Current Science

Open Access Open Access  Restricted Access Subscription Access

Characterization of Species Diversity and Forest Health using AVIRIS-NG Hyperspectral Remote Sensing Data


Affiliations
1 National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, India
2 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
3 MS University of Baroda, Vadodara 390 002, India
4 University of Wisconsin, Madison 53706, United States
5 University of California, Davis 95616, United States
 

Species diversity and vegetation health are two critical components to be monitored for sustainable forest management and conservation of biodiversity. The present study characterizes species dominance and α -diversity of a forest for the selected region in Mudumalai Wildlife Sanctuary (MWS), Western Ghats, which represents one of the most economically important forest types in India – the tropical dry deciduous forest. NASA’s Next-Generation Airborne Visible and Infrared Imaging Spectrometer (AVIRIS-NG) data at spectral resolution of 5 nm and spatial resolution of 5 m were used to analyse the forest matrix. Biodiversity (α -diversity) map thus generated from airborne platform over 14.5 sq. km area mostly represents the forest tree species diversity. Dominant tree species in the study area were also mapped using AVIRIS data for 21.7 sq. km. Canopy emergent dominant species, viz. Anogeissus latifolia, Tectona grandis, Terminalia alata, Grewia tiliifolia, Syzygium cumini and Shorea roxburghii were classified using spectral angle mapper technique and image-based spectra in the MWS study site. The study shows that nearly 40% area is dominated by A. latifolia and 27.5% by T. grandis in the study site. This study concludes that AVIRIS data can be used in the delineation of species and α -diversity mapping at community level; however, the accuracy achieved for species classification is moderate (60%) due to intermixing of species in the study area. For the Shimoga study site in Karnataka, the field spectra were collected using a spectroradiometer and used for the classification for the three dominant tree species using absorption peak decomposition technique. Fieldcollected pure spectra were analysed and species-wise absorption peaks (Gaussian) with central wavelength, peak amplitude and dispersion were used as the endmembers for classification. AVIRIS-NG data over Shoolpaneshwar Wildlife Sanctuary (SWS) study site used for fuel load estimation with narrow band indices calculated from AVIRIS-NG datasets. AVIRIS-NG data for MWS and Shimoga study site were collected during 2 and 5 January 2016, while for SWS site data were collected on 8 February 2016.

Keywords

Airborne Sensors, Forest Health, Hyperspectral Imaging, Species Diversity.
User
Notifications
Font Size

  • Reddy, C. S., Jha, C. S., Diwakar, P. G. and Dadhwal, V. K., Nationwide classification of forest types of India using remote sensing and GIS. Environ. Monit. Assess., 2015, 187, 777.

  • Reddy, C. S. et al., Quantification and monitoring of deforestation in India over eight decades (1930–2013). Biodivers. Conserv., 2016, 25, 93–116.

  • Hansen, M. C. et al., Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data. Proc. Natl. Acad. Sci. USA, 2008, 105, 9439–9444.

  • Clark, M. L. and Roberts, D. A., Species-level differences in hyperspectral metrics among tropical rainforest trees as determined by a tree-based classifier. Remote Sensing, 2012, 4, 1820–1855.

  • Asner, G. P., Hyperspectral remote sensing of canopy chemistry, physiology, and biodiversity in tropical rainforests. In Hyperspectral Remote Sensing Trop. Sub-Trop. For. (eds Kalacska, M. and Sanchez-Azofeifa, G. A.), CRC Press, 2008, pp. 261–296.

  • Clark, M. L., Identification of canopy species in tropical forests using hyperspectral data. In Hyperspectral Remote Sensing Veg. (eds Thenkabail, P. S. and Lyon, J. G.), CRC Press, 2016, p. 423.

  • Cla Barret, E. C. and Curtis, L. F., Introduction to Environmental Remote Sensing, Chapman & Hall, London, 1992, 3rd edn.

  • Ustin, S. L. et al., Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sensing Environ., 2009, 113, S67–S77.

  • Asner, G. P., Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing Environ., 1998, 64, 234– 253.

  • Gao, B.-C. and Goetz, A. F. H., Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data. J. Geophys. Res. – Atmos., 1990, 95, 3549– 3564.

  • Kokaly, R. F., Asner, G. P., Ollinger, S. V., Martin, M. E. and Wessman, C. A., Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sensing Environ., 2009, 113, S78–S91.

  • Milton, N. M. and Mouat, D. A., Remote sensing of vegetation responses to natural and cultural environmental conditions. Photogramm. Eng. Remote Sensing, 1989, 55(8), 1167–1173.

  • Slaton, M. R., Hunt, E. R. and Smith, W. E., Estimating nearinfrared leaf reflectance from leaf structural characteristics. Am. J. Bot., 2001, 88(2), 278–284.

  • Elvidge, C. D., Visible and near infrared reflectance characteristics of dry plant materials. Int. J. Remote Sensing, 1990, 11, 1775– 1795.

