Search

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g. , AMSR-E), current (e.g. , AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers. [Display omitted] • A multi-sensor semi-physical algorithm is applied to retrieve vegetation moisture. • The water component in vegetation optical depth is disentangled from dry biomass. • Vegetation moisture retrievals at X- and Ku-bands capture the in situ dynamics. • L-band has poorer performance due to lack of in situ samples in wood canopy layers. [ABSTRACT FROM AUTHOR]

Abstract The increasing knowledge in the capabilities of satellite imagery to hazard applications is especially useful in emergency situations where timing and ability to cover large areas are of the essence. For optical imagery, cloud coverage can corrupt an image rendering it unusable for intended emergency analyses. This study proposes the use of Synthetic Aperture Radar (SAR) imagery for burn severity analysis for western United States sites, as an alternative to its optical based counterpart, differenced normalized burn ratio (dNBR). Unlike optical sensors, the radar sensor is an active sensor that is able to penetrate clouds and smoke, an attribute that is crucial in emergency situations where immediate burn severity data are needed to assess the vulnerability of fire affected areas to post-fire hazards. Using C5 decision tree algorithm we developed a SAR-based metric that attempts to classify burn severities of fire affected locations in the western USA. We then compared the performance of this developed metric to that obtained by the existing dNBR metric, to determine if there is any merit to its adoption as an alternative for the western USA landscape. The results showed the SAR approach to produce higher validation metrics in comparison to the dNBR. It had an overall accuracy and kappa of 60% and 0.35, respectively, in comparison to the 35% and 0.1 of the dNBR approach. This shows an improved ability to quickly obtain burn severity data and make better informed decisions in emergency situations. [ABSTRACT FROM AUTHOR]

Airborne cloud radar and cloud microphysical data, collected in summertime stratus clouds off the Oregon coast, are analyzed to obtain an empirical threshold radar reflectivity above which drizzle can be expected in warm marine stratus. Such clouds, which are prevalent over the eastern subtropical oceans, have a profound impact on global climate.This study finds that the threshold reflectivity for drizzle is a strong function of height within the cloud layer. Both radar reflectivities and calculated reflectivities (based on measured droplet spectra) indicate that the threshold is most crisply defined in the lower half of the stratus cloud deck. The use of the threshold reflectivity profile to flag drizzle is illustrated by examining the dependency of radar reflectivity on cloud water content. Such dependency is non-existent in marine stratus in general, but a clear relationship emerges when drizzle cases are excluded. [Copyright &y& Elsevier]

Abstract In this paired study, we apply a recently developed high-resolution bathymetry estimation algorithm ("cBathy") to X-band marine radar observations at two nearshore field sites. The algorithm exploits observations of the spatial structure of wave phase to attain wavenumber estimates, inverts the linear water wave dispersion relation for depth, and then applies a Kalman filter to objectively update the bathymetry estimates. Previously, performance has only been tested using optical video observations. In this first of two papers, performance of the algorithm using X-band radar image time series is tested at two disparate barred beach environments: Duck, NC, USA, and Benson Beach, WA, USA. Each of the test beaches is either co-located with (Duck, NC) or geographically close to (Benson Beach, WA) those utilized in the original algorithm verification. Concurrent echosounder surveys are used as ground truth. The bulk performance of the radar-derived bathymetry estimate at Duck, NC, achieves 0.49 m root-mean-square error (RMSE) with 0.26 m bias deep. This compares well with the bulk performance of the concurrent estimate derived using optical video (0.44 m RMSE and 0.23 m bias deep). The radar-derived bathymetry estimate performance at Benson Beach, is similar (0.35 m RMSE and 0.11 m bias shallow), and is comparable to that of an optical video derived estimate at a similar Pacific Northwest beach (0.56 m RMSE and 0.41 m bias shallow). At both beaches, significantly higher performance is achieved at locations deeper than 2 m (offshore of the surfzone) than at locations shallower than 2 m (surfzone), where errors are often a large fraction of the total depth. Lastly, several weeks of observations are utilized to assess the sensitivity of algorithm quality control to environmental conditions. Thresholds based on the shoreward component of wind stress and offshore wave steepness are identified and shown to impact the areal coverage of radar-derived bathymetric estimates. Overall, these results demonstrate the viability of marine radar observations as input to the cBathy algorithm and delineate some environmental constraints on algorithm performance. In the companion paper, the algorithm is extended to areas where tidal currents are important, including an ebb tidal shoal and an estuary mouth. Highlights • Depth estimation algorithm, cBathy, is applied to X-band radar image time series. • Performance is assessed at two beaches (North Carolina and Washington, USA). • Accuracy via radar is comparable to video-based performance (reported previously). • Frequency of updates increases with wave height and when wind blows toward shore. [ABSTRACT FROM AUTHOR]

A low frequency (80–120 MHz) VHF RaDAR, BioSAR, specifically designed for forest biomass estimation and a profiling LiDAR, PALS, were flown over loblolly pine plantations in the southeastern United States. LiDAR-only, RaDAR-only, and joint LiDAR–RaDAR linear models were developed to determine if returns from two sensors could be used to estimate pine biomass more accurately and precisely than returns from either sensor alone. The best five-variable RaDAR model explained 81.8% (R 2) of the stem green biomass variability, with a regression RMSE of 57.5 t/ha. The best one-variable LiDAR model explained 93.3% of the biomass variation (RMSE=33.9 t/ha). Combining the RaDAR normalized volumetric returns with the profiling LiDAR ranging measurements did little to improve the best LiDAR-only model. The best LiDAR–RaDAR model explained 93.8% of the biomass variation (RSME=32.7 t/ha). Cross-validation and training/test validation procedures demonstrated (1) that all models are unbiased and (2) the increased precision of the LiDAR-only and LiDAR–RaDAR models. The results of this investigation and a companion study indicate that there is little to be gained combining VHF–RaDAR volumetric returns and profiling LiDAR ranging measurements in pine forests; a LiDAR ranging system is sufficient for accurate, precise biomass estimation. [Copyright &y& Elsevier]

Abstract: This paper investigates the transport structure of surface currents around the Monterey Bay, CA region. Currents measured by radar stations around Monterey Bay indicate the presence of strong, spatial and temporal, nonlinear patterns. To understand the geometry of the flow in the bay, Lagrangian coherent structures (LCS) are computed. These structures are mobile separatrices that divide the flow into regions of qualitatively different dynamics. They provide direct information about the flow structure but are geometrically simpler than particle trajectories themselves. The LCS patterns were used to reveal the mesoscale flow conditions observed during the 2003 Autonomous Ocean Sampling Network (AOSN-II) experiment. Drifter paths from the AOSN experiment were compared to the patterns induced by the LCS computed from high-frequency radar data. We verify that the fate of the drifters can be better characterized based on the LCS than direct interpretation of the current data. This property can be exploited to optimize drifter deployment. [Copyright &y& Elsevier]

