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Abstract: Forest canopy height is essential information for many forest management activities and is a critical parameter in models of ecosystem processes. Several methods are available to measure canopy height from single-tree to regional and global scales, but the methods vary widely in their sensitivities, leading to different height estimates even for identical stands. We compare four technologies for estimating canopy height in pine and hardwood forests of the Piedmont region of North Carolina, USA: (1) digital elevation data from the global Shuttle Radar Topography Mission (SRTM) C-band radar interferometry, (2) X- and P-band radar interferometry from the recently developed airborne Geographic Synthetic Aperture Radar (GeoSAR) sensor, (3) small footprint lidar measurements (in pine only), and (4) field measurements acquired by in situ forest mensuration. Differences between measurements were smaller in pine than in hardwood forests, with biases ranging from 5.13 to 12.17m in pine (1.60–13.77m for lidar) compared to 6.60–15.28m in hardwoods and RMSE from 8.40 to 14.21m in pine (4.73–14.92m for lidar) compared to 9.54–16.84 in hardwood. GeoSAR measurements of canopy height were among the most comparable measurements overall and showed potential for successful calibration, with R 2 =0.87 in pine canopies and R 2 =0.38 in hardwood canopies from simple linear regression. An improved calibration based on differential canopy penetration is presented and applied to SRTM measurements, resulting in canopy height estimates in pine forests with RMSE and standard error <4.00m. Each of the remotely sensed methods studied produces reasonable and consistent depictions of canopy height that can be compared with data of similar provenance, but due to differences in underlying sensitivities between the methods, comparisons between measurements from various sources require cross-calibration and will be most useful at broad scales. [Copyright &y& Elsevier]

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]

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]

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]

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: 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]

The northeastern U.S. experienced land use/land cover (LULC) changes over the last 300 years, including widespread deforestation during the 17th and 19th centuries, followed by reforestation in the 20th century. Reconstructing the extent of historic land use is crucial to understanding longer term impacts of anthropogenic activity. However, LULC datasets around the globe are typically only available for the last 50 years. This study aims to reconstruct the spatial distribution of cleared land with area estimation at two time periods based on stone walls using light detection and ranging (lidar), historic aerial photography, and deep learning. The two time periods are (1) the peak of agricultural activity period (mid-19th century) and (2) early-20th century. We utilized deep learning methods for (1) mapping the distribution of stone walls from lidar derivatives and 1934 aerial photography and (2) mapping historic land use from pre-satellite era aerial photography. The results of area estimation demonstrate strong consistency with the area indicated in the census record for both time periods. Notably, the incorporation of stone walls from 1934 orthomosaic plays a key role in significantly increasing the agreement of cleared area in the mid-19th century, as it compensated for the absence of walls that have been removed. By adding new historical time intervals of cleared land with our method, it allows for investigating the duration of land used for agricultural activities and the spatial pattern of LULC transition over the last 300 years. • Spatial reconstruction of historic land use in the northeastern U.S. using deep learning. • Filling the gap of pre-satellite period using anthropogenic landforms through lidar. • Potential to monitoring of long-term land use change. [ABSTRACT FROM AUTHOR]

The Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat) is the first spaceborne lidar instrument for continuous global observation of the Earth. GLAS records a vertical profile of the returned laser energy from its footprint. To help understand the application of the data for forest spatial structure studies in our regional projects, an evaluation of the GLAS data was conducted using NASA''s Laser Vegetation Imaging Sensor (LVIS) data in an area near NASA''s Goddard Space Flight Center in Greenbelt, Maryland, USA. The tree height indices from airborne large-footprint lidars such as LVIS have been successfully used for estimation of forest structural parameters in many previous studies and served as truth in this study. The location accuracy of the GLAS footprints was evaluated by matching the elevation profile from GLAS with the Shuttle Radar Topography Mission (SRTM) DEM. The results confirmed the location accuracy of the GLAS geolocation, and showed a high correlation between the height of the scattering phase center from SRTM and the top tree height from GLAS data. The comparisons between LVIS and GLAS data showed that the GLAS waveform is similar to the aggregation of the LVIS waveforms within the GLAS footprint, and the tree height indices derived from the GLAS and LVIS waveforms were highly correlated. The best correlations were found between the 75% waveform energy quartiles of LVIS and GLAS (r 2 =0.82 for October 2003 GLAS data, and r 2 =0.65 for June 2005 GLAS data). The correlations between the 50% waveform energy quartiles of LVIS and GLAS were also high (0.77 and 0.66 respectively). The comparisons of the top tree height and total length of waveform of the GLAS data acquired in fall of 2003 and early summer of 2005 showed a several meter bias. Because the GLAS footprints from these two orbits did not exactly overlap, several other factors may have caused this observed difference, including difference of forest structures, seasonal difference of canopy structures and errors in identifying the ground peak of waveforms. [Copyright &y& Elsevier]

