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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]

Year-round shipping is becoming common in the Canadian Arctic, a region rich in increasingly accessible natural resources. Currently, an average of two round-trip winter voyages (four single transits) (January, February, March) are made annually through the Hudson Strait to service the Raglan nickel mine in Deception Bay, Quebec. During these winter transits it is common for vessels to encounter pressured ice and become beset (stuck) for hours to days at a time. Pressured (or ridged) ice is one of the most challenging navigational hazards in the Arctic as it is difficult to predict or even detect until a vessel comes into direct contact with it. Using ship logs from 33 one-way winter transits through the Hudson Strait during winter months between 2005 and 2014, we evaluate the temporal and spatial frequency of vessel besetment events and relate these events with satellite based imagery of the region in order to examine the potential of satellite-based detection of ridges for the possible future development of real-time or prediction analytics for ridging events. Results indicate that on average the ship was beset 42% of each transit while in the Hudson Strait and often became beset in areas and times of the year associated with high ridge densities. These findings provide a basis for improving observation and prediction of hazardous ice conditions through the use satellite imagery and historic analysis of besetting events, though additional research is needed. [ABSTRACT FROM AUTHOR]

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]

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]

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]

Abstract: Flooding in general is insignificant event worldwide and also in Thailand. The Central plain, the Northern plain and the northeast of Thailand are frequently flooded areas, caused by yearly monsoons. The Thai government has extra expenditure to provide disaster relief and for the restoration of flood affected structures, persons, livestock, etc. Current flood detection in real time or near real time has become a challenge in the flood emergency response. In this paper, an automatic instant time flood detection approach consisting of a data retrieval service, flood sensor observation service (SOS), flood detection web processing service (WPS) under a sensor web environment, is presented to generate dynamically real-time flood maps. A scenario of a RADARSAT and MODIS sensor web data service for flood detection cover of the Thailand Central plain is used to test the feasibility of the proposed framework. MODIS data are used to overview the wide area, while RADARSAT data are used to classify the flood area. The proposed framework using the transactional web coverage service (WCS-T) for instant flood detection processes dynamic real-time remote sensing observations and generates instant flood maps. The results show that the proposed approach is feasible for automatic instant flood detection. [Copyright &y& Elsevier]

Abstract: Many calderas in the world show long-term unrest in the form of elevated rates of deformation and seismicity, related to pressure changes and magma movements within their magmatic plumbing systems. We present new observations of the style of deformation at the Askja caldera, Iceland, since 2000, using interferometric analysis of synthetic aperture radar images (InSAR) acquired by the Canadian RADARSAT-2 satellite. When combined with previously acquired detailed geodetic observations, by various techniques, we obtain an overview of Askja''s behaviour through more than four decades. The combined dataset reveals that, during this non-eruptive period, Askja continuously subsides at a rate of 2.5–3cm/yr in 2000–2009, somewhat lower than the ~5cm/yr rate inferred for the 1983–1998 period. This behaviour of Askja is evaluated and compared to those of other restless calderas. The wrapped interferograms show three main features: (1) concentric fringes depicting subsidence in the centre of the main Askja caldera, (2) oval fringes elongated along the rift portraying subsidence and (3) subsidence in an area north of the Vatnajökull glacier. The average line-of-sight (LOS) velocity from ground to satellite was used as input for inverse modelling, of a deflating pressure source beneath the caldera, embedded in a homogeneous, elastic half-space. Two different source geometries were tested: a point pressure source and a horizontal penny shaped crack. The concentric fringes centred in the Askja caldera are best fit by a point source located at 65.05°N 16.78°W, at a depth of 3.2–3.8km with a volume decrease of 0.0012–0.0017km3/yr from 2000 to 2009, consistent with previous studies. Provisional 2D FEM models including structural complexities in the crustal layers indicate that the tectonic setting of Askja plays an important role in the continuous, long-term high subsidence rates observed there. In order to fully understand the cause and effects of the complicated tectonic setting we encourage the use of a more realistic rheological model of the area, which could lead to reinterpretation of previous model results. [Copyright &y& Elsevier]