No-precipitation reflectivity observations from Doppler radar contain valuable information on areas lacking precipitation; however, they are often ignored in four-dimensional variational (4DVar) radar data assimilation (DA). This study incorporated a neighborhood-based scheme to assimilate no-precipitation observations as a mechanism to suppress spurious convection. The impact of the scheme on convective forecasting using 4DVar was evaluated by comparing the performance of experiments with and without assimilation (ExpCTL) using eight diverse storm cases that occurred over the central United States during summer 2016. Three no-precipitation assimilation experiments with different neighborhood radiuses of 10 (ExpR10), 30 (ExpR30), and 50 km (ExpR50) were conducted to examine the sensitivity of the scheme to neighborhood size. Results indicated that all the no-precipitation assimilation experiments significantly improved quantitative precipitation forecast skill with large reduction of the bias and the false alarm ratio from ExpCTL, as well as improving representation of the intensity and coverage of the precipitation. The horizontal wind, temperature, and water vapor were also improved, especially the latter. The scheme was found sensitive to neighborhood size and greater benefit was found in ExpR30 in comparison with ExpR10 and ExpR50. Analysis revealed that ExpR30 reduced low-level cooling and mid-level warming corresponding to decreased water vapor in areas of overpredicted and false precipitation, and it was more effective in conserving the total water content balance during cycled radar DA. The findings of this study could provide reference information for assimilation of no-precipitation observations into the Weather Research and Forecasting model using 4DVar, which would be valuable for severe weather prediction. • A neighborhood-based scheme was incorporated into 4DVar to assimilate no-precipitation observations from Doppler Radar. • The scheme significantly reduced spurious convection, and a moderate neighborhood size resulted in the best results. • The no-precipitation assimilation helps conserve balance of water content, which led to improved precipitation forecast. [ABSTRACT FROM AUTHOR]

Scanning Light Detecting and Ranging (LiDAR), Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) were analyzed to determine (1) which of the three sensor systems most accurately predicted forest biomass, and (2) if LiDAR and SAR/InSAR data sets, jointly considered, produced more accurate, precise results relative to those same data sets considered separately. LiDAR ranging measurements, VHF–SAR cross-sectional returns, and X-and P-band cross-sectional returns and interferometric ranges were regressed with ground-estimated (from dbh) forest biomass in ponderosa pine forests in the southwestern United States. All models were cross-validated. Results indicated that the average canopy height measured by the scanning LiDAR produced the best predictive equation. The simple linear LiDAR equation explained 83% of the biomass variability (n =52 plots) with a cross-validated root mean square error of 26. 0 t/ha. Additional LiDAR metrics were not significant to the model. The GeoSAR P-band (λ =86 cm) cross-sectional return and the GeoSAR/InSAR canopy height (X–P) captured 30% of the forest biomass variation with an average predictive error of 52. 5 t/ha. A second RaDAR–FOPEN collected VHF (λ ∼7. 8 m) and cross-polarized P-band (λ =88 cm) cross-sectional returns, none of which proved useful for forest biomass estimation (cross-validated R 2 =0. 09, RMSE=63. 7 t/ha). Joint consideration of LiDAR and RaDAR measurements produced a statistically significant, albeit small improvement in biomass estimation precision. The cross-validated R 2 increased from 83% to 84% and the prediction error decreased from 26. 0 t/ha to 24. 9 t/ha when the GeoSAR X–P interferometric height is considered along with the average LiDAR canopy height. Inclusion of a third LiDAR metric, the 60th decile height, further increased the R 2 to 85% and decreased the RMSE to 24. 1 t/ha. On this 11 km2 ponderosa pine study area, LiDAR data proved most useful for predicting forest biomass. RaDAR ranging measurements did not improve the LiDAR estimates. [Copyright &y& Elsevier]

Abstract: A substantial number of wind energy projects have been stalled or abandoned in the United States of America (US) due to concerns over the effects of wind turbines on radar installations. Between 2008 and 2010, military, airspace, or meteorological radar concerns in the US contributed to the delay or abandonment of an estimated 20,000 MW of wind energy capacity. These delays are a likely major factor influencing the current US Administration''s failure to double non-hydro renewable generation from 2008 to 2011; a target stated by the US President in a joint session address to Congress in February 2009. The delays are also a threat to the US Department of Energy''s target to produce 20% of electricity from wind energy by 2030 – unless radar-related barriers are mitigated. This work includes interviews with two senior representatives, from the US Department of Defence (DOD) and the American Wind Energy Association (AWEA), discussing the nature of concerns pertaining to the effects of wind turbines on radar and military/aviation in the US alongside approaches that have been trialled that aim to resolve such concerns. This research finds that the Energy Siting Clearing House, established within the DOD to review delayed wind farm projects, has brought much needed coordination to the approval process. A key challenge for any review body, however, will be to deliver an objective outcome that is not overturned by alternative political agendas. Integral to the success of any approach will be a sufficient capacity and mandate to facilitate the technical and non-technical cross-disciplinary and interagency research generating a balance between military, airspace, meteorological, and wind energy industry/political objectives. [Copyright &y& Elsevier]

Abstract: Many transportation authorities in the United States and Canada require condition assessments of concrete bridge decks and roadways as part of scheduled rehabilitation and maintenance programs. Nondestructive test (NDT) methods and laboratory studies are commonly used by engineering consultants to perform these evaluations. Amongst typical NDT methods, ground-penetrating radar (GPR) is one of the best suited to accomplish this task. Unfortunately, reported test results are oftentimes ambiguous, due to the current numerical analysis approach to evaluate the data. This paper describes some of the shortcomings of numerical analysis of GPR profiles and it introduces an alternative method developed by the authors. Several examples of actual field investigations are described, where the computer-assisted visual interpretation of GPR profiles was used to validate and eventually improve this new analysis approach. The authors’ project experiences on several structures indicate that mapping defects by visual identification of corrosion-induced anomalies in concrete is a much more accurate way of mapping defects. The results obtained from the field applications suggest that use of the computer-assisted visual interpretation of GPR data is an efficient and reliable approach that can be used by experienced GPR analysts. [Copyright &y& Elsevier]

• Precipitation annual maxima estimated from NEXRAD are evaluated over the CONUS. • Accuracy of radar-based estimates varies with magnitude and duration. • Uncertainty in radar-based IDF curves exhibits climate dependence. The lack of knowledge on precipitation frequency over ungauged areas introduces a significant source of uncertainty in relevant engineering designs and risk estimation procedures. Radar-based observations offer precipitation information over ungauged areas and thus have gained increasing attention as a potential solution to this problem. However, due to their relative short data records and inherent uncertainty sources, their ability to provide accurate estimates on the frequency of precipitation extremes requires evaluation. This study involves the evaluation of at-site precipitation frequency estimates from NEXRAD Stage IV radar precipitation dataset. We derive precipitation annual maxima series from the 16yrs record (2002–2017) of NEXRAD and we compare against 539 long-term (50yrs) hourly gauge records. In addition, Intensity-Duration-Frequency (IDF) curves are estimated from both radar and gauge dataset and compared. IDF estimation is based on fitting the Generalize Extreme Value distribution to annual precipitation maxima. Evaluation is carried out over the contiguous United States and results are grouped and presented for five dominant climate classes and for a range of return period and precipitation durations. NEXRAD was shown to overestimate intensities at shorter durations (1- and 3-h) and low quantiles, while it tends to underestimate higher quantiles at longer durations (24 h). In addition, evaluation of the IDF curves estimated from NEXRAD revealed a distinct geographic dependence with certain regions exhibiting a tendency to overestimation (e.g. east of the Rocky Mountains) or underestimation (Midwest). Overall, this analysis suggests that, while significant discrepancies may exist, there are several cases where NEXRAD provide estimates within the uncertainty bounds of the reference rain gauge dataset. The climate/geographic region and the temporal duration are important aspects to consider. Findings provided in this work on these aspects will hopefully serve as a general guideline for those interested in using NEXRAD estimates for further research or applications on precipitation extremes. [ABSTRACT FROM AUTHOR]

