Search

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]

The article discusses research done on the relationship between the divergent movements of the Pacific walrus Odobenus rosmarus divergens and sea ice in the northern Bering Sea. Concerns related to the conservation of the Pacific walrus resulted from the possibility of the occurrence of sea ice reduction in the Arctic region by mid-century. Linear mixed-effects models were used for the analysis of several factors that included the ice floe movement vector, the walrus movement vector and the distance between such ice floes and walruses.

Mapping and monitoring changes in the distribution of cropland provide information that aids sustainable approaches to agriculture and supports early warning of threats to global and regional food security. This paper tested the capability of Phased Array type L-band Synthetic Aperture Radar (SAR) (PALSAR) multipolarization and polarimetric data for crop classification. L-band results were compared with those achieved with a C-band SAR data set (ASAR and RADARSAT-1), an integrated C- and L-band data set, and a multitemporal optical data set. Using all L-band linear polarizations, corn, soybeans, cereals, and hay-pasture were classified to an overall accuracy of 70%. A more temporally rich C-band data set provided an accuracy of 80%. Larger biomass crops were well classified using the PALSAR data. C-band data were needed to accurately classify low biomass crops. With a multifrequency data set, an overall accuracy of 88.7% was reached, and many individual crops were classified to accuracies better than 90%. These results were competitive with the overall accuracy achieved using three Landsat images (88.0%). L-band parameters derived from three decomposition approaches (Cloude-Pottier, Freeman-Durden, and Krogager) produced superior crop classification accuracies relative to those achieved using the linear polarizations. Using the Krogager de- composition parameters from all three PALSAR acquisitions, an overall accuracy of 77.2% was achieved. The results reported in this paper emphasize the value of polarimetric, as well as multi- frequency SAR, data for crop classification. With such a diverse capability, a SAR-only approach to crop classification becomes increasingly viable. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

Ice concentration retrieved from spaceborne passive-microwave observations is a prime input to operational sea-ice-monitoring programs, numerical weather prediction models, and global climate models. Atmospheric Environment Service (AES)-York and the Enhanced National Aeronautics and Space Administration Team (NT2) are two algorithms that calculate ice concentration from Special Sensor Microwave/Imager observations. This paper furnishes a comparison between ice concentrations (total, thin, and thick types) output from NT2 and AES-York algorithms against the corresponding estimates from the operational analysis of Radarsat images in the Canadian Ice Service (CIS). A new data fusion technique, which incorporates the actual sensor's footprint, was developed to facilitate this study. Results have shown that the NT2 and AES-York algorithms underestimate total ice concentration by 18.35% and 9.66% concentration counts on average, with 16.8% and 15.35% standard deviation, respectively. However, the retrieved concentrations of thin and thick ice are in much more discrepancy with the operational CIS estimates when either one of these two types dominates the viewing area. This is more likely to occur when the total ice concentration approaches 100 %. If thin and thick ice types coexist in comparable concentrations, the algorithms' estimates agree with CIS's estimates. In terms of ice concentration retrieval, thin ice is more problematic than thick ice. The concept of using a single tie point to represent a thin ice surface is not realistic and provides the largest error source for retrieval accuracy. While AES-York provides total ice concentration in slightly more agreement with CIS's estimates, NT2 provides better agreement in retrieving thin and thick ice concentrations. [ABSTRACT FROM AUTHOR]

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]

During a ship voyage from Tasmania to Antarctica in summer 2000/01, radiative and meteorological measurements were continuously made, from which the surface energy budget was calculated. Sea conditions throughout the voyage ranged from open water to broken pack and finally to snow-covered unbroken sea ice in McMurdo Sound. The global radiation increased on average during the trip (to higher latitudes) as we travelled poleward. The net radiation, which was positive (toward the surface) on average, decreased however, mostly due to the increase in surface albedo. For open water, most of the net radiation is used for evapouration (61%), while for broken sea-ice conditions, nearly all energy is used for melting of the sea ice or heating of the ocean (96%). For unbroken snow-covered sea ice, the net radiation lies close to zero, due to the high surface albedo, which reached a mean value of 0.81. The sensible heat flux becomes the largest heat source and nearly all the energy is used for warming of the surface. Finally, a Radarsat image, on which the ship track was visible, was used to compare the ship observations with satellite derived ice types. [ABSTRACT FROM AUTHOR]

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]

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]

