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The maintenance and restoration of wetland habitat is a priority conservation action for most waterfowl and other wetland-dependent species in North America. Despite much progress in targeting habitat management in staging and wintering areas, methods to identify and target high-quality breeding habitats that result in the greatest potential for wildlife are still required. This is particularly true for species that breed in remote, inaccessible areas such as the American black duck, an intensively managed game bird in Eastern North America. Although evidence suggests that black ducks prefer productive, nutrient-rich waterbodies, such as beaver ponds, information about the distribution and quality of these habitats across the vast boreal forest is lacking with accurate identification remaining a challenge. Continuing advancements in remote sensing technologies that provide spatially extensive and temporally repeated information are particularly useful in meeting this information gap. In this study, we used multi-source remotely sensed information and a fuzzy analytical hierarchy process to map the spatial distribution of beaver ponds in Ontario. The use of multi-source data, including a Digital Elevation Model, a Sentinel-2 Multi-Spectral Image, and RadarSat 2 Polarimetric data, enabled us to identify individual beaver ponds on the landscape. Our model correctly identified an average of 83.0% of the known beaver dams and 72.5% of the known beaver ponds based on validation with an independent dataset. This study demonstrates that remote sensing is an effective approach for identifying beaver-modified wetland features and can be applied to map these and other wetland habitat features of interest across large spatial extents. Furthermore, the systematic acquisition strategy of the remote sensors employed is well suited for monitoring changes in wetland conditions that affect the availability of habitats important to waterfowl and other wildlife. [ABSTRACT FROM AUTHOR]

The maintenance and restoration of wetland habitat is a priority conservation action for most waterfowl and other wetland-dependent species in North America. Despite much progress in targeting habitat management in staging and wintering areas, methods to identify and target high-quality breeding habitats that result in the greatest potential for wildlife are still required. This is particularly true for species that breed in remote, inaccessible areas such as the American black duck, an intensively managed game bird in Eastern North America. Although evidence suggests that black ducks prefer productive, nutrient-rich waterbodies, such as beaver ponds, information about the distribution and quality of these habitats across the vast boreal forest is lacking with accurate identification remaining a challenge. Continuing advancements in remote sensing technologies that provide spatially extensive and temporally repeated information are particularly useful in meeting this information gap. In this study, we used multi-source remotely sensed information and a fuzzy analytical hierarchy process to map the spatial distribution of beaver ponds in Ontario. The use of multi-source data, including a Digital Elevation Model, a Sentinel-2 Multi-Spectral Image, and RadarSat 2 Polarimetric data, enabled us to identify individual beaver ponds on the landscape. Our model correctly identified an average of 83.0% of the known beaver dams and 72.5% of the known beaver ponds based on validation with an independent dataset. This study demonstrates that remote sensing is an effective approach for identifying beaver-modified wetland features and can be applied to map these and other wetland habitat features of interest across large spatial extents. Furthermore, the systematic acquisition strategy of the remote sensors employed is well suited for monitoring changes in wetland conditions that affect the availability of habitats important to waterfowl and other wildlife.

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

This paper describes an approach for combining Landsat and Radarsat satellite images to generate national statistics for urban ecosystem accounting. These accounts will inform policy related to the development of mitigation measures for climatic and hydrologic events in Canada. Milton, Ontario was used as a test case for the development of an approach identifying urban ecosystem types and assessing change from 2001 to 2019. Methods included decomposition of Radarsat images into polarimetric parameters to test their usefulness in characterizing urban areas. Geographic object-based image analysis (GEOBIA) was used to identify urban ecosystem types following an existing classification of local climate zones. Three supervised classifiers: decision tree, random forest and support vector machine, were compared for their accuracy in mapping urban ecosystems. Ancillary geospatial datasets on roads, buildings, and Landsat-based vegetation were used to better characterize individual ecosystem assets. Change detection focused on the occurrence of changes that can impact ecosystem service supply – i.e., conversions from less to more built-up urban types. Results demonstrate that combining Radarsat polarimetric parameters with the Landsat images improved urban characterization using the GEOBIA random forest classifier. This approach for mapping urban ecosystem types provides a practical method for measuring and monitoring changes in urban areas. [ABSTRACT FROM AUTHOR]

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

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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.

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.

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.

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.