Abstract: Vegetation structure retrieval accuracies from spaceborne Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat) data are affected by surface topography, background noise and sensor saturation. This study uses a physical approach to remove surface topography effect from lidar returns to retrieve vegetation height from ICESat/GLAS data over slope terrains. Slope-corrected vegetation heights from ICESat/GLAS data were compared to airborne Laser Vegetation Imaging Sensor (LVIS) (20m footprint size) and small-footprint lidar data collected in White Mountain National Forest, NH. Impact of slope on LVIS vegetation height estimates was assessed by comparing LVIS height before and after slope correction with small-footprint discrete-return lidar and field data. Slope-corrected GLAS vegetation heights match well with 98 percentile heights from small-footprint lidar (R2 =0.77, RMSE=2.2m) and top three LVIS mean (slope-corrected) heights (R2 =0.64, RMSE=3.7m). Impact of slope on LVIS heights is small, however, comparison of LVIS heights (without slope correction) with either small footprint lidar or field data indicates that our scheme improves the overall LVIS height accuracy by 0.4–0.7m in this region. Vegetation height can be overestimated by 3m over a 15° slope without slope correction. More importantly, both slope-corrected GLAS and LVIS height differences are independent of slope. Our results demonstrate the effectiveness of the physical approach to remove surface topography from large footprint lidar data to improve accuracy of maximum vegetation height estimates. GLAS waveforms were compared to aggregated LVIS waveforms in Bartlett Experimental Forest, NH, to evaluate the impact of background noise and sensor saturation on vegetation structure retrievals from ICESat/GLAS. We found that GLAS waveforms with sensor saturation and low background noise match well with aggregated LVIS waveforms, indicating these waveforms capture vertical vegetation structure well. However, waveforms with large noise often lead to mismatched waveforms with LVIS and underestimation of waveform extent and vegetation height. These results demonstrate the quality of ICESat/GLAS vegetation structure estimates. [Copyright &y& Elsevier]

Abstract: In this study retrievals of forest canopy height were obtained through adjustment of a simple geometric-optical (GO) model against red band surface bidirectional reflectance estimates from NASA''s Multiangle Imaging SpectroRadiometer (MISR), mapped to a 250 m grid. The soil-understory background contribution was partly isolated prior to inversion using regression relationships with the isotropic, geometric, and volume scattering kernel weights of a Li-Ross kernel-driven bidirectional reflectance distribution function (BRDF) model. The height retrievals were assessed using discrete return lidar data acquired over sites in Colorado as part of the Cold Land Processes Experiment (CLPX) and used with fractional crown cover retrievals to obtain aboveground woody biomass estimates. For all model runs with reasonable backgrounds and initial b/r (vertical to horizontal crown radii) values <2.0, root mean square error (RMSE) distributions were centered between 2.5 and 3.7 m while R 2 distributions were centered between 0.4 and 0.7. The MISR/GO aboveground biomass estimates predicted via regression on fractional cover and mean canopy height for the CLPX sites showed good agreement with U.S. Forest Service Interior West map data (adjusted R 2 =0.84). The implication is that multiangle sensors such as MISR can provide spatially contiguous retrievals of forest canopy height, cover, and aboveground woody biomass that are potentially useful in mapping distributions of aboveground carbon stocks, tracking disturbance, and in initializing, constraining, and validating ecosystem models. This is important because the MISR record is spatially comprehensive and extends back to the year 2000 and the launch of the NASA Earth Observing System (EOS) Terra satellite; it might thus provide a ~10-year baseline record that would enhance exploitation of data from the NASA Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission, as well as furthering realization of synergies with active instruments. [Copyright &y& Elsevier]

