Remote monitoring to predict bridge scour failure using Interferometric Synthetic Aperture Radar (InSAR) stacking techniques

Scour is the removal of ground material in water bodies due to environmental changes in water flow. It particularly occurs at bridge piers and the holes formed can make bridges susceptible to collapse. The most common cause of bridge collapse is due to scour occurring during flooding, some failures causing loss of life and most resulting in significant transport disruption and economic loss. Consequently, failure of bridges due to scour is of great concern to bridge asset owners, and is currently very difficult to predict since conventional assessment methods foresee very resource-demanding monitoring efforts in situ. This paper presents evidence of how InSAR techniques can be used to monitor bridges at risk of scour, using Tadcaster Bridge, England, as a case study. Tadcaster Bridge suffered a partial collapse due to river scour on the evening of December 29th, 2015 following a period of severe rainfall and flooding. 48 TerraSAR-X scenes over the bridge from the two-year period prior to the collapse are analysed using the small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) approach. The study highlights a distinct movement in the region of the bridge where the collapse occurred prior to the actual event. This precursor to failure observed in the data over a month before actual collapse suggests the possible use of InSAR as a means of an early warning system in structural health monitoring of bridges at risk of scour.This work was made possible by EPSRC (UK) Award 1636878, with iCase sponsorship from the National Physical Laboratory and additional funding from Laing O’Rourke

Detailed mapping of lava and ash deposits at Indonesian volcanoes by means of VHR PlanetScope change detection

Mapping of lava flows in unvegetated areas of active volcanoes using optical satellite data is challenging due to spectral similarities of volcanic deposits and the surrounding background. Using very high-resolution PlanetScope data, this study introduces a novel object-oriented classification approach for mapping lava flows in both vegetated and unvegetated areas during several eruptive phases of three Indonesian volcanoes (Karangetang 2018/2019, Agung 2017, Krakatau 2018/2019). For this, change detection analysis based on PlanetScope imagery for mapping loss of vegetation due to volcanic activity (e.g., lava flows) is combined with the analysis of changes in texture and brightness, with hydrological runoff modelling and with analysis of thermal anomalies derived from Sentinel-2 or Landsat-8. Qualitative comparison of the mapped lava flows showed good agreement with multispectral false color time series (Sentinel-2 and Landsat-8). Reports of the Global Volcanism Program support the findings, indicating the developed lava mapping approach produces valuable results for monitoring volcanic hazards. Despite the lack of bands in infrared wavelengths, PlanetScope proves beneficial for the assessment of risk and near-real-time monitoring of active volcanoes due to its high spatial (3 m) and temporal resolution (mapping of all subaerial volcanoes on a daily basis)

An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time

In the case of ongoing wildfire events, timely information on current fire parameters is crucial for informed decision making. Satellite imagery can provide valuable information in this regard, since thermal sensors can detect the exact location and intensity of an active fire at the moment the satellite passes over. This information can be derived and distributed in near-real time, allowing for a picture of current fire activity. However, the derivation of the size and shape of an already affected area is more complex and therefore most often not available within a short time frame. For urgent decision making though, it would be desirable to have this information available in near-real time, and on a large scale. The approach presented here works fully automatic and provides perimeters of burnt areas within two hours after the satellite scene acquisition. It uses the red and near-infrared bands of mid-resolution imagery to facilitate continental-scale monitoring of recently occurred burnt areas. To allow for a high detection capacity independent of the affected vegetation type, segmentation thresholds are derived dynamically from contextual information. This is done by using a Morphological Active Contour approach for perimeter determination. The results are validated against semi-automatically derived burnt areas for five wildfire incidents in Europe. Furthermore, these results are compared with three widely used burnt area datasets on a country-wide scale. It is shown that a high detection quality can be reached in near real-time. The large-scale inter-comparison shows that the results coincide with 63% to 76% of the burnt area in the reference datasets. While these established datasets are only available with a time lag of several months or are created by using manual interaction, the presented approach produces results in near-real time fully automatically. This work is therefore supposed to represent a valuable improvement in wildfire related rapid damage assessment