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 magnitude, duration, and frequency of forest disturbance caused by the spruce budworm and forest tent caterpillar in northern Minnesota and neighboring Ontario, Canada have increased over the last century due to a shift in forest species composition linked to historical fire suppression, forest management, and pesticide application that has fostered increased dominance of host tree species. Modeling approaches are currently being used to understand and forecast potential management effects in changing insect disturbance trends. However, detailed forest composition data needed for these efforts is often lacking. We used partial least squares (PLS) regression to integrate different combinations of satellite sensor data including Landsat, Radarsat-1, and PALSAR, as well as pixel-wise forest structure information derived from SPOT-5 sensor data (Wolter et al., 2009), to determine the best combination of sensor data for estimating near species-level proportional forest composition (12 types: 10 species and 2 genera). Single-sensor and various multi-sensor PLS models showed distinct species-dependent sensitivities to relative basal area (BA), with Landsat variables showing greatest overall sensitivity. However, best results were achieved using a combination of data from all these sensors, with several C-band (Radarsat-1) and L-band (PALSAR) variables showing sensitivity to the composition and abundance of specific species. Pixel-level forest structure estimates derived from SPOT-5 data were generally more sensitive to conifer species abundance (especially white pine) than to hardwood species composition. Relative BA models accounted for 68% (jack pine) to 98% (maple spp.) of the variation in ground data with RMSE values between 2.46% and 5.65% relative BA, respectively. Receiver operating characteristic (ROC) curves were used to determine the effective lower limits of usefulness of species relative BA estimates which ranged from 5.94% (jack pine) to 39.41% (black ash). These estimates were then used to produce a dominant forest species map for the study region with an overall accuracy of 78%. Most notably, this approach facilitated discrimination of aspen from paper birch as well as spruce and fir from other conifer species which is crucial for the study of forest tent caterpillar and spruce budworm dynamics in the Upper Midwest. We also demonstrate that PLS regression is an effective data fusion strategy for mapping composition of heterogeneous forests using satellite sensor data. [Copyright &y& Elsevier]

Abstract: This paper presents some new concepts in the petroleum systems of the northern Labrador and southern Baffin Island offshore region of eastern Canada. The focus of this work is the region of the Hekja O-71 gas discovery of 1979 by Aquitaine, one of only five wells drilled between 1976 and 1980 within an area covering some 166,000 square kilometers within the Saglek Basin. This study emerged from a broad scale re-examination of the petroleum potential of this area “from the crust up” using regional reflection, refraction and potential field data. An opportunity to use SAR (synthetic aperture radar) data from the RADARSAT-1 Earth observation satellite was taken to incorporate alternative data sources to support this exploratory review and resource assessment. Examination of the final map of interpreted slick-like features on the sea surface, revealed a close correlation of some of these occurrences to several bathymetric features which have underlying seismic signatures similar to previously identified gas hydrate “pipes” or chimney anomalies as seen in data from offshore Nova Scotia and in the Irish Sea. Globally, many active marine hydrocarbon seeps appear on the seafloor as “pockmarks”; in this case no seafloor depressions were seen on conventional multi channel 2-D seismic data associated with active seepage. Instead, very distinct mound-like structures are seen associated with seeps at two locations east of Hudson Strait. The presence of significant cold water coral in close proximity to the active seepage sites may show a link between seafloor petroleum seepage and coral reef development similar to that observed offshore Norway. Close examination of the near surface seismic character led to the interpretation of what was originally seen as seismic noise in vintage data, as likely being fluid escape chimneys or gas pipes, along the eastern edge of the Hekja structural complex, where favourable hydrocarbon trapping conditions are present. No active seeps were seen there; this may be due to other factors, such as the nature of the fluids that are escaping or the possibility of self sealing activity by biological agents such as bacterial matting with associated carbonate hard-ground formation. One seep anomaly was noted relatively close to the major basaltic eruptive complex near the Gjoa G-37 well. This may be due to the presence of volcaniclastic- hosted hydrocarbons similar in style to those recently discovered at the Rosebank field in Paleocene volcanics in the Faroe-Shetland Basin. The occurrence of an active seep in a volcanically-dominated terrain raises the potential for a radical shift in accepted types of petroleum leads and where they may occur. The presence of apparent oil seepage versus the past history of finding only gas-prone accumulations is a strong indicator that a second, oil-prone petroleum system exists in the Saglek Basin. The signs of active petroleum systems as shown by oil seep anomalies with confirming seismic evidence of discrete sea floor structures in close proximity makes a compelling argument for re-examining these areas with an eye towards stratigraphic instead of structural plays. [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]