Flooding is one of the natural disasters that affect the livelihood of the people living in the floodplains, like Bangladesh. Here, we proposed to employ SAR satellite images in assessing the flood extent and crop damage with the hydrometeorological observations in the southwestern region of Bangladesh. We observed that the unusual flood of the year 2000 was the combined effect of the huge amount of rainfall in the local areas as well as oncoming water flows from West Bengal in India. During late monsoon of 2000, we experienced that the amount of rainfall was in several magnitudes (250-450%) than the expected over the region. Bangladesh, one of the largest deltas in the world, in general experienced recurrent flood events from the spill of the three mighty rivers in every year. However, we observed that during 2000 the river situation was slightly above the average year conditions and below the moderate level of flood warning conditions, indicating non-riverine flooding. Therefore, we used the SAR images in delineating the flood extent and its damages for the standing aman crops. We observed that the flood extent mapping was having more than 95% agreements with the ground data and crop damage information was about 75% in agreement with the government estimates. The flood extent and crop damage map was found to be useful during the unprecedented flood in the southwestern region of Bangladesh. The use of near real-time SAR imageries thus would be helpful in developing strategies for flood management and disaster mitigation activities and could be utilized on a regular basis. [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]

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

RADARSAT and ERS-2 data collected at multiple incidence angles are used to characterize the seasonal variations in the backscatter of snow-covered landscapes in the northern Hudson Bay Lowlands during the winters of 1997/98 and 1998/99. The study evaluates the usefulness of C-band SAR systems for retrieving the snow water equivalent under dry snow conditions in the forest–tundra ecotone. The backscatter values are compared against ground measurements at six sampling sites, which are taken to be representative of the land-cover types found in the region. The contribution of dry snow to the radar return is evident when frost penetrates the first 20 cm of soil. Only then does the backscatter respond positively to changes in snow water equivalent, at least in the open and forested areas near the coast, where 1-dB increases in backscatter for each approximate 5–10 mm of accumulated water equivalent are observed at 20–31° incidence angles. Further inland, the backscatter shows either no change or a negative change with snow accumulation, which suggests that the radar signal there is dominated by ground surface scattering (e.g., fen) when not attenuated by vegetation (e.g., forested and transition). With high-frequency ground-penetrating radar, we demonstrate the presence of a 10–20-cm layer of black ice underneath the snow cover, which causes the reduced radar returns (−15 dB and less) observed in the inland fen. A correlation between the backscattering and the snow water equivalent cannot be determined due to insufficient observations at similar incidence angles. To establish a relationship between the snow water equivalent and the backscatter, only images acquired with similar incidence angles should be used, and they must be corrected for both vegetation and ground effects.

Radar and digital elevation model had been utilised in many structural studies. The main objective of this study is to compare the RADARSAT and digital elevation model for lineament interpretation which probably represent the main joints or faults along the Simpang Pulai to Pos Selim highway, Malaysia. These joints and faults may influence the instability along the highway. Manual comparison in terms of topographical aspect was undertaken between RADARSAT with 25 m spatial resolution and digital elevation model derived from 20 m contour interval of the topographical map. The previously interpreted lineaments of more than 2 km in the study area was draped over the RADARSAT and digital elevation model to compared whether the lineament concurred with the topographical representation. The interpreted lineaments were derived from Landsat TM of 1990 and 2002, where the DEM had been utilised in the negative lineament determination. It is concluded that the application RADARSAT is not very useful in terms of topographical expression in the structural geological interpretation for the study area compared to DEM derived from contour data. Further work is suggested before any conclusion can be confidently derived.

Veredas (palm swamps) are wetland complexes associated with the Brazilian savanna (cerrado) that often represent the only available source of water for the ecosystem during the dry months. Their extent and condition are mainly unknown and their cartography is an essential issue for their protection. This research article evaluates some of the fine resolution satellite data both in the radar (Radarsat-1) and optical domain (ASTER) for the delineation and characterization of veredas. Two separate approaches are evaluated. First, given the known potential of Radarsat-1 images for wetland inventories, the automatic delineation of veredas is tested using only Radarsat-1 data and a Markov random fields region-based segmentation. In this case, to increase performance, processing is limited to a buffer zone around the river network. Then, characterization of their type is attempted using traditional classification methods of ASTER optical data combined with Radarsat-1 data. The automatic classification of Radarsat data yielded results with an overall accuracy between 62 and 69%, that proved reliable enough for delineating wide and very humid veredas. Scenes from the wet season and with a smaller angle of incidence systematically yielded better results. For the classification of the main vegetation types, better results (overall success of 78.8%) were obtained by using only the visible and near infrared (VNIR) bands of the ASTER image. Radarsat data did not bring any improvement to these classification results. In fact, when using solely the Radarsat data from two different angle of incidence and two different dates, the classification results were low (50.8%) but remained powerful for delineating the permanently moist riparian forest portion of the veredas with an accuracy better than 75% in most cases. These results are considered good given the width of some types often less than 50 m wide compared with the resolution of the images (12.5 - 15 m). Comparing the classification results with the Radarsat-generated delineation allows an understanding of the relation between synthetic aperture radar (SAR) backscattering and vegetation types of the veredas.

