Spectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classification
1
Applied Remote Sensing Laboratory, Department of Geography, McGill University, Montréal, QC H3A 0B9, Canada
2
Flight Research Laboratory, National Research Council of Canada, Ottawa, ON K1A 0R6, Canada
3
Department of Geography and Environmental Systems, University of Maryland, Baltimore County (UMBC), Baltimore, MD 21250, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Francisco Javier García-Haro
Remote Sens. 2021, 13(13), 2604; https://doi.org/10.3390/rs13132604
Received: 10 June 2021
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Revised: 25 June 2021
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Accepted: 30 June 2021
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Published: 2 July 2021
(This article belongs to the Special Issue Hyperspectral Remote Sensing for Biodiversity Mapping)
Hyperspectral remote sensing across multiple spatio-temporal scales allows for mapping and monitoring mangrove habitats to support urgent conservation efforts. The use of hyperspectral imagery for assessing mangroves is less common than for terrestrial forest ecosystems. In this study, two well-known measures in statistical physics, Mean Information Gain (MIG) and Marginal Entropy (ME), have been adapted to high spatial resolution (2.5 m) full range (Visible-Shortwave-Infrared) airborne hyperspectral imagery. These two spectral complexity metrics describe the spatial heterogeneity and the aspatial heterogeneity of the reflectance. In this study, we compare MIG and ME with surface reflectance for mapping mangrove extent and species composition in the Sierpe mangroves in Costa Rica. The highest accuracy for separating mangroves from forest was achieved with visible-near infrared (VNIR) reflectance (98.8% overall accuracy), following by shortwave infrared (SWIR) MIG and ME (98%). Our results also show that MIG and ME can discriminate dominant mangrove species with higher accuracy than surface reflectance alone (e.g., MIG–VNIR = 93.6% vs. VNIR Reflectance = 89.7%).
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Keywords:
aspatial heterogeneity; spatial heterogeneity; species discrimination; airborne; mean information gain; marginal entropy; CASI; SASI
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MDPI and ACS Style
Osei Darko, P.; Kalacska, M.; Arroyo-Mora, J.P.; Fagan, M.E. Spectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classification. Remote Sens. 2021, 13, 2604. https://doi.org/10.3390/rs13132604
AMA Style
Osei Darko P, Kalacska M, Arroyo-Mora JP, Fagan ME. Spectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classification. Remote Sensing. 2021; 13(13):2604. https://doi.org/10.3390/rs13132604
Chicago/Turabian StyleOsei Darko, Patrick, Margaret Kalacska, J. P. Arroyo-Mora, and Matthew E. Fagan. 2021. "Spectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classification" Remote Sensing 13, no. 13: 2604. https://doi.org/10.3390/rs13132604
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