Utilization of hyperspectral remote sensing imagery for improving burnt area mapping accuracy

Wildfires pose a direct threat when occurring close to populated areas. Additionally, their significant carbon and climate feedbacks represent an indirect threat on a global, long-term scale. Monitoring and analyzing wildfires is therefore a crucial task to increase the understanding of interconnections between fire and ecosystems, in order to improve wildfire management activities. This study investigates the suitability of 232 different red / near-infrared band combinations based on hyperspectral imagery of the DESIS sensor with regard to burnt area detection accuracy. It is shown that the selection of wavelengths greatly influences the detection quality, and that especially the utilization of lower near-infrared wavelengths increases the yielded accuracy. For burnt area analysis based on the Normalized Difference Vegetation Index (NDVI), the optimal wavelength range has been found to be 660 - 670 nm and 810 - 835 nm for the red band and near-infrared band, respectively

Monitoring surface water dynamics in the Prairie Pothole Region of North Dakota using dual-polarised Sentinel-1 synthetic aperture radar (SAR) time series

The North American Prairie Pothole Region (PPR) represents a large system of wetlands with great importance for biodiversity, water storage and flood management. Knowledge of seasonal and inter-annual surface water dynamics in the PPR is important for understanding the functionality of these wetland ecosystems and the changing degree of hydrologic connectivity between them. Optical sensors that are widely used for retrieving such information are often limited by their temporal resolution and cloud cover, especially in the case of flood events. Synthetic aperture radar (SAR) sensors can potentially overcome such limitations. However, water extent retrieval from SAR data is often impacted by environmental factors, such as wind on water surfaces. Hence, robust retrieval methods are required to reliably monitor water extent over longer time periods. The aim of this study was to develop a robust approach for classifying open water extent in the PPR and to analyse the obtained time series covering the entire available Sentinel-1 observation period from 2015 to 2020 in the hydrometeorological context. Open water in prairie potholes was classified by fusing dual-polarised Sentinel-1 data and high-resolution topographical information using a Bayesian framework. The approach was tested for a study area in North Dakota. The resulting surface water maps were validated using high-resolution airborne optical imagery. For the observation period, the total water area, the number of waterbodies and the median area per waterbody were computed. The validation of the retrieved water maps yielded producer's accuracies between 84 % and 95 % for calm days and between 74 % and 88 % for windy days. User's accuracies were above 98 % in all cases, indicating a very low occurrence of false positives due to the constraints introduced by topographical information. The observed dynamics of total water area displayed both intra-annual and inter-annual patterns. In addition to differences in seasonality between small ( 1 ha) waterbodies due to the effect of evaporation during summer, these size classes also responded differently to an extremely wet period from 2019 to 2020 in terms of the increase in the number of waterbodies and the total area covered. The results demonstrate the potential of Sentinel-1 data for high-resolution monitoring of prairie wetlands. Limitations of this method are related to wind inhibiting the correct water extent retrieval and to the rather long acquisition interval of 12 d over the PPR, which is a result of the observation strategy of Sentinel-1

Monitoring and Automatic Change Detection of Cultural Heritage Sites using Sentinels and Copernicus Contributing Missions

Currently available very high resolution space borne imagery can be used for mapping and 3D modeling of archaeologic sites and monuments from all over the world. This allows also the continuous monitoring, protection from natural and human threatening and may also be the base for virtual or real reconstruction of monuments. As an example it is shown how a mostly automatic approach for operationally monitoring from space may work on the example of the world heritage site of Palmyra, Syria

Application of SAR time-series and deep learning for estimating landslide occurence time