For remote sensing of Great Lakes ice cover, a field experiment campaign was conducted in the 1997 winter season across the Straits of Mackinac and Lake Superior. The campaign was coordinated in two expeditions on two different United States Coast Guard icebreaker vessels, the Biscayne Bay in February and the Mackinaw in March. Aboard these icebreakers, the Jet Propulsion Laboratory C-band polarimetric scatterometer was used to measure backscatter signatures of various ice types and open water at incidence angles from 0° to 60°. The radar measurements include incidence angles and polarizations of spaceborne Synthetic Aperture Radars (SAR) on ERS, RADARSAT, and Envisat satellites. The radar data together with in situ measurements form a signature library that can be used to interpret SAR data for ice classification and mapping. Results are presented for backscatter signatures of Great Lakes ice types from thin lake ice to thick brash ice with different snow-cover and surface conditions. The signature library indicates that several ice types can be identified with multi-polarization SAR data; however, single-polarization data can result in misclassification of ice and open water at different ranges of incidence angle and wind conditions. For incidence angles larger than 30°, thick brash ice, the most difficult for icebreaking operations and the most hazardous for ship navigation, can be uniquely identified by co-polarized backscatter for all wind conditions below the gale force. [ABSTRACT FROM AUTHOR]

During the 1997 winter season, shipborne polarimetric backscatter measurements of Great Lakes (freshwater) ice types using the Jet Propulsion Laboratory C-band scatterometer, together with surface-based ice physical characterization measurements and environmental parameters, were acquired concurrently with Earth Resource Satellite 2 (ERS-2) and RADARSAT Synthetic Aperture Radar (SAR) data. This polarimetric data set, composed of over 20 variations of different ice types measured at incident angles from 0° to 60° for all polarizations, was processed to radar cross-section to establish a laboratory of signatures (look-up table)for different ice types. The library is used in the computer classification of calibrated satellite SAR data. Computer analysis of ERS-2 and RADARSAT ScanSAR images of Great Lakes ice cover using a supervised classification technique indicates that different ice types in the ice cover can be identified and mapped, and that wind speed and direction can have an influence on the classification of water as ice based on single frequency, single polarization data. Once satellite SAR data are classified into ice types, the ice map provides important and necessary input for environmental protection and management, ice control and ice breaking operations, and ice forecasting and modeling efforts. [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]

Abstract: South America''s Pantanal, the world''s largest tropical wetland, contains hundreds of thousands of geochemically diverse lakes, ranging from dilute to brackish to saline in composition. These lakes form the backbone of the habitat that supports the highly diverse flora and fauna of the Pantanal, yet the natural processes that create and destroy them are largely unknown. The quantities and types of lakes in the Pantanal and their spatial distribution are therefore essential, but missing information required to understand the dynamics of the Pantanal ecosystem. RADARSAT S1 and S7, and JERS-1 imagery were integrated with field measurements of water geochemistry and characteristics of emergent aquatic vegetation for fresh and brackish lakes of the Nhecolândia region of the Brazilian Pantanal. A supervised classification was used to classify forest, pasture, bare soil, and lakes. A mask is then applied to produce an image of only lakes. The radar backscattering values were found to have a strong relationship with the emergent aquatic plant assemblages of the lakes—S1 imagery was the most useful. The plant assemblages, in turn, were observed to be strongly controlled by the total dissolved solids (TDS) and pH of the lakes. The relationships between backscattering values, plant assemblages, and geochemistry were then exploited to map the type and distribution of the lakes in the study area. Threshold rules were used to perform Level 1 and Level 2 classifications of the lakes. For the Level 1 classification, the σ o values of RADARSAT S1 effectively separated brackish (10,000>TDS>1000 mg/kg) from fresh water lakes (TDS<1000 mg/kg) with a total accuracy of 91%. For the Level 2 classification, the σ o values of RADARSAT S1 effectively separated lakes into three geochemical groups: brackish (10,000>TDS>1000 mg/kg), hard with only Typha (1000>TDS>100 mg/kg), and fresh water lakes (TDS mg/kg<100 mg/kg) with a total accuracy of 83%. Considering that the area is very remote and the lakes are very numerous, this may be the most feasible way to map lake type in the Pantanal. [Copyright &y& Elsevier]