Abstract: The US National Weather Service (NWS) has installed a large network of weather Surveillance radars (WSR-88D) that provide precipitation maps for the United States. Many of these radars operate in mountainous regions and consequently suffer from beam blockage caused by terrain obstacles. The authors present a methodology for assessing the severity of the beam blockage and outline its implications for radar-derived precipitation estimates. The methodology involves the calculation of two-dimensional maps of power loss using a digital elevation model (DEM)-based algorithm of beam propagation for different radar antenna elevation angles. Using a large sample of actual radar data, the authors compare the simulated beam blockage results with the probability of detection of radar reflectivity above a certain threshold. The authors also compare their results with similar but coarser resolution blockage maps developed by the NWS and used in the NEXRAD system. For visualization, ArcGIS software is used to illustrate the results and offer a physical interpretation of the analyses. The study involves two NEXRAD sites: KRLX in Charleston, West Virginia and KEMX in Tucson, Arizona. The KRLX site does not suffer from significant blockage except for a single narrow sector. By contrast, the KEMX site contains several areas of blockage. The authors conclude that DEM-based prediction of radar beam occultation is a viable tool, as indicated by the good agreement of the calculated patterns of power loss with the actual long-term radar data. [Copyright &y& Elsevier]

Abstract: Impact of HF radar surface-current assimilation on ocean circulation model predictions in the Monterey Bay area is studied and evaluated during the time frame of the Autonomous Ocean Sampling Network (AOSN-II) experiment (August–September 2003). In the first instance, a previously described method for assimilation of surface current data is applied to 33-h low-pass-filtered data and a non-tidal version of the circulation model. It is demonstrated that assimilation of surface velocity data significantly improves the surface and subsurface correlation of model currents with moored current observations. These results from the AOSN-II period illustrate that surface-current assimilation is beneficial even in cases for which very high-resolution (3km) atmospheric forcing is utilized. The assimilation approach is also tested with hourly, unfiltered, CODAR-type HF radar-derived surface currents within a model configuration that includes tidal forcing. It is shown, that assimilation of unfiltered (with tides) surface-current observations into the model with tides improves the sub-tidal model predictions to the level comparable with the assimilation of filtered data into the non-tidal model, which is significant with respect to options for designing real-time nowcast and forecast systems. Finally, the approach is extended and evaluated for the direct assimilation of HF radar-derived radial velocity components. The model runs that included assimilation of radials from at least two HF radar sites show better correlations with observations than the non-assimilative run, especially those runs that included radials from the Santa Cruz site. Directions of radials for that site coincide with the directions of dominant southward flow during upwelling events and the northward flow during relaxation events. Direct assimilation of radial currents extends the range of influence of the data into regions covered by only one HF radar site. [Copyright &y& Elsevier]

Ground penetrating radar (GPR) and lithologic data reveal six Pleistocene sea-level highstand sequences, forming two composite terraces along the northern margin of Delaware Bay in southern New Jersey, USA. The upper terrace (+18 to +9 m MSL) is underlain by four estuarine-marine sequences of middle Pleistocene age, while the lower terrace (+6 m–MSL) contains 2 middle to late Pleistocene sequences. Separating the 2 terraces is a variably sloping escarpment (Cedarville) created by erosion during at least 2 high sea-level events. Intensive radar imaging and boring of these 6 unconformity-bounded units allows for their interpretation in a sequence-stratigraphic context, identifying transgressive, highstand, and regressive phases. Higher than present sea levels of +14, +12, +6, and +16 m during the middle Pleistocene, and +5 m during the middle and/or late Pleistocene are indicated by these sedimentary sequences, suggesting relative stability of this section of the Atlantic Coastal Plain during the past 500 ka. GPR analysis of coastal drainage modification reveals a contrasting lowstand sequence of incised paleovalleys, supporting the interpretation that these bay-margin deposits contain one of the most complete sections of late Quaternary highstand stratigraphy in the Mid-Atlantic region. For appendix see http://www.elsevier.nl/locate/quascirev. [Copyright &y& Elsevier]

Data gathered by electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) were used to produce a three-dimensional image of subsurface soft-sediment deformation structures developed on the modern Lake Powell delta at Hite, Utah. ERT and GPR lines were run orthogonal across the crater. ERT resolved a low-resistivity layer up to 2 m thick in the area near the vents within the crater. This low-resistivity layer thins toward the margins representing clays ejected from the vents. Below and adjacent to this layer is a high-resistivity layer that reflects delta top and pro-delta sands. The deepest zone recognized in the ERT profiles consists of a low-resistivity layer, clay deposits that accumulated during the maximum lake high stand. This clay zone is connected to the vent within the crater by a conduit that changes diameter vertically. GPR profiles recognized the presence of collapse features restricted to the proximity of the vent. The geometry of the model is consistent with those proposed for marine pockmarks that can be generated seismically or aseismically with the exception of subaerial exposure after the dome stage development. [ABSTRACT FROM AUTHOR]

Abstract: Ground-based interferometric radar (GBIR) monitoring was conducted on a slow-moving, translational failure landslide in Granby, Grand County, Colorado, USA. Radar monitoring was completed over two separate surveys in 2011 using a tripod mounted real aperture sensor. The purpose of this work is to evaluate GBIR as a temporally dense monitoring technique for monitoring landslide displacement and compare the monitoring results to ongoing GPS based surveying methods to verify measured displacements. We discuss the strengths and limitations of GBIR displacement monitoring with a variety of available sensors, and place this monitoring platform, sensor, and workflow into context of previous slope stability monitoring with GBIR. For both surveys, displacement time series were created through a small temporal baseline stacking to reduce noise and maintain high temporal resolution. The results of the displacement time series were compared to average displacement rates derived from GPS based surveying. An overall verification of radar and GPS derived displacement rates was achieved, and identifies important differences relating to the precision and uncertainty of the two techniques. This work demonstrates GBIR monitoring capability of establishing high temporal resolution on tracking variable rates of landslide movements. Spatial modeling of total observed displacements was completed for both surveys verifying a conceptual model of uniform translational landslide movement, providing greater confidence for mitigation planning. [Copyright &y& Elsevier]

Abstract: Radar instruments are part of the core payload of the two Europa Jupiter System Mission (EJSM) spacecraft: NASA-led Jupiter Europa Orbiter (JEO) and ESA-led Jupiter Ganymede Orbiter (JGO). At this point of the project, several frequency bands are under study for radar, which ranges between 5 and 50MHz. Part of this frequency range overlaps with that of the natural jovian radio emissions, which are very intense in the decametric range, below 40MHz. Radio observations above 40MHz are free of interferences, whereas below this threshold, careful observation strategies have to be investigated. We present a review of spectral intensity, variability and sources of these radio emissions. As the radio emissions are strongly beamed, it is possible to model the visibility of the radio emissions, as seen from the vicinity of Europa or Ganymede. We have investigated Io-related radio emissions as well as radio emissions related to the auroral oval. We also review the radiation belts synchrotron emission characteristics. We present radio sources visibility products (dynamic spectra and radio source location maps, on still frames or movies), which can be used for operation planning. This study clearly shows that a deep understanding of the natural radio emissions at Jupiter is necessary to prepare the future EJSM radar instrumentation. We show that this radio noise has to be taken into account very early in the observation planning and strategies for both JGO and JEO. We also point out possible synergies with RPW (Radio and Plasma Waves) instrumentations. [Copyright &y& Elsevier]

