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Mangrove Species Discrimination and Health Assessment using AVIRIS-NG Hyperspectral Data


Affiliations
1 Space Applications Centre, Ahmedabad 380 015, India
2 Department of Physics, Presidency University, Kolkata 700 073, India
3 School of Oceanographic Studies, Jadavpur University, Kolkata 700 032, India
4 Chilika Development Authority, Bhubaneshwar 751 014, India
 

Mangroves play a major role in supporting biodiversity, providing economic and ecological security to the coastal communities, mitigating the effects of climate change and global warming. Species level classification of mangrove forest, understanding physical as well as chemical properties of mangrove vegetation, mangrove health, pigments, and levels of stress are some of the key issues for making scientific and management decisions. Hyperspectral remote sensing owing to its narrow bands, yield information on structural details and canopy parameters. Hyperspectral data over Sundarban and Bhitarkanika mangrove forests are analyzed for species discrimination and forest health assessment. In all, 15 mangrove species in Sundarban and 7 mangrove species in Bhitarkanika have been identified and classified using Spectral Angle Mapper technique. In-situ spectro-radiometer data has been used along with AVIRIS-NG hyperspectral data. Based on response of vegetation in blue, red and near-infrared regions, combination of vegetation indices are used to assess mangrove forest’s health. Reduction in NIR reflectance with shift towards lower wavelength has been observed in less healthy groups.

Keywords

Coastal Forest Management, Health Assessment, Hyperspectral Data, Mangrove Species.
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  • Mangrove Species Discrimination and Health Assessment using AVIRIS-NG Hyperspectral Data

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Authors

Nilima R. Chaube
Space Applications Centre, Ahmedabad 380 015, India
Nikhil Lele
Space Applications Centre, Ahmedabad 380 015, India
Arundhati Misra
Space Applications Centre, Ahmedabad 380 015, India
T. V. R. Murthy
Space Applications Centre, Ahmedabad 380 015, India
Sudip Manna
Department of Physics, Presidency University, Kolkata 700 073, India
Sugata Hazra
School of Oceanographic Studies, Jadavpur University, Kolkata 700 032, India
Muktipada Panda
Chilika Development Authority, Bhubaneshwar 751 014, India
R. N. Samal
Chilika Development Authority, Bhubaneshwar 751 014, India

Abstract


Mangroves play a major role in supporting biodiversity, providing economic and ecological security to the coastal communities, mitigating the effects of climate change and global warming. Species level classification of mangrove forest, understanding physical as well as chemical properties of mangrove vegetation, mangrove health, pigments, and levels of stress are some of the key issues for making scientific and management decisions. Hyperspectral remote sensing owing to its narrow bands, yield information on structural details and canopy parameters. Hyperspectral data over Sundarban and Bhitarkanika mangrove forests are analyzed for species discrimination and forest health assessment. In all, 15 mangrove species in Sundarban and 7 mangrove species in Bhitarkanika have been identified and classified using Spectral Angle Mapper technique. In-situ spectro-radiometer data has been used along with AVIRIS-NG hyperspectral data. Based on response of vegetation in blue, red and near-infrared regions, combination of vegetation indices are used to assess mangrove forest’s health. Reduction in NIR reflectance with shift towards lower wavelength has been observed in less healthy groups.

Keywords


Coastal Forest Management, Health Assessment, Hyperspectral Data, Mangrove Species.

References





DOI: https://doi.org/10.18520/cs%2Fv116%2Fi7%2F1136-1142