Abstract: Southern Tunisia is dominated by early to middle Triassic continental sandstones inter-bedded with shales and conglomerates followed by late Triassic shallow marine carbonates, lower Jurassic evaporates, and upper Jurassic to lower Cretaceous clastic sedimentary rocks. These constitute the Dahar Plateau (which is part of the Ghadames Basin and it is the focus of this study) that was developed in association with regional uplift of the Saharan Platform. Efforts in mapping the details of surface geology in southern Tunisia are hindered by the lack of continuous bedrock outcrops, where some of the formations are buried under the sand of the Sahara Desert. Remote sensing data including multi-spectral optical (Landsat Enhanced Thematic Mapper (ETM+) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)), radar (RADARSAT), and Digital Elevation Models (DEMs) extracted from the Shuttle Radar Topography Mission (SRTM) data are used to trace along strike continuity of different lithological units as well as mapping morphologically defined structures in southern Tunisia. Landsat ETM+ and ASTER Red–Green–Blue (RGB) color combination images (both band and band-ratio images) have been used for the identification of various lithological units when they are exposed on the surface. On the other hand, RADARSAT images have been utilized for tracing geological formations and geological structures that are buried under thin (∼1m) sand. Fusion of optical and radar remote sensing data using Color Normalization Transformation (CNT) has been effectively implemented to further identify lithological units and geological structures. Hill-shading techniques are applied to SRTM DEMs to enhance terrain perspective views and to extract geomorphological features and morphologically defined structures through the means of lineament analysis. Results from remote sensing analysis are in good agreement with results obtained from in situ investigations including geological mapping and seismic exploration. Identifying lithological and structural features using remote sensing studies incorporated with surface and sub-surface geological investigations in southern Tunisia can aid exploration for new oil and gas fields. Such an approach of integrating remote sensing and in situ geological studies can be successfully adopted in other parts of North Africa and arid regions in general. [Copyright &y& Elsevier]

Abstract: Digital Elevation Models (DEMs) extracted from the Shuttle Radar Topography Mission (SRTM) with 90m x–y resolution, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with 15m x–y resolution have been used in conjunction with ASTER and RADARSAT images, and field studies to extract geological and geomorphological information in order to understand the geological controls on the Gorge of the Nile in Ethiopia. The Blue Nile on the NW Ethiopian Plateau forms a ∼150km bend and carves the ∼1.6km deep Gorge of the Nile. The river shows a dramatic drop in gradient from ∼4m/km to ∼0.42m/km as it spirals around Tertiary to Quaternary shield volcanoes. A ∼1200m thick section of Mesozoic sedimentary rocks bounded between Tertiary and Quaternary volcanic rocks and Neoproterozoic basement rocks is exposed within the Gorge of the Nile. Our work shows that: (1) SRTM DEMs are effective for the characterization of 3D spatial relationships between the river’s course and regional geomorphological features such as Tertiary to Quaternary shield volcanoes, Afar Depression and the Main Ethiopian Rift. These DEMs are also useful in extracting the river’s geometric properties and the analysis of drainage network. (2) ASTER band (7-3-1) and band-ratio (4/5-3/1-3/4) images better resolve lithological units and lithologically defined structures. (3) The side-looking geometry of the Standard Beam RADARSAT data is effective in mapping morphologically defined structures due to radar shadow-illumination effect. (4) Fusion of ASTER and RADARSAT data using Color Normalization Technique (CNT) enhances the mapping ability because the fused image preserves the spectral information of ASTER data and incorporates terrain morphological characteristics from RADARSAT data. (5) Three dimensional (3D) perspective views generated by draping ASTER images over ASTER DEMs are effective in mapping sub-horizontal lithological units such as those dominating the Gorge of the Nile. These perspective views are also effective in highlighting lithologically defined structures. This study also shows a number of possible geologic controls in the evolution of the Gorge of the Nile. Base-level adjustment due to regional uplift, spatial distribution of shield volcanoes, obstruction and diversion due to bed rock structures, and differential incision due to varying lithology have significant roles in deep carving of the Gorge of the Nile, deflection of the course of the Blue Nile, and in producing a diverse drainage network. [Copyright &y& Elsevier]