The sea surface imprints of Atmospheric Vortex Street (AVS) off Aleutian Volcanic Islands, Alaska were observed in two RADARSAT-1 Synthetic Aperture Radar (SAR) images separated by about 11 hours. In both images, three pairs of distinctive vortices shedding in the lee side of two volcanic mountains can be clearly seen. The length and width of the vortex street are about 60-70 km and 20 km, respectively. Although the AVS’s in the two SAR images have similar shapes, the structure of vortices within the AVS is highly asymmetrical. The sea surface wind speed is estimated from the SAR images with wind direction input from Navy NOGAPS model. In this paper we present a complete MM5 model simulation of the observed AVS. The surface wind simulated from the MM5 model is in good agreement with SAR-derived wind. The vortex shedding rate calculated from the model run is about 1 hour and 50 minutes. Other basic characteristics of the AVS including propagation speed of the vortex, Strouhal and Reynolds numbers favorable for AVS generation are also derived. The wind associated with AVS modifies the cloud structure in the marine atmospheric boundary layer. The AVS cloud pattern is also observed on a MODIS visible band image taken between the two RADARSAT SAR images. An ENVISAT advance SAR image taken 4 hours after the second RADARSAT SAR image shows that the AVS has almost vanished.

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

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

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

Indonesia is a tropical country which has dominant cloud coverage, and some of the area has cloud cover almost all year long. Mapping by optical sensors, especially with Photogrammetric method shows a very good result, but the main constraint is cloud cover conditions, and this was the weakness point of this method.This study describe the technique for deriving Topographic data from Radarsat Synthetic Aperture Radar stereo image pair and apply it to an image pair over West Kalimantan, Indonesia. This paper contains also brief discussion of the use of stereo SAR to derive Digital Elevation Model, the site condition and the source of validation data. The result shows that Radarsat data recommended only for 1 : 100.000 or smaller.

Support vector machines (SVMs) have been applied to land cover classification, and a number of studies have demonstrated their ability to increase classification accuracy. The high correlation between the data set and SVM training model parameters indicates the high performance of the classification model. To improve the correlation, research has focused on the integration of SVMs and other algorithms for data set selection and SVM training model parameter estimation. This letter proposes a novel method, based on a particle filter (PF), of estimating SVM training model parameters according to an observation system. By treating the SVM training function as the observation system of the PF, the new method automatically updates the SVM training model parameters to values that are more appropriate for the data set and can provide a better classification model than can the original model, wherein the parameters are set by trial and error. Various experiments were conducted using Radarsat-2 synthetic aperture radar data from the 2011 Thailand flood. The proposed method provides superior performance and a more accurate analysis compared with the standard SVM. [ABSTRACT FROM PUBLISHER]

The main goal of this research was to detect oil spills, to determine the oil spill frequency, and to approximate oil leak sources around the Oil Rocks Settlement, the Chilov and Pirallahi Islands in the Caspian Sea using multi-temporal ENVISAT Advanced Synthetic Aperture Radar Wide Swath Medium-resolution images acquired in the period of 2009-2010. The processing of ENVISAT satellite images for oil spill detection included the following stages: filtering of images for noise reduction and imagery enhancement, preliminary segmentation for detection of dark features, interactive visual interpretation for the discrimination among other oil spill look-alikes, computation of oil spill frequency, and approximation of oil leak sources. The analysis showed that the appearance of the oil slicks in the radar imagery significantly depends on the Caspian Sea wind speed. The best wind speed conditions for the detection of oil spill were determined to be 2.5-14 m/s. The hot spot areas of detected oil spill frequencies for 2009-2010 were the following: 2-25 (4,658.83 sq. km.), 26-45 (302.83 sq. km.), 46-75 (77.95 sq. km.). The most critical oil leak sources were observed around the Oil Rocks Settlement. The exponential regression analysis between wind speeds and oil slick areas in 2009 with R equal to 0.61 showed that larger oil spill areas were observed with lower wind speeds. The spatiotemporal patterns of currents in the Caspian Sea explained the multi-directional spatial distribution of oil spills around the Oil Rocks Settlement. [ABSTRACT FROM AUTHOR]

