Hyperspectral Remote Sensing for Mapping Foliar Pigment Concentration in Mudumalai Tiger Reserve, India

B.S. P.C. Kishore, Amit Kumar, Purabi Saikia, M L Khan


The present study focuses on mapping and identification of the pigment concentration and dominant invasive species in Mudumalai Tiger Reserve (MTR), Tamil Nadu, India using optical (Sentinel 2A) and hyperspectral (Hyperion) earth observation satellite images. The Hyperion satellite data-based study exhibited dominance of dense vegetation (40.64%) and moderate vegetation (27.81%), in contrast to low vegetation (15.71%) in MTR. While vegetation indices based analysis demonstrated more accurate results of forest cover through Modified Red Edge Normalised Difference Vegetation Index (MRENDVI) compared to other vegetation indices. The dominance of moderate Normalised Difference Vegetation Index (NDVI) values (0.55–0.6) were observed in MTR with moderate carotenoid concentration (<0.3) and low anthocyanin concentration (<0.2). The study highlights the prominence of Lantana camara, Parthenium hysterophorus, Prosopis juliflora, and Chromolaena odorata in the Mudumalai Tiger Reserve. The correlation of species and foliar pigment concentration provides a better understanding of the species distribution in tropical deciduous forests in southern India. The study highlights better results in Sentinel 2A images compared to Hyperion images in LULC classification due to higher spatial resolution, while Hyperion images provide more improved classification and diversity for species characterisation due to high spectral agility. The study necessitates concurrent monitoring of invasion in forest-rich regions through the adoption of potential forest conservation and management plans.


Hyperion, Sentinel 2A, Spectral Indices, Foliar Pigment


Aneece, I. and Thenkabail, P. 2018. Accuracies achieved in classifying five leading world crop types and their growth stages using optimal earth observing-1 hyperion hyperspectral narrow bands on google earth engine. Remote Sensing, 10(12), 2027.

Arockraj. S., Kumar, A., Hoda, N. and Jeyaseelan, A.T. 2015. Quantification and identification of tree species in open mixed forests using high resolution QuickBird satellite imagery. Journal of Tropical Forestry and Environment, 5(2), 40-53.

Ashokkumar, L. and Shanmugam, S. 2014. Hyperspectral band selection and classification of Hyperion image of Bhitarkanika mangrove ecosystem, eastern India. In: Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI (Vol. 9239, p. 923914), International Society for Optics and Photonics.

Asner, G. P. and Vitousek, P. M. 2005. Remote analysis of biological invasion and biogeochemical change. Proceedings of the National Academy of Sciences 102, 4383e4386.

Asner, G. P., Jones, M. O., Martin, R. E., Knapp, D. E., & Hughes, R. F. 2008. Remote sensing of native and invasive species in Hawaiian forests. Remote Sensing of Environment, 112(5), 1912–1926.

Barker, D. H., Seaton, G. G. R., & Robinson, S. A. 1997. Internal and external photoprotection in developing leaves of the CAM plant Cotyledon orbiculata. Plant, Cell & Environment, 20(5), 617–624.

Bartley, G. E. and Scolnik, P. A. 1995. Plant carotenoids: pigments for photoprotection, visual attraction, and human health. The Plant Cell, 7(7), 1027.

Chambers, J. Q., Asner, G. P., Morton, D. C., Anderson, L. O., Saatchi, S. S., Espírito-Santo, F. D., Palace, M., & Souza Jr, C. 2007. Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. Trends in Ecology & Evolution, 22(8), 414–423.

Chazdon, R. L. 2003. Tropical forest recovery: legacies of human impact and natural disturbances. Perspectives in Plant Ecology, Evolution and Systematics, 6(1-2), 51–71.

Chutia, D., Borah, N., Baruah, D., Bhattacharyya, D. K., Raju, P. L. N., & Sarma, K. K. 2020. An effective approach for improving the accuracy of a random forest classifier in the classification of Hyperion data. Applied Geomatics, 12(1), 95-105.

Close, D. C. and Beadle, C. L. 2003. The ecophysiology of foliar anthocyanin. The Botanical Review, 69(2), 149–161.

Czech, B. and Krausman, P. R. 1997. Distribution and causation of species endangerment in the United States. Science, 277(5329), 1116–1117.

