Satellite Imagery-Estimated Intertidal Seaweed Biomass Using UAV as an Intermediary

The aim of this study was to use unmanned aerial vehicles (UAVs) as a supplement to satellite remote sensing to accurately assess benthic seaweed biomass in intertidal zones, in order to improve inversion accuracy results and investigate the spatial distribution patterns of seaweed. By adopting non-...

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Bibliographic Details
Main Authors: Jianqu Chen, Kai Wang, Xu Zhao, Xiaopeng Cheng, Shouyu Zhang, Jie Chen, Jun Li, Xunmeng Li
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/15/18/4428
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Summary:The aim of this study was to use unmanned aerial vehicles (UAVs) as a supplement to satellite remote sensing to accurately assess benthic seaweed biomass in intertidal zones, in order to improve inversion accuracy results and investigate the spatial distribution patterns of seaweed. By adopting non-multicollinearity vegetation indices (feature sets) from PlanetScope and Sentinel-2, and using benthic seaweed biomass inverted from multispectral UAV imagery as the label set for satellite pixel biomass values, machine learning methods (Gradient boosting decision tree, GBDT) can effectively improve the accuracy of biomass estimation results for <i>Ulva pertusa</i> and <i>Sargassum thunbergii</i> species (<i>Ulva pertusa</i>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mi>S</mi><mi>e</mi><mi>n</mi><mi>t</mi><mi>i</mi><mi>n</mi><mi>e</mi><mi>l</mi><mn>2</mn></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> = 0.74, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mi>P</mi><mi>l</mi><mi>a</mi><mi>n</mi><mi>e</mi><mi>t</mi><mi>S</mi><mi>c</mi><mi>o</mi><mi>p</mi><mi>e</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> = 0.8; <i>Sargassum thunbergii</i>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mi>S</mi><mi>e</mi><mi>n</mi><mi>t</mi><mi>i</mi><mi>n</mi><mi>e</mi><mi>l</mi><mn>2</mn></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> = 0.88, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mi>P</mi><mi>l</mi><mi>a</mi><mi>n</mi><mi>e</mi><mi>t</mi><mi>S</mi><mi>c</mi><mi>o</mi><mi>p</mi><mi>e</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> = 0.69). The average biomasses of <i>Ulva pertusa</i> and <i>Sargassum thunbergii</i> in the intertidal zone of Gouqi Island are 456.84 g/m<sup>2</sup> and 2606.60 g/m<sup>2</sup>, respectively, and the total resources are 3.5 × 10<sup>8</sup> g and 1.4 × 10<sup>9</sup> g, respectively. In addition, based on the hyperspectral data, it was revealed that a major source of error is the patchy distribution of seaweed.
ISSN:2072-4292