College of Marine Ecology and Environment, Shanghai Ocean University, Shanghai 201306, China.
Engineering Technology Research Center of Marine Ranching, Shanghai Ocean University, Shanghai 201306, China.
Sensors (Basel). 2022 Jun 21;22(13):4656. doi: 10.3390/s22134656.
Probing the coverage and biomass of seaweed is necessary for achieving the sustainable utilization of nearshore seaweed resources. Remote sensing can realize dynamic monitoring on a large scale and the spectral characteristics of objects are the basis of remote sensing applications. In this paper, we measured the spectral data of six dominant seaweed species in different dry and wet conditions from the intertidal zone of Gouqi Island: , , , Harv., C. Ag., and . The different seaweed spectra were identified and analyzed using a combination of one-way analysis of variance (ANOVA), support vector machines (SVM), and a fusion model comprising extreme gradient boosting (XGBoost) and SVM. In total, 14 common spectral variables were used as input variables, and the input variables were filtered by one-way ANOVA. The samples were divided into a training set (266 samples) and a test set (116 samples) at a ratio of 3:1 for input into the SVM and fusion model. The results showed that when the input variables were the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), , , , , , and and the model parameters were g = 1.30 and c = 2.85, the maximum discrimination rate of the six different wet and dry states of seaweed was 74.99%, and the highest accuracy was 93.94% when distinguishing between the different seaweed phyla (g = 6.85 and c = 2.55). The classification of the fusion model also shows similar results: The overall accuracy is 73.98%, and the mean score of the different seaweed phyla is 97.211%. In this study, the spectral data of intertidal seaweed with different dry and wet states were classified to provide technical support for the monitoring of coastal zones via remote sensing and seaweed resource statistics.
探测海藻的覆盖范围和生物量对于实现近岸海藻资源的可持续利用是必要的。遥感可以实现大规模的动态监测，而物体的光谱特征是遥感应用的基础。在本文中，我们测量了枸杞岛潮间带六种优势海藻在不同干湿条件下的光谱数据：、、、、、和。通过单向方差分析（ANOVA）、支持向量机（SVM）以及极端梯度提升（XGBoost）和 SVM 融合模型的组合，对不同的海藻光谱进行了识别和分析。共使用了 14 个常见的光谱变量作为输入变量，并通过单向方差分析对输入变量进行了筛选。将样本按照 3:1 的比例分为训练集（266 个样本）和测试集（116 个样本），然后将其输入 SVM 和融合模型。结果表明，当输入变量为归一化差异植被指数（NDVI）、比值植被指数（RVI）、、、、、和，模型参数为 g = 1.30 和 c = 2.85 时，六种不同干湿状态海藻的最大鉴别率为 74.99%，而在鉴别不同海藻门时的准确率最高为 93.94%（g = 6.85 和 c = 2.55）。融合模型的分类也呈现出类似的结果：总准确率为 73.98%，不同海藻门的平均得分是 97.211%。本研究对不同干湿状态潮间带海藻的光谱数据进行了分类，为通过遥感监测和海藻资源统计提供了海岸带监测的技术支持。
