Spectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classification

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dc.contributor.authorDarko, Patrick Osei
dc.contributor.authorKalacska, Margaret
dc.contributor.authorArroyo-Mora, J. Pablo
dc.contributor.authorFagan, Matthew E.
dc.date.accessioned2022-06-10T17:44:16Z
dc.date.available2022-06-10T17:44:16Z
dc.date.issued2021-07-02
dc.description.abstractHyperspectral 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%).en_US
dc.description.sponsorshipThe study was supported by the Natural Sciences and Engineering Research Council of Canada NSERC grant and financial support from the Canadian Space Agency through the Flights for the Advancement of Science and Technology (FAST) program for Mission Airborne Carbon 13 (MAC13).en_US
dc.description.urihttps://www.mdpi.com/2072-4292/13/13/2604en_US
dc.format.extent28 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m29yjn-wsde
dc.identifier.citationOsei 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/rs13132604en_US
dc.identifier.urihttps://doi.org/10.3390/rs13132604
dc.identifier.urihttp://hdl.handle.net/11603/24893
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Geography and Environmental Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.titleSpectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classificationen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-8023-9251en_US

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