Subsurface imaging in arid regions is a well-known application of satellite Synthetic Aperture Radar (SAR). Archaeological prospection has often focused on L-band SAR sensors, given the ability of longer wavelengths to penetrate more deeply into sand. In contrast, this study demonstrates capabilities of shorter-wavelength, but higher spatial resolution, C-band and X-band SAR sensors in archaeological subsurface imaging at the site of ‘Uqdat al-Bakrah (Safah), Oman. Despite having varying parameters and acquisitions, both the X-band and C-band images analyzed were able to identify a subsurface paleo-channel that is not visible on the ground surface. This feature was first identified through Ground Penetrating Radar (GPR) survey, then recognized in the SAR imagery and further verified by test excavations. Both the GPR and the excavations reveal the base of the paleo-channel at a depth of 0.6 m–0.7 m. Hence, both X-band and C-band wavelengths are appropriate for subsurface archaeological prospection in suitable (dry silt and sand) conditions with specific acquisition parameters. Moreover, these results offer important new insights into the paleo-environmental context of ancient metal-working at ‘Uqdat al-Bakrah and demonstrate surface water flow roughly contemporary with the site’s occupation.
more »
« less
Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band
Flood events have become intense and more frequent due to heavy rainfall and hurricanes caused by global warming. Accurate floodwater extent maps are essential information sources for emergency management agencies and flood relief programs to direct their resources to the most affected areas. Synthetic Aperture Radar (SAR) data are superior to optical data for floodwater mapping, especially in vegetated areas and in forests that are adjacent to urban areas and critical infrastructures. Investigating floodwater mapping with various available SAR sensors and comparing their performance allows the identification of suitable SAR sensors that can be used to map inundated areas in different land covers, such as forests and vegetated areas. In this study, we investigated the performance of polarization configurations for flood boundary delineation in vegetated and open areas derived from Sentinel1b, C-band, and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band data collected during flood events resulting from Hurricane Florence in the eastern area of North Carolina. The datasets from the sensors for the flooding event collected on the same day and same study area were processed and classified for five landcover classes using a machine learning method—the Random Forest classification algorithm. We compared the classification results of linear, dual, and full polarizations of the SAR datasets. The L-band fully polarized data classification achieved the highest accuracy for flood mapping as the decomposition of fully polarized SAR data allows land cover features to be identified based on their scattering mechanisms.
more »
« less
- Award ID(s):
- 1800768
- NSF-PAR ID:
- 10409062
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 14
- Issue:
- 24
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 6374
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Numerous algorithms have been developed to automate the process of delineating water surface maps for flood monitoring and mitigation purposes by using multiple sources such as satellite sensors and digital elevation model (DEM) data. To better understand the causes of inaccurate mapping information, we aim to demonstrate the advantages and limitations of these algorithms through a case study of the 2022 Madagascar flooding event. The HYDRAFloods toolbox was used to perform preprocessing, image correction, and automated flood water detection based on the state-of-the-art Edge Otsu, Bmax Otsu, and Fuzzy Otsu algorithms for the satellite images; the FwDET tool was deployed upon the cloud computing platform (Google Earth Engine) for rapid estimation of flood area/depth using the digital elevation model (DEM) data. Generated surface water maps from the respective techniques were evaluated qualitatively against each other and compared with a reference map produced by the European Union Copernicus Emergency Management Service (CEMS). The DEM-based maps show generally overestimated flood extents. The satellite-based maps show that Edge Otsu and Bmax Otsu methods are more likely to generate consistent results than those from Fuzzy Otsu. While the synthetic-aperture radar (SAR) data are typically favorable over the optical image under undesired weather conditions, maps generated based on SAR data tend to underestimate the flood extent as compared with reference maps. This study also suggests the newly launched Landsat-9 serves as an essential supplement to the rapid delineation of flood extents.more » « less