Lack of accurate and complete utility records is a root cause of utility strikes that occur on average once every minute in the United States, leading to hundreds of deaths and billions of dollars in financial losses. Ground-penetrating radar (GPR) has emerged as a promising tool to detect, locate, and measure underground pipes. Many algorithms have been devised to process GPR scans to determine pipe location and burying depth. Their accuracy depends on the relative angles between the GPR survey trajectory and the buried pipes. Perpendicular-to-pipe scanning yields the highest detectability, and along-pipe scanning yields the highest planimetric and depth accuracy. However, the challenge in practice is to maintain such ideal angles while not knowing the exact orientation of the pipes. This paper devises a novel spatial reasoning mechanism to enable the automation of GPR survey trajectory planning and adjustment based on ill-shaped and incomplete GPR signatures to achieve ideal angles in real time. The spatial reasoning mechanism provides trajectory adjustment suggestions based on the connection between the GPR signatures extracted from GPR scans and the relative angles between the GPR trajectory and underground pipes. The adjustment process continues until the GPR signatures from synthetic data under ideal angles and from field survey converge, and further adjustment no longer improves the results. Both indoor and field experiments have been conducted for validation. The results show that the newly developed method is capable of guiding the adjustment process in real time to achieve ideal angles and collect high-quality GPR data, leading to a more accurate estimation of pipe locations and burying depths. It has the great potential to support the operation of robotic unmanned ground vehicles (UGV) or unmanned aircraft system (UAS) to fully automate the GPR field survey. • A spatial reasoning process that guides survey robots to adaptively adjust trajectory in field • The trajectory planning of robotic GPR-UGV is near real-time. • The correlation of GPR signatures and spatial configurations are modeled and established. • Indoor and outdoor experiments were performed to validate the new method. • The new method achieves high accuracy and efficacy in mapping underground pipes. [ABSTRACT FROM AUTHOR]

This research investigates the appropriate scale for watershed averaged and site specific soil moisture retrieval from high resolution radar imagery. The first approach involved filtering backscatter for input to a retrieval model that was compared against field measures of soil moisture. The second approach involved spatially averaging raw and filtered imagery in an image-based statistical technique to determine the best scale for site-specific soil moisture retrieval. Field soil moisture was measured at 1225 m2 sites in three watersheds commensurate with 7 m resolution Radarsat image acquisition. Analysis of speckle reducing block median filters indicated that 5×5 filter level was the optimum for watershed averaged estimates of soil moisture. However, median filtering alone did not provide acceptable accuracy for soil moisture retrieval on a site-specific basis. Therefore, spatial averaging of unfiltered and median filtered power values was used to generate backscatter estimates with known confidence for soil moisture retrieval. This combined approach of filtering and averaging was demonstrated at watersheds located in Arizona (AZ), Oklahoma (OK) and Georgia (GA). The optimum ground resolution for AZ, OK and GA study areas was 162 m, 310 m, and 1131 m respectively obtained with unfiltered imagery. This statistical approach does not rely on ground verification of soil moisture for validation and only requires a satellite image and average roughness parameters of the site. When applied at other locations, the resulting optimum ground resolution will depend on the spatial distribution of land surface features that affect radar backscatter. This work offers insight into the accuracy of soil moisture retrieval, and an operational approach to determine the optimal spatial resolution for the required application accuracy. [Copyright &y& Elsevier]

Highlights • Evaluates utility of scatterometer data for estimation of urban built-up volume. • Dense Sampling Method-processed scatterometer data are compared to airborne lidar. • Analysis conducted for nine urban areas in the United States. • Strong, linear correlations observed between radar- and lidar-derived datasets. • Results promote three-dimensional approach to land use/land cover analyses. Abstract Accurately mapping urban infrastructure and extent is a high priority for resource management and service allocation as well as for addressing environmental, socioeconomic, and geopolitical concerns. Most available data products only document surficial (two-dimensional) land use and land cover (LULC), yet a substantial component of urban growth occurs in the vertical dimension. Light detection and ranging (lidar) data offer the potential for monitoring three-dimensional (3D) change, but the extreme lack of systematic lidar coverage worldwide inflicts considerable gaps in both spatial and temporal coverage. Satellite scatterometer (radar) data may serve as an alternative data source for characterizing urban growth and development in both the horizontal and vertical directions. The accuracy of these radar-based datasets for estimating building volumes remains to be validated quantitatively. For nine U.S. cities, we test whether scatterometer data can be used to estimate 3D urban built-up volume. We found strong, linear correlations between the lidar-derived and radar-derived building volume estimates for all cities with r2 values as high as 0.98 when using spatial trend analysis. Given the high expense that limits lidar data acquisition to small areas at sporadic points in time, satellite scatterometer data provide a breakthrough method for monitoring both vertical growth and horizontal expansion of cities across the world with a continuous decadal time scale. [ABSTRACT FROM AUTHOR]

The procedure estimating hourly rainfalls by merging radar-derived rainfalls and gauge measurements is developed and tested. It uses simple linear regression, which is complemented by the normalization and correction of distribution. The data from radar Tulsa, Oklahoma, Weather Surveillance Radar-1988 Doppler version and rain gauge data from the radar domain are used. The quality of estimates is evaluated against independent rain gauges by the root-mean-square-error, bias and correlation coefficient in dependence on the density of a gauge network. The results indicate that even a sparse gauge network (about 50 gauges, i.e. 4000 km2 per one gauge) is sufficient to improve the radar-derived rainfalls. The improvement increases with the number of gauges. [Copyright &y& Elsevier]

Wastewater injection over the past decade has increased seismicity in the central USA, in some cases accompanied by detectable surface uplift. Here, we use this uplift to constrain subsurface properties and pore pressure evolution. We apply an advanced multitemporal interferometric algorithm to 35 synthetic aperture radar images acquired by ALOS satellite over four years before the 2012 earthquake sequence in east Texas, where large volumes of wastewater are disposed at depths of ∼800 m and ∼1800 m. To solve for the hydraulic diffusivity of the injection layers, we jointly inverted the injected volume and uplift data, considering a poroelastic layered half space. We find diffusivity values of 0.3 ± 0.1 m2/s and 0.7 ± 0.15 m2/s for shallow and deep injection layers, respectively, which combined with seismically-derived bulk moduli yields permeability values of 5.5 ± 2.6 × 10 − 14 m2 and 1.9 ± 0.25 × 10 − 13 m2 for these layers, consistent with permeability range reported for Rodessa formation and well test values. Hydraulic conductivity determines the evolution of pore pressure and thus the origin and location of induced seismicity. This study highlights the value of geodetic observations to constrain key hydrogeological properties of injection layers and to monitor the evolution of the subsurface pressure change. • Fluid injection near Timpson, East Texas caused an uplift detectable by InSAR. • Surface deformation data are inverted to solve for hydraulic diffusivity of injection layer. • Pore pressure increase due to injection has triggered the 2012 M w 4.8 earthquake. [ABSTRACT FROM AUTHOR]

Abstract: Precipitation is one important input variable for land surface hydrologic and ecological models. Next Generation Radar (NEXRAD) can provide precipitation products that cover most of the conterminous United States at high resolution (approximately 4 km × 4 km). There are two major issues concerning the application of NEXRAD data: 1) the lack of a NEXRAD geo-processing and geo-referring program and 2) bias correction of NEXRAD estimates. However, in public domain, there is no Geographic Information System (GIS) software that can use geostatistical approaches to calibrate NEXRAD data using raingauge data, and automatically process NEXRAD data for hydrologic and ecological models. In this study, we developed new GIS software for NEXRAD validation and calibration (NEXRAD-VC) using raingauge data. NEXRAD-VC can automatically read in NEXRAD data in NetCDF or XMRG format, transform projection of NEXRAD data to match with raingauge data, apply different geostatistical approaches to calibrate NEXRAD data using raingauge data, evaluate performance of different calibration methods using leave-one-out cross-validation scheme, output spatial precipitation maps in ArcGIS grid format, calculate spatial average precipitation for each spatial modeling unit used by hydrologic and ecological models. The major functions of NEXRAD-VC are illustrated in the Little River Experimental Watershed (LREW) in Georgia using daily precipitation records of fifteen raingauges and NEXRAD products of five years. The validation results show that NEXRAD has a high success rate for detecting rain and no-rain events: 82.8% and 95.6%, respectively. NEXRAD estimates have high correlation with raingauge observations (correlation coefficient of 0.91), but relatively larger relative mean absolute error value of 36%. It is also worth noting that the performance of NEXRAD increases with the decreasing of rainfall variability. Three methods (Bias Adjustment method (BA), Regressing Kriging (RK), and Simple Kriging with varying local means (SKlm)) were employed to calibrate NEXRAD using raingauge data. Overall, SKlm performed the best among these methods. Compared with NEXRAD, SKlm improved the correlation coefficient to 0.96 and the relative mean absolute error to 22.8%, respectively. SKlm also increased the success rate of detection of rain and no-rain events to 87.47% and 96.05%, respectively. Further analysis of the performance of the three calibration methods and NEXRAD for daily spatial precipitation estimation shows that no one method can consistently provide better results than the other methods for each evaluation coefficient for each day. It is suggested that multiple methods be implemented to predict spatial precipitation. The NEXRAD-VC developed in this study can serve as an effective and efficient tool to batch process large amounts of NEXRAD data for hydrologic and ecological modeling. [Copyright &y& Elsevier]

