Leveraging High Resolution Topography for Education and Outreach: Updates to OpenTopography to make EarthScope and Other Lidar Datasets more Prominent in Geoscience Education

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Kleber, E.; Crosby, C. J.; Arrowsmith, R.; Robinson, S.; Haddad, D. E.

2013-12-01

The use of Light Detection and Ranging (lidar) derived topography has become an indispensable tool in Earth science research. The collection of high-resolution lidar topography from an airborne or terrestrial platform allows landscapes and landforms to be represented at sub-meter resolution and in three dimensions. In addition to its high value for scientific research, lidar derived topography has tremendous potential as a tool for Earth science education. Recent science education initiatives and a community call for access to research-level data make the time ripe to expose lidar data and derived data products as a teaching tool. High resolution topographic data fosters several Disciplinary Core Ideas (DCIs) of the Next Generation Science Standards (NGS, 2013), presents respective Big Ideas of the new community-driven Earth Science Literacy Initiative (ESLI, 2009), teaches to a number National Science Education Standards (NSES, 1996), and Benchmarks for Science Literacy (AAAS, 1993) for science education for undergraduate physical and environmental earth science classes. The spatial context of lidar data complements concepts like visualization, place-based learning, inquiry based teaching and active learning essential to teaching in the geosciences. As official host to EarthScope lidar datasets for tectonically active areas in the western United States, the NSF-funded OpenTopography facility provides user-friendly access to a wealth of data that is easily incorporated into Earth science educational materials. OpenTopography (www.opentopography.org), in collaboration with EarthScope, has developed education and outreach activities to foster teacher, student and researcher utilization of lidar data. These educational resources use lidar data coupled with free tools such as Google Earth to provide a means for students and the interested public to visualize and explore Earth's surface in an interactive manner not possible with most other remotely sensed imagery. The

Rapid, high-resolution measurement of leaf area and leaf orientation using terrestrial LiDAR scanning data

International Nuclear Information System (INIS)

Bailey, Brian N; Mahaffee, Walter F

2017-01-01

The rapid evolution of high performance computing technology has allowed for the development of extremely detailed models of the urban and natural environment. Although models can now represent sub-meter-scale variability in environmental geometry, model users are often unable to specify the geometry of real domains at this scale given available measurements. An emerging technology in this field has been the use of terrestrial LiDAR scanning data to rapidly measure the three-dimensional geometry of trees, such as the distribution of leaf area. However, current LiDAR methods suffer from the limitation that they require detailed knowledge of leaf orientation in order to translate projected leaf area into actual leaf area. Common methods for measuring leaf orientation are often tedious or inaccurate, which places constraints on the LiDAR measurement technique. This work presents a new method to simultaneously measure leaf orientation and leaf area within an arbitrarily defined volume using terrestrial LiDAR data. The novelty of the method lies in the direct measurement of the fraction of projected leaf area G from the LiDAR data which is required to relate projected leaf area to total leaf area, and in the new way in which radiation transfer theory is used to calculate leaf area from the LiDAR data. The method was validated by comparing LiDAR-measured leaf area to (1) ‘synthetic’ or computer-generated LiDAR data where the exact area was known, and (2) direct measurements of leaf area in the field using destructive sampling. Overall, agreement between the LiDAR and reference measurements was very good, showing a normalized root-mean-squared-error of about 15% for the synthetic tests, and 13% in the field. (paper)

Modelling Soil-Landscapes in Coastal California Hills Using Fine Scale Terrestrial Lidar

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Prentice, S.; Bookhagen, B.; Kyriakidis, P. C.; Chadwick, O.

2013-12-01

Digital elevation models (DEMs) are the dominant input to spatially explicit digital soil mapping (DSM) efforts due to their increasing availability and the tight coupling between topography and soil variability. Accurate characterization of this coupling is dependent on DEM spatial resolution and soil sampling density, both of which may limit analyses. For example, DEM resolution may be too coarse to accurately reflect scale-dependent soil properties yet downscaling introduces artifactual uncertainty unrelated to deterministic or stochastic soil processes. We tackle these limitations through a DSM effort that couples moderately high density soil sampling with a very fine scale terrestrial lidar dataset (20 cm) implemented in a semiarid rolling hillslope domain where terrain variables change rapidly but smoothly over short distances. Our guiding hypothesis is that in this diffusion-dominated landscape, soil thickness is readily predicted by continuous terrain attributes coupled with catenary hillslope segmentation. We choose soil thickness as our keystone dependent variable for its geomorphic and hydrologic significance, and its tendency to be a primary input to synthetic ecosystem models. In defining catenary hillslope position we adapt a logical rule-set approach that parses common terrain derivatives of curvature and specific catchment area into discrete landform elements (LE). Variograms and curvature-area plots are used to distill domain-scale terrain thresholds from short range order noise characteristic of very fine-scale spatial data. The revealed spatial thresholds are used to condition LE rule-set inputs, rendering a catenary LE map that leverages the robustness of fine-scale terrain data to create a generalized interpretation of soil geomorphic domains. Preliminary regressions show that continuous terrain variables alone (curvature, specific catchment area) only partially explain soil thickness, and only in a subset of soils. For example, at spatial

Quantifying Standing Dead Tree Volume and Structural Loss with Voxelized Terrestrial Lidar Data

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Popescu, S. C.; Putman, E.

2017-12-01

Standing dead trees (SDTs) are an important forest component and impact a variety of ecosystem processes, yet the carbon pool dynamics of SDTs are poorly constrained in terrestrial carbon cycling models. The ability to model wood decay and carbon cycling in relation to detectable changes in tree structure and volume over time would greatly improve such models. The overall objective of this study was to provide automated aboveground volume estimates of SDTs and automated procedures to detect, quantify, and characterize structural losses over time with terrestrial lidar data. The specific objectives of this study were: 1) develop an automated SDT volume estimation algorithm providing accurate volume estimates for trees scanned in dense forests; 2) develop an automated change detection methodology to accurately detect and quantify SDT structural loss between subsequent terrestrial lidar observations; and 3) characterize the structural loss rates of pine and oak SDTs in southeastern Texas. A voxel-based volume estimation algorithm, "TreeVolX", was developed and incorporates several methods designed to robustly process point clouds of varying quality levels. The algorithm operates on horizontal voxel slices by segmenting the slice into distinct branch or stem sections then applying an adaptive contour interpolation and interior filling process to create solid reconstructed tree models (RTMs). TreeVolX estimated large and small branch volume with an RMSE of 7.3% and 13.8%, respectively. A voxel-based change detection methodology was developed to accurately detect and quantify structural losses and incorporated several methods to mitigate the challenges presented by shifting tree and branch positions as SDT decay progresses. The volume and structural loss of 29 SDTs, composed of Pinus taeda and Quercus stellata, were successfully estimated using multitemporal terrestrial lidar observations over elapsed times ranging from 71 - 753 days. Pine and oak structural loss rates

Effect of target color and scanning geometry on terrestrial LiDAR point-cloud noise and plane fitting

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Bolkas, Dimitrios; Martinez, Aaron

2018-01-01

Point-cloud coordinate information derived from terrestrial Light Detection And Ranging (LiDAR) is important for several applications in surveying and civil engineering. Plane fitting and segmentation of target-surfaces is an important step in several applications such as in the monitoring of structures. Reliable parametric modeling and segmentation relies on the underlying quality of the point-cloud. Therefore, understanding how point-cloud errors affect fitting of planes and segmentation is important. Point-cloud intensity, which accompanies the point-cloud data, often goes hand-in-hand with point-cloud noise. This study uses industrial particle boards painted with eight different colors (black, white, grey, red, green, blue, brown, and yellow) and two different sheens (flat and semi-gloss) to explore how noise and plane residuals vary with scanning geometry (i.e., distance and incidence angle) and target-color. Results show that darker colors, such as black and brown, can produce point clouds that are several times noisier than bright targets, such as white. In addition, semi-gloss targets manage to reduce noise in dark targets by about 2-3 times. The study of plane residuals with scanning geometry reveals that, in many of the cases tested, residuals decrease with increasing incidence angles, which can assist in understanding the distribution of plane residuals in a dataset. Finally, a scheme is developed to derive survey guidelines based on the data collected in this experiment. Three examples demonstrate that users should consider instrument specification, required precision of plane residuals, required point-spacing, target-color, and target-sheen, when selecting scanning locations. Outcomes of this study can aid users to select appropriate instrumentation and improve planning of terrestrial LiDAR data-acquisition.

Quantifying Vegetation Structure with Lightweight, Rapid-Scanning Terrestrial Lidar

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Paynter, I.; Genest, D.; Saenz, E. J.; Strahler, A. H.; Li, Z.; Peri, F.; Schaaf, C.

2016-12-01

Light Detection and Ranging (lidar) is proving a competent technology for observing vegetation structure. Terrestrial laser scanners (TLS) are ground-based instruments which utilize hundreds of thousands to millions of lidar observations to provide detailed structural and reflective information of their surroundings. TLS has enjoyed initial success as a validation tool for satellite and airborne estimates of vegetation structure, and are producing independent estimates with increasing accuracy. Reconstruction techniques for TLS observations of vegetation have also improved rapidly, especially for trees. However, uncertainties and challenges still remain in TLS modelling of vegetation structure, especially in geometrically complex ecosystems such as tropical forests (where observation extent and density is hampered by occlusion) and highly temporally dynamic coastal ecosystems (such as saltmarshes and mangroves), where observations may be restricted to narrow microstates. Some of these uncertainties can be mitigated, and challenges met, through the use of lidar instruments optimized for favorable deployment logistics through low weight, rapid scanning, and improved durability. We have conducted studies of vegetation structure in temperate and tropical forests, saltmarshes and mangroves, utilizing a highly portable TLS with considerable deployment flexibility, the Compact Biomass Lidar (CBL). We show results from studies in the temperate Long Term Ecological Research site of Harvard Forest (MA, USA); the tropical forested long-term Carbono sites of La Selva Biological Station (Sarapiqui, Costa Rica); and the saltmarsh LTER of Plum Island (MA, USA). These results demonstrate the improvements to observations in these ecosystems which are facilitated by the specifications of the CBL (and similar TLS) which are optimized for favorable deployment logistics and flexibility. We show the benefits of increased numbers of scanning positions, and specialized deployment

Assessing hydrometeorological impacts with terrestrial and aerial Lidar data in Monterrey, México

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Yepez Rincon, F.; Lozano Garcia, D.; Vela Coiffier, P.; Rivera Rivera, L.

2013-10-01

Light Detection Ranging (Lidar) is an efficient tool to gather points reflected from a terrain and store them in a xyz coordinate system, allowing the generation of 3D data sets to manage geoinformation. Translation of these coordinates, from an arbitrary system into a geographical base, makes data feasible and useful to calculate volumes and define topographic characteristics at different scales. Lidar technological advancement in topographic mapping enables the generation of highly accurate and densely sampled elevation models, which are in high demand by many industries like construction, mining and forestry. This study merges terrestrial and aerial Lidar data to evaluate the effectiveness of these tools assessing volumetric changes after a hurricane event of riverbeds and scour bridges The resulted information could be an optimal approach to improve hydrological and hydraulic models, to aid authorities in proper to decision making in construction, urban planning, and homeland security.

Extraction of Urban Trees from Integrated Airborne Based Digital Image and LIDAR Point Cloud Datasets - Initial Results

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Dogon-yaro, M. A.; Kumar, P.; Rahman, A. Abdul; Buyuksalih, G.

2016-10-01

Timely and accurate acquisition of information on the condition and structural changes of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting tree features include; ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraint, such as labour intensive field work, a lot of financial requirement, influences by weather condition and topographical covers which can be overcome by means of integrated airborne based LiDAR and very high resolution digital image datasets. This study presented a semi-automated approach for extracting urban trees from integrated airborne based LIDAR and multispectral digital image datasets over Istanbul city of Turkey. The above scheme includes detection and extraction of shadow free vegetation features based on spectral properties of digital images using shadow index and NDVI techniques and automated extraction of 3D information about vegetation features from the integrated processing of shadow free vegetation image and LiDAR point cloud datasets. The ability of the developed algorithms shows a promising result as an automated and cost effective approach to estimating and delineated 3D information of urban trees. The research also proved that integrated datasets is a suitable technology and a viable source of information for city managers to be used in urban trees management.

Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges

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Ranjith Gopalakrishnan

2015-08-01

Full Text Available Generating accurate and unbiased wall-to-wall canopy height maps from airborne lidar data for large regions is useful to forest scientists and natural resource managers. However, mapping large areas often involves using lidar data from different projects, with varying acquisition parameters. In this work, we address the important question of whether one can accurately model canopy heights over large areas of the Southeastern US using a very heterogeneous dataset of small-footprint, discrete-return airborne lidar data (with 76 separate lidar projects. A unique aspect of this effort is the use of nationally uniform and extensive field data (~1800 forested plots from the Forest Inventory and Analysis (FIA program of the US Forest Service. Preliminary results are quite promising: Over all lidar projects, we observe a good correlation between the 85th percentile of lidar heights and field-measured height (r = 0.85. We construct a linear regression model to predict subplot-level dominant tree heights from distributional lidar metrics (R2 = 0.74, RMSE = 3.0 m, n = 1755. We also identify and quantify the importance of several factors (like heterogeneity of vegetation, point density, the predominance of hardwoods or softwoods, the average height of the forest stand, slope of the plot, and average scan angle of lidar acquisition that influence the efficacy of predicting canopy heights from lidar data. For example, a subset of plots (coefficient of variation of vegetation heights <0.2 significantly reduces the RMSE of our model from 3.0–2.4 m (~20% reduction. We conclude that when all these elements are factored into consideration, combining data from disparate lidar projects does not preclude robust estimation of canopy heights.

3D Spatial and Spectral Fusion of Terrestrial Hyperspectral Imagery and Lidar for Hyperspectral Image Shadow Restoration Applied to a Geologic Outcrop

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Hartzell, P. J.; Glennie, C. L.; Hauser, D. L.; Okyay, U.; Khan, S.; Finnegan, D. C.

2016-12-01

Recent advances in remote sensing technology have expanded the acquisition and fusion of active lidar and passive hyperspectral imagery (HSI) from an exclusively airborne technique to terrestrial modalities. This enables high resolution 3D spatial and spectral quantification of vertical geologic structures for applications such as virtual 3D rock outcrop models for hydrocarbon reservoir analog analysis and mineral quantification in open pit mining environments. In contrast to airborne observation geometry, the vertical surfaces observed by horizontal-viewing terrestrial HSI sensors are prone to extensive topography-induced solar shadowing, which leads to reduced pixel classification accuracy or outright removal of shadowed pixels from analysis tasks. Using a precisely calibrated and registered offset cylindrical linear array camera model, we demonstrate the use of 3D lidar data for sub-pixel HSI shadow detection and the restoration of the shadowed pixel spectra via empirical methods that utilize illuminated and shadowed pixels of similar material composition. We further introduce a new HSI shadow restoration technique that leverages collocated backscattered lidar intensity, which is resistant to solar conditions, obtained by projecting the 3D lidar points through the HSI camera model into HSI pixel space. Using ratios derived from the overlapping lidar laser and HSI wavelengths, restored shadow pixel spectra are approximated using a simple scale factor. Simulations of multiple lidar wavelengths, i.e., multi-spectral lidar, indicate the potential for robust HSI spectral restoration that is independent of the complexity and costs associated with rigorous radiometric transfer models, which have yet to be developed for horizontal-viewing terrestrial HSI sensors. The spectral restoration performance is quantified through HSI pixel classification consistency between full sun and partial sun exposures of a single geologic outcrop.

Characterizing Tropical Forest Structure using Field-based Measurements and a Terrestrial Lidar

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Palace, M. W.; Sullivan, F.; Ducey, M. J.; Herrick, C.

2015-12-01

Forest structure comprises numerous quantifiable components of forest biometric characteristics, one of which is tree architecture. This structural component is important in the understanding of the past and future trajectories of these biomes. Tropical forests are often considered the most structurally complex and yet least understood of forested ecosystems. New technologies have provided novel avenues for quantifying properties of forested ecosystems, one of which is LIght Detection And Ranging (lidar). This sensor can be deployed on satellite, aircraft, unmanned aerial vehicles, and terrestrial platforms. In this study we examined the efficacy of a terrestrial lidar scanner (TLS) system in a tropical forest to estimate forest structure. Our study was conducted in January 2012 at La Selva, Costa Rica at twenty locations in predominantly undisturbed forest. At these locations we collected field measured biometric attributes using a variable plot design. We also collected TLS data from the center of each plot. Using this data we developed relative vegetation profiles (RVPs) and calculated a series of parameters including entropy, FFT, number of layers and plant area index to develop statistical relationships with field data. We developed statistical models using multiple linear regressions, all of which converged on statistically significant relationships with the strongest relationship being for mean crown depth (r2 = 0.87, p information on tropical forest structure.

Studies of fracture network geometry of reservoir outcrop analogues from terrestrial lidar data: attempts to quantify spatial variations of fracture characteristics

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Vsemirnova, E. A.; Jones, R. R.; McCaffrey, K. J. W.

2012-04-01

We describe studies analysing terrestrial lidar datasets of fracture systems from a range of reservoir analogues in clastic and carbonate lithologies that represent geological analogues of offshore hydrocarbon reservoirs for the UK continental shelf. As fracture networks (observed here from centimetre to kilometre scale) can significantly affect the permeability of a fractured reservoir, the definition of fracture network geometry at various scales has become an important goal of structural analysis. The main aim of the study has been to extend the investigation of fracture networks in order to quantify spatial variations in fracture parameters in a variety of lithologies. The datasets were pre-processed using RiSCAN PRO software, and then re-sampled and filtered to derive characteristics which are traditionally measured from outcrops, including size distributions, fracture spacing and clustering statistics. This type of analysis can significantly reduce the uncertainty associated with some field fracture network measurements. The digitised fracture networks datasets are then used to investigate various aspects of spatial heterogeneity. A series of fracture maps (joints and faults) were generated at different scales, and fracture trends were studied to test scale dependency of fracture orientations. Multiscale trend analysis was then applied to describe the trend structure of the fracture networks.

Lidar-based mapping of flood control levees in south Louisiana

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Thatcher, Cindy A.; Lim, Samsung; Palaseanu-Lovejoy, Monica; Danielson, Jeffrey J.; Kimbrow, Dustin R.

2016-01-01

Flood protection in south Louisiana is largely dependent on earthen levees, and in the aftermath of Hurricane Katrina the state’s levee system has received intense scrutiny. Accurate elevation data along the levees are critical to local levee district managers responsible for monitoring and maintaining the extensive system of non-federal levees in coastal Louisiana. In 2012, high resolution airborne lidar data were acquired over levees in Lafourche Parish, Louisiana, and a mobile terrestrial lidar survey was conducted for selected levee segments using a terrestrial lidar scanner mounted on a truck. The mobile terrestrial lidar data were collected to test the feasibility of using this relatively new technology to map flood control levees and to compare the accuracy of the terrestrial and airborne lidar. Metrics assessing levee geometry derived from the two lidar surveys are also presented as an efficient, comprehensive method to quantify levee height and stability. The vertical root mean square error values of the terrestrial lidar and airborne lidar digital-derived digital terrain models were 0.038 m and 0.055 m, respectively. The comparison of levee metrics derived from the airborne and terrestrial lidar-based digital terrain models showed that both types of lidar yielded similar results, indicating that either or both surveying techniques could be used to monitor geomorphic change over time. Because airborne lidar is costly, many parts of the USA and other countries have never been mapped with airborne lidar, and repeat surveys are often not available for change detection studies. Terrestrial lidar provides a practical option for conducting repeat surveys of levees and other terrain features that cover a relatively small area, such as eroding cliffs or stream banks, and dunes.

Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data

Directory of Open Access Journals (Sweden)

H. Ozdemir

2013-10-01

Full Text Available This paper evaluates the results of benchmark testing a new inertial formulation of the St. Venant equations, implemented within the LISFLOOD-FP hydraulic model, using different high resolution terrestrial LiDAR data (10 cm, 50 cm and 1 m and roughness conditions (distributed and composite in an urban area. To examine these effects, the model is applied to a hypothetical flooding scenario in Alcester, UK, which experienced surface water flooding during summer 2007. The sensitivities of simulated water depth, extent, arrival time and velocity to grid resolutions and different roughness conditions are analysed. The results indicate that increasing the terrain resolution from 1 m to 10 cm significantly affects modelled water depth, extent, arrival time and velocity. This is because hydraulically relevant small scale topography that is accurately captured by the terrestrial LIDAR system, such as road cambers and street kerbs, is better represented on the higher resolution DEM. It is shown that altering surface friction values within a wide range has only a limited effect and is not sufficient to recover the results of the 10 cm simulation at 1 m resolution. Alternating between a uniform composite surface friction value (n = 0.013 or a variable distributed value based on land use has a greater effect on flow velocities and arrival times than on water depths and inundation extent. We conclude that the use of extra detail inherent in terrestrial laser scanning data compared to airborne sensors will be advantageous for urban flood modelling related to surface water, risk analysis and planning for Sustainable Urban Drainage Systems (SUDS to attenuate flow.

Estimating terrestrial aboveground biomass estimation using lidar remote sensing: a meta-analysis

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Zolkos, S. G.; Goetz, S. J.; Dubayah, R.

2012-12-01

Estimating biomass of terrestrial vegetation is a rapidly expanding research area, but also a subject of tremendous interest for reducing carbon emissions associated with deforestation and forest degradation (REDD). The accuracy of biomass estimates is important in the context carbon markets emerging under REDD, since areas with more accurate estimates command higher prices, but also for characterizing uncertainty in estimates of carbon cycling and the global carbon budget. There is particular interest in mapping biomass so that carbon stocks and stock changes can be monitored consistently across a range of scales - from relatively small projects (tens of hectares) to national or continental scales - but also so that other benefits of forest conservation can be factored into decision making (e.g. biodiversity and habitat corridors). We conducted an analysis of reported biomass accuracy estimates from more than 60 refereed articles using different remote sensing platforms (aircraft and satellite) and sensor types (optical, radar, lidar), with a particular focus on lidar since those papers reported the greatest efficacy (lowest errors) when used in the a synergistic manner with other coincident multi-sensor measurements. We show systematic differences in accuracy between different types of lidar systems flown on different platforms but, perhaps more importantly, differences between forest types (biomes) and plot sizes used for field calibration and assessment. We discuss these findings in relation to monitoring, reporting and verification under REDD, and also in the context of more systematic assessment of factors that influence accuracy and error estimation.

THREE-DIMENSIONAL RECONSTRUCTION OF THE VIRTUAL PLANT BRANCHING STRUCTURE BASED ON TERRESTRIAL LIDAR TECHNOLOGIES AND L-SYSTEM

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Y. Gong

2018-04-01

Full Text Available For the purpose of extracting productions of some specific branching plants effectively and realizing its 3D reconstruction, Terrestrial LiDAR data was used as extraction source of production, and a 3D reconstruction method based on Terrestrial LiDAR technologies combined with the L-system was proposed in this article. The topology structure of the plant architectures was extracted using the point cloud data of the target plant with space level segmentation mechanism. Subsequently, L-system productions were obtained and the structural parameters and production rules of branches, which fit the given plant, was generated. A three-dimensional simulation model of target plant was established combined with computer visualization algorithm finally. The results suggest that the method can effectively extract a given branching plant topology and describes its production, realizing the extraction of topology structure by the computer algorithm for given branching plant and also simplifying the extraction of branching plant productions which would be complex and time-consuming by L-system. It improves the degree of automation in the L-system extraction of productions of specific branching plants, providing a new way for the extraction of branching plant production rules.

Three-Dimensional Reconstruction of the Virtual Plant Branching Structure Based on Terrestrial LIDAR Technologies and L-System

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Gong, Y.; Yang, Y.; Yang, X.

2018-04-01

For the purpose of extracting productions of some specific branching plants effectively and realizing its 3D reconstruction, Terrestrial LiDAR data was used as extraction source of production, and a 3D reconstruction method based on Terrestrial LiDAR technologies combined with the L-system was proposed in this article. The topology structure of the plant architectures was extracted using the point cloud data of the target plant with space level segmentation mechanism. Subsequently, L-system productions were obtained and the structural parameters and production rules of branches, which fit the given plant, was generated. A three-dimensional simulation model of target plant was established combined with computer visualization algorithm finally. The results suggest that the method can effectively extract a given branching plant topology and describes its production, realizing the extraction of topology structure by the computer algorithm for given branching plant and also simplifying the extraction of branching plant productions which would be complex and time-consuming by L-system. It improves the degree of automation in the L-system extraction of productions of specific branching plants, providing a new way for the extraction of branching plant production rules.

Georeferencing UAS Derivatives Through Point Cloud Registration with Archived Lidar Datasets

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Magtalas, M. S. L. Y.; Aves, J. C. L.; Blanco, A. C.

2016-10-01

Georeferencing gathered images is a common step before performing spatial analysis and other processes on acquired datasets using unmanned aerial systems (UAS). Methods of applying spatial information to aerial images or their derivatives is through onboard GPS (Global Positioning Systems) geotagging, or through tying of models through GCPs (Ground Control Points) acquired in the field. Currently, UAS (Unmanned Aerial System) derivatives are limited to meter-levels of accuracy when their generation is unaided with points of known position on the ground. The use of ground control points established using survey-grade GPS or GNSS receivers can greatly reduce model errors to centimeter levels. However, this comes with additional costs not only with instrument acquisition and survey operations, but also in actual time spent in the field. This study uses a workflow for cloud-based post-processing of UAS data in combination with already existing LiDAR data. The georeferencing of the UAV point cloud is executed using the Iterative Closest Point algorithm (ICP). It is applied through the open-source CloudCompare software (Girardeau-Montaut, 2006) on a `skeleton point cloud'. This skeleton point cloud consists of manually extracted features consistent on both LiDAR and UAV data. For this cloud, roads and buildings with minimal deviations given their differing dates of acquisition are considered consistent. Transformation parameters are computed for the skeleton cloud which could then be applied to the whole UAS dataset. In addition, a separate cloud consisting of non-vegetation features automatically derived using CANUPO classification algorithm (Brodu and Lague, 2012) was used to generate a separate set of parameters. Ground survey is done to validate the transformed cloud. An RMSE value of around 16 centimeters was found when comparing validation data to the models georeferenced using the CANUPO cloud and the manual skeleton cloud. Cloud-to-cloud distance computations of

Analyzing Hydro-Geomorphic Responses in Post-Fire Stream Channels with Terrestrial LiDAR

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Nourbakhshbeidokhti, S.; Kinoshita, A. M.; Chin, A.

2015-12-01

Wildfires have potential to significantly alter soil properties and vegetation within watersheds. These alterations often contribute to accelerated erosion, runoff, and sediment transport in stream channels and hillslopes. This research applies repeated Terrestrial Laser Scanning (TLS) Light Detection and Ranging (LiDAR) to stream reaches within the Pike National Forest in Colorado following the 2012 Waldo Canyon Fire. These scans allow investigation of the relationship between sediment delivery and environmental characteristics such as precipitation, soil burn severity, and vegetation. Post-fire LiDAR images provide high resolution information of stream channel changes in eight reaches for three years (2012-2014). All images are processed with RiSCAN PRO to remove vegetation and triangulated and smoothed to create a Digital Elevation Model (DEM) with 0.1 m resolution. Study reaches with two or more successive DEM images are compared using a differencing method to estimate the volume of sediment erosion and deposition. Preliminary analysis of four channel reaches within Williams Canyon and Camp Creek yielded erosion estimates between 0.035 and 0.618 m3 per unit area. Deposition was estimated as 0.365 to 1.67 m3 per unit area. Reaches that experienced higher soil burn severity or larger rainfall events produced the greatest geomorphic changes. Results from LiDAR analyses can be incorporated into post-fire hydrologic models to improve estimates of runoff and sediment yield. These models will, in turn, provide guidance for water resources management and downstream hazards mitigation.

Evaluation of short-term geomorphic changes along the Tagliamento river using LiDAR and terrestrial laser scanner surveys

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R. Rainato

2013-09-01

Full Text Available In the recent years a change in the predominant morphology of several river environments has taken place, consisting in a reduction of the braided pattern in favor to wandering or straight configurations. This evolution seems to be due, according to the scientific community, to anthropic causes and, in particular, to the alteration of flow regimes as well as the reduction of sediment transport. Braided rivers are characterized by two or more active channels, separated by bars and fluvial islands and normally feature a high morphological dynamism. This dynamism is the result of the interaction among different elements as sediment supply, flow regime and in-channel and perifluvial vegetation. These factors have a fundamental role in the erosion and deposition processes which are the basis of the morphological changes. The aims of this study are the assessment of the short period geomorphic and volumetric changes occurred along a reach of the Tagliamento River and the comparison between the results obtained from LiDAR (Light Detection and Ranging and TLS (Terrestrial Laser Scanner data. The Tagliamento river is a natural gravel-bed river located in the NE of Italy, characterized by a relatively low degree of human disturbances. The analyses were carried out considering two different scales (a reach of about 430 ha and a sub-reach of about 25 ha and were based on two subsequent datasets in order to investigate the shortterm geomorphic changes due to eight significant floods. The surveys were performed using two different datasets derived from LiDAR and TLS technologies and used to analyze the reach and sub-reach respectively. The short-term estimates of geomorphic and volumetric changes were performed using DEMs of Difference (DoD based on a Fuzzy Inference System. The results have confirmed the high dynamism of the Tagliamento river, estimating a prevalent deposition at reach and a predominant erosion at sub-reach levels. Finally, a comparative

Coastal monitoring solutions of the geomorphological response of beach-dune systems using multi-temporal LiDAR datasets (Vendée coast, France)

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Le Mauff, Baptiste; Juigner, Martin; Ba, Antoine; Robin, Marc; Launeau, Patrick; Fattal, Paul

2018-03-01

Three beach and dune systems located in the northeastern part of the Bay of Biscay in France were monitored over 5 years with a time series of three airborne LiDAR datasets. The three study sites illustrate a variety of morphological beach types found in this region. Reproducible monitoring solutions adapted to basic and complex beach and dune morphologies using LiDAR time series were investigated over two periods bounded by the three surveys. The first period (between May 2008 and August 2010) is characterized by a higher prevalence of storm events, and thus has a greater potential for eroding the coast, than the second period (between August 2010 and September 2013). During the first period, the central and northeastern part of the Bay of Biscay was notably impacted by Storm Xynthia, with water levels and wave heights exceeding the 10-year return period and 1-year return period, respectively. Despite differences in dune morphology between the sites, the dune crest (Dhigh) and the dune base (Dlow) are efficiently extracted from each DEM. Based on the extracted dune base, an original shoreline mobility indicator is built displaying a combination of the horizontal and vertical migrations of this geomorphic indicator between two LiDAR datasets. A 'Geomorphic Change Detection' is also completed by computing DEMs of Difference (DoD) resulting in segregated maps of erosion and deposition and sediment budgets. Accounting for the accuracy of LiDAR datasets, a probabilistic approach at a 95% confidence interval is used as a threshold for the Geomorphic Change Detection showing more reliable results. However, caution should be taken when interpreting thresholded maps of changes and sediment budgets because some beach processes may be masked, especially on wide tidal beaches, by only keeping the most significant changes. The results of the shoreline mobility and Geomorphic Change Detection show a high variability in the beach responses between and within the three study

Utilizing topobathy LIDAR datasets to identify shoreline variations and to direct charting updates in the northern Gulf of Mexico

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Gremillion, S. L.; Wright, S. L.

2017-12-01

Topographic and bathymetric light detection and ranging (LIDAR), remote sensing tools used to measure vertical elevations, are commonly employed to monitor shoreline fluctuations. Many of these publicly available datasets provide wide-swath, nearshore topobathy which can be used to extract shoreline positions and analyze coastlines experiencing the greatest temporal and spatial variability. This study focused on the shorelines of Mississippi's Jackson County to determine the minimum time for significant positional changes to occur, relative to currently published NOAA navigational charts. Many of these dynamic shorelines are vulnerable to relative sea level rise, storm surge, and coastal erosion. Utilizing LIDAR datasets from 1998-2015, shoreline positions were derived and analyzed against NOAA's Continually Updated Shoreline Product (CUSP) to recommend the frequency at which future surveys should be conducted. Advisement of charting updates were based upon the resolution of published charts, and the magnitude of observed variances. Jackson County shorelines were divided into four areas for analysis; the mainland, Horn Island, Petit Bois Island (PBI), and a dredge spoil area west of PBI. The mainland shoreline experienced an average change rate of +0.57 m/yr during the study period. This stability was due to engineering structures implemented in the early 1920's to protect against tropical storms. Horn Island, the most stable barrier island, changed an average of -1.34 m/yr, while PBI had an average change of -2.70 m/yr throughout. Lastly, the dredge spoil area changed by +9.06 m/yr. Based on these results, it is recommended that LIDAR surveys for Jackson County's mainland be conducted at least every two years, while surveys of the offshore barrier islands be conducted annually. Furthermore, insufficient LIDAR data for Round Island and the Round Island Marsh Restoration Project highlight these two areas as priority targets for future surveys.

CURB-BASED STREET FLOOR EXTRACTION FROM MOBILE TERRESTRIAL LIDAR POINT CLOUD

Directory of Open Access Journals (Sweden)

S. Ibrahim

2012-07-01

Full Text Available Mobile terrestrial laser scanners (MTLS produce huge 3D point clouds describing the terrestrial surface, from which objects like different street furniture can be generated. Extraction and modelling of the street curb and the street floor from MTLS point clouds is important for many applications such as right-of-way asset inventory, road maintenance and city planning. The proposed pipeline for the curb and street floor extraction consists of a sequence of five steps: organizing the 3D point cloud and nearest neighbour search; 3D density-based segmentation to segment the ground; morphological analysis to refine out the ground segment; derivative of Gaussian filtering to detect the curb; solving the travelling salesman problem to form a closed polygon of the curb and point-inpolygon test to extract the street floor. Two mobile laser scanning datasets of different scenes are tested with the proposed pipeline. The results of the extracted curb and street floor are evaluated based on a truth data. The obtained detection rates for the extracted street floor for the datasets are 95% and 96.53%. This study presents a novel approach to the detection and extraction of the road curb and the street floor from unorganized 3D point clouds captured by MTLS. It utilizes only the 3D coordinates of the point cloud.

Testing the Suitability of a Terrestrial 2D LiDAR Scanner for Canopy Characterization of Greenhouse Tomato Crops

Directory of Open Access Journals (Sweden)

Jordi Llop

2016-09-01

Full Text Available Canopy characterization is essential for pesticide dosage adjustment according to vegetation volume and density. It is especially important for fresh exportable vegetables like greenhouse tomatoes. These plants are thin and tall and are planted in pairs, which makes their characterization with electronic methods difficult. Therefore, the accuracy of the terrestrial 2D LiDAR sensor is evaluated for determining canopy parameters related to volume and density and established useful correlations between manual and electronic parameters for leaf area estimation. Experiments were performed in three commercial tomato greenhouses with a paired plantation system. In the electronic characterization, a LiDAR sensor scanned the plant pairs from both sides. The canopy height, canopy width, canopy volume, and leaf area were obtained. From these, other important parameters were calculated, like the tree row volume, leaf wall area, leaf area index, and leaf area density. Manual measurements were found to overestimate the parameters compared with the LiDAR sensor. The canopy volume estimated with the scanner was found to be reliable for estimating the canopy height, volume, and density. Moreover, the LiDAR scanner could assess the high variability in canopy density along rows and hence is an important tool for generating canopy maps.

Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR.

Science.gov (United States)

Brede, Benjamin; Lau, Alvaro; Bartholomeus, Harm M; Kooistra, Lammert

2017-10-17

In recent years, LIght Detection And Ranging (LiDAR) and especially Terrestrial Laser Scanning (TLS) systems have shown the potential to revolutionise forest structural characterisation by providing unprecedented 3D data. However, manned Airborne Laser Scanning (ALS) requires costly campaigns and produces relatively low point density, while TLS is labour intense and time demanding. Unmanned Aerial Vehicle (UAV)-borne laser scanning can be the way in between. In this study, we present first results and experiences with the RIEGL RiCOPTER with VUX ® -1UAV ALS system and compare it with the well tested RIEGL VZ-400 TLS system. We scanned the same forest plots with both systems over the course of two days. We derived Digital Terrain Model (DTMs), Digital Surface Model (DSMs) and finally Canopy Height Model (CHMs) from the resulting point clouds. ALS CHMs were on average 11.5 c m higher in five plots with different canopy conditions. This showed that TLS could not always detect the top of canopy. Moreover, we extracted trunk segments of 58 trees for ALS and TLS simultaneously, of which 39 could be used to model Diameter at Breast Height (DBH). ALS DBH showed a high agreement with TLS DBH with a correlation coefficient of 0.98 and root mean square error of 4.24 c m . We conclude that RiCOPTER has the potential to perform comparable to TLS for estimating forest canopy height and DBH under the studied forest conditions. Further research should be directed to testing UAV-borne LiDAR for explicit 3D modelling of whole trees to estimate tree volume and subsequently Above-Ground Biomass (AGB).

2008 St. Johns County, FL Countywide Lidar

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Airborne terrestrial LiDAR was collected for St. Johns County, FL. System Parameters/Flight Plan. The LiDAR system acquisition parameters were developed based on a...

Study of Diagenetic Features in Rudist Buildups of Cretaceous Edwards Formation Using Ground Based Hyperspectral Scanning and Terrestrial LiDAR

Science.gov (United States)

Krupnik, D.; Khan, S.; Okyay, U.; Hartzell, P. J.; Biber, K.

2015-12-01

Ground based remote sensing is a novel technique for development of digital outcrop models which can be instrumental in performing detailed qualitative and quantitative sedimentological analysis for the study of depositional environment, diagenetic processes, and hydrocarbon reservoir characterization. For this investigation, ground-based hyperspectral data collection is combined with terrestrial LiDAR to study outcrops of Late Albian rudist buildups of the Edwards formation in the Lake Georgetown Spillway in Williamson County, Texas. The Edwards formation consists of shallow water deposits of reef and associated inter-reef facies, including rudist bioherms and biostromes. It is a significant aquifer and was investigated as a hydrocarbon play in south central Texas. Hyperspectral data were used to map compositional variation in the outcrop by distinguishing spectral properties unique to each material. Lithological variation was mapped in detail to investigate the structure and composition of rudist buildups. Hyperspectral imagery was registered to a 3D model produced from the LiDAR point cloud with an accuracy of up to one pixel. Flat-topped toucasid-rich bioherm facies were distinguished from overlying toucasid-rich biostrome facies containing chert nodules, overlying sucrosic dolostones, and uppermost peloid wackestones and packstones of back-reef facies. Ground truth was established by petrographic study of samples from this area and has validated classification products of remote sensing data. Several types of porosity were observed and have been associated with increased dolomitization. This ongoing research involves integration of remotely sensed datasets to analyze geometrical and compositional properties of this carbonate formation at a finer scale than traditional methods have achieved and seeks to develop a workflow for quick and efficient ground based remote sensing-assisted outcrop studies.

OpenTopography: Enabling Online Access to High-Resolution Lidar Topography Data and Processing Tools

Science.gov (United States)

Crosby, Christopher; Nandigam, Viswanath; Baru, Chaitan; Arrowsmith, J. Ramon

2013-04-01

resources. Datasets hosted by other organizations, as well as lidar-specific software, can be registered into the OpenTopography catalog, providing users a "one-stop shop" for such information. With several thousand active users, OpenTopography is an excellent example of a mature Spatial Data Infrastructure system that is enabling access to challenging data for research, education and outreach. Ongoing OpenTopography design and development work includes the archive and publication of datasets using digital object identifiers (DOIs); creation of a more flexible and scalable high-performance environment for processing of large datasets; expanded support for satellite and terrestrial lidar; and creation of a "pluggable" infrastructure for third-party programs and algorithms. OpenTopography has successfully created a facility for sharing lidar data. In the project's next phase, we are working to enable equally easy and successful sharing of services for processing and analysis of these data.

2006 Fulton County Georgia Lidar

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) LAS dataset is a survey of Fulton County. The Fulton County LiDAR Survey project area consists of approximately 690.5 square...

2007 USGS Lidar: Canyon Fire (CA)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This Southern California Light Detection and Ranging (LiDAR) data is to provide high accuracy LIDAR data. These datasets will be the initial acquisition to support...

Quantifying Local Ablation Rates for the Greenland Ice Sheet Using Terrestrial LIDAR

Science.gov (United States)

Kershner, C. M.; Pitcher, L. H.; LeWinter, A.; Finnegan, D. C.; Overstreet, B. T.; Miège, C.; Cooper, M. G.; Smith, L. C.; Rennermalm, A. K.

2016-12-01

Quantifying accurate ice surface ablation or melt rates for the Greenland Ice Sheet is important for calibrating and validating surface mass balance models and constraining sea level rise estimates. Common practice is to monitor surface ablation at defined points by manually measuring ice surface lowering in relation to stakes inserted into the ice / snow. However, this method does not account for the effects of local topography, solar zenith angle, and local variations in ice surface albedo/impurities on ablation rates. To directly address these uncertainties, we use a commercially available terrestrial LIDAR scanner (TLS) to monitor daily melt rates in the ablation zone of the Greenland Ice Sheet for 7 consecutive days in July 2016. Each survey is registered to previous scans using retroreflective cylinders and is georeferenced using static GPS measurements. Bulk ablation will be calculated using multi-temporal differential LIDAR techniques, and difficulties in referencing scans and collecting high quality surveys in this dynamic environment will be discussed, as well as areas for future research. We conclude that this novel application of TLS technology provides a spatially accurate, higher fidelity measurements of ablation across a larger area with less interpolation and less time spent than using traditional manual point based methods alone. Furthermore, this sets the stage for direct calibration, validation and cross-comparison with existing airborne (e.g. NASA's Airborne Topographic Mapper - ATM - onboard Operation IceBridge and NASA's Land, Vegetation & Ice Sensor - LVIS) and forthcoming spaceborne sensors (e.g. NASA's ICESat-2).

Semi-Automated Approach for Mapping Urban Trees from Integrated Aerial LiDAR Point Cloud and Digital Imagery Datasets

Science.gov (United States)

Dogon-Yaro, M. A.; Kumar, P.; Rahman, A. Abdul; Buyuksalih, G.

2016-09-01

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

2006 Volusia County Florida LiDAR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This dataset is the lidar data for Volusia County, Florida, approximately 1,432 square miles, acquired in early March of 2006. A total of 143 flight lines of Lidar...

Comparison of Stem Map Developed from Crown Geometry Allometry Linked Census Data to Airborne and Terrestrial Lidar at Harvard Forest, MA

Science.gov (United States)

Sullivan, F.; Palace, M. W.; Ducey, M. J.; David, O.; Cook, B. D.; Lepine, L. C.

2014-12-01

Harvard Forest in Petersham, MA, USA is the location of one of the temperate forest plots established by the Center for Tropical Forest Science (CTFS) as a joint effort with Harvard Forest and the Smithsonian Institute's Forest Global Earth Observatory (ForestGEO) to characterize ecosystem processes and forest dynamics. Census of a 35 ha plot on Prospect Hill was completed during the winter of 2014 by researchers at Harvard Forest. Census data were collected according to CTFS protocol; measured variables included species, stem diameter, and relative X-Y locations. Airborne lidar data were collected over the censused plot using the high spatial resolution Goddard LiDAR, Hyperspectral, and Thermal sensor package (G-LiHT) during June 2012. As part of a separate study, 39 variable radius plots (VRPs) were randomly located and sampled within and throughout the Prospect Hill CTFS/ForestGEO plot during September and October 2013. On VRPs, biometric properties of trees were sampled, including species, stem diameter, total height, crown base height, crown radii, and relative location to plot centers using a 20 Basal Area Factor prism. In addition, a terrestrial-based lidar scanner was used to collect one lidar scan at plot center for 38 of the 39 VRPs. Leveraging allometric equations of crown geometry and tree height developed from 374 trees and 16 different species sampled on 39 VRPs, a 3-dimensional stem map will be created using the Harvard Forest ForestGEO Prospect Hill census. Vertical and horizontal structure of 3d field-based stem maps will be compared to terrestrial and airborne lidar scan data. Furthermore, to assess the quality of allometric equations, a 2d canopy height raster of the field-based stem map will be compared to a G-LiHT derived canopy height model for the 35 ha census plot. Our automated crown delineation methods will be applied to the 2d representation of the census stem map and the G-LiHT canopy height model. For future work related to this study

GRIP LIDAR ATMOSPHERIC SENSING EXPERIMENT (LASE) V1

Data.gov (United States)

National Aeronautics and Space Administration — The GRIP Lidar Atmospheric Sensing Experiment (LASE) dataset was collected by NASA's Lidar Atmospheric Sensing Experiment (LASE) system, which is an airborne...

Leveraging Open Standards and Technologies to Enhance Community Access to Earth Science Lidar Data

Science.gov (United States)

Crosby, C. J.; Nandigam, V.; Krishnan, S.; Cowart, C.; Baru, C.; Arrowsmith, R.

2011-12-01

Lidar (Light Detection and Ranging) data, collected from space, airborne and terrestrial platforms, have emerged as an invaluable tool for a variety of Earth science applications ranging from ice sheet monitoring to modeling of earth surface processes. However, lidar present a unique suite of challenges from the perspective of building cyberinfrastructure systems that enable the scientific community to access these valuable research datasets. Lidar data are typically characterized by millions to billions of individual measurements of x,y,z position plus attributes; these "raw" data are also often accompanied by derived raster products and are frequently terabytes in size. As a relatively new and rapidly evolving data collection technology, relevant open data standards and software projects are immature compared to those for other remote sensing platforms. The NSF-funded OpenTopography Facility project has developed an online lidar data access and processing system that co-locates data with on-demand processing tools to enable users to access both raw point cloud data as well as custom derived products and visualizations. OpenTopography is built on a Service Oriented Architecture (SOA) in which applications and data resources are deployed as standards compliant (XML and SOAP) Web services with the open source Opal Toolkit. To develop the underlying applications for data access, filtering and conversion, and various processing tasks, OpenTopography has heavily leveraged existing open source software efforts for both lidar and raster data. Operating on the de facto LAS binary point cloud format (maintained by ASPRS), open source libLAS and LASlib libraries provide OpenTopography data ingestion, query and translation capabilities. Similarly, raster data manipulation is performed through a suite of services built on the Geospatial Data Abstraction Library (GDAL). OpenTopography has also developed our own algorithm for high-performance gridding of lidar point cloud data

Making lidar more photogenic: creating band combinations from lidar information

Science.gov (United States)

Stoker, Jason M.

2010-01-01

Over the past five to ten years the use and applicability of light detection and ranging (lidar) technology has increased dramatically. As a result, an almost exponential amount of lidar data is being collected across the country for a wide range of applications, and it is currently the technology of choice for high resolution terrain model creation, 3-dimensional city and infrastructure modeling, forestry and a wide range of scientific applications (Lin and Mills, 2010). The amount of data that is being delivered across the country is impressive. For example, the U.S. Geological Survey’s (USGS) Center for Lidar Information Coordination and Knowledge (CLICK), which is a National repository of USGS and partner lidar point cloud datasets (Stoker et al., 2006), currently has 3.5 percent of the United States covered by lidar, and has approximately another 5 percent in the processing queue. The majority of data being collected by the commercial sector are from discrete-return systems, which collect billions of lidar points in an average project. There are also a lot of discussions involving a potential National-scale Lidar effort (Stoker et al., 2008).

SEMI-AUTOMATED APPROACH FOR MAPPING URBAN TREES FROM INTEGRATED AERIAL LiDAR POINT CLOUD AND DIGITAL IMAGERY DATASETS

Directory of Open Access Journals (Sweden)

M. A. Dogon-Yaro

2016-09-01

Full Text Available Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

NAMMA LIDAR ATMOSPHERIC SENSING EXPERIMENT (LASE) V1

Data.gov (United States)

National Aeronautics and Space Administration — The NAMMA Lidar Atmospheric Sensing Experiment (LASE) dataset used the LASE system using the Differential Absorption Lidar (DIAL) system was operated during the NASA...

2005 Mississippi Merged LiDAR Data (2005 LiDAR data merged with 2005 Post-Katrina LiDAR data to create a bare-earth product for flood plain mapping in coastal Mississippi).

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Pre- and post-hurricane Katrina LiDAR datasets of Hancock, Harrison, and Jackson Counties, MS, were merged into a seamless coverage by URS. The pre-Katrina LiDAR...

Using LiDAR to as a Potential Method for Detection Plastics in Water

Science.gov (United States)

Lee, G.; Neal, A.; Mielke, R.; Bookhagen, B.

2010-12-01

We conducted a series of experiments using Light Detection and Range (LiDAR) technology as an innovative way to detect the presence of plastics in water. The purpose of this study was to determine if LiDAR technology is a feasible, non-intrusive alternative to dredging in the ocean to determine the amount of plastics in the ocean. We used a tripod mounted RIEGL LMS-Z420i terrestrial LiDAR 3-D scanner and the associated operating software RiSCAN Pro. The terrestrial LiDAR is an optical remote sensing technology that measures the reflection of near infared light to find the range of a distant target that is most commonly used to create high precision digital elevation models of terrestrial surfaces. In theory, water should absorb the near infared light, while the plastics should reflect the light. The experiments consisted of different scale models of plastic pellets in water, ranging from a small plastic dish to a large tank to test the range of the LiDAR in different salt and fresh water mediums.

GRIP DOPPLER AEROSOL WIND LIDAR (DAWN) V1

Data.gov (United States)

National Aeronautics and Space Administration — The GRIP Doppler Aerosol WiNd Lidar (DAWN) Dataset was collected by the Doppler Aerosol WiNd (DAWN), a pulsed lidar, which operated aboard a NASA DC-8 aircraft...

2009 - 2011 CA Coastal Conservancy Coastal Lidar Project

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Light Detection and Ranging (LiDAR) data is remotely sensed high-resolution elevation data collected by an airborne collection platform. This LiDAR dataset is a...

LiDAR Vegetation Investigation and Signature Analysis System (LVISA)

Science.gov (United States)

Höfle, Bernhard; Koenig, Kristina; Griesbaum, Luisa; Kiefer, Andreas; Hämmerle, Martin; Eitel, Jan; Koma, Zsófia

2015-04-01

Our physical environment undergoes constant changes in space and time with strongly varying triggers, frequencies, and magnitudes. Monitoring these environmental changes is crucial to improve our scientific understanding of complex human-environmental interactions and helps us to respond to environmental change by adaptation or mitigation. The three-dimensional (3D) description of the Earth surface features and the detailed monitoring of surface processes using 3D spatial data have gained increasing attention within the last decades, such as in climate change research (e.g., glacier retreat), carbon sequestration (e.g., forest biomass monitoring), precision agriculture and natural hazard management. In all those areas, 3D data have helped to improve our process understanding by allowing quantifying the structural properties of earth surface features and their changes over time. This advancement has been fostered by technological developments and increased availability of 3D sensing systems. In particular, LiDAR (light detection and ranging) technology, also referred to as laser scanning, has made significant progress and has evolved into an operational tool in environmental research and geosciences. The main result of LiDAR measurements is a highly spatially resolved 3D point cloud. Each point within the LiDAR point cloud has a XYZ coordinate associated with it and often additional information such as the strength of the returned backscatter. The point cloud provided by LiDAR contains rich geospatial, structural, and potentially biochemical information about the surveyed objects. To deal with the inherently unorganized datasets and the large data volume (frequently millions of XYZ coordinates) of LiDAR datasets, a multitude of algorithms for automatic 3D object detection (e.g., of single trees) and physical surface description (e.g., biomass) have been developed. However, so far the exchange of datasets and approaches (i.e., extraction algorithms) among LiDAR users

ASSESSMENT OF BOTTOM-OF-ATMOSPHERE REFLECTANCE IN LIDAR DATA AS REFERENCE FOR HYPERSPECTRAL IMAGERY

Directory of Open Access Journals (Sweden)

A. Roncat

2017-09-01

Full Text Available While airborne lidar has confirmed its leading role in delivering high-resolution 3D topographic information during the last decade, its radiometric potential has not yet been fully exploited. However, with the increasing availability of commercial lidar systems which (a make use of full-waveform information and (b operate at several wavelengths simultaneously, this potential is increasing as well. Radiometric calibration of the full-waveform information mentioned before allows for the derivation of physical target surface parameters such as the backscatter coefficient and a diffuse reflectance value at bottom of atmosphere (BOA, i.e. the target surface. With lidar being an active remote sensing technique, these parameters can be derived from lidar data itself, accompanied by the measurement or estimation of reference data for diffuse reflectance. In contrast to this, such a radiometric calibration for passive hyperspectral imagery (HSI requires the knowledge and/or estimation of much more unknowns. However, in case of corresponding wavelength(s radiometrically calibrated lidar datasets can deliver an areawide reference for BOA reflectance. This paper presents criteria to check where the assumption of diffuse BOA reflectance behaviour is fulfilled and how these criteria are assessed in lidar data; the assessment is illustrated by an extended lidar dataset. Moreover, for this lidar dataset and an HSI dataset recorded over the same area, the corresponding reflectance values are compared for different surface types.