Abstract: Water erosion of cropland constitutes an issue for natural environments along runoff flowpaths due to property damage by soil-laden water and the associated transfer of nutrients and pesticides. In the Pays de Caux region of northwestern France, the silty soils with crusting properties induce a high risk of runoff and erosion. Changes in agricultural practices, land use and landscape patterns appear to have increased the occurrence of erosion and mud flows over the past few decades. A runoff and erosion model called STREAM, applicable to single rainfall events at catchment scale, has been developed to simulate the impacts of land-use modifications. The model takes into account processes that degrade surface states when calculating infiltration rates, as well as agricultural aspects when computing the runoff circulation network. STREAM is based on an expert-system approach that focuses on the dominant processes whilst having only a few input parameters: three of these are used to determine the runoff circulation network, and the other four to calculate infiltration rates. Input nevertheless requires field observations, which restricts application of the model to small catchments. Satellite data covering large areas is considered as an alternative input for such a model, the main objectives being to adapt STREAM accordingly, and to compare the obtained results with field data. In view of previous work involving the extraction and validation of roughness indices using RADARSAT data, this study is based on RADARSAT and LANDSAT TM data collected during the winter of 1998. After adaptation to receive remote-sensing data, the resulting STREAM-TED model requires less input, namely (1) slope and orientation, (2) land-use classification from optical remote-sensing data, (3) roughness indices from radar remote-sensing data, and (4) previous rainfall. Runoff volumes at a gauged catchment outlet (Bourville in Upper Normandy, France) are simulated by four successive versions of the model ranging from the original STREAM to the adapted STREAM-TED. Predictions of the four versions are compared, and performance of the successive simulations is assessed in relation to measured values and according to five statistical indices. Predictions of runoff volume at the catchment outlet using STREAM-TED are similar to those using the original STREAM model, but with a tendency towards overestimation. The final STREAM-TED version is capable of identifying areas sensitive to runoff within a group of catchments and could be used as a planning decision tool in the implementation of conservation practices. [Copyright &y& Elsevier]

Monitoring the dynamics of the circumpolar boreal forest (taiga) and Arctic tundra boundary is important for understanding the causes and consequences of changes observed in these areas. This ecotone, the world''s largest, stretches for over 13,400 km and marks the transition between the northern limits of forests and the southern margin of the tundra. Because of the inaccessibility and large extent of this zone, remote sensing data can play an important role for mapping the characteristics and monitoring the dynamics. Basic understanding of the capabilities of existing space borne instruments for these purposes is required. In this study we examined the use of several remote sensing techniques for characterizing the existing tundra–taiga ecotone. These include Landsat-7, MISR, MODIS and RADARSAT data. Historical cover maps, recent forest stand measurements and high-resolution IKONOS images were used for local ground truth.It was found that a tundra–taiga transitional area can be characterized using multi-spectral Landsat ETM+ summer images, multi-angle MISR red band reflectance images, RADARSAT images with larger incidence angle, or multi-temporal and multi-spectral MODIS data. Because of different resolutions and spectral regions covered, the transition zone maps derived from different data types were not identical, but the general patterns were consistent. [Copyright &y& Elsevier]