Abstract: Twenty-nine nights including 275h measurements of temperature, zonal and meridional winds, and zonal momentum flux in the mesopause region were made between September 2006 and April 2007 with the Colorado State University (CSU) sodium lidar at Fort Collins, CO (40.6N, 105W). Perturbations with periods between 6min and 4h in temperature and winds were calculated and compared to the recently reported observations by a similar lidar located at Starfire Optical Range (SOR), NM (35N, 107W), showing general agreement. The seasonal means of the observed zonal momentum flux of these wave perturbations were found to be 0.12±0.46m2 s−2 for the equinoctial season and −0.69±0.42m2 s−2 for the winter season. These results are compared to two such observations in mid-latitude mesosphere and lower thermosphere (MLT) in the literature, i.e., lidar observation at SOR and radar observation near Kyoto, Japan (35N, 106E). Both magnitudes and signs of the CSU lidar observations are consistent with those published. [Copyright &y& Elsevier]

Summary: Technological aspects of producing, delivering and updating of flood hazard maps in the US have has gone through a revolutionary change through Federal Emergency Management Agency’s Map Modernization program. In addition, the use of topographic information derived from Light Detection and Ranging (LIDAR) is enabling creation of relatively more accurate flood inundation maps. However, LIDAR is not available for the entire United States. Even for areas, where LIDAR data are available, the effect of other factors such as cross-section configuration in one-dimensional (1D) models, mesh resolution in two-dimensional models (2D), representation of river bathymetry, and modeling approach is not well studied or documented. The objective of this paper is to address some of these issues by comparing newly developed flood inundation maps from LIDAR data to maps that are developed using different topography, geometric description and modeling approach. The methodology involves use of six topographic datasets with different horizontal resolutions, vertical accuracies and bathymetry details. Each topographic dataset is used to create a flood inundation map for twelve different cross-section configurations using 1D HEC-RAS model, and two mesh resolutions using 2D FESWMS model. Comparison of resulting maps for two study areas (Strouds Creek in North Carolina and Brazos River in Texas) show that the flood inundation area reduces with improved horizontal resolution and vertical accuracy in the topographic data. This reduction is further enhanced by incorporating river bathymetry in topography data. Overall, the inundation extent predicted by FESWMS is smaller compared to prediction from HEC-RAS for the study areas, and that the variations in the flood inundation maps arising from different factors are smaller in FESWMS compared to HEC-RAS. [Copyright &y& Elsevier]

The overall goal of this study was to develop methods for assessing crown base height for individual trees using airborne lidar data in forest settings typical for the southeastern United States. More specific objectives are to: (1) develop new lidar-derived features as multiband height bins and processing techniques for characterizing the vertical structure of individual tree crowns; (2) investigate several techniques for filtering and analyzing vertical profiles of individual trees to derive crown base height, such as Fourier and wavelet filtering, polynomial fit, and percentile analysis; (3) assess the accuracy of estimating crown base height for individual trees, and (4) investigate which type of lidar data, point frequency or intensity, provides the most accurate estimate of crown base height. A lidar software application, TreeVaW, was used to locate individual trees and to obtain per tree measurements of height and crown width. Tree locations were used with lidar height bins to derive the vertical structure of tree crowns and measurements of crown base height. Lidar-derived crown base heights of individual trees were compared to field observations for 117 trees, including 94 pines and 23 deciduous trees. Linear regression models were able to explain up to 80% of the variability associated with crown base height for individual trees. Fourier filtering used for smoothing the vertical crown profile consistently provided the best results when estimating crown base height. [Copyright &y& Elsevier]