Establishing macroecological trait datasets: digitalization, extrapolation, and validation of diet preferences in terrestrial mammals worldwide

DEFF Research Database (Denmark)

Kissling, W. Daniel; Dalby, Lars; Fløjgaard, Camilla

2014-01-01

, the importance of diet for macroevolutionary and macroecological dynamics remains little explored, partly because of the lack of comprehensive trait datasets. We compiled and evaluated a comprehensive global dataset of diet preferences of mammals (“MammalDIET”). Diet information was digitized from two global...... species within the same genus, or family) and this extrapolation was subsequently validated both internally (with a jack-knife approach applied to the compiled species-level diet data) and externally (using independent species-level diet information from a comprehensive continentwide data source). Finally...... information (48% of all terrestrial mammal species), and only rarely from other species within the same genus (6%) or from family level (8%). Internal and external validation showed that: (1) extrapolations were most reliable for primary food items; (2) several diet categories (“Animal”, “Mammal...

Suwannee River Water Management District Lidar: Falmouth (FL)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of the Suwannee River G12PD00242 1.0 Meter LiDAR Survey area 5 in north-central Florida and encompasses...

LIDAR Products, State of Rhode Island: LIDAR for the North East – ARRA and LiDAR for the North East Part II; LiDAR was collected in the Winter and Spring 2011 at a 1 meter or better nominal post spacing (1m GSD) for approximately 1,074 square miles of Rhode Island, whi, Published in 2012, 1:9600 (1in=800ft) scale, Rhode Island and Providence Plantations.

Data.gov (United States)

NSGIC State | GIS Inventory — LIDAR Products dataset current as of 2012. State of Rhode Island: LIDAR for the North East – ARRA and LiDAR for the North East Part II; LiDAR was collected in the...

A re-analysis of the Lake Suigetsu terrestrial radiocarbon calibration dataset

International Nuclear Information System (INIS)

Staff, R.A.; Bronk Ramsey, C.; Nakagawa, T.

2010-01-01

Lake Suigetsu, Honshu Island, Japan provides an ideal sedimentary sequence from which to derive a wholly terrestrial radiocarbon calibration curve back to the limits of radiocarbon detection (circa 60 ka bp). The presence of well-defined, annually-deposited laminae (varves) throughout the entirety of this period provides an independent, high resolution chronometer against which radiocarbon measurements of plant macrofossils from the sediment column can be directly related. However, data from the initial Lake Suigetsu project were found to diverge significantly from alternative, marine-based calibration datasets released around the same time (e.g. ). The main source of this divergence is thought to be the result of inaccuracies in the absolute age profile of the Suigetsu project, caused by both varve counting uncertainties and gaps in the sediment column of unknown duration between successively-drilled core sections. Here, a re-analysis of the previously-published Lake Suigetsu data is conducted. The most recent developments in Bayesian statistical modelling techniques (OxCal v4.1; ) are implemented to fit the Suigetsu data to the latest radiocarbon calibration datasets and thereby estimate the duration of the inter-core section gaps in the Suigetsu data. In this way, the absolute age of the Lake Suigetsu sediment profile is more accurately defined, providing significant information for both radiocarbon calibration and palaeoenvironmental reconstruction purposes.

2013 Suwannee River Water Management District Lidar: Greenville (FL)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of Suwannee River G12PD00242 1.0 Meter LiDAR Survey Area 3, Classified Point Cloud, in north-central...

2013 Suwannee River Water Management District Lidar: Bell (FL)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of Suwannee River G13PD00141 1.0 Meter LiDAR Survey Area 4, Classified Point Cloud, in north-central...

2013 Suwannee River Water Management District Lidar: Mayo (FL)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of Suwannee River G12PD00242 1.0 Meter LiDAR Survey Area 4, Classified Point Cloud, in north-central...

2013 Suwannee River Water Management District Lidar: Obrien (FL)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of Suwannee River G13PD00141 1.0 Meter LiDAR Survey Area 1, Classified Point Cloud, in north-central...

USGS Atchafalaya 2 LiDAR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of the Atchafalaya Basin project area. The entire survey area for Atchafalaya encompasses approximately...

2014 Suwannee River Water Management District Lidar: Cooks Hammock (FL)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of the Suwannee River G14PD00206 0.7 Meter LiDAR Survey in central Florida and encompasses 571 square...

2013 Suwannee River Water Management District Lidar: Ocean Pond (FL)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of Suwannee River G13PD00141 1.0 Meter LiDAR Survey Area 3, Classified Point Cloud, in north-central...

2013 Suwannee River Water Management District (SRWMD) Lidar: Ichetucknee (FL)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of the Suwannee River G12PD00242 1.0 Meter LiDAR Survey area 2 in north-central Florida and encompasses...

Establishing macroecological trait datasets: digitalization, extrapolation, and validation of diet preferences in terrestrial mammals worldwide.

Science.gov (United States)

Kissling, Wilm Daniel; Dalby, Lars; Fløjgaard, Camilla; Lenoir, Jonathan; Sandel, Brody; Sandom, Christopher; Trøjelsgaard, Kristian; Svenning, Jens-Christian

2014-07-01

Ecological trait data are essential for understanding the broad-scale distribution of biodiversity and its response to global change. For animals, diet represents a fundamental aspect of species' evolutionary adaptations, ecological and functional roles, and trophic interactions. However, the importance of diet for macroevolutionary and macroecological dynamics remains little explored, partly because of the lack of comprehensive trait datasets. We compiled and evaluated a comprehensive global dataset of diet preferences of mammals ("MammalDIET"). Diet information was digitized from two global and cladewide data sources and errors of data entry by multiple data recorders were assessed. We then developed a hierarchical extrapolation procedure to fill-in diet information for species with missing information. Missing data were extrapolated with information from other taxonomic levels (genus, other species within the same genus, or family) and this extrapolation was subsequently validated both internally (with a jack-knife approach applied to the compiled species-level diet data) and externally (using independent species-level diet information from a comprehensive continentwide data source). Finally, we grouped mammal species into trophic levels and dietary guilds, and their species richness as well as their proportion of total richness were mapped at a global scale for those diet categories with good validation results. The success rate of correctly digitizing data was 94%, indicating that the consistency in data entry among multiple recorders was high. Data sources provided species-level diet information for a total of 2033 species (38% of all 5364 terrestrial mammal species, based on the IUCN taxonomy). For the remaining 3331 species, diet information was mostly extrapolated from genus-level diet information (48% of all terrestrial mammal species), and only rarely from other species within the same genus (6%) or from family level (8%). Internal and external

Localized Segment Based Processing for Automatic Building Extraction from LiDAR Data

Science.gov (United States)

Parida, G.; Rajan, K. S.

2017-05-01

The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

LOCALIZED SEGMENT BASED PROCESSING FOR AUTOMATIC BUILDING EXTRACTION FROM LiDAR DATA

Directory of Open Access Journals (Sweden)

G. Parida

2017-05-01

Full Text Available The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

3D Scan of Ornamental Column (huabiao Using Terrestrial LiDAR and Hand-held Imager

Directory of Open Access Journals (Sweden)

W. Zhang

2015-08-01

Full Text Available In ancient China, Huabiao was a type of ornamental column used to decorate important buildings. We carried out 3D scan of a Huabiao located in Peking University, China. This Huabiao was built no later than 1742. It is carved by white marble, 8 meters in height. Clouds and various postures of dragons are carved on its body. Two instruments were used to acquire the point cloud of this Huabiao, a terrestrial LiDAR (Riegl VZ-1000 and a hand-held imager (Mantis Vision F5. In this paper, the details of the experiment were described, including the differences between these two instruments, such as working principle, spatial resolution, accuracy, instrument dimension and working flow. The point clouds obtained respectively by these two instruments were compared, and the registered point cloud of Huabiao was also presented. These should be of interest and helpful for the research communities of archaeology and heritage.

Jean Lafitte 2013, 1.0 Meter LiDAR, Classified point cloud

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of the Jean Lafitte,G13PD00214, 1.0 Meter LiDAR Survey Area in south of New Orleans and encompasses 77...

Waveform- and Terrestrial Lidar Assessment of the Usual (Structural) Suspects in a Forest Canopy

Science.gov (United States)

van Aardt, J. A.; Romanczyk, P.; Kelbe, D.; van Leeuwen, M.; Cawse-Nicholson, K.; Gough, C. M.; Kampe, T. U.

2015-12-01

Forest inventory has evolved from standard stem diameter-height relationships, to coarse canopy metrics, to more involved ecologically-meaningful variables, such as leaf area index (LAI) and even canopy radiative transfer as a function of canopy gaps, leaf clumping, and leaf angle distributions. Accurate and precise measurement of the latter set of variables presents a challenge to the ecological and modeling communities; however, relatively novel remote sensing modalities, e.g., waveform lidar (wlidar) and terrestrial lidar systems (TLS), have the potential to adress this challenge. Research teams at Rochester Institute of Technology (RIT) and the Virginia Commonwealth University (VCU) have been collaborating with the National Ecological Observation Network (NEON) to assess vegetation canopy structure and variation at the University of Michigan Biological Research Station and the NEON Northeast domain (Harvard Forest, MA). Airborne small-footprint wlidar data, in-situ TLS data, and first-principles, physics-based simulation tools are being used to study (i) the impact of vegetation canopy geometric elements on wlidar signals (twigs and petioles have been deemed negligible), (ii) the analysis of airborne wlidar data for top-down assessment of canopy metrics such as LAI, and (iii) our ability to extract "bottom-up" canopy structure from TLS using scans registered to each other using a novel marker-free registration approach (e.g., basal area: R2=0.82, RMSE=7.43 m2/ha). Such studies indicate that we can potentially assess radiative transfer through vegetation canopies remotely using a vertically-stratified approach with wlidar, and augment such an approach via rapid-scan TLS technology to gain a better understanding of fine-scale variation in canopy structure. This in turn is key to quantifying and modeling radiative transfer based on understanding of forest canopy structural change as a function of ecosystem development, climate, and anthropogenic drivers.

Lidar-based individual tree species classification using convolutional neural network

Science.gov (United States)

Mizoguchi, Tomohiro; Ishii, Akira; Nakamura, Hiroyuki; Inoue, Tsuyoshi; Takamatsu, Hisashi

2017-06-01

Terrestrial lidar is commonly used for detailed documentation in the field of forest inventory investigation. Recent improvements of point cloud processing techniques enabled efficient and precise computation of an individual tree shape parameters, such as breast-height diameter, height, and volume. However, tree species are manually specified by skilled workers to date. Previous works for automatic tree species classification mainly focused on aerial or satellite images, and few works have been reported for classification techniques using ground-based sensor data. Several candidate sensors can be considered for classification, such as RGB or multi/hyper spectral cameras. Above all candidates, we use terrestrial lidar because it can obtain high resolution point cloud in the dark forest. We selected bark texture for the classification criteria, since they clearly represent unique characteristics of each tree and do not change their appearance under seasonable variation and aged deterioration. In this paper, we propose a new method for automatic individual tree species classification based on terrestrial lidar using Convolutional Neural Network (CNN). The key component is the creation step of a depth image which well describe the characteristics of each species from a point cloud. We focus on Japanese cedar and cypress which cover the large part of domestic forest. Our experimental results demonstrate the effectiveness of our proposed method.

Forest inventory with terrestrial LiDAR

NARCIS (Netherlands)

Bauwens, Sébastien; Bartholomeus, Harm; Calders, Kim; Lejeune, Philippe

2016-01-01

The application of static terrestrial laser scanning (TLS) in forest inventories is becoming more effective. Nevertheless, the occlusion effect is still limiting the processing efficiency to extract forest attributes. The use of a mobile laser scanner (MLS) would reduce this occlusion. In this

FY12 St Johns River Water Management LiDAR Survey: Putnam (FL)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of the FY12 St Johns River Water Management LiDAR Survey, project area in north-central Florida and...

2009 - 2011 CA Coastal Conservancy Coastal Lidar Project: Hydro-flattened Bare Earth DEM

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Light Detection and Ranging (LiDAR) data is remotely sensed high-resolution elevation data collected by an airborne collection platform. This LiDAR dataset is a...

CLOSE RANGE HYPERSPECTRAL IMAGING INTEGRATED WITH TERRESTRIAL LIDAR SCANNING APPLIED TO ROCK CHARACTERISATION AT CENTIMETRE SCALE

Directory of Open Access Journals (Sweden)

T. H. Kurz

2012-07-01

Full Text Available Compact and lightweight hyperspectral imagers allow the application of close range hyperspectral imaging with a ground based scanning setup for geological fieldwork. Using such a scanning setup, steep cliff sections and quarry walls can be scanned with a more appropriate viewing direction and a higher image resolution than from airborne and spaceborne platforms. Integration of the hyperspectral imagery with terrestrial lidar scanning provides the hyperspectral information in a georeferenced framework and enables measurement at centimetre scale. In this paper, three geological case studies are used to demonstrate the potential of this method for rock characterisation. Two case studies are applied to carbonate quarries where mapping of different limestone and dolomite types was required, as well as measurements of faults and layer thicknesses from inaccessible parts of the quarries. The third case study demonstrates the method using artificial lighting, applied in a subsurface scanning scenario where solar radiation cannot be utilised.

CALIBRATION OF LOW COST DIGITAL CAMERA USING DATA FROM SIMULTANEOUS LIDAR AND PHOTOGRAMMETRIC SURVEYS

Directory of Open Access Journals (Sweden)

E. Mitishita

2012-07-01

Full Text Available Digital photogrammetric products from the integration of imagery and lidar datasets are a reality nowadays. When the imagery and lidar surveys are performed together and the camera is connected to the lidar system, a direct georeferencing can be applied to compute the exterior orientation parameters of the images. Direct georeferencing of the images requires accurate interior orientation parameters to perform photogrammetric application. Camera calibration is a procedure applied to compute the interior orientation parameters (IOPs. Calibration researches have established that to obtain accurate IOPs, the calibration must be performed with same or equal condition that the photogrammetric survey is done. This paper shows the methodology and experiments results from in situ self-calibration using a simultaneous images block and lidar dataset. The calibration results are analyzed and discussed. To perform this research a test field was fixed in an urban area. A set of signalized points was implanted on the test field to use as the check points or control points. The photogrammetric images and lidar dataset of the test field were taken simultaneously. Four strips of flight were used to obtain a cross layout. The strips were taken with opposite directions of flight (W-E, E-W, N-S and S-N. The Kodak DSC Pro SLR/c digital camera was connected to the lidar system. The coordinates of the exposition station were computed from the lidar trajectory. Different layouts of vertical control points were used in the calibration experiments. The experiments use vertical coordinates from precise differential GPS survey or computed by an interpolation procedure using the lidar dataset. The positions of the exposition stations are used as control points in the calibration procedure to eliminate the linear dependency of the group of interior and exterior orientation parameters. This linear dependency happens, in the calibration procedure, when the vertical images and

Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations.

Science.gov (United States)

Huang, Rongyong; Zheng, Shunyi; Hu, Kun

2018-06-01

Registration of large-scale optical images with airborne LiDAR data is the basis of the integration of photogrammetry and LiDAR. However, geometric misalignments still exist between some aerial optical images and airborne LiDAR point clouds. To eliminate such misalignments, we extended a method for registering close-range optical images with terrestrial LiDAR data to a variety of large-scale aerial optical images and airborne LiDAR data. The fundamental principle is to minimize the distances from the photogrammetric matching points to the terrestrial LiDAR data surface. Except for the satisfactory efficiency of about 79 s per 6732 × 8984 image, the experimental results also show that the unit weighted root mean square (RMS) of the image points is able to reach a sub-pixel level (0.45 to 0.62 pixel), and the actual horizontal and vertical accuracy can be greatly improved to a high level of 1/4⁻1/2 (0.17⁻0.27 m) and 1/8⁻1/4 (0.10⁻0.15 m) of the average LiDAR point distance respectively. Finally, the method is proved to be more accurate, feasible, efficient, and practical in variety of large-scale aerial optical image and LiDAR data.

2007 US Army Corps of Engineers (USACE), Jacksonville District US Virgin Islands LiDAR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This Light Detection and Ranging (LiDAR) bare-earth classified LAS dataset is a topographic survey conducted for the USACE USVI LiDAR Project. These data were...

High Spectral Resolution Lidar and MPLNET Micro Pulse Lidar Aerosol Optical Property Retrieval Intercomparison During the 2012 7-SEAS Field Campaign at Singapore

Science.gov (United States)

Lolli, Simone; Welton, Ellsworth J.; Campbell, James R.; Eloranta, Edwin; Holben, Brent N.; Chew, Boon Ning; Salinas, Santo V.

2014-01-01

From August 2012 to February 2013 a High Resolution Spectral Lidar (HSRL; 532 nm) was deployed at that National University of Singapore near a NASA Micro Pulse Lidar NETwork (MPLNET; 527 nm) site. A primary objective of the MPLNET lidar project is the production and dissemination of reliable Level 1 measurements and Level 2 retrieval products. This paper characterizes and quantifies error in Level 2 aerosol optical property retrievals conducted through inversion techniques that derive backscattering and extinction coefficients from MPLNET elastic single-wavelength datasets. MPLNET Level 2 retrievals for aerosol optical depth and extinction/backscatter coefficient profiles are compared with corresponding HSRL datasets, for which the instrument collects direct measurements of each using a unique optical configuration that segregates aerosol and cloud backscattered signal from molecular signal. The intercomparison is performed, and error matrices reported, for lower (0-5km) and the upper (>5km) troposphere, respectively, to distinguish uncertainties observed within and above the MPLNET instrument optical overlap regime.

Lidar to lidar calibration of Ground-based Lidar

DEFF Research Database (Denmark)

Fernandez Garcia, Sergio; Courtney, Michael

This report presents the result of the lidar to lidar calibration performed for ground-based lidar. Calibration is here understood as the establishment of a relation between the reference lidar wind speed measurements with measurement uncertainties provided by measurement standard and corresponding...... lidar wind speed indications with associated measurement uncertainties. The lidar calibration concerns the 10 minute mean wind speed measurements. The comparison of the lidar measurements of the wind direction with that from the reference lidar measurements are given for information only....

2014 NCFMP Lidar: Statewide North Carolina (Phase 1)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Geographic Extent: North Carolina Area of Interest for Sandy, covering approximately 9,396 square miles. Dataset Description: The North Carolina - Sandy LiDAR...

North Carolina Statewide Lidar DEM 2014 Phase 1

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Geographic Extent: North Carolina Area of Interest for Sandy, covering approximately 9,396 square miles. Dataset Description: The North Carolina - Sandy LiDAR...

The OptD-multi method in LiDAR processing

International Nuclear Information System (INIS)

Błaszczak-Bąk, Wioleta; Sobieraj-Żłobińska, Anna; Kowalik, Michał

2017-01-01

New and constantly developing technology for acquiring spatial data, such as LiDAR (light detection and ranging), is a source for large volume of data. However, such amount of data is not always needed for developing the most popular LiDAR products: digital terrain model (DTM) or digital surface model. Therefore, in many cases, the number of contained points are reduced in the pre-processing stage. The degree of reduction is determined by the algorithm used, which should enable the user to obtain a dataset appropriate and optimal for the planned purpose. The aim of this article is to propose a new Optimum Dataset method (OptD method) in the processing of LiDAR point clouds. The OptD method can reduce the number of points in a dataset for the specified optimization criteria concerning the characteristics of generated DTM. The OptD method can be used in two variants: OptD-single (one criterion for optimization) and OptD-multi (two or more optimization criteria). The OptD-single method has been thoroughly tested and presented by Błaszczak-Bąk (2016 Acta Geodyn. Geomater . 13/4 379–86). In this paper the authors discussed the OptD-multi method. (paper)

North Carolina Statewide Lidar DEM 2015 Phase 3

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Geographic Extent: North Carolina Area of Interest, covering approximately 7,197 square miles. Dataset Description: The North Carolina LiDAR project called for the...

2015 NCFMP Lidar: Statewide North Carolina (Phase 3)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Geographic Extent: North Carolina Area of Interest, covering approximately 7,197 square miles. Dataset Description: The North Carolina LiDAR project called for the...

Lidar to lidar calibration

DEFF Research Database (Denmark)

Fernandez Garcia, Sergio; Villanueva, Héctor

This report presents the result of the lidar to lidar calibration performed for ground-based lidar. Calibration is here understood as the establishment of a relation between the reference lidar wind speed measurements with measurement uncertainties provided by measurement standard and corresponding...... lidar wind speed indications with associated measurement uncertainties. The lidar calibration concerns the 10 minute mean wind speed measurements. The comparison of the lidar measurements of the wind direction with that from the reference lidar measurements are given for information only....

2008 FEMA Lidar: South Oneida County (NY)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — For Oneida County, NY, there were two types of elevation datasets. The first type is LiDAR and the second one is Auto-correlation DEM. Auto-correlation DEM data was...

Magnitude-frequency and Spatial Distribution of Rockfalls in the White Canyon, British Columbia using Terrestrial LiDAR and Microseismic Monitoring Systems

Science.gov (United States)

van Veen, M.

2015-12-01

Transportation corridors built along natural slopes are subject to frequent rockfall hazards, which can disrupt service and cause damage to infrastructure. Many of these areas exist along the Fraser-Thompson corridor of the CN rail line in Southern British Columbia, particularly in the White Canyon area near Lytton. Here the rail track is situated between the 500 m high slopes and the river, for 2.4 km. The frequency-magnitude relationship between these events and the percentage of rockfalls making it to track level are important components of hazard assessment for these slopes. Traditional methods of collecting rockfall data in this area involve visual inspection by maintenance personnel, however this is an onerous task for such a large slope with frequent rockfall activity, and therefore the rockfall record for this area is often lacking data. Since 2012, high-resolution terrestrial LiDAR (Light detection and ranging) data has been collected for the White Canyon area and analysis of change from sequential LiDAR scans provides detailed data that can't be obtained from traditional rockfall databases, including the magnitude and spatial distribution of rockfall events. While the LiDAR change detection can be useful in identifying rockfall volumes and source zones, it can be difficult to determine the end location of each rockfall and the exact timing of events, as scan data is usually collected over a period of several months. Recently, a microseismic monitoring system has been deployed over a section of the railway track and data is available on time and location of impact at the track level, which permits assessment of the number of rockfalls traversing the whole slope down to track level. This, in combination with data on rockfall magnitudes and source zones obtained from the LiDAR change detection can provide useful information for management of tracks in these hazardous settings and also provides data for calibration of rockfall modelling.

Lidar-Based Rock-Fall Hazard Characterization of Cliffs

Science.gov (United States)

Collins, Brian D.; Greg M.Stock,

2017-01-01

Rock falls from cliffs and other steep slopes present numerous challenges for detailed geological characterization. In steep terrain, rock-fall source areas are both dangerous and difficult to access, severely limiting the ability to make detailed structural and volumetric measurements necessary for hazard assessment. Airborne and terrestrial lidar survey methods can provide high-resolution data needed for volumetric, structural, and deformation analyses of rock falls, potentially making these analyses straightforward and routine. However, specific methods to collect, process, and analyze lidar data of steep cliffs are needed to maximize analytical accuracy and efficiency. This paper presents observations showing how lidar data sets should be collected, filtered, registered, and georeferenced to tailor their use in rock fall characterization. Additional observations concerning surface model construction, volumetric calculations, and deformation analysis are also provided.

RAPID INSPECTION OF PAVEMENT MARKINGS USING MOBILE LIDAR POINT CLOUDS

Directory of Open Access Journals (Sweden)

H. Zhang

2016-06-01

Full Text Available This study aims at building a robust semi-automated pavement marking extraction workflow based on the use of mobile LiDAR point clouds. The proposed workflow consists of three components: preprocessing, extraction, and classification. In preprocessing, the mobile LiDAR point clouds are converted into the radiometrically corrected intensity imagery of the road surface. Then the pavement markings are automatically extracted with the intensity using a set of algorithms, including Otsu’s thresholding, neighbor-counting filtering, and region growing. Finally, the extracted pavement markings are classified with the geometric parameters using a manually defined decision tree. Case studies are conducted using the mobile LiDAR dataset acquired in Xiamen (Fujian, China with different road environments by the RIEGL VMX-450 system. The results demonstrated that the proposed workflow and our software tool can achieve 93% in completeness, 95% in correctness, and 94% in F-score when using Xiamen dataset.

2010 ARRA Lidar: California Coastal Project (Zone 3)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The primary purpose of this project was to develop a consistent and accurate surface elevation dataset derived from high-accuracy Light Detection and Ranging (LiDAR)...

2010 ARRA Lidar: California Coastal Project (Zone 4)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The primary purpose of this project was to develop a consistent and accurate surface elevation dataset derived from high-accuracy Light Detection and Ranging (LiDAR)...

Characterizing the Vertical Distribution of Aerosols using Ground-based Multiwavelength Lidar Data

Science.gov (United States)

Ferrare, R. A.; Thorsen, T. J.; Clayton, M.; Mueller, D.; Chemyakin, E.; Burton, S. P.; Goldsmith, J.; Holz, R.; Kuehn, R.; Eloranta, E. W.; Marais, W.; Newsom, R. K.; Liu, X.; Sawamura, P.; Holben, B. N.; Hostetler, C. A.

2016-12-01

Observations of aerosol optical and microphysical properties are critical for developing and evaluating aerosol transport model parameterizations and assessing global aerosol-radiation impacts on climate. During the Combined HSRL And Raman lidar Measurement Study (CHARMS), we investigated the synergistic use of ground-based Raman lidar and High Spectral Resolution Lidar (HSRL) measurements to retrieve aerosol properties aloft. Continuous (24/7) operation of these co-located lidars during the ten-week CHARMS mission (mid-July through September 2015) allowed the acquisition of a unique, multiwavelength ground-based lidar dataset for studying aerosol properties above the Southern Great Plains (SGP) site. The ARM Raman lidar measured profiles of aerosol backscatter, extinction and depolarization at 355 nm as well as profiles of water vapor mixing ratio and temperature. The University of Wisconsin HSRL simultaneously measured profiles of aerosol backscatter, extinction and depolarization at 532 nm and aerosol backscatter at 1064 nm. Recent advances in both lidar retrieval theory and algorithm development demonstrate that vertically-resolved retrievals using such multiwavelength lidar measurements of aerosol backscatter and extinction can help constrain both the aerosol optical (e.g. complex refractive index, scattering, etc.) and microphysical properties (e.g. effective radius, concentrations) as well as provide qualitative aerosol classification. Based on this work, the NASA Langley Research Center (LaRC) HSRL group developed automated algorithms for classifying and retrieving aerosol optical and microphysical properties, demonstrated these retrievals using data from the unique NASA/LaRC airborne multiwavelength HSRL-2 system, and validated the results using coincident airborne in situ data. We apply these algorithms to the CHARMS multiwavelength (Raman+HSRL) lidar dataset to retrieve aerosol properties above the SGP site. We present some profiles of aerosol effective

Modelling the subsurface geomorphology of an active landslide using LIDAR.

Science.gov (United States)

2014-07-01

The focus of this research was twofold: : 1. To determine millimeter/sub-millimeter movement within a slide body using high precision terrestrial LIDAR and : artificial targets. This allows movement not apparent to the naked eye to be verified. : 2. ...

LARGE SCALE TEXTURED MESH RECONSTRUCTION FROM MOBILE MAPPING IMAGES AND LIDAR SCANS

Directory of Open Access Journals (Sweden)

M. Boussaha

2018-05-01

Full Text Available The representation of 3D geometric and photometric information of the real world is one of the most challenging and extensively studied research topics in the photogrammetry and robotics communities. In this paper, we present a fully automatic framework for 3D high quality large scale urban texture mapping using oriented images and LiDAR scans acquired by a terrestrial Mobile Mapping System (MMS. First, the acquired points and images are sliced into temporal chunks ensuring a reasonable size and time consistency between geometry (points and photometry (images. Then, a simple, fast and scalable 3D surface reconstruction relying on the sensor space topology is performed on each chunk after an isotropic sampling of the point cloud obtained from the raw LiDAR scans. Finally, the algorithm proposed in (Waechter et al., 2014 is adapted to texture the reconstructed surface with the images acquired simultaneously, ensuring a high quality texture with no seams and global color adjustment. We evaluate our full pipeline on a dataset of 17 km of acquisition in Rouen, France resulting in nearly 2 billion points and 40000 full HD images. We are able to reconstruct and texture the whole acquisition in less than 30 computing hours, the entire process being highly parallel as each chunk can be processed independently in a separate thread or computer.

Large Scale Textured Mesh Reconstruction from Mobile Mapping Images and LIDAR Scans

Science.gov (United States)

Boussaha, M.; Vallet, B.; Rives, P.

2018-05-01

The representation of 3D geometric and photometric information of the real world is one of the most challenging and extensively studied research topics in the photogrammetry and robotics communities. In this paper, we present a fully automatic framework for 3D high quality large scale urban texture mapping using oriented images and LiDAR scans acquired by a terrestrial Mobile Mapping System (MMS). First, the acquired points and images are sliced into temporal chunks ensuring a reasonable size and time consistency between geometry (points) and photometry (images). Then, a simple, fast and scalable 3D surface reconstruction relying on the sensor space topology is performed on each chunk after an isotropic sampling of the point cloud obtained from the raw LiDAR scans. Finally, the algorithm proposed in (Waechter et al., 2014) is adapted to texture the reconstructed surface with the images acquired simultaneously, ensuring a high quality texture with no seams and global color adjustment. We evaluate our full pipeline on a dataset of 17 km of acquisition in Rouen, France resulting in nearly 2 billion points and 40000 full HD images. We are able to reconstruct and texture the whole acquisition in less than 30 computing hours, the entire process being highly parallel as each chunk can be processed independently in a separate thread or computer.

2012 NOAA American Samoa Lidar: Islands of Tutuila, Aunu'u, Ofu, Olosega, Ta'u and Rose Atoll

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Light Detection and Ranging (LiDAR) data is remotely sensed high-resolution elevation data collected by an airborne collection platform. This LiDAR dataset is a...

2011 USGS Topographic LiDAR: Suwannee River Expansion

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — USGS Task Order No. G10PD00236 USGS Contract No. G10PC00093 The Light Detection and Ranging (LiDAR) dataset is a survey of the Suwannee River Expansion in...

i-LOVE: ISS-JEM lidar for observation of vegetation environment

Science.gov (United States)

Asai, Kazuhiro; Sawada, Haruo; Sugimoto, Nobuo; Mizutani, Kohei; Ishii, Shoken; Nishizawa, Tomoaki; Shimoda, Haruhisa; Honda, Yoshiaki; Kajiwara, Koji; Takao, Gen; Hirata, Yasumasa; Saigusa, Nobuko; Hayashi, Masatomo; Oguma, Hiroyuki; Saito, Hideki; Awaya, Yoshio; Endo, Takahiro; Imai, Tadashi; Murooka, Jumpei; Kobatashi, Takashi; Suzuki, Keiko; Sato, Ryota

2012-11-01

It is very important to watch the spatial distribution of vegetation biomass and changes in biomass over time, representing invaluable information to improve present assessments and future projections of the terrestrial carbon cycle. A space lidar is well known as a powerful remote sensing technology for measuring the canopy height accurately. This paper describes the ISS(International Space Station)-JEM(Japanese Experimental Module)-EF(Exposed Facility) borne vegetation lidar using a two dimensional array detector in order to reduce the root mean square error (RMSE) of tree height due to sloped surface.

G-LiHT: Goddard's LiDAR, Hyperspectral and Thermal Airborne Imager

Science.gov (United States)

Cook, Bruce; Corp, Lawrence; Nelson, Ross; Morton, Douglas; Ranson, Kenneth J.; Masek, Jeffrey; Middleton, Elizabeth

2012-01-01

Scientists at NASA's Goddard Space Flight Center have developed an ultra-portable, low-cost, multi-sensor remote sensing system for studying the form and function of terrestrial ecosystems. G-LiHT integrates two LIDARs, a 905 nanometer single beam profiler and 1550 nm scanner, with a narrowband (1.5 nanometers) VNIR imaging spectrometer and a broadband (8-14 micrometers) thermal imager. The small footprint (approximately 12 centimeters) LIDAR data and approximately 1 meter ground resolution imagery are advantageous for high resolution applications such as the delineation of canopy crowns, characterization of canopy gaps, and the identification of sparse, low-stature vegetation, which is difficult to detect from space-based instruments and large-footprint LiDAR. The hyperspectral and thermal imagery can be used to characterize species composition, variations in biophysical variables (e.g., photosynthetic pigments), surface temperature, and responses to environmental stressors (e.g., heat, moisture loss). Additionally, the combination of LIDAR optical, and thermal data from G-LiHT is being used to assess forest health by sensing differences in foliage density, photosynthetic pigments, and transpiration. Low operating costs (approximately $1 ha) have allowed us to evaluate seasonal differences in LiDAR, passive optical and thermal data, which provides insight into year-round observations from space. Canopy characteristics and tree allometry (e.g., crown height:width, canopy:ground reflectance) derived from G-LiHT data are being used to generate realistic scenes for radiative transfer models, which in turn are being used to improve instrument design and ensure continuity between LiDAR instruments. G-LiHT has been installed and tested in aircraft with fuselage viewports and in a custom wing-mounted pod that allows G-LiHT to be flown on any Cessna 206, a common aircraft in use throughout the world. G-LiHT is currently being used for forest biomass and growth estimation

Probabilistic change mapping from airborne LiDAR for post-disaster damage assessment

Science.gov (United States)

Jalobeanu, A.; Runyon, S. C.; Kruse, F. A.

2013-12-01

When both pre- and post-event LiDAR point clouds are available, change detection can be performed to identify areas that were most affected by a disaster event, and to obtain a map of quantitative changes in terms of height differences. In the case of earthquakes in built-up areas for instance, first responders can use a LiDAR change map to help prioritize search and recovery efforts. The main challenge consists of producing reliable change maps, robust to collection conditions, free of processing artifacts (due for instance to triangulation or gridding), and taking into account the various sources of uncertainty. Indeed, datasets acquired within a few years interval are often of different point density (sometimes an order of magnitude higher for recent data), different acquisition geometries, and very likely suffer from georeferencing errors and geometric discrepancies. All these differences might not be important for producing maps from each dataset separately, but they are crucial when performing change detection. We have developed a novel technique for the estimation of uncertainty maps from the LiDAR point clouds, using Bayesian inference, treating all variables as random. The main principle is to grid all points on a common grid before attempting any comparison, as working directly with point clouds is cumbersome and time consuming. A non-parametric approach based on local linear regression was implemented, assuming a locally linear model for the surface. This enabled us to derive error bars on gridded elevations, and then elevation differences. In this way, a map of statistically significant changes could be computed - whereas a deterministic approach would not allow testing of the significance of differences between the two datasets. This approach allowed us to take into account not only the observation noise (due to ranging, position and attitude errors) but also the intrinsic roughness of the observed surfaces occurring when scanning vegetation. As only

Brief communication: 3-D reconstruction of a collapsed rock pillar from Web-retrieved images and terrestrial lidar data - the 2005 event of the west face of the Drus (Mont Blanc massif)

Science.gov (United States)

Guerin, Antoine; Abellán, Antonio; Matasci, Battista; Jaboyedoff, Michel; Derron, Marc-Henri; Ravanel, Ludovic

2017-07-01

In June 2005, a series of major rockfall events completely wiped out the Bonatti Pillar located in the legendary Drus west face (Mont Blanc massif, France). Terrestrial lidar scans of the west face were acquired after this event, but no pre-event point cloud is available. Thus, in order to reconstruct the volume and the shape of the collapsed blocks, a 3-D model has been built using photogrammetry (structure-from-motion (SfM) algorithms) based on 30 pictures collected on the Web. All these pictures were taken between September 2003 and May 2005. We then reconstructed the shape and volume of the fallen compartment by comparing the SfM model with terrestrial lidar data acquired in October 2005 and November 2011. The volume is calculated to 292 680 m3 (±5.6 %). This result is close to the value previously assessed by Ravanel and Deline (2008) for this same rock avalanche (265 000 ± 10 000 m3). The difference between these two estimations can be explained by the rounded shape of the volume determined by photogrammetry, which may lead to a volume overestimation. However it is not excluded that the volume calculated by Ravanel and Deline (2008) is slightly underestimated, the thickness of the blocks having been assessed manually from historical photographs.

2006-2008 PAMAP LiDAR Data of Pennsylvania (Southern Counties)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This dataset consists of classified LiDAR (Light Detection and Ranging) elevation points produced by the PAMAP Program. Additional information is available at the...

GIS-Based Detection of Gullies in Terrestrial LiDAR Data of the Cerro Llamoca Peatland (Peru

Directory of Open Access Journals (Sweden)

Markus Forbriger

2013-11-01

Full Text Available Cushion peatlands are typical features of the high altitude Andes in South America. Due to the adaptation to difficult environmental conditions, they are very fragile ecosystems and therefore vulnerable to environmental and climate changes. Peatland erosion has severe effects on their ecological functions, such as water storage capacity. Thus, erosion monitoring is highly advisable. Erosion quantification and monitoring can be supported by high-resolution terrestrial Light Detection and Ranging (LiDAR. In this study, a novel Geographic Information System (GIS-based method for the automatic delineation and geomorphometric description of gullies in cushion peatlands is presented. The approach is a multi-step workflow based on a gully edge extraction and a sink filling algorithm applied to a conditioned digital terrain model. Our method enables the creation of GIS-ready polygons of the gullies and the derivation of geomorphometric parameters along the entire channel course. Automatically derived boundaries and gully area values correspond to a high degree (93% with manually digitized reference polygons. The set of methods developed in this study offers a suitable tool for the monitoring and scientific analysis of fluvial morphology in cushion peatlands.

A Scalable Infrastructure for Lidar Topography Data Distribution, Processing, and Discovery

Science.gov (United States)

Crosby, C. J.; Nandigam, V.; Krishnan, S.; Phan, M.; Cowart, C. A.; Arrowsmith, R.; Baru, C.

2010-12-01

High-resolution topography data acquired with lidar (light detection and ranging) technology have emerged as a fundamental tool in the Earth sciences, and are also being widely utilized for ecological, planning, engineering, and environmental applications. Collected from airborne, terrestrial, and space-based platforms, these data are revolutionary because they permit analysis of geologic and biologic processes at resolutions essential for their appropriate representation. Public domain lidar data collection by federal, state, and local agencies are a valuable resource to the scientific community, however the data pose significant distribution challenges because of the volume and complexity of data that must be stored, managed, and processed. Lidar data acquisition may generate terabytes of data in the form of point clouds, digital elevation models (DEMs), and derivative products. This massive volume of data is often challenging to host for resource-limited agencies. Furthermore, these data can be technically challenging for users who lack appropriate software, computing resources, and expertise. The National Science Foundation-funded OpenTopography Facility (www.opentopography.org) has developed a cyberinfrastructure-based solution to enable online access to Earth science-oriented high-resolution lidar topography data, online processing tools, and derivative products. OpenTopography provides access to terabytes of point cloud data, standard DEMs, and Google Earth image data, all co-located with computational resources for on-demand data processing. The OpenTopography portal is built upon a cyberinfrastructure platform that utilizes a Services Oriented Architecture (SOA) to provide a modular system that is highly scalable and flexible enough to support the growing needs of the Earth science lidar community. OpenTopography strives to host and provide access to datasets as soon as they become available, and also to expose greater application level functionalities to

Landforms in Lidar: Building a Catalog of Digital Landforms for Education and Outreach

Science.gov (United States)

Kleber, E.; Crosby, C.; Olds, S. E.; Arrowsmith, R.

2012-12-01

Lidar (Light Detection and Ranging) has emerged as a fundamental tool in the earth sciences. The collection of high-resolution lidar topography from an airborne or terrestrial platform allows landscapes and landforms to be spatially represented in at sub-meter resolution and in three dimensions. While the growing availability of lidar has led to numerous new scientific findings, these data also have tremendous value for earth science education. The study of landforms is an essential and basic element of earth science education that helps students to grasp fundamental earth system processes and how they manifest themselves in the world around us. Historically students are introduced to landforms and related processes through diagrams and images seen in earth science textbooks. Lidar data, coupled with free tools such as Google Earth, provide a means to allow students and the interested public to visualize, explore, and interrogate these same landforms in an interactive manner not possible in two-dimensional remotely sensed imagery. The NSF-funded OpenTopography facility hosts data collected for geologic, hydrologic, and biological research, covering a diverse range of landscapes, and thus provides a wealth of data that could be incorporated into educational materials. OpenTopography, in collaboration with UNAVCO, are developing a catalog of classic geologic landforms depicted in lidar. Beginning with textbook-examples of features such as faults and tectonic landforms, dunes, fluvial and glacial geomorphology, and natural hazards such as landslides and volcanoes, the catalog will be an online resource for educators and the interested public. Initially, the landforms will be sourced from pre-existing datasets hosted by OpenTopography. Users will see an image representative of the landform then have the option to download the data in Google Earth KMZ format, as a digital elevation model, or the original lidar point cloud file. By providing the landform in a range of

2006-2008 PAMAP LiDAR Data of Pennsylvania (Northern Counties)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This dataset consists of classified LiDAR (Light Detection and Ranging) elevation points produced by the PAMAP Program. PAMAP data are organized into blocks, which...

Simulation of Satellite, Airborne and Terrestrial LiDAR with DART (I):Waveform Simulation with Quasi-Monte Carlo Ray Tracing

Science.gov (United States)

Gastellu-Etchegorry, Jean-Philippe; Yin, Tiangang; Lauret, Nicolas; Grau, Eloi; Rubio, Jeremy; Cook, Bruce D.; Morton, Douglas C.; Sun, Guoqing

2016-01-01

Light Detection And Ranging (LiDAR) provides unique data on the 3-D structure of atmosphere constituents and the Earth's surface. Simulating LiDAR returns for different laser technologies and Earth scenes is fundamental for evaluating and interpreting signal and noise in LiDAR data. Different types of models are capable of simulating LiDAR waveforms of Earth surfaces. Semi-empirical and geometric models can be imprecise because they rely on simplified simulations of Earth surfaces and light interaction mechanisms. On the other hand, Monte Carlo ray tracing (MCRT) models are potentially accurate but require long computational time. Here, we present a new LiDAR waveform simulation tool that is based on the introduction of a quasi-Monte Carlo ray tracing approach in the Discrete Anisotropic Radiative Transfer (DART) model. Two new approaches, the so-called "box method" and "Ray Carlo method", are implemented to provide robust and accurate simulations of LiDAR waveforms for any landscape, atmosphere and LiDAR sensor configuration (view direction, footprint size, pulse characteristics, etc.). The box method accelerates the selection of the scattering direction of a photon in the presence of scatterers with non-invertible phase function. The Ray Carlo method brings traditional ray-tracking into MCRT simulation, which makes computational time independent of LiDAR field of view (FOV) and reception solid angle. Both methods are fast enough for simulating multi-pulse acquisition. Sensitivity studies with various landscapes and atmosphere constituents are presented, and the simulated LiDAR signals compare favorably with their associated reflectance images and Laser Vegetation Imaging Sensor (LVIS) waveforms. The LiDAR module is fully integrated into DART, enabling more detailed simulations of LiDAR sensitivity to specific scene elements (e.g., atmospheric aerosols, leaf area, branches, or topography) and sensor configuration for airborne or satellite LiDAR sensors.

2012 FEMA Topographic Lidar: Hudson-Hoosic and Deerfield Watersheds, Massachusetts

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of the Hudson-Hoosic and Deerfield project area. The entire survey area for Massachusetts is...

2006 Florida LiDAR: Escambia, Santa Rosa, and Walton Counties

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — ESCAMBIA: The Light Detection and Ranging (LiDAR) LAS dataset is a survey of select areas within Escambia County, Florida. These data were produced for Dewberry and...

2005 Southwest Florida Water Management District (SWFWMD) Lidar: Manatee District

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) LAS dataset is a survey of select areas within Southwest Florida. These data were produced for the Southwest Florida Water...

VT Lidar-based Aspect, Symbolized, Not Cached, VT State Plane Meters

Data.gov (United States)

Vermont Center for Geographic Information — ASPECT service (compass direction that a slope faces) derived from various VT Lidar datasets. A pre-defined SYMbology has been applied to the service. VT State Plane...

Improving stream studies with a small-footprint green lidar

Science.gov (United States)

Jim McKean; Dan Isaak; Wayne Wright

2009-01-01

Technology is changing how scientists and natural resource managers describe and study streams and rivers. A new generation of airborne aquatic-terrestrial lidars is being developed that can penetrate water and map the submerged topography inside a stream as well as the adjacent subaerial terrain and vegetation in one integrated mission. A leading example of these new...

Increasing the Impact of High-Resolution Lidar Topography Through Online Data Access and Processing

Science.gov (United States)

Crosby, C. J.; Nandigam, V.; Baru, C.; Arrowsmith, R.

2013-12-01

Topography data acquired with lidar (light detection and ranging) technology are revolutionizing the way we study the Earth's surface and overlying vegetation. These data, collected from satellite, airborne, tripod, or mobile-mounted scanners have emerged as a fundamental tool for research on topics including earthquake hazards, hillslope processes, and cyrosphere change. The U.S. National Science Foundation-funded OpenTopography (OT) Facility (http://www.opentopography.org) is a web-based system designed to democratize access to earth science-oriented lidar topography data. OT provides free, online access to lidar data in a number of forms, including the point cloud and associated geospatial-processing tools for customized analysis. The point cloud data are co-located with on-demand processing tools to generate digital elevation models, and derived products and visualizations which allow users to quickly access data in a format appropriate for their scientific application. The OT system is built using a service-oriented architecture (SOA) that leverages cyberinfrastructure resources at the San Diego Supercomputer Center at the University of California San Diego to allow users, regardless of expertise, to access these massive lidar datasets and derived raster data products for use in research and teaching. OT hosts over 600 billion lidar returns covering more than 120,000 km2. These data are provided by a variety of partners under joint agreements and memoranda of understanding with OT. Partners include national facilities such as the NSF-funded National Center for Airborne Lidar Mapping (NCALM), as well as non-governmental organizations and local, state, and federal agencies. OT has become a hub for high-resolution topography resources. Datasets hosted by other organizations, as well as lidar-specific software, can be registered into the OT catalog, providing users a 'one-stop shop' for such information. OT is also a partner on the NASA Lidar Access System (NLAS

Systematic variations in multi-spectral lidar representations of canopy height profiles and gap probability

Science.gov (United States)

Chasmer, L.; Hopkinson, C.; Gynan, C.; Mahoney, C.; Sitar, M.

2015-12-01

Airborne and terrestrial lidar are increasingly used in forest attribute modeling for carbon, ecosystem and resource monitoring. The near infra-red wavelength at 1064nm has been utilised most in airborne applications due to, for example, diode manufacture costs, surface reflectance and eye safety. Foliage reflects well at 1064nm and most of the literature on airborne lidar forest structure is based on data from this wavelength. However, lidar systems also operate at wavelengths further from the visible spectrum (e.g. 1550nm) for eye safety reasons. This corresponds to a water absorption band and can be sensitive to attenuation if surfaces contain moisture. Alternatively, some systems operate in the visible range (e.g. 532nm) for specialised applications requiring simultaneous mapping of terrestrial and bathymetric surfaces. All these wavelengths provide analogous 3D canopy structure reconstructions and thus offer the potential to be combined for spatial comparisons or temporal monitoring. However, a systematic comparison of wavelength-dependent foliage profile and gap probability (index of transmittance) is needed. Here we report on two multispectral lidar missions carried out in 2013 and 2015 over conifer, deciduous and mixed stands in Ontario, Canada. The first used separate lidar sensors acquiring comparable data at three wavelengths, while the second used a single sensor with 3 integrated laser systems. In both cases, wavelenegths sampled were 532nm, 1064nm and 1550nm. The experiment revealed significant differences in proportions of returns at ground level, the vertical foliage distribution and gap probability across wavelengths. Canopy attenuation was greatest at 532nm due to photosynthetic plant tissue absorption. Relative to 1064nm, foliage was systematically undersampled at the 10% to 60% height percentiles at both 1550nm and 532nm (this was confirmed with coincident terrestrial lidar data). When using all returns to calculate gap probability, all

Remote identification of potential polar bear maternal denning habitat in northern Alaska using airborne LiDAR

Science.gov (United States)

Jones, B. M.; Durner, G. M.; Stoker, J.; Shideler, R.; Perham, C.; Liston, G. E.

2013-12-01

Polar bear (Ursus maritimus) populations throughout the Arctic are being threatened by reductions in critical sea ice habitat. Throughout much of their range, polar bears give birth to their young in winter dens that are excavated in snowdrifts. New-born cubs, which are unable to survive exposure to Arctic winter weather, require 2-3 months of the relatively warm, stable, and undisturbed environment of the den for their growth. In the southern Beaufort Sea (BS), polar bears may den on the Alaskan Arctic Coastal Plain (ACP).The proportion of dens occurring on land has increased because of reductions in stable multi-year ice, increases in unconsolidated ice, and lengthening of the fall open-water period. Large portions of the ACP are currently being used for oil and gas activities and proposed projects will likely expand this footprint in the near future. Since petroleum exploration and development activities increase during winter there is the potential for human activities to disturb polar bears in maternal dens. Thus, maps showing the potential distribution of terrestrial denning habitat can help to mitigate negative interactions. Prior remote sensing efforts have consisted of manual interpretation of vertical aerial photography and automated classification of Interferometric Synthetic Aperture (IfSAR) derived digital terrain models (DTM) (5-m spatial resolution) focused on the identification of snowdrift forming landscape features. In this study, we assess the feasibility of airborne Light Detection and Ranging (LiDAR) data (2-m spatial resolution) for the automated classification of potential polar bear maternal denning habitat in a 1,400 km2 area on the central portion of the ACP. The study region spans the BS coast from the Prudhoe Bay oilfield in the west to near Point Thompson in the east and extends inland from 10 to 30 km. Approximately 800 km2 of the study area contains 19 known den locations, 51 field survey sites with information on bank height and

2007 Florida Division of Emergency Management (FDEM) Lidar Project: Southwest Florida

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This Light Detection and Ranging (LiDAR) LAS dataset is a topographic survey conducted for a coalition of GIS practitioners, including the Florida Division of...

2007 Florida Division of Emergency Management (FDEM) Lidar Project: Bay County

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This Light Detection and Ranging (LiDAR) LAS dataset is a topographic survey conducted for a coalition of GIS practitioners, including the Florida Division of...

Data Integration Framework Data Management Plan Remote Sensing Dataset

Science.gov (United States)

2016-07-01

Department of the Army position unless so designated by other authorized documents. DESTROY THIS REPORT WHEN NO LONGER NEEDED . DO NOT RETURN IT TO THE...2013 — Point clouds . ..................................................... 25 Table A2. Mobile terrestrial lidar data 2011–2013 — Digital elevation...HQUSACE) Navigation Business Line Manager overseeing the CODS Program. W. Jeff Lillycrop, CHL, was the ERDC Technical Director for Civil Works and

The Ålesund slide, March 2008: Lidar analysis of an urban human-induced rockslide

Science.gov (United States)

Derron, M.-H.; Melchiorre, C.; Blikra, L.; Loye, A.; Pedrazzini, A.

2009-04-01

On March 26 2008, a 2400 m3 rock block slid from an excavated slope on a six floors house in the city of Aalesund (Western coast of Norway). The whole house was displaced horizontally on its basement on about 6 meters and the two lower floors of the building collapsed. Five persons died during the event. Almost 500 inhabitants living in the surroundings had to be evacuated for 6 to 8 days because of a gas leakage. The site has been scanned three times with a terrestrial lidar. In the first dataset, made a couple of days after the event, the block and the house are present. In the second dataset, acquired about 3 months later, the house had been removed but most of the sliding block is still in place. The last scan, made six months after the events, shows very well the sliding surface, as the block had been removed too. These three scans completed by field measurement and observations have been used: 1) to characterize the geometry and volume of the block, 2) to determine the displacement vector, 3) to perform a analysis of discontinuities and some kinematics tests, 4) to estimate the roughness and the waviness of the sliding surface.

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase two, volume 3 : advanced consideration in LiDAR technology for bridge evaluation.

Science.gov (United States)

2012-03-01

This report describes Phase Two enhancement of terrestrial LiDAR scanning for bridge damage : evaluation that was initially developed in Phase One. Considering the spatial and reflectivity : information contained in LiDAR scans, two detection algorit...

