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The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called "pixels weighted differential gradient", to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. [ABSTRACT FROM AUTHOR]

Land cover mapping is an important part of resource management, planning, and economic predictions. Improvements in remote sensing, machine learning, image processing, and object based image analysis (OBIA) has made the process of identifying land cover types increasingly faster and reliable but these advances have not been able to utilize all of the information encompassed within ultra-high (sub-meter) resolution imagery. There have been few known attempts to try and maximize this detailed information in high resolution imagery using advanced textural components. Hierarchical land classes are also rarely used as an attribute within the machine learning step of object-based image analysis. In this study we try to circumnavigate the inherent problems associated with high resolution imagery by combining well researched data transformations that aid the OBIA process with a seldom used texture transformation in Geographic Object Based Image Analyses (GEOBIA/OBIA) known as the Gabor Transform and the hierarchal organization of landscapes. We will observe the difference made in segmentation and classification accuracy of a random forest classifier when we fuse a Gabor transformed image to a Normalized Difference Vegetation Index (NDVI), high resolution multi-spectral imagery (RGB and NIR) and Light Detection and Ranging (LiDAR) derived canopy height model (CHM) within a riparian area in Southeast Iowa, United States. Additionally, we will observe the effects on classification accuracy when adding multi-scale land cover data to objects. Both, the addition of hierarchical information and Gabor textural information, could aid the GEOBIA process in delineating and classifying the same objects that human experts would delineate within this riparian landscape. [ABSTRACT FROM AUTHOR]

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]

Publicly available Light Detection and Ranging (LiDAR) datasets have become widely accessible in the northeastern United States and beyond in the past 10 years. The increase in dataset availability and accessibility coupled with a number of publications detailing the types of cultural features that can be identified has made it necessary to explore and discuss positive impacts and risks to cultural features on this landscape. Access to detailed, documented locations of archaeological resources at state or federal agencies in the United States is typically limited to those with certain credentials, yet many locations of features and sites, both documented and undocumented, are now available to anyone who can access these datasets and effectively interpret them. This presents a challenge for cultural resource management professionals and the field of archaeology; for while LiDAR datasets have had many positive impacts, it is not yet obvious what the unintended impacts of feature exposure might be. Risks to sites are worth considering in the northeastern United States, where (1) region‐wide LiDAR data are publicly available and accessible, (2) many cultural features are widely accessible and not well monitored and (3) case studies have been published that provide guidance on how to identify specific types of cultural landscape features using LiDAR data. We discuss the nuances of those topics here, provide examples of how the datasets have impacted archaeology in the northeastern United States and explore possible mitigation strategies to maintain data accessibility while also protecting important cultural features in this region. [ABSTRACT FROM AUTHOR]

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]

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]

Habitat mapping can be accomplished using many techniques and types of data. There are pros and cons for each technique and dataset, therefore, the goal of this project was to investigate the capabilities of new satellite sensor technology and to assess map accuracy for a variety of image classification techniques based on hundreds of field-work sites. The study area was Masonboro Island, an undeveloped area in coastal North Carolina, USA. Using the best map results, a habitat change assessment was conducted between 2002 and 2010. WorldView-2, QuickBird, and IKONOS satellite sensors were tested using unsupervised and supervised methods using a variety of spectral band combinations. Light Detection and Ranging (LiDAR) elevation and texture data pan-sharpening, and spatial filtering were also tested. In total, 200 maps were generated and results indicated that WorldView-2 was consistently more accurate than QuickBird and IKONOS. Supervised maps were more accurate than unsupervised in 80% of the maps. Pan-sharpening the images did not consistently improve map accuracy but using a majority filter generally increased map accuracy. During the relatively short eight-year period, 20% of the coastal study area changed with intertidal marsh experiencing the most change. Smaller habitat classes changed substantially as well. For example, 84% of upland scrub-shrub experienced change. These results document the dynamic nature of coastal habitats, validate the use of the relatively new Worldview-2 sensor, and may be used to guide future coastal habitat mapping. [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]

