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Identification of the mangrove species using UAV hyperspectral images: A case study of Zhangjiangkou mangrove national nature reserve
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Zaiming Zhou1, Benqing Chen1, Ran Xu2, Wei Fang3
Haiyang Xuebao | 2021, 43(9) : 137 - 145
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Haiyang Xuebao | 2021, 43(9): 137-145
Article
Identification of the mangrove species using UAV hyperspectral images: A case study of Zhangjiangkou mangrove national nature reserve
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Zaiming Zhou1, Benqing Chen1, Ran Xu2, Wei Fang3
Affiliations
  • 1Ocean Acoustics and Remote Sensing Laboratory, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
  • 2Forestry College, Fujian Agriculture and Forestry University, Fuzhou 350002, China
  • 3Zhangjiangkou Mangrove National Nature Reserve, Yunxiao 363000, China
Published: 2021-09-25 doi: 10.12284/hyxb2021136
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The composition and distribution of mangrove species are crucial to the protection and restoration of mangrove wetland ecosystems. In this study, mangrove species distribution was identified by unmanned aerial vehicle (UAV) hyperspectral images from Zhangjiangkou mangrove national nature reserve. Spectral characteristics, spectral differential, and spectral continuum removal were analyzed, 17 spectral parameters of 911 group spectral data from different vegetation species were obtained. Furthermore, 13 parameters for decision tree construction were selected by stepwise discriminant analysis. As a result, an accurate distribution map of mangrove species in the study area was obtained through C5.0 decision tree classification model. The vegetation species present different distribution types from top to bottom in the Zhangjiangkou mangrove national nature reserve. The upper part of the study area was dominated by the mixed type of Aegiceras corniculatum and Kandelia obovata. The middle area showed symbiosis status of three different mangrove species Avicennia marina, Aegiceras corniculatum and Kandelia obovata. The lower part of the study area was dominated by Avicennia marina, and a small amount of Kandelia obovata. Through the confusion matrix, the overall classification accuracy is 87.95% and the Kappa coefficient is 83.81%, showed a satisfactory precision. Therefore, our mangrove species identification results from UAV hyperspectral images could be used as a reference for ecological protection of regional mangrove wetland, and also as a identification method reference for mangrove species.

mangrove  /  Zhangjiangkou  /  unmanned aerial vehicle (UAV)  /  hyperspectral images  /  species identification
Zaiming Zhou, Benqing Chen, Ran Xu, Wei Fang. Identification of the mangrove species using UAV hyperspectral images: A case study of Zhangjiangkou mangrove national nature reserve[J]. Haiyang Xuebao, 2021 , 43 (9) : 137 -145 . DOI: 10.12284/hyxb2021136
Year 2021 volume 43 Issue 9
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Article Info
doi: 10.12284/hyxb2021136
  • Receive Date:2020-12-08
  • Online Date:2026-02-26
  • Published:2021-09-25
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  • Received:2020-12-08
  • Revised:2021-05-06
Funding
Affiliations
    1Ocean Acoustics and Remote Sensing Laboratory, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
    2Forestry College, Fujian Agriculture and Forestry University, Fuzhou 350002, China
    3Zhangjiangkou Mangrove National Nature Reserve, Yunxiao 363000, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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