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Sea-surface petroleum pollution is observed as "oil slicks" (i.e., "oil spills" or "oil seeps") and can be confused with "look-alike slicks" (i.e., environmental phenomena, such as low-wind speed, upwelling conditions, chlorophyll, etc.) in synthetic aperture radar (SAR) measurements, the most proficient satellite sensor to detect mineral oil on the sea surface. Even though machine learning (ML) has become widely used to classify remotely-sensed petroleum signatures, few papers have been published comparing various ML methods to distinguish spills from look-alikes. Our research fills this gap by comparing and evaluating six traditional techniques: simple (naive Bayes (NB), K-nearest neighbor (KNN), decision trees (DT)) and advanced (random forest (RF), support vector machine (SVM), artificial neural network (ANN)) applied to different combinations of satellite-retrieved attributes. 36 ML algorithms were used to discriminate "ocean-slick signatures" (spills versus look-alikes) with ten-times repeated random subsampling cross validation (70-30 train-test partition). Our results found that the best algorithm (ANN: 90%) was >20% more effective than the least accurate one (DT: ~68%). Our empirical ML observations contribute to both scientific ocean remote-sensing research and to oil and gas industry activities, in that: (i) most techniques were superior when morphological information and Meteorological and Oceanographic (MetOc) parameters were included together, and less accurate when these variables were used separately; (ii) the algorithms with the better performance used more variables (without feature selection), while lower accuracy algorithms were those that used fewer variables (with feature selection); (iii) we created algorithms more effective than those of benchmark-past studies that used linear discriminant analysis (LDA: ~85%) on the same dataset; and (iv) accurate algorithms can assist in finding new offshore fossil fuel discoveries (i.e., misclassification reduction). [ABSTRACT FROM AUTHOR]

Estimating sea ice motion from synthetic aperture radar (SAR) imagery at C-band is the most reliable approach because of its high spatial resolution and ever increasing temporal resolution given the multiple current and upcoming SAR platforms. However, there is still uncertainty in SAR derived sea ice motion depending on the ice type and its thermodynamic state. There have been suggestions (mostly theoretical) that use of L-band SAR and its inherent longer wavelength (15–30 cm) and subsequent increased penetration capability could be beneficial for estimating sea ice motion, especially during the melt season. Here, we estimate and analyze sea ice motion for 9 pairs of C- and L-band SAR imagery from RADARSAT-2, PALSAR-1 and PALSAR-2 located in the Canadian Arctic over a variety of sea ice types at different thermodynamic states. Results show that the increased signal penetration of L-band SAR into multi-year ice (MYI) during the melt season facilitates the detection of more motion vectors with stronger cross-correlation coefficients compared to C-band SAR. Over newly formed ice and dry first-year ice, the reduced sensitivity to surface scattering and richer texture from L-band SAR imagery facilitates the detection of more motion vectors with stronger cross-correlation coefficients compared to C-band SAR. Over dry MYI, L-band provided stronger cross-correlation coefficients but C-band detected more motion vectors with a more representative spatial distribution. With Arctic sea ice continuing shift from a multi-year to first-year dominated icescape, coupled with an increasing melt season length, L-band SAR's ability to provide improved sea ice motion estimates during both the melt and freeze-up time periods could prove even more useful in the coming decades. [ABSTRACT FROM AUTHOR]

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Our research focuses on refining the ability to discriminate two petrogenic oil-slick categories: the sea surface expression of naturally-occurring oil seeps and man-made oil spills. For that, a long-term RADARSAT-2 dataset (244 scenes imaged between 2008 and 2012) is analyzed to investigate oil slicks (4562) observed in the Gulf of Mexico (Campeche Bay, Mexico). As the scientific literature on the use of satellite-derived measurements to discriminate the oil-slick category is sparse, our research addresses this gap by extending our previous investigations aimed at discriminating seeps from spills. To reveal hidden traits of the available satellite information and to evaluate an existing Oil-Slick Discrimination Algorithm, distinct processing segments methodically inspect the data at several levels: input data repository, data transformation, attribute selection, and multivariate data analysis. Different attribute selection strategies similarly excel at the seep-spill differentiation. The combination of different Oil-Slick Information Descriptors presents comparable discrimination accuracies. Among 8 non-linear transformations, the Logarithm and Cube Root normalizations disclose the most effective discrimination power of almost 70%. Our refined analysis corroborates and consolidates our earlier findings, providing a firmer basis and useful accuracies of the seep-spill discrimination practice using information acquired with space-borne surveillance systems based on Synthetic Aperture Radars.