Abstract: The performance of ground-penetrating radar (GPR) in soils strongly depends on soil electrical conductivity. Soils having high electrical conductivity rapidly attenuate radar energy, which restricts penetration depths and severely limits the effectiveness of GPR. Factors influencing the electrical conductivity of soils include the amount and type of salts in solution and the clay content. The Ground-Penetrating Radar Soil Suitability Map of the Conterminous United States is a thematic map showing the relative suitability of soils for GPR applications within comparatively large areas of the United States. This map is based on over twenty-five years of field observations made throughout the United States and soil attribute data contained in the State Soil Geographic (STATSGO) database. Attribute data used to determine the suitability of soils include taxonomic criteria, clay content, salinity, sodium absorption ratio, and calcium carbonate content. Based on additional testing, several GPR specific adjustments have been made. This paper describes these revisions and outlines the procedures used to develop the revised map. The revised map limits areas that are rated as being “Unsuited” for GPR to saline and sodic soils, reassesses calcareous and gypsiferous soils, and provides a mineralogy override for soils with low activity clays. This map can be used to assess the relative appropriateness of GPR for soil investigations within comparatively large areas of the conterminous United States. [Copyright &y& Elsevier]

Abstract: NASA has been making statistical measurements of the orbital debris environment for more than a decade using the MIT Lincoln Laboratory Haystack radar. The goal has been to characterize the environment for debris sizes as small as possible. Like all sensors which operate in the presence of noise, the Haystack radar has limited sensitivity. As the returned energy from small targets begins to approach the sensitivity limit of the radar, the probability-of-detection decreases, eventually approaching zero. The slope of the cumulative size distribution of debris begins to flatten out. This paper explores the possibility of extending the cumulative size distribution to smaller sizes by adjusting the distribution for probability-of-detection. [Copyright &y& Elsevier]

Abstract: We examine the warm season (April–September) rainfall climatology of the northeastern US through analyses of high-resolution radar rainfall fields from the Hydro-NEXRAD system and regional climate model simulations using the weather research and forecasting (WRF) model. Analyses center on the 5-year period from 2003 to 2007 and the study area includes the New York–New Jersey metropolitan region covered by radar rainfall fields from the Fort Dix, NJ WSR-88D. The objective of this study is to develop and test tools for examining rainfall climatology, with a special focus on heavy rainfall. An additional emphasis is on rainfall climatology in regions of complex terrain, like the northeastern US, which is characterized by land–water boundaries, large heterogeneity in land use and cover, and mountainous terrain in the western portion of the region. We develop a 5-year record of warm season radar rainfall fields for the study region using the Hydro-NEXRAD system. We perform regional downscaling simulations for the 5-year study period using the WRF model. Radar rainfall fields are used to characterize the interannual, seasonal and diurnal variation of rainfall over the study region and to examine spatial heterogeneity of rainfall. Regional climate model simulations are characterized by a wet bias in the rainfall fields, with the largest bias in the high-elevation regions of the model domain. We show that model simulations capture broad features of the interannual, seasonal, and diurnal variation of rainfall. Model simulations do not capture spatial gradients in radar rainfall fields around the New York metropolitan region and land–water boundaries to the east. The model climatology of convective available potential energy (CAPE) is used to interpret the regional distribution of warm season rainfall and the seasonal and diurnal variability of rainfall. We use hydrologic and meteorological observations from July 2007 to examine the interactions of land surface processes and rainfall from a regional perspective. [Copyright &y& Elsevier]

Summary: The worth of archival, long-term, high spatial and temporal resolution radar-rainfall estimates for hydrologic modeling has not yet been established. In particular, uncertainties remain regarding accuracy at the event time-scale, which is a modeling requirement for mass conservation. To explore this issue, an archival radar-rainfall precipitation dataset produced from the National Reflectivity Composite for the GEWEX Continental-Scale International Project (GCIP) and US National Weather Service, Weather Surveillance Radar 1988-D (WSR-88D) Stage III (SIII) data were compared with data from 982 National Climatic Data Center (NCDC) rain gages throughout the Mississippi River Basin. These two datasets are derived from the same radar-reflectivity observations but they are different. The GCIP dataset was developed from low resolution (5dBZ bins) national radar mosaic imagery using a systematic procedure that employed one climatologically-derived reflectivity-rainfall relationship, while the SIII radar-rainfall estimates are produced using a processing algorithm developed by the US National Weather Service that employs near-real time adjustment and varied parameterizations in different River Forecast Centers around the Mississippi River Basin. Statistical analyses were performed to compare both radar-rainfall datasets with rain gage data on an event time-scale for the entire year of 1997. The investigation shows that the GCIP dataset, given a change in the equation used to calculate rainfall from reflectivity, has a lower root mean square difference for the majority of gages in four of the five river forecast centers that cover the Mississippi River Basin compared to SIII in 1997. [Copyright &y& Elsevier]

An analysis is presented of a 2-h-long time series of X-band marine radar images, collected at Duck, North Carolina (USA), that captured the evolution of a buoyant ocean front as it propagated onshore, following a period of upwelling-favorable winds. In plan view, the front exhibits a scallop-shaped structure similar to that previously observed along strongly convergent fronts. This alongshore structure consists of broad frontal crests (a few hundred meters in length) alternating with sharply angled troughs, or frontal cusps. The evolution of these frontal shapes is explored using a reduced-gravity model (Cooper et al., J. Geophys. Res.-Oceans 106 (2001) 16887) that allows for nonlinear self-interaction of a propagating front. A model simulation shows cusps that develop quickly from initially broad troughs and that point toward the buoyant water, features resembling the observations. However, the simulation also shows a continuous oscillation of frontal shapes, while the observed front reaches a quasi-steady plan form. We attribute this difference in behavior to the gradual shoaling of the observed front as it steadily advances, ultimately reaching water depths of less than 2 m, which is comparable to the thickness of the buoyant layer. As a consequence of the shoaling, we suggest the cusps become sites of enhanced mixing, where water inshore of the front is also accelerated seaward. [Copyright &y& Elsevier]