Dao, P. D., Axiotis, A., & He, Y. 2021. Mapping native and invasive grassland species and characterizing topography-driven species dynamics using high spatial resolution hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation, 104, 102542.

Datt, B. 1999. A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using Eucalyptus leaves. Journal of Plant Physiology, 154(1), 30-36.

Dawson, T. P., North, P. R. J., Plummer, S. E., & Curran, P. J. 2003. Forest ecosystem chlorophyll content: implications for remotely sensed estimates of net primary productivity. International Journal of Remote Sensing, 24(3), 611–617.

Dewey, S. A., Price, K. P., & Ramsey, D. 1991. Satellite remote sensing to predict potential distribution of dyers woad (Isatis tinctoria). Weed Technology, 5(3), 479–484.

Dixon, B. and Candade, N. 2008. Multispectral land use classification using neural networks and support vector machines: one or the other, or both? International Journal of Remote Sensing, 29(4), 1185-1206.

Edge, R., McGarvey, D. J., & Truscott, T. G. 1997. The carotenoids as antioxidants-a review. Journal of Photochemistry and Photobiology B: Biology, 41, 189–200.

Elatawneh, A., Kalaitzidis, C., Petropoulos, G. P., & Schneider, T. 2014. Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data. International Journal of Digital Earth, 7(3), 194-216.

Filella, I., Serrano, L., Serra, J., & Penuelas, J. 1995. Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science, 35(5), 1400–1405.

George, R., Padalia, H., & Kushwaha, S. P. S. 2014. Forest tree species discrimination in western Himalaya using EO-1 Hyperion. International Journal of Applied Earth Observation and Geoinformation, 28, 140-149.

Gitelson, A. A., Merzlyak, M. N., & Chivkunova, O. B. 2001. Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74(1), 38-45.

Gitelson, A. A., Zur, Y., Chivkunova, O. B., & Merzlyak, M. N. 2002. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 75(3), 272-281.

Gitelson, A. and Merzlyak, M. N. 1994. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143(3), 286-292.

Gould, K., Davies, K. M., & Winefield, C. 2008. Anthocyanins: biosynthesis, functions, and applications. Springer Science & Business Media.

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.

Karimi, Y., Prasher, S. O., Patel, R. M., & Kim, S. H. 2006. Application of support vector machine technology for weed and nitrogen stress detection in corn. Computers and Electronics in Agriculture, 51(1-2), 99-109.

Kaufman, Y. J. and Tanre, D. 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE transactions on Geoscience and Remote Sensing, 30(2), 261-270.

Kaufman, Y. J., Wald, A. E., Remer, L. A., Gao, B. -C., Li, R. -R., & Flynn, L. 1997. The MODIS 2.1-/spl mu/m channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE transactions on Geoscience and Remote Sensing, 35(5), 1286–1298.

Kayet, N., Pathak, K., Chakrabarty, A., Singh, C. P., Chowdary, V. M., Kumar, S., & Sahoo, S. 2019. Forest health assessment for geo-environmental planning and management in hilltop mining areas using Hyperion and Landsat data. Ecological Indicators, 106, 105471.

Kishore, B. S. P. C., Kumar, A., Lele, N., Srivastava, P., Saikia, P., Pandey, A. C., Bhattacharya, B., & Khan, M. L. 2020. Major forests and plant species discrimination in Mudumalai forest region using airborne hyperspectral sensing. Journal of Asia Pacific Biodiversity, 13(4), 637-651.

Kokaly, R. F., Despain, D. G., Clark, R. N., & Livo, K. E. 2003. Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote Sensing of Environment, 84(3), 437-456.

Kothandaraman, S., Dar, J. A., Sundarapandian, S., Dayanandan, S., & Khan, M. L. 2020. Ecosystem-level carbon storage and its links to diversity, structural and environmental drivers in tropical forests of Western Ghats. Scientific Reports, 10(1), 13444.

Kumar, R. and Saikia, P. 2020. Forests resources of Jharkhand, Eastern India: Socio-economic and bioecological perspectives. In: Socio-economic and Eco-Biological Dimensions in Resource use and Conservation - Strategies for Sustainability, Roy, N., Roychoudhury, S., Nautiyal, S., Agarwal, S. K., & Baksi, S., (eds.), Chapter 4, Springer International Publishing, Switzerland, pp. 61-101.