In 2013, the National Oceanic and Atmospheric Administration (NOAA) brought to operations a synthetic aperture radar (SAR)-derived subkilometer resolution wind speed product. This transition from research to operations comes 35 years after the 1978 launch of the US Seasat satellite, which demonstrated that radar backscatter from active microwave instruments in orbit can provide detailed information about ocean surface waves, winds, and sea surface height. NOAA's initial source of data for operational SAR winds is Radarsat-2, which was launched in 2007 by the Canadian Space Agency. In this paper, we discuss the history of our understanding of the relationship between microwave measurements, particularly SAR measurements, and wind speed, and how a spaceborne instrument first designed to measure ocean waves is now routinely used to derive wind speeds.

Hurricanes are among the most destructive global natural disasters. Thus recognizing and extracting their morphology is important for understanding their dynamics. Conventional optical sensors, due to cloud cover associated with hurricanes, cannot reveal the intense air–sea interaction occurring at the sea surface. In contrast, the unique capabilities of spaceborne synthetic aperture radar (SAR) data for cloud penetration, and its backscattering signal characteristics enable the extraction of the sea surface roughness. Therefore, SAR images enable the measurement of the size and shape of hurricane eyes, which reveal their evolution and strength. In this study, using six SAR hurricane images, we have developed a mathematical morphology method for automatically extracting the hurricane eyes from C-band SAR data. Skeleton pruning based on discrete skeleton evolution (DSE) was used to ensure global and local preservation of the hurricane eye shape. This distance weighted algorithm applied in a hierarchical structure for extraction of the edges of the hurricane eyes, can effectively avoid segmentation errors by reducing redundant skeletons attributed to speckle noise along the edges of the hurricane eye. As a consequence, the skeleton pruning has been accomplished without deficiencies in the key hurricane eye skeletons. A morphology-based analyses of the subsequent reconstructions of the hurricane eyes shows a high degree of agreement with the hurricane eye areas derived from reference data based on NOAA manual work. [ABSTRACT FROM AUTHOR]

Doppler velocity can be derived by calculating Doppler shift anomalies between predicted and estimated Doppler centroids. The predicted Doppler centroid is calculated based on a geometric model of satellite assuming that the target is not moving. The estimated Doppler centroid can be directly extracted from the raw SAR signal data by applying the average cross-correlation coefficient method. It is known that wind-generated ocean waves can significantly contribute to Doppler velocity due to the correlation between orbital motions of the waves and (tilt and hydrodynamic) modulated radar cross sections, in addition to what sea surface current contributes. In this study, the characteristics of Doppler velocities under hurricane conditions were investigated using RADARSAT-1 ScanSAR raw data. Five different hurricanes (Hurricane Dean, Hurricane Ivan, Hurricane Kyle, Hurricane Lili, and Typhoon Xangsane) and sequential acquisitions of two hurricanes (Hurricane Kyle and Hurricane Lili) were selected to study the contribution of wind-induced waves to Doppler velocities and compared with in situ measurements of drifting buoys. The results show that hurricane-generated seas and associated winds and waves appear to be different from ordinary sea state. This leads to lower estimates of Doppler velocities than expected and much closer to sea surface current velocities. [ABSTRACT FROM PUBLISHER]