KML-Based Access and Visualization of High Resolution LiDAR Topography

Science.gov (United States)

Crosby, C. J.; Blair, J. L.; Nandigam, V.; Memon, A.; Baru, C.; Arrowsmith, J. R.

2008-12-01

Over the past decade, there has been dramatic growth in the acquisition of LiDAR (Light Detection And Ranging) high-resolution topographic data for earth science studies. Capable of providing digital elevation models (DEMs) more than an order of magnitude higher resolution than those currently available, LiDAR data allow earth scientists to study the processes that contribute to landscape evolution at resolutions not previously possible yet essential for their appropriate representation. These datasets also have significant implications for earth science education and outreach because they provide an accurate representation of landforms and geologic hazards. Unfortunately, the massive volume of data produced by LiDAR mapping technology can be a barrier to their use. To make these data available to a larger user community, we have been exploring the use of Keyhole Markup Language (KML) and Google Earth to provide access to LiDAR data products and visualizations. LiDAR digital elevation models are typically delivered in a tiled format that lends itself well to a KML-based distribution system. For LiDAR datasets hosted in the GEON OpenTopography Portal (www.opentopography.org) we have developed KML files that show the extent of available LiDAR DEMs and provide direct access to the data products. Users interact with these KML files to explore the extent of the available data and are able to select DEMs that correspond to their area of interest. Selection of a tile loads a download that the user can then save locally for analysis in their software of choice. The GEON topography system also has tools available that allow users to generate custom DEMs from LiDAR point cloud data. This system is powerful because it enables users to access massive volumes of raw LiDAR data and to produce DEM products that are optimized to their science applications. We have developed a web service that converts the custom DEM models produced by the system to a hillshade that is delivered to

Canopy rainfall partitioning across an urbanization gradient in forest structure as characterized by terrestrial LiDAR

Science.gov (United States)

Mesta, D. C.; Van Stan, J. T., II; Yankine, S. A.; Cote, J. F.; Jarvis, M. T.; Hildebrandt, A.; Friesen, J.; Maldonado, G.

2017-12-01

As urbanization expands, greater forest area is shifting from natural stand structures to urban stand structures, like forest fragments and landscaped tree rows. Changes in forest canopy structure have been found to drastically alter the amount of rainwater reaching the surface. However, stormwater management models generally treat all forest structures (beyond needle versus broadleaved) similarly. This study examines the rainfall partitioning of Pinus spp. canopies along a natural-to-urban forest gradient and compares these to canopy structural measurements using terrestrial LiDAR. Throughfall and meteorological observations were also used to estimate parameters of the commonly-used Gash interception model. Preliminary findings indicate that as forest structure changed from natural, closed canopy conditions to semi-closed canopy fragments and, ultimately, to exposed urban landscaping tree rows, the interchange between throughfall and rainfall interception also changed. This shift in partitioning between throughfall and rainfall interception may be linked to intuitive parameters, like canopy closure and density, as well as more complex metrics, like the fine-scale patterning of gaps (ie, lacunarity). Thus, results indicate that not all forests of the same species should be treated the same by stormwater models. Rather, their canopy structural characteristics should be used to vary their hydrometeorological interactions.

Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems.

Science.gov (United States)

Paynter, Ian; Genest, Daniel; Peri, Francesco; Schaaf, Crystal

2018-04-06

Volumetric models with known biases are shown to provide bounds for the uncertainty in estimations of volume for ecologically interesting objects, observed with a terrestrial laser scanner (TLS) instrument. Bounding cuboids, three-dimensional convex hull polygons, voxels, the Outer Hull Model and Square Based Columns (SBCs) are considered for their ability to estimate the volume of temperate and tropical trees, as well as geomorphological features such as bluffs and saltmarsh creeks. For temperate trees, supplementary geometric models are evaluated for their ability to bound the uncertainty in cylinder-based reconstructions, finding that coarser volumetric methods do not currently constrain volume meaningfully, but may be helpful with further refinement, or in hybridized models. Three-dimensional convex hull polygons consistently overestimate object volume, and SBCs consistently underestimate volume. Voxel estimations vary in their bias, due to the point density of the TLS data, and occlusion, particularly in trees. The response of the models to parametrization is analysed, observing unexpected trends in the SBC estimates for the drumlin dataset. Establishing that this result is due to the resolution of the TLS observations being insufficient to support the resolution of the geometric model, it is suggested that geometric models with predictable outcomes can also highlight data quality issues when they produce illogical results.

2007 Southwest Florida Water Management District (SWFWMD) LiDAR: Hernando County

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This dataset is one component of a digital terrain model (DTM) for the Southwest Florida Water Management Districts FY2006 Digital Orthophoto (B089) and LiDAR...

2010 U.S. Geological Survey Topographic LiDAR: Atchafalaya Basin, Louisiana

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) dataset is a survey of the Atchafalaya Basin in south-central Louisiana. The entire survey area encompasses 981 square miles....

2005 Southwest Florida Water Management District (SWFWMD) Lidar: Little Manatee District

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) LAS dataset is a survey of select areas within Southwest Florida. These data were produced for the Southwest Florida Water...

2011 - 2012 New York State Department of Environmental Conservation (NYSDEC) Lidar: Coastal New York (Long Island and along the Hudson River)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Light Detection and Ranging (LiDAR) data is remotely sensed high-resolution elevation data collected by an airborne collection platform. This LiDAR dataset is a...

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase one, volume 3 : use of scanning LiDAR in structural evaluation of bridges.

Science.gov (United States)

2009-12-01

This volume introduces several applications of remote bridge inspection technologies studied in : this Integrated Remote Sensing and Visualization (IRSV) study using ground-based LiDAR : systems. In particular, the application of terrestrial LiDAR fo...

Applying a weighted random forests method to extract karst sinkholes from LiDAR data

Science.gov (United States)

Zhu, Junfeng; Pierskalla, William P.

2016-02-01

Detailed mapping of sinkholes provides critical information for mitigating sinkhole hazards and understanding groundwater and surface water interactions in karst terrains. LiDAR (Light Detection and Ranging) measures the earth's surface in high-resolution and high-density and has shown great potentials to drastically improve locating and delineating sinkholes. However, processing LiDAR data to extract sinkholes requires separating sinkholes from other depressions, which can be laborious because of the sheer number of the depressions commonly generated from LiDAR data. In this study, we applied the random forests, a machine learning method, to automatically separate sinkholes from other depressions in a karst region in central Kentucky. The sinkhole-extraction random forest was grown on a training dataset built from an area where LiDAR-derived depressions were manually classified through a visual inspection and field verification process. Based on the geometry of depressions, as well as natural and human factors related to sinkholes, 11 parameters were selected as predictive variables to form the dataset. Because the training dataset was imbalanced with the majority of depressions being non-sinkholes, a weighted random forests method was used to improve the accuracy of predicting sinkholes. The weighted random forest achieved an average accuracy of 89.95% for the training dataset, demonstrating that the random forest can be an effective sinkhole classifier. Testing of the random forest in another area, however, resulted in moderate success with an average accuracy rate of 73.96%. This study suggests that an automatic sinkhole extraction procedure like the random forest classifier can significantly reduce time and labor costs and makes its more tractable to map sinkholes using LiDAR data for large areas. However, the random forests method cannot totally replace manual procedures, such as visual inspection and field verification.

Lidar to lidar calibration phase 1

DEFF Research Database (Denmark)

Yordanova, Ginka; Courtney, Michael

This report presents a feasibility study of a lidar to lidar (L2L) calibration procedure. Phase one of the project was conducted at Høvsøre, Denmark. Two windcubes were placed next to the 116m met mast and different methods were applied to obtain the sensing height error of the lidars. The purpose...... is to find the most consistent method and use it in a potential lidar to lidar calibration procedure....

2006 Southwest Florida Water Management District (SWFWMD) Lidar: Rutland Ranch District

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) LAS dataset is a survey of select areas within Rutland Ranch. This data falls in Manatee County. These data were produced for...

Measuring Oscillating Walking Paths with a LIDAR

Directory of Open Access Journals (Sweden)

Jordi Palacín

2011-05-01

Full Text Available This work describes the analysis of different walking paths registered using a Light Detection And Ranging (LIDAR laser range sensor in order to measure oscillating trajectories during unsupervised walking. The estimate of the gait and trajectory parameters were obtained with a terrestrial LIDAR placed 100 mm above the ground with the scanning plane parallel to the floor to measure the trajectory of the legs without attaching any markers or modifying the floor. Three different large walking experiments were performed to test the proposed measurement system with straight and oscillating trajectories. The main advantages of the proposed system are the possibility to measure several steps and obtain average gait parameters and the minimum infrastructure required. This measurement system enables the development of new ambulatory applications based on the analysis of the gait and the trajectory during a walk.

Lidar to lidar calibration phase 2

DEFF Research Database (Denmark)

Yordanova, Ginka; Courtney, Michael

This report presents the results from phase 2 of a lidar to lidar (L2L) calibration procedure. Phase two of the project included two measurement campaigns conducted at given sites. The purpose was to find out if the lidar-to-lidar calibration procedure can be conducted with similar results...

Evaluating lidar point densities for effective estimation of aboveground biomass

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Wu, Zhuoting; Dye, Dennis G.; Stoker, Jason M.; Vogel, John M.; Velasco, Miguel G.; Middleton, Barry R.

2016-01-01

The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. High point density airborne lidar data, were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m2, corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m2. Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m2, and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m2, our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.

Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study

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Rubini Mahalingam

2016-11-01

Full Text Available Landslides are a significant geohazard, which frequently result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping using GIS and remote sensing can help communities prepare for these damaging events. Current mapping efforts utilize a wide variety of techniques and consider multiple factors. Unfortunately, each study is relatively independent of others in the applied technique and factors considered, resulting in inconsistencies. Further, input data quality often varies in terms of source, data collection, and generation, leading to uncertainty. This paper investigates if lidar-derived data-sets (slope, slope roughness, terrain roughness, stream power index, and compound topographic index can be used for predictive mapping without other landslide conditioning factors. This paper also assesses the differences in landslide susceptibility mapping using several, widely used statistical techniques. Landslide susceptibility maps were produced from the aforementioned lidar-derived data-sets for a small study area in Oregon using six representative statistical techniques. Most notably, results show that only a few factors were necessary to produce satisfactory maps with high predictive capability (area under the curve >0.7. The sole use of lidar digital elevation models and their derivatives can be used for landslide mapping using most statistical techniques without requiring additional detailed data-sets that are often difficult to obtain or of lower quality.

2008 Northwest Florida Water Management District (NWFWMD) LiDAR: Inland Okaloosa County

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National Oceanic and Atmospheric Administration, Department of Commerce — This Light Detection and Ranging (LiDAR) LAS dataset is a survey of inland Okaloosa County, Florida not covered in the 2008 Florida Department of Emergency...

Automated Detection of Geomorphic Features in LiDAR Point Clouds of Various Spatial Density

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Dorninger, Peter; Székely, Balázs; Zámolyi, András.; Nothegger, Clemens

2010-05-01

LiDAR, also referred to as laser scanning, has proved to be an important tool for topographic data acquisition. Terrestrial laser scanning allows for accurate (several millimeter) and high resolution (several centimeter) data acquisition at distances of up to some hundred meters. By contrast, airborne laser scanning allows for acquiring homogeneous data for large areas, albeit with lower accuracy (decimeter) and resolution (some ten points per square meter) compared to terrestrial laser scanning. Hence, terrestrial laser scanning is preferably used for precise data acquisition of limited areas such as landslides or steep structures, while airborne laser scanning is well suited for the acquisition of topographic data of huge areas or even country wide. Laser scanners acquire more or less homogeneously distributed point clouds. These points represent natural objects like terrain and vegetation and artificial objects like buildings, streets or power lines. Typical products derived from such data are geometric models such as digital surface models representing all natural and artificial objects and digital terrain models representing the geomorphic topography only. As the LiDAR technology evolves, the amount of data produced increases almost exponentially even in smaller projects. This means a considerable challenge for the end user of the data: the experimenter has to have enough knowledge, experience and computer capacity in order to manage the acquired dataset and to derive geomorphologically relevant information from the raw or intermediate data products. Additionally, all this information might need to be integrated with other data like orthophotos. In all theses cases, in general, interactive interpretation is necessary to determine geomorphic structures from such models to achieve effective data reduction. There is little support for the automatic determination of characteristic features and their statistical evaluation. From the lessons learnt from automated

2007 Bureau of Land Management (BLM) Lidar: Panther Creek Watershed, Yamhill County

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National Oceanic and Atmospheric Administration, Department of Commerce — The dataset represents LiDAR elevations acquired during a leaf-off and a leaf-on vegetative condition for the Upper Panther Creek Watershed in the Yamhill County...

MONITORING CONCEPTS FOR COASTAL AREAS USING LIDAR DATA

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A. Schmidt

2013-05-01

Full Text Available Coastal areas are characterized by high spatial and temporal variability. In order to detect undesired changes at early stages, enabling rapid countermeasures to mitigate or minimize potential harm or hazard, a recurrent monitoring becomes necessary. In this paper, we focus on two monitoring task: the analysis of morphological changes and the classification and mapping of habitats. Our concepts are solely based on airborne lidar data which provide substantial information in coastal areas. For the first task, we generate a digital terrain model (DTM from the lidar point cloud and analyse the dynamic of an island by comparing the DTMs of different epochs with a time difference of six years. For the deeper understanding of the habitat composition in coastal areas, we classify the lidar point cloud by a supervised approach based on Conditional Random Fields. From the classified point cloud, water-land-boundaries as well as mussel bed objects are derived afterwards. We evaluate our approaches on two datasets of the German Wadden Sea.

Foredune Classification and Storm Response: Automated Analysis of Terrestrial Lidar DEMs

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2015-06-15

since Hurricane Sandy. 041 20/3/2015 4 Figure 1. A. The study site in Duck , NC showing the alongshore coordinates of the local coordinate...waves on March 10: Hs = 4.8 m at 16 sec Coastal Lidar and Radar Imaging System (CLARIS) Nor’easter Storm Conditions Study Site: Duck , NC...Engineer Research and Development Center, Coastal & Hydraulics Laboratory, Coastal Observation & Analysis Branch, 1261 Duck Rd, Duck , NC 27949, USA

A generalized adaptive mathematical morphological filter for LIDAR data

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Cui, Zheng

Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in

THE BENEFITS OF TERRESTRIAL LASER SCANNING AND HYPERSPECTRAL DATA FUSION PRODUCTS

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S. J. Buckley

2012-10-01

Full Text Available Close range hyperspectral imaging is a developing method for the analysis and identification of material composition in many applications, such as in within the earth sciences. Using compact imaging devices in the field allows near-vertical topography to be imaged, thus bypassing the key limitations of viewing angle and resolution that preclude the use of airborne and spaceborne platforms. Terrestrial laser scanning allows 3D topography to be captured with high precision and spatial resolution. The combination of 3D geometry from laser scanning, and material properties from hyperspectral imaging allows new fusion products to be created, adding new information for solving application problems. This paper highlights the advantages of terrestrial lidar and hyperspectral integration, focussing on the qualitative and quantitative aspects, with examples from a geological field application. Accurate co-registration of the two data types is required. This allows 2D pixels to be linked to the 3D lidar geometry, giving increased quantitative analysis as classified material vectors are projected to 3D space for calculation of areas and examination of spatial relationships. User interpretation of hyperspectral results in a spatially-meaningful manner is facilitated using visual methods that combine the geometric and mineralogical products in a 3D environment. Point cloud classification and the use of photorealistic modelling enhance qualitative validation and interpretation, and allow image registration accuracy to be checked. A method for texture mapping of lidar meshes with multiple image textures, both conventional digital photos and hyperspectral results, is described. The integration of terrestrial laser scanning and hyperspectral imaging is a valuable means of providing new analysis methods, suitable for many applications requiring linked geometric and chemical information.

First Airborne IPDA Lidar Measurements of Methane and Carbon Dioxide Applying the DLR Greenhouse Gas Sounder CHARM-F

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Amediek, A.; Ehret, G.; Fix, A.; Wirth, M.; Quatrevalet, M.; Büdenbender, C.; Kiemle, C.; Loehring, J.; Gerbig, C.

2015-12-01

First airborne measurement using CHARM-F, the four-wavelengths lidar for simultaneous soundings of atmospheric CO2 and CH4, were performed in Spring 2015 onboard the German research aircraft HALO. The lidar is designed in the IPDA (integrated path differential absorption) configuration using short double pulses, which gives column averaged gas mixing ratios between aircraft and ground. HALO's maximum flight altitude of 15 km and special features of the lidar, such as a relatively large laser ground spot, enable the CHARM-F system to be an airborne demonstrator for future spaceborne greenhouse gas lidars. Due to a high technological conformity this applies in particular to the French-German satellite mission MERLIN, the spaceborne methane IPDA lidar. The successfully completed flight measurements provide a valuable dataset, which supports the retrieval algorithm development for MERLIN notably. The flights covered different ground cover types, different orography types as well as the sea. Additionally, we captured different cloud conditions, at which the broken cloud case is a matter of particular interest. This dataset allows detailed analyses of measurement sensitivities, general studies on the IPDA principle and on technical details of the system. These activities are supported by another instrument onboard: a cavity ring down spectrometer, providing in-situ data of carbon dioxide, methane and water vapor with high accuracy and precision, which is ideal for validation purposes of the lidar. Additionally the onboard instrumentation of HALO gives information about pressure and temperature for cross-checking the ECMWF data, which are intended to be used for calculating the weighting function, the key quantity for the retrieval of gas column mixing ratios from the measured gas optical depths. In combination with dedicated descents into the boundary layer and subsequent ascents, a self-contained dataset for characterizations of CHARM-F is available.

2010 U.S. Geological Survey (USGS) Topographic LiDAR: San Francisco Bay, California

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National Oceanic and Atmospheric Administration, Department of Commerce — The primary purpose of this project was to develop a consistent and accurate surface elevation dataset derived from high-accuracy Light Detection and Ranging (LiDAR)...

NOSQL FOR STORAGE AND RETRIEVAL OF LARGE LIDAR DATA COLLECTIONS

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J. Boehm

2015-08-01

Full Text Available Developments in LiDAR technology over the past decades have made LiDAR to become a mature and widely accepted source of geospatial information. This in turn has led to an enormous growth in data volume. The central idea for a file-centric storage of LiDAR point clouds is the observation that large collections of LiDAR data are typically delivered as large collections of files, rather than single files of terabyte size. This split of the dataset, commonly referred to as tiling, was usually done to accommodate a specific processing pipeline. It makes therefore sense to preserve this split. A document oriented NoSQL database can easily emulate this data partitioning, by representing each tile (file in a separate document. The document stores the metadata of the tile. The actual files are stored in a distributed file system emulated by the NoSQL database. We demonstrate the use of MongoDB a highly scalable document oriented NoSQL database for storing large LiDAR files. MongoDB like any NoSQL database allows for queries on the attributes of the document. As a specialty MongoDB also allows spatial queries. Hence we can perform spatial queries on the bounding boxes of the LiDAR tiles. Inserting and retrieving files on a cloud-based database is compared to native file system and cloud storage transfer speed.

Nosql for Storage and Retrieval of Large LIDAR Data Collections

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Boehm, J.; Liu, K.

2015-08-01

Developments in LiDAR technology over the past decades have made LiDAR to become a mature and widely accepted source of geospatial information. This in turn has led to an enormous growth in data volume. The central idea for a file-centric storage of LiDAR point clouds is the observation that large collections of LiDAR data are typically delivered as large collections of files, rather than single files of terabyte size. This split of the dataset, commonly referred to as tiling, was usually done to accommodate a specific processing pipeline. It makes therefore sense to preserve this split. A document oriented NoSQL database can easily emulate this data partitioning, by representing each tile (file) in a separate document. The document stores the metadata of the tile. The actual files are stored in a distributed file system emulated by the NoSQL database. We demonstrate the use of MongoDB a highly scalable document oriented NoSQL database for storing large LiDAR files. MongoDB like any NoSQL database allows for queries on the attributes of the document. As a specialty MongoDB also allows spatial queries. Hence we can perform spatial queries on the bounding boxes of the LiDAR tiles. Inserting and retrieving files on a cloud-based database is compared to native file system and cloud storage transfer speed.

Lidar Remote Sensing for Characterizing Forest Vegetation - Special Issue. Foreword

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Popescu, Sorin C.; Nelson, Ross F.

2011-01-01

, and 6) forest inventory, (7) silvicultural and ecological applications, and (8) terrestrial lidar applications. Within the constraint limiting the number of papers that could be fitted into the special issue we attempted to select those papers that best represented these conference topics and sections, giving special consideration to studies using forestry lidar data collected from each of the three platforms -- terrestrial, airborne, and spaceborne. Reflecting the international participation and reach of the conference, the studies presented here took place in the USA, Canada, Taiwan, the UK, and China.

Noctilucent clouds in the polar sumer mesopause: Investigations using the ALOMAR Rayleigh/Mie/Raman Lidar; Leuchtende Nachtwolken an der polaren Sommermesopause: Untersuchungen mit dem ALOMAR Rayleigh/Mie/Raman Lidar

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Baumgarten, G.

2001-09-01

Noctilucent clouds (NLC) are rare, tenuous clouds in the terrestrial atmosphere that occur at polar latitudes in summer near 83 km altitude. These clouds where studied using the ALOMAR Rayleigh/Mie/Raman lidar located at 69 N, 16 E. The depolarization of light, which was backscattered on NLC particles was measured for the first time by the ALOMAR RMR-Lidar. Considering the small ratio of particle size over wavelength an unexpectedly large depolarization of 1.7% was observed. Comparing this result to T-matrix calculations for scattering on randomly oriented aspherical particles implies that the shape of the NLC particles is needle like. The ALOMAR RMR-Lidar is a twin-lidar equipped with two steerable telescopes which were used to observe a single NLC layer in two separate measurement volumes about 50 km apart at NLC altitudes. Cross correlation technique reveal the layer to be tilted with imbedded periodic horizontal structures showing wavelengths of about 30 to 50 km. These structures drift horizontally through the measurement volumes rather than being microphysically formed during the observation period. (orig.)

Parallel Landscape Driven Data Reduction & Spatial Interpolation Algorithm for Big LiDAR Data

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Rahil Sharma

2016-06-01

Full Text Available Airborne Light Detection and Ranging (LiDAR topographic data provide highly accurate digital terrain information, which is used widely in applications like creating flood insurance rate maps, forest and tree studies, coastal change mapping, soil and landscape classification, 3D urban modeling, river bank management, agricultural crop studies, etc. In this paper, we focus mainly on the use of LiDAR data in terrain modeling/Digital Elevation Model (DEM generation. Technological advancements in building LiDAR sensors have enabled highly accurate and highly dense LiDAR point clouds, which have made possible high resolution modeling of terrain surfaces. However, high density data result in massive data volumes, which pose computing issues. Computational time required for dissemination, processing and storage of these data is directly proportional to the volume of the data. We describe a novel technique based on the slope map of the terrain, which addresses the challenging problem in the area of spatial data analysis, of reducing this dense LiDAR data without sacrificing its accuracy. To the best of our knowledge, this is the first ever landscape-driven data reduction algorithm. We also perform an empirical study, which shows that there is no significant loss in accuracy for the DEM generated from a 52% reduced LiDAR dataset generated by our algorithm, compared to the DEM generated from an original, complete LiDAR dataset. For the accuracy of our statistical analysis, we perform Root Mean Square Error (RMSE comparing all of the grid points of the original DEM to the DEM generated by reduced data, instead of comparing a few random control points. Besides, our multi-core data reduction algorithm is highly scalable. We also describe a modified parallel Inverse Distance Weighted (IDW spatial interpolation method and show that the DEMs it generates are time-efficient and have better accuracy than the one’s generated by the traditional IDW method.

Performance testing of LiDAR exploitation software

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Varela-González, M.; González-Jorge, H.; Riveiro, B.; Arias, P.

2013-04-01

Mobile LiDAR systems are being used widely in recent years for many applications in the field of geoscience. One of most important limitations of this technology is the large computational requirements involved in data processing. Several software solutions for data processing are available in the market, but users are often unknown about the methodologies to verify their performance accurately. In this work a methodology for LiDAR software performance testing is presented and six different suites are studied: QT Modeler, AutoCAD Civil 3D, Mars 7, Fledermaus, Carlson and TopoDOT (all of them in x64). Results depict as QTModeler, TopoDOT and AutoCAD Civil 3D allow the loading of large datasets, while Fledermaus, Mars7 and Carlson do not achieve these powerful performance. AutoCAD Civil 3D needs large loading time in comparison with the most powerful softwares such as QTModeler and TopoDOT. Carlson suite depicts the poorest results among all the softwares under study, where point clouds larger than 5 million points cannot be loaded and loading time is very large in comparison with the other suites even for the smaller datasets. AutoCAD Civil 3D, Carlson and TopoDOT show more threads than other softwares like QTModeler, Mars7 and Fledermaus.

APPLICABILITY ANALYSIS OF CLOTH SIMULATION FILTERING ALGORITHM FOR MOBILE LIDAR POINT CLOUD

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S. Cai

2018-04-01

Full Text Available Classifying the original point clouds into ground and non-ground points is a key step in LiDAR (light detection and ranging data post-processing. Cloth simulation filtering (CSF algorithm, which based on a physical process, has been validated to be an accurate, automatic and easy-to-use algorithm for airborne LiDAR point cloud. As a new technique of three-dimensional data collection, the mobile laser scanning (MLS has been gradually applied in various fields, such as reconstruction of digital terrain models (DTM, 3D building modeling and forest inventory and management. Compared with airborne LiDAR point cloud, there are some different features (such as point density feature, distribution feature and complexity feature for mobile LiDAR point cloud. Some filtering algorithms for airborne LiDAR data were directly used in mobile LiDAR point cloud, but it did not give satisfactory results. In this paper, we explore the ability of the CSF algorithm for mobile LiDAR point cloud. Three samples with different shape of the terrain are selected to test the performance of this algorithm, which respectively yields total errors of 0.44 %, 0.77 % and1.20 %. Additionally, large area dataset is also tested to further validate the effectiveness of this algorithm, and results show that it can quickly and accurately separate point clouds into ground and non-ground points. In summary, this algorithm is efficient and reliable for mobile LiDAR point cloud.

Novel Methods for Measuring LiDAR

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Ayrey, E.; Hayes, D. J.; Fraver, S.; Weiskittel, A.; Cook, B.; Kershaw, J.

2017-12-01

The estimation of forest biometrics from airborne LiDAR data has become invaluable for quantifying forest carbon stocks, forest and wildlife ecology research, and sustainable forest management. The area-based approach is arguably the most common method for developing enhanced forest inventories from LiDAR. It involves taking a series of vertical height measurements of the point cloud, then using those measurements with field measured data to develop predictive models. Unfortunately, there is considerable variation in methodology for collecting point cloud data, which can vary in pulse density, seasonality, canopy penetrability, and instrument specifications. Today there exists a wealth of public LiDAR data, however the variation in acquisition parameters makes forest inventory prediction by traditional means unreliable across the different datasets. The goal of this project is to test a series of novel point cloud measurements developed along a conceptual spectrum of human interpretability, and then to use the best measurements to develop regional enhanced forest inventories on Northern New England's and Atlantic Canada's public LiDAR. Similarly to a field-based inventory, individual tree crowns are being segmented, and summary statistics are being used as covariates. Established competition and structural indices are being generated using each tree's relationship to one another, whilst existing allometric equations are being used to estimate diameter and biomass of each tree measured in the LiDAR. Novel metrics measuring light interception, clusteredness, and rugosity are also being measured as predictors. On the other end of the human interpretability spectrum, convolutional neural networks are being employed to directly measure both the canopy height model, and the point clouds by scanning each using two and three dimensional kernals trained to identify features useful for predicting biological attributes such as biomass. Predictive models will be trained and

Fusion of multi-temporal Airborne Snow Observatory (ASO) lidar data for mountainous vegetation ecosystems studies.

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Ferraz, A.; Painter, T. H.; Saatchi, S.; Bormann, K. J.

2016-12-01

Fusion of multi-temporal Airborne Snow Observatory (ASO) lidar data for mountainous vegetation ecosystems studies The NASA Jet Propulsion Laboratory developed the Airborne Snow Observatory (ASO), a coupled scanning lidar system and imaging spectrometer, to quantify the spatial distribution of snow volume and dynamics over mountains watersheds (Painter et al., 2015). To do this, ASO weekly over-flights mountainous areas during snowfall and snowmelt seasons. In addition, there are additional flights in snow-off conditions to calculate Digital Terrain Models (DTM). In this study, we focus on the reliability of ASO lidar data to characterize the 3D forest vegetation structure. The density of a single point cloud acquisition is of nearly 1 pt/m2, which is not optimal to properly characterize vegetation. However, ASO covers a given study site up to 14 times a year that enables computing a high-resolution point cloud by merging single acquisitions. In this study, we present a method to automatically register ASO multi-temporal lidar 3D point clouds. Although flight specifications do not change between acquisition dates, lidar datasets might have significant planimetric shifts due to inaccuracies in platform trajectory estimation introduced by the GPS system and drifts of the IMU. There are a large number of methodologies that address the problem of 3D data registration (Gressin et al., 2013). Briefly, they look for common primitive features in both datasets such as buildings corners, structures like electric poles, DTM breaklines or deformations. However, they are not suited for our experiment. First, single acquisition point clouds have low density that makes the extraction of primitive features difficult. Second, the landscape significantly changes between flights due to snowfall and snowmelt. Therefore, we developed a method to automatically register point clouds using tree apexes as keypoints because they are features that are supposed to experience little change

Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods

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Preston Hartzell

2013-05-01

Full Text Available Terrestrial laser scanners are sophisticated instruments that operate much like high-speed total stations. It has previously been shown that unmodelled systematic errors can exist in modern terrestrial laser scanners that deteriorate their geometric measurement precision and accuracy. Typically, signalised targets are used in point-based self-calibrations to identify and model the systematic errors. Although this method has proven its effectiveness, a large quantity of signalised targets is required and is therefore labour-intensive and limits its practicality. In recent years, feature-based self-calibration of aerial, mobile terrestrial, and static terrestrial laser scanning systems has been demonstrated. In this paper, the commonalities and differences between point-based and plane-based self-calibration (in terms of model identification and parameter correlation are explored. The results of this research indicate that much of the knowledge from point-based self-calibration can be directly transferred to plane-based calibration and that the two calibration approaches are nearly equivalent. New network configurations, such as the inclusion of tilted scans, were also studied and prove to be an effective means for strengthening the self-calibration solution, and improved recoverability of the horizontal collimation axis error for hybrid scanners, which has always posed a challenge in the past.

Optimizing the Use of LiDAR for Hydraulic and Sediment Transport Model Development: Case Studies from Marin and Sonoma Counties, CA

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Kobor, J. S.; O'Connor, M. D.; Sherwood, M. N.

2013-12-01

Effective floodplain management and restoration requires a detailed understanding of floodplain processes not readily achieved using standard one-dimensional hydraulic modeling approaches. The application of more advanced numerical models is, however, often limited by the relatively high costs of acquiring the high-resolution topographic data needed for model development using traditional surveying methods. The increasing availability of LiDAR data has the potential to significantly reduce these costs and thus facilitate application of multi-dimensional hydraulic models where budget constraints would have otherwise prohibited their use. The accuracy and suitability of LiDAR data for supporting model development can vary widely depending on the resolution of channel and floodplain features, the data collection density, and the degree of vegetation canopy interference among other factors. More work is needed to develop guidelines for evaluating LiDAR accuracy and determining when and how best the data can be used to support numerical modeling activities. Here we present two recent case studies where LiDAR datasets were used to support floodplain and sediment transport modeling efforts. One LiDAR dataset was collected with a relatively low point density and used to study a small stream channel in coastal Marin County and a second dataset was collected with a higher point density and applied to a larger stream channel in western Sonoma County. Traditional topographic surveying was performed at both sites which provided a quantitative means of evaluating the LiDAR accuracy. We found that with the lower point density dataset, the accuracy of the LiDAR varied significantly between the active stream channel and floodplain whereas the accuracy across the channel/floodplain interface was more uniform with the higher density dataset. Accuracy also varied widely as a function of the density of the riparian vegetation canopy. We found that coupled 1- and 2-dimensional hydraulic

Water Mapping Using Multispectral Airborne LIDAR Data

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Yan, W. Y.; Shaker, A.; LaRocque, P. E.

2018-04-01

This study investigates the use of the world's first multispectral airborne LiDAR sensor, Optech Titan, manufactured by Teledyne Optech to serve the purpose of automatic land-water classification with a particular focus on near shore region and river environment. Although there exist recent studies utilizing airborne LiDAR data for shoreline detection and water surface mapping, the majority of them only perform experimental testing on clipped data subset or rely on data fusion with aerial/satellite image. In addition, most of the existing approaches require manual intervention or existing tidal/datum data for sample collection of training data. To tackle the drawbacks of previous approaches, we propose and develop an automatic data processing workflow for land-water classification using multispectral airborne LiDAR data. Depending on the nature of the study scene, two methods are proposed for automatic training data selection. The first method utilizes the elevation/intensity histogram fitted with Gaussian mixture model (GMM) to preliminarily split the land and water bodies. The second method mainly relies on the use of a newly developed scan line elevation intensity ratio (SLIER) to estimate the water surface data points. Regardless of the training methods being used, feature spaces can be constructed using the multispectral LiDAR intensity, elevation and other features derived from these parameters. The comprehensive workflow was tested with two datasets collected for different near shore region and river environment, where the overall accuracy yielded better than 96 %.

2005/2006 Southwest Florida Water Management District (SWFWMD) Lidar: Peace River South (including Carter Creek)

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National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) LAS dataset is a survey of select areas within Southwest Florida. These data were produced for the Southwest Florida Water...

Enhancing Conservation with High Resolution Productivity Datasets for the Conterminous United States

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Robinson, Nathaniel Paul

Human driven alteration of the earth's terrestrial surface is accelerating through land use changes, intensification of human activity, climate change, and other anthropogenic pressures. These changes occur at broad spatio-temporal scales, challenging our ability to effectively monitor and assess the impacts and subsequent conservation strategies. While satellite remote sensing (SRS) products enable monitoring of the earth's terrestrial surface continuously across space and time, the practical applications for conservation and management of these products are limited. Often the processes driving ecological change occur at fine spatial resolutions and are undetectable given the resolution of available datasets. Additionally, the links between SRS data and ecologically meaningful metrics are weak. Recent advances in cloud computing technology along with the growing record of high resolution SRS data enable the development of SRS products that quantify ecologically meaningful variables at relevant scales applicable for conservation and management. The focus of my dissertation is to improve the applicability of terrestrial gross and net primary productivity (GPP/NPP) datasets for the conterminous United States (CONUS). In chapter one, I develop a framework for creating high resolution datasets of vegetation dynamics. I use the entire archive of Landsat 5, 7, and 8 surface reflectance data and a novel gap filling approach to create spatially continuous 30 m, 16-day composites of the normalized difference vegetation index (NDVI) from 1986 to 2016. In chapter two, I integrate this with other high resolution datasets and the MOD17 algorithm to create the first high resolution GPP and NPP datasets for CONUS. I demonstrate the applicability of these products for conservation and management, showing the improvements beyond currently available products. In chapter three, I utilize this dataset to evaluate the relationships between land ownership and terrestrial production

An error reduction algorithm to improve lidar turbulence estimates for wind energy

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J. F. Newman

2017-02-01

-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

Comparison of the filtering models for airborne LiDAR data by three classifiers with exploration on model transfer

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Ma, Hongchao; Cai, Zhan; Zhang, Liang

2018-01-01

This paper discusses airborne light detection and ranging (LiDAR) point cloud filtering (a binary classification problem) from the machine learning point of view. We compared three supervised classifiers for point cloud filtering, namely, Adaptive Boosting, support vector machine, and random forest (RF). Nineteen features were generated from raw LiDAR point cloud based on height and other geometric information within a given neighborhood. The test datasets issued by the International Society for Photogrammetry and Remote Sensing (ISPRS) were used to evaluate the performance of the three filtering algorithms; RF showed the best results with an average total error of 5.50%. The paper also makes tentative exploration in the application of transfer learning theory to point cloud filtering, which has not been introduced into the LiDAR field to the authors' knowledge. We performed filtering of three datasets from real projects carried out in China with RF models constructed by learning from the 15 ISPRS datasets and then transferred with little to no change of the parameters. Reliable results were achieved, especially in rural area (overall accuracy achieved 95.64%), indicating the feasibility of model transfer in the context of point cloud filtering for both easy automation and acceptable accuracy.

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

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Alireza G. Kashani

2015-11-01

Full Text Available In addition to precise 3D coordinates, most light detection and ranging (LIDAR systems also record “intensity”, loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of “normalization”, “correction”, or “calibration” techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration.

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Kashani, Alireza G; Olsen, Michael J; Parrish, Christopher E; Wilson, Nicholas

2015-11-06

In addition to precise 3D coordinates, most light detection and ranging (LIDAR) systems also record "intensity", loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of "normalization", "correction", or "calibration" techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.

Comparison of Surface Flow Features from Lidar-Derived Digital Elevation Models with Historical Elevation and Hydrography Data for Minnehaha County, South Dakota

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Poppenga, Sandra K.; Worstell, Bruce B.; Stoker, Jason M.; Greenlee, Susan K.

2009-01-01

The U.S. Geological Survey (USGS) has taken the lead in the creation of a valuable remote sensing product by incorporating digital elevation models (DEMs) derived from Light Detection and Ranging (lidar) into the National Elevation Dataset (NED), the elevation layer of 'The National Map'. High-resolution lidar-derived DEMs provide the accuracy needed to systematically quantify and fully integrate surface flow including flow direction, flow accumulation, sinks, slope, and a dense drainage network. In 2008, 1-meter resolution lidar data were acquired in Minnehaha County, South Dakota. The acquisition was a collaborative effort between Minnehaha County, the city of Sioux Falls, and the USGS Earth Resources Observation and Science (EROS) Center. With the newly acquired lidar data, USGS scientists generated high-resolution DEMs and surface flow features. This report compares lidar-derived surface flow features in Minnehaha County to 30- and 10-meter elevation data previously incorporated in the NED and ancillary hydrography datasets. Surface flow features generated from lidar-derived DEMs are consistently integrated with elevation and are important in understanding surface-water movement to better detect surface-water runoff, flood inundation, and erosion. Many topographic and hydrologic applications will benefit from the increased availability of accurate, high-quality, and high-resolution surface-water data. The remotely sensed data provide topographic information and data integration capabilities needed for meeting current and future human and environmental needs.

Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network

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Hasan Asy’ari Arief

2018-06-01

Full Text Available Inspired by the success of deep learning techniques in dense-label prediction and the increasing availability of high precision airborne light detection and ranging (LiDAR data, we present a research process that compares a collection of well-proven semantic segmentation architectures based on the deep learning approach. Our investigation concludes with the proposition of some novel deep learning architectures for generating detailed land resource maps by employing a semantic segmentation approach. The contribution of our work is threefold. (1 First, we implement the multiclass version of the intersection-over-union (IoU loss function that contributes to handling highly imbalanced datasets and preventing overfitting. (2 Thereafter, we propose a novel deep learning architecture integrating the deep atrous network architecture with the stochastic depth approach for speeding up the learning process, and impose a regularization effect. (3 Finally, we introduce an early fusion deep layer that combines image-based and LiDAR-derived features. In a benchmark study carried out using the Follo 2014 LiDAR data and the NIBIO AR5 land resources dataset, we compare our proposals to other deep learning architectures. A quantitative comparison shows that our best proposal provides more than 5% relative improvement in terms of mean intersection-over-union over the atrous network, providing a basis for a more frequent and improved use of LiDAR data for automatic land cover segmentation.

Automatic Matching of Large Scale Images and Terrestrial LIDAR Based on App Synergy of Mobile Phone

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Xia, G.; Hu, C.

2018-04-01

The digitalization of Cultural Heritage based on ground laser scanning technology has been widely applied. High-precision scanning and high-resolution photography of cultural relics are the main methods of data acquisition. The reconstruction with the complete point cloud and high-resolution image requires the matching of image and point cloud, the acquisition of the homonym feature points, the data registration, etc. However, the one-to-one correspondence between image and corresponding point cloud depends on inefficient manual search. The effective classify and management of a large number of image and the matching of large image and corresponding point cloud will be the focus of the research. In this paper, we propose automatic matching of large scale images and terrestrial LiDAR based on APP synergy of mobile phone. Firstly, we develop an APP based on Android, take pictures and record related information of classification. Secondly, all the images are automatically grouped with the recorded information. Thirdly, the matching algorithm is used to match the global and local image. According to the one-to-one correspondence between the global image and the point cloud reflection intensity image, the automatic matching of the image and its corresponding laser radar point cloud is realized. Finally, the mapping relationship between global image, local image and intensity image is established according to homonym feature point. So we can establish the data structure of the global image, the local image in the global image, the local image corresponding point cloud, and carry on the visualization management and query of image.

Line-Based Registration of Panoramic Images and LiDAR Point Clouds for Mobile Mapping

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Tingting Cui

2016-12-01

Full Text Available For multi-sensor integrated systems, such as the mobile mapping system (MMS, data fusion at sensor-level, i.e., the 2D-3D registration between an optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and a LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for the panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and the LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, with the latter being more reliable.

Retrievals of Aerosol Microphysics from Simulations of Spaceborne Multiwavelength Lidar Measurements

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Whiteman, David N.; Perez-Ramírez, Daniel; Veselovskii, Igor; Colarco, Peter; Buchard, Virginie

2017-01-01

In support of the Aerosol, Clouds, Ecosystems mission, simulations of a spaceborne multiwavelength lidar are performed based on global model simulations of the atmosphere along a satellite orbit track. The yield for aerosol microphysical inversions is quantified and comparisons are made between the aerosol microphysics inherent in the global model and those inverted from both the model's optical data and the simulated three backscatter and two extinction lidar measurements, which are based on the model's optical data. We find that yield can be significantly increased if inversions based on a reduced optical dataset of three backscatter and one extinction are acceptable. In general, retrieval performance is better for cases where the aerosol fine mode dominates although a lack of sensitivity to particles with sizes less than 0.1 microns is found. Lack of sensitivity to coarse mode cases is also found, in agreement with earlier studies. Surface area is generally the most robustly retrieved quantity. The work here points toward the need for ancillary data to aid in the constraints of the lidar inversions and also for joint inversions involving lidar and polarimeter measurements.

Doppler Lidar Vector Retrievals and Atmospheric Data Visualization in Mixed/Augmented Reality

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Cherukuru, Nihanth Wagmi

Environmental remote sensing has seen rapid growth in the recent years and Doppler wind lidars have gained popularity primarily due to their non-intrusive, high spatial and temporal measurement capabilities. While lidar applications early on, relied on the radial velocity measurements alone, most of the practical applications in wind farm control and short term wind prediction require knowledge of the vector wind field. Over the past couple of years, multiple works on lidars have explored three primary methods of retrieving wind vectors viz., using homogeneous windfield assumption, computationally extensive variational methods and the use of multiple Doppler lidars. Building on prior research, the current three-part study, first demonstrates the capabilities of single and dual Doppler lidar retrievals in capturing downslope windstorm-type flows occurring at Arizona's Barringer Meteor Crater as a part of the METCRAX II field experiment. Next, to address the need for a reliable and computationally efficient vector retrieval for adaptive wind farm control applications, a novel 2D vector retrieval based on a variational formulation was developed and applied on lidar scans from an offshore wind farm and validated with data from a cup and vane anemometer installed on a nearby research platform. Finally, a novel data visualization technique using Mixed Reality (MR)/ Augmented Reality (AR) technology is presented to visualize data from atmospheric sensors. MR is an environment in which the user's visual perception of the real world is enhanced with live, interactive, computer generated sensory input (in this case, data from atmospheric sensors like Doppler lidars). A methodology using modern game development platforms is presented and demonstrated with lidar retrieved wind fields. In the current study, the possibility of using this technology to visualize data from atmospheric sensors in mixed reality is explored and demonstrated with lidar retrieved wind fields as well as

Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar

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Hao Tang

2016-09-01

Full Text Available Airborne single-photon lidar (SPL is a new technology that holds considerable potential for forest structure and carbon monitoring at large spatial scales because it acquires 3D measurements of vegetation faster and more efficiently than conventional lidar instruments. However, SPL instruments use green wavelength (532 nm lasers, which are sensitive to background solar noise, and therefore SPL point clouds require more elaborate noise filtering than other lidar instruments to determine canopy heights, particularly in daytime acquisitions. Histogram-based aggregation is a commonly used approach for removing noise from photon counting lidar data, but it reduces the resolution of the dataset. Here we present an alternate voxel-based spatial filtering method that filters noise points efficiently while largely preserving the spatial integrity of SPL data. We develop and test our algorithms on an experimental SPL dataset acquired over Garrett County in Maryland, USA. We then compare canopy attributes retrieved using our new algorithm with those obtained from the conventional histogram binning approach. Our results show that canopy heights derived using the new algorithm have a strong agreement with field-measured heights (r2 = 0.69, bias = 0.42 m, RMSE = 4.85 m and discrete return lidar heights (r2 = 0.94, bias = 1.07 m, RMSE = 2.42 m. Results are consistently better than height accuracies from the histogram method (field data: r2 = 0.59, bias = 0.00 m, RMSE = 6.25 m; DRL: r2 = 0.78, bias = −0.06 m and RMSE = 4.88 m. Furthermore, we find that the spatial-filtering method retains fine-scale canopy structure detail and has lower errors over steep slopes. We therefore believe that automated spatial filtering algorithms such as the one presented here can support large-scale, canopy structure mapping from airborne SPL data.

Analysis of lidar elevation data for improved identification and delineation of lands vulnerable to sea-level rise

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Gesch, Dean B.

2009-01-01

The importance of sea-level rise in shaping coastal landscapes is well recognized within the earth science community, but as with many natural hazards, communicating the risks associated with sea-level rise remains a challenge. Topography is a key parameter that influences many of the processes involved in coastal change, and thus, up-to-date, high-resolution, high-accuracy elevation data are required to model the coastal environment. Maps of areas subject to potential inundation have great utility to planners and managers concerned with the effects of sea-level rise. However, most of the maps produced to date are simplistic representations derived from older, coarse elevation data. In the last several years, vast amounts of high quality elevation data derived from lidar have become available. Because of their high vertical accuracy and spatial resolution, these lidar data are an excellent source of up-to-date information from which to improve identification and delineation of vulnerable lands. Four elevation datasets of varying resolution and accuracy were processed to demonstrate that the improved quality of lidar data leads to more precise delineation of coastal lands vulnerable to inundation. A key component of the comparison was to calculate and account for the vertical uncertainty of the elevation datasets. This comparison shows that lidar allows for a much more detailed delineation of the potential inundation zone when compared to other types of elevation models. It also shows how the certainty of the delineation of lands vulnerable to a given sea-level rise scenario is much improved when derived from higher resolution lidar data.

Detection and classification of pole-like road objects from mobile LiDAR data in motorway environment

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Yan, Li; Li, Zan; Liu, Hua; Tan, Junxiang; Zhao, Sainan; Chen, Changjun

2017-12-01

Mobile LiDAR Scanning (MLS) can collect 3-dimensional (3D) road and road-related geospatial information accurately and efficiently. Pole-like objects located in road environment are important street furniture and they are necessary information in road inventory and road mapping. The automatic detection and classification of pole-like road objects from mobile LiDAR data can greatly reduce the cost and improve the efficiency. This paper provides a complete workflow for the detection and classification of pole-like road objects from mobile LiDAR data in motorway environment. The major workflow includes three steps: data preprocessing, pole-like road objects detection and pole-like road objects classification. In data preprocessing step, ground points are removed by an automatic ground filtering algorithm, and then off-ground points are clustered into segments and the overlapped segments containing pole-like road objects are further separated through an iterative min-cut based segmentation approach. In detection step, filters utilizing both prior and shape information are used to detect the target objects. In classification step, features of objects are calculated and classified using Random Forest classifier. Our method was tested on two datasets scanned in motorway environment, and the results showed that the Matthews correlation coefficient of the two datasets in detection step was 93.7% and 95.9% respectively and the overall accuracy of the two datasets in classification step was 96.5% and 97.9% respectively.

Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes

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Buffington, Kevin J.; Dugger, Bruce D.; Thorne, Karen M.; Takekawa, John Y.

2016-01-01

Airborne light detection and ranging (lidar) is a valuable tool for collecting large amounts of elevation data across large areas; however, the limited ability to penetrate dense vegetation with lidar hinders its usefulness for measuring tidal marsh platforms. Methods to correct lidar elevation data are available, but a reliable method that requires limited field work and maintains spatial resolution is lacking. We present a novel method, the Lidar Elevation Adjustment with NDVI (LEAN), to correct lidar digital elevation models (DEMs) with vegetation indices from readily available multispectral airborne imagery (NAIP) and RTK-GPS surveys. Using 17 study sites along the Pacific coast of the U.S., we achieved an average root mean squared error (RMSE) of 0.072 m, with a 40–75% improvement in accuracy from the lidar bare earth DEM. Results from our method compared favorably with results from three other methods (minimum-bin gridding, mean error correction, and vegetation correction factors), and a power analysis applying our extensive RTK-GPS dataset showed that on average 118 points were necessary to calibrate a site-specific correction model for tidal marshes along the Pacific coast. By using available imagery and with minimal field surveys, we showed that lidar-derived DEMs can be adjusted for greater accuracy while maintaining high (1 m) resolution.

LIDAR e Fotogrammetria Digitale verso una nuova integrazione

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Fulvio Rinaudo

2012-04-01

Full Text Available In tutte le applicazioni del rilevo metrico la tecnica LIDAR e la Fotogrammetria Digitale giocano ormai un ruoloquasi esclusivo. Gli efficienti servizi forniti dalle reti di stazioni permanenti e dalle piattaforme GNSS/IMU hannoconsentito una rapida diffusione e una affidabilità mai raggiunta per altre tecniche di rilievo in ambito territoriale,così come pure nelle applicazioni terrestri quali il rilievo architettonico o il monitoraggio di fenomeni naturali, ovesono stati sostituiti quasi integralmente gli approcci tradizionali del rilievo topografico terrestre.LIDAR and Digital Photogrammetry towards a new integration A new integration approach between digital photogrammetry and LiDAR is presented in this paper. Up to now LiDAR use some  photogrammetric  topics  to  manage  point  clouds  and digital photogrammetry extract useful information from LiDAR data to produce traditional and accurate orthophoto. Considering that in traditional photogrammetric plotting the surveyor define the 3D location of the break-lines and add points just in those parts of the object where smooth surfaces can be defined, the proposed solution foresees a continuous exchange of information between those techniques in order to automatically extract the break-lines of the surveyed object. The final results  is  the  complete  breack-lines  3D  description  and  the point clouds portions useful to define smooth surfaces.  At the end of the automatic procedure a complete 3D set of information is produced in order to build up the fine 3D model of the surveyed object.

LIDAR e Fotogrammetria Digitale verso una nuova integrazione

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Fulvio Rinaudo

2012-04-01

Full Text Available In tutte le applicazioni del rilevo metrico la tecnica LIDAR e la Fotogrammetria Digitale giocano ormai un ruoloquasi esclusivo. Gli efficienti servizi forniti dalle reti di stazioni permanenti e dalle piattaforme GNSS/IMU hannoconsentito una rapida diffusione e una affidabilità mai raggiunta per altre tecniche di rilievo in ambito territoriale,così come pure nelle applicazioni terrestri quali il rilievo architettonico o il monitoraggio di fenomeni naturali, ovesono stati sostituiti quasi integralmente gli approcci tradizionali del rilievo topografico terrestre. LIDAR and Digital Photogrammetry towards a new integration A new integration approach between digital photogrammetry and LiDAR is presented in this paper. Up to now LiDAR use some  photogrammetric  topics  to  manage  point  clouds  and digital photogrammetry extract useful information from LiDAR data to produce traditional and accurate orthophoto. Considering that in traditional photogrammetric plotting the surveyor define the 3D location of the break-lines and add points just in those parts of the object where smooth surfaces can be defined, the proposed solution foresees a continuous exchange of information between those techniques in order to automatically extract the break-lines of the surveyed object. The final results  is  the  complete  breack-lines  3D  description  and  the point clouds portions useful to define smooth surfaces.  At the end of the automatic procedure a complete 3D set of information is produced in order to build up the fine 3D model of the surveyed object.

