This article outlines a semi‐autonomous approach for using a fusion of light detection and ranging (LiDAR) and optical remote sensing data to identify and measure small impoundments (SIs) and their dams. Quantifying such water bodies as hydrologic network features is critical for ecosystem and species conservation, emergency management, and water resource planning; however, such features are incompletely mapped at national and state levels. By merging an airborne LiDAR‐derived point cloud with a normalized water index using airborne optical imagery we demonstrate an improvement upon single‐source methods for identifying these water bodies; classification accuracies increased over 10% by using this multi‐source fusion method. Furthermore, the method presented here illustrates a cost‐effective pathway to improve the National Inventory of Dams (NID) and includes a framework for estimating dam heights, with results showing strong correlations between derived dam heights and those recorded in the NID (r=.78). With the steady increase in available LiDAR coverage, the 87,000+ dams in the NID could be updated using this technique, a method which could also be expanded for global inventories of SIs and dams.

中文翻译：

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LiDAR-光学数据融合方法，用于识别和测量小河蓄水和水坝

本文概述了一种使用光检测和测距（LiDAR）与光学遥感数据的融合来识别和测量小型蓄水池（SI）及其坝的半自治方法。对诸如水文网络特征之类的水体进行量化对于生态系统和物种保护，应急管理和水资源规划至关重要；但是，这些功能在国家和州级别上的映射不完整。通过使用机载光学图像将机载LiDAR派生的点云与归一化水指数合并，我们证明了用于识别这些水体的单源方法的改进。使用这种多源融合方法，分类准确性提高了10％以上。此外，r = .78）。随着可用LiDAR覆盖范围的稳定增长，可以使用此技术更新NID中的87,000多个水坝，该方法也可以扩展到SI和水坝的全球清单。