Seasonal snow-derived water is a critical component of the water supply in the mountains and downstream regions, and the accurate characterization of available water in the form of snow-water-equivalent (SWE), peak SWE, and snowmelt onset are essential inputs for water management efforts. Arising from recent advancements in artificial intelligence (AI) and machine learning (ML), we introduce a large-scale ML SWE model leveraging publicly available data sources and open-source software. The model demonstrates the application of a limited feature space in a relatively simple ML architecture without the need for process-based formulations to effectively estimate spatially continuous SWE at a daily temporal resolution. Beginning with in situ SWE measurements (i.e., SNOTEL), lidar-derived terrain features, and temporal variables, we employ localized feature engineering and optimization via gradient-boosting decision trees to identify regionally unique drivers of snowpack dynamics and use the optimal features to train regionally independent artificial neural networks to estimate regional SWE at a 1 km spatial resolution. The model results yield respectable skill in reconstructed 1 km gridded SWE magnitudes in a hindcast simulation of the 2019 water year that is independent of the training and testing data. Comparing model estimates to over 6200 observations, the model demonstrates a weighted RMSE of 15.4 cm, Kling-Gupta Efficiency metric of 0.86, and a percent bias of 0.71% across 23 snow-influenced regions in the western U.S. The model simulation produces peak SWE estimates within 10 cm for twenty of the twenty-three regions, demonstrating capability in effectively capturing regional snow accumulation processes. The demonstration of low-error ML workflows capable of providing near-real-time, spatially continuous SWE estimates at a high spatial resolution provides proof-of-concept and a foundation to effectively update snow state variables that drive water supply forecasts in snow-dominated regions. • A new Machine Learning-based SWE estimation model for the Western United States. • Utilization of open-source tools and up-to-date in-situ observations to estimate SWE. • Reconstructed SWE magnitudes exhibit respectable skill in a hindcast simulation. • Output may serve as a supplemental state-of-snowpack input to hydrologic models. [ABSTRACT FROM AUTHOR]

Abstract: The NASA IceBridge flights have obtained critical observations for Earth''s polar ice since ICESat stopped collecting data in 2009. This study develops an automatic method in processing IceBridge Airborne Topographic Mapper (ATM) altimeter L1B data (one elevation per 3–4m horizontally) to derive a local sea level height for referencing snow freeboards and then computing sea ice thicknesses. Four 30-km L1B profiles (A, B, C and D) flown on October 21, 2009 over the Bellingshausen Sea in Antarctica are selected. The local sea level reference is first obtained by visual examination of ATM L1B heights over leads or thin ice identified on images simultaneously acquired from the Digital Mapping System camera (called manual selection). This sea level reference is then used as ground truth to validate sea level heights derived by automatic calculations using five thresholds of 2%, 1%, 0.5%, 0.2% and 0.1% of the lowest L1B data. The L1B_0.2% method gives a similar sea level height as from the L1B manual selection, by mean (absolute) difference of −0.01 (0.06) m. The sea level heights demonstrate a near linear gradient of 0.01m/km to 0.03m/km within each ~30-km L1B profile along the flight track from section A to D. The resulting mean snow freeboards are 0.59m, 0.67m, 0.53m, and 0.60m on sections A, B, C and D, respectively. Three empirical equations and the buoyancy equation (with zero ice freeboard assumption) all give similar statistics in ice thickness estimation with mean ice thicknesses of 1.91m for section A, 2.16m for B, 1.76m for C, and 1.94m for D. The sea level over leads cannot be accurately resolved from the ATM L2 data (~60m x 80m horizontal averaging). However, by using a sea level reference obtained from the L1B data, the ATM L2 data can achieve reasonable ice thickness estimates with mean absolute difference of only 0.10m compared to thickness derived from the L1B data. [Copyright &y& Elsevier]