Abstract: The US Army owns and maintains approximately 2000 bridges on its installations spread out in the United States and around the world. From this inventory, 67% are concrete bridges, most of which do not have design or as-built plans to effectively evaluate their condition/capacity. In addition, military vehicles have unique characteristics that are difficult to take into account when analytical analyses are performed. For these purposes, the US Army Engineer Research and Development Center has been adapting non-destructive techniques. The most promising concept explored thus far involves bridge rating through non-destructive load testing in combination with Ground Penetrating Radar (GPR) techniques. Field load testing techniques are valuable for evaluating the capacity of existing bridges and accurately characterize the structure’s live-load response while GPR techniques provide valuable information regarding the internal reinforcement as well as the condition of the concrete. This article will provide a brief description as well as the results from recent evaluations performed on reinforced concrete bridges at military installations. [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]

Satellite Radar Interferometry (InSAR) is suited to monitoring ground deformation on the scale of volcanic arcs, providing insight into the eruptive cycle over both long and short time periods. However, these measurements are often contaminated with atmospheric artefacts caused by changes in the refractivity of the atmosphere. Here, we test the use of two large-scale atmospheric models, ERA-Interim (ERA-I) and North American Regional Reanalysis (NARR), to correct atmospheric uncertainties in InSAR data from the Cascades Volcanic Arc, United States. At Lassen Volcanic Center, we find that NARR reduces interferogram standard deviation in 79% of cases by an average of 22%. Using NARR, we develop a strategy to produce a priori estimates of atmospheric uncertainties on an arc-wide basis. We show that in the Cascades, the RMS variation in range change is dependent upon volcano topography and increases by 0.7 cm per kilometre of relief. We use this to estimate detection thresholds for long-term monitoring of small magnitude (1 cm/yr) deformation signals, and short-term monitoring of ground deformation associated with pre-eruptive unrest. This new approach of assessing atmospheric uncertainties a priori is widely applicable to other volcanic arcs, and provides realistic estimates of atmospheric uncertainties suitable for use in near-real-time analysis of InSAR data during periods of volcanic unrest. [ABSTRACT FROM AUTHOR]

• Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. • Noncontact radar-based discharge estimates can be as accurate as in-situ, conventional streamgauges. • There was a notable phase sequencing between the surface velocity and stage, indicative of unsteady flows. • Real-time alerting capabilities of an impending flash flood are demonstrated in a vulnerable, ungauged basin. Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams. [ABSTRACT FROM AUTHOR]

Abstract: The integration of geophysical data into the subsurface characterization problem has been shown in many cases to significantly improve hydrological knowledge by providing information at spatial scales and locations that is unattainable using conventional hydrological measurement techniques. In particular, crosshole ground-penetrating radar (GPR) tomography has shown much promise in hydrology because of its ability to provide highly detailed images of subsurface radar wave velocity, which is strongly linked to soil water content. Here, we develop and demonstrate a procedure for inverting together multiple crosshole GPR data sets in order to characterize the spatial distribution of radar wave velocity below the water table at the Boise Hydrogeophysical Research Site (BHRS) near Boise, Idaho, USA. Specifically, we jointly invert 31 intersecting crosshole GPR profiles to obtain a highly resolved and consistent radar velocity model along the various profile directions. The model is found to be strongly correlated with complementary neutron porosity-log data and is further corroborated by larger-scale structural information at the BHRS. This work is an important prerequisite to using crosshole GPR data together with existing hydrological measurements for improved groundwater flow and contaminant transport modeling. [Copyright &y& Elsevier]

Abstract: In this paper we quantify the effects of temporal decorrelation in repeat pass synthetic aperture radar interferometry (InSAR). Temporal decorrelation causes significant uncertainties in vegetation parameter estimates obtained using various InSAR techniques, which are desired on a global scale. Because of its stochastic nature temporal decorrelation is hard to model and isolate. In this paper we analyze temporal decorrelation statistically as observed in a large swath of SIR-C L-Band InSAR data collected over the eastern United States, with a repeat pass duration of one day in October 1994 and a near zero perpendicular baseline. The very small baseline for this particular pair makes the effect of volumetric scattering on correlation magnitude statistics nearly imperceptible, allowing for a quantitative analysis of temporal effects alone. The swath analyzed in this paper spans more than a million hectares of terrain comprised primarily of deciduous and evergreen forests, agricultural land, water and urban areas. The relationships of these different land-cover types, phenology and weather conditions (i.e. precipitation and wind) on the measures of interferometric correlation is analyzed in what amounts to be the most geographically extensive analysis of this phenomenon to date. [Copyright &y& Elsevier]

Abstract: Horizontal wind data over Indonesia obtained by global reanalyses were compared with wind-profiling radar (WPR) observations in Indonesia at Kototabang (100.32°E, 0.20°S), Pontianak (109.37°E, 0.00°S), Manado (124.92°E, 1.55°N), and Biak (136.10°E, 1.18°S). The global-reanalysis data were 850- and 700-hPa horizontal winds for 2007 and 2008. These data were obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis (R1), the NCEP-Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (R2), the Japan Meteorological Agency Climate Data Assimilation System (JCDAS), the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis-interim (ERA-Interim). All the four reanalyses showed good agreement with the WPR zonal and meridional wind data (correlation coefficient >0.7 in most cases). Of the four reanalyses, ERA-Interim had the highest correlation coefficient with the WPR winds in both zonal (correlation coefficient 0.89) and meridional (0.77) components. It can be explained that ERA-Interim used four-dimensional variational analysis (4D-var) for data assimilation, whereas the other reanalyses used three-dimensional variational analysis (3D-var). The zonal winds of the four reanalyses had smaller (78–87%) amplitudes than did the WPR winds. Further comparison of the zonal winds between the global reanalyses and operational radiosondes indicated that global-reanalysis zonal winds with smaller amplitudes are common in and around Indonesia. [Copyright &y& Elsevier]

Abstract: The Shuttle Radar Topography Mission distinguished itself as the first near-global spaceborne mission to demonstrate direct sensitivity to vertical vegetation structure. Whether this sensitivity is viewed as exploitable signal or unwanted bias, a great deal of interest exists in retrieving vegetation canopy height information from the SRTM data. This study presents a comprehensive application-specific assessment of SRTM data quality, focusing on the characterization and mitigation of two primary sources of relative vertical error: uncompensated Shuttle mast motion and random phase noise. The assessment spans four test sites located in the upper Midwestern United States and examines the dependence of data quality on both frequency, i. E. , C-band vs. X-band, and the number of acquired datatakes. The results indicate that the quality of SRTM data may be higher than previously thought. Novel mitigation strategies include a knowledge-based approach to sample averaging, which has the potential to reduce phase noise error by 43 to 80%. The strategies presented here are being implemented as part of an ongoing effort to produce regional-to continental-scale estimates of vegetation canopy height within the conterminous U. S. [Copyright &y& Elsevier]

To aid in modeling studies over the Mississippi River Basin, we have developed an archival precipitation data set for the GEWEX Continental-Scale International Project. The data set spans from 1996–2000, a 5-year continuous period of record. Inputs for the data set are the National Reflectivity Composite that we obtained in Hierarchical Data Format. The size of the input data is 40 GB. In order to significantly reduce the size of the data set, we have developed an efficient data format that can be read from a disc faster than the input Hierarchical Data Format. We have also developed browsing tools for users of the data set. The data browser allows for fast and efficient viewing of the precipitation data and was produced using a common Geographic Information System software. Peripheral data are also included in the browser as an aid to the user. [Copyright &y& Elsevier]