Forest ecosystems are the most important terrestrial carbon (C) storage globally, and presently mitigate anthropogenic climate change by acting as a large and persistent sink for atmospheric CO. Yet, forest C density varies greatly in space, both globally and at stand and landscape levels. Understanding the multi-scale drivers of this variation is a prerequisite for robust and effective climate change mitigation in ecosystem management. Here, we used airborne light detection and ranging (Lidar) and a novel high-resolution simulation model of landscape dynamics (iLand) to identify the drivers of variation in C density for an old-growth forest landscape in Oregon, USA. With total ecosystem C in excess of 1 Gt ha these ecosystems are among the most C-rich globally. Our findings revealed considerable spatial variability in stand-level C density across the landscape. Notwithstanding the distinct environmental gradients in our mountainous study area only 55.3% of this variation was explained by environmental drivers, with radiation and soil physical properties having a stronger influence than temperature and precipitation. The remaining variation in C stocks was largely attributable to emerging properties of stand dynamics (that is, stand structure and composition). Not only were density- and size-related indicators positively associated with C stocks but also diversity in composition and structure, documenting a close link between biodiversity and ecosystem functioning. We conclude that the complexity of old-growth forests contributes to their sustained high C levels, a finding that is relevant to managing forests for climate change mitigation. [ABSTRACT FROM AUTHOR]

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

Three-dimensional (3D) slip vectors re corded by displaced landforms are diffi- cult to constrain across complex fault zones, and the uncertainties associated with such measure ments become increasingly challenging to assess as landforms degrade over time. We approach this problem from a remote sensing perspective by using terrestrial laser scanning (TLS) and 3D structural analysis. We have developed an integrated TLS data collection and point-based analysis workflow that incorporates accurate assessments of aleatoric and epistemic uncertainties using experimental surveys, Monte Carlo simulations, and iterative site reconstructions. Our scanning workflow and equipment requirements are optimized for single-operator surveying, and our data analysis process is largely completed using new point-based computing tools in an immersive 3D virtual reality environment. In a case study, we measured slip vector orientations at two sites along the rupture trace of the 1954 Dixie Valley earthquake (central Nevada, United States), yielding measurements that are the first direct constraints on the 3D slip vector for this event. These observations are consistent with a previous approximation of net extension direction for this event. We find that errors introduced by variables in our survey method result in <2.5 cm of variability in components of displacement, and are eclipsed by the 10-60 cm epistemic errors introduced by reconstructing the field sites to their preerosion geometries. Although the higher resolution TLS data sets enabled visualization and data interactivity critical for reconstructing the 3D slip vector and for assessing uncertainties, dense topographic constraints alone were not sufficient to significantly narrow the wide (<26°) range of allowable slip vector orientations that resulted from accounting for epistemic uncertainties. [ABSTRACT FROM AUTHOR]

Marine terraces are landforms that record both paleohorizontal and true surface uplift above mean sea level, and thus serve as longbaseline geodetic markers. Here we present a method for extending terrace records back in time by analyzing 2003 National Aeronautics and Space Administration (NASA) airborne LiDAR data to discover a suite of probable marine terraces along a 70-kmlong section of the northern California coast. In particular, we present a semiautomated surface classification model, based on slope and surface roughness properties, to identify low-slope and low-relief topographic platforms with characteristics similar to marine terraces. Between Fort Ross and Point Arena (California, United States), the method identifies 851 individual platforms that range in elevation from 100 to 450 m above sea level. The total platform area is 24.5 km2 over a total area of 394 km2; the majority of platforms are found west of the San Andreas fault. We interpret these platforms as a suite of marine terraces because they are generally elongate parallel to the modern coastline and one another, and their elevations increase systematically from west to east in a step-like fashion. Geologic evidence supporting this conclusion includes locally abundant well-rounded and well-polished clasts with heterogeneous compositions, beveled outcrops of the underlying bedrock (German Rancho Formation), and provenance studies indicating transport of clasts contained in probable marine deposits across the San Andreas fault. The platforms contain 21 different elevation bands, each of which is 2-6 m high, suggesting that they may define a flight of 21 distinct marine terraces at elevations between 113 and 405 m above sea level. Matching the 21 observed platform elevations with those predicted from sealevel curves suggests that the surface uplift rate for the northern coast of California has been ∼0.2 mm/yr over the past 2 Ma. This rate is similar to, but lower than the previously determined uplift rate of ∼0.3 mm/yr for a ∼23 m marine terrace in the area. [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]