We have investigated the use of C-band compact polarimetric synthetic aperture radar for estimation of ocean surface wind speeds. Using 1399 buoy observations collocated with Radarsat-2 scenes, compact polarimetric data was simulated for two of the Radarsat Constellation’s planned wide swath imaging modes. Provided the wind direction is known or can be estimated, our results demonstrate that wind speed can be estimated from the right-vertical polarization channel of the compact polarimetry using a combination of the CMOD5 geophysical model function and a linear model. If wind speed estimation without wind direction input is desired, the randomly-polarized component of the backscattered power can be used in a similar fashion to that of the linear cross-polarizations, but is less affected by increases in the noise effective sigma-zero of the data. A model is proposed for the randomly-polarized power as a function of incidence angle and wind speed, independent of wind direction. The results suggest that compact polarimetry is a strong alternative to linearly polarized synthetic aperture radar data for wind speed estimation applications, particularly for wide swath imaging modes with a high noise floor. [ABSTRACT FROM AUTHOR]

We map and monitor the condition of linear structures using Measurement Points (MPs) from satellite Interferometric Synthetic Aperture Radar (InSAR), and deal with the uncertainty of localising the detected deformation along the building elevation. We combine spatial information of the MPs with elevation measurements collected by Real Time Kinematic (RTK) GPS surveying to understand where structural motions occurred. The MPs are geolocated along the z-direction by exploiting their height information (h MP ) compared to the elevation of the surveyed buildings and surrounding ground (h GPS ). This approach aims to find a good compromise between the required accuracy and repeatability, and the advantages of reduced time-consumption and cost-effectiveness offered by RTK GPS. Reliability of the method is proved via the experiment on the Roman Aqueducts in the southern peri-urban quarters of the city of Rome, Italy. We focus on the linear man-made structures of the ancient to modern aqueduct systems. These are challenging anthropogenic features to monitor with InSAR due to their huge extent, variety of condition and architectural complexity. Of the total 13,519 MPs retrieved from SqueeSAR™ processing of 87 RADARSAT-1 Fine Beam Mode 3 ascending scenes (2003–2010), the MPs spatially attributed to the local linear features and the surroundings are analysed with regard to: (i) their densities against building type, structure planimetric orientation and vegetation coverage; and (ii) their height distribution against RTK GPS micro-topographic surveying in seven sample areas. Numerical analysis of h MP –h GPS pairs result in high correlation (R 2 equals 0.970), and their cross-comparison allows validation of 3D geolocation of the MPs, also demonstrating the usefulness of complementary surveying by laser distance meter device whenever RTK GPS is not feasible. Cross-referenced h MP values are then used to reclassify the MPs and generate final map products to support the design of in-situ inspection activities. We discuss beneficial impacts for condition monitoring and assessment at the scale of single building through the examples of the medieval tower Torre del Fiscale and the Roman arcades of the Claudian Aqueduct. The MP height information improves the understanding of the deformation estimates, and also contributes to address hazard mitigation measures and restorations. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar technology has revolutionized earth observation with very high resolutions of below 5m, making it possible to distinguish individual urban features like buildings and even cars on the surface of the earth. But, the difficulty in interpretation of these images has hindered their use. The geometry of target objects and their orientation with respect to the SAR sensor contribute enormously to unexpected signatures on SAR images. Geometry of objects can cause single, double or multiple reflections which, in turn, affect the brightness value on the SAR images. Occlusions, shadow and layover effects are present in the SAR images as a result of orientation of target objects with respect to the incident microwaves. Simulation of SAR images is the best and easiest way to study and understand the anomalies. This paper discusses synthetic aperture radar image simulation, with the study of effect of target geometry as the main aim. Simulation algorithm has been developed in the time domain to provide greater modularity and to increase the ease of implementation. This algorithm takes into account the sensor and target characteristics, their locations with respect to the earth, 3-dimensional model of the target, sensor velocity, and SAR parameters. two methods have been discussed to obtain position and velocity vectors of SAR sensor -- the first, from the metadata of real SAR image used to verify the simulation algorithm, and the second, from satellite orbital parameters. Using these inputs, the SAR image coordinates and backscatter coefficients for each point on the target are calculated. The backscatter coefficients at target points are calculated based on the local incidence angles using Muhleman's backscatter model. The present algorithm has been successfully implemented on radarsat-2 image of San Francisco bay area. Digital elevation models (DEMs) of the area under consideration are used as the 3d models of the target area. DEMs of different resolutions have been used to simulate SAR images in order to study how the target models affect the accuracy of simulation algorithm. The simulated images have been compared with radarsat-2 images to observe the efficiency of the simulation algorithm in accurately representing the locations and extents of different objects in the target area. The simulated algorithm implemented in this paper has given satisfactory results as the simulated images accurately show the different features present in the DEM of the target area. [ABSTRACT FROM AUTHOR]

