Abstract

The goal of this research was to develop a fully automated method to map open water extent that is operationally practical on a national scale. Such a system needs to produce acceptable results in all regions of the country and particularly in the Prairie Potholes Region where understanding water surface dynamics is important for predicting flooding, agriculture/water availability and for evaporation calculations in weather models. A system was developed to automate, ingest, and process Radarsat-2 (RS2) imagery, from which mapping open water body extents in near-real time was carried out using a machine learning classification technique. A Random Forest classification algorithm was trained using the data extracted from the Global Surface Water (GSW) occurrence dataset. The GSW occurrence thresholds used to extract the training data were examined and there was little influence of uncertainty on the classification. The quality of classifications generated from RS2 Fine Wide mode imagery improved with increasing incidence angle. All Fine Quad incident angles produced acceptable results, but Standard and Wide mode imagery produced results below the accuracy thresholds deemed acceptable for this operational solution. Validation was carried out by comparing mapped water extents to temporally coincident high resolution multi-spectral imagery and to the USGS Global Land Cover Characteristics dataset, that is currently used as a land-water mask by ECCC in weather numerical weather modelling. The system that has been developed will allow new image datasets (e.g. Radarsat Constellation Mission) or training data that becomes available to be used to improve models. The open source code will be made available on Github.

摘要

这项研究的目的是开发一种完全自动化的方法来绘制开放水域范围的地图，该方法在全国范围内都是可行的。这种系统需要在该国所有地区，特别是在草原坑洼地区产生令人满意的结果，在该地区，了解水面动态对于预测洪水，农业/水的可获得性以及天气模型中的蒸发量非常重要。开发了一个系统来自动化，摄取和处理Radarsat-2（RS2）图像，然后使用机器学习分类技术从中实时绘制开放水域范围的地图。使用从全球地表水（GSW）发生数据集中提取的数据对随机森林分类算法进行了训练。检查了用于提取训练数据的GSW发生阈值，并且不确定性对分类的影响很小。RS2精细广角模式影像产生的分类质量随入射角的增加而提高。所有的精细四边形入射角均产生可接受的结果，但标准和宽模式图像产生的结果低于该操作解决方案可接受的精度阈值。通过将映射的水域与时间上一致的高分辨率多光谱图像以及USGS全球土地覆盖特征数据集进行比较来进行验证，该数据集目前在气候数值天气建模中被ECCC用作陆地水面罩。已开发的系统将允许新的图像数据集（例如 Radarsat星座任务）或训练数据可用于改进模型。开源代码将在Github上可用。