Abstract: While LiDAR-based digital elevation models (DEM) are more accurate and precise than the USGS-sourced DEM that are widely used in soil mapping in the US, their high cost and other problems prohibit an easy decision of adopting them in service-oriented soil mapping conducted by a government agency like USDA-NRCS. This study compares the performances of LiDAR-based DEM and the USGS-sourced DEM in calculating slope gradient as an input for knowledge-based digital soil mapping (KBDSM), aiming to provide scientific evidence and more importantly, propose a scientific approach to evaluating the two types of DEM for KBDSM. We conducted the comparison by evaluating how closely the DEM-based slope gradient values match the field-measured values. For a small watershed in northern Vermont, US, we prepared three DEM, including a 10-m DEM interpolated from the 7.5-minute USGS topographic map, a 1-m DEM based on LiDAR points, and a 5-m DEM resampled from the 1-m DEM. When calculating slope gradient, we applied two neighborhood sizes (10m and 30m), two neighborhood shapes (square and circular), and three slope gradient algorithms (Evans–Young, Horn, and modified Zevenbergen–Thorne) to the three DEM. We then compared the calculated slope gradient values with the values measured by soil scientists at 159 sample locations in the study area. Statistics show that across all the tested settings, the LiDAR-based DEM perform significantly better than the USGS-sourced DEM. We conclude that LiDAR-based DEM may considerably improve the quality of inputs for KBDSM. We also find that the results from the 1-m LiDAR-based DEM and the resampled 5-m DEM do not show considerable and consistent differences. [Copyright &y& Elsevier]

Abstract: Changes in the structural state of forests of the semi-arid U.S.A., such as an increase in tree density, are widely believed to be leading to an ecological crisis, but accurate methods of quantifying forest density and configuration are lacking at landscape scales. An individual tree canopy (ITC) method based on aerial LiDAR has been developed to assess forest structure by estimating the density and spatial configuration of trees in four different height classes. The method has been tested against field measured forest inventory data from two geographically distinct forests with independent LiDAR acquisitions. The results show two distinct patterns: accurate, unbiased density estimates for trees taller than 20m, and underestimation of density in trees less than 20m tall. The underestimation of smaller trees is suggested to be a limitation of LiDAR remote sensing. Ecological applications of the method are demonstrated through landscape metrics analysis of density and configuration rasters. [Copyright &y& Elsevier]

Abstract: SeaWinds scatterometer data from 1999 to 2009 has been used to detect and track large Antarctic icebergs on a daily basis. Here, we develop two estimation algorithms to supplement iceberg position reports with estimates of the iceberg''s major-axis length, minor-axis length, and angle of rotational orientation. The first algorithm is an image-based approach while the second is based on raw SeaWinds measurements. Maximum-likelihood objective functions that relate SeaWinds backscatter observations to model predictions are developed. The utility of both estimation approaches is analyzed in simulation and with actual data via a case study of iceberg A22a. The image-based approach is found to outperform the measurement-based technique. Results are validated using high-resolution imagery. Estimates are also compared with reports compiled by the United States National Ice Center. [Copyright &y& Elsevier]

Summary: Typically, flash floods are studied ex post facto in response to a major impact event. A complement to field investigations is developing a detailed database of flash flood events, including minor events and null reports (i.e., where heavy rain occurred but there was no flash flooding), based on public survey questions conducted in near-real time. The Severe hazards analysis and verification experiment (SHAVE) has been in operation at the National Severe Storms Laboratory (NSSL) in Norman, OK, USA during the summers since 2006. The experiment employs undergraduate students to analyse real-time products from weather radars, target specific regions within the conterminous US, and poll public residences and businesses regarding the occurrence and severity of hail, wind, tornadoes, and now flash floods. In addition to providing a rich learning experience for students, SHAVE has also been successful in creating high-resolution datasets of severe hazards used for algorithm and model verification. This paper describes the criteria used to initiate the flash flood survey, the specific questions asked and information entered to the database, and then provides an analysis of results for flash flood data collected during the summer of 2008. It is envisioned that specific details provided by the SHAVE flash flood observation database will complement databases collected by operational agencies (i.e., US National Weather Service Storm Data reports) and thus lead to better tools to predict the likelihood of flash floods and ultimately reduce their impacts on society. [ABSTRACT FROM AUTHOR]

Abstract: This article explores whether the use of LiDAR data in detecting transport network obstructions shortens the time required to reach disaster sites. It presents a method for doing this using LiDAR data collected in New Orleans, Louisiana (USA) before and after Hurricane Katrina. It involves identifying all the LiDAR data points that lie within transport links (e.g., highways or streets) and performing blockage detection analysis with the Quick Terrain Modeler (QT Modeler) software. After performing this blockage detection, routing analysis was performed to determine the effect of these obstructions on the time needed to reach 30 randomly chosen locations in the study area from the centrally located City of New Orleans Fire Station. The results show that the use of LiDAR data in emergency response situations can significantly reduce the response times for first responders to reach disaster sites. [Copyright &y& Elsevier]

Abstract: Accurate estimation of live and dead biomass in forested ecosystems is important for studies of carbon dynamics, biodiversity, wildfire behavior, and for forest management. Lidar remote sensing has been used successfully to estimate live biomass, but studies focusing on dead biomass are rare. We used lidar data, in conjunction with field measurements from 58 plots to distinguish between and map standing live and dead tree biomass in the mixed coniferous forest of the North Rim of Grand Canyon National Park, USA. Lidar intensity and canopy volume were key variables for estimating live biomass, whereas for dead biomass, lidar intensity alone was critical for accurate estimation. Regression estimates of both live and dead biomass ranged between 0 and 600 Mg ha−1, with means of 195.08 Mg ha−1 and 65.73 Mg ha−1, respectively. Cross validation with field data resulted in correlation coefficients for predicted vs. observed of 0.85 for live biomass (RMSE=50 Mg ha−1 and %RMSE (RMSE as a percent of the mean)=26). For dead biomass, correlation was 0.79, RMSE was 42 Mg ha−1, and %RMSE was 63. Biomass maps revealed interesting patterns of live and dead standing tree biomass. Live biomass was highest in the ponderosa pine zone, and decreased from south to north through the mixed conifer and spruce–fir forest zones. Dead biomass exhibited a background range of values in these mature forests from zero to 100 Mg ha−1, with lower values in locations having higher live biomass. In areas with high dead biomass values, live biomass was near zero. These areas were associated with recent wildfires, as indicated by fire maps derived from the Monitoring Trends in Burn Severity Project (MTBS). Combining our dead biomass maps with the MTBS maps, we demonstrated the complementary power of these two datasets, revealing that MTBS burn intensity class can be described quantitatively in terms of dead biomass. Assuming a background range of dead biomass up to 100 Mg ha−1, it is possible to estimate and map the contribution to the standing dead tree biomass pool associated with recent wildfire. [Copyright &y& Elsevier]

Quantifying forest structure is important for sustainable forest management, as it relates to a wide variety of ecosystem processes and services. Lidar data have proven particularly useful for measuring or estimating a suite of forest structural attributes such as canopy height, basal area, and LAI. However, the potential of this technology to characterize forest succession remains largely untested. The objective of this study was to evaluate the use of lidar data for characterizing forest successional stages across a structurally diverse, mixed-species forest in Northern Idaho. We used a variety of lidar-derived metrics in conjunction with an algorithmic modeling procedure (Random Forests) to classify six stages of three-dimensional forest development and achieved an overall accuracy>95%. The algorithmic model presented herein developed ecologically meaningful classifications based upon lidar metrics quantifying mean vegetation height and canopy cover, among others. This study highlights the utility of lidar data for accurately classifying forest succession in complex, mixed coniferous forests; but further research should be conducted to classify forest successional stages across different forests types. The techniques presented herein can be easily applied to other areas. Furthermore, the final classification map represents a significant advancement for forest succession modeling and wildlife habitat assessment. [Copyright &y& Elsevier]