  • Castro-Esau, K. L., Sánchez-Azofeifa, G. A., Rivard, B., Wright, S. J. and Quesada, M., Variability in leaf optical properties of Mesoamerican trees and the potential for species classification. Am. J. Bot., 2006, 93, 517–530.

  • Zhang, J., Rivard, B., Sánchez-Azofeifa, A. and Castro-Esau, K., Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: implications for species identification using HYDICE imagery. Remote Sensing Environ., 2006, 105, 129–141.

  • Carlson, K. M., Asner, G. P., Hughes, R. F., Ostertag, R. and Martin, R. E., Hyperspectral remote sensing of canopy biodiversity in Hawaiian lowland rainforests. Ecosystems, 2007, 10, 536–549.

  • Skoupý, O. et al., The use of hyperspectral remote sensing for mapping the age composition of forest stands. J. For. Sci., 2012, 58, 287–297.

  • Christian, B. and Krishnayya, N. S. R., Classification of tropical trees growing in a sanctuary using hyperion (EO-1) and SAM algorithm. Curr. Sci., 2009, 96(12), 1601–1607.

  • Mitri, G. H. and Gitas, I. Z., Mapping postfire vegetation recovery using EO-1 Hyperion imagery. IEEE Trans. Geosci. Remote Sensing, 2010, 48, 1613–1618.

  • Vyas, D., Krishnayya, N. S. R., Manjunath, K. R., Ray, S. S. and Panigrahy, S., Evaluation of classifiers for processing hyperion (EO-1) data of tropical vegetation. Int. J. Appl. Earth Obs. Geoinf., 2011, 13, 228–235.

  • Wu, C., Wang, L., Niu, Z., Gao, S. and Wu, M., Nondestructive estimation of canopy chlorophyll content using hyperion and landsat/TM images. Int. J. Remote Sensing, 2010, 31, 2159–2167.

  • Thenkabail, P. S., Mariotto, I., Gumma, M. K., Middleton, E. M., Landis, D. R. and Huemmrich, K. F., Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2013, 6, 427–439.

  • Vyas, D., Christian, B. and Krishnayya, N. S. R., Canopy level estimations of chlorophyll and LAI for two tropical species (teak and bamboo) from hyperion (EO1) data. Int. J. Remote Sensing, 2013, 34, 1676–1690.

  • Cho, M. A., Sobhan, I., Skidmore, A. K. and De Leeuw, J., Discriminating species using hyperspectral indices at leaf and canopy scales. Int. Arch. Photogramm. Remote Sensing Spat. Inf. Sci., 2008, 37.

  • Sánchez-Azofeifa, G. A., Castro, K. L., Rivard, B., Kalascka, M. R. and Harriss, R. C., Remote sensing research priorities in tropical dry forest environments. Biotropica, 2003, 35, 134–142.

  • Xiao, Q., Ustin, S. L. and McPherson, E. G., Using AVIRIS data and multiple-masking techniques to map urban forest tree species. Int. J. Remote Sensing, 2004, 25, 5637–5654.

  • Williams, A. P. and Hunt Jr, E. R., Using AVIRIS imagery to map invasive plants on rangelands: leafy spurge in northeastern Wyoming; https://aviris.jpl.nasa.gov/proceedings/workshops/02_docs/2002_Parker_Williams_web.pdf

  • Wessman, C. A., Aber, J. D., Peterson, D. L. and Melillo, J. M., Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature, 1988, 335, 154.

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

  • Goodenough, D. G., Bhogal, A. S., Dyk, A. and Hudson, D., Data fusion of remote sensing data for forest parameter estimation. In Proceedings of the Fourth International Airborne Remote Sensing Conference and Exhibition/21 Canadian Symposium on Remote Sensing, ERIM International Inc, United States, 1999, p. 1816.

  • Gamon, J. A., Field, C. B., Roberts, D. A., Ustin, S. L. and Valentini, R., Functional patterns in an annual grassland during an AVIRIS overflight. Remote Sensing Environ., 1993, 44, 239–253.

  • Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A. and Schwartz, M. D., Shifting plant phenology in response to global change. Trends Ecol. Evol., 2007, 22, 357–365.

  • Morisette, J. T. et al., Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front. Ecol. Environ., 2009, 7, 253–260.

  • Wolkovich, E. M., Cook, B. I. and Davies, T. J., Progress towards an interdisciplinary science of plant phenology: building predictions across space, time and species diversity. New Phytol., 2014, 201, 1156–1162.

  • Féret, J.-B. and Asner, G. P., Tree species discrimination in tropical forests using airborne imaging spectroscopy. IEEE Trans. Geosci. Remote Sensing, 2013, 51, 73–84.

  • Clark, M. L., Roberts, D. A. and Clark, D. B., Hyperspectral discrimination of tropical rainforest tree species at leaf to crown scales. Remote Sensing Environ., 2005, 96, 375–398.

  • Castro-Esau, K. L., Sánchez-Azofeifa, G. A. and Caelli, T., Discrimination of lianas and trees with leaf-level hyperspectral data. Remote Sensing Environ., 2004, 90, 353–372.