Abstract: This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms – maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better land-cover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification. [Copyright &y& Elsevier]

An analysis of the sensitivity of synthetic aperture radar (SAR) backscatter (σo) to crop and soil conditions was conducted using 57 RADARSAT-2 C-band quad-polarized SAR images acquired from April to September 2009 for large fields of wheat, barley, oat, corn, onion, and alfalfa in Barrax, Spain. Preliminary results showed that the cross-polarized σHVo was particularly useful for monitoring both crop and soil conditions and was the least sensitive to differences in beam incidence angle. The greatest separability of barley, corn, and onion occurred in spring after the barley had been harvested or in the narrow time window associated with grain crop heading when corn and onion were still immature. The time series of σo offered reliable information about crop growth stage, such as jointing and heading in grain crops and leaf growth and reproduction in corn and onion. There was a positive correlation between σo and the Normalized Difference Vegetation Index for onion and corn but not for all crops, and the impact of view direction and incidence angle on the time series was minimal compared to the signal response to crop and soil conditions. Related to planning for future C-band SAR missions, we found that quad-polarization with image acquisition frequency from 3-6 days was best suited for distinguishing crop types and for monitoring crop phenology, single- or dual-polarization with an acquisition frequency of 3-6 days was sufficient for mapping crop green biomass, and single- or dual-polarization with daily image acquisition was necessary to capture rapid changes in soil moisture condition. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims at contributing to the elaboration of new concepts for an efficient and standardized Synthetic Aperture Radar (SAR) based monitoring of floods. Algorithms that enable an automatic delineation of flooded areas are an essential component of any SAR-based monitoring service but are to date quasi non-existent. Here we propose a hybrid methodology, which combines radiometric thresholding and region growing as an approach enabling the automatic, objective and reliable flood extent extraction from SAR images. The method relies on the calibration of a statistical distribution of ‘open water’ backscatter values inferred from SAR images of floods. A radiometric thresholding provides the seed region for a subsequent region growing process. Change detection is included as an additional step that limits over-detection of inundated areas. Two variants of the proposed flood extraction algorithm (with and without integration of reference images) are tested against four state-of-the-art benchmark methods. The methods are evaluated through two case studies: the July 2007 flood of the Severn river (UK) and the February 1997 flood of the Red river (US). Our trial cases show that considering a reference pre- or post-flood image gives the same performance as optimized manual approaches. This encouraging result indicates that the proposed method may indeed outperform all manual approaches if no training data are available and the parameters associated with these methods are determined in a non-optimal way. The results further demonstrate the algorithm’s potential for accurately processing data from different SAR sensors. [Copyright &y& Elsevier]

Abstract: The status quo in forestry practice is to leave standard width buffers around water bodies in order to prevent the excess transport of sediments and nutrients from terrestrial to aquatic systems. This practice does not seem to be effective in the sub-humid boreal forest where climatic and physiographic variability produces complex hydrologic pathways not well protected by standard width buffers. We developed a remote sensing technique that forms a hydrologic basis for buffer strip design. Synthetic aperture radar (SAR) imagery was used to map, both inundated and saturated areas (herein called wet areas) amenable for surface transport of nutrients and sediments on a low relief landscape in northern Alberta, Canada. Wet areas coverage of the Moose Lake drainage basin showed a semi-logarithmic relationship with daily discharge (r 2 =0.72, p <0.001, n =18). This relationship was used to define a flow–duration curve for wet areas that could be used as an aspatial assessment of what proportion of a drainage basin should be protected. A probability map of wet areas formation was calculated from the database of 18 images. We demonstrated how the probability map may be used to design adaptive buffer strips for the mitigation of increased nutrient loading to boreal lakes following timber harvesting. [Copyright &y& Elsevier]

