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<p><i>Spartina alterniflora</i>’s robust reproductive capacity has enabled it to spread rapidly, posing a serious threat to native ecosystems in China. Therefore, accurate quantification of <i>Spartina alterniflora</i> aboveground biomass at a fine scale is crucial for understanding its growth dynamics and managing its invasion. This study focuses on the coastal wetlands of central Jiangsu Province, China, utilizing multispectral unmanned aerial vehicle (UAV) data to map the distribution of <i>Spartina alterniflora</i>. Object-based image analysis (OBIA) combined with support vector machines (SVM) was employed for classification. Additionally, multiple regression models, including univariate, band-based, vegetation index (VI)-based, and multivariate linear regression models integrating both band and VI data, were developed to estimate biomass: (1) the Bands + VIs multiple linear regression model based on fresh weight exhibited the highest estimation accuracy; (2) the optimal model achieved <i>R</i><sup>2</sup> values of 0.81 and 0.82 at Dafeng and Tiaozini Nature Reserve, with RMSE values of 591.78 g/m<sup>2</sup> and 337.62 g/m<sup>2</sup>, and MAE values of 576.82 g/m<sup>2</sup> and 287.71 g/m<sup>2</sup>, respectively; and (3) the aboveground biomass of <i>Spartina alterniflora</i> primarily ranged from 994.60 g/m<sup>2</sup> to 5 351.48 g/m<sup>2</sup> at Dafeng and from 796.05 g/m<sup>2</sup> to 1 994.02 g/m<sup>2</sup> in Tiaozini Nature Reserve. These findings highlight the effectiveness of multispectral UAV technology for accurately estimating <i>Spartina alterniflora</i> biomass, providing a robust methodology for wetland vegetation monitoring and invasive species management.</p>

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Inversion of aboveground biomass of Spartina alterniflora based on multispectral UAV

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Ziyao Chen,
  • Yan Gu,
  • Jianchun Chen,
  • Dingchen Liu,
  • Junjie Rui,
  • Shibing Zhu,
  • Yaping Wang

摘要

Spartina alterniflora’s robust reproductive capacity has enabled it to spread rapidly, posing a serious threat to native ecosystems in China. Therefore, accurate quantification of Spartina alterniflora aboveground biomass at a fine scale is crucial for understanding its growth dynamics and managing its invasion. This study focuses on the coastal wetlands of central Jiangsu Province, China, utilizing multispectral unmanned aerial vehicle (UAV) data to map the distribution of Spartina alterniflora. Object-based image analysis (OBIA) combined with support vector machines (SVM) was employed for classification. Additionally, multiple regression models, including univariate, band-based, vegetation index (VI)-based, and multivariate linear regression models integrating both band and VI data, were developed to estimate biomass: (1) the Bands + VIs multiple linear regression model based on fresh weight exhibited the highest estimation accuracy; (2) the optimal model achieved R2 values of 0.81 and 0.82 at Dafeng and Tiaozini Nature Reserve, with RMSE values of 591.78 g/m2 and 337.62 g/m2, and MAE values of 576.82 g/m2 and 287.71 g/m2, respectively; and (3) the aboveground biomass of Spartina alterniflora primarily ranged from 994.60 g/m2 to 5 351.48 g/m2 at Dafeng and from 796.05 g/m2 to 1 994.02 g/m2 in Tiaozini Nature Reserve. These findings highlight the effectiveness of multispectral UAV technology for accurately estimating Spartina alterniflora biomass, providing a robust methodology for wetland vegetation monitoring and invasive species management.