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Article: GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong
Title | GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong |
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Authors | |
Keywords | hyperspectral remote sensing GF-5 mangrove Mai Po Nature Reserve random forests |
Issue Date | 2020 |
Publisher | MDPI AG. The Journal's web site is located at http://www.mdpi.com/journal/remotesensing/ |
Citation | Remote Sensing, 2020, v. 12 n. 4, p. article no. 656 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/289339 |
ISSN | 2023 Impact Factor: 4.2 2023 SCImago Journal Rankings: 1.091 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wan, L | - |
dc.contributor.author | Lin, Y | - |
dc.contributor.author | Zhang, H | - |
dc.contributor.author | Wang, F | - |
dc.contributor.author | Liu, M | - |
dc.contributor.author | Lin, H | - |
dc.date.accessioned | 2020-10-22T08:11:14Z | - |
dc.date.available | 2020-10-22T08:11:14Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Remote Sensing, 2020, v. 12 n. 4, p. article no. 656 | - |
dc.identifier.issn | 2072-4292 | - |
dc.identifier.uri | http://hdl.handle.net/10722/289339 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.publisher | MDPI AG. The Journal's web site is located at http://www.mdpi.com/journal/remotesensing/ | - |
dc.relation.ispartof | Remote Sensing | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | hyperspectral remote sensing | - |
dc.subject | GF-5 | - |
dc.subject | mangrove | - |
dc.subject | Mai Po Nature Reserve | - |
dc.subject | random forests | - |
dc.title | GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong | - |
dc.type | Article | - |
dc.identifier.email | Zhang, H: zhanghs@hku.hk | - |
dc.identifier.authority | Zhang, H=rp02616 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3390/rs12040656 | - |
dc.identifier.scopus | eid_2-s2.0-85080899621 | - |
dc.identifier.hkuros | 317426 | - |
dc.identifier.volume | 12 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | article no. 656 | - |
dc.identifier.epage | article no. 656 | - |
dc.identifier.isi | WOS:000519564600067 | - |
dc.publisher.place | Switzerland | - |
dc.identifier.issnl | 2072-4292 | - |