plant stands. Dr. W. Junk Publishers, London
Schmidt KS, Skidmore AK (2003) Spectral discrimination of vegetation types in a coastal wetland. Remote Sens Environ 85:92–108
Seshavatharam V, Srivalli M (1989) Systematic leaf anatomy of some Indian mangroves. Proc Indian Acad Sci (Plant Sci) 99:557–565
Sun Y, Liu X, Wu Y, Liao C (2008) Identifying hyperspectral characters of wetland species using in situ data. Int Arch Photogramm Remote Sens Spa Inf Sci 107:459–465
Thenkabail PS, Enclona EA, Ashton MS, van der Meer V (2004) Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens Environ 91:354–376
Tomlinson PB (1994) The botany of mangroves. Cambridge University Press, Cambridge
Vaiphasa C, Ongsomwang S, Vaiphasa T, Skidmore AK (2005) Tropical mangrove species discrimination using hyperspectral data: a laboratory study. Estuar Coast Shelf Sci 65:371–379
Yuangue L, Zhongbao L, Peng L (2009) The study on the leaf anatomy of some mangrove species of China. In: Proceedings of the international conference on Environmental Science and Information Application Technology (ESIAT). Jimei University, Xiamen, pp 47–51
Acknowledgments
We express our sincere gratitude to Dr. R. R. Navalgund (Director, SAC) and Dr. J. S. Parihar (Deputy Director, EPSA, SAC) for overall encouragement. We also acknowledge the anonymous reviewers for their critical and useful suggestions for improving the manuscript. Funding sources: The work was funded by SAC, ISRO, Ahmedabad, Gujarat.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Panigrahy, S., Kumar, T. & Manjunath, K.R. Hyperspectral leaf signature as an added dimension for species discrimination: case study of four tropical mangroves. Wetlands Ecol Manage 20, 101–110 (2012). https://doi.org/10.1007/s11273-011-9245-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11273-011-9245-z