In precision forestry, tree species identification is one of the critical variables of forest inventory. Lidar, specifically full-waveform Lidar, holds high promise in the ability to identify dominant hardwood tree species in forests. Raw waveform Lidar data contain more information than can be represented by a limited series of fitted peaks. Here we attempt to use this information with a simple transformation of the raw waveform data into the frequency domain using a fast Fourier transform. Some relationships are found among the influences of component frequencies across a given species. These relationships are exploited using a classification tree approach to separate three hardwood tree species native to the Pacific Northwest of the United States. We are able to correctly classify 75% of the trees ([image omitted] 0.615) in our training data set. Each tree's species was predicted using a classification tree built from all the other training trees. Two of the species grow in proximity and grow to a similar form, making differentiation difficult. Across all the classification trees built during the analysis, a small group of frequencies is predominantly used as predictors to separate the species. [ABSTRACT FROM AUTHOR]

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: 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]

The article examines the effectiveness of airborne High Spectral Resolution Lidar (HSRL) measurements for aerosol classification. It states that the U.S. National Aeronautics and Space Administration's (NASA) HSRL applies technique to retrieve aerosol extinction, aerosol optical thickness, backscatter, and depolarization profiles. The HSRL-based classification found vertical variability of aerosol types during the field mission conducted in Alaska and northwest Canada.

Net changes in thickness and volume of glacial ice and perennial snow at Mount Rainier, Washington State, have been mapped over the entire edifice by differencing between a high-resolution LiDAR (light detection and ranging) topographic survey of September-October 2007/2008 and the 10 m lateral resolution U.S. Geological Survey digital elevation model derived from September 1970 aerial photography. Excepting the large Emmons and Winthrop Glaciers, all of Mount Rainier's glaciers thinned and retreated in their terminal regions, with substantial thinning mainly at elevations <2000 m and the greatest thinning on south-facing glaciers. Mount Rainier's glaciers and snowfields also lost volume over the interval, excepting the east-flank Fryingpan and Emmons Glaciers and minor near-summit snowfields; maximum volume losses were centered from ~1750 m (north flank) to ~2250 m (south flank) elevation. The greatest single volume loss was from the Carbon Glacier, despite its northward aspect, due to its sizeable area at <2000 m elevation. Overall, Mount Rainier lost ~14 vol% glacial ice and perennial snow over the 37 to 38 yr interval between surveys. Enhanced thinning of south-flank glaciers may be meltback from the high snowfall period of the mid-1940s to mid-1970s associated with the cool phase of the Pacific Decadal Oscillation. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration's (NOAA) National Geodetic Survey (NGS) is mandated to map the national shoreline, which is depicted on NOAA nautical charts, serves as an important source in determining territorial limits, and is widely used in various coastal science and management applications. The National Geodetic Survey's primary method of mapping the national shoreline is through stereo compilation from tide-coordinated aerial photography. However, over the past decade, NGS has conducted several phases of research to develop, test, and refine light detection and ranging (LIDAR)-based shoreline mapping procedures. Although important, reliable estimates of uncertainty of these products have, unfortunately, lagged behind in development. We attempt here to outline possible solutions to this lack. Specifically, this study presents and compares two new methods of assessing the uncertainty of NGS' LIDAR-derived shoreline: an empirical (ground-based) approach and a stochastic (Monte Carlo) approach. We observe uncertainties in the horizontal position of the shorelines on the order of 1 to 6 m (95%) depending on location and, especially, beach slope. We show that appropriate adjustment for biases can reduce these to about 1 m (95%) and that the two methods of assessing the uncertainty show good agreement in our test cases. [ABSTRACT FROM AUTHOR]

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]

A tropospheric ozone Differential Absorption Lidar system, developed jointly by The University of Alabama in Huntsville and the National Aeronautics and Space Administration, is making regular observations of ozone vertical distributions between 1 and 8 km with two receivers under both daytime and nighttime conditions using lasers at 285 and 291 nm. This paper describes the lidar system and analysis technique with some measurement examples. An iterative aerosol correction procedure reduces the retrieval error arising from differential aerosol backscatter in the lower troposphere. Lidar observations with coincident ozonesonde flights demonstrate that the retrieval accuracy ranges from better than 10% below 4 km to better than 20% below 8 km with 750-m vertical resolution and 10-min temporal integration. [ABSTRACT FROM AUTHOR]