Earth observation from active microwave satellites such as RADARSAT-1 is an excellent tool to monitor and forecast floods. Two complementary approaches are described in this paper: (a) real time or near-real time monitoring of flood extent and (b) mapping of hydrological properties of drainage basins. Since it can penetrate through clouds, which usually occur during precipitation periods, and due to the fact that it can be programmed with different incidence angles, RADARSAT-1 enables frequent coverage over specific areas of interest. It has been used successfully to monitor a major flood of the Red River in Manitoba in 1997, by providing frequent coverage of the flood during its progression and decrease. Resulting data and images have been useful in planning the emergency measures and in assessing flood damage. RADARSAT has also the ability to characterize hydrological properties of watersheds. It has been used in agricultural catchments in Europe for mapping soil surface roughness, which affects runoff coefficients, concentration time and resistance to erosion processes. Used to complement optical data, RADARSAT has provided information on the status of land use and soil protective cover in drainage basins. This information can then be translated into parameters and coefficients that hydrological models can use for runoff and flood forecasting [ABSTRACT FROM AUTHOR]

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]

Many of today’s agricultural landscapes once held vast amounts of wetland habitat for waterbirds and other wildlife. Successful restoration of these landscapes relies on access to accurate maps of the wetlands that remain. We used C-band (5.6-cm-wavelength), HH-polarized radar remote sensing (RADARSAT) at a 38° incidence angle (8-m resolution) to map the distribution of winter shorebird (Charadriiformes) habitat on agricultural lands in the Willamette Valley of western Oregon. We acquired imagery on three dates (10 December 1999, 27 January 2000, and 15 March 2000) and simultaneously collected ground reference data to classify radar signatures and evaluate map accuracy of four habitat classes: (1) wet with ≤ 50% vegetation (considered optimal shorebird habitat), (2) wet with > 50% vegetation, (3) dry with ≤ 50% vegetation, and (4) dry with > 50% vegetation. Overall accuracy varied from 45 to 60% among the three images, but the accuracy of focal class 1 was greater, ranging from 72 to 80%. Class 4 coverage was stable and dominated maps (40% of mapped study area) for all three dates, while coverage of class 3 decreased slightly throughout the study period. Among wet classes, class 1 was most abundant (about 30% coverage) in December and January, decreasing in March to approximately 15%. Conversely, class 2 increased dramatically from January to March, likely due to transition from class 1 as vegetation grew. This approach was successful in detecting optimal habitat for shorebirds on agricultural lands. For modest classification schemes, radar remote sensing is a valuable option for wetland mapping in areas where cloud cover is persistent. [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 paper presents an application of neural networks to the extraction of bare soil surface parameters such as roughness and soil moisture content using synthetic aperture radar (SAR) satellite data. It uses a fast learning algorithm for training a multilayer feedforward neural network using the Kalman filter technique. Two different databases (theoretical and empirical) were used for the learning stage. Each database was configured as single and multiangular sets of input data (data acquired at two different incidence angles) that are compatible with data from one and two satellite images, respectively. All the configurations are trained and then evaluated using RADARSAT-1 and simulated data. The empirical (measured) database with the multiangular set of input data configuration had the best accuracy with a mean error of 1.54 cm for root mean square (rms) height of the surface roughness and 2.45 for soil dielectric constant in the study area. Based on these results the proposed approach was applied on RADARSAT-1 images from the Chateauguay watershed area (Quebec, Canada) and the final results are presented in the form of roughness and humidity maps. [ABSTRACT FROM AUTHOR]

An approach for mapping near-surface soil moisture at the watershed scale from RADARSAT-1 synthetic aperture radar (SAR) data was developed and tested on seven RADARSAT-1 SAR images acquired over the northern portion of the Châteauguay River Basin in southwestern Quebec, Canada, dominated by agricultural and herbaceous fields. A soil surface roughness map was first retrieved from a SAR image by inverting an empirical backscatter model with known (or assumed) soil moisture. The resulting map was then used with the backscatter model to recover near-surface soil moisture for the remaining SAR images. Field campaigns were conducted concurrent to SAR image acquisitions to measure soil moisture and surface roughness in 24 fields. Good agreement was observed between watershed-scale soil moisture values and measurements averaged for all sampled fields, with a correlation coefficient of 0.96 and an RMS error of 2.2%. However, considerable scatter was found between observed and SAR-derived soil moisture estimates at the field scale. Although the generated maps reveal reasonable small-scale soil moisture variability, no definitive conclusions could be drawn as to whether or not the proposed approach can quantify soil moisture at the field or within field scale due to insufficient ground measurements. Furrows and herbaceous and crop vegetation, which are known to affect the radar signal, appeared to have little influence on the ability to retrieve soil moisture at the watershed scale for the images analyzed in this study. [ABSTRACT FROM AUTHOR]

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]