Rockfall hazard analysis using LiDAR and spatial modeling

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Lan, Hengxing; Martin, C. Derek; Zhou, Chenghu; Lim, Chang Ho

2010-05-01

Rockfalls have been significant geohazards along the Canadian Class 1 Railways (CN Rail and CP Rail) since their construction in the late 1800s. These rockfalls cause damage to infrastructure, interruption of business, and environmental impacts, and their occurrence varies both spatially and temporally. The proactive management of these rockfall hazards requires enabling technologies. This paper discusses a hazard assessment strategy for rockfalls along a section of a Canadian railway using LiDAR and spatial modeling. LiDAR provides accurate topographical information of the source area of rockfalls and along their paths. Spatial modeling was conducted using Rockfall Analyst, a three dimensional extension to GIS, to determine the characteristics of the rockfalls in terms of travel distance, velocity and energy. Historical rockfall records were used to calibrate the physical characteristics of the rockfall processes. The results based on a high-resolution digital elevation model from a LiDAR dataset were compared with those based on a coarse digital elevation model. A comprehensive methodology for rockfall hazard assessment is proposed which takes into account the characteristics of source areas, the physical processes of rockfalls and the spatial attribution of their frequency and energy.

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

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Carlos Alberto Silva

2017-10-01

Full Text Available Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Pará, Brazil. Field-derived AGB was computed at 85 square 50 × 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses·m−2 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses·m−2. For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg·ha−1 when pulse density decreased from 12 to 0.2 pulses·m−2. The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg·ha−1. The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national

Simulating Various Terrestrial and Uav LIDAR Scanning Configurations for Understory Forest Structure Modelling

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Hämmerle, M.; Lukač, N.; Chen, K.-C.; Koma, Zs.; Wang, C.-K.; Anders, K.; Höfle, B.

2017-09-01

Information about the 3D structure of understory vegetation is of high relevance in forestry research and management (e.g., for complete biomass estimations). However, it has been hardly investigated systematically with state-of-the-art methods such as static terrestrial laser scanning (TLS) or laser scanning from unmanned aerial vehicle platforms (ULS). A prominent challenge for scanning forests is posed by occlusion, calling for proper TLS scan position or ULS flight line configurations in order to achieve an accurate representation of understory vegetation. The aim of our study is to examine the effect of TLS or ULS scanning strategies on (1) the height of individual understory trees and (2) understory canopy height raster models. We simulate full-waveform TLS and ULS point clouds of a virtual forest plot captured from various combinations of max. 12 TLS scan positions or 3 ULS flight lines. The accuracy of the respective datasets is evaluated with reference values given by the virtually scanned 3D triangle mesh tree models. TLS tree height underestimations range up to 1.84 m (15.30 % of tree height) for single TLS scan positions, but combining three scan positions reduces the underestimation to maximum 0.31 m (2.41 %). Combining ULS flight lines also results in improved tree height representation, with a maximum underestimation of 0.24 m (2.15 %). The presented simulation approach offers a complementary source of information for efficient planning of field campaigns aiming at understory vegetation modelling.

SIMULATING VARIOUS TERRESTRIAL AND UAV LIDAR SCANNING CONFIGURATIONS FOR UNDERSTORY FOREST STRUCTURE MODELLING

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M. Hämmerle

2017-09-01

Full Text Available Information about the 3D structure of understory vegetation is of high relevance in forestry research and management (e.g., for complete biomass estimations. However, it has been hardly investigated systematically with state-of-the-art methods such as static terrestrial laser scanning (TLS or laser scanning from unmanned aerial vehicle platforms (ULS. A prominent challenge for scanning forests is posed by occlusion, calling for proper TLS scan position or ULS flight line configurations in order to achieve an accurate representation of understory vegetation. The aim of our study is to examine the effect of TLS or ULS scanning strategies on (1 the height of individual understory trees and (2 understory canopy height raster models. We simulate full-waveform TLS and ULS point clouds of a virtual forest plot captured from various combinations of max. 12 TLS scan positions or 3 ULS flight lines. The accuracy of the respective datasets is evaluated with reference values given by the virtually scanned 3D triangle mesh tree models. TLS tree height underestimations range up to 1.84 m (15.30 % of tree height for single TLS scan positions, but combining three scan positions reduces the underestimation to maximum 0.31 m (2.41 %. Combining ULS flight lines also results in improved tree height representation, with a maximum underestimation of 0.24 m (2.15 %. The presented simulation approach offers a complementary source of information for efficient planning of field campaigns aiming at understory vegetation modelling.

Voxel-Based LIDAR Analysis and Applications

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Hagstrom, Shea T.

One of the greatest recent changes in the field of remote sensing is the addition of high-quality Light Detection and Ranging (LIDAR) instruments. In particular, the past few decades have been greatly beneficial to these systems because of increases in data collection speed and accuracy, as well as a reduction in the costs of components. These improvements allow modern airborne instruments to resolve sub-meter details, making them ideal for a wide variety of applications. Because LIDAR uses active illumination to capture 3D information, its output is fundamentally different from other modalities. Despite this difference, LIDAR datasets are often processed using methods appropriate for 2D images and that do not take advantage of its primary virtue of 3-dimensional data. It is this problem we explore by using volumetric voxel modeling. Voxel-based analysis has been used in many applications, especially medical imaging, but rarely in traditional remote sensing. In part this is because the memory requirements are substantial when handling large areas, but with modern computing and storage this is no longer a significant impediment. Our reason for using voxels to model scenes from LIDAR data is that there are several advantages over standard triangle-based models, including better handling of overlapping surfaces and complex shapes. We show how incorporating system position information from early in the LIDAR point cloud generation process allows radiometrically-correct transmission and other novel voxel properties to be recovered. This voxelization technique is validated on simulated data using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, a first-principles based ray-tracer developed at the Rochester Institute of Technology. Voxel-based modeling of LIDAR can be useful on its own, but we believe its primary advantage is when applied to problems where simpler surface-based 3D models conflict with the requirement of realistic geometry. To

Ensemble classification of individual Pinus crowns from multispectral satellite imagery and airborne LiDAR

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Kukunda, Collins B.; Duque-Lazo, Joaquín; González-Ferreiro, Eduardo; Thaden, Hauke; Kleinn, Christoph

2018-03-01

Distinguishing tree species is relevant in many contexts of remote sensing assisted forest inventory. Accurate tree species maps support management and conservation planning, pest and disease control and biomass estimation. This study evaluated the performance of applying ensemble techniques with the goal of automatically distinguishing Pinus sylvestris L. and Pinus uncinata Mill. Ex Mirb within a 1.3 km2 mountainous area in Barcelonnette (France). Three modelling schemes were examined, based on: (1) high-density LiDAR data (160 returns m-2), (2) Worldview-2 multispectral imagery, and (3) Worldview-2 and LiDAR in combination. Variables related to the crown structure and height of individual trees were extracted from the normalized LiDAR point cloud at individual-tree level, after performing individual tree crown (ITC) delineation. Vegetation indices and the Haralick texture indices were derived from Worldview-2 images and served as independent spectral variables. Selection of the best predictor subset was done after a comparison of three variable selection procedures: (1) Random Forests with cross validation (AUCRFcv), (2) Akaike Information Criterion (AIC) and (3) Bayesian Information Criterion (BIC). To classify the species, 9 regression techniques were combined using ensemble models. Predictions were evaluated using cross validation and an independent dataset. Integration of datasets and models improved individual tree species classification (True Skills Statistic, TSS; from 0.67 to 0.81) over individual techniques and maintained strong predictive power (Relative Operating Characteristic, ROC = 0.91). Assemblage of regression models and integration of the datasets provided more reliable species distribution maps and associated tree-scale mapping uncertainties. Our study highlights the potential of model and data assemblage at improving species classifications needed in present-day forest planning and management.

Illuminating wildfire erosion and deposition patterns with repeat terrestrial lidar

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Rengers, Francis K.; Tucker, G.E.; Moody, J.A.; Ebel, Brian

2016-01-01

Erosion following a wildfire is much greater than background erosion in forests because of wildfire-induced changes to soil erodibility and water infiltration. While many previous studies have documented post-wildfire erosion with point and small plot-scale measurements, the spatial distribution of post-fire erosion patterns at the watershed scale remains largely unexplored. In this study lidar surveys were collected periodically in a small, first-order drainage basin over a period of 2 years following a wildfire. The study site was relatively steep with slopes ranging from 17° to > 30°. During the study period, several different types of rain storms occurred on the site including low-intensity frontal storms (2.4 mm h−1) and high-intensity convective thunderstorms (79 mm h−1). These storms were the dominant drivers of erosion. Erosion resulting from dry ravel and debris flows was notably absent at the site. Successive lidar surveys were subtracted from one another to obtain digital maps of topographic change between surveys. The results show an evolution in geomorphic response, such that the erosional response after rain storms was strongly influenced by the previous erosional events and pre-fire site morphology. Hillslope and channel roughness increased over time, and the watershed armored as coarse cobbles and boulders were exposed. The erosional response was spatially nonuniform; shallow erosion from hillslopes (87% of the study area) contributed 3 times more sediment volume than erosion from convergent areas (13% of the study area). However, the total normalized erosion depth (volume/area) was highest in convergent areas. From a detailed understanding of the spatial locations of erosion, we made inferences regarding the processes driving erosion. It appears that hillslope erosion is controlled by rain splash (for detachment) and overland flow (for transport and quasi-channelized erosion), with the sites of highest erosion corresponding to locations

Multispectral LiDAR Data for Land Cover Classification of Urban Areas

Directory of Open Access Journals (Sweden)

Salem Morsy

2017-04-01

Full Text Available Airborne Light Detection And Ranging (LiDAR systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy.

Multispectral LiDAR Data for Land Cover Classification of Urban Areas.

Science.gov (United States)

Morsy, Salem; Shaker, Ahmed; El-Rabbany, Ahmed

2017-04-26

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy.

An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

Directory of Open Access Journals (Sweden)

Wuming Zhang

2016-06-01

Full Text Available Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs from airborne LiDAR (light detection and ranging data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. Within the proposed approach, a LiDAR point cloud is inverted, and then a rigid cloth is used to cover the inverted surface. By analyzing the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface. Finally, the ground points can be extracted from the LiDAR point cloud by comparing the original LiDAR points and the generated surface. Benchmark datasets provided by ISPRS (International Society for Photogrammetry and Remote Sensing working Group III/3 are used to validate the proposed filtering method, and the experimental results yield an average total error of 4.58%, which is comparable with most of the state-of-the-art filtering algorithms. The proposed easy-to-use filtering method may help the users without much experience to use LiDAR data and related technology in their own applications more easily.

Detecting Multi-layered Forest Stands Using High Density Airborne LiDAR Data. GI_Forum|GI_Forum 2015 – Geospatial Minds for Society|

OpenAIRE

Schultz, Alfred; Mund, Jan-Peter; Körner, Michael; Wilke, Robert

2016-01-01

Since two decades, the use of terrestrial laser scanning (TLS) and Airborne Light Detection and Ranging (LIDAR) has become very prominent in analysing 3D forest structures (AKAY et al. 2009). The potential of full waveform analysis of high density Airborne LiDAR data (ALS) for the detection and structural analysis of multi-layered forest stands is not yet well investigated (JASKIERNIAK et al. 2011), although ALS data provide exact information on tree heights of multi-layered forest stands usi...

CO-REGISTRATION OF PHOTOGRAMMETRIC AND LIDAR DATA: METHODOLOGY AND CASE STUDY

Directory of Open Access Journals (Sweden)

Mwafag Ghanma

2004-07-01

Full Text Available Registration activities combine data from different sources in order to attain higher accuracy and derive more information than available from one source. The increasing availability of a wide variety of sensors capable of capturing high quality and complementary data requires parallel efforts for developing accurate and robust registration techniques. Currently, photogrammetric and LIDAR systems are being incorporated in a wide spectrum of mapping applica¬tions such as city modeling, surface reconstruction, and object recognition. Photogrammetric processing of overlapping imagery provides accurate information regarding object space break-lines in addition to an explicit semantic description of the photographed objects. On the other hand, LIDAR systems supply dense geometric surface information in the form of non-selective points. Considering the properties of photogrammetric and LIDAR data, it is clear that the two technologies provide complementary information. However, the synergic characteristics of both systems can be fully utilized only after successful registration of the photogrammetric and LIDAR data relative to a common reference frame. The registration methodology has to deal with three issues: registration primitives, transformation function, and similarity measure. This paper presents two methodologies for utilizing straight-line features derived from both datasets as the registration primitives. The first methodology directly incorporates the LIDAR lines as control information in the photogrammetric triangulation. The second methodology starts by generating a photogrammetric model relative to an arbitrary datum. Then, LIDAR features are used as control information for the absolute orientation of the photogram¬metric model. In addition to the registration methodologies, the paper presents a comparative analysis between two approaches for extracting linear features from raw and processed/interpolated LIDAR data. Also, a comparative

Geotechnical applications of LiDAR pertaining to geomechanical evaluation and hazard identification

Science.gov (United States)

Lato, Matthew J.

Natural hazards related to ground movement that directly affect the safety of motorists and highway infrastructure include, but are not limited to, rockfalls, rockslides, debris flows, and landslides. This thesis specifically deals with the evaluation of rockfall hazards through the evaluation of LiDAR data. Light Detection And Ranging (LiDAR) is an imaging technology that can be used to delineate and evaluate geomechanically-controlled hazards. LiDAR has been adopted to conduct hazard evaluations pertaining to rockfall, rock-avalanches, debris flows, and landslides. Characteristics of LiDAR surveying, such as rapid data acquisition rates, mobile data collection, and high data densities, pose problems to traditional CAD or GIS-based mapping methods. New analyses methods, including tools specifically oriented to geomechanical analyses, are needed. The research completed in this thesis supports development of new methods, including improved survey techniques, innovative software workflows, and processing algorithms to aid in the detection and evaluation of geomechanically controlled rockfall hazards. The scientific research conducted between the years of 2006-2010, as presented in this thesis, are divided into five chapters, each of which has been published by or is under review by an international journal. The five research foci are: (i) geomechanical feature extraction and analysis using LiDAR data in active mining environments; (ii) engineered monitoring of rockfall hazards along transportation corridors: using mobile terrestrial LiDAR; (iii) optimization of LiDAR scanning and processing for automated structural evaluation of discontinuities in rockmasses; (iv) location orientation bias when using static LiDAR data for geomechanical analysis; and (v) evaluating roadside rockmasses for rockfall hazards from LiDAR data: optimizing data collection and processing protocols. The research conducted pertaining to this thesis has direct and significant implications with

Lidar calibration experiments

DEFF Research Database (Denmark)

Ejsing Jørgensen, Hans; Mikkelsen, T.; Streicher, J.

1997-01-01

detection to test the reproducibility and uncertainty of lidars. Lidar data were obtained from both single-ended and double-ended Lidar configurations. A backstop was introduced in one of the experiments and a new method was developed where information obtained from the backstop can be used in the inversion...... algorithm. Independent in-situ aerosol plume concentrations were obtained from a simultaneous tracer gas experiment with SF6, and comparisons with the two lidars were made. The study shows that the reproducibility of the lidars is within 15%, including measurements from both sides of a plume...

A conceptual prototype for the next-generation national elevation dataset

Science.gov (United States)

Stoker, Jason M.; Heidemann, Hans Karl; Evans, Gayla A.; Greenlee, Susan K.

2013-01-01

In 2012 the U.S. Geological Survey's (USGS) National Geospatial Program (NGP) funded a study to develop a conceptual prototype for a new National Elevation Dataset (NED) design with expanded capabilities to generate and deliver a suite of bare earth and above ground feature information over the United States. This report details the research on identifying operational requirements based on prior research, evaluation of what is needed for the USGS to meet these requirements, and development of a possible conceptual framework that could potentially deliver the kinds of information that are needed to support NGP's partners and constituents. This report provides an initial proof-of-concept demonstration using an existing dataset, and recommendations for the future, to inform NGP's ongoing and future elevation program planning and management decisions. The demonstration shows that this type of functional process can robustly create derivatives from lidar point cloud data; however, more research needs to be done to see how well it extends to multiple datasets.

Quantifying spatial distribution of snow depth errors from LiDAR using Random Forests

Science.gov (United States)

Tinkham, W.; Smith, A. M.; Marshall, H.; Link, T. E.; Falkowski, M. J.; Winstral, A. H.

2013-12-01

There is increasing need to characterize the distribution of snow in complex terrain using remote sensing approaches, especially in isolated mountainous regions that are often water-limited, the principal source of terrestrial freshwater, and sensitive to climatic shifts and variations. We apply intensive topographic surveys, multi-temporal LiDAR, and Random Forest modeling to quantify snow volume and characterize associated errors across seven land cover types in a semi-arid mountainous catchment at a 1 and 4 m spatial resolution. The LiDAR-based estimates of both snow-off surface topology and snow depths were validated against ground-based measurements across the catchment. Comparison of LiDAR-derived snow depths to manual snow depth surveys revealed that LiDAR based estimates were more accurate in areas of low lying vegetation such as shrubs (RMSE = 0.14 m) as compared to areas consisting of tree cover (RMSE = 0.20-0.35 m). The highest errors were found along the edge of conifer forests (RMSE = 0.35 m), however a second conifer transect outside the catchment had much lower errors (RMSE = 0.21 m). This difference is attributed to the wind exposure of the first site that led to highly variable snow depths at short spatial distances. The Random Forest modeled errors deviated from the field measured errors with a RMSE of 0.09-0.34 m across the different cover types. Results show that snow drifts, which are important for maintaining spring and summer stream flows and establishing and sustaining water-limited plant species, contained 30 × 5-6% of the snow volume while only occupying 10% of the catchment area similar to findings by prior physically-based modeling approaches. This study demonstrates the potential utility of combining multi-temporal LiDAR with Random Forest modeling to quantify the distribution of snow depth with a reasonable degree of accuracy. Future work could explore the utility of Terrestrial LiDAR Scanners to produce validation of snow-on surface

Automatic building extraction from LiDAR data fusion of point and grid-based features

Science.gov (United States)

Du, Shouji; Zhang, Yunsheng; Zou, Zhengrong; Xu, Shenghua; He, Xue; Chen, Siyang

2017-08-01

This paper proposes a method for extracting buildings from LiDAR point cloud data by combining point-based and grid-based features. To accurately discriminate buildings from vegetation, a point feature based on the variance of normal vectors is proposed. For a robust building extraction, a graph cuts algorithm is employed to combine the used features and consider the neighbor contexture information. As grid feature computing and a graph cuts algorithm are performed on a grid structure, a feature-retained DSM interpolation method is proposed in this paper. The proposed method is validated by the benchmark ISPRS Test Project on Urban Classification and 3D Building Reconstruction and compared to the state-art-of-the methods. The evaluation shows that the proposed method can obtain a promising result both at area-level and at object-level. The method is further applied to the entire ISPRS dataset and to a real dataset of the Wuhan City. The results show a completeness of 94.9% and a correctness of 92.2% at the per-area level for the former dataset and a completeness of 94.4% and a correctness of 95.8% for the latter one. The proposed method has a good potential for large-size LiDAR data.

Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar.

Science.gov (United States)

Wang, Dongliang; Xin, Xiaoping; Shao, Quanqin; Brolly, Matthew; Zhu, Zhiliang; Chen, Jin

2017-01-19

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass ( R ² = 0.340, root-mean-square error (RMSE) = 81.89 g·m -2 , and relative error of 14.1%). The improvement of multiple regressions to the R ² and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (lidar returns.

Tropospheric ozone climatology at two Southern Hemisphere tropical/subtropical sites, (Reunion Island and Irene, South Africa from ozonesondes, LIDAR, and in situ aircraft measurements

Directory of Open Access Journals (Sweden)

G. Clain

2009-03-01

Full Text Available This paper presents a climatology and trends of tropospheric ozone in the Southwestern Indian Ocean (Reunion Island and South Africa (Irene and Johannesburg. This study is based on a multi-instrumental dataset: PTU-O₃ ozonesondes, DIAL LIDAR and MOZAIC airborne instrumentation.

The seasonal profiles of tropospheric ozone at Reunion Island have been calculated from two different data sets: ozonesondes and LIDAR. The two climatological profiles are similar, except in austral summer when the LIDAR profiles show greater values in the free troposphere, and in the upper troposphere when the LIDAR profiles show lower values during all seasons. These results show that the climatological value of LIDAR profiles must be discussed with care since LIDAR measurements can be performed only under clear sky conditions, and the upper limit of the profile depends on the signal strength.

In addition, linear trends have been calculated from ozonesonde data at Reunion and Irene. Considering the whole tropospheric column, the trend is slightly positive for Reunion, and more clearly positive for Irene. Trend calculations have also been made separating the troposphere into three layers, and separating the dataset into seasons. Results show that the positive trend for Irene is governed by the lower layer that is affected by industrial pollution and biomass burning. On the contrary, for Reunion Island, the strongest trends are observed in the upper troposphere, and in winter when stratosphere-troposphere exchange is more frequently expected.

GLEAM version 3: Global Land Evaporation Datasets and Model

Science.gov (United States)

Martens, B.; Miralles, D. G.; Lievens, H.; van der Schalie, R.; de Jeu, R.; Fernandez-Prieto, D.; Verhoest, N.

2015-12-01

Terrestrial evaporation links energy, water and carbon cycles over land and is therefore a key variable of the climate system. However, the global-scale magnitude and variability of the flux, and the sensitivity of the underlying physical process to changes in environmental factors, are still poorly understood due to limitations in in situ measurements. As a result, several methods have risen to estimate global patterns of land evaporation from satellite observations. However, these algorithms generally differ in their approach to model evaporation, resulting in large differences in their estimates. One of these methods is GLEAM, the Global Land Evaporation: the Amsterdam Methodology. GLEAM estimates terrestrial evaporation based on daily satellite observations of meteorological variables, vegetation characteristics and soil moisture. Since the publication of the first version of the algorithm (2011), the model has been widely applied to analyse trends in the water cycle and land-atmospheric feedbacks during extreme hydrometeorological events. A third version of the GLEAM global datasets is foreseen by the end of 2015. Given the relevance of having a continuous and reliable record of global-scale evaporation estimates for climate and hydrological research, the establishment of an online data portal to host these data to the public is also foreseen. In this new release of the GLEAM datasets, different components of the model have been updated, with the most significant change being the revision of the data assimilation algorithm. In this presentation, we will highlight the most important changes of the methodology and present three new GLEAM datasets and their validation against in situ observations and an alternative dataset of terrestrial evaporation (ERA-Land). Results of the validation exercise indicate that the magnitude and the spatiotemporal variability of the modelled evaporation agree reasonably well with the estimates of ERA-Land and the in situ

Unveiling topographical changes using LiDAR mapping capability: case study of Belaga in Sarawak, East-Malaysia

Science.gov (United States)

Ganendra, T. R.; Khan, N. M.; Razak, W. J.; Kouame, Y.; Mobarakeh, E. T.

2016-06-01

The use of Light Detection and Ranging (LiDAR) remote sensing technology to scan and map landscapes has proven to be one of the most popular techniques to accurately map topography. Thus, LiDAR technology is the ultimate method of unveiling the surface feature under dense vegetation, and, this paper intends to emphasize the diverse techniques that can be utilized to elucidate topographical changes over the study area, using multi-temporal airborne full waveform LiDAR datasets collected in 2012 and 2014. Full waveform LiDAR data offers access to an almost unlimited number of returns per shot, which enables the user to explore in detail topographical changes, such as vegetation growth measurement. The study also found out topography changes at the study area due to earthwork activities contributing to soil consolidation, soil erosion and runoff, requiring cautious monitoring. The implications of this study not only concurs with numerous investigations undertaken by prominent researchers to improve decision making, but also corroborates once again that investigations employing multi-temporal LiDAR data to unveil topography changes in vegetated terrains, produce more detailed and accurate results than most other remote sensing data.

Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and Lidar Data

Science.gov (United States)

Babcock, Chad; Finley, Andrew O.; Cook, Bruce D.; Weiskittel, Andrew; Woodall, Christopher W.

2016-01-01

Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of AGB and estimate AGB growth using LiDAR data. The proposed model accommodates temporal misalignment between field measurements and remotely sensed data-a problem pervasive in such settings-by including multiple time-indexed measurements at plot locations to estimate AGB growth. We pursue a Bayesian modeling framework that allows for appropriately complex parameter associations and uncertainty propagation through to prediction. Specifically, we identify a space-varying coefficients model to predict and map AGB and its associated growth simultaneously. The proposed model is assessed using LiDAR data acquired from NASA Goddard's LiDAR, Hyper-spectral & Thermal imager and field inventory data from the Penobscot Experimental Forest in Bradley, Maine. The proposed model outperformed the time-invariant counterpart models in predictive performance as indicated by a substantial reduction in root mean squared error. The proposed model adequately accounts for temporal misalignment through the estimation of forest AGB growth and accommodates residual spatial dependence. Results from this analysis suggest that future AGB models informed using remotely sensed data, such as LiDAR, may be improved by adapting traditional modeling frameworks to account for temporal misalignment and spatial dependence using random effects.

The use of lidar images in Costa Rica : case studies applied in geology, engineering, and archaeology

International Nuclear Information System (INIS)

Ruiz, Paulo; Garro, Jose F.; Soto, Gerardo J.

2014-01-01

A historical review is made about evolution of technology Lidar (Laser Imaging Detection and Ranging). The improvements of this technology and additions from other technologies are annotated in the last 15 years. The information of the aerial and terrestrial lidar operation, technical parameters for data acquisition and resolutions are presented. The results from four studies are presented in Costa Rica: 1) The discovery of pre-Columbian trails in the area of Volcan Arenal, 2) A case of geological mapping and discovery of new volcanic structures in the North area of the Volcan Poas, 3) The characterization of a landslide near Palmares that represents a hazard for the road network, 4) A detail survey of a pre-Columbian sphere. The results from new Lidar will become more frequent in the following years because its cost will be more affordable. The transference of data between institutions will generate new applications in different fields. (author) [es

Evaluation of the Wind Flow Variability Using Scanning Doppler Lidar Measurements

Science.gov (United States)

Sand, S. C.; Pichugina, Y. L.; Brewer, A.

2016-12-01

Better understanding of the wind flow variability at the heights of the modern turbines is essential to accurately assess of generated wind power and efficient turbine operations. Nowadays the wind energy industry often utilizes scanning Doppler lidar to measure wind-speed profiles at high spatial and temporal resolution.The study presents wind flow features captured by scanning Doppler lidars during the second Wind Forecast and Improvement Project (WFIP 2) sponsored by the Department of Energy (DOE) and National Oceanic and Atmospheric Administration (NOAA). This 18-month long experiment in the Columbia River Basin aims to improve model wind forecasts complicated by mountain terrain, coastal effects, and numerous wind farms.To provide a comprehensive dataset to use for characterizing and predicting meteorological phenomena important to Wind Energy, NOAA deployed scanning, pulsed Doppler lidars to two sites in Oregon, one at Wasco, located upstream of all wind farms relative to the predominant westerly flow in the region, and one at Arlington, located in the middle of several wind farms.In this presentation we will describe lidar scanning patterns capable of providing data in conical, or vertical-slice modes. These individual scans were processed to obtain 15-min averaged profiles of wind speed and direction in real time. Visualization of these profiles as time-height cross sections allows us to analyze variability of these parameters with height, time and location, and reveal periods of rapid changes (ramp events). Examples of wind flow variability between two sites of lidar measurements along with examples of reduced wind velocity downwind of operating turbines (wakes) will be presented.

2010 Northern San Francisco Bay Area Lidar: Portions of Alameda, Contra Costa, Marin, Napa, San Francisco, Solano, and Sonoma Counties

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This Light Detection and Ranging (LiDAR) dataset is a survey of northern San Francisco Bay, California. The project area consists of approximately 437 square miles...

Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar

Directory of Open Access Journals (Sweden)

Juan Carlos Fernandez-Diaz

2016-11-01

Full Text Available In this paper we present a description of a new multispectral airborne mapping light detection and ranging (lidar along with performance results obtained from two years of data collection and test campaigns. The Titan multiwave lidar is manufactured by Teledyne Optech Inc. (Toronto, ON, Canada and emits laser pulses in the 1550, 1064 and 532 nm wavelengths simultaneously through a single oscillating mirror scanner at pulse repetition frequencies (PRF that range from 50 to 300 kHz per wavelength (max combined PRF of 900 kHz. The Titan system can perform simultaneous mapping in terrestrial and very shallow water environments and its multispectral capability enables new applications, such as the production of false color active imagery derived from the lidar return intensities and the automated classification of target and land covers. Field tests and mapping projects performed over the past two years demonstrate capabilities to classify five land covers in urban environments with an accuracy of 90%, map bathymetry under more than 15 m of water, and map thick vegetation canopies at sub-meter vertical resolutions. In addition to its multispectral and performance characteristics, the Titan system is designed with several redundancies and diversity schemes that have proven to be beneficial for both operations and the improvement of data quality.

Improving low-relief coastal LiDAR DEMs with hydro-conditioning of fine-scale and artificial drainages

Directory of Open Access Journals (Sweden)

Thomas Richard Allen

2015-11-01

Full Text Available Improvements in Light Detection and Ranging (LiDAR technology and spatial analysis of high-resolution digital elevation models (DEMs have advanced the accuracy and diversity of applications for coastal hazards and natural resources management. This article presents a concise synthesis of LiDAR analysis for coastal flooding and management applications in low-relief coastal plains and a case study demonstration of a new, efficient drainage mapping algorithm. The impetus for these LiDAR applications follows historic flooding from Hurricane Floyd in 1999, after which the State of North Carolina and the Federal Emergency Management Agency undertook extensive LiDAR data acquisition and technological developments for high-resolution floodplain mapping. An efficient algorithm is outlined for hydro-conditioning bare earth LiDAR DEMs using available US Geological Survey National Hydrography Dataset canal and ditch vectors. The methodology is illustrated in Moyock, North Carolina, for refinement of hydro-conditioning by combines pre-existing bare earth DEMs with spatial analysis of LiDAR point clouds in segmented and buffered ditch and canal networks. The methodology produces improved maps of fine-scale drainage, reduced omission of areal flood inundation, and subwatershed delineations that typify heavily ditched and canalled drainage areas. These preliminary results illustrate the capability of the technique to improve the representation of ditches in DEMs as well as subsequent flow and inundation modeling that could spur further research on low-relief coastal LiDAR applications.

Lunar and Planetary Science XXXV: Mars: Remote Sensing and Terrestrial Analogs

Science.gov (United States)

2004-01-01

The session "Mars: Remote Sensing and Terrestrial Analogs" included the following:Physical Meaning of the Hapke Parameter for Macroscopic Roughness: Experimental Determination for Planetary Regolith Surface Analogs and Numerical Approach; Near-Infrared Spectra of Martian Pyroxene Separates: First Results from Mars Spectroscopy Consortium; Anomalous Spectra of High-Ca Pyroxenes: Correlation Between Ir and M ssbauer Patterns; THEMIS-IR Emissivity Spectrum of a Large Dark Streak near Olympus Mons; Geomorphologic/Thermophysical Mapping of the Athabasca Region, Mars, Using THEMIS Infrared Imaging; Mars Thermal Inertia from THEMIS Data; Multispectral Analysis Methods for Mapping Aqueous Mineral Depostis in Proposed Paleolake Basins on Mars Using THEMIS Data; Joint Analysis of Mars Odyssey THEMIS Visible and Infrared Images: A Magic Airbrush for Qualitative and Quantitative Morphology; Analysis of Mars Thermal Emission Spectrometer Data Using Large Mineral Reference Libraries ; Negative Abundance : A Problem in Compositional Modeling of Hyperspectral Images; Mars-LAB: First Remote Sensing Data of Mineralogy Exposed at Small Mars-Analog Craters, Nevada Test Site; A Tool for the 2003 Rover Mini-TES: Downwelling Radiance Compensation Using Integrated Line-Sight Sky Measurements; Learning About Mars Geology Using Thermal Infrared Spectral Imaging: Orbiter and Rover Perspectives; Classifying Terrestrial Volcanic Alteration Processes and Defining Alteration Processes they Represent on Mars; Cemented Volcanic Soils, Martian Spectra and Implications for the Martian Climate; Palagonitic Mars: A Basalt Centric View of Surface Composition and Aqueous Alteration; Combining a Non Linear Unmixing Model and the Tetracorder Algorithm: Application to the ISM Dataset; Spectral Reflectance Properties of Some Basaltic Weathering Products; Morphometric LIDAR Analysis of Amboy Crater, California: Application to MOLA Analysis of Analog Features on Mars; Airborne Radar Study of Soil Moisture at

Collection, Processing and Accuracy of Mobile Terrestrial Lidar Survey Data in the Coastal Environment

Science.gov (United States)

2017-04-01

is built from sequential two-dimensional linescans. A typical survey will start with the vehicle positioned parallel to the dune near the dune toe ...these maps to assess where wave runup impacted the foreshore, upper beach, and dune toe , as well as areas where aeolian transport has deposited...the lidar data. These techniques can be expanded to calculate beach/berm widths, slopes, and dune toe and dune crest positions. ERDC/CHL TR-17-5 27

SEMI-AUTOMATIC CO-REGISTRATION OF PHOTOGRAMMETRIC AND LIDAR DATA USING BUILDINGS

Directory of Open Access Journals (Sweden)

C. Armenakis

2012-07-01

Full Text Available In this work, the co-registration steps between LiDAR and photogrammetric DSM 3Ddata are analyzed and a solution based on automated plane matching is proposed and implemented. For a robust 3D geometric transformation both planes and points are used. Initially planes are chosen as the co-registration primitives. To confine the search space for the plane matching a sequential automatic building matching is performed first. For matching buildings from the LiDAR and the photogrammetric data, a similarity objective function is formed based on the roof height difference (RHD, the 3D histogram of the building attributes, and the building boundary area of a building. A region growing algorithm based on a Triangulated Irregular Network (TIN is implemented to extract planes from both datasets. Next, an automatic successive process for identifying and matching corresponding planes from the two datasets has been developed and implemented. It is based on the building boundary region and determines plane pairs through a robust matching process thus eliminating outlier pairs. The selected correct plane pairs are the input data for the geometric transformation process. The 3D conformal transformation method in conjunction with the attitude quaternion is applied to obtain the transformation parameters using the normal vectors of the corresponding plane pairs. Following the mapping of one dataset onto the coordinate system of the other, the Iterative Closest Point (ICP algorithm is then applied, using the corresponding building point clouds to further refine the transformation solution. The results indicate that the combination of planes and points improve the co-registration outcomes.

Doppler Lidar Sensor for Precision Landing on the Moon and Mars

Science.gov (United States)

Amzajerdian, Farzin; Petway, Larry; Hines, Glenn; Barnes, Bruce; Pierrottet, Diego; Lockhard, George

2012-01-01

Landing mission concepts that are being developed for exploration of planetary bodies are increasingly ambitious in their implementations and objectives. Most of these missions require accurate position and velocity data during their descent phase in order to ensure safe soft landing at the pre-designated sites. To address this need, a Doppler lidar is being developed by NASA under the Autonomous Landing and Hazard Avoidance (ALHAT) project. This lidar sensor is a versatile instrument capable of providing precision velocity vectors, vehicle ground relative altitude, and attitude. The capabilities of this advanced technology have been demonstrated through two helicopter flight test campaigns conducted over a vegetation-free terrain in 2008 and 2010. Presently, a prototype version of this sensor is being assembled for integration into a rocket-powered terrestrial free-flyer vehicle. Operating in a closed loop with vehicle's guidance and navigation system, the viability of this advanced sensor for future landing missions will be demonstrated through a series of flight tests in 2012.

Development of teaching modules for geology and engineering coursework using terrestrial LiDAR scanning systems

Science.gov (United States)

Yarbrough, L. D.; Katzenstein, K.

2012-12-01

Exposing students to active and local examples of physical geologic processes is beneficial to the learning process. Students typically respond with interest to examples that use state-of-the-art technologies to investigate local or regional phenomena. For lower cognitive level of learning (e.g. knowledge, comprehension, and application), the use of "close-to-home" examples ensures that students better understand concepts. By providing these examples, the students may already have a familiarity or can easily visit the location. Furthermore, these local and regional examples help students to offer quickly other examples of similar phenomena. Investigation of these examples using normal photographic techniques, as well as a more sophisticated 3-D Light Detection And Ranging (LiDAR) (AKA Terrestrial Laser Scanning or TLS) system, allows students to gain a better understanding of the scale and the mechanics of the geologic processes and hazards. The systems are used for research, teaching and outreach efforts and depending on departmental policies can be accessible to students are various learning levels. TLS systems can yield scans at sub-centimeter resolution and contain surface reflectance of targets. These systems can serve a number of learning goals that are essential for training geoscientists and engineers. While querying the data to answer geotechnical or geomorphologic related questions, students will develop skills using large, spatial databases. The upper cognitive level of learning (e.g. analysis, synthesis, and evaluation) is also promoted by using a subset of the data and correlating the physical geologic process of stream bank erosion and rock slope failures with mathematical and computer models using the scanned data. Students use the examples and laboratory exercises to help build their engineering judgment skills with Earth materials. The students learn not only applications of math and engineering science but also the economic and social implication

Using the Rapid-Scanning, Ultra-Portable, Canopy Biomass Lidar (CBL) Alone and In Tandem with the Full-Waveform Dual-Wavelength Echidna® Lidar (DWEL) to Establish Forest Structure and Biomass Estimates in a Variety of Ecosystems

Science.gov (United States)

Schaaf, C.; Paynter, I.; Saenz, E. J.; Li, Z.; Strahler, A. H.; Peri, F.; Erb, A.; Raumonen, P.; Muir, J.; Howe, G.; Hewawasam, K.; Martel, J.; Douglas, E. S.; Chakrabarti, S.; Cook, T.; Schaefer, M.; Newnham, G.; Jupp, D. L. B.; van Aardt, J. A.; Kelbe, D.; Romanczyk, P.; Faulring, J.

2014-12-01

Terrestrial lidars are increasingly being deployed in a variety of ecosystems to calibrate and validate large scale airborne and spaceborne estimates of forest structure and biomass. While these lidars provide a wealth of high resolution information on canopy structure and understory vegetation, they tend to be expensive, slow scanning and somewhat ponderous to deploy. Therefore, frequent deployments and characterization of larger areas of a hectare or more can still be challenging. This suggests a role for low cost, ultra-portable, rapid scanning (but lower resolution) instruments -- particularly in scanning extreme environments and as a way to augment and extend strategically placed scans from the more highly capable lidars. The Canopy Biomass Lidar (CBL) is an inexpensive, highly portable, fast-scanning (33 seconds), time-of-flight, terrestrial laser scanning (TLS) instrument, built in collaboration with RIT, by U Mass Boston. The instrument uses a 905nm SICK time of flight laser with a 0.25o resolution and 30m range. The higher resolution, full-waveform Dual Wavelength Echidna® Lidar (DWEL), developed by Boston University, U Mass Lowell and U Mass Boston, builds on the Australian CSIRO single wavelength, full-waveform Echidna® Validation Instrument (EVI), but utilizes two simultaneous laser pulses at 1064 and 1548 nm to separate woody returns from those of foliage at a range of up to 100m range. The UMass Boston CBL has been deployed in rangelands (San Joaquin Experimental Range, CA), high altitude conifers (Sierra National Forest, CA), mixed forests (Harvard Forest LTER MA), tropical forests (La Selva and Sirena Biological Stations, Costa Rica), eucalypts (Karawatha, Brisbane TERN, Australia), and woodlands (Alice Holt Forest, UK), frequently along-side the DWEL, as well as in more challenging environments such as mangrove forests (Corcovado National Park, Costa Rica) and Massachusetts salt marshes and eroding bluffs (Plum Island LTER, and UMass Boston

Intercomparison of aerosol measurements performed with multi-wavelength Raman lidars, automatic lidars and ceilometers in the framework of INTERACT-II campaign

Science.gov (United States)

Madonna, Fabio; Rosoldi, Marco; Lolli, Simone; Amato, Francesco; Vande Hey, Joshua; Dhillon, Ranvir; Zheng, Yunhui; Brettle, Mike; Pappalardo, Gelsomina

2018-04-01

Following the previous efforts of INTERACT (INTERcomparison of Aerosol and Cloud Tracking), the INTERACT-II campaign used multi-wavelength Raman lidar measurements to assess the performance of an automatic compact micro-pulse lidar (MiniMPL) and two ceilometers (CL51 and CS135) in providing reliable information about optical and geometric atmospheric aerosol properties. The campaign took place at the CNR-IMAA Atmospheric Observatory (760 m a. s. l. ; 40.60° N, 15.72° E) in the framework of ACTRIS-2 (Aerosol Clouds Trace gases Research InfraStructure) H2020 project. Co-located simultaneous measurements involving a MiniMPL, two ceilometers and two EARLINET multi-wavelength Raman lidars were performed from July to December 2016. The intercomparison highlighted that the MiniMPL range-corrected signals (RCSs) show, on average, a fractional difference with respect to those of CNR-IMAA Atmospheric Observatory (CIAO) lidars ranging from 5 to 15 % below 2.0 km a.s.l. (above sea level), largely due to the use of an inaccurate overlap correction, and smaller than 5 % in the free troposphere. For the CL51, the attenuated backscatter values have an average fractional difference with respect to CIAO lidars performance is similar to the CL51 below 2.0 km a. s. l. , while in the region above 3 km a. s. l. the differences are about ±40 %. The variability of the CS135 normalization constant is within ±47 %.Finally, additional tests performed during the campaign using the CHM15k ceilometer operated at CIAO showed the clear need to investigate the CHM15k historical dataset (2010-2016) to evaluate potential effects of ceilometer laser fluctuations on calibration stability. The number of laser pulses shows an average variability of 10 % with respect to the nominal power which conforms to the ceilometer specifications. Nevertheless, laser pulses variability follows seasonal behavior with an increase in the number of laser pulses in summer and a decrease in winter. This contributes to

GRACE-derived terrestrial water storage depletion associated with the 2003 European heat wave

DEFF Research Database (Denmark)

Andersen, Ole Baltazar; Seneviratne, S.I.; Hinderer, J.

2005-01-01

water storage depletion observed from GRACE can be related to the record-breaking heat wave that occurred in central Europe in 2003. We validate the measurements from GRACE using two independent hydrological estimates and direct gravity observations from superconducting gravimeters in Europe. All...... datasets agree well with the GRACE measurements despite the disparity of the employed information; the difference between datasets tends to be within GRACE margin of error. The April-to-August terrestrial water storage depletion is found to be significantly larger in 2003 than in 2002 from both models......The GRACE twin satellites reveal large inter-annual terrestrial water-storage variations between 2002 and 2003 for central Europe. GRACE observes a negative trend in regional water storage from 2002 to 2003 peaking at -7.8 cm in central Europe with an accuracy of 1 cm. The 2003 excess terrestrial...

Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+

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Leitold, Veronika; Keller, Michael; Morton, Douglas C; Cook, Bruce D; Shimabukuro, Yosio E

2015-12-01

Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing. We compared digital terrain models (DTM) derived from airborne lidar data from a mountainous region of the Atlantic Forest in Brazil to 35 ground control points measured with survey grade GNSS receivers. The terrain model generated from full-density (~20 returns m -2 ) data was highly accurate (mean signed error of 0.19 ± 0.97 m), while those derived from reduced-density datasets (8 m -2 , 4 m -2 , 2 m -2 and 1 m -2 ) were increasingly less accurate. Canopy heights calculated from reduced-density lidar data declined as data density decreased due to the inability to accurately model the terrain surface. For lidar return densities below 4 m -2 , the bias in height estimates translated into errors of 80-125 Mg ha -1 in predicted aboveground biomass. Given the growing emphasis on the use of airborne lidar for forest management, carbon monitoring, and conservation efforts, the results of this study highlight the importance of careful survey planning and consistent sampling for accurate quantification of aboveground biomass stocks and dynamics. Approaches that rely primarily on canopy height to estimate aboveground biomass are sensitive to DTM errors from variability in lidar sampling density.

Macrophysical and optical properties of midlatitude cirrus clouds from four ground-based lidars and collocated CALIOP observations

Energy Technology Data Exchange (ETDEWEB)

Dupont, Jean-Charles; Haeffelin, M.; Morille, Y.; Noel, V.; Keckhut, P.; Winker, D.; Comstock, Jennifer M.; Chervet, P.; Roblin, A.

2010-05-27

Ground-based lidar and CALIOP datasets gathered over four mid-latitude sites, two US and two French sites, are used to evaluate the consistency of cloud macrophysical and optical property climatologies that can be derived by such datasets. The consistency in average cloud height (both base and top height) between the CALIOP and ground datasets ranges from -0.4km to +0.5km. The cloud geometrical thickness distributions vary significantly between the different datasets, due in part to the original vertical resolutions of the lidar profiles. Average cloud geometrical thicknesses vary from 1.2 to 1.9km, i.e. by more than 50%. Cloud optical thickness distributions in subvisible, semi-transparent and moderate intervals differ by more than 50% between ground and space-based datasets. The cirrus clouds with 2 optical thickness below 0.1 (not included in historical cloud climatologies) represent 30-50% of the non-opaque cirrus class. The differences in average cloud base altitude between ground and CALIOP datasets of 0.0-0.1 km, 0.0-0.2 km and 0.0-0.2 km can be attributed to irregular sampling of seasonal variations in the ground-based data, to day-night differences in detection capabilities by CALIOP, and to the restriction to situations without low-level clouds in ground-based data, respectively. The cloud geometrical thicknesses are not affected by irregular sampling of seasonal variations in the ground-based data, while up to 0.0-0.2 km and 0.1-0.3 km differences can be attributed to day-night differences in detection capabilities by CALIOP, and to the restriction to situations without lowlevel clouds in ground-based data, respectively.

Handling Low-Density LiDAR Data: Calculating the Heights of Civil Constructions and the Accuracy Expected

Directory of Open Access Journals (Sweden)

Rubén Martínez Marín

2013-01-01

Full Text Available During the last years, in many developed countries, administrations and private companies have devoted considerable amounts of money to obtain mapping data using airborne LiDAR. For many civil activities, we can take advantage of it, since those data are available with no cost. Some important questions arise: Are those data good enough to be used for determining the heights of the civil constructions with the accuracy we need in some civil work? What accuracy can we expect when using low-density LiDAR data (0.5 pts/m2? In order to answer those questions, we have developed a specific methodology based on establishing a set of control points on the top of several constructions and calculating the elevation of each one using postprocessing GPS. Those results have been taken as correct values and the comparison between those values and the elevations obtained, assigning values to the control points by the interpolation of the LiDAR dataset, has been carried out. This paper shows the results obtained using low-density airborne LiDAR data and the accuracy obtained. Results have shown that LiDAR can be accurate enough (10–25 cm to determine the height of civil constructions and apply those data in many civil engineering activities.

PROGRESSIVE DENSIFICATION AND REGION GROWING METHODS FOR LIDAR DATA CLASSIFICATION

Directory of Open Access Journals (Sweden)

J. L. Pérez-García

2012-07-01

Full Text Available At present, airborne laser scanner systems are one of the most frequent methods used to obtain digital terrain elevation models. While having the advantage of direct measurement on the object, the point cloud obtained has the need for classification of their points according to its belonging to the ground. This need for classification of raw data has led to appearance of multiple filters focused LiDAR classification information. According this approach, this paper presents a classification method that combines LiDAR data segmentation techniques and progressive densification to carry out the location of the points belonging to the ground. The proposed methodology is tested on several datasets with different terrain characteristics and data availability. In all case, we analyze the advantages and disadvantages that have been obtained compared with the individual techniques application and, in a special way, the benefits derived from the integration of both classification techniques. In order to provide a more comprehensive quality control of the classification process, the obtained results have been compared with the derived from a manual procedure, which is used as reference classification. The results are also compared with other automatic classification methodologies included in some commercial software packages, highly contrasted by users for LiDAR data treatment.

INTEGRATING SMARTPHONE IMAGES AND AIRBORNE LIDAR DATA FOR COMPLETE URBAN BUILDING MODELLING

Directory of Open Access Journals (Sweden)

S. Zhang

2016-06-01

Full Text Available A complete building model reconstruction needs data collected from both air and ground. The former often has sparse coverage on building façades, while the latter usually is unable to observe the building rooftops. Attempting to solve the missing data issues in building reconstruction from single data source, we describe an approach for complete building reconstruction that integrates airborne LiDAR data and ground smartphone imagery. First, by taking advantages of GPS and digital compass information embedded in the image metadata of smartphones, we are able to find airborne LiDAR point clouds for the corresponding buildings in the images. In the next step, Structure-from-Motion and dense multi-view stereo algorithms are applied to generate building point cloud from multiple ground images. The third step extracts building outlines respectively from the LiDAR point cloud and the ground image point cloud. An automated correspondence between these two sets of building outlines allows us to achieve a precise registration and combination of the two point clouds, which ultimately results in a complete and full resolution building model. The developed approach overcomes the problem of sparse points on building façades in airborne LiDAR and the deficiency of rooftops in ground images such that the merits of both datasets are utilized.

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

Directory of Open Access Journals (Sweden)

Elias Ayrey

2018-04-01

Full Text Available As light detection and ranging (LiDAR technology becomes more available, it has become common to use these datasets to generate remotely sensed forest inventories across landscapes. Traditional methods for generating these inventories employ the use of height and proportion metrics to measure LiDAR returns and relate these back to field data using predictive models. Here, we employ a three-dimensional convolutional neural network (CNN, a deep learning technique that scans the LiDAR data and automatically generates useful features for predicting forest attributes. We test the accuracy in estimating forest attributes using the three-dimensional implementations of different CNN models commonly used in the field of image recognition. Using the best performing model architecture, we compared CNN performance to models developed using traditional height metrics. The results of this comparison show that CNNs produced 12% less prediction error when estimating biomass, 6% less in estimating tree count, and 2% less when estimating the percentage of needleleaf trees. We conclude that using CNNs can be a more accurate means of interpreting LiDAR data for forest inventories compared to standard approaches.

Multi-sourced, 3D geometric characterization of volcanogenic karst features: Integrating lidar, sonar, and geophysical datasets (Invited)

Science.gov (United States)

Sharp, J. M.; Gary, M. O.; Reyes, R.; Halihan, T.; Fairfield, N.; Stone, W. C.

2009-12-01

Karstic aquifers can form very complex hydrogeological systems and 3-D mapping has been difficult, but Lidar, phased array sonar, and improved earth resistivity techniques show promise in this and in linking metadata to models. Zacatón, perhaps the Earth’s deepest cenote, has a sub-aquatic void space exceeding 7.5 x 106 cubic m3. It is the focus of this study which has created detailed 3D maps of the system. These maps include data from above and beneath the the water table and within the rock matrix to document the extent of the immense karst features and to interpret the geologic processes that formed them. Phase 1 used high resolution (20 mm) Lidar scanning of surficial features of four large cenotes. Scan locations, selected to achieve full feature coverage once registered, were established atop surface benchmarks with UTM coordinates established using GPS and Total Stations. The combined datasets form a geo-registered mesh of surface features down to water level in the cenotes. Phase 2 conducted subsurface imaging using Earth Resistivity Imaging (ERI) geophysics. ERI identified void spaces isolated from open flow conduits. A unique travertine morphology exists in which some cenotes are dry or contain shallow lakes with flat travertine floors; some water-filled cenotes have flat floors without the cone of collapse material; and some have collapse cones. We hypothesize that the floors may have large water-filled voids beneath them. Three separate flat travertine caps were imaged: 1) La Pilita, which is partially open, exposing cap structure over a deep water-filled shaft; 2) Poza Seca, which is dry and vegetated; and 3) Tule, which contains a shallow (<1 m) lake. A fourth line was run adjacent to cenote Verde. La Pilita ERI, verified by SCUBA, documented the existence of large water-filled void zones ERI at Poza Seca showed a thin cap overlying a conductive zone extending to at least 25 m depth beneath the cap with no lower boundary of this zone evident

Evaluation of Terrestrial Carbon Cycle with the Land Use Harmonization Dataset

Science.gov (United States)

Sasai, T.; Nemani, R. R.

2017-12-01

CO2 emission by land use and land use change (LULUC) has still had a large uncertainty (±50%). We need to more accurately reveal a role of each LULUC process on terrestrial carbon cycle, and to develop more complicated land cover change model, leading to improve our understanding of the mechanism of global warming. The existing biosphere model studies do not necessarily have enough major LULUC process in the model description (e.g., clear cutting and residual soil carbon). The issue has the potential for causing an underestimation of the effect of LULUC on the global carbon exchange. In this study, the terrestrial biosphere model was modified with several LULUC processes according to the land use harmonization data set. The global mean LULUC emission from the year 1850 to 2000 was 137.2 (PgC 151year-1), and we found the noticeable trend in tropical region. As with the case of primary production in the existing studies, our results emphasized the role of tropical forest on wood productization and residual soil organic carbon by cutting. Global mean NEP was decreased by LULUC. NEP is largely affected by decreasing leaf biomass (photosynthesis) by deforestation process and increasing plant growth rate by regrowth process. We suggested that the model description related to deforestation, residual soil decomposition, wood productization and plant regrowth is important to develop a biosphere model for estimating long-term global carbon cycle.

