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

USGS Publications Warehouse

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.

Simulation Framework to Estimate the Performance of CO2 and O2 Sensing from Space and Airborne Platforms for the ASCENDS Mission Requirements Analysis

NASA Technical Reports Server (NTRS)

Plitau, Denis; Prasad, Narasimha S.

2012-01-01

The Active Sensing of CO2 Emissions over Nights Days and Seasons (ASCENDS) mission recommended by the NRC Decadal Survey has a desired accuracy of 0.3% in carbon dioxide mixing ratio (XCO2) retrievals requiring careful selection and optimization of the instrument parameters. NASA Langley Research Center (LaRC) is investigating 1.57 micron carbon dioxide as well as the 1.26-1.27 micron oxygen bands for our proposed ASCENDS mission requirements investigation. Simulation studies are underway for these bands to select optimum instrument parameters. The simulations are based on a multi-wavelength lidar modeling framework being developed at NASA LaRC to predict the performance of CO2 and O2 sensing from space and airborne platforms. The modeling framework consists of a lidar simulation module and a line-by-line calculation component with interchangeable lineshape routines to test the performance of alternative lineshape models in the simulations. As an option the line-by-line radiative transfer model (LBLRTM) program may also be used for line-by-line calculations. The modeling framework is being used to perform error analysis, establish optimum measurement wavelengths as well as to identify the best lineshape models to be used in CO2 and O2 retrievals. Several additional programs for HITRAN database management and related simulations are planned to be included in the framework. The description of the modeling framework with selected results of the simulation studies for CO2 and O2 sensing is presented in this paper.

Independent evaluation of the SNODAS snow depth product using regional scale LiDAR-derived measurements

NASA Astrophysics Data System (ADS)

Hedrick, A.; Marshall, H.-P.; Winstral, A.; Elder, K.; Yueh, S.; Cline, D.

2014-06-01

Repeated Light Detection and Ranging (LiDAR) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km2 LiDAR-derived dataset of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically-based energy-and-mass-balance snow model with satellite, airborne and automated ground-based observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the coterminous United States. Independent validation data is scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation dataset with substantial geographic coverage. Within twelve distinctive 500 m × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 LiDAR acquisitions. This supplied a dataset for constraining the uncertainty of upscaled LiDAR estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled LiDAR snow depths were then compared to the SNODAS-estimates over the entire study area for the dates of the LiDAR flights. The remotely-sensed snow depths provided a more spatially continuous comparison dataset and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between LiDAR observations and SNODAS estimates were most drastic, suggesting natural processes specific to these regions as causal influences on model uncertainty.

Moving Beyond 2% Uncertainty: A New Framework for Quantifying Lidar Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Newman, Jennifer F.; Clifton, Andrew

2017-03-08

Remote sensing of wind using lidar is revolutionizing wind energy. However, current generations of wind lidar are ascribed a climatic value of uncertainty, which is based on a poor description of lidar sensitivity to external conditions. In this presentation, we show how it is important to consider the complete lidar measurement process to define the measurement uncertainty, which in turn offers the ability to define a much more granular and dynamic measurement uncertainty. This approach is a progression from the 'white box' lidar uncertainty method.

Levels of Autonomy and Autonomous System Performance Assessment for Intelligent Unmanned Systems

DTIC Science & Technology

2014-04-01

LIDAR and camera sensors that is driven entirely by teleoperation would be AL 0. If that same robot used its LIDAR and camera data to generate a...obstacle detection, mapping, path planning 3 CMMAD semi- autonomous counter- mine system (Few 2010) Talon UGV, camera, LIDAR , metal detector...NCAP framework are performed on individual UMS components and do not require mission level evaluations. For example, bench testing of camera, LIDAR

3D Modeling of Landslide in Open-pit Mining on Basis of Ground-based LIDAR Data

NASA Astrophysics Data System (ADS)

Hu, H.; Fernandez-Steeger, T. M.; Azzam, R.; Arnhardt, C.

