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Assessment of Sentinel-2A multispectral image for benthic habitat composition mapping

机译：评估Sentinel-2A多光谱图像用于底栖生境组成图

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Sentinel-2A accuracy for benthic habitat composition mapping was tested and compared to ALOS AVNIR-2. Aerial image acquired using custom-made unmanned aerial vehicle was used to train and validate the model. The mapping was conducted regardless of the benthic class and at individual benthic class. Benthic habitat class spatial distribution was obtained using the combination of image segmentation and classification tree analysis. The aerial image was interpreted based on the percentage of the constructed and non-constructed classes. The constructed class includes coral reefs, dead coral, seagrass, and macroalgae, while non-constructed class covers carbonate sand, rock, and rubble. Sentinel-2A produced higher accuracy (92%) than ALOS AVNIR-2 (78%) for benthic habitat spatial distribution mapping. However, in the empirical modelling of benthic habitat composition, ALOS AVNIR-2 (SE 23-24%) produced slightly better accuracy than Sentinel-2A (SE 23-27%). Several factors affected the low accuracy, which include the sub-pixel mixing of benthic habitat and constructed class, the delay between dates of acquisition, and radiometric quality of the images. Since the fundamental relationship between reflectance value and the percentage of the constructed class has been justified and consistent, given more experiments it has the potential to predict benthic habitat composition with higher accuracy in the future.
机译：测试了底栖生境组成图的Sentinel-2A准确性，并与ALOS AVNIR-2进行了比较。使用定制的无人机获取的航空图像用于训练和验证模型。不论底栖类别和单个底栖类别如何，都进行了映射。利用图像分割和分类树分析相结合的方法获得了底栖生境类空间分布。航空图像是根据已构造和未构造类别的百分比进行解释的。建造类别包括珊瑚礁，死珊瑚，海草和大型藻类，而非建造类别包括碳酸盐沙子，岩石和瓦砾。对于底栖生境空间分布图，Sentinel-2A的准确度比ALOS AVNIR-2（78％）高（92％）。但是，在底栖生境组成的经验模型中，ALOS AVNIR-2（SE 23-24％）的准确度略高于Sentinel-2A（SE 23-27％）。影响低精度的几个因素包括底栖生境和构造等级的亚像素混合，采集日期之间的延迟以及图像的辐射质量。由于反射率值与所构造类别的百分比之间的基本关系是合理且一致的，因此，在进行更多的实验后，它有可能在未来更准确地预测底栖生境的组成。

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