VEHICLE LOCALIZATION BY LIDAR POINT CORRELATION IMPROVED BY CHANGE DETECTION

Directory of Open Access Journals (Sweden)

A. Schlichting

2016-06-01

Full Text Available LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position and 0.06° (heading, and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.

Vehicle Localization by LIDAR Point Correlation Improved by Change Detection

Science.gov (United States)

Schlichting, A.; Brenner, C.

2016-06-01

LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.

Measurement and Study of Lidar Ratio by Using a Raman Lidar in Central China

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Wei Wang

2016-05-01

Full Text Available We comprehensively evaluated particle lidar ratios (i.e., particle extinction to backscatter ratio at 532 nm over Wuhan in Central China by using a Raman lidar from July 2013 to May 2015. We utilized the Raman lidar data to obtain homogeneous aerosol lidar ratios near the surface through the Raman method during no-rain nights. The lidar ratios were approximately 57 ± 7 sr, 50 ± 5 sr, and 22 ± 4 sr under the three cases with obviously different pollution levels. The haze layer below 1.8 km has a large particle extinction coefficient (from 5.4e-4 m−1 to 1.6e-4 m−1 and particle backscatter coefficient (between 1.1e-05 m−1sr−1 and 1.7e-06 m−1sr−1 in the heavily polluted case. Furthermore, the particle lidar ratios varied according to season, especially between winter (57 ± 13 sr and summer (33 ± 10 sr. The seasonal variation in lidar ratios at Wuhan suggests that the East Asian monsoon significantly affects the primary aerosol types and aerosol optical properties in this region. The relationships between particle lidar ratios and wind indicate that large lidar ratio values correspond well with weak winds and strong northerly winds, whereas significantly low lidar ratio values are associated with prevailing southwesterly and southerly wind.

Measurement and Study of Lidar Ratio by Using a Raman Lidar in Central China.

Science.gov (United States)

Wang, Wei; Gong, Wei; Mao, Feiyue; Pan, Zengxin; Liu, Boming

2016-05-18

We comprehensively evaluated particle lidar ratios (i.e., particle extinction to backscatter ratio) at 532 nm over Wuhan in Central China by using a Raman lidar from July 2013 to May 2015. We utilized the Raman lidar data to obtain homogeneous aerosol lidar ratios near the surface through the Raman method during no-rain nights. The lidar ratios were approximately 57 ± 7 sr, 50 ± 5 sr, and 22 ± 4 sr under the three cases with obviously different pollution levels. The haze layer below 1.8 km has a large particle extinction coefficient (from 5.4e-4 m(-1) to 1.6e-4 m(-1)) and particle backscatter coefficient (between 1.1e-05 m(-1)sr(-1) and 1.7e-06 m(-1)sr(-1)) in the heavily polluted case. Furthermore, the particle lidar ratios varied according to season, especially between winter (57 ± 13 sr) and summer (33 ± 10 sr). The seasonal variation in lidar ratios at Wuhan suggests that the East Asian monsoon significantly affects the primary aerosol types and aerosol optical properties in this region. The relationships between particle lidar ratios and wind indicate that large lidar ratio values correspond well with weak winds and strong northerly winds, whereas significantly low lidar ratio values are associated with prevailing southwesterly and southerly wind.

Bioinspired electronic white cane implementation based on a LIDAR, a tri-axial accelerometer and a tactile belt.

Science.gov (United States)

Pallejà, Tomàs; Tresanchez, Marcel; Teixidó, Mercè; Palacin, Jordi

2010-01-01

This work proposes the creation of a bioinspired electronic white cane for blind people using the whiskers principle for short-range navigation and exploration. Whiskers are coarse hairs of an animal's face that tells the animal that it has touched something using the nerves of the skin. In this work the raw data acquired from a low-size terrestrial LIDAR and a tri-axial accelerometer is converted into tactile information using several electromagnetic devices configured as a tactile belt. The LIDAR and the accelerometer are attached to the user's forearm and connected with a wire to the control unit placed on the belt. Early validation experiments carried out in the laboratory are promising in terms of usability and description of the environment.

Bioinspired Electronic White Cane Implementation Based on a LIDAR, a Tri-Axial Accelerometer and a Tactile Belt

Directory of Open Access Journals (Sweden)

Jordi Palacin

2010-12-01

Full Text Available This work proposes the creation of a bioinspired electronic white cane for blind people using the whiskers principle for short-range navigation and exploration. Whiskers are coarse hairs of an animal's face that tells the animal that it has touched something using the nerves of the skin. In this work the raw data acquired from a low-size terrestrial LIDAR and a tri-axial accelerometer is converted into tactile information using several electromagnetic devices configured as a tactile belt. The LIDAR and the accelerometer are attached to the user’s forearm and connected with a wire to the control unit placed on the belt. Early validation experiments carried out in the laboratory are promising in terms of usability and description of the environment.

Terrestrial multi-view photogrammetry for landslide monitoring

Science.gov (United States)

Stumpf, A.; Malet, J.; Allemand, P.; Skupinski, G.; Pierrot-Deseilligny, M.

2013-12-01

Multi-view stereo (MVS) surface reconstruction from large photo collections is being increasingly used for geoscience applications, and a number of different software solution and processing streamlines have been suggested. Open source libraries to perform feature point extraction, pose estimation, bundle adjustment and dense matching are available providing high quality results at low costs, and transparency of the implemented algorithms. Within the computer vision community benchmark datasets with toy examples and architectural scenes are frequently used to evaluate dense matching algorithms but relatively few studies have addressed the evaluation of complete processing pipelines for complex natural landscapes such as landslides developed in high mountain terrains. In order to obtain surface displacement maps of an active landslide (Super-Sauze, Southern French Alps) from multi-temporal terrestrial photographs over a period of three years, this work targeted the evaluation of three different non-commercial processing pipelines. The tested packages include VisualSfM[1], CMVS-PMVS [2], Apero and MicMac [URL]. The image acquisition focused on either subparts of the landslide (toe, main scarp) or targeted the reconstruction of a global model of the entire landslide. All images were processed with three different pipelines namely VisualSfM + CMVS-PMVS, Apero + CMVS-PMVS and Apero + MicMac and the resulting point clouds were evaluated with terrestrial and airborne LiDAR. Our results show that all multi-view stereo pipelines provide useful results to quantify surface displacement at accuracies between 1-10 cm depending on the acquisition geometry and the object distance. For pose estimation and bundle adjustment, Apero is the more accurate and versatile tool allowing the use of more sophisticated lens models and the direct integration of ground control points in the bundle adjustment. The dense matching algorithms with MicMac enables the reconstruction of denser point

The design, development, and test of balloonborne and groundbased lidar systems. Volume 3: Groundbased lidar systems

Science.gov (United States)

Shepherd, O.; Aurilio, G.; Bucknam, R. D.; Hurd, A. G.; Robertie, N. F.

1991-06-01

This is Volume 3 of a three volume final report on the design, development and test of balloonborne and groundbased lidar systems. Volume 1 describes the design and fabrication of a balloonborne CO2 coherent payload to measure the 10.6 micrometers backscatter from atmospheric aerosols as a function of altitude. Volume 2 describes the August 1987 flight test of Atmospheric Balloonborne Lidar Experiment, ABLE 2. In this volume we describe groundbased lidar development and measurements. A design was developed for installation of the ABLE lidar in the GL rooftop dome. A transportable shed was designed to house the ABLE lidar at the various remote measurement sites. Refurbishment and modification of the ABLE lidar were completed to permit groundbased lidar measurements of clouds and aerosols. Lidar field measurements were made at Ascension Island during SABLE 89. Lidar field measurements were made at Terciera, Azores during GABLE 90. These tasks have been successfully completed, and recommendations for further lidar measurements and data analysis have been made.

4D Near Real-Time Environmental Monitoring Using Highly Temporal LiDAR

Science.gov (United States)

Höfle, Bernhard; Canli, Ekrem; Schmitz, Evelyn; Crommelinck, Sophie; Hoffmeister, Dirk; Glade, Thomas

2016-04-01

The last decade has witnessed extensive applications of 3D environmental monitoring with the LiDAR technology, also referred to as laser scanning. Although several automatic methods were developed to extract environmental parameters from LiDAR point clouds, only little research has focused on highly multitemporal near real-time LiDAR (4D-LiDAR) for environmental monitoring. Large potential of applying 4D-LiDAR is given for landscape objects with high and varying rates of change (e.g. plant growth) and also for phenomena with sudden unpredictable changes (e.g. geomorphological processes). In this presentation we will report on the most recent findings of the research projects 4DEMON (http://uni-heidelberg.de/4demon) and NoeSLIDE (https://geomorph.univie.ac.at/forschung/projekte/aktuell/noeslide/). The method development in both projects is based on two real-world use cases: i) Surface parameter derivation of agricultural crops (e.g. crop height) and ii) change detection of landslides. Both projects exploit the "full history" contained in the LiDAR point cloud time series. One crucial initial step of 4D-LiDAR analysis is the co-registration over time, 3D-georeferencing and time-dependent quality assessment of the LiDAR point cloud time series. Due to the high amount of datasets (e.g. one full LiDAR scan per day), the procedure needs to be performed fully automatically. Furthermore, the online near real-time 4D monitoring system requires to set triggers that can detect removal or moving of tie reflectors (used for co-registration) or the scanner itself. This guarantees long-term data acquisition with high quality. We will present results from a georeferencing experiment for 4D-LiDAR monitoring, which performs benchmarking of co-registration, 3D-georeferencing and also fully automatic detection of events (e.g. removal/moving of reflectors or scanner). Secondly, we will show our empirical findings of an ongoing permanent LiDAR observation of a landslide (Gresten

Linear LIDAR versus Geiger-mode LIDAR: impact on data properties and data quality

Science.gov (United States)

Ullrich, A.; Pfennigbauer, M.

2016-05-01

LIDAR has become the inevitable technology to provide accurate 3D data fast and reliably even in adverse measurement situations and harsh environments. It provides highly accurate point clouds with a significant number of additional valuable attributes per point. LIDAR systems based on Geiger-mode avalanche photo diode arrays, also called single photon avalanche photo diode arrays, earlier employed for military applications, now seek to enter the commercial market of 3D data acquisition, advertising higher point acquisition speeds from longer ranges compared to conventional techniques. Publications pointing out the advantages of these new systems refer to the other category of LIDAR as "linear LIDAR", as the prime receiver element for detecting the laser echo pulses - avalanche photo diodes - are used in a linear mode of operation. We analyze the differences between the two LIDAR technologies and the fundamental differences in the data they provide. The limitations imposed by physics on both approaches to LIDAR are also addressed and advantages of linear LIDAR over the photon counting approach are discussed.

A Robust Gold Deconvolution Approach for LiDAR Waveform Data Processing to Characterize Vegetation Structure

Science.gov (United States)

Zhou, T.; Popescu, S. C.; Krause, K.; Sheridan, R.; Ku, N. W.

2014-12-01

Increasing attention has been paid in the remote sensing community to the next generation Light Detection and Ranging (lidar) waveform data systems for extracting information on topography and the vertical structure of vegetation. However, processing waveform lidar data raises some challenges compared to analyzing discrete return data. The overall goal of this study was to present a robust de-convolution algorithm- Gold algorithm used to de-convolve waveforms in a lidar dataset acquired within a 60 x 60m study area located in the Harvard Forest in Massachusetts. The waveform lidar data was collected by the National Ecological Observatory Network (NEON). Specific objectives were to: (1) explore advantages and limitations of various waveform processing techniques to derive topography and canopy height information; (2) develop and implement a novel de-convolution algorithm, the Gold algorithm, to extract elevation and canopy metrics; and (3) compare results and assess accuracy. We modeled lidar waveforms with a mixture of Gaussian functions using the Non-least squares (NLS) algorithm implemented in R and derived a Digital Terrain Model (DTM) and canopy height. We compared our waveform-derived topography and canopy height measurements using the Gold de-convolution algorithm to results using the Richardson-Lucy algorithm. Our findings show that the Gold algorithm performed better than the Richardson-Lucy algorithm in terms of recovering the hidden echoes and detecting false echoes for generating a DTM, which indicates that the Gold algorithm could potentially be applied to processing of waveform lidar data to derive information on terrain elevation and canopy characteristics.

Doppler lidar sensor for precision navigation in GPS-deprived environment

Science.gov (United States)

Amzajerdian, F.; Pierrottet, D. F.; Hines, G. D.; Petway, L. B.; Barnes, B. W.

2013-05-01

Landing mission concepts that are being developed for exploration of solar system bodies are increasingly ambitious in their implementations and objectives. Most of these missions require accurate position and velocity data during their descent phase in order to ensure safe, soft landing at the pre-designated sites. Data from the vehicle's Inertial Measurement Unit will not be sufficient due to significant drift error after extended travel time in space. Therefore, an onboard sensor is required to provide the necessary data for landing in the GPS-deprived environment of space. For this reason, NASA Langley Research Center has been developing an advanced Doppler lidar sensor capable of providing accurate and reliable data suitable for operation in the highly constrained environment of space. The Doppler lidar transmits three laser beams in different directions toward the ground. The signal from each beam provides the platform velocity and range to the ground along the laser line-of-sight (LOS). The six LOS measurements are then combined in order to determine the three components of the vehicle velocity vector, and to accurately measure altitude and attitude angles relative to the local ground. These measurements are used by an autonomous Guidance, Navigation, and Control system to accurately navigate the vehicle from a few kilometers above the ground to the designated location and to execute a gentle touchdown. A prototype version of our lidar sensor has been completed for a closed-loop demonstration onboard a rocket-powered terrestrial free-flyer vehicle.

Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification

Directory of Open Access Journals (Sweden)

Shezhou Luo

2015-12-01

Full Text Available Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging and CASI (Compact Airborne Spectrographic Imager hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity and CASI data (48 bands with 1 m spatial resolution were spatially resampled to 2, 4, 8, 10, 20 and 30 m resolutions using the nearest neighbor resampling method. These data were thereafter fused using the layer stacking and principal components analysis (PCA methods. Land cover was classified by commonly used supervised classifications in remote sensing images, i.e., the support vector machine (SVM and maximum likelihood (MLC classifiers. Each classifier was applied to four types of datasets (at seven different spatial resolutions: (1 the layer stacking fusion data; (2 the PCA fusion data; (3 the LiDAR data alone; and (4 the CASI data alone. In this study, the land cover category was classified into seven classes, i.e., buildings, road, water bodies, forests, grassland, cropland and barren land. A total of 56 classification results were produced, and the classification accuracies were assessed and compared. The results show that the classification accuracies produced from two fused datasets were higher than that of the single LiDAR and CASI data at all seven spatial resolutions. Moreover, we find that the layer stacking method produced higher overall classification accuracies than the PCA fusion method using both the SVM and MLC classifiers. The highest classification accuracy obtained (OA = 97.8%, kappa = 0.964 using the SVM classifier on the layer stacking fusion data at 1 m spatial resolution. Compared with the best classification results of the CASI and LiDAR data alone, the overall classification accuracies improved by 9.1% and 19.6%, respectively. Our findings also demonstrated that the

A FAST AND ROBUST ALGORITHM FOR ROAD EDGES EXTRACTION FROM LIDAR DATA

Directory of Open Access Journals (Sweden)

K. Qiu

2016-06-01

Full Text Available Fast mapping of roads plays an important role in many geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance. How to extract various road edges fast and robustly is a challenging task. In this paper, we present a fast and robust algorithm for the automatic road edges extraction from terrestrial mobile LiDAR data. The algorithm is based on a key observation: most roads around edges have difference in elevation and road edges with pavement are seen in two different planes. In our algorithm, we firstly extract a rough plane based on RANSAC algorithm, and then multiple refined planes which only contains pavement are extracted from the rough plane. The road edges are extracted based on these refined planes. In practice, there is a serious problem that the rough and refined planes usually extracted badly due to rough roads and different density of point cloud. To eliminate the influence of rough roads, the technology which is similar with the difference of DSM (digital surface model and DTM (digital terrain model is used, and we also propose a method which adjust the point clouds to a similar density to eliminate the influence of different density. Experiments show the validities of the proposed method with multiple datasets (e.g. urban road, highway, and some rural road. We use the same parameters through the experiments and our algorithm can achieve real-time processing speeds.

EXTRACTION OF BUILDING BOUNDARY LINES FROM AIRBORNE LIDAR POINT CLOUDS

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Y.-H. Tseng

2016-10-01

Full Text Available Building boundary lines are important spatial features that characterize the topographic maps and three-dimensional (3D city models. Airborne LiDAR Point clouds provide adequate 3D spatial information for building boundary mapping. However, information of boundary features contained in point clouds is implicit. This study focuses on developing an automatic algorithm of building boundary line extraction from airborne LiDAR data. In an airborne LiDAR dataset, top surfaces of buildings, such as roofs, tend to have densely distributed points, but vertical surfaces, such as walls, usually have sparsely distributed points or even no points. The intersection lines of roof and wall planes are, therefore, not clearly defined in point clouds. This paper proposes a novel method to extract those boundary lines of building edges. The extracted line features can be used as fundamental data to generate topographic maps of 3D city model for an urban area. The proposed method includes two major process steps. The first step is to extract building boundary points from point clouds. Then the second step is followed to form building boundary line features based on the extracted boundary points. In this step, a line fitting algorithm is developed to improve the edge extraction from LiDAR data. Eight test objects, including 4 simple low buildings and 4 complicated tall buildings, were selected from the buildings in NCKU campus. The test results demonstrate the feasibility of the proposed method in extracting complicate building boundary lines. Some results which are not as good as expected suggest the need of further improvement of the method.

Measurement Axis Searching Model for Terrestrial Laser Scans Registration

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Shaoxing Hu

2016-01-01

Full Text Available Nowadays, terrestrial Lidar scans can cover rather a large area; the point densities are strongly varied because of the line-of-sight measurement principle in potential overlaps with scans taken from different viewpoints. Most of the traditional methods focus on registration algorithm and ignore searching model. Sometimes the traditional methods are directly used to align two point clouds; a large critically unsolved problem of the large biases will be created in areas distant from the overlaps while the local overlaps are often aligned well. So a novel measurement axis searching model (MASM has been proposed in this paper. The method includes four steps: (1 the principal axis fitting, (2 the measurement axis generation, (3 low-high-precision search, and (4 result generation. The principal axis gives an orientation to the point cloud; the search scope is limited by the measurement axis. The point cloud orientation can be adjusted gradually until the achievement of the global optimum using low- and high-precision search. We perform some experiments with simulated point clouds and real terrestrial laser scans. The results of simulated point clouds have shown the processing steps of our method, and the results of real terrestrial laser scans have shown the sensitivity of the approach with respect to the indoor and outdoor scenes.

Digital Elevation Model (DEM), LiDAR acquired and processed over the entire county to support the generation of 1"=100' scale orthophotos & 2' contours. The Lidar LAS data has been classified to bare-earth as well as first-return points., Published in 2009, 1:1200 (1in=100ft) scale, Maryland National Capital Park and Planning Commission.

Data.gov (United States)

NSGIC Non-Profit | GIS Inventory — Digital Elevation Model (DEM) dataset current as of 2009. LiDAR acquired and processed over the entire county to support the generation of 1"=100' scale orthophotos...

Improving LiDAR Biomass Model Uncertainty through Non-Destructive Allometry and Plot-level 3D Reconstruction with Terrestrial Laser Scanning

Science.gov (United States)

Stovall, A. E.; Shugart, H. H., Jr.

2017-12-01

Future NASA and ESA satellite missions plan to better quantify global carbon through detailed observations of forest structure, but ultimately rely on uncertain ground measurement approaches for calibration and validation. A significant amount of the uncertainty in estimating plot-level biomass can be attributed to inadequate and unrepresentative allometric relationships used to convert plot-level tree measurements to estimates of aboveground biomass. These allometric equations are known to have high errors and biases, particularly in carbon rich forests because they were calibrated with small and often biased samples of destructively harvested trees. To overcome this issue, a non-destructive methodology for estimating tree and plot-level biomass has been proposed through the use of Terrestrial Laser Scanning (TLS). We investigated the potential for using TLS as a ground validation approach in LiDAR-based biomass mapping though virtual plot-level tree volume reconstruction and biomass estimation. Plot-level biomass estimates were compared on the Virginia-based Smithsonian Conservation Biology Institute's SIGEO forest with full 3D reconstruction, TLS allometry, and Jenkins et al. (2003) allometry. On average, full 3D reconstruction ultimately provided the lowest uncertainty estimate of plot-level biomass (9.6%), followed by TLS allometry (16.9%) and the national equations (20.2%). TLS offered modest improvements to the airborne LiDAR empirical models, reducing RMSE from 16.2% to 14%. Our findings suggest TLS plot acquisitions and non-destructive allometry can play a vital role for reducing uncertainty in calibration and validation data for biomass mapping in the upcoming NASA and ESA missions.

TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015

OpenAIRE

Abatzoglou, John T.; Dobrowski, Solomon Z.; Parks, Sean A.; Hegewisch, Katherine C.

2018-01-01

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and sol...

New Visualization Techniques to Analyze Ultra-High Resolution Four-dimensional Surface Deformation Imagery Collected With Ground-based Tripod LiDAR

Science.gov (United States)

Kreylos, O.; Bawden, G. W.; Kellogg, L. H.

2005-12-01

We are developing a visualization application to display and interact with very large (tens of millions of points) four-dimensional point position datasets in an immersive environment such that point groups from repeated Tripod LiDAR (Light Detection And Ranging) surveys can be selected, measured, and analyzed for land surface change using 3D~interactions. Ground-based tripod or terrestrial LiDAR (T-LiDAR) can remotely collect ultra-high resolution (centimeter to subcentimeter) and accurate (± 4 mm) digital imagery of the scanned target, and at scanning rates of 2,000 (x, y, z, i) (3D~position~+ intensity) points per second over 7~million points can be collected for a given target in an hour. We developed a multiresolution point set data representation based on octrees to display large T-LiDAR point cloud datasets at the frame rates required for immersive display (between 60 Hz and 120 Hz). Data inside an observer's region of interest is shown in full detail, whereas data outside the field of view or far away from the observer is shown at reduced resolution to provide context. Using 3D input devices at the University of California Davis KeckCAVES, users can navigate large point sets, accurately select related point groups in two or more point sets by sweeping regions of space, and guide the software in deriving positional information from point groups to compute their displacements between surveys. We used this new software application in the KeckCAVES to analyze 4D T-LiDAR imagery from the June~1, 2005 Blue Bird Canyon landslide in Laguna Beach, southern California. Over 50~million (x, y, z, i) data points were collected between 10 and 21~days after the landslide to evaluate T-LiDAR as a natural hazards response tool. The visualization of the T-LiDAR scans within the immediate landslide showed minor readjustments in the weeks following the primarily landslide with no observable continued motion on the primary landslide. Recovery and demolition efforts across the

Lidar Remote Sensing for Industry and Environment Monitoring

Science.gov (United States)

Singh, Upendra N. (Editor); Itabe, Toshikazu (Editor); Sugimoto, Nobuo (Editor)

2000-01-01

Contents include the following: 1. Keynote paper: Overview of lidar technology for industrial and environmental monitoring in Japan. 2. lidar technology I: NASA's future active remote sensing mission for earth science. Geometrical detector consideration s in laser sensing application (invited paper). 3. Lidar technology II: High-power femtosecond light strings as novel atmospheric probes (invited paper). Design of a compact high-sensitivity aerosol profiling lidar. 4. Lasers for lidars: High-energy 2 microns laser for multiple lidar applications. New submount requirement of conductively cooled laser diodes for lidar applications. 5. Tropospheric aerosols and clouds I: Lidar monitoring of clouds and aerosols at the facility for atmospheric remote sensing (invited paper). Measurement of asian dust by using multiwavelength lidar. Global monitoring of clouds and aerosols using a network of micropulse lidar systems. 6. Troposphere aerosols and clouds II: Scanning lidar measurements of marine aerosol fields at a coastal site in Hawaii. 7. Tropospheric aerosols and clouds III: Formation of ice cloud from asian dust particles in the upper troposphere. Atmospheric boundary layer observation by ground-based lidar at KMITL, Thailand (13 deg N, 100 deg. E). 8. Boundary layer, urban pollution: Studies of the spatial correlation between urban aerosols and local traffic congestion using a slant angle scanning on the research vessel Mirai. 9. Middle atmosphere: Lidar-observed arctic PSC's over Svalbard (invited paper). Sodium temperature lidar measurements of the mesopause region over Syowa Station. 10. Differential absorption lidar (dIAL) and DOAS: Airborne UV DIAL measurements of ozone and aerosols (invited paper). Measurement of water vapor, surface ozone, and ethylene using differential absorption lidar. 12. Space lidar I: Lightweight lidar telescopes for space applications (invited paper). Coherent lidar development for Doppler wind measurement from the International Space

Registration of Urban Aerial Image and LiDAR Based on Line Vectors

Directory of Open Access Journals (Sweden)

Qinghong Sheng

2017-09-01

Full Text Available In a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of the adjustment model. To solve this problem, this paper presents a line vector-based registration mode (LVR in which image rays and LiDAR lines are expressed by a line vector that integrates the line direction and the line position. A registration equation of line vector is set up by coplanar imaging rays and corresponding control lines. Three types of datasets consisting of synthetic, theInternational Society for Photogrammetry and Remote Sensing (ISPRS test project, and real aerial data are used. A group of progressive experiments is undertaken to evaluate the robustness of the LVR. Experimental results demonstrate that the integrated line direction and the line position contributes a great deal to the theoretical and real accuracies of the unknowns, as well as the stability of the adjustment model. This paper provides a new suggestion that, for a single image and LiDAR data, registration in urban areas can be accomplished by accommodating rich line features.

2015 Lowndes County (GA) Lidar

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME: NOAA OCM Lidar for Lowndes County, GA with the option to Collect Lidar in Cook and Tift Counties, GA Lidar Data Acquisition and Processing Production Task...

2015 OLC Lidar: Wasco, WA

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — WSI collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Wasco County, WA, study area. The Oregon LiDAR Consortium's Wasco County...

Let’s agree on the casing of Lidar

Science.gov (United States)

Deering, Carol; Stoker, Jason M.

2014-01-01

Is it lidar, Lidar, LiDAR, LIDAR, LiDar, LiDaR, or liDAR? A comprehensive review of the scientific/technical literature reveals seven different casings of this short form for light detection and ranging. And there could be more.

Lidar and Hyperspectral Remote Sensing for the Analysis of Coniferous Biomass Stocks and Fluxes

Science.gov (United States)

Halligan, K. Q.; Roberts, D. A.

2006-12-01

Airborne lidar and hyperspectral data can improve estimates of aboveground carbon stocks and fluxes through their complimentary responses to vegetation structure and biochemistry. While strong relationships have been demonstrated between lidar-estimated vegetation structural parameters and field data, research is needed to explore the portability of these methods across a range of topographic conditions, disturbance histories, vegetation type and climate. Additionally, research is needed to evaluate contributions of hyperspectral data in refining biomass estimates and determination of fluxes. To address these questions we are a conducting study of lidar and hyperspectral remote sensing data across sites including coniferous forests, broadleaf deciduous forests and a tropical rainforest. Here we focus on a single study site, Yellowstone National Park, where tree heights, stem locations, above ground biomass and basal area were mapped using first-return small-footprint lidar data. A new method using lidar intensity data was developed for separating the terrain and vegetation components in lidar data using a two-scale iterative local minima filter. Resulting Digital Terrain Models (DTM) and Digital Canopy Models (DCM) were then processed to retrieve a diversity of vertical and horizontal structure metrics. Univariate linear models were used to estimate individual tree heights while stepwise linear regression was used to estimate aboveground biomass and basal area. Three small-area field datasets were compared for their utility in model building and validation of vegetation structure parameters. All structural parameters were linearly correlated with lidar-derived metrics, with higher accuracies obtained where field and imagery data were precisely collocated . Initial analysis of hyperspectral data suggests that vegetation health metrics including measures of live and dead vegetation and stress indices may provide good indicators of carbon flux by mapping vegetation

2012 MEGIS Topographic Lidar: Statewide Lidar Project Area 1 (Aroostook), Maine

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — LiDAR data is a remotely sensed high resolution elevation data collected by an airborne platform. The LiDAR sensor uses a combination of laser range finding, GPS...

ALTERNATIVE METHODOLOGIES FOR THE ESTIMATION OF LOCAL POINT DENSITY INDEX: MOVING TOWARDS ADAPTIVE LIDAR DATA PROCESSING

Directory of Open Access Journals (Sweden)

Z. Lari

2012-07-01

Full Text Available Over the past few years, LiDAR systems have been established as a leading technology for the acquisition of high density point clouds over physical surfaces. These point clouds will be processed for the extraction of geo-spatial information. Local point density is one of the most important properties of the point cloud that highly affects the performance of data processing techniques and the quality of extracted information from these data. Therefore, it is necessary to define a standard methodology for the estimation of local point density indices to be considered for the precise processing of LiDAR data. Current definitions of local point density indices, which only consider the 2D neighbourhood of individual points, are not appropriate for 3D LiDAR data and cannot be applied for laser scans from different platforms. In order to resolve the drawbacks of these methods, this paper proposes several approaches for the estimation of the local point density index which take the 3D relationship among the points and the physical properties of the surfaces they belong to into account. In the simplest approach, an approximate value of the local point density for each point is defined while considering the 3D relationship among the points. In the other approaches, the local point density is estimated by considering the 3D neighbourhood of the point in question and the physical properties of the surface which encloses this point. The physical properties of the surfaces enclosing the LiDAR points are assessed through eigen-value analysis of the 3D neighbourhood of individual points and adaptive cylinder methods. This paper will discuss these approaches and highlight their impact on various LiDAR data processing activities (i.e., neighbourhood definition, region growing, segmentation, boundary detection, and classification. Experimental results from airborne and terrestrial LiDAR data verify the efficacy of considering local point density variation for

Fully Convolutional Networks for Ground Classification from LIDAR Point Clouds

Science.gov (United States)

Rizaldy, A.; Persello, C.; Gevaert, C. M.; Oude Elberink, S. J.

2018-05-01

Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs). In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

Extensive Sampling of Forest Carbon using High Density Power Line Lidar

Science.gov (United States)

Hampton, H. M.; Chen, Q.; Dye, D. G.; Hungate, B. A.

2013-12-01

unmanaged areas, using high point density lidar collected over transmission line corridors. The lidar metric of quadratic mean height guided our selection of field plots spanning the full range from low to high levels of aboveground biomass across the study region. Before model selection, we minimized two of the major sources of errors in lidar calibration: variance in tree allometry across landscapes and plot edge effects (spatial mismatch between field measurements and lidar points). We tested an assortment of model selection techniques and goodness of fit measures for deriving forest structural metrics of interest. For example, we obtained an R-squared value for aboveground biomass (Mg/ha) of 0.9 using stepwise regression. The forest metrics obtained are being used in the next stage of the project to parameterize biogeochemical models linking terrestrial carbon pools and atmospheric greenhouse gas exchanges.

Balloonborne lidar payloads for remote sensing

Science.gov (United States)

Shepherd, O.; Aurilio, G.; Hurd, A. G.; Rappaport, S. A.; Reidy, W. P.; Rieder, R. J.; Bedo, D. E.; Swirbalus, R. A.

1994-02-01

A series of lidar experiments has been conducted using the Atmospheric Balloonborne Lidar Experiment payload (ABLE). These experiments included the measurement of atmospheric Rayleigh and Mie backscatter from near space (approximately 30 km) and Raman backscatter measurements of atmospheric constituents as a function of altitude. The ABLE payload consisted of a frequency-tripled Nd:YAG laser transmitter, a 50 cm receiver telescope, and filtered photodetectors in various focal plane configurations. The payload for lidar pointing, thermal control, data handling, and remote control of the lidar system. Comparison of ABLE performance with that of a space lidar shows significant performance advantages and cost effectiveness for balloonborne lidar systems.

Development and validation of fuel height models for terrestrial lidar - RxCADRE 2012

Science.gov (United States)

Eric M. Rowell; Carl A. Seielstad; Roger D. Ottmar

2016-01-01

Terrestrial laser scanning (TLS) was used to collect spatially continuous measurements of fuelbed characteristics across the plots and burn blocks of the 2012 RxCADRE experiments in Florida. Fuelbeds were scanned obliquely from plot/block edges at a height of 20 m above ground. Pre-fire blocks were scanned from six perspectives and four perspectives for post-...

Lidar configurations for wind turbine control

DEFF Research Database (Denmark)

Mirzaei, Mahmood; Mann, Jakob

2016-01-01

Lidar sensors have proved to be very beneficial in the wind energy industry. They can be used for yaw correction, feed-forward pitch control and load verification. However, the current lidars are expensive. One way to reduce the price is to use lidars with few measurement points. Finding the best...... by the lidar is compared against the effective wind speed on a wind turbine rotor both theoretically and through simulations. The study provides some results to choose the best configuration of the lidar with few measurement points....

IEA Wind Task 32: Wind lidar identifying and mitigating barriers to the adoption of wind lidar

DEFF Research Database (Denmark)

Clifton, Andrew; Clive, Peter; Gottschall, Julia

2018-01-01

IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex...... flows. Since its initiation in 2011, Task 32 has been responsible for several recommended practices and expert reports that have contributed to the adoption of ground-based, nacelle-based, and floating lidar by the wind industry. Future challenges include the development of lidar uncertainty models......, best practices for data management, and developing community-based tools for data analysis, planning of lidar measurements and lidar configuration. This paper describes the barriers that Task 32 identified to the deployment of wind lidar in each of these application areas, and the steps that have been...

Remote sensing the vulnerability of vegetation in natural terrestrial ecosystems

Science.gov (United States)

Alistair M. S. Smith; Crystal A. Kolden; Wade T. Tinkham; Alan F. Talhelm; John D. Marshall; Andrew T. Hudak; Luigi Boschetti; Michael J. Falkowski; Jonathan A. Greenberg; John W. Anderson; Andrew Kliskey; Lilian Alessa; Robert F. Keefe; James R. Gosz

2014-01-01

Climate change is altering the species composition, structure, and function of vegetation in natural terrestrial ecosystems. These changes can also impact the essential ecosystem goods and services derived from these ecosystems. Following disturbances, remote-sensing datasets have been used to monitor the disturbance and describe antecedent conditions as a means of...

LIDAR Research & Development Lab

Data.gov (United States)

Federal Laboratory Consortium — The LIDAR Research and Development labs are used to investigate and improve LIDAR components such as laser sources, optical signal detectors and optical filters. The...

Using Satellite and Airborne LiDAR to Model Woodpecker Habitat Occupancy at the Landscape Scale

Science.gov (United States)

Vierling, Lee A.; Vierling, Kerri T.; Adam, Patrick; Hudak, Andrew T.

2013-01-01

Incorporating vertical vegetation structure into models of animal distributions can improve understanding of the patterns and processes governing habitat selection. LiDAR can provide such structural information, but these data are typically collected via aircraft and thus are limited in spatial extent. Our objective was to explore the utility of satellite-based LiDAR data from the Geoscience Laser Altimeter System (GLAS) relative to airborne-based LiDAR to model the north Idaho breeding distribution of a forest-dependent ecosystem engineer, the Red-naped sapsucker (Sphyrapicus nuchalis). GLAS data occurred within ca. 64 m diameter ellipses spaced a minimum of 172 m apart, and all occupancy analyses were confined to this grain scale. Using a hierarchical approach, we modeled Red-naped sapsucker occupancy as a function of LiDAR metrics derived from both platforms. Occupancy models based on satellite data were weak, possibly because the data within the GLAS ellipse did not fully represent habitat characteristics important for this species. The most important structural variables influencing Red-naped Sapsucker breeding site selection based on airborne LiDAR data included foliage height diversity, the distance between major strata in the canopy vertical profile, and the vegetation density near the ground. These characteristics are consistent with the diversity of foraging activities exhibited by this species. To our knowledge, this study represents the first to examine the utility of satellite-based LiDAR to model animal distributions. The large area of each GLAS ellipse and the non-contiguous nature of GLAS data may pose significant challenges for wildlife distribution modeling; nevertheless these data can provide useful information on ecosystem vertical structure, particularly in areas of gentle terrain. Additional work is thus warranted to utilize LiDAR datasets collected from both airborne and past and future satellite platforms (e.g. GLAS, and the planned IceSAT2

Model of the Correlation between Lidar Systems and Wind Turbines for Lidar-Assisted Control

DEFF Research Database (Denmark)

Schlipf, David; Cheng, Po Wen; Mann, Jakob

2013-01-01

- or spinner-based lidar system. If on the one hand, the assumed correlation is overestimated, then the uncorrelated frequencies of the preview will cause unnecessary control action, inducing undesired loads. On the other hand, the benefits of the lidar-assisted controller will not be fully exhausted......, if correlated frequencies are filtered out. To avoid these miscalculations, this work presents a method to model the correlation between lidar systems and wind turbines using Kaimal wind spectra. The derived model accounts for different measurement configurations and spatial averaging of the lidar system......Investigations of lidar-assisted control to optimize the energy yield and to reduce loads of wind turbines have increased significantly in recent years. For this kind of control, it is crucial to know the correlation between the rotor effective wind speed and the wind preview provided by a nacelle...

Development of Prototype Micro-Lidar using Narrow Linewidth Semiconductor Lasers for Mars Boundary Layer Wind and Dust Opacity Profiles

Science.gov (United States)

Menzies, Robert T.; Cardell, Greg; Chiao, Meng; Esproles, Carlos; Forouhar, Siamak; Hemmati, Hamid; Tratt, David

1999-01-01

We have developed a compact Doppler lidar concept which utilizes recent developments in semiconductor diode laser technology in order to be considered suitable for wind and dust opacity profiling in the Mars lower atmosphere from a surface location. The current understanding of the Mars global climate and meteorology is very limited, with only sparse, near-surface data available from the Viking and Mars Pathfinder landers, supplemented by long-range remote sensing of the Martian atmosphere. The in situ measurements from a lander-based Doppler lidar would provide a unique dataset particularly for the boundary layer. The coupling of the radiative properties of the lower atmosphere with the dynamics involves the radiative absorption and scattering effects of the wind-driven dust. Variability in solar irradiance, on diurnal and seasonal time scales, drives vertical mixing and PBL (planetary boundary layer) thickness. The lidar data will also contribute to an understanding of the impact of wind-driven dust on lander and rover operations and lifetime through an improvement in our understanding of Mars climatology. In this paper we discuss the Mars lidar concept, and the development of a laboratory prototype for performance studies, using, local boundary layer and topographic target measurements.

Dietary characterization of terrestrial mammals.

Science.gov (United States)

Pineda-Munoz, Silvia; Alroy, John

2014-08-22

Understanding the feeding behaviour of the species that make up any ecosystem is essential for designing further research. Mammals have been studied intensively, but the criteria used for classifying their diets are far from being standardized. We built a database summarizing the dietary preferences of terrestrial mammals using published data regarding their stomach contents. We performed multivariate analyses in order to set up a standardized classification scheme. Ideally, food consumption percentages should be used instead of qualitative classifications. However, when highly detailed information is not available we propose classifying animals based on their main feeding resources. They should be classified as generalists when none of the feeding resources constitute over 50% of the diet. The term 'omnivore' should be avoided because it does not communicate all the complexity inherent to food choice. Moreover, the so-called omnivore diets actually involve several distinctive adaptations. Our dataset shows that terrestrial mammals are generally highly specialized and that some degree of food mixing may even be required for most species.

Calibration of Ground-based Lidar instrument

DEFF Research Database (Denmark)

Yordanova, Ginka; Gómez Arranz, Paula

This report presents the result of the lidar calibration performed for the given Ground-based Lidar at DTU’s test site for large wind turbines at Høvsøre, Denmark. Calibration is here understood as the establishment of a relation between the reference wind speed measurements with measurement...... uncertainties provided by measurement standard and corresponding lidar wind speed indications with associated measurement uncertainties. The lidar calibration concerns the 10 minute mean wind speed measurements. The comparison of the lidar measurements of the wind direction with that from wind vanes...

2013 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Tulalip Partnership

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In October 2012, WSI (Watershed Sciences, Inc.) was contracted by the Puget Sound LiDAR Consortium (PSLC)to collect Light Detection and Ranging (LiDAR) data on a...

2013 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Saddle Mountain

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In October 2013, WSI, a Quantum Spatial Company (QSI), was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR) data...

Data Descriptor: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

Science.gov (United States)

John T. Abatzoglou; Solomon Z. Dobrowski; Sean A. Parks; Katherine C. Hegewisch

2018-01-01

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from...

Modeling habitat for Marbled Murrelets on the Siuslaw National Forest, Oregon, using lidar data

Science.gov (United States)

Hagar, Joan C.; Aragon, Ramiro; Haggerty, Patricia; Hollenbeck, Jeff P.

2018-03-28

Habitat models using lidar-derived variables that quantify fine-scale variation in vegetation structure can improve the accuracy of occupancy estimates for canopy-dwelling species over models that use variables derived from other remote sensing techniques. However, the ability of models developed at such a fine spatial scale to maintain accuracy at regional or larger spatial scales has not been tested. We tested the transferability of a lidar-based habitat model for the threatened Marbled Murrelet (Brachyramphus marmoratus) between two management districts within a larger regional conservation zone in coastal western Oregon. We compared the performance of the transferred model against models developed with data from the application location. The transferred model had good discrimination (AUC = 0.73) at the application location, and model performance was further improved by fitting the original model with coefficients from the application location dataset (AUC = 0.79). However, the model selection procedure indicated that neither of these transferred models were considered competitive with a model trained on local data. The new model trained on data from the application location resulted in the selection of a slightly different set of lidar metrics from the original model, but both transferred and locally trained models consistently indicated positive relationships between the probability of occupancy and lidar measures of canopy structural complexity. We conclude that while the locally trained model had superior performance for local application, the transferred model could reasonably be applied to the entire conservation zone.

Differential absorption lidar measurements of atmospheric water vapor using a pseudonoise code modulated AlGaAs laser. Thesis

Science.gov (United States)

Rall, Jonathan A. R.

1994-01-01

Lidar measurements using pseudonoise code modulated AlGaAs lasers are reported. Horizontal path lidar measurements were made at night to terrestrial targets at ranges of 5 and 13 km with 35 mW of average power and integration times of one second. Cloud and aerosol lidar measurements were made to thin cirrus clouds at 13 km altitude with Rayleigh (molecular) backscatter evident up to 9 km. Average transmitter power was 35 mW and measurement integration time was 20 minutes. An AlGaAs laser was used to characterize spectral properties of water vapor absorption lines at 811.617, 816.024, and 815.769 nm in a multipass absorption cell using derivative spectroscopy techniques. Frequency locking of an AlGaAs laser to a water vapor absorption line was achieved with a laser center frequency stability measured to better than one-fifth of the water vapor Doppler linewidth over several minutes. Differential absorption lidar measurements of atmospheric water vapor were made in both integrated path and range-resolved modes using an externally modulated AlGaAs laser. Mean water vapor number density was estimated from both integrated path and range-resolved DIAL measurements and agreed with measured humidity values to within 6.5 percent and 20 percent, respectively. Error sources were identified and their effects on estimates of water vapor number density calculated.

IEA Wind Task 32: Wind Lidar Identifying and Mitigating Barriers to the Adoption of Wind Lidar

Directory of Open Access Journals (Sweden)

Andrew Clifton

2018-03-01

Full Text Available IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex flows. Since its initiation in 2011, Task 32 has been responsible for several recommended practices and expert reports that have contributed to the adoption of ground-based, nacelle-based, and floating lidar by the wind industry. Future challenges include the development of lidar uncertainty models, best practices for data management, and developing community-based tools for data analysis, planning of lidar measurements and lidar configuration. This paper describes the barriers that Task 32 identified to the deployment of wind lidar in each of these application areas, and the steps that have been taken to confirm or mitigate the barriers. Task 32 will continue to be a meeting point for the international wind lidar community until at least 2020 and welcomes old and new participants.

Recording Cultural Heritage Using Terrestrial Laserscanning - Dealing with the System, the Huge Datasets they Create and Ways to Extract the Necessary Deliverables you can Work with

Science.gov (United States)

Christofori, E.; Bierwagen, J.

2013-07-01

Recording Cultural Heritage objects using terrestrial laserscanning becomes more and more popular over the last years. Since terrestrial Laserscanning System (TLS) Manufacturers have strongly increased the amount and speed of data captured with a single scan at each system upgrade and cutting down system costs the use of TLS Systems for recording cultural heritage is an option for recording worth to think about beside traditional methods like Photogrammetric. TLS Systems can be a great tool for capturing complex cultural heritage object within a short amount of time beside the traditional methods but can be a nightmare to handle for further process if not used right while capturing. Furthermore TLS Systems still have to be recognized as survey equipment, even though some of the manufactures promote them as everyday tool. They have to be used in an intelligent way having in mind the clients and the individual cultural objects needs. Thus the efficient way to use TLS Systems for data recording becomes a relevant topic to deal with the huge Amount of data the Systems collect while recording. Already small projects can turn into huge Pointcloud Datasets that End user, like Architects or Archaeologist neither can't deal with as their technical equipment doesn't fit the requirements of the Dataset nor do they have the software tools to use the Data as the current software tools still are high prized. Even the necessary interpretation of the Dataset can be a tough task if the people who have to work on with the Pointcloud aren't educated right in order to understand TLS and the results it creates. The use of TLS Systems has to have in mind the project requirements of the individual Heritage Object, like the required accuracy, standards for Levels of Details (e.g. "Empfehlungen für die Baudokumentation, Günther Eckstein, Germany"), the required kind of Deliverables (Visualization, 2D Drawings, True Deformation Drawings, 3D Models, BIM or 4D - Animations) as well as the

Demonstration of coherent Doppler lidar for navigation in GPS-denied environments

Science.gov (United States)

Amzajerdian, Farzin; Hines, Glenn D.; Pierrottet, Diego F.; Barnes, Bruce W.; Petway, Larry B.; Carson, John M.

2017-05-01

A coherent Doppler lidar has been developed to address NASA's need for a high-performance, compact, and cost-effective velocity and altitude sensor onboard its landing vehicles. Future robotic and manned missions to solar system bodies require precise ground-relative velocity vector and altitude data to execute complex descent maneuvers and safe, soft landing at a pre-designated site. This lidar sensor, referred to as a Navigation Doppler Lidar (NDL), meets the required performance of the landing missions while complying with vehicle size, mass, and power constraints. Operating from up to four kilometers altitude, the NDL obtains velocity and range precision measurements reaching 2 cm/sec and 2 meters, respectively, dominated by the vehicle motion. Terrestrial aerial vehicles will also benefit from NDL data products as enhancement or replacement to GPS systems when GPS is unavailable or redundancy is needed. The NDL offers a viable option to aircraft navigation in areas where the GPS signal can be blocked or jammed by intentional or unintentional interference. The NDL transmits three laser beams at different pointing angles toward the ground to measure range and velocity along each beam using a frequency modulated continuous wave (FMCW) technique. The three line-of-sight measurements are then combined in order to determine the three components of the vehicle velocity vector and its altitude relative to the ground. This paper describes the performance and capabilities that the NDL demonstrated through extensive ground tests, helicopter flight tests, and onboard an autonomous rocket-powered test vehicle while operating in closedloop with a guidance, navigation, and control (GN and C) system.

Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

Directory of Open Access Journals (Sweden)

Jian Yang

2015-11-01

Full Text Available Delineating canopy gaps and quantifying gap characteristics (e.g., size, shape, and dynamics are essential for understanding regeneration dynamics and understory species diversity in structurally complex forests. Both high spatial resolution optical and light detection and ranging (LiDAR remote sensing data have been used to identify canopy gaps through object-based image analysis, but few studies have quantified the pros and cons of integrating optical and LiDAR for image segmentation and classification. In this study, we investigate whether the synergistic use of optical and LiDAR data improves segmentation quality and classification accuracy. The segmentation results indicate that the LiDAR-based segmentation best delineates canopy gaps, compared to segmentation with optical data alone, and even the integration of optical and LiDAR data. In contrast, the synergistic use of two datasets provides higher classification accuracy than the independent use of optical or LiDAR (overall accuracy of 80.28% ± 6.16% vs. 68.54% ± 9.03% and 64.51% ± 11.32%, separately. High correlations between segmentation quality and object-based classification accuracy indicate that classification accuracy is largely dependent on segmentation quality in the selected experimental area. The outcome of this study provides valuable insights of the usefulness of data integration into segmentation and classification not only for canopy gap identification but also for many other object-based applications.

DECISION LEVEL FUSION OF ORTHOPHOTO AND LIDAR DATA USING CONFUSION MATRIX INFORMATION FOR LNAD COVER CLASSIFICATION

Directory of Open Access Journals (Sweden)

S. Daneshtalab

2017-09-01

Full Text Available Automatic urban objects extraction from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Lidar data available today. The aim of this study is to propose a new approach for the integration of high-resolution aerial imagery and Lidar data to improve the accuracy of classification in the city complications. In the proposed method, first, the classification of each data is separately performed using Support Vector Machine algorithm. In this case, extracted Normalized Digital Surface Model (nDSM and pulse intensity are used in classification of LiDAR data, and three spectral visible bands (Red, Green, Blue are considered as feature vector for the orthoimage classification. Moreover, combining the extracted features of the image and Lidar data another classification is also performed using all the features. The outputs of these classifications are integrated in a decision level fusion system according to the their confusion matrices to find the final classification result. The proposed method was evaluated using an urban area of Zeebruges, Belgium. The obtained results represented several advantages of image fusion with respect to a single shot dataset. With the capabilities of the proposed decision level fusion method, most of the object extraction difficulties and uncertainty were decreased and, the overall accuracy and the kappa values were improved 7% and 10%, respectively.

FULLY CONVOLUTIONAL NETWORKS FOR GROUND CLASSIFICATION FROM LIDAR POINT CLOUDS

Directory of Open Access Journals (Sweden)

A. Rizaldy

2018-05-01

Full Text Available Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs. In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN, a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher. The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

OPEN-SOURCE DIGITAL ELEVATION MODEL (DEMs EVALUATION WITH GPS AND LiDAR DATA

Directory of Open Access Journals (Sweden)

N. F. Khalid

2016-09-01

Full Text Available Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM, Shuttle Radar Topography Mission (SRTM, and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010 are freely available Digital Elevation Model (DEM datasets for environmental modeling and studies. The quality of spatial resolution and vertical accuracy of the DEM data source has a great influence particularly on the accuracy specifically for inundation mapping. Most of the coastal inundation risk studies used the publicly available DEM to estimated the coastal inundation and associated damaged especially to human population based on the increment of sea level. In this study, the comparison between ground truth data from Global Positioning System (GPS observation and DEM is done to evaluate the accuracy of each DEM. The vertical accuracy of SRTM shows better result against ASTER and GMTED10 with an RMSE of 6.054 m. On top of the accuracy, the correlation of DEM is identified with the high determination of coefficient of 0.912 for SRTM. For coastal zone area, DEMs based on airborne light detection and ranging (LiDAR dataset was used as ground truth data relating to terrain height. In this case, the LiDAR DEM is compared against the new SRTM DEM after applying the scale factor. From the findings, the accuracy of the new DEM model from SRTM can be improved by applying scale factor. The result clearly shows that the value of RMSE exhibit slightly different when it reached 0.503 m. Hence, this new model is the most suitable and meets the accuracy requirement for coastal inundation risk assessment using open source data. The suitability of these datasets for further analysis on coastal management studies is vital to assess the potentially vulnerable areas caused by coastal inundation.

Lidar Inter-Comparison Exercise Final Campaign Report

Energy Technology Data Exchange (ETDEWEB)

Protat, A [Australian Bureau of Meterology; Young, S

2015-02-01

The objective of this field campaign was to evaluate the performance of the new Leosphere R-MAN 510 lidar, procured by the Australian Bureau of Meteorology, by testing it against the MicroPulse Lidar (MPL) and Raman lidars, at the Darwin Atmospheric Radiation Measurement (ARM) site. This lidar is an eye-safe (355 nm), turn-key mini Raman lidar, which allows for the detection of aerosols and cloud properties, and the retrieval of particulate extinction profiles. To accomplish this evaluation, the R-MAN 510 lidar has been operated at the Darwin ARM site, next to the MPL, Raman lidar, and Vaisala ceilometer (VCEIL) for three months (from 20 January 2013 to 20 April 2013) in order to collect a sufficient sample size for statistical comparisons.

2015 OLC Lidar DEM: Wasco, WA

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — WSI collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Wasco County, WA, study area. The Oregon LiDAR Consortium's Wasco County...