Validation of predictive models in remote sensing requires a good coregistration of field and sensor data sets. However, previous research has demonstrated that Global Navigation Satellite System survey techniques often produce large positioning errors when applied to areas under forest canopies. In this article, we present a repeatable methodology for analyzing the effect of such errors when validating models that predict tree-height distributions from LiDAR data sets. The method is based on conditional probability theory applied to error positioning and includes an error assessment of the surveying technique. A technical criterion for selecting the plot radius that avoids significant effects of positioning errors was proposed. We demonstrated that for a plot radius greater than 10 m, the effects of positioning errors introduced by a phase-differential device were insignificant when studying forest tree-height distributions. La validation des modèles de prédiction en matière de télédétection nécessite une bonne correspondance entre les données terrain et les données des capteurs. Toutefois, des recherches antérieures ont démontré que les techniques d'inventaire par la système mondial de navigation par satellite produisent souvent d'importantes erreurs de positionnement lorsqu'elles sont appliquées à des zones situées sous le couvert forestier. Dans cet article, nous présentons une méthode reproductible pour analyser l'effet de telles erreurs lors de la validation des modèles de prédiction de la distribution de la hauteur des arbres à partir de données lidar. La méthode est basée sur la théorie des probabilités conditionnelles appliquée à l'erreur de positionnement et comprend une évaluation de l'erreur de la technique d'inventaire. Nous proposons un critère technique pour choisir le rayon des placettes de manière à éviter que l'erreur de positionnement ait des effets significatifs. Nous avons démontré que, pour une placette dont le rayon est supérieur à 10 m, les effets des erreurs de positionnement produites par un appareil à phase différentielle ne sont pas significatifs lorsqu'on étudie la distribution de la hauteur des arbres. [ABSTRACT FROM AUTHOR]

The effectiveness of simulating surge inundation using the Eulerian-Lagrangian circulation (ELCIRC) model over multi-scale unstructured grids was examined in this study. The large domain model grid encompasses the western North Atlantic Ocean, the Gulf of Mexico, and the Caribbean Sea to appropriately account for remote and resonance effects during hurricane events and simplify the specification of the open boundary condition. The U.S. East and Gulf Coasts were divided into 12 overlapping basins with fine-resolution (up to 30 × 30 m) grids to model overland surge flooding. These overlapping basins have different fine-resolution grids near the coastal region, but have an identical coarse-resolution grid in the offshore region within the large model domain. Thus, the storm surge prediction can be conducted without reducing computation efficiency by executing multiple model runs with local fine-resolution grids where potential hurricane landfalls may occur. The capability of the multi-scale approach was examined by simulating storm surge caused by Hurricanes Andrew (1992) and Isabel (2003) along the South Florida coast and in the Chesapeake Bay. Comparisons between simulated and observed results suggest that multi-scale models proficiently simulated storm surges in the Biscayne Bay and the Chesapeake Bay during two hurricanes. A series of sensitivity tests demonstrated that the simulation of surge flooding was improved when LiDAR topographic data and special bottom drag coefficient values for mangrove forests were employed. The tests also showed that appropriate representation of linear hydrologic features is important for computing surge inundation in an urban area. [ABSTRACT FROM AUTHOR]

Aerosols and clouds have important effects on Earth's climate through their effects on the radiation budget and the cycling of water between the atmosphere and Earth's surface. Limitations in our understanding of the global distribution and properties of aerosols and clouds are partly responsible for the current uncertainties in modeling the global climate system and predicting climate change. The CALIPSO satellite was developed as a joint project between NASA and the French space agency CNES to provide needed capabilities to observe aerosols and clouds from space. CALIPSO carries CALIOP, a two-wavelength, polarization-sensitive lidar, along with two passive sensors operating in the visible and thermal infrared spectral regions. CALIOP is the first lidar to provide long-term atmospheric measurements from Earth's orbit. Its profiling and polarization capabilities offer unique measurement capabilities. Launched together with the CloudSat satellite in April 2006 and now flying in formation with the A-train satellite constellation, CALIPSO is now providing information on the distribution and properties of aerosols and clouds, which is fundamental to advancing our understanding and prediction of climate. This paper provides an overview of the CALIPSO mission and instruments, the data produced, and early results. [ABSTRACT FROM AUTHOR]