Abstract: In this paper, a methodology for individual tree-based species classification using high sampling density and small footprint lidar data is clarified, corrected and improved. For this purpose, a well-defined directed graph (digraph) is introduced and it plays a fundamental role in the approach. It is argued that there exists one and only one such unique digraph that describes all four pure events and resulting disjoint sets of laser points associated with a single tree in data from a two-return lidar system. However, the digraph is extendable so that it fits an n-return lidar system (n >2) with higher logical resolution. Furthermore, a mathematical notation for different types of groupings of the laser points is defined, and a new terminology for various types of individual tree-based concepts defined by the digraph is proposed. A novel calibration technique for estimating individual tree heights is evaluated. The approach replaces the unreliable maximum single laser point height of each tree with a more reliable prediction based on shape characteristics of a marginal height distribution of the whole first-return point cloud of each tree. The result shows a reduction of the RMSE of the tree heights of about 20% (stddev=1.1 m reduced to stddev=0.92 m). The method improves the species classification accuracy markedly, but it could also be used for reducing the sampling density at the time of data acquisition. Using the calibrated tree heights, a scale-invariant rescaled space for the universal set of points for each tree is defined, in which all individual tree-based geometric measurements are conducted. With the corrected and improved classification methodology the total accuracy raises from 60% to 64% for classifying three leaf-off individual tree deciduous species (N =200 each) in West Virginia, USA: oaks (Quercus spp.), red maple (Acer rubrum), and yellow poplar (Liriodendron tuliperifera). [Copyright &y& Elsevier]

Abstract: The direct retrieval of canopy height and the estimation of aboveground biomass are two important measures of forest structure that can be quantified by airborne laser scanning at landscape scales. These and other metrics are central to studies attempting to quantify global carbon cycles and to improve understanding of the spatial variation in forest structure evident within differing biomes. Data acquired using NASA''s Laser Vegetation Imaging Sensor (LVIS) over the Bartlett Experimental Forest (BEF) in central New Hampshire (USA) was used to assess the performance of waveform lidar in a northern temperate mixed conifer and deciduous forest. Using coincident plots established for this study, we found strong agreement between field and lidar measurements of height (r 2 =0.80, p <0.000) at the footprint level. Allometric calculations of aboveground biomass (AGBM) and LVIS metrics (AGBM: r 2 =0.61, PRESS RMSE=58.0 Mg ha−1, p <0.000) and quadratic mean stem diameter (QMSD) and LVIS metrics (r 2 =0.54, p =0.002) also showed good agreement at the footprint level. Application of a generalized equation for determining AGBM proposed by to footprint-level field data from Bartlett resulted in a coefficient of determination of 0.55; RMSE=64.4 Mg ha−1; p =0.002. This is slightly weaker than the strongest relationship found with the best-fit single term regression model. Relationships between a permanent grid of USDA Forest Service inventory plots and the mean values of aggregated LVIS metrics, however, were not as strong. This discrepancy suggests that validation efforts must be cautious in using pre-existing field data networks as a sole means of calibrating and verifying such remote sensing data. Stratification based on land-use or species composition, however, did provide the means to improve regression relationships at this scale. Regression models established at the footprint level for AGBM and QMSD were applied to LVIS data to generate predicted values for the whole of Bartlett. The accuracy of these models was assessed using varying subsets of the USFS NERS plot data. Coefficient of determinations ranged from fair to strong with aspects of land-use history and species composition influencing both the fit and the level of error seen in the predicted relationships. [Copyright &y& Elsevier]

Abstract: One thousand three hundred and eleven 15-min profiles of nocturnal mesopause region (80–105km) temperature and horizontal wind, observed by Colorado State University sodium lidar over Fort Collins, CO (41°N, 105°W), between May 2002 and April 2003, were analyzed. From these profiles, taken over 390h and each possessing vertical resolution of 2km, a statistical analysis of seasonal variations in wind shears, convective and dynamical instabilities was performed. Large wind shears were most often observed near 100km and during winter months. Thirty-five percent of the winter profiles contained wind shears exceeding 40m/s per km at some altitude. In spite of large winds and shears, the mesopause region (at a resolution of 2km and 15min) is a very stable region. At a given altitude, the probability for convective instability is less than 1.4% for all seasons and the probability for dynamic instability (in the sense of Richardson number) ranges from 2.7% to 6.0%. Wind shear measurements are compared with four decades of chemical release measurements, compiled in a study by Larson [2002. Winds and shears in the mesosphere and lower thermosphere: results from four decades of chemical release wind measurements. Journal of Geophysical Research 107(A8), 1215]. Instability results are compared with those deduced from an annual lidar study conducted with higher spatial and temporal resolution at the Starfire Optical Range (SOR) in Albuquerque, NM, by Zhao et al. [2003. Measurements of atmospheric stability in the mesopause region at Starfire Optical Range, NM. Journal of Atmospheric and Solar-Terrestrial Physics 65, 219–232], and from a study by Li et al. [2005b. Characteristics of instabilities in the mesopause region over Maui, Hawaii. Journal of Geophysical Research 110, D09S12] with 19 days of data acquired from Maui Mesosphere and Lower Thermosphere (Maui MALT) Campaign . The Fort Collins lidar profiles were also analyzed using 1-h temporal resolution to compare instances of instabilities observed on different time scales. [Copyright &y& Elsevier]

Abstract: A capability to remotely measure the vertical and spatial distribution of forest structure is required for more accurate modeling of energy, carbon, water, and climate over regional, continental, and global scales. We examined the potential of using a multi-angle spectral sensor to predict forest vertical structure as measured by an airborne lidar system. Data were acquired from AirMISR (Airborne Multi-Angle Imaging Spectrometer) and airborne LVIS (Laser Vegetation Imaging Sensor) for a 7000 ha study site near Howland Maine, consisting of small plantations, multi-generation clearings and large natural forest stands. The LVIS data set provided a relatively direct measure of forest vertical structure at a fine scale (20 m diameter footprints). Multivariate linear regression and neural network models were developed to predict the LVIS forest energy height measures from 28 AirMISR multi-angle spectral radiance values. The best model accurately predicted the maximum canopy height (as measured from LVIS) using AirMISR data (rmse=0.92 m, R 2 =0.89). The models developed in this study achieved high accuracies over a study site with an elaborate patchwork of forest communities with exceptional diversity in forest structure. We conclude that models using MISR-like data are capable of accurately predicting the vertical structure of forest canopies. [Copyright &y& Elsevier]

This study presents comparisons of Colorado State University''s prognostic mesoscale Regional Atmospheric Modeling System (RAMS 4.3) results with observational data obtained from aircraft, wind profiler, lidar, tethered balloon and RASS during the Northeast Oxidant and Particle Study (NE-OPS) field program at Philadelphia, PA during a summer episode in 1999. Model simulations were performed for the 15–20 July 1999 period. The comparison of model-predicted temperatures with aircraft and tethered balloon data revealed that the mean relative error exhibited the same general trend in time for temperature noted by earlier investigators. The comparisons of model relative humidity with aircraft and tethered balloon indicate that the mean relative error varied from −13% to −21%. The mean relative error for water vapor mixing ratio with respect to lidar data exhibited a negative bias consistent with humidity bias corresponding to aircraft and tethered balloon. The largest root mean square (rms) errors obtained from 36 km resolution RAMS results and the regular upper air rawinsonde stations are associated with the relative humidity values. The smallest rms errors for any variable are all associated with the lower atmosphere while the largest rms errors are associated with the upper/mid-tropospheric region. The difficulty in correctly predicting upper level humidity is due to lack of consistency in the upper level moisture observations while the lower rms errors of relative humidity close to surface are due to increased availability of moisture data at surface. The results of the present study, by utilizing a variety of diverse observational platforms, broadly confirm the general traits of RAMS performance noted by earlier investigators. [Copyright &y& Elsevier]