The time series of normalized difference vegetation index (NDVI) and interferometric coherence extracted from optical and Synthetic Aperture Radar (SAR) images, respectively, have strong responses to sudden landslide failures in vegetated regions, which is expressed by a sudden increase or decrease in the values of NDVI and coherence. Compared with optical sensors, SAR sensors are not affected by cloud and daylight conditions and can detect the occurrence time of failure in near real-time. The purpose of this paper is to automatically determine the time of failure occurrence using time series coherence values. We propose, based on some existing anomaly detection algorithms, a deep neural network-based anomaly detection strategy that combines supervised and unsupervised learning without a priori knowledge about failure time. Our experiment using July 21, 2020 Shaziba landslide in China shows that in comparison to widely used unsupervised methodology, the use of our algorithm leads to a more accurate detection of the timing of the landslide failure

Sentinel-1-based water and flood mapping: benchmarking convolutional neural networks against an operational rule-based processing chain

In this study, the effectiveness of several convolutional neural network architectures (AlbuNet-34/FCN/DeepLabV3+/U-Net/U-Net++) for water and flood mapping using Sentinel-1 amplitude data is compared to an operational rule-based processor (S-1FS). This comparison is made using a globally distributed dataset of Sentinel-1 scenes and the corresponding ground truth water masks derived from Sentinel-2 data to evaluate the performance of the classifiers on a global scale in various environmental conditions. The impact of using single versus dual-polarized input data on the segmentation capabilities of AlbuNet-34 is evaluated. The weighted cross entropy loss is combined with the Lovász loss and various data augmentation methods are investigated. Furthermore, the concept of atrous spatial pyramid pooling used in DeepLabV3+ and the multiscale feature fusion inherent in U-Net++ are assessed. Finally, the generalization capacity of AlbuNet-34 is tested in a realistic flood mapping scenario by using additional data from two flood events and the Sen1Floods11 dataset. The model trained using dual polarized data outperforms the S-1FS significantly and increases the intersection over union (IoU) score by 5%. Using a weighted combination of the cross entropy and the Lovász loss increases the IoU score by another 2%. Geometric data augmentation degrades the performance while radiometric data augmentation leads to better testing results. FCN/DeepLabV3+/U-Net/U-Net++ perform not significantly different to AlbuNet-34. Models trained on data showing no distinct inundation perform very well in mapping the water extent during two flood events, reaching IoU scores of 0.96 and 0.94, respectively, and perform comparatively well on the Sen1Floods11 dataset

Assessment of recent trends of wildfire activity in Europe

DLR's Earth Observation Center (EOC) is operating a burnt area monitoring service for Europe. It is based on mid-resolution Sentinel-3 OLCI (Ocean and Land Color Instrument) satellite imagery, research of methodologies and developments of processing chains (Nolde et al. 2020) and provides burnt area information twice a day in near-real time. The service is fully automated and targeted at supporting both, rapid mapping activities and timely post fire damage assessment. It is designed incrementally, in a way that generated results are refined and optimized as soon as new satellite data becomes available. Besides the burn perimeter and detection date, the output data also contains detailed information regarding the burn severity of each detected burnt area While the service is primarily intended for continental-scale monitoring of wildfire occurrence, the accumulated results allows the analysis of multi-year development trends regarding the mentioned parameters in addition. This study, firstly, demonstrates the capabilities of the wildfire monitoring service, and secondly, analyses trends regarding fire extent, seasonality, and burn severity for the region of Europe regarding the recent years. The results are set in relation with findings derived for study areas outside Europe, namely California / USA and New South Wales / Australia. The focus of the study is put on fire severity, since this information is not present in most common, large scale burnt area datasets. Yet, fire severity is a critical aspect of fire regimes, determining fire impacts on ecosystem attributes and associated post-fire recovery. In addition to the analysis of large-scale wildfire activity, the results of the burnt area monitoring service can be utilized to monitor the spatio-temporal evolution of large lava flow events in near-real time, as for example the 2018 Lower East Rift Zone eruption at Kilauea Volcano, Hawaii or the 2021 eruption on La Palma. Reference: Nolde, M., Plank, S., & Riedlinger, T. (2020). An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time. Remote Sensing, 12(13), 2162