Abstract: Hydrology is the single most important abiotic factor in the formation and functioning of a wetland. Many limitations still exist to accurately characterizing wetland hydrology over large spatial extents, especially in forested wetlands. Imaging radar has emerged as a viable tool for wetland flood mapping, although the limitations of radar data remain uncertain. The influence of incidence angle on the ability to detect flooding in different forest types was examined using C-HH Radarsat-1 data (23.5°, 27.5°, 33.5°, 39.0°, 43.5°, and 47.0°) during the leaf-off and leaf-on seasons. The ability to detect flooding under leaf-on conditions varied much more according to incidence angle while forest type (open canopy tupelo-cypress, tupelo-cypress, and bottomland hardwood) had a greater effect during the leaf-off season. When all forest types were considered together, backscatter generally decreased with increasing incidence angle under all conditions (2.45 dB between 23.5° and 47.0° flooded, leaf-off; 2.28 dB between 23.5° and 47.0° not flooded, leaf-off; 0.62 between 23.5° and 43.5° flooded, leaf-on; 1.73 dB between 23.5° and 43.5° not flooded, leaf-on; slope was not constant between incidence angles), but the distinction between flooded and non-flooded areas did not decline sharply with incidence angle. Differentiation of flooded and non-flooded forests was similar during the leaf-off and leaf-on seasons. The ability to detect inundation under forest canopies was less than expected at smaller incidence angles and greater than expected at larger incidence angles, based on the results of previous studies. Use of a wider range of incidence angles during the entire year increases the temporal resolution of imagery which may, in turn, enhance mapping of inundation beneath forest canopies. [Copyright &y& Elsevier]

Industrial activity in Canada’s north is increasing, placing demands on the use of water from lakes to build ice roads. Winter water withdrawal from these lakes has the potential to impact overwintering fish. Removal of water from small lakes can decrease oxygen and habitat available to fish. To address this issue, a protocol has been developed by the Department of Fisheries and Oceans outlining water withdrawal thresholds. Bathymetric surveys are the traditional method to determine lake depth, but are costly given the remoteness of northern lakes. This paper investigates the use of satellite C-band synthetic aperture radar (SAR) remote sensing technology as a potential alternative or complement to traditional survey methods. Previous research has shown that a SAR can detect the transition from grounded to floating ice on lakes, or if a lake is completely frozen. Grounded ice has a dark signature while floating ice appears very bright in contrast. Similar results were observed for the datasets acquired in the study area. This suggests that lakes that freeze completely to the bottom can be identified using SAR. Such water bodies would not be viable fish overwintering habitat and can therefore be used as water sources without thresholds necessary. However, attempts to accurately calculate the depth of the ice at the grounded-floating ice boundary using bathymetric profiles acquired in the summer and lake ice thickness measurements from a reference lake near Inuvik proved to be unreliable. [ABSTRACT FROM AUTHOR]

