Abstract
Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), provides potentially promising new hyperspectral dataset with 330 spectral bands in visible and near infrared range. Therefore, there is much demand for assessing the effectiveness and superiority of GF-5 hyperspectral data in plants species mapping, particularly mangrove species mapping, to better support the efficient mangrove management. In this study, mangrove forest in Mai Po Nature Reserve (MPNR), Hong Kong was selected as the study area. Four dominant native mangrove species were investigated in this study according to the field surveys. Two machine learning methods, Random Forests and Support Vector Machines, were employed to classify mangrove species with Landsat 8, Simulated Hyperion and GF-5 data sets. The results showed that 97 more bands of GF-5 over Hyperion brought a higher over accuracy of 87.12%, in comparison with 86.82% from Hyperion and 73.89% from Landsat 8. The higher spectral resolution of 5 nm in GF-5 was identified as making the major contribution, especially for the mapping of Aegiceras corniculatum. Therefore, GF-5 is likely to improve the classification accuracy of mangrove species mapping via enhancing spectral resolution and thus has promising potential to improve mangrove monitoring at species level to support mangrove management.
Highlights
Mangrove forests are tropical and subtropical ecosystems, growing in inter-tidal areas, being the interface of land and oceans [1]
In this study, we aim to assess the efficiency of GF-5 hyperspectral data on mangrove mapping at species level through the comparison of Landsat 8 and Hyperion, and to answer two questions: 1) how does the GF-5 hyperspectral data apply to mangrove species discrimination; and 2) whether the increase in spectral resolution can improve the capacity of mangrove discrimination
Because we focus on mangroves, the remaining objects like urban areas were firstly masked by manually outlining the initial region containing mangrove forests
Summary
Mangrove forests are tropical and subtropical ecosystems, growing in inter-tidal areas, being the interface of land and oceans [1]. They can provide coastal protection, economic benefit from aquaculture, and significant eco-services, such as habitat provision and carbon sequestration [2,3]. Monitoring mangrove at species level can provide essential information on biodiversity, which may support mangrove management and protection in terms of biodiversity conservation, ecological succession analysis, biomass and carbon estimation etc. Remote sensing has been widely used as a cost-effective way to monitor mangrove forests, especially for large-scale observation, with focus on area quantification, structure complexity analysis, and above-ground biomass estimation [5,6,7,8]. Spectral similarity between different mangrove species makes discrimination difficult, and high plant density with overlap intensifies the difficulty [10]
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