A topic of recurring interest in ecological research is the degree to which vegetation structure influences the distribution and abundance of species. Here we test the applicability of remote sensing, particularly novel use of waveform lidar measurements, for quantifying the habitat heterogeneity of a contiguous northern hardwoods forest in the northeastern United States. We apply these results to predict the breeding habitat quality, an indicator of reproductive output of a well-studied Neotropical migrant songbird, the Black-throated Blue Warbler (Dendroica caerulescens). We found that using canopy vertical structure metrics provided unique information for models of habitat quality and spatial patterns of prevalence. An ensemble decision tree modeling approach (random forests) consistently identified lidar metrics describing the vertical distribution and complexity of canopy elements as important predictors of habitat use over multiple years. Although other aspects of habitat were important, including the seasonality of vegetation cover, the canopy structure variables provided unique and complementary information that systematically improved model predictions. We conclude that canopy structure metrics derived from waveform lidar, which will be available on future satellite missions, can advance multiple aspects of biodiversity research, and additional studies should be extended to other organisms and regions. [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]

Measuring individual trees can provide valuable information about forests, and airborne light detection and ranging (LiDAR) sensors have been used recently to identify individual trees and measure structural tree parameters. Past results, however, have been mixed because of reliance on interpolated (image) versions of the LiDAR measurements and search methods that do not adapt to variations in canopies. In this work, an adaptive clustering method is developed using airborne LiDAR data acquired over two distinctly different managed pine forests in North-Central Florida, USA. A crucial issue in isolating individual trees is determining the appropriate size of the moving window (search radius) when locating seed points. The proposed approach works directly on the three-dimensional (3D) 'cloud' of LiDAR points and adapts to irregular canopies sizes. The region growing step yields collectively exhaustive sets in an initial segmentation of tree canopies. An agglomerative clustering step is then used to merge clusters that represent parts of whole canopies using locally varying height distribution. The overall tree detection accuracy achieved is 95.1% with no significant bias. The tree detection enables subsequent estimation of tree height and vertical crown length to an accuracy better than 0.8 and 1.5 m, respectively. [ABSTRACT FROM AUTHOR]

A study of air quality was performed using a compact, aircraft aerosol lidar designed in the Science Directorate at NASA Langley Research Center and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals. Five flights of lidar measurements conducted in the Hampton–Norfolk–Virginia Beach, Virginia, region showed complex regional aerosol distributions. Comparisons with MODIS AOD at 10 km × 10 km and 5 km × 5 km resolutions show good agreement, with correlation R2 values of 0.82 and 0.88, respectively. Linear regressions of particulate matter with a diameter of less than 2.5 μm (PM2.5) and AOD within the ranges of 5–40 μg m-3 and 0.05–0.7, respectively, result in R2 values of ∼0.64 and ∼0.82 for MODIS and the Compact Aerosol Lidar, respectively. The linear regressions reflect approximately 51 μg m-3 to 1 AOD. These relationships are in agreement with previous findings for air pollution aerosols in the eastern United States and in northern Italy. However, large vertical variation is seen case by case, with planetary boundary layer heights ranging between 0.7 and 2 km and uncertainties ranging between 0.1 and 0.4 km. The results of the case studies suggest that AOD can be used as an indicator of surface measurements of PM2.5 but with larger uncertainties associated with small aerosol loading (AOD < 0.3). [ABSTRACT FROM AUTHOR]

Recent years have seen the progression of light detection and ranging (lidar) from the realm of research to operational use in natural resource management. Numerous government agencies, private industries, and public/private stakeholder consortiums are planning or have recently acquired large-scale acquisitions, and a national U.S. lidar acquisition is likely before 2020. Before it is feasible for land managers to integrate lidar into decision making, resource assessment, or monitoring across the gambit of natural resource applications, consistent standards in project planning, data processing, and user-driven products are required. This paper introduces principal lidar acquisition parameters, and makes recommendations for project planning, processing, and product standards to better serve natural resource managers across multiple disciplines. [ABSTRACT FROM AUTHOR]

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]

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]

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]