The Transantarctic Mountains represent a major Cenozoic rift-flank uplift that crosses the interior of Antarctica. On one side of the mountains lies the Ross embayment, a submarine portion of the West Antarctic rift system. The East Antarctic Ice Sheet overlies the cratonic margin of the mountains. The Transantarctic Mountains restrict flow of the ice sheet to lower elevations within the Ross embayment. Using RADARSAT imagery, radio-echo sounding data and field-based structural data, we demonstrate that RADARSAT imagery can be used for regional structural mapping of the Transantarctic Mountains and, in particular, that bedrock structures can be traced subglacially beneath the ice-covered mountain flanks. Our analysis shows that regional structures in the mountains have provided preferred pathways for ice flow from the East Antarctic Ice Sheet across the Transantarctic Mountains barrier to the Ross embayment in West Antarctica. Further regional mapping using RADARSAT imagery has the potential to delineate the regional structural architecture of the Transantarctic Mountains and to establish the degree of spatial coupling between structures associated with mountain uplift and regional drainage patterns of the Antarctic ice sheets. [Copyright &y& Elsevier]

This work introduces a new approach for utilizing RADARSAT data for oil slick detection and oil slick trajectory model in the coastal water of Malacca Straits. The concept of the study involves two sub-models. The first model contains texture algorithms for oil slick automatic detection. The second model contains the oil slick trajectory forecasting model. The oil slick trajectory model contains the integration between Doppler frequency shift model and Lagrangian model. Doppler frequency shift model is implemented to simulate tidal current pattern from RADARSAT data, while the Lagrangian model is used to predict oil slick spreading pattern. The classical Fay’s algorithm was implemented in the two models to simulate the oil slick trajectory movement.The results of this study are for a specific RADARSAT image in Malacca Straits and not a generalization for all oil slick incidents. The study shows that the entropy and homogeneity texture algorithms are appropriate for oil slick detection. The results shows that oil slick length is effected by the tidal current V component which had a maximum velocity of 1.4 m/s. The tidal current V component moved towards the north direction. The oil slick trajectory path is moved towards the coastline due to the effect of the tidal current U component. The oil slick approached the coastline within 48 h.In conclusion, RADARSAT HH polarization data can image the oil slick by using the entropy texture algorithm. The forecasting of oil slick trajectory movement can be detected by the integration of Fay’s algorithm model and Doppler frequency shift model. [Copyright &y& Elsevier]

The coastal zone of Lake Malawi contains the greatest diversity of freshwater ichthyofauna in the world. The distribution of habitats along the coast has played an important role in the speciation of fishes but has not been mapped using remote sensing due to cloud cover. The discrimination of rock, sand, and vegetated coasts of tropical Lake Malawi are investigated using synthetic aperture radar (SAR) and optical remote sensing data. The effects of coherent fading, look direction, and incident angle on SAR backscatter are investigated using eight fine-beam RADARSAT SGX images covering 31 km of coast. Adaptive filter trials demonstrate pixels with relatively low backscatter values are identified as image noise most frequently. For each class most of the SAR backscatter averages derived from high and low incident angles within each look direction are similar or within sensor calibration limits. Average rock, sand, and vegetated image tones derived from shoreline segments 150 m in length are statistically separable. Linear discriminant Analyses (LDA) of the RADARSAT and SPOT data are used to attain maximal sep- aration for 33% of rock, sand, and vegetated coastal data and to predict class membership for the 67% of coast for which class membership is known. The RADARSAT and SPOT are treated independently and then combined to use the complementary information available in multi-sensor data sets. LDA of a single extra fine beam RADARSAT image can separate rock, sand, and vegetation using SAR backscatter, coastal slope, and the angle of the shoreline relative to the satellite. Overall classification agreement is 98.5%. LDA of the four multispectral SPOT bands provided classification agreement of 79.3%. Coastal class discrimination is improved with the addition of one or more SAR images to the SPOT data. Classification is not improved above 98.9% when more than one SAR image is added to the data from SPOT. SAR data can be used to map rock, sand, and vegetated coastal zones in areas of persistent cloud cover. [ABSTRACT FROM AUTHOR]

Block kriging is applied to geographically register digital images from the RADARSAT-1 satellite to soil moisture samples. Both satellite and soil moisture data are interpolated in this process to obtain precise registration. Median and adaptive Lee filtering of images are also used to correlate pixel values with soil moisture. A case study is presented using a playa in the western Great Basin, Nevada, of North America. A statistically significant correlation is found between interpolated RADARSAT-1 digital numbers and interpolated soil moisture. Results indicate that RADARSAT-1 is sensitive to median soil moisture levels; however, filtering does not significantly improve this sensitivity. The study results indicate the ability of synthetic aperture radar to delineate and map temporal soil moisture variability with the use of geostatistical methods to interpolate values over pixel areas. [Copyright &y& Elsevier]