We present a flexible, integrated statistical-based modeling approach to improve the robustness of soil moisture data predictions. We apply this approach in exploring the consequence of different choices of leading predictors and covariates. Competing models, predictors, covariates and changing spatial correlation are often ignored in empirical analyses and validation studies. An optimal choice of model and predictors may, however, provide a more consistent and reliable explanation of the high environmental variability and stochasticity of soil moisture observational data. We integrate active polarimetric satellite remote-sensing data (RADARSAT-2, C-band) with ground-based in-situ data across an agricultural monitoring site in Canada. We apply a grouped step-wise algorithm to iteratively select best-performing predictors of soil moisture. Integrated modeling approaches may better account for observed uncertainty and be tuned to different applications that vary in scale and scope, while also providing greater insights into spatial scaling (upscaling and downscaling) of soil moisture variability from the field- to regional scale. We discuss several methodological extensions and data requirements to enable further statistical modeling and validation for improved agricultural decision-support. [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]

Flood risk management serves to reduce the negative consequences of flood disaster to a certain extent. In agriculture-dominated countries, the extent of damage incurred in crop land by heavy and frequent floods is quite high. The present study aims to develop an optimal rice planning procedure considering the flood risk through hydrodynamic floodplain modeling in flood-prone delta region of Mahanadi river basin in India. As high-resolution topographic data and surveyed river cross sections are unavailable for the study area, MIKE FLOOD model setup is prepared using river cross sections and floodplain elevation model derived from freely available SRTM DEM. In this study, MIKE FLOOD setup is prepared and flood inundation simulation is carried out. Flood inundation extent obtained is compared with RADARSAT-1 image-based inundation extent. Subsequently, flood risk is evaluated for cropping pattern in floodplains using functional relationships between flood characteristics and the expected damage of different rice varieties. Based on the flood risk, an optimal rice planning model is developed for maximizing the net benefits in the floodplain. The average annual expected net benefit of optimal rice allocation model for the study area is to the tune of INR 601 million compared to INR 432 million for normal rice variety cultivation throughout the study area. [ABSTRACT FROM AUTHOR]

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

The potential of quad polarization radar data for the target discrimination has been analyzed. Quad polarization data of the RADARSAT-2 fine resolution mode has been utilized. Class separability analysis has been carried out on different polarization combinations using Transformed Divergence (TD) method and it is observed that HH-HV/VH-VV polarization combination gives better class separability when compared to other polarization combinations. Classification has been carried out on the optimized polarization combination using Maximum likelihood (MLC) and Support Vector Machine (SVM) classifiers. It is observed that SVM classification gives better classification accuracy compared to MLC. Overall classification accuracy is 93.03% for SVM and 88.78% for MLC. Class separability and classification accuracy comparison results are presented. [ABSTRACT FROM AUTHOR]

Our ability to simulate and understand oceanic conditions of the Gulf of St Lawrence (GSL) has significantly increased in the last decade with the development of regional ocean models combining scientific knowledge, oceanographic data, and computing techniques. This has created opportunities for their integration with meteorological forecast models where the importance of sea ice-ocean dynamics in air-sea exchanges was recently demonstrated. In order to produce accurate initial conditions for coupled weather forecasts, a GSL ice-ocean 'pseudo' analysis (IOPA) system has been developed and implemented operationally at the Canadian Meteorological Centre (CMC). Based on prognostic ice-ocean model solutions the IOPA system has sufficient skill to produce realistic daily fields without flux correction or data assimilation of temperature and salinity. Using only a simple assimilation method for sea-ice data, daily oceanic fields are obtained and can be used to initialize ice-ocean or coupled atmospheric forecasting systems. A 3-year retrospective evaluation of this operational method is presented focusing on sea-surface temperature (SST) and sea-ice conditions. Model estimates are compared with in situ and satellite-derived data. Results show that the IOPA system has an accuracy equivalent to or better than the current operational SST and ice analyses produced at the CMC. Together with preceding modelling efforts, this work lays the foundation for the first state-of-the-art GSL ocean analysis and forecast systems combining ocean modelling and real-time three-dimensional data assimilation. Coupled atmosphere-ice-ocean forecasts produced with the same ice-ocean model and initialized with the IOPA system are presented in a companion paper. Copyright © 2012 Crown in the right of Canada. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

RADARSAT and ERS-2 data collected at multiple incidence angles are used to characterize the seasonal variations in the backscatter of snow-covered landscapes in the northern Hudson Bay Lowlands during the winters of 1997/98 and 1998/99. The study evaluates the usefulness of C-band SAR systems for retrieving the snow water equivalent under dry snow conditions in the forest-tundra ecotone. The backscatter values are compared against ground measurements at six sampling sites, which are taken to be representative of the land-cover types found in the region. The contribution of dry snow to the radar return is evident when frost penetrates the first 20 cm of soil. Only then does the backscatter respond positively to changes in snow water equivalent, at least in the open and forested areas near the coast, where 1-dB increases in backscatter for each approximate 5-10 mm of accumulated water equivalent are observed at 20-31° incidence angles. Further inland, the backscatter shows either no change or a negative change with snow accumulation, which suggests that the radar signal there is dominated by ground surface scattering (e.g., fen) when not attenuated by vegetation (e.g., forested and transition). With high-frequency ground-penetrating radar, we demonstrate the presence of a 10-20-cm layer of black ice underneath the snow cover, which causes the reduced radar returns (-15 dB and less) observed in the inland fen. A correlation between the backscattering and the snow water equivalent cannot be determined due to insufficient observations at similar incidence angles. To establish a relationship between the snow water equivalent and the backscatter, only images acquired with similar incidence angles should be used, and they must be corrected for both vegetation and ground effects. [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]

This work proposes a method for detecting inundation between semi-diurnal low and high water conditions in the northern Gulf of Mexico using high-resolution satellite imagery. Radarsat 1, Landsat imagery and aerial photography from the Apalachicola region in Florida were used to demonstrate and validate the algorithm. A change detection approach was implemented through the analysis of red, green and blue (RGB) false colour composites image to emphasise differences in high and low tide inundation patterns. To alleviate the effect of inherent speckle in the SAR images, we also applied ancillary optical data. The flood-prone area for the site was delineated a priori through the determination of lower and higher water contour lines with Landsat images combined with a high-resolution digital elevation model. This masking technique improved the performance of the proposed algorithm with respect to detection techniques using the entire Radarsat scene. The resulting inundation maps agreed well with historical aerial photography as the probability of detection reached 83%. The combination of SAR data and optical images, when coupled with a high-resolution digital elevation model, was shown to be useful for inundation mapping and have a great potential for evaluating wetting/drying algorithms of inland and coastal hydrodynamic models. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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

Abstract: 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]

Remote sensing is the most practical method available to managers of flood-prone areas for quantifying and mapping flood impacts. This study explored large inundation areas in the Maghna River Basin, around the northeastern Bangladesh, as determined from passive sensor LANDSAT data and the cloud-penetrating capabilities of the active sensors of the remote imaging microwave RADARSAT. This study also used passive sensor LANDSAT wet and dry images for the year 2000. Spatial resolution was 30 m by 30 m for comparisons of the inundation area with RADARSAT images. RADARSAT images with spatial resolution of 50 m by 50 m were used for frequency analysis of floods from 2000 to 2004. Time series images for 2004 were also used. RADARSAT remote sensing data, GIS data, and ground data were used for the purpose of flood monitoring, mapping and assessing. A supervised classification technique was used for this processing. They were processed for creating a maximum water extent map and for estimating inundation areas. The results of this study indicated that the maximum extent of the inundation area as estimated using RADARSAT satellite imaging was about 29, 900.72 km in 2004, which corresponded well with the heavy rainfall around northeast region, as seen at the Bhairab Bazar station and with the highest water level of the Ganges-Brahmaputra-Meghna (GBM) Rivers. A composite of 5 years of RADARSAT inundation maps from 2000 to 2004, GIS data, and damage data, was used to create unique flood hazard maps. Using the damage data for 2004 and the GIS data, a set of damage maps was also created. These maps are expected to be useful for future planning and flood disaster management. Thus, it has been demonstrated that RADARSAT imaging data acquired over the Bangladesh have the ability to precisely assess and clarify inundation areas allowing for successful flood monitoring, mapping and disaster management. [ABSTRACT FROM AUTHOR]

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]