Time-frequency techniques to characterize cuspate patterns in light detection and ranging (lidar) data are introduced using examples from the Atlantic coast of Florida, United States. These techniques permit the efficient study of beach face landforms over many kilometers of coastline at multiple spatial scales. From a lidar image, a beach-parallel spatial series is generated. Here, this series is the shore-normal position of a specific elevation (contour line). Well-established time-frequency analysis techniques, wavelet transforms, and S-Transforms, are then applied to the spatial series. These methods yield results entirely compatible with the traditional method of estimating the spacing of cuspate features. In addition, confidence intervals are readily established for the spatial extent and wavelengths of cuspate landforms simultaneously at multiple scales. Examples show this method is useful for capturing transitions in cuspate shapes. With the advent of land-based time-lapse lidar, such techniques should be particularly useful for characterizing the evolution of cuspate landforms and testing models for beach face dynamics. [ABSTRACT FROM AUTHOR]

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]

We utilized a Doppler lidar to measure spectra of vertical velocity w from 390m above the surface to the top of the daytime convective boundary layer (CBL). The high resolution 2μm wavelength Doppler lidar developed by the NOAA Environmental Technology Laboratory was used to detect the mean radial velocity of aerosol particles. It operated continuously during the daytime in the zenith-pointing mode for several days in summer 1996 during the Lidars-in-Flat-Terrain experiment over level farmland in central Illinois, U.S.A. The temporal resolution of the lidar was about 1 s, and the range-gate resolution was about 30m. The vertical cross-sections were used to calculate spectra as a function of height with unprecedented vertical resolution throughout much of the CBL, and, in general, we find continuity of the spectral peaks throughout the depth of the CBL. We compare the observed spectra with previous formulations based on both measurements and numerical simulations, and discuss the considerable differences, both on an averaged and a case-by-case basis. We fit the observed spectra to a model that takes into account the wavelength of the spectral peak and the curvature of the spectra across the transition from low wavenumbers to the inertial subrange. The curvature generally is as large or larger than the von Kármán spectra. There is large case-to-case variability, some of which can be linked to the mean structure of the CBL, especially the mean wind and the convective instability. We also find a large case-to-case variability in our estimates of normalized turbulent kinetic energy dissipation deduced from the spectra, likely due for the most part to a varying ratio of entrainment flux to surface flux. Finally, we find a relatively larger contribution to the low wavenumber region of the spectra in cases with smaller shear across the capping inversion, and suggest that this may be due partly to gravity waves in the inversion and overlying free atmosphere. [ABSTRACT FROM AUTHOR]

Vegetation canopy heights derived from the SRTM 30 m grid DEM minus USGS National Elevation Data (NED) DTM were compared to three vegetation metrics derived from a medium footprint LIDAR data (LVIS) for the US Sierra Nevada forest in California. Generally the SRTM minus NED was found to underestimate the vegetation canopy height. Comparing the SRTM-NED-derived heights as a function of the canopy percentile height (shape/vertical structure) derived from LVIS, the SRTM SAR signal was found to penetrate, on average, into about 44% of the canopy and 85% after adjustment of the data. On the canopy type analysis, it was found that the SRTM phase scattering centres occurred at 60% for red fir, 53% for Sierra mixed conifer, 50% for ponderosa pine and 50% for montane hardwood-conifer. Whereas analysing the residual errors of the SRTM-NED minus the LVIS-derived canopy height as a function of LVIS canopy height and cover it was observed that the residuals generally increase with increasing canopy height and cover. Likewise, the behaviour of the RMSE as a function of canopy height and cover was observed to initially increase with canopy height and cover but saturates at 50 m canopy height and 60% canopy cover. On the other hand, the behaviour of the correlation coefficient as a function of canopy height and cover was found to be high at lower canopy height (<15 m) and cover (<20%) and decrease rapidly making a depression at medium canopy heights (>15 m and <50 m) and cover (>20% and <50%). It then increases with increasing canopy height and cover yielding a plateau at canopies higher than 50 m and cover above 70%. [ABSTRACT FROM AUTHOR]

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]

Over the past decade, the National Oceanic and Atmospheric Administration’s National Geodetic Survey, in collaboration with multiple organizations, has conducted research into airport obstruction surveying using airborne light detection and ranging (LIDAR). What was initially envisioned as a relatively straightforward demonstration of the utility of this emerging remote sensing technology for airport surveys was quickly shown to be a challenging undertaking fraught with both technical and practical issues. We provide a brief history of previous work in LIDAR airport obstruction surveying, including a discussion of both past achievements and previously unsolved problems. We then present a new processing workflow, specifically designed to overcome the remaining problems. A key facet of our approach is the use of a new LIDAR waveform deconvolution and georeferencing strategy that produces very dense, detailed point clouds in which the vertical structures of objects are well characterized. Additional processing steps have been carefully selected and ordered based on the objectives of meeting Federal Aviation Administration requirements and maximizing efficiency. Tests conducted using LIDAR waveform data for two project sites demonstrate the efficacy of the approach. [ABSTRACT FROM AUTHOR]

