Levee Slide Detection using Synthetic Aperture Radar Magnitude and Phase

The objectives of this research are to support the development of state-of-the-art methods using remotely sensed data to detect slides or anomalies in an efficient and cost-effective manner based on the use of SAR technology. Slough or slump slides are slope failures along a levee, which leave areas of the levee vulnerable to seepage and failure during high water events. This work investigates the facility of detecting the slough slides on an earthen levee with different types of polarimetric Synthetic Aperture Radar (polSAR) imagery. The source SAR imagery is fully quad-polarimetric L-band data from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area encompasses a portion of the levees of the lower Mississippi river, located in Mississippi, United States. The obtained classification results reveal that the polSAR data unsupervised classification with features extraction produces more appropriate results than the unsupervised classification with no features extraction. Obviously, supervised classification methods provide better classification results compared to the unsupervised methods. The anomaly identification is good with these results and was improved with the use of a majority filter. The classification accuracy is further improved with a morphology filter. The classification accuracy is significantly improved with the use of GLCM features. The classification results obtained for all three cases (magnitude, phase, and complex data), with classification accuracies for the complex data being higher, indicate that the use of synthetic aperture radar in combination with remote sensing imagery can effectively detect anomalies or slides on an earthen levee. For all the three samples it consistently shows that the accuracies for the complex data are higher when compared to those from the magnitude and phase data alone. The tests comparing complex data features to magnitude and phase data alone, and full complex data, and use of post-processing filter, all had very high accuracy. Hence we included more test samples to validate and distinguish results

Accuracy Analysis Comparison of Supervised Classification Methods for Anomaly Detection on Levees Using SAR Imagery

This paper analyzes the use of a synthetic aperture radar (SAR) imagery to support levee condition assessment by detecting potential slide areas in an efficient and cost-effective manner. Levees are prone to a failure in the form of internal erosion within the earthen structure and landslides (also called slough or slump slides). If not repaired, slough slides may lead to levee failures. In this paper, we compare the accuracy of the supervised classification methods minimum distance (MD) using Euclidean and Mahalanobis distance, support vector machine (SVM), and maximum likelihood (ML), using SAR technology to detect slough slides on earthen levees. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) uninhabited aerial vehicle synthetic aperture radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers

Identifying the Vulnerability of Earthen Levees to Slump Slides using Geotechnical and Geomorphological Parameters

The main goal of this research is to investigate vulnerability of levees to future slump slides. In the first part, polarimetric synthetic aperture radar (PolSAR) imagery is used as input in an automated classification system for characterizing areas on the levee having anomalies. In addition, a set of in-situ soil data is collected to provide detailed soil properties over the study area. In-situ soil properties of different classes characterized by the classifier are analyzed to determine how similarities between different areas. The second part, a database including of 34 slump slides that occurred in the lower Mississippi River levee system over a period of two years is used. The impacts of rainfall as well as several spatial geometrical and geomorphological variables (including channel width, river sinuosity index, riverbank erosion, channel shape condition and distance to river) are analyzed and tested for significance and used for developing a logistic regression model

Levee as a Flood Mitigation Option in Malaysia, Its' Susceptibility to Failure and Design Approach

Flooding is a major concern globally affecting many countries around the world, including Malaysia. As Earth undergo global climate change, intensified flood events are also expected to increase. One of the available structural flood mitigation measures is the implementation of levees. Effective and economical in preventing floods, levees can also be integrated into urban landscaping works and consequently improve the aesthetic appeal of the river frontage. However, levees also pose a critical risk as it can be catastrophic if any stretch of the levee structure fails. This paper summarizes the type of levee failures that can occur and discusses the basic design checks required thus providing levee designers an overview of the risks that needs to be addressed to ensure adequate levee design

Flood mapping in vegetated areas using an unsupervised clustering approach on Sentinel-1 and-2 imagery

The European Space Agency's Sentinel-1 constellation provides timely and freely available dual-polarized C-band Synthetic Aperture Radar (SAR) imagery. The launch of these and other SAR sensors has boosted the field of SAR-based flood mapping. However, flood mapping in vegetated areas remains a topic under investigation, as backscatter is the result of a complex mixture of backscattering mechanisms and strongly depends on the wave and vegetation characteristics. In this paper, we present an unsupervised object-based clustering framework capable of mapping flooding in the presence and absence of flooded vegetation based on freely and globally available data only. Based on a SAR image pair, the region of interest is segmented into objects, which are converted to a SAR-optical feature space and clustered using K-means. These clusters are then classified based on automatically determined thresholds, and the resulting classification is refined by means of several region growing post-processing steps. The final outcome discriminates between dry land, permanent water, open flooding, and flooded vegetation. Forested areas, which might hide flooding, are indicated as well. The framework is presented based on four case studies, of which two contain flooded vegetation. For the optimal parameter combination, three-class F1 scores between 0.76 and 0.91 are obtained depending on the case, and the pixel- and object-based thresholding benchmarks are outperformed. Furthermore, this framework allows an easy integration of additional data sources when these become available

Burned area detection and mapping using Sentinel-1 backscatter coefficient and thermal anomalies

This paper presents a burned area mapping algorithm based on change detection of Sentinel-1 backscatter data guided by thermal anomalies. The algorithm self-adapts to the local scattering conditions and it is robust to variations of input data availability. The algorithm applies the Reed-Xiaoli detector (RXD) to distinguish anomalous changes of the backscatter coefficient. Such changes are linked to fire events, which are derived from thermal anomalies (hotspots) acquired during the detection period by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Land cover maps were used to account for changing backscatter behaviour as the RXD is class dependent. A machine learning classifier (random forests) was used to detect burned areas where hotspots were not available. Burned area perimeters derived from optical images (Landsat-8 and Sentinel-2) were used to validate the algorithm results. The validation dataset covers 21 million hectares in 18 locations that represent the main biomes affected by fires, from boreal forests to tropical and sub-tropical forests and savannas. A mean Dice coefficient (DC) over all studied locations of 0.59±0.06 (±confidence interval, 95%) was obtained. Mean omission (OE) and commission errors (CE) were 0.43±0.08 and 0.37±0.06, respectively. Comparing results with the MODIS based MCD64A1 Version 6, our detections are quite promising, improving on average DC by 0.13 and reducing OE and CE by 0.12 and 0.06, respectively.European Space AgencyMinisterio de Educación, Cultura y Deport

Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography

This book focuses on remote sensing for urban deformation monitoring. In particular, it highlights how deformation monitoring in urban areas can be carried out using Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions show the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. Some of them show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This book is dedicated to the technical and scientific community interested in urban applications. It is useful for choosing the appropriate technique and gaining an assessment of the expected performance. The book will also be useful to researchers, as it provides information on the state-of-the-art and new trends in this fiel