Adaptive Data Processing Technique for Lidar-Assisted Control to Bridge the Gap between Lidar Systems and Wind Turbines: Preprint

Energy Technology Data Exchange (ETDEWEB)

Schlipf, David; Raach, Steffen; Haizmann, Florian; Cheng, Po Wen; Fleming, Paul; Scholbrock, Andrew, Krishnamurthy, Raghu; Boquet, Mathieu

2015-12-14

This paper presents first steps toward an adaptive lidar data processing technique crucial for lidar-assisted control in wind turbines. The prediction time and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be fully exploited, or can even result in harmful control action. An online analysis of the lidar and turbine data are necessary to continually reassess the prediction time and lidar data quality. In this work, a structured process to develop an analysis tool for the prediction time and a new hardware setup for lidar-assisted control are presented. The tool consists of an online estimation of the rotor effective wind speed from lidar and turbine data and the implementation of an online cross correlation to determine the time shift between both signals. Further, initial results from an ongoing campaign in which this system was employed for providing lidar preview for feed-forward pitch control are presented.

2015 OLC Lidar: Chelan

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Quantum Spatial has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Chelan FEMA study area. This study area is located in...

Extraction of Features from High-resolution 3D LiDaR Point-cloud Data

Science.gov (United States)

Keller, P.; Kreylos, O.; Hamann, B.; Kellogg, L. H.; Cowgill, E. S.; Yikilmaz, M. B.; Hering-Bertram, M.; Hagen, H.

2008-12-01

Airborne and tripod-based LiDaR scans are capable of producing new insight into geologic features by providing high-quality 3D measurements of the landscape. High-resolution LiDaR is a promising method for studying slip on faults, erosion, and other landscape-altering processes. LiDaR scans can produce up to several billion individual point returns associated with the reflection of a laser from natural and engineered surfaces; these point clouds are typically used to derive a high-resolution digital elevation model (DEM). Currently, there exist only few methods that can support the analysis of the data at full resolution and in the natural 3D perspective in which it was collected by working directly with the points. We are developing new algorithms for extracting features from LiDaR scans, and present method for determining the local curvature of a LiDaR data set, working directly with the individual point returns of a scan. Computing the curvature enables us to rapidly and automatically identify key features such as ridge-lines, stream beds, and edges of terraces. We fit polynomial surface patches via a moving least squares (MLS) approach to local point neighborhoods, determining curvature values for each point. The size of the local point neighborhood is defined by a user. Since both terrestrial and airborne LiDaR scans suffer from high noise, we apply additional pre- and post-processing smoothing steps to eliminate unwanted features. LiDaR data also captures objects like buildings and trees complicating greatly the task of extracting reliable curvature values. Hence, we use a stochastic approach to determine whether a point can be reliably used to estimate curvature or not. Additionally, we have developed a graph-based approach to establish connectivities among points that correspond to regions of high curvature. The result is an explicit description of ridge-lines, for example. We have applied our method to the raw point cloud data collected as part of the Geo

2009 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Lewis County, Washington

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Watershed Sciences, Inc. (WSI) collected Light Detection and Ranging (LiDAR) data for the Lewis County survey area for the Puget Sound LiDAR Consortium. This data...

Towards lidar-based mapping of tree age at the Arctic forest tundra ecotone.

Science.gov (United States)

Jensen, J.; Maguire, A.; Oelkers, R.; Andreu-Hayles, L.; Boelman, N.; D'Arrigo, R.; Griffin, K. L.; Jennewein, J. S.; Hiers, E.; Meddens, A. J.; Russell, M.; Vierling, L. A.; Eitel, J.

2017-12-01

Climate change may cause spatial shifts in the forest-tundra ecotone (FTE). To improve our ability to study these spatial shifts, information on tree demography along the FTE is needed. The objective of this study was to assess the suitability of lidar derived tree heights as a surrogate for tree age. We calculated individual tree age from 48 tree cores collected at basal height from white spruce (Picea glauca) within the FTE in northern Alaska. Tree height was obtained from terrestrial lidar scans (= 3 m), yielding strong predictive relationships between height and age (R2 = 0.86, RMSE 12.21 years, and R2 = 0.93, RMSE = 25.16 years, respectively). The slope coefficient for small and large tree models (16.83 and 12.98 years/m, respectively) indicate that small trees grow 1.3 times faster than large trees at these FTE study sites. Although a strong, predictive relationship between age and height is uncommon in light-limited forest environments, our findings suggest that the sparseness of trees within the FTE may explain the strong tree height-age relationships found herein. Further analysis of 36 additional tree cores recently collected within the FTE near Inuvik, Canada will be performed. Our preliminary analysis suggests that lidar derived tree height could be a reliable proxy for tree age at the FTE, thereby establishing a new technique for scaling tree structure and demographics across larger portions of this sensitive ecotone.

Charactering lidar optical subsystem using four quadrants method

Science.gov (United States)

Tian, Xiaomin; Liu, Dong; Xu, Jiwei; Wang, Zhenzhu; Wang, Bangxin; Wu, Decheng; Zhong, Zhiqing; Xie, Chenbo; Wang, Yingjian

2018-02-01

Lidar is a kind of active optical remote sensing instruments , can be applied to sound atmosphere with a high spatial and temporal resolution. Many parameter of atmosphere can be get by using different inverse algorithm with lidar backscatter signal. The basic setup of a lidar consist of a transmitter and a receiver. To make sure the quality of lidar signal data, the lidar must be calibrated before being used to measure the atmospheric variables. It is really significant to character and analyze lidar optical subsystem because a well equiped lidar optical subsystem contributes to high quality lidar signal data. we pay close attention to telecover test to character and analyze lidar optical subsystem.The telecover test is called four quadrants method consisting in dividing the telescope aperture in four quarants. when a lidar is well configured with lidar optical subsystem, the normalized signal from four qudrants will agree with each other on some level. Testing our WARL-II lidar by four quadrants method ,we find the signals of the four basically consistent with each other both in near range and in far range. But in detail, the signals in near range have some slight distinctions resulting from overlap function, some signals distinctions are induced by atmospheric instability.

The design, development, and test of balloonborne and groundbased lidar systems. Volume 1: Balloonborne coherent CO2 lidar system

Science.gov (United States)

Shepherd, O.; Aurilio, G.; Bucknam, R. D.; Hurd, A. G.; Rappaport, S. A.

1991-06-01

This is Volume 1 of a three volume final report on the design, development, and test of balloonborne and groundbased lidar systems. Volume 2 describes the flight test of Atmospheric Balloonborne Lidar Experiment, ABLE 2, which successfully made atmospheric density backscatter measurements during a flight over White Sands Missile Range. Volume 3 describes groundbased lidar development and measurements, including the design of a telescope dome lidar installation, the design of a transportable lidar shed for remote field sites, and field measurements of atmospheric and cloud backscatter from Ascension Island during SABLE 89 and Terciera, Azores during GABLE 90. In this volume, Volume 1, the design and fabrication of a balloonborne CO2 coherent lidar payload are described. The purpose of this payload is to measure, from altitudes greater than 20 km, the 10.6 micrometers backscatter from atmospheric aerosols as a function of altitude. Minor modifications to the lidar would provide for aerosol velocity measurements to be made. The lidar and payload system design was completed, and major components were fabricated and assembled. These tasks have been successfully completed, and recommendations for further lidar measurements and data analysis have been made.

Semiconductor Laser Wind Lidar for Turbine Control

DEFF Research Database (Denmark)

Hu, Qi

This thesis describes an experimentally oriented study of continuous wave (CW) coherent Doppler lidar system design. The main application is remote wind sensing for active wind turbine control using nacelle mounted lidar systems; and the primary focus is to devise an industrial instrument that can...... historical overview within the topic of wind lidar systems. Both the potential and the challenges of an industrialized wind lidar has been addressed here. Furthermore, the basic concept behind the heterodyne detection and a brief overview of the lidar signal processing is explained; and a simple...... investigation of the telescope truncation and lens aberrations is conducted, both numerically and experimentally. It is shown that these parameters dictate the spatial resolution of the lidar system, and have profound impact on the SNR. In this work, an all-semiconductor light source is used in the lidar design...

RECORDING CULTURAL HERITAGE USING TERRESTRIAL LASERSCANNING – DEALING WITH THE SYSTEM, THE HUGE DATASETS THEY CREATE AND WAYS TO EXTRACT THE NECESSARY DELIVERABLES YOU CAN WORK WITH

Directory of Open Access Journals (Sweden)

E. Christofori

2013-07-01

Full Text Available Recording Cultural Heritage objects using terrestrial laserscanning becomes more and more popular over the last years. Since terrestrial Laserscanning System (TLS Manufacturers have strongly increased the amount and speed of data captured with a single scan at each system upgrade and cutting down system costs the use of TLS Systems for recording cultural heritage is an option for recording worth to think about beside traditional methods like Photogrammetric. TLS Systems can be a great tool for capturing complex cultural heritage object within a short amount of time beside the traditional methods but can be a nightmare to handle for further process if not used right while capturing. Furthermore TLS Systems still have to be recognized as survey equipment, even though some of the manufactures promote them as everyday tool. They have to be used in an intelligent way having in mind the clients and the individual cultural objects needs. Thus the efficient way to use TLS Systems for data recording becomes a relevant topic to deal with the huge Amount of data the Systems collect while recording. Already small projects can turn into huge Pointcloud Datasets that End user, like Architects or Archaeologist neither can't deal with as their technical equipment doesn't fit the requirements of the Dataset nor do they have the software tools to use the Data as the current software tools still are high prized. Even the necessary interpretation of the Dataset can be a tough task if the people who have to work on with the Pointcloud aren't educated right in order to understand TLS and the results it creates. The use of TLS Systems has to have in mind the project requirements of the individual Heritage Object, like the required accuracy, standards for Levels of Details (e.g. "Empfehlungen für die Baudokumentation, Günther Eckstein, Germany", the required kind of Deliverables (Visualization, 2D Drawings, True Deformation Drawings, 3D Models, BIM or 4D �

A Three Tier Architecture Applied to LiDAR Processing and Monitoring

Directory of Open Access Journals (Sweden)

Efrat Jaeger-Frank

2006-01-01

Full Text Available Emerging Grid technologies enable solving scientific problems that involve large datasets and complex analyses, which in the past were often considered difficult to solve. Coordinating distributed Grid resources and computational processes requires adaptable interfaces and tools that provide modularized and configurable environments for accessing Grid clusters and executing high performance computational tasks. Computationally intensive processes are also subject to a high risk of component failures and thus require close monitoring. In this paper we describe a scientific workflow approach to coordinate various resources via data analysis pipelines. We present a three tier architecture for LiDAR interpolation and analysis, a high performance processing of point intensive datasets, utilizing a portal, a scientific workflow engine and Grid technologies. Our proposed solution is available to the community in a unified framework through a shared cyberinfrastructure, the GEON portal, enabling scientists to focus on their scientific work and not be concerned with the implementation of the underlying infrastructure.

Development of lidar techniques for environmental studies

Energy Technology Data Exchange (ETDEWEB)

Andersson, Mats

1996-09-01

The lidar group in Lund has performed many DIAL measurements with a mobile lidar system that was first described in 1987. The lidar system is based on a Nd:YAG-pumped dye laser. During the last few years the lidar group has focused on fluorescence imaging and mercury measurements in the troposphere. In 1994 we performed two campaigns: one fluorescence imaging measurement campaign outside Avignon, France and one unique lidar campaign at a mercury mine in Almaden, Spain. Both campaigns are described in this thesis. This thesis also describes how the mobile lidar system was updated with the graphical programming language LabVIEW to obtain a user friendly lidar system. The software controls the lidar system and analyses measured data. The measurement results are shown as maps of species concentration. All electronics and the major parts of the program are described. A new graphical technique to estimate wind speed from plumes is also discussed. First measurements have been performed with the new system. 31 refs, 19 figs, 1 tab

Quantification of waves in lidar observations of noctilucent clouds at scales from seconds to minutes

Science.gov (United States)

Kaifler, N.; Baumgarten, G.; Fiedler, J.; Lübken, F.-J.

2013-12-01

We present small-scale structures and waves observed in noctilucent clouds (NLC) by lidar at an unprecedented temporal resolution of 30 s or less. The measurements were taken with the Rayleigh/Mie/Raman lidar at the ALOMAR observatory in northern Norway (69° N) in the years 2008-2011. We find multiple layer NLC in 7.9% of the time for a brightness threshold of δ β = 12 × 10-10 m-1 sr-1. In comparison to 10 min averaged data, the 30 s dataset shows considerably more structure. For limited periods, quasi-monochromatic waves in NLC altitude variations are common, in accord with ground-based NLC imagery. For the combined dataset, on the other hand, we do not find preferred periods but rather significant periods at all timescales observed (1 min to 1 h). Typical wave amplitudes in the layer vertical displacements are 0.2 km with maximum amplitudes up to 2.3 km. Average spectral slopes of temporal altitude and brightness variations are -2.01 ± 0.25 for centroid altitude, -1.41 ± 0.24 for peak brightness and -1.73 ± 0.25 for integrated brightness. Evaluating a new single-pulse detection system, we observe altitude variations of 70 s period and spectral slopes down to a scale of 10 s. We evaluate the suitability of NLC parameters as tracers for gravity waves.

Installation report - Lidar

DEFF Research Database (Denmark)

Georgieva Yankova, Ginka; Villanueva, Héctor

The report describes the installation, configuration and data transfer for the ground-based lidar. The unit is provided by a customer but is installed and operated by DTU while in this project.......The report describes the installation, configuration and data transfer for the ground-based lidar. The unit is provided by a customer but is installed and operated by DTU while in this project....

Application of LiDAR to hydrologic flux estimation in Australian eucalypt forests (Invited)

Science.gov (United States)

Lane, P. N.; Mitchell, P. J.; Jaskierniak, D.; Hawthorne, S. N.; Griebel, A.

2013-12-01

inventory methods.These models can be used to parameterize hydrologic models to explore disturbance and age-related ET changes, and develop spatial-temporal maps of ET based on accurate representation of sapwood areas in complex terrain. The third example involves analyses of stand growth and long term streamflow response to thinning treatments in eucalyptus regnans forests. These forests have a strong age-streamflow relationship that can lead to streamflow declines as disturbed stands regrow. A set of thinning treatments in small experimental catchments (uniform, strip and understorey removal) were implemented in 1978-1982. The streamflow analysis supported early findings that flows increase and then relaxed, but also detected a flow decline below expected undisturbed levels for most catchments. Airborne LiDAR was used to analyse the structural recovery of treated stands, estimate LAI and canopy coverage via gap-fraction analysis, and scale ET measurements. The LiDAR data revealed the association of treatment type and regrowth and demonstrated that despite a net reduction in overstorey stem density, stand LAI had recovered and may explain the flow response. Finally, new terrestrial LiDAR instruments are being used in conjunction with eddy-covariance flux tower and sapflow measurement to measure fine temporal scale carbon-water dynamics. These instruments can be combined with airborne derived data to produce 3 dimensional canopy profile for linkage with ET processes.

Frequency Stepped Pulse Train Modulated Wind Sensing Lidar

DEFF Research Database (Denmark)

Olesen, Anders Sig; Pedersen, Anders Tegtmeier; Rottwitt, Karsten

2011-01-01

of frequency shifts corresponding to a specific distance. The spatial resolution depends on the repetition rate of the pulses in the pulse train. Directional wind measurements are shown and compared to a CW lidar measurement. The carrier to noise ratio of the FSPT lidar compared to a CW lidar is discussed......In this paper a wind sensing lidar utilizing a Frequency Stepped Pulse Train (FSPT) is demonstrated. One of the advantages in the FSTP lidar is that it enables direct measurement of wind speed as a function of distance from the lidar. Theoretically the FSPT lidar continuously produces measurements...... as is the case with a CW lidar, but at the same time with a spatial resolution, and without the range ambiguity originating from e.g. clouds. The FSPT lidar utilizes a frequency sweeping source for generation of the FSPT. The source generates a pulse train where each pulse has an optical carrier frequency...

Damage Assessment for Disaster Relief Efforts in Urban Areas Using Optical Imagery and LiDAR Data

Science.gov (United States)

Bahr, Thomas

2014-05-01

Imagery combined with LiDAR data and LiDAR-derived products provides a significant source of geospatial data which is of use in disaster mitigation planning. Feature rich building inventories can be constructed from tools with 3D rooftop extraction capabilities, and two dimensional outputs such as DSMs and DTMs can be used to generate layers to support routing efforts in Spatial Analyst and Network Analyst workflows. This allows us to leverage imagery and LiDAR tools for disaster mitigation or other scenarios. Software such as ENVI, ENVI LiDAR, and ArcGIS® Spatial and Network Analyst can therefore be used in conjunction to help emergency responders route ground teams in support of disaster relief efforts. This is exemplified by a case study against the background of the magnitude 7.0 earthquake that struck Haiti's capital city of Port-au-Prince on January 12, 2010. Soon after, both LiDAR data and an 8-band WorldView-2 scene were collected to map the disaster zone. The WorldView-2 scene was orthorectified and atmospherically corrected in ENVI prior to use. ENVI LiDAR was used to extract the DSM, DTM, buildings, and debris from the LiDAR data point cloud. These datasets provide a foundation for the 2D portion of the analysis. As the data was acquired over an area of dense urbanization, the majority of ground surfaces are roads, and standing buildings and debris are actually largely separable on the basis of elevation classes. To extract the road network of Port-au-Prince, the LiDAR-based feature height information was fused with the WorldView-2 scene, using ENVI's object-based feature extraction approach. This road network was converted to a network dataset for further analysis by the ArcGIS Network Analyst. For the specific case of Haiti, the distribution of blue tarps, used as accommodations for refugees, provided a spectrally distinct target. Pure blue tarp pixel spectra were selected from the WorldView-2 scene and input as a reference into ENVI's Spectral Angle

The design, development, and test of balloonborne and groundbased lidar systems. Volume 2: Flight test of Atmospheric Balloon Lidar Experiment, ABLE 2

Science.gov (United States)

Shepherd, O.; Bucknam, R. D.; Hurd, A. G.; Sheehan, W. H.

1991-06-01

This is Volume 3 of a three volume final report on the design, development, and test of balloonborne and groundbased lidar systems. Volume 1 describes the design and fabrication of a balloonborne CO2 coherent payload to measure the 10.6 micrometers backscatter from atmospheric aerosols as a function of altitude. Volume 2 describes the Aug. 1987 flight test of Atmospheric Balloonborne Lidar Experiment, ABLE 2. In this volume we describe groundbased lidar development and measurements. A design was developed for installation of the ABLE lidar in the GL rooftop dome. A transportable shed was designed to house the ABLE lidar at the various remote measurement sites. Refurbishment and modification of the ABLE lidar were completed to permit groundbased lidar measurements of clouds and aerosols. Lidar field measurements were made at Ascension Island during SABLE 89. Lidar field measurements were made at Terciera, Azores during GABLE 90. These tasks were successfully completed, and recommendations for further lidar measurements and data analysis were made.

2006 MDEQ Camp Shelby, MS Lidar Survey

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This metadata record describes the acquisition and processing of bare earth lidar data, raw point cloud lidar data, lidar intensity data, and floodmap breaklines...

2015 Puget Sound LiDAR Consortium (PSLC) LiDAR: WA DNR Lands (P1)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In June 2014, WSI, a Quantum Spatial Inc. (QSI) company, was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR)...

2014 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Willapa Valley (Delivery 1)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In January, 2014 WSI, a Quantum Spatial (QSI) company, was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR) data...

2015 Puget Sound LiDAR Consortium (PSLC) LiDAR: WA DNR Lands (P2)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In June 2014, WSI, a Quantum Spatial Inc. (QSI) company, was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR)...

Towards 250 m mapping of terrestrial primary productivity over Canada

Science.gov (United States)

Gonsamo, A.; Chen, J. M.

2011-12-01

Terrestrial ecosystems are an important part of the climate and global change systems. Their role in climate change and in the global carbon cycle is yet to be well understood. Dataset from satellite earth observation, coupled with numerical models provide the unique tools for monitoring the spatial and temporal dynamics of territorial carbon cycle. The Boreal Ecosystems Productivity Simulator (BEPS) is a remote sensing based approach to quantifying the terrestrial carbon cycle by that gross and net primary productivity (GPP and NPP) and terrestrial carbon sinks and sources expressed as net ecosystem productivity (NEP). We have currently implemented a scheme to map the GPP, NPP and NEP at 250 m for first time over Canada using BEPS model. This is supplemented by improved mapping of land cover and leaf area index (LAI) at 250 m over Canada from MODIS satellite dataset. The results from BEPS are compared with MODIS GPP product and further evaluated with estimated LAI from various sources to evaluate if the results capture the trend in amount of photosynthetic biomass distributions. Final evaluation will be to validate both BEPS and MODIS primary productivity estimates over the Fluxnet sites over Canada. The primary evaluation indicate that BEPS GPP estimates capture the over storey LAI variations over Canada very well compared to MODIS GPP estimates. There is a large offset of MODIS GPP, over-estimating the lower GPP value compared to BEPS GPP estimates. These variations will further be validated based on the measured values from the Fluxnet tower measurements over Canadian. The high resolution GPP (NPP) products at 250 m will further be used to scale the outputs between different ecosystem productivity models, in our case the Canadian carbon budget model of Canadian forest sector CBM-CFS) and the Integrated Terrestrial Ecosystem Carbon model (InTEC).

LiDAR derived high resolution topography: the next challenge for the analysis of terraces stability and vineyard soil erosion

Directory of Open Access Journals (Sweden)

Federico Preti

2013-09-01

Full Text Available The soil erosion in the vineyards is a critical issue that could affect their productivity, but also, when the cultivation is organized in terraces, increase the risk due to derived slope failure processes. If terraces are not correctly designed or maintained, a progressively increasing of gully erosion affects the structure of the walls. The results of this process is the increasing of connectivity and runoff. In order to overcome such issues it is really important to recognize in detail all the surface drainage paths, thus providing a basis upon which develop a suitable drainage system or provide structural measures for the soil erosion risk mitigation. In the last few years, the airborne LiDAR technology led to a dramatic increase in terrain information. Airborne LiDAR and Terrestrial Laser Scanner derived high-resolution Digital Terrain Models (DTMs have opened avenues for hydrologic and geomorphologic studies (Tarolli et al., 2009. In general, all the main surface process signatures are correctly recognized using a DTM with cell sizes of 1 m. However sub-meter grid sizes may be more suitable in those situations where the analysis of micro topography related to micro changes is critical for slope failures risk assessment or for the design of detailed drainage flow paths. The Terrestrial Laser Scanner (TLS has been proven to be an useful tool for such detailed field survey. In this work, we test the effectiveness of high resolution topography derived by airborne LiDAR and TLS for the recognition of areas subject to soil erosion risk in a typical terraced vineyard landscape of “Chianti Classico” (Tuscany, Italy. The algorithm proposed by Tarolli et al. (2013, for the automatic recognition of anthropic feature induced flow direction changes, has been tested. The results underline the effectiveness of LiDAR and TLS data in the analysis of soil erosion signatures in vineyards, and indicate the high resolution topography as a useful tool to

The potential to characterize ecological data with terrestrial laser scanning in Harvard Forest, MA

Science.gov (United States)

Boucher, P.; Saenz, E.; Li, Z.

2018-01-01

Contemporary terrestrial laser scanning (TLS) is being used widely in forest ecology applications to examine ecosystem properties at increasing spatial and temporal scales. Harvard Forest (HF) in Petersham, MA, USA, is a long-term ecological research (LTER) site, a National Ecological Observatory Network (NEON) location and contains a 35 ha plot which is part of Smithsonian Institution's Forest Global Earth Observatory (ForestGEO). The combination of long-term field plots, eddy flux towers and the detailed past historical records has made HF very appealing for a variety of remote sensing studies. Terrestrial laser scanners, including three pioneering research instruments: the Echidna Validation Instrument, the Dual-Wavelength Echidna Lidar and the Compact Biomass Lidar, have already been used both independently and in conjunction with airborne laser scanning data and forest census data to characterize forest dynamics. TLS approaches include three-dimensional reconstructions of a plot over time, establishing the impact of ice storm damage on forest canopy structure, and characterizing eastern hemlock (Tsuga canadensis) canopy health affected by an invasive insect, the hemlock woolly adelgid (Adelges tsugae). Efforts such as those deployed at HF are demonstrating the power of TLS as a tool for monitoring ecological dynamics, identifying emerging forest health issues, measuring forest biomass and capturing ecological data relevant to other disciplines. This paper highlights various aspects of the ForestGEO plot that are important to current TLS work, the potential for exchange between forest ecology and TLS, and emphasizes the strength of combining TLS data with long-term ecological field data to create emerging opportunities for scientific study. PMID:29503723

The potential to characterize ecological data with terrestrial laser scanning in Harvard Forest, MA.

Science.gov (United States)

Orwig, D A; Boucher, P; Paynter, I; Saenz, E; Li, Z; Schaaf, C

2018-04-06

Contemporary terrestrial laser scanning (TLS) is being used widely in forest ecology applications to examine ecosystem properties at increasing spatial and temporal scales. Harvard Forest (HF) in Petersham, MA, USA, is a long-term ecological research (LTER) site, a National Ecological Observatory Network (NEON) location and contains a 35 ha plot which is part of Smithsonian Institution's Forest Global Earth Observatory (ForestGEO). The combination of long-term field plots, eddy flux towers and the detailed past historical records has made HF very appealing for a variety of remote sensing studies. Terrestrial laser scanners, including three pioneering research instruments: the Echidna Validation Instrument, the Dual-Wavelength Echidna Lidar and the Compact Biomass Lidar, have already been used both independently and in conjunction with airborne laser scanning data and forest census data to characterize forest dynamics. TLS approaches include three-dimensional reconstructions of a plot over time, establishing the impact of ice storm damage on forest canopy structure, and characterizing eastern hemlock ( Tsuga canadensis ) canopy health affected by an invasive insect, the hemlock woolly adelgid ( Adelges tsugae ). Efforts such as those deployed at HF are demonstrating the power of TLS as a tool for monitoring ecological dynamics, identifying emerging forest health issues, measuring forest biomass and capturing ecological data relevant to other disciplines. This paper highlights various aspects of the ForestGEO plot that are important to current TLS work, the potential for exchange between forest ecology and TLS, and emphasizes the strength of combining TLS data with long-term ecological field data to create emerging opportunities for scientific study.

1km Global Terrestrial Carbon Flux: Estimations and Evaluations

Science.gov (United States)

Murakami, K.; Sasai, T.; Kato, S.; Saito, M.; Matsunaga, T.; Hiraki, K.; Maksyutov, S. S.

2017-12-01

Estimating global scale of the terrestrial carbon flux change with high accuracy and high resolution is important to understand global environmental changes. Furthermore the estimations of the global spatiotemporal distribution may contribute to the political and social activities such as REDD+. In order to reveal the current state of terrestrial carbon fluxes covering all over the world and a decadal scale. The satellite-based diagnostic biosphere model is suitable for achieving this purpose owing to observing on the present global land surface condition uniformly at some time interval. In this study, we estimated the global terrestrial carbon fluxes with 1km grids by using the terrestrial biosphere model (BEAMS). And we evaluated our new carbon flux estimations on various spatial scales and showed the transition of forest carbon stocks in some regions. Because BEAMS required high resolution meteorological data and satellite data as input data, we made 1km interpolated data using a kriging method. The data used in this study were JRA-55, GPCP, GOSAT L4B atmospheric CO2 data as meteorological data, and MODIS land product as land surface satellite data. Interpolating process was performed on the meteorological data because of insufficient resolution, but not on MODIS data. We evaluated our new carbon flux estimations using the flux tower measurement (FLUXNET2015 Datasets) in a point scale. We used 166 sites data for evaluating our model results. These flux sites are classified following vegetation type (DBF, EBF, ENF, mixed forests, grass lands, croplands, shrub lands, Savannas, wetlands). In global scale, the BEAMS estimations was underestimated compared to the flux measurements in the case of carbon uptake and release. The monthly variations of NEP showed relatively high correlations in DBF and mixed forests, but the correlation coefficients of EBF, ENF, and grass lands were less than 0.5. In the meteorological factors, air temperature and solar radiation showed

Multi-model analysis of terrestrial carbon cycles in Japan: reducing uncertainties in model outputs among different terrestrial biosphere models using flux observations

Science.gov (United States)

Ichii, K.; Suzuki, T.; Kato, T.; Ito, A.; Hajima, T.; Ueyama, M.; Sasai, T.; Hirata, R.; Saigusa, N.; Ohtani, Y.; Takagi, K.

2009-08-01

Terrestrial biosphere models show large uncertainties when simulating carbon and water cycles, and reducing these uncertainties is a priority for developing more accurate estimates of both terrestrial ecosystem statuses and future climate changes. To reduce uncertainties and improve the understanding of these carbon budgets, we investigated the ability of flux datasets to improve model simulations and reduce variabilities among multi-model outputs of terrestrial biosphere models in Japan. Using 9 terrestrial biosphere models (Support Vector Machine-based regressions, TOPS, CASA, VISIT, Biome-BGC, DAYCENT, SEIB, LPJ, and TRIFFID), we conducted two simulations: (1) point simulations at four flux sites in Japan and (2) spatial simulations for Japan with a default model (based on original settings) and an improved model (based on calibration using flux observations). Generally, models using default model settings showed large deviations in model outputs from observation with large model-by-model variability. However, after we calibrated the model parameters using flux observations (GPP, RE and NEP), most models successfully simulated seasonal variations in the carbon cycle, with less variability among models. We also found that interannual variations in the carbon cycle are mostly consistent among models and observations. Spatial analysis also showed a large reduction in the variability among model outputs, and model calibration using flux observations significantly improved the model outputs. These results show that to reduce uncertainties among terrestrial biosphere models, we need to conduct careful validation and calibration with available flux observations. Flux observation data significantly improved terrestrial biosphere models, not only on a point scale but also on spatial scales.

Calibration Methods for a Space Borne Backscatter Lidar

NARCIS (Netherlands)

Kunz, G.J.

1996-01-01

Lidar returns from cloud decks and from the Earth's surface are useful for calibrating single scatter lidar signals from space. To this end analytical methods (forward and backward) are presented for inverting lidar waveforms in terms of the path integrated lidar retum and the transmission losses

New Generation Lidar Technology and Applications

Science.gov (United States)

Spinhirne, James D.

1999-01-01

Lidar has been a tool for atmospheric research for several decades. Until recently routine operational use of lidar was not known. Problems have involved a lack of appropriate technology rather than a lack of applications. Within the last few years, lidar based on a new generation of solid state lasers and detectors have changed the situation. Operational applications for cloud and aerosol research applications are now well established. In these research applications, the direct height profiling capability of lidar is typically an adjunct to other types of sensing, both passive and active. Compact eye safe lidar with the sensitivity for ground based monitoring of all significant cloud and aerosol structure and the reliability to operate full time for several years is now in routine use. The approach is known as micro pulse lidar (MPL). For MPL the laser pulse repetition rate is in the kilohertz range and the pulse energies are in the micro-Joule range. The low pulse energy permits the systems to be eye safe and reliable with solid state lasers. A number of MPL systems have been deployed since 1992 at atmospheric research sites at a variety of global locations. Accurate monitoring of cloud and aerosol vertical distribution is a critical measurement for atmospheric radiation. An airborne application of lidar cloud and aerosol profiling is retrievals of parameters from combined lidar and passive sensing involving visible, infrared and microwave frequencies. A lidar based on a large pulse, solid state diode pumped ND:YAG laser has been deployed on the NASA ER-2 high altitude research aircraft along with multi-spectral visible/IR and microwave imaging radiometers since 1993. The system has shown high reliability in an extensive series of experimental projects for cloud remote sensing. The retrieval of cirrus radiation parameters is an effective application for combined lidar and passive sensing. An approved NASA mission will soon begin long term lidar observation of

Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data

Directory of Open Access Journals (Sweden)

Weimin Wang

2016-11-01

Full Text Available The segmentation of point clouds is an important aspect of automated processing tasks such as semantic extraction. However, the sparsity and non-uniformity of the point clouds gathered by the popular 3D mobile LiDAR devices pose many challenges for existing segmentation methods. To improve the segmentation results of point clouds from mobile LiDAR devices, we propose an optimized segmentation method based on Scanline Continuity Constraint (SLCC in this work. Unlike conventional scanline-based segmentation methods, SLCC clusters scanlines using the continuity constraints in terms of the distance as well as the direction of two consecutive points. In addition, scanline clusters are agglomerated not only into primitive geometrical shapes but also irregular shapes. Another downside to existing segmentation methods is that they are not capable of incremental processing. This causes unnecessary memory and time consumption for applications that require frame-wise segmentation or when new point clouds are added. In order to address this, we propose an incremental scheme—the Incremental Recursive Segmentation (IRIS, that can be easily applied to any segmentation method. IRIS is achieved by combining the segments of newly added point clouds and the previously segmented results. Furthermore, as an example application, we construct a processing pipeline consisting of plane fitting and surface reconstruction using the segmentation results. Finally, we evaluate the proposed methods on three datasets acquired from a handheld Velodyne HDL-32E LiDAR device. The experimental results verify the efficiency of IRIS for any segmentation method and the advantages of SLCC for processing mobile LiDAR data.

2012 USGS Lidar: Juneau (AK)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This task order is for planning, acquisition, processing, and derivative products of LiDAR data to be collected for Juneau, Alaska. LiDAR data, and derivative...

A Least Squares Collocation Method for Accuracy Improvement of Mobile LiDAR Systems

Directory of Open Access Journals (Sweden)

Qingzhou Mao

2015-06-01

Full Text Available In environments that are hostile to Global Navigation Satellites Systems (GNSS, the precision achieved by a mobile light detection and ranging (LiDAR system (MLS can deteriorate into the sub-meter or even the meter range due to errors in the positioning and orientation system (POS. This paper proposes a novel least squares collocation (LSC-based method to improve the accuracy of the MLS in these hostile environments. Through a thorough consideration of the characteristics of POS errors, the proposed LSC-based method effectively corrects these errors using LiDAR control points, thereby improving the accuracy of the MLS. This method is also applied to the calibration of misalignment between the laser scanner and the POS. Several datasets from different scenarios have been adopted in order to evaluate the effectiveness of the proposed method. The results from experiments indicate that this method would represent a significant improvement in terms of the accuracy of the MLS in environments that are essentially hostile to GNSS and is also effective regarding the calibration of misalignment.

Generic methodology for calibrating profiling nacelle lidars

DEFF Research Database (Denmark)

Borraccino, Antoine; Courtney, Michael; Wagner, Rozenn

Improving power performance assessment by measuring at different heights has been demonstrated using ground-based profiling LIDARs. More recently, nacelle-mounted lidars studies have shown promising capabilities to assess power performance. Using nacelle lidars avoids the erection of expensive me...

Iowa LiDAR Mapping Project

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — This is collection level metadata for LAS and ASCII data files from the statewide Iowa Lidar Project. The Iowa Light Detection and Ranging (LiDAR) Project collects...

Clear-air lidar dark band

Science.gov (United States)

Girolamo, Paolo Di; Scoccione, Andrea; Cacciani, Marco; Summa, Donato; Schween, Jan H.

2018-04-01

This paper illustrates measurements carried out by the Raman lidar BASIL in the frame of HOPE, revealing the presence of a clear-air dark band phenomenon (i.e. the appearance of a minimum in lidar backscatter echoes) in the upper portion of the convective boundary layer. The phenomenon is clearly distinguishable in the lidar backscatter echoes at 1064 nm. This phenomenon is attributed to the presence of lignite aerosol particles advected from the surrounding open pit mines in the vicinity of the measuring site.

Introduction of a simple-model-based land surface dataset for Europe

Science.gov (United States)

Orth, Rene; Seneviratne, Sonia I.

2015-04-01

Land surface hydrology can play a crucial role during extreme events such as droughts, floods and even heat waves. We introduce in this study a new hydrological dataset for Europe that consists of soil moisture, runoff and evapotranspiration (ET). It is derived with a simple water balance model (SWBM) forced with precipitation, temperature and net radiation. The SWBM dataset extends over the period 1984-2013 with a daily time step and 0.5° × 0.5° resolution. We employ a novel calibration approach, in which we consider 300 random parameter sets chosen from an observation-based range. Using several independent validation datasets representing soil moisture (or terrestrial water content), ET and streamflow, we identify the best performing parameter set and hence the new dataset. To illustrate its usefulness, the SWBM dataset is compared against several state-of-the-art datasets (ERA-Interim/Land, MERRA-Land, GLDAS-2-Noah, simulations of the Community Land Model Version 4), using all validation datasets as reference. For soil moisture dynamics it outperforms the benchmarks. Therefore the SWBM soil moisture dataset constitutes a reasonable alternative to sparse measurements, little validated model results, or proxy data such as precipitation indices. Also in terms of runoff the SWBM dataset performs well, whereas the evaluation of the SWBM ET dataset is overall satisfactory, but the dynamics are less well captured for this variable. This highlights the limitations of the dataset, as it is based on a simple model that uses uniform parameter values. Hence some processes impacting ET dynamics may not be captured, and quality issues may occur in regions with complex terrain. Even though the SWBM is well calibrated, it cannot replace more sophisticated models; but as their calibration is a complex task the present dataset may serve as a benchmark in future. In addition we investigate the sources of skill of the SWBM dataset and find that the parameter set has a similar

2014 OLC Lidar: Colville, WA

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — WSI, a Quantum Spatial company, has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Colville study area. This study area is...

2015 OLC Lidar DEM: Chelan

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Quantum Spatial has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Chelan FEMA study area. This study area is located in...

2015 OLC Lidar: Okanogan WA

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Quantum Spatial has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Okanogan FEMA study area. This study area is located in...

Occurrence and characteristics of mutual interference between LIDAR scanners

Science.gov (United States)

Kim, Gunzung; Eom, Jeongsook; Park, Seonghyeon; Park, Yongwan

2015-05-01

The LIDAR scanner is at the heart of object detection of the self-driving car. Mutual interference between LIDAR scanners has not been regarded as a problem because the percentage of vehicles equipped with LIDAR scanners was very rare. With the growing number of autonomous vehicle equipped with LIDAR scanner operated close to each other at the same time, the LIDAR scanner may receive laser pulses from other LIDAR scanners. In this paper, three types of experiments and their results are shown, according to the arrangement of two LIDAR scanners. We will show the probability that any LIDAR scanner will interfere mutually by considering spatial and temporal overlaps. It will present some typical mutual interference scenario and report an analysis of the interference mechanism.

Methods from Information Extraction from LIDAR Intensity Data and Multispectral LIDAR Technology

Science.gov (United States)

Scaioni, M.; Höfle, B.; Baungarten Kersting, A. P.; Barazzetti, L.; Previtali, M.; Wujanz, D.

2018-04-01

LiDAR is a consolidated technology for topographic mapping and 3D reconstruction, which is implemented in several platforms On the other hand, the exploitation of the geometric information has been coupled by the use of laser intensity, which may provide additional data for multiple purposes. This option has been emphasized by the availability of sensors working on different wavelength, thus able to provide additional information for classification of surfaces and objects. Several applications ofmonochromatic and multi-spectral LiDAR data have been already developed in different fields: geosciences, agriculture, forestry, building and cultural heritage. The use of intensity data to extract measures of point cloud quality has been also developed. The paper would like to give an overview on the state-of-the-art of these techniques, and to present the modern technologies for the acquisition of multispectral LiDAR data. In addition, the ISPRS WG III/5 on `Information Extraction from LiDAR Intensity Data' has collected and made available a few open data sets to support scholars to do research on this field. This service is presented and data sets delivered so far as are described.

METHODS FROM INFORMATION EXTRACTION FROM LIDAR INTENSITY DATA AND MULTISPECTRAL LIDAR TECHNOLOGY

Directory of Open Access Journals (Sweden)

M. Scaioni

2018-04-01

Full Text Available LiDAR is a consolidated technology for topographic mapping and 3D reconstruction, which is implemented in several platforms On the other hand, the exploitation of the geometric information has been coupled by the use of laser intensity, which may provide additional data for multiple purposes. This option has been emphasized by the availability of sensors working on different wavelength, thus able to provide additional information for classification of surfaces and objects. Several applications ofmonochromatic and multi-spectral LiDAR data have been already developed in different fields: geosciences, agriculture, forestry, building and cultural heritage. The use of intensity data to extract measures of point cloud quality has been also developed. The paper would like to give an overview on the state-of-the-art of these techniques, and to present the modern technologies for the acquisition of multispectral LiDAR data. In addition, the ISPRS WG III/5 on ‘Information Extraction from LiDAR Intensity Data’ has collected and made available a few open data sets to support scholars to do research on this field. This service is presented and data sets delivered so far as are described.

The Influence of Sub-Block Position on Performing Integrated Sensor Orientation Using In Situ Camera Calibration and Lidar Control Points

Directory of Open Access Journals (Sweden)

Felipe A. L. Costa

2018-02-01

Full Text Available The accuracy of photogrammetric and Lidar dataset integration is dependent on the quality of a group of parameters that models accurately the conditions of the system at the moment of the survey. In this sense, this paper aims to study the effect of the sub-block position in the entire image block to estimate the interior orientation parameters (IOP in flight conditions to be used in integrated sensor orientation (ISO. For this purpose, five sub-blocks were extracted in different regions of the entire block. Then, in situ camera calibrations were performed using sub-blocks and sets of Lidar control points (LCPs, computed by a three planes’ intersection extracted from the Lidar point cloud on building roofs. The ISO experiments were performed using IOPs from in situ calibrations, the entire image block, and the exterior orientation parameters (EOP from the direct sensor orientation (DSO. Analysis of the results obtained from the ISO experiments performed show that the IOP from the sub-block positioned at the center of the entire image block can be recommended.

2014 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Cedar River Watershed (Delivery 2)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In September 2013, WSI, a Quantum Spatial company (QSI), was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR)...

2014 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Cedar River Watershed (Delivery 1)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In September 2013, WSI, a Quantum Spatial company (QSI), was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR)...

SAR and LIDAR fusion: experiments and applications

Science.gov (United States)

Edwards, Matthew C.; Zaugg, Evan C.; Bradley, Joshua P.; Bowden, Ryan D.

2013-05-01

In recent years ARTEMIS, Inc. has developed a series of compact, versatile Synthetic Aperture Radar (SAR) systems which have been operated on a variety of small manned and unmanned aircraft. The multi-frequency-band SlimSAR has demonstrated a variety of capabilities including maritime and littoral target detection, ground moving target indication, polarimetry, interferometry, change detection, and foliage penetration. ARTEMIS also continues to build upon the radar's capabilities through fusion with other sensors, such as electro-optical and infrared camera gimbals and light detection and ranging (LIDAR) devices. In this paper we focus on experiments and applications employing SAR and LIDAR fusion. LIDAR is similar to radar in that it transmits a signal which, after being reflected or scattered by a target area, is recorded by the sensor. The differences are that a LIDAR uses a laser as a transmitter and optical sensors as a receiver, and the wavelengths used exhibit a very different scattering phenomenology than the microwaves used in radar, making SAR and LIDAR good complementary technologies. LIDAR is used in many applications including agriculture, archeology, geo-science, and surveying. Some typical data products include digital elevation maps of a target area and features and shapes extracted from the data. A set of experiments conducted to demonstrate the fusion of SAR and LIDAR data include a LIDAR DEM used in accurately processing the SAR data of a high relief area (mountainous, urban). Also, feature extraction is used in improving geolocation accuracy of the SAR and LIDAR data.

A cloud masking algorithm for EARLINET lidar systems

Science.gov (United States)

Binietoglou, Ioannis; Baars, Holger; D'Amico, Giuseppe; Nicolae, Doina

2015-04-01

Cloud masking is an important first step in any aerosol lidar processing chain as most data processing algorithms can only be applied on cloud free observations. Up to now, the selection of a cloud-free time interval for data processing is typically performed manually, and this is one of the outstanding problems for automatic processing of lidar data in networks such as EARLINET. In this contribution we present initial developments of a cloud masking algorithm that permits the selection of the appropriate time intervals for lidar data processing based on uncalibrated lidar signals. The algorithm is based on a signal normalization procedure using the range of observed values of lidar returns, designed to work with different lidar systems with minimal user input. This normalization procedure can be applied to measurement periods of only few hours, even if no suitable cloud-free interval exists, and thus can be used even when only a short period of lidar measurements is available. Clouds are detected based on a combination of criteria including the magnitude of the normalized lidar signal and time-space edge detection performed using the Sobel operator. In this way the algorithm avoids misclassification of strong aerosol layers as clouds. Cloud detection is performed using the highest available time and vertical resolution of the lidar signals, allowing the effective detection of low-level clouds (e.g. cumulus humilis). Special attention is given to suppress false cloud detection due to signal noise that can affect the algorithm's performance, especially during day-time. In this contribution we present the details of algorithm, the effect of lidar characteristics (space-time resolution, available wavelengths, signal-to-noise ratio) to detection performance, and highlight the current strengths and limitations of the algorithm using lidar scenes from different lidar systems in different locations across Europe.

Remote Sensing of Aerosol Backscatter and Earth Surface Targets By Use of An Airborne Focused Continuous Wave CO2 Doppler Lidar Over Western North America

Science.gov (United States)

Jarzembski, Maurice A.; Srivastava, Vandana; Goodman, H. Michael (Technical Monitor)

2000-01-01

Airborne lidar systems are used to determine wind velocity and to measure aerosol or cloud backscatter variability. Atmospheric aerosols, being affected by local and regional sources, show tremendous variability. Continuous wave (cw) lidar can obtain detailed aerosol loading with unprecedented high resolution (3 sec) and sensitivity (1 mg/cubic meter) as was done during the 1995 NASA Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) mission over western North America and the Pacific Ocean. Backscatter variability was measured at a 9.1 micron wavelength cw focused CO2 Doppler lidar for approximately 52 flight hours, covering an equivalent horizontal distance of approximately 30,000 km in the troposphere. Some quasi-vertical backscatter profiles were also obtained during various ascents and descents at altitudes that ranged from approximately 0.1 to 12 km. Similarities and differences for aerosol loading over land and ocean were observed. Mid-tropospheric aerosol backscatter background mode was approximately 6 x 10(exp -11)/ms/r, consistent with previous lidar datasets. While these atmospheric measurements were made, the lidar also retrieved a distinct backscatter signal from the Earth's surface from the unfocused part of the focused cw lidar beam during aircraft rolls. Atmospheric backscatter can be highly variable both spatially and temporally, whereas, Earth-surface backscatter is relatively much less variant and can be quite predictable. Therefore, routine atmospheric backscatter measurements by an airborne lidar also give Earth surface backscatter which can allow for investigating the Earth terrain. In the case where the Earth's surface backscatter is coming from a well-known and fairly uniform region, then it can potentially offer lidar calibration opportunities during flight. These Earth surface measurements over varying Californian terrain during the mission were compared with laboratory backscatter measurements using the same lidar of various

2012 MEGIS Topographic Lidar: Statewide Lidar Project Areas 2 and 3 (Mid-Coastal Cleanup), Maine

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — LiDAR data is a remotely sensed high resolution elevation data collected by an airborne platform. The LiDAR sensor uses a combination of laser range finding, GPS...

Standards – An Important Step for the (Public Use of Lidars

Directory of Open Access Journals (Sweden)

Althausen Dietrich

2016-01-01

Full Text Available Lidar standards are needed to ensure quality and lidar product control at the interface between lidar manufacturers and lidar users. Meanwhile three lidar standards have been published by German and international standardization organizations. This paper describes the cooperation between the lidar technique inventors, lidar instrument constructors, and lidar product users to establish useful standards. Presently a backscatter lidar standard is elaborated in Germany. Key points of this standard are presented here. Two German standards were already accepted as international standards by the International Organization for Standardization (ISO. Hence, German and international organizations for the establishment of lidar standards are introduced to encourage a cooperative work on lidar standards by lidar scientists.

The impact of forest structure and spatial scale on the relationship between ground plot above ground biomass and GEDI lidar waveforms

Science.gov (United States)

Armston, J.; Marselis, S.; Hancock, S.; Duncanson, L.; Tang, H.; Kellner, J. R.; Calders, K.; Disney, M.; Dubayah, R.

2017-12-01

The NASA Global Ecosystem Dynamics Investigation (GEDI) will place a multi-beam waveform lidar instrument on the International Space Station (ISS) to provide measurements of forest vertical structure globally. These measurements of structure will underpin empirical modelling of above ground biomass density (AGBD) at the scale of individual GEDI lidar footprints (25m diameter). The GEDI pre-launch calibration strategy for footprint level models relies on linking AGBD estimates from ground plots with GEDI lidar waveforms simulated from coincident discrete return airborne laser scanning data. Currently available ground plot data have variable and often large uncertainty at the spatial resolution of GEDI footprints due to poor colocation, allometric model error, sample size and plot edge effects. The relative importance of these sources of uncertainty partly depends on the quality of ground measurements and region. It is usually difficult to know the magnitude of these uncertainties a priori so a common approach to mitigate their influence on model training is to aggregate ground plot and waveform lidar data to a coarser spatial scale (0.25-1ha). Here we examine the impacts of these principal sources of uncertainty using a 3D simulation approach. Sets of realistic tree models generated from terrestrial laser scanning (TLS) data or parametric modelling matched to tree inventory data were assembled from four contrasting forest plots across tropical rainforest, deciduous temperate forest, and sclerophyll eucalypt woodland sites. These tree models were used to simulate geometrically explicit 3D scenes with variable tree density, size class and spatial distribution. GEDI lidar waveforms are simulated over ground plots within these scenes using monte carlo ray tracing, allowing the impact of varying ground plot and waveform colocation error, forest structure and edge effects on the relationship between ground plot AGBD and GEDI lidar waveforms to be directly assessed. We

Turbulence estimation from a continuous-wave scanning lidar (SpinnerLidar)

DEFF Research Database (Denmark)

Barnhoorn, J.G.; Sjöholm, Mikael; Mikkelsen, Torben Krogh

2017-01-01

out, and 2) the mixing of velocity covariances from other components into the line-of-sight variance measurements. However, turbulence measurements based on upwind horizontal rotor plane scanning of the line-of-sight variance measurements combined with ensemble-averaged Doppler spectra width...... deviations averaged over 10-min sampling periods are compared. Lidar variances are inherently more prone to noise which always yields a positive bias. The 5.3 % higher turbulence level measured by the SpinnerLidar relative to the cup anemometer may equally well be attributed to truncation of turbulent...

Saginaw Bay, MI LiDAR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME:(NRCS) Saginaw Bay, MI LiDAR LiDAR Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No. G11PD01254 Woolpert Order...

Quantification of waves in lidar observations of noctilucent clouds at scales from seconds to minutes

Directory of Open Access Journals (Sweden)

N. Kaifler

2013-12-01

Full Text Available We present small-scale structures and waves observed in noctilucent clouds (NLC by lidar at an unprecedented temporal resolution of 30 s or less. The measurements were taken with the Rayleigh/Mie/Raman lidar at the ALOMAR observatory in northern Norway (69° N in the years 2008–2011. We find multiple layer NLC in 7.9% of the time for a brightness threshold of δ β = 12 × 10−10 m−1 sr−1. In comparison to 10 min averaged data, the 30 s dataset shows considerably more structure. For limited periods, quasi-monochromatic waves in NLC altitude variations are common, in accord with ground-based NLC imagery. For the combined dataset, on the other hand, we do not find preferred periods but rather significant periods at all timescales observed (1 min to 1 h. Typical wave amplitudes in the layer vertical displacements are 0.2 km with maximum amplitudes up to 2.3 km. Average spectral slopes of temporal altitude and brightness variations are −2.01 ± 0.25 for centroid altitude, −1.41 ± 0.24 for peak brightness and −1.73 ± 0.25 for integrated brightness. Evaluating a new single-pulse detection system, we observe altitude variations of 70 s period and spectral slopes down to a scale of 10 s. We evaluate the suitability of NLC parameters as tracers for gravity waves.

Detection of Wind Evolution and Lidar Trajectory Optimization for Lidar-Assisted Wind Turbine Control

Directory of Open Access Journals (Sweden)

David Schlipf

2015-11-01

Full Text Available Recent developments in remote sensing are offering a promising opportunity to rethink conventional control strategies of wind turbines. With technologies such as lidar, the information about the incoming wind field - the main disturbance to the system - can be made available ahead of time. Initial field testing of collective pitch feedforward control shows, that lidar measurements are only beneficial if they are filtered properly to avoid harmful control action. However, commercial lidar systems developed for site assessment are usually unable to provide a usable signal for real time control. Recent research shows, that the correlation between the measurement of rotor effective wind speed and the turbine reaction can be modeled and that the model can be used to optimize a scan pattern. This correlation depends on several criteria such as turbine size, position of the measurements, measurement volume, and how the wind evolves on its way towards the rotor. In this work the longitudinal wind evolution is identified with the line-of-sight measurements of a pulsed lidar system installed on a large commercial wind turbine. This is done by staring directly into the inflowing wind during operation of the turbine and fitting the coherence between the wind at different measurement distances to an exponential model taking into account the yaw misalignment, limitation to line-of-sight measurements and the pulse volume. The identified wind evolution is then used to optimize the scan trajectory of a scanning lidar for lidar-assisted feedforward control in order to get the best correlation possible within the constraints of the system. Further, an adaptive filer is fitted to the modeled correlation to avoid negative impact of feedforward control because of uncorrelated frequencies of the wind measurement. The main results of the presented work are a first estimate of the wind evolution in front of operating wind turbines and an approach which manufacturers of

Holographic Raman lidar

International Nuclear Information System (INIS)

Andersen, G.

