Flood Hazard and Risk Assessment of Extreme Weather Events Using Synthetic Aperture Radar and Auxiliary Data: A Case Study

Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations

This work aims to clarify the potential of incoherent and coherent change detection (CD) approaches for detecting and monitoring ground surface changes using sequences of synthetic aperture radar (SAR) images. Nowadays, the growing availability of remotely sensed data collected by the twin Sentinel-1A/B sensors of the European (EU) Copernicus constellation allows fast mapping of damage after a disastrous event using radar data. In this research, we address the role of SAR (amplitude) backscattered signal variations for CD analyses when a natural (e.g., a fire, a flash flood, etc.) or a human-induced (disastrous) event occurs. Then, we consider the additional pieces of information that can be recovered by comparing interferometric coherence maps related to couples of SAR images collected between a principal disastrous event date. This work is mainly concerned with investigating the capability of different coherent/incoherent change detection indices (CDIs) and their mutual interactions for the rapid mapping of “changed” areas. In this context, artificial intelligence (AI) algorithms have been demonstrated to be beneficial for handling the different information coming from coherent/incoherent CDIs in a unique corpus. Specifically, we used CDIs that synthetically describe ground surface changes associated with a disaster event (i.e., the pre-, cross-, and post-disaster phases), based on the generation of sigma nought and InSAR coherence maps. Then, we trained a random forest (RF) to produce CD maps and study the impact on the final binary decision (changed/unchanged) of the different layers representing the available synthetic CDIs. The proposed strategy was effective for quickly assessing damage using SAR data and can be applied in several contexts. Experiments were conducted to monitor wildfire’s effects in the 2021 summer season in Italy, considering two case studies in Sardinia and Sicily. Another experiment was also carried out on the coastal city of Houston, Texas, the US, which was affected by a large flood in 2017; thus, demonstrating the validity of the proposed integrated method for fast mapping of flooded zones using SAR data.

Flood Depth Estimation during Hurricane Harvey Using Sentinel-1 and UAVSAR Data

In August 2017, Hurricane Harvey was one of the most destructive storms to make landfall in the Houston area, causing loss of life and property. Temporal and spatial changes in the depth of floodwater and the extent of inundation form an essential part of flood studies. This work estimates the flood extent and depth from LiDAR DEM (light detection and ranging digital elevation model) using data from the Synthetic Aperture Radar (SAR)–Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and satellite sensor—Sentinel-1. The flood extent showed a decrease between 29–30 August and 5 September 2017. The flood depths estimated using the DEM were compared with the USGS gauge data and showed a correlation (R2) greater than 0.88. The use of Sentinel-1 and UAVSAR resulted in a daily temporal repeat, which helped to document the changes in the flood area and the water depth. These observations are significant for efficient disaster management and to assist relief organizations by providing spatially precise information for the affected areas.

Land Subsidence in the Texas Coastal Bend: Locations, Rates, Triggers, and Consequences

Land subsidence and sea level rise are well-known, ongoing problems that are negatively impacting the entire Texas coast. Although ground-based monitoring techniques using long-term global navigation satellite systems (GNSS) records provide accurate subsidence rates, they are labor intensive, expensive, time-consuming, and spatially limited. In this study, interferometric synthetic aperture radar (InSAR) data and techniques were used to map the locations and quantify rates of land subsidence in the Texas Coastal Bend region during the period from October 2016 to July 2019. InSAR-derived land subsidence rates were then validated and calibrated against GNSS-derived rates. The factors controlling the observed land subsidence rates and locations were investigated. The consequences of spatial variability in land subsidence rates in Coastal Bend were also examined. The results indicated that: (1) land subsidence rates in the Texas Coastal Bend exhibited spatial variability, (2) InSAR-derived land subsidence rates were consistent with GNSS-derived deformation rates, (3) land subsidence in the Texas Coastal Bend could be attributed mainly to hydrocarbon and groundwater extraction as well as vertical movements along growth faults, and (4) land subsidence increased both flood frequency and severity in the Texas Coastal Bend. Our results provide valuable information regarding not only land deformation rates in the Texas Coastal Bend region, but also the effectiveness of interferometric techniques for other coastal rural areas around the globe.