Abstract: We investigated the influence on flow resistance of flow structure and turbulence at the reach scale in a mountain channel using 3-D velocity measurements and geostatistical analysis to understand the complexity of the flow structure in a reach with limited bed irregularities. The increase in flow resistance at low flows in a plane-bed reach was not fully explained by grain resistance, therefore detailed 3-D velocity measurements were made to investigate spatial variability in velocity and turbulence components and potential controls on flow resistance. One plane-bed reach was surveyed over two stages in Fraser Experimental Forest, Colorado, using a combination of a total station, LiDAR (Light Detection and Ranging), and a SonTek Flowtracker handheld ADV (acoustic Doppler velocimeter). LiDAR was used to capture bank and channel geometry at low flows, whereas the water surface and bed data were collected with the total station at all flows. We used the standard deviation of bed elevation (σ b ) within a moving window as an index of roughness height (k s ) and calculated the relative submergence of the bed at different stages as h/k s , where h is the local flow depth. ADV measurements were collected on a grid with a 0.3m to 0.5m spacing. Geostatistical analysis of the velocity data indicated that the flow was highly three-dimensional and varied based on stage, demonstrating that even small irregularities in the bed have a significant influence on the flow characteristics. The streamwise component was the largest at both low and high flow, but varied more throughout the reach at low flow. At high flow, the greatest streamwise velocities were located within the thalweg. Areas of upwelling and downwelling also varied based on stage, with this component being strongly influenced by small changes in the morphology at high flow, and by protuberant grains at low flows. The cross-stream velocity and turbulence components were controlled by the flow structure and less by the roughness of the bed. The turbulence intensity is significant when considering hydraulics for predicting sediment transport and for habitat assessment. The plane-bed flow patterns were compared to results from a pool-riffle reach in the North Fork Cache La Poudre River, a step-pool reach in East St. Louis Creek, and a step-pool reach in Italy on the Rio Cordon. The comparison of the 3-D velocities among channel types showed that the plane-bed reach has higher streamwise velocities, but similar values of cross-stream and transverse velocities. Streamwise turbulence intensities were similar in both the plane-bed and step-pool reach in East St. Louis Creek. The analysis revealed that the connection between the flow characteristics and the channel was not only related to the gross morphology and location of larger clasts, but also to the development of shear layers from the convergence of flow. Therefore, it is essential to understand how water moving in one direction can alter the characteristics of another component of flow and how this interaction is connected to the bed morphology. [Copyright &y& Elsevier]

Abstract: Large areas of forest in the US and Canada are affected by insects and disease each year. Over the past century, outbreaks of the Eastern spruce budworm have become more frequent and severe. The notion of designing a more pest resistant landscape through prescriptive management practices hinges on our ability to effectively model forest–insect dynamics at regional scales. Increasingly, more detailed pixel-wise estimates of forest biophysical parameters are needed for such endeavors. Lidar technology, although promising, is not yet viable for repeated regional accounting, necessitating the development of methods which take advantage of existing spaceborne assets. Our objective is to use one of these assets (SPOT-5) to estimate a large set of forest structural attributes at a finer spatial grain size (5 m and 10 m) over a broader area than is currently available for the purpose of supplying needed input data for disturbance simulation modeling. We employ neighborhood statistics (standard deviation, variance, sill variance, and ratios of these metrics at 5 and 10 m) calculated from SPOT-5 sensor data and derivatives to estimate and map tree canopy diameter (CDIA), bole diameter at breast height (DBH), tree height (HT), crown closure (CC), vertical length of live crown (LC), and basal area (BA). A partial least squares (PLS) regression approach was used with these local statistics and field data to produce models for pixel-wise estimation and mapping of mean values, respectively, for hardwood and coniferous forest CDIA (R 2 =0.82 and 0.93, RMSE 0.62 and 0.47 m), DBH (R 2 =0.82 and 0.90, RMSE 2.92 and 3.75 cm), HT (R 2 =0.69 and 0.92, RMSE 1.27 and 1.59 m), CC (R 2 =0.52 and 0.68, RMSE 5.49 and 6.02%), LC (R 2 =0.58 and 0.81, RMSE 0.96 and 1.25 m), and BA (R 2 =0.71 and 0.74, RMSE 2.47 and 4.58 m2 ha−1) for a 3600 km2 area in northeast Minnesota. This approach for quantifying forest structure is robust in the sense that a detailed forest cover type map is not required to stratify analysis at any step in the process. Hence, we show that multi-resolution SPOT-5 data are a practical alternative to lidar for regional characterization of forest biophysical parameters. However, lidar data may potentially be used to calibrate these SPOT-based structure models in the future. [Copyright &y& Elsevier]