| Download | - View final version: Spectral complexity of hyperspectral images: a new approach for mangrove classification (PDF, 7.2 MiB)
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| DOI | Resolve DOI: https://doi.org/10.3390/rs13132604 |
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| Author | Search for: Osei Darko, PatrickORCID identifier: https://orcid.org/0000-0002-6928-3462; Search for: Kalacska, MargaretORCID identifier: https://orcid.org/0000-0002-1676-481X; Search for: Arroyo-Mora, J. Pablo1ORCID identifier: https://orcid.org/0000-0003-0287-8960; Search for: Fagan, Matthew E. |
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| Affiliation | - National Research Council of Canada. Aerospace
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| Funder | Search for: Canadian Space Agency; Search for: Natural Sciences and Engineering Research Council of Canada |
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| Format | Text, Article |
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| Subject | aspatial heterogeneity; spatial heterogeneity; species discrimination; airborne; mean information gain; marginal entropy; CASI; SASI |
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| Abstract | 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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| Publication date | 2021-07-02 |
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| Publisher | MDPI |
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| Licence | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | f0290766-821a-44e7-9cb5-27c4095eb9db |
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| Record created | 2023-01-27 |
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| Record modified | 2023-01-27 |
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