-
more » « less
Abstract We mapped the Soi crater region at 1:800,000 scale and produced a geomorphological map using methodology presented by Malaska, Lopes, Williams, et al. (2016),
https://doi.org/10.1016/j.icarus.2016.02.021 and Schoenfeld et al. (2021),https://doi.org/10.1016/j.icarus.2021.114516 . This region spans longitude 110° to 180°W and latitude 0° to 60°N and is representative of the transition between the equatorial, mid‐latitude, and high‐latitude northern regions of Titan. We used Cassini Synthetic Aperture Radar (SAR) as our primary mapping data set. For areas where SAR was not available, we used lower resolution data from the Imaging Science Subsystem, the Visible and Infrared Mapping Spectrometer, radiometry, and high‐altitude SAR for complete mapping coverage of the region. We identified 22 geomorphological units, 3 of which have been discussed in existing literature but have not yet been incorporated into our mapping investigations. These include sharp‐edged depressions (bse ), ramparts (brh ), and bright gradational plains (pgh ). All six major terrain classes are represented in this region: Craters, Labyrinth, Hummocky/mountainous, Plains, Dunes, and Basin and Lakes. We find that plains dominate the surface of the Soi crater region, comprising ∼73% of the mapped area, followed by dunes (∼14%), mountains/hummocky terrains (∼12%), basin and lakes (∼0.7%), labyrinth terrains (∼0.5%), and crater terrains (∼0.4%). We also observe empty lakes as far south as 40°N. The Soi crater region largely has the same collection and proportion of geomorphological units to other mapped regions on Titan. These results further support the hypothesis that surface processes are, broadly speaking, the same across Titan's middle and equatorial latitudes, with the exception of Xanadu. -
more » « less
Abstract The increased need for water resources in urban sprawls and intense droughts has forced more aggressive groundwater extraction resulting in numerous urban areas undergoing land subsidence. In most cases, only some large metropolitan areas have been well-characterized for subsidence. However, there is no existing country-wide assessment of urban areas, population, and households exposed to this process. This research showcases a methodology to systematically evaluate urban localities with land subsidence higher than − 2.8 cm/year throughout Mexico. We used Interferometric Synthetic Aperture Radar (InSAR) tools with a dataset of 4611 scenes from European Space Agency’s Sentinel-1 A/B SAR sensors acquired from descending orbits from September 2018 through October 2019. This dataset was processed at a supercomputer using InSAR Scientific Computing Environment and the Miami InSAR Time Series software in Python software. The quality and calibration of the resulting velocity maps are assessed through a large-scale comparison with observations from 100 continuous GPS sites throughout Mexico. Our results show that an urban area of 3797 km2, 6.9 million households, and 17% of the total population in Mexico is exposed to subsidence velocities of faster than − 2.8 cm/year, in more than 853 urban localities within 29 land subsidence regions. We also confirm previous global potential estimations of subsidence occurrence in low relief areas over unconsolidated deposits and where groundwater aquifers are under stress. The presented research demonstrates the capabilities for surveying urban areas exposed to land subsidence at a country-scale level by combining Sentinel-1 velocities with spatial national census data.
-
more » « less
Abstract This study develops a new, highly efficient method to produce accurate, high‐resolution surface water maps. The “active‐passive surface water classification” method leverages cloud‐based computing resources and machine learning techniques to merge Sentinel 1 synthetic aperture radar and Landsat observations and generate monthly 10‐m‐resolution water body maps. The skill of the active‐passive surface water classification method is demonstrated by mapping surface water change over the Awash River basin in Ethiopia during the 2015 East African regional drought and 2016 localized flood events. Errors of omission (water incorrectly classified as nonwater) and commission (nonwater incorrectly classified as water) in the case study area are 7.16% and 1.91%, respectively. The case study demonstrates the method's ability to generate accurate, high‐resolution water body maps depicting surface water dynamics in data‐sparse regions. The developed technique will facilitate better monitoring and understanding of the impact of environmental change and climate extremes on global freshwater ecosystems.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs14246374