2000-01-01

Full text: We have constructed a Raman lidar system that incorporates a holographic optical element. By resolving just 3 nitrogen lines in the Resonance Raman spectroscopy (RRS) spectrum, temperature fits as good as 1% at altitudes of 20km can be made in 30 minutes. Due to the narrowband selectivity of the HOE, the lidar provides measurements over a continuous 24hr period. By adding a 4th channel to capture the Rayleigh backscattered light, temperature profiles can be extended to 80km

Pointing Verification Method for Spaceborne Lidars

Directory of Open Access Journals (Sweden)

Axel Amediek

2017-01-01

Full Text Available High precision acquisition of atmospheric parameters from the air or space by means of lidar requires accurate knowledge of laser pointing. Discrepancies between the assumed and actual pointing can introduce large errors due to the Doppler effect or a wrongly assumed air pressure at ground level. In this paper, a method for precisely quantifying these discrepancies for airborne and spaceborne lidar systems is presented. The method is based on the comparison of ground elevations derived from the lidar ranging data with high-resolution topography data obtained from a digital elevation model and allows for the derivation of the lateral and longitudinal deviation of the laser beam propagation direction. The applicability of the technique is demonstrated by using experimental data from an airborne lidar system, confirming that geo-referencing of the lidar ground spot trace with an uncertainty of less than 10 m with respect to the used digital elevation model (DEM can be obtained.

Lidar-Radiometer Inversion Code (LIRIC) for the Retrieval of Vertical Aerosol Properties from Combined Lidar Radiometer Data: Development and Distribution in EARLINET

Science.gov (United States)

Chaikovsky, A.; Dubovik, O.; Holben, Brent N.; Bril, A.; Goloub, P.; Tanre, D.; Pappalardo, G.; Wandinger, U.; Chaikovskaya, L.; Denisov, S.;

Terrestrial carbon turnover time constraints on future carbon cycle-climate feedback

Science.gov (United States)

Fan, N.; Carvalhais, N.; Reichstein, M.

2017-12-01

Understanding the terrestrial carbon cycle-climate feedback is essential to reduce the uncertainties resulting from the between model spread in prognostic simulations (Friedlingstein et al., 2006). One perspective is to investigate which factors control the variability of the mean residence times of carbon in the land surface, and how these may change in the future, consequently affecting the response of the terrestrial ecosystems to changes in climate as well as other environmental conditions. Carbon turnover time of the whole ecosystem is a dynamic parameter that represents how fast the carbon cycle circulates. Turnover time τ is an essential property for understanding the carbon exchange between the land and the atmosphere. Although current Earth System Models (ESMs), supported by GVMs for the description of the land surface, show a strong convergence in GPP estimates, but tend to show a wide range of simulated turnover times (Carvalhais, 2014). Thus, there is an emergent need of constraints on the projected response of the balance between terrestrial carbon fluxes and carbon stock which will give us more certainty in response of carbon cycle to climate change. However, the difficulty of obtaining such a constraint is partly due to lack of observational data on temporal change of terrestrial carbon stock. Since more new datasets of carbon stocks such as SoilGrid (Hengl, et al., 2017) and fluxes such as GPP (Jung, et al., 2017) are available, improvement in estimating turnover time can be achieved. In addition, previous study ignored certain aspects such as the relationship between τ and nutrients, fires, etc. We would like to investigate τ and its role in carbon cycle by combining observatinoal derived datasets and state-of-the-art model simulations.

Atmospheric lidar: legislative, scientific and technological aspects; Lidar atmosferico. Aspetti legislativi, scientifici e tecnologici

Energy Technology Data Exchange (ETDEWEB)

Barbini, R.; Colao, F.; Fiorani, L.; Palucci, A. [ENEA, Divisione Fisica Applicata, Centro Ricerche Frascati, Frascati, RM (Italy)

2000-07-01

The Atmospheric Lidar is one of the systems of the Mobile Laboratory of Laser Remote Sensing under development at the ENEA Research Center of Frascati. This technical report addresses the legislative, scientific and technological aspects that are the basis for the identification of the requirements, the definition of the architecture and the fixation of the specifications of the Atmospheric Lidar. The problems of air pollution are introduced in section 2. A summary of the Italian laws on that topic is then given. Section 4 provides a survey of the atmospheric measurements that can be achieved with the lidar. The sensitivity in the monitoring of pollutants is discussed in section 5. The other systems of the Mobile Laboratory of Laser Remote Sensing are shortly described in section 6. The last section is devoted to conclusions and perspectives. [Italian] Il lidar atmosferico e' uno dei sistemi del Laboratorio Mobile di Telerilevamento Laser in corso di realizzazione presso il Centro Ricerche di Frascati dell'ENEA. Questo rapporto tecnico discute gli aspetti legislativi, scientifici, tecnologici che sono alla base dell'individuazione dei requisiti, della definizione dell'architettura e della fissazione delle specifiche del Lidar atmosferico. La problematica dell'inquinamento dell'aria e' introdotta nella sezione 2. Segue un riassunto della legislazione italiana su tale tematica. La sezione 4 offre una panoramica delle misure atmosferiche realizzabili con il Lidar. La sensibilita' nel monitoraggio di inquinanti e' discussa nella sezione 5. Gli altri sistemi del Laboratorio Mobile di Telerilevamento Laser sono descritti brevemente nella sezione 6. L'ultima sezione e' dedicata alle conclusioni e alle prospettive.

Nonlinear filtering for LIDAR signal processing

Directory of Open Access Journals (Sweden)

D. G. Lainiotis

1996-01-01

Full Text Available LIDAR (Laser Integrated Radar is an engineering problem of great practical importance in environmental monitoring sciences. Signal processing for LIDAR applications involves highly nonlinear models and consequently nonlinear filtering. Optimal nonlinear filters, however, are practically unrealizable. In this paper, the Lainiotis's multi-model partitioning methodology and the related approximate but effective nonlinear filtering algorithms are reviewed and applied to LIDAR signal processing. Extensive simulation and performance evaluation of the multi-model partitioning approach and its application to LIDAR signal processing shows that the nonlinear partitioning methods are very effective and significantly superior to the nonlinear extended Kalman filter (EKF, which has been the standard nonlinear filter in past engineering applications.

Derivation of Sky-View Factors from LIDAR Data

Science.gov (United States)

Kidd, Christopher; Chapman, Lee

2013-01-01

The use of Lidar (Light Detection and Ranging), an active light-emitting instrument, is becoming increasingly common for a range of potential applications. Its ability to provide fine resolution spatial and vertical resolution elevation data makes it ideal for a wide range of studies. This paper demonstrates the capability of Lidar data to measure sky view factors (SVF). The Lidar data is used to generate a spatial map of SVFs which are then compared against photographically-derived SVF at selected point locations. At each location three near-surface elevations measurements were taken and compared with collocated Lidar-derived estimated. It was found that there was generally good agreement between the two methodologies, although with decreasing SVF the Lidar-derived technique tended to overestimate the SVF: this can be attributed in part to the spatial resolution of the Lidar sampling. Nevertheless, airborne Lidar systems can map sky view factors over a large area easily, improving the utility of such data in atmospheric and meteorological models.

On Feature Extraction from Large Scale Linear LiDAR Data

Science.gov (United States)

Acharjee, Partha Pratim

Airborne light detection and ranging (LiDAR) can generate co-registered elevation and intensity map over large terrain. The co-registered 3D map and intensity information can be used efficiently for different feature extraction application. In this dissertation, we developed two algorithms for feature extraction, and usages of features for practical applications. One of the developed algorithms can map still and flowing waterbody features, and another one can extract building feature and estimate solar potential on rooftops and facades. Remote sensing capabilities, distinguishing characteristics of laser returns from water surface and specific data collection procedures provide LiDAR data an edge in this application domain. Furthermore, water surface mapping solutions must work on extremely large datasets, from a thousand square miles, to hundreds of thousands of square miles. National and state-wide map generation/upgradation and hydro-flattening of LiDAR data for many other applications are two leading needs of water surface mapping. These call for as much automation as possible. Researchers have developed many semi-automated algorithms using multiple semi-automated tools and human interventions. This reported work describes a consolidated algorithm and toolbox developed for large scale, automated water surface mapping. Geometric features such as flatness of water surface, higher elevation change in water-land interface and, optical properties such as dropouts caused by specular reflection, bimodal intensity distributions were some of the linear LiDAR features exploited for water surface mapping. Large-scale data handling capabilities are incorporated by automated and intelligent windowing, by resolving boundary issues and integrating all results to a single output. This whole algorithm is developed as an ArcGIS toolbox using Python libraries. Testing and validation are performed on a large datasets to determine the effectiveness of the toolbox and results are

Coherent Lidar Turbulence Measurement for Gust Load Alleviation

Science.gov (United States)

Bogue, Rodney K.; Ehernberger, L. J.; Soreide, David; Bagley, Hal

1996-01-01

Atmospheric turbulence adversely affects operation of commercial and military aircraft and is a design constraint. The airplane structure must be designed to survive the loads imposed by turbulence. Reducing these loads allows the airplane structure to be lighter, a substantial advantage for a commercial airplane. Gust alleviation systems based on accelerometers mounted in the airplane can reduce the maximum gust loads by a small fraction. These systems still represent an economic advantage. The ability to reduce the gust load increases tremendously if the turbulent gust can be measured before the airplane encounters it. A lidar system can make measurements of turbulent gusts ahead of the airplane, and the NASA Airborne Coherent Lidar for Advanced In-Flight Measurements (ACLAIM) program is developing such a lidar. The ACLAIM program is intended to develop a prototype lidar system for use in feasibility testing of gust load alleviation systems and other airborne lidar applications, to define applications of lidar with the potential for improving airplane performance, and to determine the feasibility and benefits of these applications. This paper gives an overview of the ACLAIM program, describes the lidar architecture for a gust alleviation system, and describes the prototype ACLAIM lidar system.

Airborne LIDAR Power Line Classification Based on Spatial Topological Structure Characteristics

Science.gov (United States)

Wang, Y.; Chen, Q.; Li, K.; Zheng, D.; Fang, J.

2017-09-01

Automatic extraction of power lines has become a topic of great importance in airborne LiDAR data processing for transmission line management. In this paper, we present a new, fully automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) neighbourhood selection, (iii) feature extraction based on spatial topology, and (iv) SVM classification. In a detailed evaluation involving seven neighbourhood definitions, 26 geometric features and two datasets, we demonstrated that the use of multi-scale neighbourhoods for individual 3D points significantly improved the power line classification. Additionally, we showed that the spatial topological features may even further improve the results while reducing data processing time.

Lidar instruments for ESA Earth observation missions

Science.gov (United States)

Hélière, Arnaud; Armandillo, Errico; Durand, Yannig; Culoma, Alain; Meynart, Roland

2017-11-01

The idea of deploying a lidar system on an Earthorbiting satellite stems from the need for continuously providing profiles of our atmospheric structure with high accuracy and resolution and global coverage. Interest in this information for climatology, meteorology and the atmospheric sciences in general is huge. Areas of application range from the determination of global warming and greenhouse effects, to monitoring the transport and accumulation of pollutants in the different atmospheric regions (such as the recent fires in Southeast Asia), to the assessment of the largely unknown microphysical properties and the structural dynamics of the atmosphere itself. Spaceborne lidar systems have been the subject of extensive investigations by the European Space Agency since mid 1970's, resulting in mission and instrument concepts, such as ATLID, the cloud backscatter lidar payload of the EarthCARE mission, ALADIN, the Doppler wind lidar of the Atmospheric Dynamics Mission (ADM) and more recently a water vapour Differential Absorption Lidar considered for the WALES mission. These studies have shown the basic scientific and technical feasibility of spaceborne lidars, but they have also demonstrated their complexity from the instrument viewpoint. As a result, the Agency undertook technology development in order to strengthen the instrument maturity. This is the case for ATLID, which benefited from a decade of technology development and supporting studies and is now studied in the frame of the EarthCARE mission. ALADIN, a Direct Detection Doppler Wind Lidar operating in the Ultra -Violet, will be the 1st European lidar to fly in 2007 as payload of the Earth Explorer Core Mission ADM. WALES currently studied at the level of a phase A, is based upon a lidar operating at 4 wavelengths in near infrared and aims to profile the water vapour in the lower part of the atmosphere with high accuracy and low bias. Lastly, the European Space Agency is extending the lidar instrument field

IEA Task 32: Wind Lidar Systems for Wind Energy Deployment (LIDAR)

Energy Technology Data Exchange (ETDEWEB)

Kuhn, Martin; Trabucchi, Davide; Clifton, Andrew; Courtney, Mike; Rettenmeier, Andreas

2016-05-25

Under the International Energy Agency Wind Implementing Agreement (IEA Wind) Task 11, researchers started examining novel applications for remote sensing and the issues around them during the 51st topical expert meeting about remote sensing in January 2007. The 59th topical expert meeting organized by Task 11 in October 2009 was also dedicated to remote sensing, and the first draft of the Task's recommended practices on remote sensing was published in January 2013. The results of the Task 11 topical expert meetings provided solid groundwork for a new IEA Wind Task 32 on wind lidar technologies. Members of the wind community identified the need to consolidate the knowledge about wind lidar systems to facilitate their use, and to investigate how to exploit the advantages offered by this technology. This was the motivation that led to the start of IEA Wind Task 32 'Lidar Application for Wind Energy Deployment' in November 2011. The kick-off was meeting was held in May 2012.

Lidar extinction measurement in the mid infrared

Science.gov (United States)

Mitev, Valentin; Babichenko, S.; Borelli, R.; Fiorani, L.; Grigorov, I.; Nuvoli, M.; Palucci, A.; Pistilli, M.; Puiu, Ad.; Rebane, Ott; Santoro, S.

2014-11-01

We present a lidar measurement of atmospheric extinction coefficient. The measurement is performed by inversion of the backscatter lidar signal at wavelengths 3'000nm and 3'500nm. The inversion of the backscatter lidar signal was performed with constant extinction-to-backscatter ration values of 104 and exponential factor 0.1.

2012 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Quinault River Watershed, Washington (Delivery 1)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Watershed Sciences, Inc. (WSI) collected Light Detection and Ranging (LiDAR) data on the Quinault watershed survey area for the Puget Sound LiDAR Consortium. This...

LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

Science.gov (United States)

Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

Analysis of long-term terrestrial water storage variations in the Yangtze River basin

NARCIS (Netherlands)

Huang, Ying; Salama, M.S.; Krol, Martinus S.; van der Velde, R.; Hoekstra, Arjen Ysbert; Zhou, Y.; Su, Zhongbo

2013-01-01

In this study, we analyze 32 yr of terrestrial water storage (TWS) data obtained from the Interim Reanalysis Data (ERA-Interim) and Noah model from the Global Land Data Assimilation System (GLDAS-Noah) for the period 1979 to 2010. The accuracy of these datasets is validated using 26 yr (1979–2004)

Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to maximize detection of forested inundation extent in the Delmarva Peninsula, USA

Science.gov (United States)

Vanderhoof, Melanie; Distler, Hayley; Mendiola, Di Ana; Lang, Megan

2017-01-01

Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring within both Maryland and Delaware. We integrated six quad-polarized Radarsat-2 images, Worldview-3 imagery, and an enhanced topographic wetness index in a random forest model. Output maps were filtered using light detection and ranging (lidar)-derived depressions to maximize the accuracy of forested inundation extent. Overall accuracy within the integrated and filtered model was 94.3%, with 5.5% and 6.0% errors of omission and commission for inundation, respectively. Accuracy of inundation maps obtained using Radarsat-2 alone were likely detrimentally affected by less than ideal angles of incidence and recent precipitation, but were likely improved by targeting the period between snowmelt and leaf-out for imagery collection. Across the six Radarsat-2 dates, filtering inundation outputs by lidar-derived depressions slightly elevated errors of omission for water (+1.0%), but decreased errors of commission (−7.8%), resulting in an average increase of 5.4% in overall accuracy. Depressions were derived from lidar datasets collected under both dry and average wetness conditions. Although antecedent wetness conditions influenced the abundance and total area mapped as depression, the two versions of the depression datasets showed a similar ability to reduce error in the inundation maps. Accurate mapping of surface water is critical to predicting and monitoring the effect of human-induced change and interannual variability on water quantity and quality.

Calibrating nacelle lidars

Energy Technology Data Exchange (ETDEWEB)

Courtney, M.

2013-01-15

Nacelle mounted, forward looking wind lidars are beginning to be used to provide reference wind speed measurements for the power performance testing of wind turbines. In such applications, a formal calibration procedure with a corresponding uncertainty assessment will be necessary. This report presents four concepts for performing such a nacelle lidar calibration. Of the four methods, two are found to be immediately relevant and are pursued in some detail. The first of these is a line of sight calibration method in which both lines of sight (for a two beam lidar) are individually calibrated by accurately aligning the beam to pass close to a reference wind speed sensor. A testing procedure is presented, reporting requirements outlined and the uncertainty of the method analysed. It is seen that the main limitation of the line of sight calibration method is the time required to obtain a representative distribution of radial wind speeds. An alternative method is to place the nacelle lidar on the ground and incline the beams upwards to bisect a mast equipped with reference instrumentation at a known height and range. This method will be easier and faster to implement and execute but the beam inclination introduces extra uncertainties. A procedure for conducting such a calibration is presented and initial indications of the uncertainties given. A discussion of the merits and weaknesses of the two methods is given together with some proposals for the next important steps to be taken in this work. (Author)

Estimation of Global 1km-grid Terrestrial Carbon Exchange Part I: Developing Inputs and Modelling

Science.gov (United States)

Sasai, T.; Murakami, K.; Kato, S.; Matsunaga, T.; Saigusa, N.; Hiraki, K.

2015-12-01

Global terrestrial carbon cycle largely depends on a spatial pattern in land cover type, which is heterogeneously-distributed over regional and global scales. However, most studies, which aimed at the estimation of carbon exchanges between ecosystem and atmosphere, remained within several tens of kilometers grid spatial resolution, and the results have not been enough to understand the detailed pattern of carbon exchanges based on ecological community. Improving the sophistication of spatial resolution is obviously necessary to enhance the accuracy of carbon exchanges. Moreover, the improvement may contribute to global warming awareness, policy makers and other social activities. In this study, we show global terrestrial carbon exchanges (net ecosystem production, net primary production, and gross primary production) with 1km-grid resolution. As methodology for computing the exchanges, we 1) developed a global 1km-grid climate and satellite dataset based on the approach in Setoyama and Sasai (2013); 2) used the satellite-driven biosphere model (Biosphere model integrating Eco-physiological And Mechanistic approaches using Satellite data: BEAMS) (Sasai et al., 2005, 2007, 2011); 3) simulated the carbon exchanges by using the new dataset and BEAMS by the use of a supercomputer that includes 1280 CPU and 320 GPGPU cores (GOSAT RCF of NIES). As a result, we could develop a global uniform system for realistically estimating terrestrial carbon exchange, and evaluate net ecosystem production in each community level; leading to obtain highly detailed understanding of terrestrial carbon exchanges.

Aerosol backscatter measurements at 10.6 microns with airborne and ground-based CO2 Doppler lidars over the Colorado High Plains. I - Lidar intercomparison

Science.gov (United States)

Bowdle, David A.; Rothermel, Jeffry; Vaughan, J. Michael; Brown, Derek W.; Post, Madison J.

1991-01-01

An airborne continuous-wave (CW) focused CO2 Doppler lidar and a ground-based pulsed CO2 Doppler lidar were to obtain seven pairs of comparative measurements of tropospheric aerosol backscatter profiles at 10.6-micron wavelength, near Denver, Colorado, during a 20-day period in July 1982. In regions of uniform backscatter, the two lidars show good agreement, with differences usually less than about 50 percent near 8-km altitude and less than a factor of 2 or 3 elsewhere but with the pulsed lidar often lower than the CW lidar. Near sharp backscatter gradients, the two lidars show poorer agreement, with the pulsed lidar usually higher than the CW lidar. Most discrepancies arise from a combination of atmospheric factors and instrument factors, particularly small-scale areal and temporal backscatter heterogeneity above the planetary boundary layer, unusual large-scale vertical backscatter structure in the upper troposphere and lower stratosphere, and differences in the spatial resolution, detection threshold, and noise estimation for the two lidars.

Can Wind Lidars Measure Turbulence?

DEFF Research Database (Denmark)

Sathe, Ameya; Mann, Jakob; Gottschall, Julia

2011-01-01

Modeling of the systematic errors in the second-order moments of wind speeds measured by continuous-wave (ZephIR) and pulsed (WindCube) lidars is presented. These lidars use the conical scanning technique to measure the velocity field. The model captures the effect of volume illumination and coni...

Forest biomass mapping from fusion of GEDI Lidar data and TanDEM-X InSAR data

Science.gov (United States)

Qi, W.; Hancock, S.; Armston, J.; Marselis, S.; Dubayah, R.

2017-12-01

Mapping forest above-ground biomass (hereafter biomass) can significantly improve our ability to assess the role of forest in terrestrial carbon budget and to analyze the ecosystem productivity. Global Ecosystem Dynamic Investigation (GEDI) mission will provide the most complete lidar observations of forest vertical structure and has the potential to provide global-scale forest biomass data at 1-km resolution. However, GEDI is intrinsically a sampling mission and will have a between-track spacing of 600 m. An increase in adjacent-swath distance and the presence of cloud cover may also lead to larger gaps between GEDI tracks. In order to provide wall-to-wall forest biomass maps, fusion algorithms of GEDI lidar data and TanDEM-X InSAR data were explored in this study. Relationship between biomass and lidar RH metrics was firstly developed and used to derive biomass values over GEDI tracks which were simulated using airborne lidar data. These GEDI biomass values were then averaged in each 1-km cell to represent the biomass density within that cell. Whereas for cells without any GEDI observations, regression models developed between GEDI-derived biomass and TDX InSAR variables were applied to predict biomass over those places. Based on these procedures, contiguous biomass maps were finally generated at 1-km resolution over three representative forest types. Uncertainties for these biomass maps were also estimated at 1 km following methods developed in Saarela et al. (2016). Our results indicated great potential of GEDI/TDX fusion for large-scale biomass mapping. Saarela, S., Holm, S., Grafstrom, A., Schnell, S., Naesset, E., Gregoire, T.G., Nelson, R.F., & Stahl, G. (2016). Hierarchical model-based inference for forest inventory utilizing three sources of information. Annals of Forest Science, 73, 895-910

Fusion of LiDAR and aerial imagery for the estimation of downed tree volume using Support Vector Machines classification and region based object fitting

Science.gov (United States)

Selvarajan, Sowmya

The study classifies 3D small footprint full waveform digitized LiDAR fused with aerial imagery to downed trees using Support Vector Machines (SVM) algorithm. Using small footprint waveform LiDAR, airborne LiDAR systems can provide better canopy penetration and very high spatial resolution. The small footprint waveform scanner system Riegl LMS-Q680 is addition with an UltraCamX aerial camera are used to measure and map downed trees in a forest. The various data preprocessing steps helped in the identification of ground points from the dense LiDAR dataset and segment the LiDAR data to help reduce the complexity of the algorithm. The haze filtering process helped to differentiate the spectral signatures of the various classes within the aerial image. Such processes, helped to better select the features from both sensor data. The six features: LiDAR height, LiDAR intensity, LiDAR echo, and three image intensities are utilized. To do so, LiDAR derived, aerial image derived and fused LiDAR-aerial image derived features are used to organize the data for the SVM hypothesis formulation. Several variations of the SVM algorithm with different kernels and soft margin parameter C are experimented. The algorithm is implemented to classify downed trees over a pine trees zone. The LiDAR derived features provided an overall accuracy of 98% of downed trees but with no classification error of 86%. The image derived features provided an overall accuracy of 65% and fusion derived features resulted in an overall accuracy of 88%. The results are observed to be stable and robust. The SVM accuracies were accompanied by high false alarm rates, with the LiDAR classification producing 58.45%, image classification producing 95.74% and finally the fused classification producing 93% false alarm rates The Canny edge correction filter helped control the LiDAR false alarm to 35.99%, image false alarm to 48.56% and fused false alarm to 37.69% The implemented classifiers provided a powerful tool for

Lidar signal-to-noise ratio improvements: Considerations and techniques

Science.gov (United States)

Hassebo, Yasser Y.

The primary objective of this study is to improve lidar signal-to-noise ratio (SNR) and hence extend attainable lidar ranges through reduction of the sky background noise (BGP), which dominates other sources of noise in daytime operations. This is particularly important for Raman lidar techniques where the Raman backscattered signal of interest is relatively weak compared with the elastic backscatter lidars. Two approaches for reduction of sky background noise are considered: (1) Improvements in lidar SNR by optimization of the design of the lidar receiver were examined by a series of simulations. This part of the research concentrated on biaxial lidar systems, where overlap between laser beam and receiver field of view (FOV) is an important aspect of noise considerations. The first optimized design evolved is a wedge shaped aperture. While this design has the virtue of greatly reducing background light, it is difficult to implement practically, requiring both changes in area and position with lidar range. A second more practical approach, which preserves some of the advantages of the wedge design, was also evolved. This uses a smaller area circular aperture optimally located in the image plane for desired ranges. Simulated numerical results for a biaxial lidar have shown that the best receiver parameters selection is one using a small circular aperture (field stop) with a small telescope focal length f, to ensure the minimum FOV that accepts all return signals over the entire lidar range while at the same time minimizing detected BGP and hence maximizing lidar SNR and attainable lidar ranges. The improvement in lidar SNR was up to 18%. (2) A polarization selection technique was implemented to reduce sky background signal for linearly polarized monostatic elastic backscatter lidar measurements. The technique takes advantage of naturally occurring polarization properties in scattered sky light, and then ensures that both the lidar transmitter and receiver track and

A machine learning pipeline for automated registration and classification of 3D lidar data

Science.gov (United States)

Rajagopal, Abhejit; Chellappan, Karthik; Chandrasekaran, Shivkumar; Brown, Andrew P.

2017-05-01

Despite the large availability of geospatial data, registration and exploitation of these datasets remains a persis- tent challenge in geoinformatics. Popular signal processing and machine learning algorithms, such as non-linear SVMs and neural networks, rely on well-formatted input models as well as reliable output labels, which are not always immediately available. In this paper we outline a pipeline for gathering, registering, and classifying initially unlabeled wide-area geospatial data. As an illustrative example, we demonstrate the training and test- ing of a convolutional neural network to recognize 3D models in the OGRIP 2007 LiDAR dataset using fuzzy labels derived from OpenStreetMap as well as other datasets available on OpenTopography.org. When auxiliary label information is required, various text and natural language processing filters are used to extract and cluster keywords useful for identifying potential target classes. A subset of these keywords are subsequently used to form multi-class labels, with no assumption of independence. Finally, we employ class-dependent geometry extraction routines to identify candidates from both training and testing datasets. Our regression networks are able to identify the presence of 6 structural classes, including roads, walls, and buildings, in volumes as big as 8000 m3 in as little as 1.2 seconds on a commodity 4-core Intel CPU. The presented framework is neither dataset nor sensor-modality limited due to the registration process, and is capable of multi-sensor data-fusion.

Ground-Truthing of Airborne LiDAR Using RTK-GPS Surveyed Data in Coastal Louisiana's Wetlands

Science.gov (United States)

Lauve, R. M.; Alizad, K.; Hagen, S. C.

2017-12-01

Airborne LiDAR (Light Detection and Ranging) data are used by engineers and scientists to create bare earth digital elevation models (DEM), which are essential to modeling complex coastal, ecological, and hydrological systems. However, acquiring accurate bare earth elevations in coastal wetlands is difficult due to the density of marsh grasses that prevent the sensors reflection off the true ground surface. Previous work by Medeiros et al. [2015] developed a technique to assess LiDAR error and adjust elevations according to marsh vegetation density and index. The aim of this study is the collection of ground truth points and the investigation on the range of potential errors found in existing LiDAR datasets within coastal Louisiana's wetlands. Survey grids were mapped out in an area dominated by Spartina alterniflora and a survey-grade Trimble Real Time Kinematic (RTK) GPS device was employed to measure bare earth ground elevations in the marsh system adjacent to Terrebonne Bay, LA. Elevations were obtained for 20 meter-spaced surveyed grid points and were used to generate a DEM. The comparison between LiDAR derived and surveyed data DEMs yield an average difference of 23 cm with a maximum difference of 68 cm. Considering the local tidal range of 45 cm, these differences can introduce substantial error when the DEM is used for ecological modeling [Alizad et al., 2016]. Results from this study will be further analyzed and implemented in order to adjust LiDAR-derived DEMs closer to their true elevation across Louisiana's coastal wetlands. ReferencesAlizad, K., S. C. Hagen, J. T. Morris, S. C. Medeiros, M. V. Bilskie, and J. F. Weishampel (2016), Coastal wetland response to sea-level rise in a fluvial estuarine system, Earth's Future, 4(11), 483-497, 10.1002/2016EF000385. Medeiros, S., S. Hagen, J. Weishampel, and J. Angelo (2015), Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density, Remote Sensing, 7

Multiangle lidar observations of the Atmosphere

Science.gov (United States)

Lalitkumar Prakash, Pawar; Choukiker, Yogesh Kumar; Raghunath, K.

2018-04-01

Atmospheric Lidars are used extensively to get aerosol parameters like backscatter coefficient, backscatter ratio etc. National Atmospheric Research Laboratory, Gadanki (13°N, 79°E), India has a powerful lidar which has alt-azimuth capability. Inversion method is applied to data from observations of lidar system at different azimuth and elevation angles. Data Analysis is described and Observations in 2D and 3D format are discussed. Presence of Cloud and the variation of backscatter parameters are seen in an interesting manner.

LIDAR for atmosphere research over Africa

CSIR Research Space (South Africa)

Sivakumar, V

2008-11-01

Full Text Available d’aéronomie, CNRS, Paris, France 1Email: SVenkataraman@csir.co.za – www.csir.co.za K-6665 [www.kashangroup.com] Lidar for atmospheric studies: The CSIR’s laser research into monitoring various pollutants in the lower atmosphere via... to lidar applications for atmosphere studies including pollutant monitoring. The following salient features emanated from the survey: • Around 80% of the lidars are in the northern hemisphere • Of the 20% in the southern hemisphere region...

2012 Oregon Lidar Consortium (OLC) Lidar: Keno (OR)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Watershed Sciences, Inc. (WSI) collected Light Detection and Ranging (LiDAR) data of the Oregon Keno Study Area for the Oregon Department of Geology and Mineral...

Laser remote sensing of water vapor: Raman lidar development

International Nuclear Information System (INIS)

Goldsmith, J.E.M.; Lapp, M.; Bisson, S.E.; Melfi, S.H.; Whiteman, D.N.; Ferrare, R.A.; Evans, K.D.

1994-01-01

The goal of this research is the development of a critical design for a Raman lidar system optimized to match ARM Program needs for profiling atmospheric water vapor at CART sites. This work has emphasized the development of enhanced daytime capabilities using Raman lidar techniques. This abstract touches briefly on the main components of the research program, summarizing results of the efforts. A detailed Raman lidar instrument model has been developed to predict the daytime and nighttime performance capabilities of Raman lidar systems. The model simulates key characteristics of the lidar system, using realistic atmospheric profiles, modeled background sky radiance, and lidar system parameters based on current instrument capabilities. The model is used to guide development of lidar systems based on both the solar-blind concept and the narrowband, narrow field-of-view concept for daytime optimization

Mapping Fearscapes of a Mammalian Herbivore using Terrestrial LiDAR and UAV Imagery

Science.gov (United States)

Olsoy, P.; Nobler, J. D.; Forbey, J.; Rachlow, J. L.; Burgess, M. A.; Glenn, N. F.; Shipley, L. A.

2013-12-01

Concealment allows prey animals to remain hidden from a predator and can influence both real and perceived risks of predation. The heterogeneous nature of vegetative structure can create a variable landscape of concealment - a 'fearscape' - that may influence habitat quality and use by prey. Traditional measurements of concealment rely on a limited number of distances, heights, and vantage points, resulting in small snapshots of concealment available to a prey animal. Our objective was to demonstrate the benefits of emerging remote sensing techniques to map fearscapes for pygmy rabbits (Brachylagus idahoensis) in sagebrush steppe habitat across a continuous range of scales. Specifically, we used vegetation height rasters derived from terrestrial laser scanning (TLS) to create viewsheds from multiple vantage points, representing predator visibility. The sum of all the viewsheds modeled horizontal concealment of prey at both the shrub and patch scales. We also used a small, unmanned aerial vehicle (UAV) to determine vertical concealment at a habitat scale. Terrestrial laser scanning provided similar estimates of horizontal concealment at the shrub scale when compared to photographic methods (R2 = 0.85). Both TLS and UAV provide the potential to quantify concealment of prey from multiple distances, heights, or vantage points, allowing the creation of a manipulable fearscape map that can be correlated with habitat use by prey animals. The predictive power of such a map also could identify shrubs or patches for fine scale nutritional and concealment analysis for future investigation and conservation efforts. Fearscape map at the mound-scale. Viewsheds were calculated from 100 equally spaced observer points located 4 m from the closest on-mound sagebrush of interest. Red areas offer low concealment, while green areas provide high concealment.

Tropical Airborne LiDAR for Landslide Assessment in Malaysia: a technical perspective

Science.gov (United States)

Abd Manap, Mohamad; Azhari Razak, Khamarrul; Mohamad, Zakaria; Ahmad, Azhari; Ahmad, Ferdaus; Mohamad Zin, Mazlan; A'zad Rosle, Qalam

2015-04-01

Malaysia has faced a substantial number of landslide events every year. Cameron Highlands, Pahang is one of the badly areas affected by slope failures characterized by extreme climate, rugged topographic and weathered geological structures in a tropical environment. A high frequency of landslide occurrence in the hilly areas is predominantly due to the geological materials, tropical monsoon seasons and uncontrolled agricultural activities. Therefore the Government of Malaysia through the Prime Minister Department has allocated a special budget to conduct national level hazard and risk mapping project through Minerals and Geoscience Department Malaysia, the Ministry of Natural Resources and Environment. The primary aim of this project is to provide slope hazard risk information for a better slope management in Malaysia. In addition this project will establish national infrastructure for geospatial information on the geological terrain and slope by emphasizing the disaster risk throughout the country. The areas of interest are located in the three different selected areas i.e. Cameron Highlands (275 square kilometers), Ipoh (200 square kilometers) and Cheras Kajang -- Batang kali (650 square kilometers). These areas are selected based on National Slope Master Plan (2009 -- 2023) that endorsed by Malaysia Government Cabinet. The national hazard and risk mapping project includes six parts of major tasks: (1) desk study and mobilization, (2) airborne LiDAR data acquisition and analysis, (3) field data acquisition and verification, (4) hazard and risk for natural terrain, (5) hazard and risk analysis for man-made slope and (6) Man-made slope mitigation/preventive measures. The project was authorized in September, 2014 and will be ended in March, 2016. In this paper, the main focus is to evaluate the suitability of integrated capability of airborne- and terrestrial LiDAR data acquisition and analysis, and also digital photography for regional landslide assessment. The

Global screening for Critical Habitat in the terrestrial realm.

Science.gov (United States)

Brauneder, Kerstin M; Montes, Chloe; Blyth, Simon; Bennun, Leon; Butchart, Stuart H M; Hoffmann, Michael; Burgess, Neil D; Cuttelod, Annabelle; Jones, Matt I; Kapos, Val; Pilgrim, John; Tolley, Melissa J; Underwood, Emma C; Weatherdon, Lauren V; Brooks, Sharon E

2018-01-01

Critical Habitat has become an increasingly important concept used by the finance sector and businesses to identify areas of high biodiversity value. The International Finance Corporation (IFC) defines Critical Habitat in their highly influential Performance Standard 6 (PS6), requiring projects in Critical Habitat to achieve a net gain of biodiversity. Here we present a global screening layer of Critical Habitat in the terrestrial realm, derived from global spatial datasets covering the distributions of 12 biodiversity features aligned with guidance provided by the IFC. Each biodiversity feature is categorised as 'likely' or 'potential' Critical Habitat based on: 1. Alignment between the biodiversity feature and the IFC Critical Habitat definition; and 2. Suitability of the spatial resolution for indicating a feature's presence on the ground. Following the initial screening process, Critical Habitat must then be assessed in-situ by a qualified assessor. This analysis indicates that a total of 10% and 5% of the global terrestrial environment can be considered as likely and potential Critical Habitat, respectively, while the remaining 85% did not overlap with any of the biodiversity features assessed and was classified as 'unknown'. Likely Critical Habitat was determined principally by the occurrence of Key Biodiversity Areas and Protected Areas. Potential Critical Habitat was predominantly characterised by data representing highly threatened and unique ecosystems such as ever-wet tropical forests and tropical dry forests. The areas we identified as likely or potential Critical Habitat are based on the best available global-scale data for the terrestrial realm that is aligned with IFC's Critical Habitat definition. Our results can help businesses screen potential development sites at the early project stage based on a range of biodiversity features. However, the study also demonstrates several important data gaps and highlights the need to incorporate new and

TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

Science.gov (United States)

Abatzoglou, John T.; Dobrowski, Solomon Z.; Parks, Sean A.; Hegewisch, Katherine C.

2018-01-01

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.

Sensitivity analysis of nacelle lidar free stream wind speed measurements to wind-induction reconstruction model and lidar range configuration

DEFF Research Database (Denmark)

Svensson, Elin; Borraccino, Antoine; Meyer Forsting, Alexander Raul

The sensitivity of nacelle lidar wind speed measurements to wind-induction models and lidar range configurations is studied using experimental data from the Nørrekær Enge (NKE) measurement campaign and simulated lidar data from Reynold-Averaged Navier Stokes (RANS) aerodynamic computational fluid...... the ZDM was configured to measure at five distances. From the configured distances, a large number of range configurations were created and systematically tested to determine the sensitivity of the reconstructed wind speeds to the number of ranges, minimum range and maximum range in the range......) of the fitting residuals. The results demonstrate that it is not possible to use RANS CFD simulated lidar data to determine optimal range configurations for real-time nacelle lidars due to their perfect (unrealistic) representation of the simulated flow field. The recommended range configurations are therefore...

Spaceborne Lidar in the Study of Marine Systems.

Science.gov (United States)

Hostetler, Chris A; Behrenfeld, Michael J; Hu, Yongxiang; Hair, Johnathan W; Schulien, Jennifer A

2018-01-03

Satellite passive ocean color instruments have provided an unbroken ∼20-year record of global ocean plankton properties, but this measurement approach has inherent limitations in terms of spatial-temporal sampling and ability to resolve vertical structure within the water column. These limitations can be addressed by coupling ocean color data with measurements from a spaceborne lidar. Airborne lidars have been used for decades to study ocean subsurface properties, but recent breakthroughs have now demonstrated that plankton properties can be measured with a satellite lidar. The satellite lidar era in oceanography has arrived. Here, we present a review of the lidar technique, its applications in marine systems, a perspective on what can be accomplished in the near future with an ocean- and atmosphere-optimized satellite lidar, and a vision for a multiplatform virtual constellation of observational assets that would enable a three-dimensional reconstruction of global ocean ecosystems.

Spaceborne Lidar in the Study of Marine Systems

Science.gov (United States)

Hostetler, Chris A.; Behrenfeld, Michael J.; Hu, Yongxiang; Hair, Johnathan W.; Schulien, Jennifer A.

2018-01-01

Satellite passive ocean color instruments have provided an unbroken ˜20-year record of global ocean plankton properties, but this measurement approach has inherent limitations in terms of spatial-temporal sampling and ability to resolve vertical structure within the water column. These limitations can be addressed by coupling ocean color data with measurements from a spaceborne lidar. Airborne lidars have been used for decades to study ocean subsurface properties, but recent breakthroughs have now demonstrated that plankton properties can be measured with a satellite lidar. The satellite lidar era in oceanography has arrived. Here, we present a review of the lidar technique, its applications in marine systems, a perspective on what can be accomplished in the near future with an ocean- and atmosphere-optimized satellite lidar, and a vision for a multiplatform virtual constellation of observational assets that would enable a three-dimensional reconstruction of global ocean ecosystems.

A New Framework for Quantifying Lidar Uncertainty

Energy Technology Data Exchange (ETDEWEB)

Newman, Jennifer, F.; Clifton, Andrew; Bonin, Timothy A.; Churchfield, Matthew J.

2017-03-24

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards discuss uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device. However, real-world experience has shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we propose the development of a new lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from an operational wind farm to assess the ability of the framework to predict errors in lidar-measured wind speed.

Multiangle lidar observations of the Atmosphere

Directory of Open Access Journals (Sweden)

Lalitkumar Prakash Pawar

2018-01-01

Full Text Available Atmospheric Lidars are used extensively to get aerosol parameters like backscatter coefficient, backscatter ratio etc. National Atmospheric Research Laboratory, Gadanki (13°N, 79°E, India has a powerful lidar which has alt-azimuth capability. Inversion method is applied to data from observations of lidar system at different azimuth and elevation angles. Data Analysis is described and Observations in 2D and 3D format are discussed. Presence of Cloud and the variation of backscatter parameters are seen in an interesting manner.

Wind measurement via direct detection lidar

Science.gov (United States)

Afek, I.; Sela, N.; Narkiss, N.; Shamai, G.; Tsadka, S.

2013-10-01

Wind sensing Lidar is considered a promising technology for high quality wind measurements required for various applications such as hub height wind resource assessment, power curve measurements and advanced, real time, forward looking turbine control. Until recently, the only available Lidar technology was based on coherent Doppler shift detection, whose market acceptance has been slow primarily due to its exuberant price. Direct detection Lidar technology provides an alternative to remote sensing of wind by incorporating high precision measurement, a robust design and an affordable price tag.

2014 OLC Lidar DEM: Colville, WA

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — WSI, a Quantum Spatial company, has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Colville study area. This study area is...

Object Classification Using Airborne Multispectral LiDAR Data

Directory of Open Access Journals (Sweden)

PAN Suoyan

2018-02-01

Full Text Available Airborne multispectral LiDAR system,which obtains surface geometry and spectral data of objects,simultaneously,has become a fast effective,large-scale spatial data acquisition method.Multispectral LiDAR data are characteristics of completeness and consistency of spectrum and spatial geometric information.Support vector machine (SVM,a machine learning method,is capable of classifying objects based on small samples.Therefore,by means of SVM,this paper performs land cover classification using multispectral LiDAR data. First,all independent point cloud with different wavelengths are merged into a single point cloud,where each pixel contains the three-wavelength spectral information.Next,the merged point cloud is converted into range and intensity images.Finally,land-cover classification is performed by means of SVM.All experiments were conducted on the Optech Titan multispectral LiDAR data,containing three individual point cloud collected by 532 nm,1024 nm,and 1550 nm laser beams.Experimental results demonstrate that ①compared to traditional single-wavelength LiDAR data,multispectral LiDAR data provide a promising solution to land use and land cover applications;②SVM is a feasible method for land cover classification of multispectral LiDAR data.

ASSESSING TEMPORAL BEHAVIOR IN LIDAR POINT CLOUDS OF URBAN ENVIRONMENTS

Directory of Open Access Journals (Sweden)

J. Schachtschneider

2017-05-01

Full Text Available Self-driving cars and robots that run autonomously over long periods of time need high-precision and up-to-date models of the changing environment. The main challenge for creating long term maps of dynamic environments is to identify changes and adapt the map continuously. Changes can occur abruptly, gradually, or even periodically. In this work, we investigate how dense mapping data of several epochs can be used to identify the temporal behavior of the environment. This approach anticipates possible future scenarios where a large fleet of vehicles is equipped with sensors which continuously capture the environment. This data is then being sent to a cloud based infrastructure, which aligns all datasets geometrically and subsequently runs scene analysis on it, among these being the analysis for temporal changes of the environment. Our experiments are based on a LiDAR mobile mapping dataset which consists of 150 scan strips (a total of about 1 billion points, which were obtained in multiple epochs. Parts of the scene are covered by up to 28 scan strips. The time difference between the first and last epoch is about one year. In order to process the data, the scan strips are aligned using an overall bundle adjustment, which estimates the surface (about one billion surface element unknowns as well as 270,000 unknowns for the adjustment of the exterior orientation parameters. After this, the surface misalignment is usually below one centimeter. In the next step, we perform a segmentation of the point clouds using a region growing algorithm. The segmented objects and the aligned data are then used to compute an occupancy grid which is filled by tracing each individual LiDAR ray from the scan head to every point of a segment. As a result, we can assess the behavior of each segment in the scene and remove voxels from temporal objects from the global occupancy grid.

Assessing Temporal Behavior in LIDAR Point Clouds of Urban Environments

Science.gov (United States)

Schachtschneider, J.; Schlichting, A.; Brenner, C.

2017-05-01

Self-driving cars and robots that run autonomously over long periods of time need high-precision and up-to-date models of the changing environment. The main challenge for creating long term maps of dynamic environments is to identify changes and adapt the map continuously. Changes can occur abruptly, gradually, or even periodically. In this work, we investigate how dense mapping data of several epochs can be used to identify the temporal behavior of the environment. This approach anticipates possible future scenarios where a large fleet of vehicles is equipped with sensors which continuously capture the environment. This data is then being sent to a cloud based infrastructure, which aligns all datasets geometrically and subsequently runs scene analysis on it, among these being the analysis for temporal changes of the environment. Our experiments are based on a LiDAR mobile mapping dataset which consists of 150 scan strips (a total of about 1 billion points), which were obtained in multiple epochs. Parts of the scene are covered by up to 28 scan strips. The time difference between the first and last epoch is about one year. In order to process the data, the scan strips are aligned using an overall bundle adjustment, which estimates the surface (about one billion surface element unknowns) as well as 270,000 unknowns for the adjustment of the exterior orientation parameters. After this, the surface misalignment is usually below one centimeter. In the next step, we perform a segmentation of the point clouds using a region growing algorithm. The segmented objects and the aligned data are then used to compute an occupancy grid which is filled by tracing each individual LiDAR ray from the scan head to every point of a segment. As a result, we can assess the behavior of each segment in the scene and remove voxels from temporal objects from the global occupancy grid.

2014 PSLC Lidar: City of Redmond

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In February 2014, Quantum Spatial (QSI) was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR) data for the City of...

2014 Horry County, South Carolina Lidar

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set is comprised of lidar point cloud data. This project required lidar data to be acquired over Horry County, South Carolina. The total area of the Horry...

Elevation - LIDAR Survey - Roseau County, Minnesota

Data.gov (United States)

Army Corps of Engineers, Department of the Army, Department of Defense — LIDAR Data for Roseau County Minnesota. This project consists of approximately 87 square miles of LIDAR mapping in Roseau County, Minnesota at two sites: area 1,...

3D pulsed chaos lidar system.

Science.gov (United States)

Cheng, Chih-Hao; Chen, Chih-Ying; Chen, Jun-Da; Pan, Da-Kung; Ting, Kai-Ting; Lin, Fan-Yi

2018-04-30

We develop an unprecedented 3D pulsed chaos lidar system for potential intelligent machinery applications. Benefited from the random nature of the chaos, conventional CW chaos lidars already possess excellent anti-jamming and anti-interference capabilities and have no range ambiguity. In our system, we further employ self-homodyning and time gating to generate a pulsed homodyned chaos to boost the energy-utilization efficiency. Compared to the original chaos, we show that the pulsed homodyned chaos improves the detection SNR by more than 20 dB. With a sampling rate of just 1.25 GS/s that has a native sampling spacing of 12 cm, we successfully achieve millimeter-level accuracy and precision in ranging. Compared with two commercial lidars tested side-by-side, namely the pulsed Spectroscan and the random-modulation continuous-wave Lidar-lite, the pulsed chaos lidar that is in compliance with the class-1 eye-safe regulation shows significantly better precision and a much longer detection range up to 100 m. Moreover, by employing a 2-axis MEMS mirror for active laser scanning, we also demonstrate real-time 3D imaging with errors of less than 4 mm in depth.

Complex terrain and wind lidars

Energy Technology Data Exchange (ETDEWEB)

Bingoel, F.

2009-08-15

This thesis includes the results of a PhD study about complex terrain and wind lidars. The study mostly focuses on hilly and forested areas. Lidars have been used in combination with cups, sonics and vanes, to reach the desired vertical measurement heights. Several experiments are performed in complex terrain sites and the measurements are compared with two different flow models; a linearised flow model LINCOM and specialised forest model SCADIS. In respect to the lidar performance in complex terrain, the results showed that horizontal wind speed errors measured by a conically scanning lidar can be of the order of 3-4% in moderately-complex terrain and up to 10% in complex terrain. The findings were based on experiments involving collocated lidars and meteorological masts, together with flow calculations over the same terrains. The lidar performance was also simulated with the commercial software WAsP Engineering 2.0 and was well predicted except for some sectors where the terrain is particularly steep. Subsequently, two experiments were performed in forested areas; where the measurements are recorded at a location deep-in forest and at the forest edge. Both sites were modelled with flow models and the comparison of the measurement data with the flow model outputs showed that the mean wind speed calculated by LINCOM model was only reliable between 1 and 2 tree height (h) above canopy. The SCADIS model reported better correlation with the measurements in forest up to approx6h. At the forest edge, LINCOM model was used by allocating a slope half-in half out of the forest based on the suggestions of previous studies. The optimum slope angle was reported as 17 deg.. Thus, a suggestion was made to use WAsP Engineering 2.0 for forest edge modelling with known limitations and the applied method. The SCADIS model worked better than the LINCOM model at the forest edge but the model reported closer results to the measurements at upwind than the downwind and this should be

2012 USGS Lidar: Elwha River (WA)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME: Elwha River, WA LiDAR LiDAR Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No. G11PD01088 Woolpert Order No....

Lidar data used in the COFIN project

DEFF Research Database (Denmark)

Ejsing Jørgensen, Hans; Nielsen, Morten

1999-01-01

This report presents the Lidar data used in the COFIN project. The Lidar data have been obtained from several ground level dispersion experiments over flat and complex terrain. The method for treating the data and the conditons under which the data wereobtained are described in detail. Finally we...... describe the Tools to extract and visualize the Lidar data. Data, report, and visualisation tools are available on the Risø FTP server....

Towards Linking 3D SAR and Lidar Models with a Spatially Explicit Individual Based Forest Model

Science.gov (United States)

Osmanoglu, B.; Ranson, J.; Sun, G.; Armstrong, A. H.; Fischer, R.; Huth, A.

2017-12-01

In this study, we present a parameterization of the FORMIND individual-based gap model (IBGM)for old growth Atlantic lowland rainforest in La Selva, Costa Rica for the purpose of informing multisensor remote sensing techniques for above ground biomass techniques. The model was successfully parameterized and calibrated for the study site; results show that the simulated forest reproduces the structural complexity of Costa Rican rainforest based on comparisons with CARBONO inventory plot data. Though the simulated stem numbers (378) slightly underestimated the plot data (418), particularly for canopy dominant intermediate shade tolerant trees and shade tolerant understory trees, overall there was a 9.7% difference. Aboveground biomass (kg/ha) showed a 0.1% difference between the simulated forest and inventory plot dataset. The Costa Rica FORMIND simulation was then used to parameterize a spatially explicit (3D) SAR and lidar backscatter models. The simulated forest stands were used to generate a Look Up Table as a tractable means to estimate aboveground forest biomass for these complex forests. Various combinations of lidar and radar variables were evaluated in the LUT inversion. To test the capability of future data for estimation of forest height and biomass, we considered data of 1) L- (or P-) band polarimetric data (backscattering coefficients of HH, HV and VV); 2) L-band dual-pol repeat-pass InSAR data (HH/HV backscattering coefficients and coherences, height of scattering phase center at HH and HV using DEM or surface height from lidar data as reference); 3) P-band polarimetric InSAR data (canopy height from inversion of PolInSAR data or use the coherences and height of scattering phase center at HH, HV and VV); 4) various height indices from waveform lidar data); and 5) surface and canopy top height from photon-counting lidar data. The methods for parameterizing the remote sensing models with the IBGM and developing Look Up Tables will be discussed. Results

Optimizing Lidar Scanning Strategies for Wind Energy Measurements (Invited)

Science.gov (United States)

Newman, J. F.; Bonin, T. A.; Klein, P.; Wharton, S.; Chilson, P. B.

