Terrestrial laser scanning (TLS) has been used extensively in Earth Science for acquisition of digital outcrop data over the past decade. Structure-frommotion (SfM) photogrammetry has recently emerged as an alternative and competing technology. The real-world performance of these technologies for ground-based digital outcrop acquisition is assessed using outcrops from North East England and the United Arab Emirates. Both TLS and SfM are viable methods, although no single technology is universally best suited to all situations. There are a range of practical considerations and operating conditions where each method has clear advantages. In comparison to TLS, SfM benefits from being lighter, more compact, cheaper, more easily replaced and repaired, with lower power requirements. TLS in comparison to SfM provides intrinsically validated data and more robust data acquisition in a wide range of operating conditions. Data post-processing is also swifter. The SfM data sets were found to contain systematic inaccuracies when compared to their TLS counterparts. These inaccuracies are related to the triangulation approach of the SfM, which is distinct from the time-of-flight principle employed by TLS. An elaborate approach is required for SfM to produce comparable results to TLS under most circumstances.
The increasing number of flood events combined with coastal urbanization has contributed to significant economic losses and damage to buildings and infrastructure. Development of higher resolution SAR flood mapping that accurately identifies flood features at all scales can be incorporated into operational flood forecasting tools, improving response and resilience to large flood events. Here, we present a comparison of several methods for characterizing flood inundation using a combination of synthetic aperture radar (SAR) remote sensing data and machine learning methods. We implement two applications with SAR GRD data, an amplitude thresholding technique applied, for the first time, to Sentinel-1A/B SAR data, and a machine learning technique, DeepLabv3+. We also apply DeepLabv3+ to a false color RGB characterization of dual polarization SAR data. Analyses at 10 m pixel spacing are performed for the major flood event associated with Hurricane Harvey and associated inundation in Houston, TX in August of 2017. We compare these results with high-resolution aerial optical images over this time period, acquired by the NOAA Remote Sensing Division. We compare the results with NDWI produced from Sentinel-2 images, also at 10 m pixel spacing, and statistical testing suggests that the amplitude thresholding technique is the most effective, although the machine learning analysis is successful at reproducing the inundation shape and extent. These results demonstrate the effectiveness of flood inundation mapping at unprecedented resolutions and its potential for use in operational emergency hazard response to large flood events.
The rising number of flooding events combined with increased urbanization is contributing to significant economic losses due to damages to structures and infrastructures. Here we present a method for producing all weather maps of flood inundation using a combination of synthetic aperture radar (SAR) remote sensing data and machine learning methods that can be used to provide information on the evolution of flood hazards to DisasterAware©, a global alerting system, that is used to disseminate flood risk information to stakeholders across the globe. While these efforts are still in development, a case study is presented for the major flood event associated with Hurricane Harvey and associated floods that impacted Houston, TX in August of 2017.
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