This paper shows results of digital elevation models (DEMs) generated from various RADARSAT stereopairs (fine, standard, extended) over three different topographic study sites (flat-to-rolling, rolling-to-mountainous, mountainous-to-steep) using 3-D parametric modeling developed at the Canada Centre for Remote Sensing (CCRS) and an automated image matching method. Reference topographic DEMs were first used to compute terrain slope and aspect and then to evaluate the accuracy of the stereo-derived DEMs as a function of the slopes and aspects. The DEM accuracy was almost linearly correlated with the terrain slope, with the larger errors in the steepest slopes. In addition, the stronger the stereo radar geometry, the more heterogeneous the DEM is. For the aspect, the best and worse results generally occurred in the foreslopes and backslopes, respectively, and there is no specific trend in the azimuth. Finally, the more pronounced the relief, the higher the correlation between the elevation accuracy and the aspect is and the larger the variations of the elevation accuracy are. [Copyright &y& Elsevier]

The Special Sensor Microwave Imager (SSM/I) microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea ice and climate studies. However, comparisons of SSM/I, Landsat, AVHRR, and ERS-1 synthetic aperture radar (SAR) have shown substantial seasonal and regional differences in their estimates of sea ice concentration. To evaluate these differences, we compared SSM/I estimates of sea ice coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents Sea, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. Ice concentration estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellite''s two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I ice concentrations were derived at National Snow and Ice Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT ice concentrations were calculated and compared. Overall, total sea ice concentration estimates derived independently from near-coincident RADARSAT, OKEAN-01, and SSM/I satellite imagery demonstrated mean differences of less than 5.5% (S.D.<9.5%) during the winter period. Differences between the SSM/I NASA Team and the SSM/I Bootstrap concentrations were no more than 3.1% (S.D.<5.4%) during this period. RADARSAT and OKEAN-01 data both yielded higher total ice concentrations than the NASA Team and the Bootstrap algorithms. The Bootstrap algorithm yielded higher total ice concentrations than the NASA Team algorithm. Total ice concentrations derived from OKEAN-01 and SSM/I satellite imagery were highly correlated during winter, spring, and fall, with mean differences of less than 8.1% (S.D.<15%) for the NASA Team algorithm, and less than 2.8% (S.D.<13.8%) for the Bootstrap algorithm. Respective differences between SSM/I NASA Team and SSM/I Bootstrap total concentrations were less than 5.3% (S.D.<6.9%). Monthly mean differences between SSM/I and OKEAN differed annually by less than 6%, with smaller differences primarily in winter. The NASA Team and Bootstrap algorithms underestimated the total sea ice concentrations relative to the RADARSAT ScanSAR no more than 3.0% (S.D.<9%) and 1.2% (S.D.<7.5%) during cold months, and no more than 12% and 7% during summer, respectively. ScanSAR tended to estimate higher ice concentrations for ice concentrations greater than 50%, when compared to SSM/I during all months. ScanSAR underestimated total sea ice concentration by 2% compared to the OKEAN-01 algorithm during cold months, and gave an overestimation by 2% during spring and summer months. Total NASA Team and Bootstrap sea ice concentration estimates derived from coincident SSM/I and OKEAN-01 data demonstrated mean differences of no more than 5.3% (S.D.<7%), 3.1% (S.D.<5.5%), 2.0% (S.D.<5.5%), and 7.3% (S.D.<10%) for fall, winter, spring, and summer periods, respectively. Large disagreements were observed between the OKEAN and NASA Team results in spring and summer for estimates of the first-year (FY) and multiyear (MY) age classes. The OKEAN-01 algorithm and data tended to estimate, on average, lower concentrations of young or FY ice and higher concentrations of total and MY ice for all months and seasons. Our results contribute to the growing body of documentation about the levels of disparity obtained when seasonal sea ice concentrations are estimated using various types of satellite data and algorithms. [Copyright &y& Elsevier]

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]