Since the completion of a 398,000 m3 nourishment project along 2 km of Rehoboth Beach, Delaware, in August 2005, the subaerial volume and area of the northern 25% of the beach has been monitored monthly through the use of terrestrial-based light detection and ranging (LIDAR) surveys. Traditionally, the Delaware Department of Natural Resources and Environmental Control and the U.S. Army Corps of Engineers use analyses of beach width from aerial imagery and volumes estimated from widely spaced profile surveys to assess nourishment performance and assist in determining renourishment quantities. However, these survey methods lack the spatial and temporal resolution needed for short-term management strategies. Recent efforts at monitoring Atlantic Coast beaches using airborne LIDAR show the potential of this technology for providing more detailed representations of beach volumetric change over time, but the operational costs still limit the frequency of surveys. Alternatively, our terrestrial LIDAR study allows for the development of models of subaerial beach topography with both high temporal and spatial resolution. Although geographically less extensive than airborne surveys, the digital elevation models from our data (1) allow for a better understanding of the range in variation in beach area and volume, especially that due to storm events, (2) provide more accurate volume estimates than traditional profile surveys by as much as 8%, and (3) indicate that the area and volume do not covary, limiting the usefulness of using aerial imagery in estimating volume. [ABSTRACT FROM AUTHOR]

This paper concerns a new methodology for accuracy assessment of GPS (Global Positioning System) verified experimentally with LiDAR (Light Detection and Ranging) data alignment at continent scale for autonomous driving safety analysis. Accuracy of an autonomous driving vehicle positioning within a lane on the road is one of the key safety considerations and the main focus of this paper. The accuracy of GPS positioning is checked by comparing it with mobile mapping tracks in the recorded high-definition source. The aim of the comparison is to see if the GPS positioning remains accurate up to the dimensions of the lane where the vehicle is driving. The goal is to align all the available LiDAR car trajectories to confirm the of accuracy of GNSS + INS (Global Navigation Satellite System + Inertial Navigation System). For this reason, the use of LiDAR metric measurements for data alignment implemented using SLAM (Simultaneous Localization and Mapping) was investigated, assuring no systematic drift by applying GNSS+INS constraints. The methodology was verified experimentally using arbitrarily chosen measurement instruments (NovAtel GNSS + INS, Velodyne HDL32 LiDAR) mounted onto mobile mapping systems. The accuracy was assessed and confirmed by the alignment of 32,785 trajectories with a total length of 1,159,956.9 km and a total of 186.4 × 10 9 optimized parameters (six degrees of freedom of poses) that cover the United States region in the 2016–2019 period. The alignment improves the trajectories; thus the final map is consistent. The proposed methodology extends the existing methods of global positioning system accuracy assessment, focusing on realistic environmental and driving conditions. The impact of global positioning system accuracy on autonomous car safety is discussed. It is shown that 99% of the assessed data satisfy the safety requirements (driving within lanes of 3.6 m) for Mid-Size (width 1.85 m, length 4.87 m) vehicles and 95% for Six-Wheel Pickup (width 2.03–2.43 m, length 5.32–6.76 m). The conclusion is that this methodology has great potential for global positioning accuracy assessment at the global scale for autonomous driving applications. LiDAR data alignment is introduced as a novel approach to GNSS + INS accuracy confirmation. Further research is needed to solve the identified challenges. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEMs) vary in resolution and accuracy by the production method. DEMs with different resolutions and accuracies can generate varied topographic and hydrological features, which can in turn affect predictions by soil erosion models, such as the WEPP (Water Erosion Prediction Project) model. This study investigates the effects of DEMs on deriving topographic and hydrological attributes, and on predicting watershed erosion using WEPP v2006.5. Six DEMs at three resolutions from three sources were prepared for two small forested watersheds located in northern Idaho, USA. These DEMs were used to calculate topographic and hydrological parameters that served as inputs to WEPP. The model results of sediment yields and runoffs were compared with field observations. For both watersheds, DEMs with different resolutions and sources generated varied watershed shapes and structures, which in turn led to different extracted hill slope and channel lengths and gradients, and produced substantially different erosion predictions by WEPP. [ABSTRACT FROM AUTHOR]

