Automated high-resolution satellite-derived coastal bathymetry mapping
Automated high-resolution satellite-derived coastal bathymetry mapping
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DOI：
10.1016/j.jag.2022.102693
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发表时间：
2022-01-28
影响因子：
7.5
通讯作者：
Muller-Karger, Frank E.
中科院分区：
文献类型：
--
作者：
McCarthy, Matthew J.;Otis, Daniel B.;Muller-Karger, Frank E.
准确和最新的沿海水深测量图是沿海资源管理、商业和军事导航以及水产养殖等许多应用的基础。现有的测深制图方法需要密集和昂贵的实地调查或有针对性的空中捕获，这两种方法都不容易或负担不起重复制图和变化监测。然而，卫星测深（SDB）提供了以高空间和时间分辨率重复和有效地绘制浅水水体的潜力（即，每天-每周在5 m或更好）。SDB的大规模实施的一个挑战在于固有水柱特性的自动推导，使得它们可以在各种深度和基底条件下精确地补偿。在这里，我们提出了一个算法，利用WorldView（Maxar/数字地球仪（TM））卫星图像映射整个3700公里（2）佛罗里达群岛（美国）岛链在2米的分辨率，而不需要任何现场数据收集。预处理包括辐射校准、大气校正、自动化去光泽和光学深水的自动化检测，然后将其用于估计叶绿素a浓度，假设研究区域主要由Case-I水组成（即，其中光信号由水、浮游植物中的叶绿素a以及与叶绿素a浓度成比例变化的性质主导的那些）。估算叶绿素a浓度使我们能够计算适当的调谐系数中使用的光谱波段比方程估计水深。从摄取1B级图像到测深光栅输出，整个过程完全自动化。将佛罗里达群岛从基拉戈映射到基韦斯特需要34个WorldView图像，并且使用单个GPU核心（即，不需要超级计算资源）。在ArcMap中组合产品（镶嵌）后，将墙到墙的测深图与由激光雷达衍生的测深模型进行了验证，该模型具有超过600，000个点;结果显示，从0到15米深度的RMSE为1.95米。
Accurate and up-to-date maps of coastal bathymetry are fundamental for coastal resource management, commercial and military navigation, and aquaculture, among many applications. Existing methods for bathymetry mapping require intensive and costly field surveys or targeted aerial captures, neither of which are easily or affordably replicated for repeat mapping and change monitoring. Satellite-derived bathymetry (SDB), however, offers the potential to map shallow water bodies repeatedly and efficiently with high spatial and temporal resolution (i.e., daily-weekly at 5 m or better). One challenge to large-scale implementation of SDB lies in the automated derivation of inherent water column properties such that they may be accurately compensated for across a variety of depth and substrate conditions. Here we present an algorithm that leverages WorldView (Maxar/Digital Globe (TM)) satellite imagery to map the entire 3700 km(2) Florida Keys (USA) island chain at 2-meter resolution without the need for any in-situ data collections. Preprocessing included radiometric calibration, atmospheric correction, automated deglinting, and automated detection of optically deep water, which was then used to estimate chlorophyll-a concentration assuming that the study area is primarily comprised of Case-I water (i.e., those where the optical signal is dominated by water, Chlorophyll-a in phytoplankton, and properties that vary in proportion to Chlorophyll-a concentration). Estimating Chlorophyll-a concentration allowed us to calculate the appropriate tuning coefficients used in a spectral band ratio equation for estimating bathymetry. The entire process was fully automated from ingestion of Level-1B image to bathymetry raster output. Mapping the Florida Keys from Key Largo to Key West required 34 WorldView images and was completed in approximately 27 min for an average processing time of 47 s per image using a single GPU core (i.e., supercomputing resources were not needed). After combining the products (mosaicking) in ArcMap, the wall-to-wall bathymetry map was validated against a LiDAR-derived bathymetry model with over 600,000 points; results show an RMSE of 1.95 m over depths from 0 to 15 m.