![]() ![]() ![]() To address the issue of cloud cover, Synthetic Aperture Radar (SAR) has been employed as the sensor signals penetrate clouds, thus can be used in all weather conditions and during the day or night. Fortunately, the rise of cloud-based data providers and computational resources such as Google Earth Engine (GEE) offer a means to address these computational challenges enabling satellite image processes to be scaled. However, to date, there are few automated surface water mapping methods implemented due to uncertainties in the large-scale accuracy of these methods and the need for robust computational resources. To address these shortcomings, many methods have been developed leveraging satellite remote sensing to map the surface water extent, particularly during floods. Furthermore, stream gauge-based monitoring provides a simple means to identify floods based on pre-determined water level thresholds set to individual gauge locations, bu fail to capture the spatial extent of flooding, a critical component in disaster response and damage assessments. Traditionally, ground-based stream gauges are used to monitor water level or streamflow/discharge in major water bodies however, these observations fail to provide a large scale overview of conditions in regions where stream gauges are sparsely located. As more people are negatively affected by floods in Asia than in any other place in the world, there is a need for increased hydrologic monitoring to guide flood response efforts. Monitoring these variations is critical in monsoonal regions, such as Southeast Asia, where annual variation in rainfall results in hydrologic extremes that affect local communities. Satellite remote sensing offers a means to monitor water resources and their change in time across large areas. High accuracy surface water maps are critical to disaster planning and response efforts, thus results from this study can help inform SAR data users on the pre-processing steps needed and its effects as inputs on algorithms for surface water mapping applications. However, differences between the approaches presented in this paper were not found to be significant suggesting both methods are valid for generating accurate surface water maps. Overall, it was found that algorithms using terrain correction yield higher overall accuracy and yielded a greater spatial agreement between methods. Furthermore, the surface water maps generated from the terrain corrected data resulted in a intersection over union metrics of 95.8%–96.4%, showing greater spatial agreement, as compared to 92.3%–93.1% intersection over union using the non-terrain corrected data. While the accuracies varied between methods it was found that there is no statistical significant difference between the errors of the different collections. The thresholding algorithm that samples a histogram based on water edge information performed best with a maximum accuracy of 95%. It was found that the overall accuracy from the four collections ranged from 92% to 95% with Cohen’s Kappa coefficients ranging from 0.7999 to 0.8427. The resulting surface water maps from the four different collections were validated with user-interpreted samples from high-resolution Planet Scope data. ![]() This study leverages the Google Earth Engine to compare two unsupervised histogram-based thresholding surface water mapping algorithms utilizing two distinct pre-processed Sentinel-1 SAR datasets, specifically one with and one without terrain correction. ![]() However, few studies have explored the effects of SAR pre-processing steps used and the subsequent results as inputs into surface water mapping algorithms. Due to its cloud penetrating capability, many studies have focused on providing efficient and high quality methods for surface water mapping using Synthetic Aperture Radar (SAR). Satellite remote sensing plays an important role in the monitoring of surface water for historical analysis and near real-time applications. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |