Search

Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography. [ABSTRACT FROM PUBLISHER]

We analyzed 4-years of Advanced Scatterometer (ASCAT) incidence angle normalized C-band backscatter, Next-Generation Radar (NEXRAD) precipitation and United States Department of Agriculture (USDA) crop yield data over the contiguous United States (U.S.). Large negative anomalies in backscatter are evident over the U.S. during the 2011 and 2012 growing seasons, coincident with two instances of severe drought. The backscatter anomalies were correlated with diminished growing season precipitation and with reduced crop yields reflecting where drought-induced impacts on above-ground biomass production occurred. During periods of acute drought, differences in diurnal backscatter were reduced and in some cases reversed reflecting diminished nocturnal leave rehydration. The results indicate that the C-band, VV-polarized ASCAT backscatter data is sensitive to interannual variability in agricultural productivity rather than soil moisture, and can offer an important geophysical monitoring tool capable of identifying and assessing drought-related vegetation responses independent of and complementary to satellite optical/near-infrared remote sensing data. This technique is flexible and adaptable, providing a capability to address region-specific needs. The twice-daily, weather independent and near-real time global monitoring capabilities of ASCAT and similar microwave instruments (e.g. RapidSCAT, Sentinel-1) can help improve drought preparedness and mitigation planning allowing improved water resource management for agriculture on the local as well as the state level. [ABSTRACT FROM AUTHOR]

The National Aeronautics and Space Administration's (NASA) proposed Soil Moisture Active Passive (SMAP) satellite mission (\sim2014) will include a radar system that will provide L-band multi-polarization backscatter at a constant incidence angle of 40^\circ. During the pre-launch phase of the project, there is a need for observations that will support the radar-based soil moisture algorithm development and validation. A valuable resource for providing these observations is the NASA Jet Propulsion Laboratory Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). However, SMAP will observe at a constant incidence angle of 40^ \circ, and UAVSAR collects data over a wide range of incidence angles (25 ^\circ–60^\circ). In this investigation, a technique was developed and tested for normalizing UAVSAR data to a constant incidence angle. The approach is based on a histogram matching procedure. The data used to develop and demonstrate this approach were collected as part of the Canadian Soil Moisture Experiment 2010 (CanEx-SM10). Land cover in the region included agriculture and forest. Evaluation was made possible by the acquisition of numerous overlapping UAVSAR flight lines that provided multiple incidence angle observations of the same locations. Actual observations at a 40^\circ incidence angle were compared to the normalized data to assess performance of the normalization technique. An optimum technique should be able to reduce the systematic error (Bias) to 0 dB and to lower the total root mean square error (RMSE) computed after correction to the level of the initial residual error (\RMSEres) present in the data set. The normalization approach developed here achieved both of these. Bias caused by the incidence angle variability was minimized to \sim 0 dB, whereas the residual error caused by instrument related random errors and amplitude fluctuations due to ground variability was reduced to approximately 3 dB for agricultural areas and 2.6 dB for forests; these values were consistent with the initial \RMSEres estimated using the un-corrected data. The residual error can be reduced further by aggregating the radar observations to a coarser grid spacing. The technique adequately adjusted the backscatter over the full swath width irrespective of the original incidence angle, polarization, and ground conditions (vegetation cover and soil moisture). In addition to providing a basis for fully exploiting UAVSAR (or similar aircraft systems) for SMAP algorithm development and validation, the technique could also be adapted to satellite radar systems. This normalization approach will also be beneficial in terms of reducing the number of flight lines required to cover a study area, which would eventually result in more cost-effective soil moisture field campaigns. [ABSTRACT FROM PUBLISHER]

For more than a decade, the U.S. government has been developing laser-based sensors for detecting, locating, and classifying aerosols in the atmosphere at safe standoff ranges. The motivation for this work is the need to discriminate aerosols of biological origin from interferent materials such as smoke and dust using the backscatter from multiple wavelengths in the long wave infrared (LWIR) spectral region. Through previous work, algorithms have been developed for estimating the aerosol spectral dependence and concentration range dependence from these data. The range dependence is required for locating and tracking the aerosol plumes, and the backscatter spectral dependence is used for discrimination by a support vector machine classifier. Substantial progress has been made in these algorithms for the case of a single aerosol present in the lidar line-of-sight (LOS). Often, however, mixtures of aerosols are present along the same LOS overlapped in range and time. Analysis of these mixtures of aerosols presents a difficult inverse problem that cannot be successfully treated by the methods used for single aerosols. Fortunately, recent advances have been made in the analysis of inverse problems using shrinkage-based L1-regularization techniques. Of the several L1-regularization methods currently known, the split Bregman algorithm is straightforward to implement, converges rapidly, and is applicable to a broad range of inverse problems including our aerosol unmixing. In this paper, we show how the split Bregman algorithm can successfully resolve LWIR lidar data containing mixtures of bioaerosol simulants and interferents into their separate components. The individual components then can be classified as bio- or nonbioaerosol by our SVM classifier. We illustrate the approach through data collected in field tests over the past several years using the U.S. Army FAL sensor in testing at Dugway Proving Ground, UT. [ABSTRACT FROM PUBLISHER]

Active and passive microwave remote sensing data offer complementary information on the properties of the observed scene. This study investigates the relationship of L-band radiometer and radar signals to vegetation and soil during four field campaigns conducted in United States between 1999 and 2008. The study shows complex relationship between radiometer observed reflectivity and radar observed backscatter over various landscapes, as expected. Vegetation classification was attempted with different in situ and remote sensing parameters but only a purely empirical combination of Vegetation Water Content and cross-polarized backscatter was able to successfully divide the observations into broad categories of high and low vegetation. The study suggests that a combination of radar and radiometer signal is necessary for establishing a parameter that can fully describe the vegetation state. [ABSTRACT FROM AUTHOR]

The Regional Atmospheric Modeling System (RAMS)-based Forest Large-Eddy Simulation (RAFLES), developed and evaluated here, is used to explore the effects of three-dimensional canopy heterogeneity, at the individual tree scale, on the statistical properties of turbulence most pertinent to mass and momentum transfer. In RAFLES, the canopy interacts with air by exerting a drag force, by restricting the open volume and apertures available for flow (i.e. finite porosity), and by acting as a heterogeneous source of heat and moisture. The first and second statistical moments of the velocity and flux profiles computed by RAFLES are compared with turbulent velocity and scalar flux measurements collected during spring and winter days. The observations were made at a meteorological tower situated within a southern hardwood canopy at the Duke Forest site, near Durham, North Carolina, U.S.A. Each of the days analyzed is characterized by distinct regimes of atmospheric stability and canopy foliage distribution conditions. RAFLES results agreed with the 30-min averaged flow statistics profiles measured at this single tower. Following this intercomparison, two case studies are numerically considered representing end-members of foliage and midday atmospheric stability conditions: one representing the winter season with strong winds above a sparse canopy and a slightly unstable boundary layer; the other representing the spring season with a dense canopy, calm conditions, and a strongly convective boundary layer. In each case, results from the control canopy, simulating the observed heterogeneous canopy structure at the Duke Forest hardwood stand, are compared with a test case that also includes heterogeneity commensurate in scale to tree-fall gaps. The effects of such tree-scale canopy heterogeneity on the flow are explored at three levels pertinent to biosphere-atmosphere exchange. The first level (zero-dimensional) considers the effects of such heterogeneity on the common representation of the canopy via length scales such as the zero-plane displacement, the aerodynamic roughness length, the surface-layer depth, and the eddy-penetration depth. The second level (one-dimensional) considers the normalized horizontally-averaged profiles of the first and second moments of the flow to assess how tree-scale heterogeneities disturb the entire planar-averaged profiles from their canonical (and well-studied planar-homogeneous) values inside the canopy and in the surface layer. The third level (three-dimensional) considers the effects of such tree-scale heterogeneities on the spatial variability of the ejection-sweep cycle and its propagation to momentum and mass fluxes. From these comparisons, it is shown that such microscale heterogeneity leads to increased spatial correlations between attributes of the ejection-sweep cycle and measures of canopy heterogeneity, resulting in correlated spatial heterogeneity in fluxes. This heterogeneity persisted up to four times the mean height of the canopy ( h c) for some variables. Interestingly, this estimate is in agreement with the working definition of the thickness of the canopy roughness sublayer (2 h c–5 h c). [ABSTRACT FROM AUTHOR]

As part of the U.S. Office of Naval Research (ONR, Arlington, VA) mine burial program, an experiment was conducted off the pier at Santa Cruz, CA, to measure the near-bed suspended sediment reference concentration under waves and currents. Two tripods were deployed to carry out the measurements; one consisting mainly of acoustical instrumentation and the other solely of optical instruments. The tripods were located within 15 m of one another on a sandy bed and measurements of the suspended sediment were made using acoustics and optics. Although the experiment was not primarily designed to conduct an intercomparison of acoustical and optical measurements, it was considered interesting to take advantage of the situation and to examine if these two techniques gave comparable results. In particular, measurements of particle size and concentration, obtained using a triple frequency acoustic backscatter system (ABS) have been compared with the commercially available laser miniature scattering and transmissometry instrument (MSCAT). It was found that the mean grain size estimated by the two methods was consistent; however, in contrast, the concentration time series showed differences, both in magnitude and form. [ABSTRACT FROM AUTHOR]

Nearly 100 collocated grab samples and in situ 65 kHz acoustic measurements were collected on the New Jersey middle and outer shelf within an area that had previously been mapped with multibeam backscatter and bathymetry data, and more recently with chirp seismic reflection profiling. Eighteen short cores were also collected and probed for resistivity-based porosity measurements. The combined data set provides a basis for empirically exploring the relationship among the remotely sensed data, such as backscatter and reflection coefficients, and directly measured seabed properties such as grain size distribution, velocity, attenuation and porosity. We also investigate the spatial variability of these properties through semi-variogram analysis to facilitate acoustic modeling of natural environmental variability.Grain size distributions on the New Jersey shelf are commonly multi-modal, leading us to separately characterize coarse % (>4 mm), fine % (<63 μm) and mean sand grain size to quantify the distribution. We find that the backscatter is dominated by the coarse component (expressed as weight %), typically shell hash and occasionally terrigenous gravel. In sediment types where coarse material is not significant, backscatter correlates with velocity and fine weight %. Mean sand grain size and fine % are partially correlated with each other, and combined represent the primary control on velocity. The fine %, rather than mean grain size as a whole, appears to be the primary control on attenuation, although coarse % may increase attenuation marginally through scattering. Vertical-incidence seismic reflection coefficients, carefully culled of unreliable values, exhibit a strong correlation with the in situ velocity measurements, suggesting that such data may prove more reliable than backscatter at deriving sediment physical properties from remote sensing data. The velocity and mean sand grain size semi-variograms can be fitted with a von Kármán statistical model with horizontal scale ∼12.6 km, which provides a basis for generating synthetic realizations. The backscatter and coarse % semi-variograms exhibit two horizontal scales: one ∼8 km and the other too small to quantify with available data. [Copyright &y& Elsevier]

A multi-year study was carried out to evaluate ERS synthetic aperture radar (SAR) imagery for monitoring surface hydrologic conditions in wetlands of southern Florida. Surface conditions (water level, aboveground biomass, soil moisture) were measured in 13 study sites (representing three major wetland types) over a 25-month period. ERS SAR imagery was collected over these sites on 22 different occasions and correlated with the surface observations. The results show wide variation in ERS backscatter in individual sites when they were flooded and non-flooded. The range (minimum vs. maximum) in SAR backscatter for the sites when they were flooded was between 2.3 and 8.9 dB, and between 5.0 and 9.0 dB when they were not flooded. Variations in backscatter in the non-flooded sites were consistent with theoretical scattering models for the most part. Backscatter was positively correlated to field measurements of soil moisture. The MIchigan MIcrowave Canopy Scattering (MIMICS) model predicts that backscatter should decrease sharply when a site becomes inundated, but the data show that this drop is only 1–2 dB. This decrease was observed in both non-wooded and wooded sites. The drop in backscatter as water depth increases predicted by MIMICS was observed in the non-wooded wetland sites, and a similar decrease was observed in wooded wetlands as well. Finally, the sensitivity of backscatter and attenuation to variations in aboveground biomass predicted by MIMICS was not observed in the data.The results show that the inter- and intra-annual variations in ERS SAR image intensity in the study region are the result of changes in soil moisture and degree of inundation in the sites. The correlation between changes in SAR backscatter and water depth indicates the potential for using spaceborne SAR systems, such as the ERS for monitoring variations in flooding in south Florida wetlands. [Copyright &y& Elsevier]

Santa Monica Bay was mapped in 1996 using a high-resolution multibeam system, providing the first substantial update of the submarine geomorphology since the initial compilation by Shepard and Emery [(1941) Geol. Soc. Amer. Spec. Paper 31]. The multibeam mapping generated not only high-resolution bathymetry, but also coregistered, calibrated acoustic backscatter at 95 kHz. The geomorphology has been subdivided into six provinces; shelf, marginal plateau, submarine canyon, basin slope, apron, and basin. The dimensions, gradients, and backscatter characteristics of each province is described and related to a combination of tectonics, climate, sea level, and sediment supply. Fluctuations of eustatic sea level have had a profound effect on the area; by periodically eroding the surface of Santa Monica plateau, extending the mouth of the Los Angeles River to various locations along the shelf break, and by connecting submarine canyons to rivers. A wetter glacial climate undoubtedly generated more sediment to the rivers that then transported the increased sediment load to the low-stand coastline and canyon heads. The trends of Santa Monica Canyon and several bathymetric highs suggest a complex tectonic stress field that has controlled the various segments. There is no geomorphic evidence to suggest Redondo Canyon is fault controlled. The San Pedro fault can be extended more than 30 km to the northwest by the alignment of a series of bathymetric highs and abrupt changes in direction of channel thalwegs. [Copyright &y& Elsevier]

Detailed acoustic surveys of benthic sediments were conducted in July 1995 and September 1998 in the vicinity of Humboldt Bay, California. During these surveys, a band of enhanced acoustic backscatter was observed offshore from the bay entrance, approximately parallel to the isobaths, in water depths ranging from 16-24 m. In order to assess the cause of the increase in backscatter levels, a more comprehensive study was conducted in August and September 1999 using 100 kHz side-scan sonar, bottom grab sampling and underwater video recording. New observations indicated that a dense population of sand dollars (Dendraster excentricus) coincided with the enhanced backscatter band. Compared to the two previous acoustic studies, the central section of the band expanded westward by 180 m and the southern section of the band shifted eastward by 160 m, possibly resulting from a change in the biological or physical factors which influence the location and breadth of sand dollars. The relationship between high sand dollar abundance and enhanced acoustic backscatter was further verified in the nearshore region off Samoa Beach California, where a dense, banded population of sand dollars was previously observed. Video footage confirmed the presence of a band of sand dollars, also nominally parallel to the isobaths, in water depths of 8–15 m. A band of enhanced backscatter coincided with the dense sand dollar population. The identification of dense aggregations of sand dollars through enhanced acoustic backscatter could lead to the use of acoustic techniques to study sand dollar distributions and abundance. [ABSTRACT FROM AUTHOR]

The TU Wien Soil Moisture Retrieval (TUW SMR) approach is used to produce several operational soil moisture products from the Advanced Scatterometer (ASCAT) on the Metop series of satellites as part of the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The incidence angle dependence of backscatter is described by a second-order Taylor polynomial, the coefficients of which are used to normalize ASCAT observations to the reference incidence angle of 40 ∘ and for correcting vegetation effects. Recently, a kernel smoother was developed to estimate the coefficients dynamically, in order to account for interannual variability. In this study, we used the kernel smoother for estimating these coefficients, where we distinguished for the first time between their two uses, meaning that we used a short and fixed window width for the backscatter normalisation while we tested different window widths for optimizing the vegetation correction. In particular, we investigated the impact of using the dynamic vegetation parameters on soil moisture retrieval. We compared soil moisture retrievals based on the dynamic vegetation parameters to those estimated using the current operational approach by examining their agreement, in terms of the Pearson correlation coefficient, unbiased RMSE and bias with respect to in situ soil moisture. Data from the United States Climate Research Network were used to study the influence of climate class and land cover type on performance. The sensitivity to the kernel smoother half-width was also investigated. Results show that estimating the vegetation parameters with the kernel smoother can yield an improvement when there is interannual variability in vegetation due to a trend or a change in the amplitude or timing of the seasonal cycle. However, using the kernel smoother introduces high-frequency variability in the dynamic vegetation parameters, particularly for shorter kernel half-widths. [ABSTRACT FROM AUTHOR]

Building height is one of the key variables in studying anthropogenic activities and built environments in the urban system. However, the information of urban building height over large areas is still limited due to the lack of available data. In this study, we developed an approach to estimate building height using the Sentinel-1 Ground Range Detected data. First, we proposed an indicator of VVH that integrates the dual-polarization information (i.e., VV and VH) from the Sentinel-1 synthetic aperture radar data. Second, we developed a building height model using the indicator of VVH and the reference building height from airborne light detection and ranging (LiDAR) images in seven cities in the United States (US). Third, we estimated building height of major cities with area larger than 500 km2 in the US using the developed model. The root mean square error between estimated and reference height is 1.5 m in seven study cities in the US. The comparison with the Laser observations from the Ice, Cloud, and land Elevation Satellite data in available footprints also indicates that the estimated building height from Sentinel-1 data is reliable. The estimated building height from Sentinel-1 data shows a better agreement with the reference data compared to the Advanced Land Observing Satellite data in seven US cities. Our results of building height in major US cities indicate that there are more cities with tall buildings in the Northeastern US compared to the Western and Southern US, with more aggregated tall buildings in central business districts of cities. The information of building height from this study is of great value in urban studies, such as estimating energy consumption and carbon emissions in urban areas. • We developed a building height model using Sentinel-1 data. • We estimated building height of major cities in the US. • The model performs well in estimating building height with a root mean square error of 1.5 m. • The information of building height is valuable in urban studies such as energy consumption. [ABSTRACT FROM AUTHOR]

Machine learning classifiers have been rarely used for the identification of seafloor sediment types in the rapidly changing dredge pits for coastal restoration. Our study uses multiple machine learning classifiers to identify the sediment types of the Caminada dredge pit in the eastern part of the submarine sandy Ship Shoal of the Louisiana inner shelf of the United States (USA), and compares the performance of multiple supervised classification methods. High-resolution bathymetry and backscatter data, as well as 58 sediment grab samples were collected in the Caminada pit in August 2018, about two years after dredging. Two primary features (bathymetry and backscatter) and four secondary features were selected in the machine learning models. Three supervised classifications were tested in the study area: Decision Trees, Random Forest, and Regularized Logistic Regression. The models were trained using three different combinations of features: (1) all six features, (2) only bathymetry and backscatter features, and (3) a subset of selected features. The best performing model was the Random Forest method, but its performance was relatively poor when dealing with a few mixed (sand and mud) surficial sediment samples. The model provides a new and efficient method to predict the change of sediment distribution inside the Caminada pit over time, and is more reliable when predicting mixed bed with rough pit bottoms. Our results can be used to better understand the impacts on biological communities by (1) direct defaunation after initial sand excavation, (2) later mud accumulation in topographic lows, and (3) other geological and physical processes. In the future, the deposition and redistribution of mud inside the Caminada pit will continue, likely impacting benthos and water quality. Backscatter, roughness derived from bathymetry, rugosity derived from backscatter, and bathymetry (in the importance order from high to low) were identified as the most effective predictors of sediment texture for mineral resources management. [ABSTRACT FROM AUTHOR]

The establishment of multibeam echosounders (MBES), as a mainstream tool in ocean mapping, has facilitated integrative approaches towards nautical charting, benthic habitat mapping, and seafloor geotechnical surveys. The combined acoustic response of the seabed and the subsurface can vary with MBES operating frequency. At worst, this can make for difficulties in merging the results from different mapping systems or mapping campaigns. However, at best, having observations of the same seafloor at different acoustic wavelengths allows for increased discriminatory power in seabed classification and characterization efforts. Here, we present the results from trials of a multispectral multibeam system (R2Sonic 2026 MBES, manufactured by R2Sonic, LLC, Austin, TX, USA) in the Bedford Basin, Nova Scotia. In this system, the frequency can be modified on a ping-by-ping basis, which can provide multi-spectral acoustic measurements with a single pass of the survey platform. The surveys were conducted at three operating frequencies (100, 200, and 400 kHz), and the resulting backscatter mosaics revealed differences in parts of the survey area between the frequencies. Ground validation surveys using a combination of underwater video transects and benthic grab and core sampling confirmed that these differences were due to coarse, dredge spoil material underlying a surface cover of mud. These innovations offer tremendous potential for application in the area of seafloor geological and benthic habitat mapping. [ABSTRACT FROM AUTHOR]