Abstract: Since its launch on November 4, 1995 and the start of the routine operation on April 1, 1996, RADARSAT-1, the first Canadian Synthetic Aperture Radar (SAR) remote sensing satellite, has provided calibrated data to worldwide users for their intended applications. From the early qualification stages of the mission, both single beams and ScanSAR operating modes are monitored routinely for radiometric calibration performance using images of the Amazon Rainforest, and for image quality performance using images of RADARSAT-1 Precision Transponders. After the initial Calibration Phase and the Antarctic Mapping Mission in 1997, a systematic calibration monitoring strategy showed changes in the characteristics of several previously calibrated elevation antenna patterns. Compensations for these changes are made in the processor by re-calibrating the beams. In addition, a major upgrade of the ScanSAR processor completed at the Canadian Data Processing Facility (CDPF) in 2002 yielded to significant improvements in image quality and radiometry. Throughout the nominal mission life of 5 years and the 3 years of the current extended mission, which started in early 2001, the Canadian Data Processing Facility continued to provide radiometrically and geometrically calibrated RADARSAT-1 products to users. In late October 2000, concerns began to rise of the possibility of failure of the Horizon Scanner 1, which would result in operating the spacecraft in a mode known as ‘Attitude Determination Method 3’ (ADM3), causing a decrease in attitude control performance of the spacecraft compared to the current operation in primary ADM1. Experiments were conducted to better understand the impact on processing and image quality when in ADM3 mode. No major impact on image quality was noticed with adapted re-processing. In mid 2002, due to aging considerations for the On-Board Recorder, natural sites within Canadian data reception masks have been envisioned for their potential to support radiometric analyses, as an alternative to the Amazon Rainforest where images are recorded. From several sites, a Boreal Forest location near Hearst, Ontario, Canada was chosen for testing radiometric measurements, using specific beams to cover the entire range of incidence angles. [Copyright &y& Elsevier]

In this letter, a new wind-vector algorithm is presented that uses radar backscatter sigma0 measurements at two adjacent subscenes of RADARSAT-1 synthetic aperture radar (SAR) images, with each subscene having slightly different geometry. Resultant wind vectors are validated using in situ buoy measurements and compared with wind vectors determined from a hybrid wind-retrieval model using wind directions determined by spectral analysis of wind-induced image streaks and observed by colocated QuikSCAT measurements. The hybrid wind-retrieval model consists of CMOD-IFR2 [applicable to C-band vertical-vertical (VV) polarization] and a C-band copolarization ratio according to Kirchhoff scattering. The new algorithm displays improved skill in wind-vector estimation for RADARSAT-1 SAR data when compared to conventional wind-retrieval methodology. In addition, unlike conventional methods, the present method is applicable to RADARSAT-1 images both with and without visible streaks. However, this method requires ancillary data such as buoy measurements to resolve the ambiguity in retrieved wind direction [ABSTRACT FROM PUBLISHER]

A multisensor data fusion algorithm based on a multilayer neural network is presented for sea ice classification in the winter period. The algorithm uses European Remote Sensing (ERS), RADARSAT synthetic aperture radar (SAR), and low-resolution television camera images and image texture features. Based on a set of in situ observations made at the Kara Sea, a neural network is trained, and its structure is optimized using a pruning method. The performance of the algorithm with different combinations of input features (sensors) is assessed and compared with the performance of a linear discriminant analysis (LDA)-based algorithm. We show that for both algorithms a substantial improvement can be gained by fusion of the three different types of data (91.2% for the neural network) as compared with single-source ERS (66.0%) and RADARSAT (70.7%) SAR image classification. Incorporation of texture increases classification accuracy. This positive effect of texture becomes weaker with increasing number of sensors (from 8.4 to 6.4 percent points for the use of two and three sensors, respectively). In view of the short training time and smaller number of adjustable parameters, this result suggests that semiparametric classification methods can be considered as a good alternative to the neural networks and traditional parametric statistical classifiers applied for the sea ice classification. [ABSTRACT FROM AUTHOR]

RADARSAT-1 synthetic aperture radar (SAR) images from the western Great Basin, North America are used to map geomorphic features using environmental data (increased soil moisture), differences in incidence angles and ascending/descending satellite passes. These attributes are shown to increase the ability to delineate subtle geomorphic features along old shorelines. The change in moisture and the temporal resolution of the images provides a unique opportunity to use moisture as a geomorphic mapping tool in addition to traditional techniques such as image texture and the size and shape of the image features. [Copyright &y& Elsevier]