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System (EOS) Terra and Aqua platforms provides unique measurements for deriving global and regional cloud properties. MODIS has spectral coverage combined with spatial resolution in key atmospheric bands, which is not available on previous imagers and sounders. This increased spectral coverage/spatial resolution, along with improved onboard calibration, enhances the capability for global cloud property retrievals. MODIS operational cloud products are derived globally at spatial resolutions of 5 km (referred to as level-2 products) and are aggregated to a 1° equal-angle grid (referred to as level-3 product), available for daily, 8-day, and monthly time periods. The MODIS cloud algorithm produces cloud-top pressures that are found to be within 50 hPa of lidar determinations in single-layer cloud situations. In multilayer clouds, where the upper-layer cloud is semitransparent, the MODIS cloud pressure is representative of the radiative mean between the two cloud layers. In atmospheres prone to temperature inversions, the MODIS cloud algorithm places the cloud above the inversion and hence is as much as 200 hPa off its true location. The wealth of new information available from the MODIS operational cloud products offers the promise of improved cloud climatologies. This paper 1) describes the cloud-top pressure and amount algorithm that has evolved through collection 5 as experience has been gained with in-flight data from NASA Terra and Aqua platforms; 2) compares the MODIS cloud-top pressures, converted to cloud-top heights, with similar measurements from airborne and space-based lidars; and 3) introduces global maps of MODIS and High Resolution Infrared Sounder (HIRS) cloud-top products. [ABSTRACT FROM AUTHOR]

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]

A statistical study is presented of the errors in sodium Doppler lidar measurements of wind and temperature in the mesosphere that arise from the statistics of the photon-counting process that is inherent in the technique. The authors use data from the Colorado State University (CSU) sodium Doppler wind-temperature lidar, acquired at a midlatitude site, to define the statistics of the lidar measurements in different seasons under both daytime and nighttime conditions. The CSU lidar measurements are scaled, based on a 35-cm-diameter receiver telescope, to the use of large-aperture telescopes (i.e., 1-, 1.8-, and 3.5-m diameters). The expected biases in vertical heat flux measurements at a resolution of 480 m and 150 s are determined and compared to Gardner and Yang’s reported geophysical values of 2.3 K m s-1. A cross-correlation coefficient of 2%–7% between the lidar wind and temperature estimates is found. It is also found that the biases vary from -4 × 10-3 K m s-1 for wintertime measurements at night with a 3.5-m telescope to -61 K m s-1 for summertime measurements at midday with a 1-m telescope. During winter, at night, the three telescope systems yield biases in their heat flux measurements that are less than 10% of the reported value of the heat flux; and during summer, at night, the 1.8- and 3.5-m systems yield biases in their heat flux measurements that are less than 10% of the geophysical value. While during winter at midday the 3.5-m system yields biases in their heat flux measurements that are less than 10% of the geophysical value, during summer at midday all of the systems yield flux biases that are greater than the geophysical value of the heat flux. The results are discussed in terms of current lidar measurements and proposed measurements at high-latitude sites. [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]

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]

Mapping shallow-water bathymetry with acoustic techniques is complicated and expensive. The environmental parameters in shallow-water (< 2 m) areas become more variable and greatly impact the depth extraction from the survey measurements. Current airborne light detection and ranging (LIDAR) bathymetry surveying in shallow-water depths uses green-channel waveforms to measure the water depth. Unfortunately, due to difficulties in distinguishing between the surface and bottom return of the water column, the timing of the bottom return is often ambiguous. Furthermore, the water often becomes optically ‘dirty’ due to turbulence at these shallow depths. Therefore, it is common to find coastal areas that lack any measured depths. This paper presents a novel approach for measuring water depths in these shallow coastal waters with airborne LIDAR. Observations of the red-channel waveforms show that the waveforms are divided into two groups, namely: 1) waveforms in deep waters (> 2 m) whose shape is invariant with respect to the water depth and 2) waveforms in shallow-water depths that show a change in shape as a function of the depth in the water column. The data for this study are from the US Geological Survey LIDAR surveys of Lake Tahoe, CA, and Lake Michigan, using a SHOALS-400 LIDAR system. [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: 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]