Many of today’s agricultural landscapes once held vast amounts of wetland habitat for waterbirds and other wildlife. Successful restoration of these landscapes relies on access to accurate maps of the wetlands that remain. We used C-band (5.6-cm-wavelength), HH-polarized radar remote sensing (RADARSAT) at a 38° incidence angle (8-m resolution) to map the distribution of winter shorebird (Charadriiformes) habitat on agricultural lands in the Willamette Valley of western Oregon. We acquired imagery on three dates (10 December 1999, 27 January 2000, and 15 March 2000) and simultaneously collected ground reference data to classify radar signatures and evaluate map accuracy of four habitat classes: (1) wet with ≤ 50% vegetation (considered optimal shorebird habitat), (2) wet with > 50% vegetation, (3) dry with ≤ 50% vegetation, and (4) dry with > 50% vegetation. Overall accuracy varied from 45 to 60% among the three images, but the accuracy of focal class 1 was greater, ranging from 72 to 80%. Class 4 coverage was stable and dominated maps (40% of mapped study area) for all three dates, while class 3 coverage decreased slightly throughout the study period. Among wet classes, class 1 was most abundant (30% coverage) in December and January, decreasing in March by 15%. Conversely, class 2 increased dramatically from January to March, likely due to transition from class 1 as vegetation grew. This approach was successful in detecting optimal habitat for shorebirds on agricultural lands. For modest classification schemes, radar remote sensing is a valuable option for wetland mapping in areas where cloud cover is persistent. [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]

The Arctic land surface water balance plays an important role in regulating the planetary heat balance and global ocean circulation. Lakes and wetlands are common features in the low-gradient Putuligayuk River watershed in northern Alaska, with important implications for the annual water balance. Evapotranspiration exceeds precipitation over the summer, and there is a gradual reduction in wetland extent. Total inundated area derived from RADARSAT ScanSAR synthetic aperture radar images throughout 1999 and 2000 varied from 15 to 67 percent of the 471 km2 watershed. The hydrological system becomes disconnected within 2 weeks of snowmelt, and overland flow largely ceases. End-of-winter snow water equivalent and discharge during the melt period for 1999, 2000, and 2001 were used to estimate that between 30 and 37 mm (24-42 percent) of snow meltwater serves to recharge the evaporation deficit of the previous summer. The percent of snowmelt entering storage is dependent on the available surface storage. [ABSTRACT FROM AUTHOR]

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]

This study investigates the backscattering properties of C-band RADARSAT (SAR) signal over sloping mountainous areas, and evaluates the utility of RADARSAT in natural disaster areas (e.g., forest fires), geological mapping, monitoring of natural hazards, natural and associated Earths environmental changes. Conventional change detection techniques in remote sensing are adequate for most applications. However, the changes in backscattering coefficients (σ0) before and after, for example, forest fires are not apparent in certain cases, mainly because of the effects of steeply sloping target scattering surface areas. The slopes of a mountainous test site can be divided into two cases with respect to the incident SAR signal look direction: the front and back slopes, where local incidence angles can vary significantly. Depending on the slope of the scattering surface, the backscattering coefficient increases to the maximum of 3.3. dB in the front-slope, but decreases 4.5 dB in back-slope after forest fires. Forest fire burnt areas provide a good example of a natural sloping scattering surface, which exhibits subtle changes in backscattering coefficients similar to the lithological changes in nature. The April 2000 forest fire in Kangwon province, Korea, burned for several weeks and was one of the worst ones in recent years. Among several firedamaged sites, an area near Samchuk city was investigated in detail with a set of multi-temporal RADARSAT Synthetic Aperture Radar (SAR) data to estimate the fire damages as compared to the unburnt background. [ABSTRACT FROM AUTHOR]

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]

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]

Space-borne Synthetic Aperture Radar (SAR) techniques have recently become one of the most flexible and cost-effective Earth-observation tools for monitoring surface processes, including natural hazard monitoring and management tasks such as landslides, volcanic activities, and earthquake-related problems. This study investigates the feasibility of applying space-borne SAR interferometry to the monitoring of earthquake hazards and investigation of the earthquake-related tectonic processes, focusing on the investigation of the geological setting and associated tectonic processes in the Nahanni Earthquake area, NWT, Canada. Investigation of seismotectonics in the Nahanni area was carried out in two stages: traditional analysis of remote-sensing data, including both optical and microwave data, for static aspects of tectonic processes (Moon et al., 1991); and new SAR interferometry using RADARSAT, ERS-1/2, and JERS-1 SAR data to study relative movements of several active geological tectonic blocks. Preliminary results indicate that (1) conventional geological remote-sensing methods provide us with important basic information on the tectonic setting associated with local earthquake activities, including several newly discovered structural features, and remain important as Earth-observation tools, (2) the newly discovered tectonic features correlate well with the earthquake focal plane solutions obtained from teleseismic data, (3) multi-temporal SAR interferometric (or differential InSAR) analysis can provide us with detailed tectonic moeements and understanding of the earthquake processes in the study area, but (4) availability of suitable space-borne SAR data suitable for differential SAR interferometry may pose more problems than the technical development. [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]