This study quantified relationships between forest composition and flooding gradients on the Roanoke River floodplain, North Carolina. Because flooding is highly variable in time and space, the research was designed to determine the specific hydrological parameters that control woody species abundance on the landscape scale. I specifically tested the importance of spring vs. yearly flood duration, as well as flood duration during hydrologically wet vs. dry years. Field vegetation samples of woody species composition were integrated with spatial data from a Landsat Thematic Mapper (TM) classification and a flood simulation model derived in part from synthetic aperture radar (SAR) imagery. Flood simulations were output and summarized for the periods 1912–1950 (before dams were constructed on the river) and 1965–1996 (after all of the dams were completed). Tenth percentile (dry), median, and 90th percentile (wet) hydroperiod (flood duration) regimes were generated for the spring and year, both pre- and post-dam. Detrended correspondence analysis (DCA) was used to ordinate the plot data, and correlation/regression between ordination axis scores and the flood variables were used to explore the relationships between flooding and species composition. Nineteenth percentile hydroperiod (i.e., wet conditions) correlated most strongly with DCA axis 1 ( r>0.9), indicating that inundation during extremely wet years strongly controls species composition on the floodplain. The results were used to quantitatively determine the niche width for both species and mapped vegetation classes in terms of number of days flooded annually and during the spring growth period. The results suggest that spring hydroperiod is an important mechanism that may drive competitive sorting along the flooding gradient, especially during the early years of succession (i.e., pre-dam, which represents the period during which most of the forests sampled were established), and that annual hydroperiod affects the relative dominance of species as the forests mature. [ABSTRACT FROM AUTHOR]

The development of effective strategies to manage the river ice breakup process or the associated risks is hindered by a lack of understanding and information. Radar earth observation satellites offer excellent potential for collecting up-to-date information on the conditions of and changes in river ice cover during the breakup period. This text describes the development, performance and limitations of an automated procedure to map breaking river ice by means of C-band, HH-polarized Synthetic Aperture Radar (SAR) images. An original two-step supervised classification model (IceBC), which uses backscatter intensities, lies at the core of the procedure. First, IceBC identifies three primary classes: water, sheet ice and rubble ice. Next, each primary ice class is divided in three secondary classes that denote top surface roughness scale differences. Input images must have incidence angles from ~27° to ~60°. Below ~36°, IceBC may assign a class labelled "unclassified" to water or sheet ice pixels. The primary classification model yields overall accuracies of ~86% and ~93% for independent test pixels with incidence angles ≤ ~49° and ≥ ~29° or ≥ ~36°, respectively. The associated class accuracies for water, sheet ice and rubble ice are ~97% & 96%, ~69% & 85% and ~97% & 99%. Given its connection to ice jam flood events, the classification accuracy achieved for rubble ice is particularly important. Maps produced by means of IceBC comprise detailed spatial information regarding ice cover conditions and the development of the breakup process. Their quality may be affected by: freezing conditions, wet snow cover, meltwater pools, infrastructure, rapids or high winds. Monitoring is the key to managing the impacts of most of these challenges. An IceBC prototype has been used operationally since 2015. Unlabelled Image • One out of three floods in Canada results from the jamming of breaking river ice. • River ice breakup processes are difficult to document and thus poorly understood. • Radar satellites are very useful for collecting data on breaking river ice cover. • Radar images are used to map water and ice cover with different roughness levels. • Our supervised classifier performs better than popular unsupervised classifiers. [ABSTRACT FROM AUTHOR]

A novel empirical approach to categorize oil slicks' sea surface expressions in synthetic aperture radar (SAR) measurements into oil seeps or oil spills is investigated, contributing both to academic remote sensing research and to practical applications for the petroleum industry. We use linear discriminant analysis (LDA) to try accuracy improvements from our previously published methods of discriminating seeps from spills that achieved ~70% of overall accuracy. Analyzing 244 RADARSAT-2 scenes containing 4562 slicks observed in Campeche Bay (Gulf of Mexico), our exploratory data analysis evaluates the impact of 61 combinations of SAR backscatter coefficients (σ°, β°, γ°), SAR calibrated products (received radar beam given in amplitude or decibel, with or without a despeckle filter), and data transformations (none, cube root, log10). The LDA ability to discriminate the oil-slick category is rather independent of backscatter coefficients and calibrated products, but influenced by data transformations. The combination of attributes plays a role in the discrimination; combining oil-slicks' size and SAR information is more effective. We have simplified our analyses using fewer attributes to reach accuracies comparable to those of our earlier studies, and we suggest using other multivariate data analyses—cubist or random forest—to attempt to further improve oil-slick category discrimination. [ABSTRACT FROM AUTHOR]

