College of Ecology and Environment, Shanghai Ocean University, Shanghai, China.
Engineering Technology Research Center of Marine Ranching, Shanghai Ocean University, Shanghai, China.
PLoS One. 2022 Feb 24;17(2):e0263416. doi: 10.1371/journal.pone.0263416. eCollection 2022.
The Above Ground Biomass (AGB) of seaweeds is the most fundamental ecological parameter as the material and energy basis of intertidal ecosystems. Therefore, there is a need to develop an efficient survey method that has less impact on the environment. With the advent of technology and the availability of popular filming devices such as smartphones and cameras, intertidal seaweed wet biomass can be surveyed by remote sensing using popular RGB imaging sensors. In this paper, 143 in situ sites of seaweed in the intertidal zone of GouQi Island, ShengSi County, Zhejiang Province, were sampled and biomass inversions were performed. The hyperspectral data of seaweed at different growth stages were analyzed, and it was found that the variation range was small (visible light range < 0.1). Through Principal Component Analysis (PCA), Most of the variance is explained in the first principal component, and the load allocated to the three kinds of seaweed is more than 90%. Through Pearson correlation analysis, 24 parameters of spectral features, 9 parameters of texture features (27 in total for the three RGB bands) and parameters of combined spectral and texture features of the images were selected for screening, and regression prediction was performed using two methods: Random Forest (RF), and Gradient Boosted Decision Tree (GBDT), combined with Pearson correlation coefficients. Compared with the other two models, GBDT has better fitting accuracy in the inversion of seaweed biomass, and the highest R2 was obtained when the top 17, 17 and 11 parameters with strong correlation were selected for the regression prediction by Pearson's correlation coefficient for Ulva australis, Sargassum thunbergii, and Sargassum fusiforme, and the R2 for Ulva australis was 0.784, RMSE 156.129, MAE 50.691 and MAPE 28.201, the R2 for Sargassum thunbergii was 0.854, RMSE 790.487, MAE 327.108 and MAPE 19.039, and the R2 for Sargassum fusiforme was 0.808, RMSE 445.067 and MAPE 28.822. MAE was 180.172 and MAPE was 28.822. The study combines in situ survey with machine learning methods, which has the advantages of being popular, efficient and environmentally friendly, and can provide technical support for intertidal seaweed surveys.
海藻的地上生物量（AGB）是最基本的生态参数，是潮间带生态系统的物质和能量基础。因此，需要开发一种对环境影响较小的高效调查方法。随着技术的出现和智能手机和相机等流行摄像设备的普及，利用流行的 RGB 成像传感器，可以通过遥感对潮间带海藻的湿生物量进行调查。本文对浙江省嵊泗县枸杞岛潮间带的 143 个海藻原位站点进行了采样，并进行了生物量反演。分析了不同生长阶段海藻的高光谱数据，发现变化范围较小（可见光范围<0.1）。通过主成分分析（PCA），第一主成分解释了大部分方差，分配给三种海藻的负荷超过 90%。通过 Pearson 相关分析，筛选了图像的 24 个光谱特征参数、9 个纹理特征参数（三种 RGB 波段共 27 个）和光谱和纹理特征组合参数，分别采用随机森林（RF）和梯度提升决策树（GBDT）两种方法进行回归预测，并结合 Pearson 相关系数进行筛选。与其他两种模型相比，GBDT 在海藻生物量的反演中具有更好的拟合精度，当通过 Pearson 相关系数选择前 17、17 和 11 个具有强相关性的参数进行回归预测时，得到了最高的 R2 值，用于 Ulva australis、Sargassum thunbergii 和 Sargassum fusiforme 的回归预测，其中 Ulva australis 的 R2 为 0.784，RMSE 为 156.129，MAE 为 50.691，MAPE 为 28.201，Sargassum thunbergii 的 R2 为 0.854，RMSE 为 790.487，MAE 为 327.108，MAPE 为 19.039，Sargassum fusiforme 的 R2 为 0.808，RMSE 为 445.067，MAPE 为 28.822。MAE 为 180.172，MAPE 为 28.822。本研究将现场调查与机器学习方法相结合，具有普及、高效、环保的优点，可为潮间带海藻调查提供技术支持。