  • Kalacska, M., Bohlman, S., Sanchez-Azofeifa, G. A., Castro-Esau, K. and Caelli, T., Hyperspectral discrimination of tropical dry forest lianas and trees: comparative data reduction approaches at the leaf and canopy levels. Remote Sensing Environ., 2007, 109, 406–415.

  • Arroyo-Mora, J. P., Kalacska, M., Chazdon, R., Civco, D., Obando-Vargas, G. and Sanchun, A., Assessing Recovery following Selective Logging of Lowland Tropical Forests based on Hyperspectral Imagery, Taylor and Francis Group-CRC Press, Boca Raton, FL, USA, 2008.

  • Féret, J.-B. and Asner, G. P., Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecol. Appl., 2014, 24, 1289–1296.

  • Champion, H. G. and Seth, S. K., A revised survey of the forest types of India. Govt Publication, New Delhi, 1968.

  • Joseph, S., Reddy, S. C., Pattanaik, C. and Sudhakar, S., Distribution of plant communities along climatic and topographic gradients in Mudumalia Wildlife Sanctuary (southern India). Biol. Lett., 2008, 45, 29–41.

  • Thompson, R. D., Gao, B.-C., Robert, O. G., Roberts, D. A., Dennison, P. E. and Sarah, R. L., Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign. Remote Sensing Environ., 2015; ISSN 00344257, http://dx.doi.org/10.1016/j.rse.2015.02.010.

  • Sims, D. A. and Gamon, J. A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing Environ., 2002, 81(2–3), 337–354.

  • Kruse, F. A. et al., The spectral image processing system (SIPS) interactive visualization and analysis of imaging spectrometer data. Remote Sensing Environ., 1993, 44, 145–163.

  • Shen, M., Tang, Y., Desai, A. R., Gough, C. and Chen, J., Can EVI-derived land-surface phenology be used as a surrogate for phenology of canopy photosynthesis? Int. J. Remote Sensing, 2014, 35, 1162–1174.

  • D’Odorico, P. et al., The match and mismatch between photosynthesis and land surface phenology of deciduous forests. Agric. For. Meteorol., 2015, 214, 25–38.


Abstract Views: 383

PDF Views: 126




  • Characterization of Species Diversity and Forest Health using AVIRIS-NG Hyperspectral Remote Sensing Data

Abstract Views: 383  |  PDF Views: 126

Authors

C. S. Jha
National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, India
Rakesh
National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, India
J. Singhal
National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, India
C. S. Reddy
National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, India
G. Rajashekar
National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, India
S. Maity
Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
C. Patnaik
Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
Anup Das
Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
Arundhati Misra
Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
C. P. Singh
Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
Jakesh Mohapatra
Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India
N. S. R. Krishnayya
MS University of Baroda, Vadodara 390 002, India
Sandhya Kiran
MS University of Baroda, Vadodara 390 002, India
Phil Townsend
University of Wisconsin, Madison 53706, United States
Margarita Huesca Martinez
University of California, Davis 95616, United States

Abstract


Species diversity and vegetation health are two critical components to be monitored for sustainable forest management and conservation of biodiversity. The present study characterizes species dominance and α -diversity of a forest for the selected region in Mudumalai Wildlife Sanctuary (MWS), Western Ghats, which represents one of the most economically important forest types in India – the tropical dry deciduous forest. NASA’s Next-Generation Airborne Visible and Infrared Imaging Spectrometer (AVIRIS-NG) data at spectral resolution of 5 nm and spatial resolution of 5 m were used to analyse the forest matrix. Biodiversity (α -diversity) map thus generated from airborne platform over 14.5 sq. km area mostly represents the forest tree species diversity. Dominant tree species in the study area were also mapped using AVIRIS data for 21.7 sq. km. Canopy emergent dominant species, viz. Anogeissus latifolia, Tectona grandis, Terminalia alata, Grewia tiliifolia, Syzygium cumini and Shorea roxburghii were classified using spectral angle mapper technique and image-based spectra in the MWS study site. The study shows that nearly 40% area is dominated by A. latifolia and 27.5% by T. grandis in the study site. This study concludes that AVIRIS data can be used in the delineation of species and α -diversity mapping at community level; however, the accuracy achieved for species classification is moderate (60%) due to intermixing of species in the study area. For the Shimoga study site in Karnataka, the field spectra were collected using a spectroradiometer and used for the classification for the three dominant tree species using absorption peak decomposition technique. Fieldcollected pure spectra were analysed and species-wise absorption peaks (Gaussian) with central wavelength, peak amplitude and dispersion were used as the endmembers for classification. AVIRIS-NG data over Shoolpaneshwar Wildlife Sanctuary (SWS) study site used for fuel load estimation with narrow band indices calculated from AVIRIS-NG datasets. AVIRIS-NG data for MWS and Shimoga study site were collected during 2 and 5 January 2016, while for SWS site data were collected on 8 February 2016.

Keywords


Airborne Sensors, Forest Health, Hyperspectral Imaging, Species Diversity.

References





DOI: https://doi.org/10.18520/cs%2Fv116%2Fi7%2F1124-1135