Mariners navigating within Canadian waters rely on Canadian Hydrographic Service (CHS) navigational charts to safely reach their destinations. To fulfil this need, CHS charts must accurately reflect the current state of Canadian coastal regions. While many coastal regions are stable, others are dynamic and require frequent updates. In order to ensure that important and potentially dangerous changes are reflected in CHS products, the organization, in partnership with the Canadian Space Agency, is exploring coastal change detection through satellite remote sensing (SRS). In this work, CHS examined a hybrid shoreline extraction approach which uses both Synthetic Aperture Radar (SAR) and optical data. The approach was applied for a section of the Mackenzie River, one of Canada's most dynamic river systems. The approach used RADARSAT-2 imagery as its primary information source, due to its high positioning accuracy (5 m horizontal accuracy) and ability to allow for low and high water line charting. Landsat represented the primary optical data source due to its long historical record of Earth observation data. Additional sensors, such as Sentinel-2 and WorldView, were also used where a higher resolution was required. The shoreline extraction process is based on an image segmentation approach that uses both the radar and optical data. Critical information was collected using the automated approach to support chart updates, resulting in reductions to the financial, human and time factors present within the ship-based hydrographic survey techniques traditionally used for chart improvements. The results demonstrate the potential benefit of wide area SRS change detection within dynamic waterways for navigational chart improvements. The work also demonstrates that the approach developed for RADARSAT-2 could be implemented with data from the forthcoming RADARSAT Constellation Mission (RCM), which is critical to ensure project continuity. [ABSTRACT FROM AUTHOR]

Our research focuses on refining the ability to discriminate two petrogenic oil-slick categories: the sea surface expression of naturally-occurring oil seeps and man-made oil spills. For that, a long-term RADARSAT-2 dataset (244 scenes imaged between 2008 and 2012) is analyzed to investigate oil slicks (4562) observed in the Gulf of Mexico (Campeche Bay, Mexico). As the scientific literature on the use of satellite-derived measurements to discriminate the oil-slick category is sparse, our research addresses this gap by extending our previous investigations aimed at discriminating seeps from spills. To reveal hidden traits of the available satellite information and to evaluate an existing Oil-Slick Discrimination Algorithm, distinct processing segments methodically inspect the data at several levels: input data repository, data transformation, attribute selection, and multivariate data analysis. Different attribute selection strategies similarly excel at the seep-spill differentiation. The combination of different Oil-Slick Information Descriptors presents comparable discrimination accuracies. Among 8 non-linear transformations, the Logarithm and Cube Root normalizations disclose the most effective discrimination power of almost 70%. Our refined analysis corroborates and consolidates our earlier findings, providing a firmer basis and useful accuracies of the seep-spill discrimination practice using information acquired with space-borne surveillance systems based on Synthetic Aperture Radars. [ABSTRACT FROM AUTHOR]

This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal and edge detection filtering on RADARSAT images for preprocessing to remove salt and pepper distortion. Different segmentation analyses were also applied to the filtered images. Firstly, multiresolution segmentation, maximum spectral difference and chessboard segmentation were performed to separate land pixels from ocean ones. Next, the Taguchi method was used to optimise segmentation parameters. Subsequently, a support vector machine algorithm was applied on the optimised segments to classify shorelines with an accuracy of 98% for both temporal images. Results were validated using a thematic map from the Department of Survey and Mapping of Malaysia. The change detection showed an average difference in the shoreline of 12.5 m between 2003 and 2014. The methods developed in this study demonstrate the ability of active SAR sensors to map and detect shoreline changes, especially during low or high tides in tropical regions where passive sensor imagery is often masked by clouds. [ABSTRACT FROM AUTHOR]