Changes in biomass and organic carbon content during the growth period of dominant seaweed in the intertidal zone of Gouqi Island, China

Seaweed biomass is mainly investigated using quadrat sampling. However, the spatial limitations of quadrat sampling pose significant challenges to the thorough evaluation of seaweed resources at a larger scale. We combined multispectral photography collected using an unmanned aerial vehicle with satellite imagery and in situ hyperspectral reflectance measurement to determine the biomass of Ulva pertusa and Sargassum spp. in the intertidal zone of Gouqi Island, Zhejiang Province, China. The statistical metrics derived from aerial imagery for the biomass estimation of Ulva pertusa and Sargassum spp. achieved coefficients of determination (R2) of 0.81 and 0.74, root mean square errors (RMSE) of 96.54 and 788.64 g/m2, and mean absolute errors (MAE) of 40.71 and 327.12 g/m2, respectively. For satellite imagery, the R2 values were 0.80 and 0.68, RMSE were 97.65 and 910.45 g/m2, and MAE were 71.78 and 719.69 g/m2, respectively. The biomass of U. pertusa and Sargassum spp. fluctuated monthly, reaching their highest values in June and July, respectively. The total biomass of U. pertusa and Sargassum spp. was 1.69×107 and 9.93×107 g, and the organic carbon content was 7.98×105 and 5.04×106 g, respectively. Our research offers a new perspective and methodology, supplying critical data and a scientific rationale for the management of marine assets and the safeguarding of coastal ecologies.

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Publication Details

Journal
Journal of Applied Remote Sensing
Published
2024-08-20
DOI
https://doi.org/10.1117/1.jrs.18.034513
Citations
2
Primary Topic
Marine and coastal plant biology
Type
article
Field-Weighted Citation Impact
0.96
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article

Changes in biomass and organic carbon content during the growth period of dominant seaweed in the intertidal zone of Gouqi Island, China

Shouyu Zhang, Zhangbin Liu, Yuanmin Sun, Kai Wang et al.
2 citations
Journal of Applied Remote Sensing
Marine and coastal plant biology
0.96
article

Changes in biomass and organic carbon content during the growth period of dominant seaweed in the intertidal zone of Gouqi Island, China

Shouyu Zhang, Zhangbin Liu, Yuanmin Sun, Kai Wang, Xunmeng Li, Jianqu Chen, Jie Chen, Xu Zhao, Jun Li
article en
2 citations

Abstract

Seaweed biomass is mainly investigated using quadrat sampling. However, the spatial limitations of quadrat sampling pose significant challenges to the thorough evaluation of seaweed resources at a larger scale. We combined multispectral photography collected using an unmanned aerial vehicle with satellite imagery and in situ hyperspectral reflectance measurement to determine the biomass of Ulva pertusa and Sargassum spp. in the intertidal zone of Gouqi Island, Zhejiang Province, China. The statistical metrics derived from aerial imagery for the biomass estimation of Ulva pertusa and Sargassum spp. achieved coefficients of determination (R2) of 0.81 and 0.74, root mean square errors (RMSE) of 96.54 and 788.64 g/m2, and mean absolute errors (MAE) of 40.71 and 327.12 g/m2, respectively. For satellite imagery, the R2 values were 0.80 and 0.68, RMSE were 97.65 and 910.45 g/m2, and MAE were 71.78 and 719.69 g/m2, respectively. The biomass of U. pertusa and Sargassum spp. fluctuated monthly, reaching their highest values in June and July, respectively. The total biomass of U. pertusa and Sargassum spp. was 1.69×107 and 9.93×107 g, and the organic carbon content was 7.98×105 and 5.04×106 g, respectively. Our research offers a new perspective and methodology, supplying critical data and a scientific rationale for the management of marine assets and the safeguarding of coastal ecologies.

Journal of Applied Remote SensingVol. 18(03)
Kyushu University (JP), Ministry of Natural Resources (CN), Zhejiang Ocean University (CN), Second Institute of Oceanography (CN), Zhejiang Environmental Monitoring Center (CN), Ministry of Transport (CN), Shanghai Ocean University (CN)
Life below water
Openalex Percentile: Top 26%
Marine and coastal plant biology
0.96
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