2013-12-01

Environmental concerns and rising fossil fuel prices have prompted rapid development in the renewable energy sector. Wind energy, in particular, has become increasingly popular in the United States. However, the intermittency of available wind energy makes it difficult to integrate wind energy into the power grid. Thus, the expansion and successful implementation of wind energy requires accurate wind resource assessments and wind power forecasts. The actual power produced by a turbine is affected by the wind speeds and turbulence levels experienced across the turbine rotor disk. Because of the range of measurement heights required for wind power estimation, remote sensing devices (e.g., lidar) are ideally suited for these purposes. However, the volume averaging inherent in remote sensing technology produces turbulence estimates that are different from those estimated by a sonic anemometer mounted on a standard meteorological tower. In addition, most lidars intended for wind energy purposes utilize a standard Doppler beam-swinging or Velocity-Azimuth Display technique to estimate the three-dimensional wind vector. These scanning strategies are ideal for measuring mean wind speeds but are likely inadequate for measuring turbulence. In order to examine the impact of different lidar scanning strategies on turbulence measurements, a WindCube lidar, a scanning Halo lidar, and a scanning Galion lidar were deployed at the Southern Great Plains Atmospheric Radiation Measurement (ARM) site in Summer 2013. Existing instrumentation at the ARM site, including a 60-m meteorological tower and an additional scanning Halo lidar, were used in conjunction with the deployed lidars to evaluate several user-defined scanning strategies. For part of the experiment, all three scanning lidars were pointed at approximately the same point in space and a tri-Doppler analysis was completed to calculate the three-dimensional wind vector every 1 second. In another part of the experiment, one of

Infrastructure Investment Protection with LiDAR

Science.gov (United States)

2012-10-15

The primary goal of this research effort was to explore the wide variety of uses of LiDAR technology and to evaluate their : applicability to NCDOT practices. NCDOT can use this information about LiDAR in determining how and when the : technology can...

2013 NRCS-USGS Lidar: Lauderdale (MS)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME:NRCS LAUDERDALE MS 0.7M NPS LIDAR. LiDAR Data Acquisition and Processing Production Task. USGS Contract No. G10PC00057. Task Order No. G12PD000125 Woolpert...

2014 USGS/NRCS Lidar: Central MS

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME: USGS-NRCS Laurel MS 0.7m NPS LIDAR Lidar Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No. G13PD01086 Woolpert...

Telescope aperture optimization for spacebased coherent wind lidar

Science.gov (United States)

Ge, Xian-ying; Zhu, Jun; Cao, Qipeng; Zhang, Yinchao; Yin, Huan; Dong, Xiaojing; Wang, Chao; Zhang, Yongchao; Zhang, Ning

2015-08-01

Many studies have indicated that the optimum measurement approach for winds from space is a pulsed coherent wind lidar, which is an active remote sensing tool with the characteristics that high spatial and temporal resolutions, real-time detection, high mobility, facilitated control and so on. Because of the significant eye safety, efficiency, size, and lifetime advantage, 2μm wavelength solid-state laser lidar systems have attracted much attention in spacebased wind lidar plans. In this paper, the theory of coherent detection is presented and a 2μm wavelength solid-state laser lidar system is introduced, then the ideal aperture is calculated from signal-to-noise(SNR) view at orbit 400km. However, considering real application, even if the lidar hardware is perfectly aligned, the directional jitter of laser beam, the attitude change of the lidar in the long round trip time of the light from the atmosphere and other factors can bring misalignment angle. So the influence of misalignment angle is considered and calculated, and the optimum telescope diameter(0.45m) is obtained as the misalignment angle is 4 μrad. By the analysis of the optimum aperture required for spacebased coherent wind lidar system, we try to present the design guidance for the telescope.

Applications of KHZ-CW Lidar in Ecological Entomology

Science.gov (United States)

Malmqvist, Elin; Brydegaard, Mikkel

2016-06-01

The benefits of kHz lidar in ecological entomology are explained. Results from kHz-measurements on insects, carried out with a CW-lidar system, employing the Scheimpflug principle to obtain range resolution, are presented. A method to extract insect events and analyze the large amount of lidar data is also described.

INTERACT-II campaign:comparison of commercial lidars and ceilometers with advanced multi-wavelength Raman lidars

Science.gov (United States)

Rosoldi, Marco; Madonna, Fabio; Pappalardo, Gelsomina; Vande Hey, Joshua; Zheng, Yunhui; Vaisala Team

2017-04-01

Knowledge of aerosol spatio-temporal distribution in troposphere is essential for the study of climate and air quality. For this purpose, global scale high resolution continuous measurements of tropospheric aerosols are needed. Global coverage high resolution networks of ground-based low-cost and low-maintenance remote sensing instruments, such as commercial automatic lidars and ceilometers, can strongly contribute to this scientific mission. Therefore, it is very interesting for scientific community to understand to which extent these instruments are able to provide reliable aerosol measurements and fill in the geographical gaps of existing networks of the advanced lidars, like EARLINET (European Aerosol Research LIdar NETwork). The INTERACT-II (INTERcomparison of Aerosol and Cloud Tracking) campaign, carried out at CIAO (CNR-IMAA Atmospheric Observatory) in Tito Scalo, Potenza, Italy (760m a.s.l., 40.60°N, 15.72°E), aims to evaluate the performances of commercial automatic lidars and ceilometers for tropospheric aerosol profiling. The campaign has been performed in the period from July 2016 to January 2017 in the framework of ACTRIS-2 (Aerosol Clouds Trace gases Research InfraStructure) H2020 research infrastructure project. Besides the commercial ceilometers operational at CIAO (VAISALA CT25K and Luftt CHM15k), the performance of a CL51 VAISALA ceilometer, a Campbell CS135 ceilometer and a mini-Micro Pulse Lidar (MPL) have been assessed using the EARLINET multi-wavelengths Raman lidars operative at CIAO as reference. Following a similar approach used in the first INTERACT campaign (Madonna et al., AMT 2015), attenuated backscatter coefficient profiles and signals obtained from all the instruments have been compared, over a vertical resolution of 60 meters and a temporal integration ranging between 1 and 2 hours, depending on the observed atmospheric scenario. CIAO lidars signals have been processed using the EARLINET Single Calculus Chain (SCC) also with the

Modelling Forest α-Diversity and Floristic Composition — On the Added Value of LiDAR plus Hyperspectral Remote Sensing

Directory of Open Access Journals (Sweden)

Martin Wegmann

2012-09-01

Full Text Available The decline of biodiversity is one of the major current global issues. Still, there is a widespread lack of information about the spatial distribution of individual species and biodiversity as a whole. Remote sensing techniques are increasingly used for biodiversity monitoring and especially the combination of LiDAR and hyperspectral data is expected to deliver valuable information. In this study spatial patterns of vascular plant community composition and alpha-diversity of a temperate montane forest in Germany were analysed for different forest strata. The predictive power of LiDAR (LiD and hyperspectral (MNF datasets alone and combined (MNF+LiD was compared using random forest regression in a ten-fold cross-validation scheme that included feature selection and model tuning. The final models were used for spatial predictions. Species richness could be predicted with varying accuracy (R2 = 0.26 to 0.55 depending on the forest layer. In contrast, community composition of the different layers, obtained by multivariate ordination, could in part be modelled with high accuracies for the first ordination axis (R2 = 0.39 to 0.78, but poor accuracies for the second axis (R2 ≤ 0.3. LiDAR variables were the best predictors for total species richness across all forest layers (R2 LiD = 0.3, R2 MNF = 0.08, R2 MNF+LiD = 0.2, while for community composition across all forest layers both hyperspectral and LiDAR predictors achieved similar performances (R2 LiD = 0.75, R2 MNF = 0.76, R2 MNF+LiD = 0.78. The improvement in R2 was small (≤0.07—if any—when using both LiDAR and hyperspectral data as compared to using only the best single predictor set. This study shows the high potential of LiDAR and hyperspectral data for plant biodiversity modelling, but also calls for a critical evaluation of the added value of combining both with respect to acquisition costs.

Software design of control system of CCD side-scatter lidar

Science.gov (United States)

Kuang, Zhiqiang; Liu, Dong; Deng, Qian; Zhang, Zhanye; Wang, Zhenzhu; Yu, Siqi; Tao, Zongming; Xie, Chenbo; Wang, Yingjian

2018-03-01

Because of the existence of blind zone and transition zone, the application of backscattering lidar in near-ground is limited. The side-scatter lidar equipped with the Charge Coupled Devices (CCD) can separate the transmitting and receiving devices to avoid the impact of the geometric factors which is exited in the backscattering lidar and, detect the more precise near-ground aerosol signals continuously. Theories of CCD side-scatter lidar and the design of control system are introduced. The visible control of laser and CCD and automatic data processing method of the side-scatter lidar are developed by using the software of Visual C #. The results which are compared with the calibration of the atmospheric aerosol lidar data show that signals from the CCD side- scatter lidar are convincible.

An Aerosol Extinction-to-Backscatter Ratio Database Derived from the NASA Micro-Pulse Lidar Network: Applications for Space-based Lidar Observations

Science.gov (United States)

Welton, Ellsworth J.; Campbell, James R.; Spinhime, James D.; Berkoff, Timothy A.; Holben, Brent; Tsay, Si-Chee; Bucholtz, Anthony

2004-01-01

Backscatter lidar signals are a function of both backscatter and extinction. Hence, these lidar observations alone cannot separate the two quantities. The aerosol extinction-to-backscatter ratio, S, is the key parameter required to accurately retrieve extinction and optical depth from backscatter lidar observations of aerosol layers. S is commonly defined as 4*pi divided by the product of the single scatter albedo and the phase function at 180-degree scattering angle. Values of S for different aerosol types are not well known, and are even more difficult to determine when aerosols become mixed. Here we present a new lidar-sunphotometer S database derived from Observations of the NASA Micro-Pulse Lidar Network (MPLNET). MPLNET is a growing worldwide network of eye-safe backscatter lidars co-located with sunphotometers in the NASA Aerosol Robotic Network (AERONET). Values of S for different aerosol species and geographic regions will be presented. A framework for constructing an S look-up table will be shown. Look-up tables of S are needed to calculate aerosol extinction and optical depth from space-based lidar observations in the absence of co-located AOD data. Applications for using the new S look-up table to reprocess aerosol products from NASA's Geoscience Laser Altimeter System (GLAS) will be discussed.

Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data.

Science.gov (United States)

Silva, Carlos Alberto; Hudak, Andrew Thomas; Klauberg, Carine; Vierling, Lee Alexandre; Gonzalez-Benecke, Carlos; de Padua Chaves Carvalho, Samuel; Rodriguez, Luiz Carlos Estraviz; Cardil, Adrián

2017-12-01

LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m -2 (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m. The results show that LiDAR pulse density of 5 pulses m -2 provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m -2 in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system. LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m -2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory.

Multi-wavelength Ocean Profiling and Atmospheric Lidar

Data.gov (United States)

National Aeronautics and Space Administration — We propose to build and demonstrate the world's first multi-wavelength ocean-profiling high spectral resolution lidar (HSRL). The lidar will provide profiles of...

2012 Oregon Lidar Consortium (OLC) Lidar DEM: Keno (OR)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Watershed Sciences, Inc. (WSI) collected Light Detection and Ranging (LiDAR) data of the Oregon Keno Study Area for the Oregon Department of Geology and Mineral...

Shipborne LiDAR system for coastal change monitoring

Science.gov (United States)

Kim, chang hwan; Park, chang hong; Kim, hyun wook; hyuck Kim, won; Lee, myoung hoon; Park, hyeon yeong

2016-04-01

Coastal areas, used as human utilization areas like leisure space, medical care, ports and power plants, etc., are regions that are continuously changing and interconnected with oceans and land and the sea level has risen by about 8cm (1.9mm / yr) due to global warming from 1964 year to 2006 year in Korea. Coastal erosion due to sea-level rise has caused the problem of marine ecosystems and loss of tourism resources, etc. Regular monitoring of coastal erosion is essential at key locations with such volatility. But the survey method of land mobile LiDAR (light detection and ranging) system has much time consuming and many restrictions. For effective monitoring beach erosion, KIOST (Korea Institute of Ocean Science & Technology) has constructed a shipborne mobile LiDAR system. The shipborne mobile LiDAR system comprised a land mobile LiDAR (RIEGL LMS-420i), an INS (inertial navigation system, MAGUS Inertial+), a RTKGPS (LEICA GS15 GS25), and a fixed platform. The shipborne mobile LiDAR system is much more effective than a land mobile LiDAR system in the measuring of fore shore areas without shadow zone. Because the vessel with the shipborne mobile LiDAR system is continuously moved along the shoreline, it is possible to efficiently survey a large area in a relatively short time. Effective monitoring of the changes using the constructed shipborne mobile LiDAR system for seriously eroded coastal areas will be able to contribute to coastal erosion management and response.

Light Detection and Ranging (LIDAR) From Space - Laser Altimeters

Science.gov (United States)

Sun, Xiaoli

2016-01-01

Light detection and ranging, or lidar, is like radar but atoptical wavelengths. The principle of operation and theirapplications in remote sensing are similar. Lidars havemany advantages over radars in instrument designs andapplications because of the much shorter laser wavelengthsand narrower beams. The lidar transmitters and receiveroptics are much smaller than radar antenna dishes. Thespatial resolution of lidar measurement is much finer thanthat of radar because of the much smaller footprint size onground. Lidar measurements usually give a better temporalresolution because the laser pulses can be much narrowerthan radio frequency (RF) signals. The major limitation oflidar is the ability to penetrate clouds and ground surfaces.

Comparison of a UAV-derived point-cloud to Lidar data at Haig Glacier, Alberta, Canada

Science.gov (United States)

Bash, E. A.; Moorman, B.; Montaghi, A.; Menounos, B.; Marshall, S. J.

2016-12-01

The use of unmanned aerial vehicles (UAVs) is expanding rapidly in glaciological research as a result of technological improvements that make UAVs a cost-effective solution for collecting high resolution datasets with relative ease. The cost and difficult access traditionally associated with performing fieldwork in glacial environments makes UAVs a particularly attractive tool. In the small, but growing, body of literature using UAVs in glaciology the accuracy of UAV data is tested through the comparison of a UAV-derived DEM to measured control points. A field campaign combining simultaneous lidar and UAV flights over Haig Glacier in April 2015, provided the unique opportunity to directly compare UAV data to lidar. The UAV was a six-propeller Mikrokopter carrying a Panasonic Lumix DMC-GF1 camera with a 12 Megapixel Live MOS sensor and Lumix G 20 mm lens flown at a height of 90 m, resulting in sub-centimetre ground resolution per image pixel. Lidar data collection took place April 20, while UAV flights were conducted April 20-21. A set of 65 control points were laid out and surveyed on the glacier surface on April 19 and 21 using a RTK GPS with a vertical uncertainty of 5 cm. A direct comparison of lidar points to these control points revealed a 9 cm offset between the control points and the lidar points on average, but the difference changed distinctly from points collected on April 19 versus those collected April 21 (7 cm and 12 cm). Agisoft Photoscan was used to create a point-cloud from imagery collected with the UAV and CloudCompare was used to calculate the difference between this and the lidar point cloud, revealing an average difference of less than 17 cm. This field campaign also highlighted some of the benefits and drawbacks of using a rotary UAV for glaciological research. The vertical takeoff and landing capabilities, combined with quick responsiveness and higher carrying capacity, make the rotary vehicle favourable for high-resolution photos when

Integrated remotely sensed datasets for disaster management

Science.gov (United States)

McCarthy, Timothy; Farrell, Ronan; Curtis, Andrew; Fotheringham, A. Stewart

2008-10-01

Video imagery can be acquired from aerial, terrestrial and marine based platforms and has been exploited for a range of remote sensing applications over the past two decades. Examples include coastal surveys using aerial video, routecorridor infrastructures surveys using vehicle mounted video cameras, aerial surveys over forestry and agriculture, underwater habitat mapping and disaster management. Many of these video systems are based on interlaced, television standards such as North America's NTSC and European SECAM and PAL television systems that are then recorded using various video formats. This technology has recently being employed as a front-line, remote sensing technology for damage assessment post-disaster. This paper traces the development of spatial video as a remote sensing tool from the early 1980s to the present day. The background to a new spatial-video research initiative based at National University of Ireland, Maynooth, (NUIM) is described. New improvements are proposed and include; low-cost encoders, easy to use software decoders, timing issues and interoperability. These developments will enable specialists and non-specialists collect, process and integrate these datasets within minimal support. This integrated approach will enable decision makers to access relevant remotely sensed datasets quickly and so, carry out rapid damage assessment during and post-disaster.

Quantifying the eroded volume of mercury-contaminated sediment using terrestrial laser scanning at Stocking Flat, Deer Creek, Nevada County, California, 2010–13

Science.gov (United States)

Howle, James F.; Alpers, Charles N.; Bawden, Gerald W.; Bond, Sandra

2016-07-28

High-resolution ground-based light detection and ranging (lidar), also known as terrestrial laser scanning, was used to quantify the volume of mercury-contaminated sediment eroded from a stream cutbank at Stocking Flat along Deer Creek in the Sierra Nevada foothills, about 3 kilometers west of Nevada City, California. Terrestrial laser scanning was used to collect sub-centimeter, three-dimensional images of the complex cutbank surface, which could not be mapped non-destructively or in sufficient detail with traditional surveying techniques.The stream cutbank, which is approximately 50 meters long and 8 meters high, was surveyed on four occasions: December 1, 2010; January 20, 2011; May 12, 2011; and February 4, 2013. Volumetric changes were determined between the sequential, three-dimensional lidar surveys. Volume was calculated by two methods, and the average value is reported. Between the first and second surveys (December 1, 2010, to January 20, 2011), a volume of 143 plus or minus 15 cubic meters of sediment was eroded from the cutbank and mobilized by Deer Creek. Between the second and third surveys (January 20, 2011, to May 12, 2011), a volume of 207 plus or minus 24 cubic meters of sediment was eroded from the cutbank and mobilized by the stream. Total volumetric change during the winter and spring of 2010–11 was 350 plus or minus 28 cubic meters. Between the third and fourth surveys (May 12, 2011, to February 4, 2013), the differencing of the three-dimensional lidar data indicated that a volume of 18 plus or minus 10 cubic meters of sediment was eroded from the cutbank. The total volume of sediment eroded from the cutbank between the first and fourth surveys was 368 plus or minus 30 cubic meters.

AUTOMATIC 3D BUILDING MODEL GENERATION FROM LIDAR AND IMAGE DATA USING SEQUENTIAL MINIMUM BOUNDING RECTANGLE

Directory of Open Access Journals (Sweden)

E. Kwak

2012-07-01

Full Text Available Digital Building Model is an important component in many applications such as city modelling, natural disaster planning, and aftermath evaluation. The importance of accurate and up-to-date building models has been discussed by many researchers, and many different approaches for efficient building model generation have been proposed. They can be categorised according to the data source used, the data processing strategy, and the amount of human interaction. In terms of data source, due to the limitations of using single source data, integration of multi-senor data is desired since it preserves the advantages of the involved datasets. Aerial imagery and LiDAR data are among the commonly combined sources to obtain 3D building models with good vertical accuracy from laser scanning and good planimetric accuracy from aerial images. The most used data processing strategies are data-driven and model-driven ones. Theoretically one can model any shape of buildings using data-driven approaches but practically it leaves the question of how to impose constraints and set the rules during the generation process. Due to the complexity of the implementation of the data-driven approaches, model-based approaches draw the attention of the researchers. However, the major drawback of model-based approaches is that the establishment of representative models involves a manual process that requires human intervention. Therefore, the objective of this research work is to automatically generate building models using the Minimum Bounding Rectangle algorithm and sequentially adjusting them to combine the advantages of image and LiDAR datasets.

2010 ARRA Lidar: 4 Southeast Counties (MI)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME: Southeast Michigan LiDAR LiDAR Data Acquisition and Processing Production Task- Monroe, St. Clair, Macomb, and Livingston Counties SEMCOG CONTRACT:...

Wind turbine control applications of turbine-mounted LIDAR

International Nuclear Information System (INIS)

Bossanyi, E A; Kumar, A; Hugues-Salas, O

2014-01-01

In recent years there has been much interest in the possible use of LIDAR systems for improving the performance of wind turbine controllers, by providing preview information about the approaching wind field. Various potential benefits have been suggested, and experimental measurements have sometimes been used to claim surprising gains in performance. This paper reports on an independent study which has used detailed analytical methods for two main purposes: firstly to try to evaluate the likely benefits of LIDAR-assisted control objectively, and secondly to provide advice to LIDAR manufacturers about the characteristics of LIDAR systems which are most likely to be of value for this application. Many different LIDAR configurations were compared: as a general conclusion, systems should be able to sample at least 10 points every second, reasonably distributed around the swept area, and allowing a look-ahead time of a few seconds. An important conclusion is that the main benefit of the LIDAR will be to enhance of collective pitch control to reduce thrust-related fatigue loads; there is some indication that extreme loads can also be reduced, but this depends on other considerations which are discussed in the paper. LIDAR-assisted individual pitch control, optimal C p tracking and yaw control were also investigated, but the benefits over conventional methods are less clear

Calibration of Ground -based Lidar instrument

DEFF Research Database (Denmark)

Villanueva, Héctor; Yordanova, Ginka

This report presents the result of the lidar calibration performed for the given Ground-based Lidar at DTU’s test site for large wind turbines at Høvsøre, Denmark. Calibration is here understood as the establishment of a relation between the reference wind speed measurements with measurement...

Lidar sounding of volcanic plumes

Science.gov (United States)

Fiorani, Luca; Aiuppa, Alessandro; Angelini, Federico; Borelli, Rodolfo; Del Franco, Mario; Murra, Daniele; Pistilli, Marco; Puiu, Adriana; Santoro, Simone

2013-10-01

Accurate knowledge of gas composition in volcanic plumes has high scientific and societal value. On the one hand, it gives information on the geophysical processes taking place inside volcanos; on the other hand, it provides alert on possible eruptions. For this reasons, it has been suggested to monitor volcanic plumes by lidar. In particular, one of the aims of the FP7 ERC project BRIDGE is the measurement of CO2 concentration in volcanic gases by differential absorption lidar. This is a very challenging task due to the harsh environment, the narrowness and weakness of the CO2 absorption lines and the difficulty to procure a suitable laser source. This paper, after a review on remote sensing of volcanic plumes, reports on the current progress of the lidar system.

Lidar investigations of atmospheric aerosols over Sofia

International Nuclear Information System (INIS)

Dreischuh, T.; Deleva, A.; Peshev, Z.; Grigorov, I.; Kolarov, G.; Stoyanov, D.

2016-01-01

An overview is given of the laser remote sensing of atmospheric aerosols and related processes over the Sofia area performed in the Institute of Electronics, Bulgarian Academy of Sciences, during the last three years. Results from lidar investigations of the optical characteristics of atmospheric aerosols obtained in the frame of the European Aerosol Research Lidar Network, as well as from the lidar mapping of near-surface aerosol fields for remote monitoring of atmospheric pollutants are presented and discussed in this paper.

Developing Verification Systems for Building Information Models of Heritage Buildings with Heterogeneous Datasets

Science.gov (United States)

Chow, L.; Fai, S.

2017-08-01

The digitization and abstraction of existing buildings into building information models requires the translation of heterogeneous datasets that may include CAD, technical reports, historic texts, archival drawings, terrestrial laser scanning, and photogrammetry into model elements. In this paper, we discuss a project undertaken by the Carleton Immersive Media Studio (CIMS) that explored the synthesis of heterogeneous datasets for the development of a building information model (BIM) for one of Canada's most significant heritage assets - the Centre Block of the Parliament Hill National Historic Site. The scope of the project included the development of an as-found model of the century-old, six-story building in anticipation of specific model uses for an extensive rehabilitation program. The as-found Centre Block model was developed in Revit using primarily point cloud data from terrestrial laser scanning. The data was captured by CIMS in partnership with Heritage Conservation Services (HCS), Public Services and Procurement Canada (PSPC), using a Leica C10 and P40 (exterior and large interior spaces) and a Faro Focus (small to mid-sized interior spaces). Secondary sources such as archival drawings, photographs, and technical reports were referenced in cases where point cloud data was not available. As a result of working with heterogeneous data sets, a verification system was introduced in order to communicate to model users/viewers the source of information for each building element within the model.

A user friendly Lidar system based on LabVIEW

Energy Technology Data Exchange (ETDEWEB)

Andersson, Mats; Weibring, P.

1996-09-01

Mobile differential absorption lidar (DIAL) systems have been used for the last two decades. The lidar group in Lund has performed many DIAL measurements with a mobile lidar system which was first described in 1987. This report describes how that system was updated with the graphical programming language LabVIEW in order to get a user friendly system. The software controls the lidar system and analyses measurement data. The measurement results are shown as maps of species concentration. New electronics to support the new lidar program have also been installed. The report describes how all supporting electronics and the program work. A user manual for the new program is also given. 19 refs, 79 figs, 23 charts

COMPARISON OF 2D AND 3D APPROACHES FOR THE ALIGNMENT OF UAV AND LIDAR POINT CLOUDS

Directory of Open Access Journals (Sweden)

R. A. Persad

2017-08-01

Full Text Available The automatic alignment of 3D point clouds acquired or generated from different sensors is a challenging problem. The objective of the alignment is to estimate the 3D similarity transformation parameters, including a global scale factor, 3 rotations and 3 translations. To do so, corresponding anchor features are required in both data sets. There are two main types of alignment: i Coarse alignment and ii Refined Alignment. Coarse alignment issues include lack of any prior knowledge of the respective coordinate systems for a source and target point cloud pair and the difficulty to extract and match corresponding control features (e.g., points, lines or planes co-located on both point cloud pairs to be aligned. With the increasing use of UAVs, there is a need to automatically co-register their generated point cloud-based digital surface models with those from other data acquisition systems such as terrestrial or airborne lidar point clouds. This works presents a comparative study of two independent feature matching techniques for addressing 3D conformal point cloud alignment of UAV and lidar data in different 3D coordinate systems without any prior knowledge of the seven transformation parameters.

Triple-Pulse Integrated Path Differential Absorption Lidar for Carbon Dioxide Measurement - Novel Lidar Technologies and Techniques with Path to Space

Science.gov (United States)

Singh, Upendra N.; Refaat, Tamer F.; Petros, Mulugeta

2017-01-01

The societal benefits of understanding climate change through identification of global carbon dioxide sources and sinks led to the desired NASA's active sensing of carbon dioxide emissions over nights, days, and seasons (ASCENDS) space-based missions of global carbon dioxide measurements. For more than 15 years, NASA Langley Research Center (LaRC) have developed several carbon dioxide active remote sensors using the differential absorption lidar (DIAL) technique operating at the two-micron wavelength. Currently, an airborne two-micron triple-pulse integrated path differential absorption (IPDA) lidar is under development. This IPDA lidar measures carbon dioxide as well as water vapor, the dominant interfering molecule on carbon dioxide remote sensing. Advancement of this triple-pulse IPDA lidar development is presented.

2015 Oregon Department Forestry Lidar: Northwest OR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — GeoTerra, Inc. was selected by Oregon Department of Forestry to provide Lidar remote sensing data including LAZ files of the classified Lidar points and surface...

Power curve measurement with a nacelle mounted lidar

DEFF Research Database (Denmark)

Wagner, Rozenn; Friis Pedersen, Troels; Courtney, Michael

2014-01-01

is tested. A pulsed lidar prototype, measuring horizontally, was installed on the nacelle of a multi-megawatt wind turbine. A met mast with a top-mounted cup anemometer standing at two rotor diameters in front of the turbine was used as a reference. After a data-filtering step, the comparison of the 10 min......Nacelle-based lidars are an attractive alternative to conventional mast base reference wind instrumentation where the erection of a mast is expensive, for example offshore. In this paper, the use of this new technology for the specific application of wind turbine power performance measurement...... in wind speed measurements. A lower scatter in the power curve was observed for the lidar than for the mast. Since the lidar follows the turbine nacelle as it yaws, it always measures upwind. The wind measured by the lidar therefore shows a higher correlation with the turbine power fluctuations than...

LIDAR and atmosphere remote sensing [DST Space Science Initiatives

CSIR Research Space (South Africa)

Venkataraman, S

2009-04-01

Full Text Available Energy Source included in the measurement. Slide 2 © CSIR 2008 www.csir.co.za The observer can control the source Eg. Radar, Lidar, Sodar, Sonar etc. (b) Passive remote sensors. Energy source is not included in the measurement... Instrument Passive Slide 3 © CSIR 2008 www.csir.co.za Active LiDAR Principle • LIDAR (Light Detection and Ranging) • LiDAR employs a laser as a source of pulsed energy • Lasers are advantageous because – checkbld Monochromatic...

LESTO: an Open Source GIS-based toolbox for LiDAR analysis

Science.gov (United States)

Franceschi, Silvia; Antonello, Andrea; Tonon, Giustino

2015-04-01

During the last five years different research institutes and private companies stared to implement new algorithms to analyze and extract features from LiDAR data but only a few of them also created a public available software. In the field of forestry there are different examples of software that can be used to extract the vegetation parameters from LiDAR data, unfortunately most of them are closed source (even if free), which means that the source code is not shared with the public for anyone to look at or make changes to. In 2014 we started the development of the library LESTO (LiDAR Empowered Sciences Toolbox Opensource): a set of modules for the analysis of LiDAR point cloud with an Open Source approach with the aim of improving the performance of the extraction of the volume of biomass and other vegetation parameters on large areas for mixed forest structures. LESTO contains a set of modules for data handling and analysis implemented within the JGrassTools spatial processing library. The main subsections are dedicated to 1) preprocessing of LiDAR raw data mainly in LAS format (utilities and filtering); 2) creation of raster derived products; 3) flight-lines identification and normalization of the intensity values; 4) tools for extraction of vegetation and buildings. The core of the LESTO library is the extraction of the vegetation parameters. We decided to follow the single tree based approach starting with the implementation of some of the most used algorithms in literature. These have been tweaked and applied on LiDAR derived raster datasets (DTM, DSM) as well as point clouds of raw data. The methods range between the simple extraction of tops and crowns from local maxima, the region growing method, the watershed method and individual tree segmentation on point clouds. The validation procedure consists in finding the matching between field and LiDAR-derived measurements at individual tree and plot level. An automatic validation procedure has been developed

2009 Bayfield County Lake Superior Lidar Survey

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The LIDAR survey presents digital elevation data sets of a bald earth surface model and 2ft interval contours covering Bayfield County, Wisconsin. The LIDAR data was...

2007 South Carolina DNR Lidar: Dorchester County

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Woolpert Inc. conducted a LiDAR survey to acquire LiDAR capable of producing a DEM for orthophoto rectification and able to support 2-foot contour specifications....

Self-supervised Traversability Assessment in Field Environments with Lidar and Camera

DEFF Research Database (Denmark)

Hansen, Mikkel Kragh; Underwood, James; Karstoft, Henrik

, the visual classifier detects non-traversable image patches as outliers from a Gaussian Mixture Model that maintains the appearance of only traversable ground. Results Our method is evaluated using a diverse dataset of agricultural fields and orchards gathered with a perception research robot developed......Introduction The application of robotic automation within agriculture is increasing. There is a high demand for fully autonomous robots that are both efficient, reliable and affordable. In order to ensure safety, autonomous agricultural vehicles must perceive the environment and detect potential...... obstacles and threats across a variety of environmental conditions. In this paper, a self-supervised framework is proposed, combining laser range sensing from a lidar with images from a monocular camera to reliably assess terrain traversability/navigability. Methods The method uses a near-to-far approach...

Contours, 2 foot contours automatically generated from 2008 LIDAR for the purpose of supporting FEMA floodplain mapping. Limited manual editing, breaklines for waterbodies greater than 5 acres created and use.10' index contours labeled., Published in 2008, 1:1200 (1in=100ft) scale, City of Portage Government.

Data.gov (United States)

NSGIC Local Govt | GIS Inventory — Contours dataset current as of 2008. 2 foot contours automatically generated from 2008 LIDAR for the purpose of supporting FEMA floodplain mapping. Limited manual...

2015 OLC FEMA Lidar: Snake River, ID

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Quantum Spatial has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Snake River FEMA study area. This study area is located...

2007 South Carolina DNR Lidar: Anderson County

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The LiDAR data acquisition was executed in 5 sessions, from March 7 to March 9, 2007. The airborne GPS (ABGPS) base stations supporting the LiDAR acquisition...

2011 South Carolina DNR Lidar: York County

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Towill Inc. collected LiDAR for over 3,500 square miles in York, Pickens, Anderson, and Oconee Counties in South Carolina. This metadata covers the LiDAR produced...

2012 NRCS-USGS Tupelo, MS Lidar Survey

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — LiDAR data is a remotely sensed high resolution elevation data collected by an airborne platform. The LiDAR sensor uses a combination of laser range finding, GPS...

Gluing for Raman lidar systems using the lamp mapping technique.

Science.gov (United States)

Walker, Monique; Venable, Demetrius; Whiteman, David N

2014-12-20

In the context of combined analog and photon counting (PC) data acquisition in a Lidar system, glue coefficients are defined as constants used for converting an analog signal into a virtual PC signal. The coefficients are typically calculated using Lidar profile data taken under clear, nighttime conditions since, in the presence of clouds or high solar background, it is difficult to obtain accurate glue coefficients from Lidar backscattered data. Here we introduce a new method in which we use the lamp mapping technique (LMT) to determine glue coefficients in a manner that does not require atmospheric profiles to be acquired and permits accurate glue coefficients to be calculated when adequate Lidar profile data are not available. The LMT involves scanning a halogen lamp over the aperture of a Lidar receiver telescope such that the optical efficiency of the entire detection system is characterized. The studies shown here involve two Raman lidar systems; the first from Howard University and the second from NASA/Goddard Space Flight Center. The glue coefficients determined using the LMT and the Lidar backscattered method agreed within 1.2% for the water vapor channel and within 2.5% for the nitrogen channel for both Lidar systems. We believe this to be the first instance of the use of laboratory techniques for determining the glue coefficients for Lidar data analysis.

2013-2014 USGS Lidar: Olympic Peninsula (WA)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME: USGS Olympic Peninsula Washington LIDAR LiDAR Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No. G13PD00849...

2015 OLC Lidar DEM: Big Wood, ID

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Quantum Spatial has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Big Wood 2015 study area. This study area is located in...

Elevation - LiDAR Survey - Roseau County, Minnesota

Data.gov (United States)

Army Corps of Engineers, Department of the Army, Department of Defense — LIDAR Data for Roseau County Minnesota. This project consists of approximately 87 square miles of LIDAR mapping in Roseau County, Minnesota at two sites: area 1,...

Temporal Analysis and Automatic Calibration of the Velodyne HDL-32E LiDAR System

Directory of Open Access Journals (Sweden)

T. O. Chan

2013-10-01

Full Text Available At the end of the first quarter of 2012, more than 600 Velodyne LiDAR systems had been sold worldwide for various robotic and high-accuracy survey applications. The ultra-compact Velodyne HDL-32E LiDAR has become a predominant sensor for many applications that require lower sensor size/weight and cost. For high accuracy applications, cost-effective calibration methods with minimal manual intervention are always desired by users. However, the calibrations are complicated by the Velodyne LiDAR's narrow vertical field of view and the very highly time-variant nature of its measurements. In the paper, the temporal stability of the HDL-32E is first analysed as the motivation for developing a new, automated calibration method. This is followed by a detailed description of the calibration method that is driven by a novel segmentation method for extracting vertical cylindrical features from the Velodyne point clouds. The proposed segmentation method utilizes the Velodyne point cloud's slice-like nature and first decomposes the point clouds into 2D layers. Then the layers are treated as 2D images and are processed with the Generalized Hough Transform which extracts the points distributed in circular patterns from the point cloud layers. Subsequently, the vertical cylindrical features can be readily extracted from the whole point clouds based on the previously extracted points. The points are passed to the calibration that estimates the cylinder parameters and the LiDAR's additional parameters simultaneously by constraining the segmented points to fit to the cylindrical geometric model in such a way the weighted sum of the adjustment residuals are minimized. The proposed calibration is highly automatic and this allows end users to obtain the time-variant additional parameters instantly and frequently whenever there are vertical cylindrical features presenting in scenes. The methods were verified with two different real datasets, and the results suggest

Big data; sensor networks and remotely-sensed data for mapping; feature extraction from lidar

Science.gov (United States)

Tlhabano, Lorato

2018-05-01

Unmanned aerial vehicles (UAVs) can be used for mapping in the close range domain, combining aerial and terrestrial photogrammetry and now the emergence of affordable platforms to carry these technologies has opened up new opportunities for mapping and modeling cadastral boundaries. At the current state mainly low cost UAVs fitted with sensors are used in mapping projects with low budgets, the amount of data produced by the UAVs can be enormous hence the need for big data techniques' and concepts. The past couple of years have witnessed the dramatic rise of low-cost UAVs fitted with high tech Lidar sensors and as such the UAVS have now reached a level of practical reliability and professionalism which allow the use of these systems as mapping platforms. UAV based mapping provides not only the required accuracy with respect to cadastral laws and policies as well as requirements for feature extraction from the data sets and maps produced, UAVs are also competitive to other measurement technologies in terms of economic aspects. In the following an overview on how the various technologies of UAVs, big data concepts and lidar sensor technologies can work together to revolutionize cadastral mapping particularly in Africa and as a test case Botswana in particular will be used to investigate these technologies. These technologies can be combined to efficiently provide cadastral mapping in difficult to reach areas and over large areas of land similar to the Land Administration Procedures, Capacity and Systems (LAPCAS) exercise which was recently undertaken by the Botswana government, we will show how the uses of UAVS fitted with lidar sensor and utilizing big data concepts could have reduced not only costs and time for our government but also how UAVS could have provided more detailed cadastral maps.

2012-2013 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Hoh River Watershed, Washington (Deliveries 1 and 2)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Watershed Sciences, Inc. (WSI) collected Light Detection and Ranging (LiDAR) data on the Hoh River watershed survey area for the Puget Sound LiDAR Consortium and the...

Detecting wind turbine wakes with nacelle lidars

DEFF Research Database (Denmark)

Held, D. P.; Larvol, A.; Mann, Jakob

2017-01-01

variance is used as a detection parameter for wakes. A one month long measurement campaign, where a continuous-wave lidar on a turbine has been exposed to multiple wake situations, is used to test the detection capabilities. The results show that it is possible to identify situation where a downstream...... turbine is in wake by comparing the peak widths. The used lidar is inexpensive and brings instalments on every turbine within economical reach. Thus, the information gathered by the lidars can be used for improved control at wind farm level....

Dickinson County, MI LIDAR_LAS_1.2

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME:(NRCS) Dickinson County, MI LIDAR LiDAR Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No. G12PD00721 Woolpert...

System, method, and apparatus for remote measurement of terrestrial biomass

Science.gov (United States)

Johnson, Patrick W [Jefferson, MD

2011-04-12

A system, method, and/or apparatus for remote measurement of terrestrial biomass contained in vegetative elements, such as large tree boles or trunks present in an area of interest, are provided. The method includes providing an airborne VHF radar system in combination with a LiDAR system, overflying the area of interest while directing energy toward the area of interest, using the VHF radar system to collect backscatter data from the trees as a function of incidence angle and frequency, and determining a magnitude of the biomass from the backscatter data and data from the laser radar system for each radar resolution cell. A biomass map is generated showing the magnitude of the biomass of the vegetative elements as a function of location on the map by using each resolution cell as a unique location thereon. In certain preferred embodiments, a single frequency is used with a linear array antenna.

NASA ESTO Lidar Technologies Investment Strategy: 2016 Decadal Update

Science.gov (United States)

Valinia, Azita; Komar, George J.; Tratt, David M.; Lotshaw, William T.; Gaab, Kevin M.

2017-01-01

The NASA Earth Science Technology Office (ESTO) recently updated its investment strategy in the area of lidar technologies as it pertains to NASA's Earth Science measurement goals in the next decade. The last ESTO lidar strategy was documented in 2006. The current (2016) report assesses the state-of-the-art in lidar technologies a decade later. Lidar technology maturation in the past decade has been evaluated, and the ESTO investment strategy is updated and laid out in this report according to current NASA Earth science measurement needs and new emerging technologies.

2006 South Carolina DNR Lidar: Aiken County

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The LiDAR data acquisition was executed in five sessions, on March 15, 16 & 17, 2006, using a Leica ALS50 LiDAR System. Specific details about the ALS50 system...

NAMMA LIDAR ATMOSPHERIC SENSING EXPERIMENT (LASE) V1

Data.gov (United States)

National Aeronautics and Space Administration — NASA's Lidar Atmospheric Sensing Experiment (LASE) system using the DIAL (Differential Absorption Lidar) system was operated during the NASA African Monsoon...

LIDAR Wind Speed Measurements of Evolving Wind Fields

Energy Technology Data Exchange (ETDEWEB)

Simley, E.; Pao, L. Y.

2012-07-01

Light Detection and Ranging (LIDAR) systems are able to measure the speed of incoming wind before it interacts with a wind turbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbine loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. Past studies have assumed Taylor's frozen turbulence hypothesis, which implies that turbulence remains unchanged as it advects downwind at the mean wind speed. With Taylor's hypothesis applied, the only source of wind speed measurement error is distortion caused by the LIDAR. This study introduces wind evolution, characterized by the longitudinal coherence of the wind, to LIDAR measurement simulations to create a more realistic measurement model. A simple model of wind evolution is applied to a frozen wind field used in previous studies to investigate the effects of varying the intensity of wind evolution. LIDAR measurements are also evaluated with a large eddy simulation of a stable boundary layer provided by the National Center for Atmospheric Research. Simulation results show the combined effects of LIDAR errors and wind evolution for realistic turbine-mounted LIDAR measurement scenarios.

Processing LiDAR Data to Predict Natural Hazards

Science.gov (United States)

Fairweather, Ian; Crabtree, Robert; Hager, Stacey

2008-01-01

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

Break Lines, This data was produced for the USGS according to specific project requirements. The Lidar derived breaklines cover Somerset County and the Western portion of Wicomico County, Maryland. Inland streams, rivers, lakes, ponds and tidal features are present., Published in 2012, Not Applicable scale, Eastern Shore Regional GIS Cooperative.

Data.gov (United States)

NSGIC Regional | GIS Inventory — Break Lines dataset current as of 2012. This data was produced for the USGS according to specific project requirements. The Lidar derived breaklines cover Somerset...

Forest Biomass Mapping From Lidar and Radar Synergies

Science.gov (United States)

Sun, Guoqing; Ranson, K. Jon; Guo, Z.; Zhang, Z.; Montesano, P.; Kimes, D.

2011-01-01

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed. In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R(exp 2) of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI Mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R2 of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar- SAR synergy 63 was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the

Development of a Dynamic Lidar Uncertainty Framework

Energy Technology Data Exchange (ETDEWEB)

Newman, Jennifer [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Clifton, Andrew [WindForS; Bonin, Timothy [CIRES/NOAA ESRL; Choukulkar, Aditya [CIRES/NOAA ESRL; Brewer, W. Alan [NOAA ESRL; Delgado, Ruben [University of Maryland Baltimore County

2017-08-07

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote-sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote-sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote-sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards for quantifying remote sensing device uncertainty for power performance testing consider uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device and are generally fixed, leading to climatic uncertainty values that apply to the entire measurement campaign. However, real-world experience and a consideration of the fundamentals of the measurement process have shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we describe the development of a new dynamic lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from a field measurement site to assess the ability of the framework to predict

Wind Ressources in Complex Terrain investigated with Synchronized Lidar Measurements

Science.gov (United States)

Mann, J.; Menke, R.; Vasiljevic, N.

2017-12-01

The Perdigao experiment was performed by a number of European and American universities in Portugal 2017, and it is probably the largest field campaign focussing on wind energy ressources in complex terrain ever conducted. 186 sonic anemometers on 50 masts, 20 scanning wind lidars and a host of other instruments were deployed. The experiment is a part of an effort to make a new European wind atlas. In this presentation we investigate whether scanning the wind speed over ridges in this complex terrain with multiple Doppler lidars can lead to an efficient mapping of the wind resources at relevant positions. We do that by having pairs of Doppler lidars scanning 80 m above the ridges in Perdigao. We compare wind resources obtained from the lidars and from the mast-mounted sonic anemometers at 80 m on two 100 m masts, one on each of the two ridges. In addition, the scanning lidar measurements are also compared to profiling lidars on the ridges. We take into account the fact that the profiling lidars may be biased due to the curvature of the streamlines over the instrument, see Bingol et al, Meteorolog. Z. vol. 18, pp. 189-195 (2009). We also investigate the impact of interruptions of the lidar measurements on the estimated wind resource. We calculate the relative differences of wind along the ridge from the lidar measurements and compare those to the same obtained from various micro-scale models. A particular subject investigated is how stability affects the wind resources. We often observe internal gravity waves with the scanning lidars during the night and we quantify how these affect the relative wind speed on the ridges.

Four-wavelength lidar evaluation of particle characteristics and aerosol densities

Science.gov (United States)

Uthe, E. E.; Livingston, J. M.; Delateur, S. A.; Nielsen, N. B.

1985-06-01

The SRI International four-wavelength (0.53, 1.06, 3.8, 10.6 micron) lidar systems was used during the SNOW-ONE-B and Smoke Week XI/SNOW-TWO field experiments to validate its capabilities in assessing obscurant optical and physical properties. The lidar viewed along a horizontal path terminated by a passive reflector. Data examples were analyzed in terms of time-dependent transmission, wavelength dependence of optical depth, and range-resolved extinction coefficients. Three methods were used to derive extinction data from the lidar signatures. These were target method, Klett method and experimental data method. The results of the field and analysis programs are reported in the journal and conference papers that are appended to this report, and include: comparison study of lidar extinction methods, submitted to applied optics, error analysis of lidar solution techniques for range-resolved extinction coefficients based on observational data, smoke/obscurants symposium 9, Four--Wavelength Lidar Measurements from smoke week 6/SNOW-TWO, smoke/obscurants symposium 8, SNOW-ONE-B multiple-wavelength lidar measurements. Snow symposium 3, and lidar applications for obscurant evaluations, smoke/obscurants Symposium 7. The report also provides a summary of background work leading to this project, and of project results.

LIDAR and atmosphere remote sensing

CSIR Research Space (South Africa)

Venkataraman, S

2008-05-01

Full Text Available using state of the art Light Detection And Ranging (LiDAR) instrumentation and other active and passive remote sensing tools. First “Lidar Field Campaign” • 2-day measurement campaign at University of Pretoria • First 23-hour continuous measurement... head2rightCirrus cloud morphology and dynamics. Atmospheric Research in Southern Africa and Indian Ocean (ARSAIO) Slide 24 © CSIR 2008 www.csir.co.za Middle atmosphere dynamics and thermal structure: comparative studies from...

Underwater lidar system: design challenges and application in pollution detection

Science.gov (United States)

Gupta, Pradip; Sankolli, Swati; Chakraborty, A.

2016-05-01

The present remote sensing techniques have imposed limitations in the applications of LIDAR Technology. The fundamental sampling inadequacy of the remote sensing data obtained from satellites is that they cannot resolve in the third spatial dimension, the vertical. This limits our possibilities of measuring any vertical variability in the water column. Also the interaction between the physical and biological process in the oceans and their effects at subsequent depths cannot be modeled with present techniques. The idea behind this paper is to introduce underwater LIDAR measurement system by using a LIDAR mounted on an Autonomous Underwater Vehicle (AUV). The paper introduces working principles and design parameters for the LIDAR mounted AUV (AUV-LIDAR). Among several applications the papers discusses the possible use and advantages of AUV-LIDAR in water pollution detection through profiling of Dissolved Organic Matter (DOM) in water bodies.

Quantifying TOLNet Ozone Lidar Accuracy During the 2014 DISCOVER-AQ and FRAPPE Campaigns

Science.gov (United States)

Wang, Lihua; Newchurch, Michael J.; Alvarez, Raul J., II; Berkoff, Timothy A.; Brown, Steven S.; Carrion, William; De Young, Russell J.; Johnson, Bryan J.; Ganoe, Rene; Gronoff, Guillaume;

Assessment and Optimization of Lidar Measurement Availability for Wind Turbine Control: Preprint

Energy Technology Data Exchange (ETDEWEB)

Davoust, S.; Jehu, A.; Bouillet, M.; Bardon, M.; Vercherin, B.; Scholbrock, A.; Fleming, P.; Wright, A.

2014-05-01

Turbine-mounted lidars provide preview measurements of the incoming wind field. By reducing loads on critical components and increasing the potential power extracted from the wind, the performance of wind turbine controllers can be improved [2]. As a result, integrating a light detection and ranging (lidar) system has the potential to lower the cost of wind energy. This paper presents an evaluation of turbine-mounted lidar availability. Availability is a metric which measures the proportion of time the lidar is producing controller-usable data, and is essential when a wind turbine controller relies on a lidar. To accomplish this, researchers from Avent Lidar Technology and the National Renewable Energy Laboratory first assessed and modeled the effect of extreme atmospheric events. This shows how a multirange lidar delivers measurements for a wide variety of conditions. Second, by using a theoretical approach and conducting an analysis of field feedback, we investigated the effects of the lidar setup on the wind turbine. This helps determine the optimal lidar mounting position at the back of the nacelle, and establishes a relationship between availability, turbine rpm, and lidar sampling time. Lastly, we considered the role of the wind field reconstruction strategies and the turbine controller on the definition and performance of a lidar's measurement availability.

Automated integration of lidar into the LANDFIRE product suite

Science.gov (United States)

Peterson, Birgit; Nelson, Kurtis; Seielstad, Carl; Stoker, Jason M.; Jolly, W. Matt; Parsons, Russell

2015-01-01

Accurate information about three-dimensional canopy structure and wildland fuel across the landscape is necessary for fire behaviour modelling system predictions. Remotely sensed data are invaluable for assessing these canopy characteristics over large areas; lidar data, in particular, are uniquely suited for quantifying three-dimensional canopy structure. Although lidar data are increasingly available, they have rarely been applied to wildland fuels mapping efforts, mostly due to two issues. First, the Landscape Fire and Resource Planning Tools (LANDFIRE) program, which has become the default source of large-scale fire behaviour modelling inputs for the US, does not currently incorporate lidar data into the vegetation and fuel mapping process because spatially continuous lidar data are not available at the national scale. Second, while lidar data are available for many land management units across the US, these data are underutilized for fire behaviour applications. This is partly due to a lack of local personnel trained to process and analyse lidar data. This investigation addresses these issues by developing the Creating Hybrid Structure from LANDFIRE/lidar Combinations (CHISLIC) tool. CHISLIC allows individuals to automatically generate a suite of vegetation structure and wildland fuel parameters from lidar data and infuse them into existing LANDFIRE data sets. CHISLIC will become available for wider distribution to the public through a partnership with the U.S. Forest Service’s Wildland Fire Assessment System (WFAS) and may be incorporated into the Wildland Fire Decision Support System (WFDSS) with additional design and testing. WFAS and WFDSS are the primary systems used to support tactical and strategic wildland fire management decisions.

2015 USGS Lidar: 3DEP Co-Op South Central MS

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Mississippi Coastal QL2 Lidar with 3DEP Extension Lidar 0.7m NPS Lidar Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No....

Lightweight Inexpensive Ozone Lidar Telescope Using a Plastic Fresnel Lens

Science.gov (United States)

DeYoung, Russell J.; Notari, Anthony; Carrion, William; Pliutau, Denis

2014-01-01

An inexpensive lightweight ozone lidar telescope was designed, constructed and operated during an ozone lidar field campaign. This report summarizes the design parameters and performance of the plastic Fresnel lens telescope and shows the ozone lidar performance compared to Zemax calculations.

An Improved Calibration Method for a Rotating 2D LIDAR System.

Science.gov (United States)

Zeng, Yadan; Yu, Heng; Dai, Houde; Song, Shuang; Lin, Mingqiang; Sun, Bo; Jiang, Wei; Meng, Max Q-H

2018-02-07

This paper presents an improved calibration method of a rotating two-dimensional light detection and ranging (R2D-LIDAR) system, which can obtain the 3D scanning map of the surroundings. The proposed R2D-LIDAR system, composed of a 2D LIDAR and a rotating unit, is pervasively used in the field of robotics owing to its low cost and dense scanning data. Nevertheless, the R2D-LIDAR system must be calibrated before building the geometric model because there are assembled deviation and abrasion between the 2D LIDAR and the rotating unit. Hence, the calibration procedures should contain both the adjustment between the two devices and the bias of 2D LIDAR itself. The main purpose of this work is to resolve the 2D LIDAR bias issue with a flat plane based on the Levenberg-Marquardt (LM) algorithm. Experimental results for the calibration of the R2D-LIDAR system prove the reliability of this strategy to accurately estimate sensor offsets with the error range from -15 mm to 15 mm for the performance of capturing scans.