Lamine, S., Petropoulos, G. P., Singh, S. K., Szabó, S., Bachari, N. E. I., Srivastava, P. K., & Suman, S. 2018. Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS®. Geocarto International, 33(8), 862-878.

Lawrence, R. L., Wood, S. D., & Sheley, R. L. 2006. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest). Remote Sensing of Environment, 100(3), 356-362.

Lin, Z. and Zhang, G. 2020. Genetic algorithm-based parameter optimization for EO-1 Hyperion remote sensing image classification. European Journal of Remote Sensing, 53(1), 124-131.

Lobell, D. and Asner, G. 2004. Hyperion studies of crop stress in Mexico. In Proceedings of the 12 Annual JPL Airborne Earth Science Workshop, Pasadena, CA, USA, 24–28.

Matthew, M. W., Adler-Golden, S. M., Berk, A., Richtsmeier, S. C., Levine, R. Y., Bernstein, L. S., Acharya, P. K., Anderson, G. P., Felde, G. W., & Hoke, M. L. 2000. Status of atmospheric correction using a MODTRAN4-based algorithm, in: Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI. International Society for Optics and Photonics, pp. 199–207.

Moran, J. A., Mitchell, A. K., Goodmanson, G., & Stockburger, K. A. 2000. Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: a comparison of methods. Tree Physiology, 20(16), 1113–1120.

Otukei, J. R. and Blaschke, T. 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27-S31.

Petropoulos, G. P., Kalaitzidis, C., & Vadrevu, K. P. 2012. Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Computers & Geosciences, 41, 99-107.

Pimentel, D., Lach, L., Zuniga, R., & Morrison, D. 2000. Environmental and economic costs of nonindigenous species in the United States. BioScience, 50, 53–65.

Porcar-Castell, A., Juurola, E., Ensminger, I., Berninger, F., Hari, P., & Nikinmaa, E. 2008. Seasonal acclimation of photosystem II in Pinus sylvestris. II. Using the rate constants of sustained thermal energy dissipation and photochemistry to study the effect of the light environment. Tree Physiology, 28(10), 1483–1491.

Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351, 309.

Saikia, P., Deka, J., Bharali, S., Kumar, A., Tripathi, O.P., Singha, L.B., Khan, M.L., Dayanandan, S. 2017. Plant Diversity Patterns and Conservation Status of Eastern Himalayan Forests in Arunachal Pradesh, Northeast India. Forest Ecosystem, 4(1), 1-12,

Steyn, W. J., Wand, S. J. E., Holcroft, D. M., & Jacobs, G. 2002. Anthocyanins in vegetative tissues: a proposed unified function in photoprotection. New Phytologist, 155(3), 349–361.

Treitz, P. M. and Howarth, P. J. 1999. Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems. Progress in Physical Geography, 23(3), 359-390.

Ustin, S. L., Roberts, D. A., Gamon, J. A., Asner, G. P., & Green, R. O. 2004. Using imaging spectroscopy to study ecosystem processes and properties. BioScience, 54(6), 523–534.

Vogelmann, J. E., Rock, B. N., & Moss, D. M. 1993. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14(8), 1563-1575.

Vyas, D., Krishnayya, N. S. R., Manjunath, K. R., Ray, S. S., & Panigrahy, S. 2011. Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation. International Journal of Applied Earth Observation and Geoinformation, 13(2), 228-235.

Whitmore, T. C. and Burslem, D. 1998. Major disturbances in tropical rainforests. Dynamics of Tropical Communities, Newbery, D. M., Prins, H. H. T., & Brown, N. D., (eds.), Blackwell Science, Oxford, pp. 549–565.

Wilcove, D. S., & Chen, L. Y., (1998). Management costs for endangered species. Conservation Biology, 12(6), 1405–1407.

Woolley, J. T. 1971. Reflectance and transmittance of light by leaves. Plant physiology, 47(5), 656–662.

Zarco-Tejada, P. J., Hornero, A., Beck, P. S. A., Kattenborn, T., Kempeneers, P., & Hernández-Clemente, R. 2019. Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline. Remote Sensing of Environment, 223, 320-335.

Zarco-Tejada, P. J., Hornero, A., Hernández-Clemente, R., & Beck, P. S. A. 2018. Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 137, 134–14.

Full Text: PDF


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

COPYRIGHT of this Journal vests fully with the National Instional Institute of Ecology. Any commercial use of the content on this site in any form is legally prohibited.