Extracting mangrove information using MNF transformation based on HY-1C CZI spectral indices reconstruction data

Liang Chao; Liu Li; Liu Jianqiang; Zou Bin; Zou Yarong; Cui Songxue

doi:10.3969/j.issn.0253-4193.2020.04.012

Volume 42 Issue 4

Nov. 2020

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Liang Chao，Liu Li，Liu Jianqiang, et al. Extracting mangrove information using MNF transformation based on HY-1C CZI spectral indices reconstruction data[J]. Haiyang Xuebao，2020, 42(4)：104–112，doi:10.3969/j.issn.0253−4193.2020.04.012

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Extracting mangrove information using MNF transformation based on HY-1C CZI spectral indices reconstruction data

doi: 10.3969/j.issn.0253-4193.2020.04.012

Liang Chao^{1, 2
,},
Liu Li^{3
,
,},
Liu Jianqiang^{1, 2},
Zou Bin^{1, 2},
Zou Yarong^{1, 2},
Cui Songxue^{1, 2}

1.
National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China
2.
Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources , Beijing 100081, China
3.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Received Date: 2019-05-17
Rev Recd Date: 2019-08-21

Available Online: 2020-11-18

Publish Date: 2020-04-25

Abstract

Abstract

In this study, we first used the spectral vegetation indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), atmospheric impedance vegetation index (ARPI) and visible spectrum slope ratio of coastal zone imager (CVSSR) to reconstruct the HY-1C coastal zone imager (CZI) image data of the Shankou mangrove national ecosystem nature reserve in Guangxi. And then, the minimum noise fraction rotation (MNF) was used to enhance the spectral difference between mangroves and general terrestrial vegetation on the reconstructed multi-band data set. We established a decision tree based on the MNF components to achieve automatic extracting mangrove information. The results show that the spectral indices reconstruction data and its MNF transformation can effectively enhance the difference between the mangroves and the general terrestrial vegetation on CZI images, the mangrove information can be effectively extracted by our decision tree. Compared with the experts’ interpretation results, the extracted accuracy of area of our method is over 90%. The overall detection accuracy is 88% after verification by random sample points.
- HY-1C satellite,
- CZI,
- mangrove,
- spectral indices,
- MNF transformation

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References(23)

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