基于无人机与卫星图像的大型绿藻孔石莼(Ulva pertusa)遥感估算研究

doi:10.11978/2021088

Abstract

Abstract:

Satellite images are valuable data sources for monitoring floating green macroalgae on the sea surface. However, there are large errors in green macroalgae coverage derived on mixed pixels. It is thus important to solve the problem of sub-pixel coverage of green macroalgae, for precise monitoring of benthic green macroalgae in coastal area. In this paper, retrieval models were established to link sub-pixel coverage of green macroalgae with vegetation indexes and reflectance of characteristic bands by analyzing spectral characteristics of green macroalgae from the Landsat images, based on the results of green macroalgae coverage derived from unmanned aerial vehicle (UAV). The results show excellent linear relationships between the reflectance of blue, green, and red bands and the sub-pixel coverage of green macroalgae, and the reflectance decreases monotonically with increasing sub-pixel coverage. These three models were verified, and the results show that the model based on the reflectance of green band was more accuracy than the other indexes or index combination, with the highest coefficient of determination (R²), root mean square error (RMSE), and mean relative error (MRE) values of 0.92, 0.07, and 10.85%, respectively. Hence, we provide a model that can estimate the sub-pixel coverage of green macroalgae, and realize the precise monitoring of the coverage of green macroalgae extracted from Landsat images.

Key words: unmanned aerial vehicle (UAV), satellite image, green macroalgae, sub-pixel coverage

CLC Number:

MENG Miaomiao, ZHENG Xiangyang, XING Qianguo, LIU Hailong. Remote sensing estimation of green macroalgae Ulva pertusa based on unmanned aerial vehicle and satellite image[J].Journal of Tropical Oceanography, 2022, 41(3): 46-53.

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动态阈值法	提取一致性	Kappa	F1 score
NGRDI	89%	0.72	0.83
NGBDI	83%	0.68	0.79
RGRI	79%	0.62	0.74
ExG	77%	0.38	0.65
红波段DN值	94%	0.88	0.92

Remote sensing estimation of green macroalgae Ulva pertusa based on unmanned aerial vehicle and satellite image

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