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首页> 外文期刊>International journal of remote sensing >Mapping submergent aquatic vegetation in the US Great Lakes using Quickbird satellite data
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Mapping submergent aquatic vegetation in the US Great Lakes using Quickbird satellite data

机译：使用Quickbird卫星数据绘制美国五大湖中的水下水生植被图

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摘要

Submergent aquatic vegetation (SAV) is a powerful indicator of environmental conditions in both marine and fresh water ecosystems. Quickbird imagery was used to map SAV at three sites across the Great Lakes. Unsupervised classifications were performed at each site using summer Quickbird sensor data. At one site, a multi-temporal classification approach was added, combining visible red difference (May-August) with August red and green visible band data. Multi-temporal SAV classification was superior to single-date results at this site. Muck bottom was not seriously confused with SAV, which was unexpected. Multi-temporal classification results showed less confusion between deep water and SAV, although spectral variability due to sub-surface sandbar structure was a source of error in both single- and multi-date classifications. Nevertheless, some of the confounding effects of water column on SAV classification appear to have been mitigated using this multi-temporal approach. Future efforts would be well served by incorporating detailed, continuous, bathymetry data in the classification process. Quickbird sensor data are very useful for classifying SAV under US Great Lakes conditions. However, regional classification efforts using these data may be impractical at this time, as high cost, rigid tasking parameters and unpredictable water conditions limit availability of suitable imagery.
机译：淹没水生植被（SAV）是海洋和淡水生态系统中环境状况的有力指标。 Quickbird影像用于在大湖区的三个地点绘制SAV的地图。使用夏季Quickbird传感器数据在每个站点进行无监督分类。在一个站点上，添加了一种多时间分类方法，将可见的红色差异（5月至8月）与8月的红色和绿色可见波段数据相结合。在该站点上，多时间SAV分类优于单日结果。渣土并未与SAV严重混淆，这是出乎意料的。多时相分类结果显示，深水和SAV之间的混淆较少，尽管由于地下沙洲结构引起的光谱变化是单日和多日分类的误差源。尽管如此，使用这种多时相方法似乎已减轻了水柱对SAV分类的一些混杂影响。通过在分类过程中合并详细的，连续的测深数据，可以为将来的工作提供良好的服务。 Quickbird传感器数据对于在美国大湖地区条件下对SAV进行分类非常有用。但是，此时使用这些数据进行区域分类的工作可能不切实际，因为高成本，严格的任务参数和不可预测的水况限制了合适图像的可用性。

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