2009-04-01

Slope stability is not only an important problem which is related to production and safety in open-pit mining, but also very complex task. There are three main reasons which affect the slope stability as follows: geotechnical factors: Geological structure, lithologic characteristics, water, cohesion, friction, etc.; climate factors: Rainfall and temperature; and external factors: Open-pit mining process, explosion vibration, dynamic load, etc.. The 3rd reason, as a specially one in open-pit mining, not only causes some dynamic problems but also induces the fast geometry changing which must be considered in the following research using numerical simulation and stability analysis. Recently, LIDAR technology has been applied in many fields and places in the world wide. Ground-based LIDAR technology with high accuracy up to 3mm increasingly accommodates to monitoring landslides and detecting changing. LIDAR data collection and preprocessing research have been carried out by Department of Engineering Geology and Hydrogeology at RWTH Aachen University. LIDAR data, so-called a point-cloud of mass data in high density can be obtained in short time for the sensitive open-pit mining area by using ground-based LIDAR. To obtain a consistent surface model, it is necessary to set up multiple scans with the ground-based LIDAR. The framework of data preprocessing which can be implemented by Poly-Works is introduced as follows: gross error detection and elimination, integration of reference frame, model fusion of different scans (re-sampled in overlap region), data reduction without removing the useful information which is a challenge and research front in LIDAR data processing. After data preprocessing, 3D surface model can be directly generated in Poly-Works or generated in other software by building the triangular meshes. The 3D surface landslide model can be applied to further researches such as: real time landslide geometry monitoring due to the fast data collection and processing; change detecting by means of overlying different periods of topographic or geometric data; FEM (Finite Element Method) numerical simulation on basis of combining with the geotechnical properties and parameters to analyze slope stability and predict future movements for designing and rectifying the open-pit mining process; using the reverse engineering thought for developing constitutive models. An improved 3D surface model (HRDEM) which is based on fast data collection and precise data processing on basis of ground-based LIDAR technology is important contribution for further researches of slope stability in open-pit mining area.

Ground-based aerosol measurements during CHARMEX/ADRIMED campaign at Granada station

NASA Astrophysics Data System (ADS)

Granados-Muñoz, Maria Jose; Bravo-Aranda, Juan Antonio; Navas-Guzman, Francisco; Guerro-Rascado, Juan Luis; Titos, Gloria; Lyamani, Hassan; Valenzuela, Antonio; Cazorla, Alberto; Olmo, Francisco Jose; Mallet, Marc; Alados-Arboledas, Lucas

2015-04-01

In the framework of ChArMEx/ADRIMED (Chemistry-Aerosol Mediterranean Experiment, http://charmex.lsce.ipsl.fr/; Aerosol Direct Radiative Impact on the regional climate in the MEDiterranean region) projects, a field experiment based on in situ and remote sensing measurements from surface and airborne platforms was performed. The ADRIMED project aimed to capture the high complexity of the Mediterranean region by using an integrated approach based on intensive experimental field campaign and spaceborne observations, radiative transfer calculations and climate modelling with Regional Climate Models better adapted than global circulation models. For this purpose, measurements were performed at different surface super-sites (including Granada station) over the Occidental Mediterranean region during summer 2013 for creating an updated database of the physical, chemical, optical properties and the vertical distribution of the major "Mediterranean aerosols". Namely, measurements at Granada station were performed on 16 and 17 July 2013, in coincidence with the overpasses of the ATR aircraft over the station. The instrumentation used for the campaign includes both remote sensing instruments (a multiwavelength Raman lidar and a sun photometer) and in-situ measurements (a nephelometer, a Multi-Angle Absorption Photometer (MAAP), an Aerodynamic particle sizer (APS), a high volume sampler of PM10 and an aethalometer). During the measurement period a mineral dust event was detected, with similar dust load on both days. According to in-situ measurements, the event reached the surface level on 16 of June. Vertically resolved lidar measurements indicated presence of mineral dust layers up to 5 km asl both on 16 and 17 June 2013. Temporal evolution analysis indicated that on 17 June the dust layer decoupled from the boundary layer and disappeared around 14:00 UTC. In addition, lidar and sun-photometer data were used to retrieve volume concentration profiles by means of LIRIC (Lidar-Radiometer Inversion Code algorithm) [Chaikovsky et al., 2008]. The retrieved volume concentration profiles were compared with data from ATR flights above the station at 14:30 UTC on 16 June and 07:30 UTC on 17 June, obtaining in general good agreement in the location of the aerosol layers and discrepancies in the volume concentration values ranging between 15 and 40 µm3/cm3 for the coarse mode. References: Chaikovsky, A., O. Dubovik, et a., (2008), Software package for the retrieval of aerosol microphysical properties in the vertical column using combined lidar/photometer data, Tech. Rep., Institute of Physics, National Academy of Sciences of Belarus. Acknowledgments: EARLINET lidar measurements are supported by the 7th Framework Programme project Aerosols, Clouds, and Trace Gases Research Infrastructure Network (ACTRIS) (grant agreement no. 262254). The field campaign was performed in the framework of work package 4 on aerosol-radiation-climate interactions of the coordinated programme ChArMEx.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Newman, Jennifer; Clifton, Andrew; Bonin, Timothy

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 considermore » 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 errors in lidar-measured wind speed. The results show how uncertainty varies over time and can be used to help select data with different levels of uncertainty for different applications, for example, low uncertainty data for power performance testing versus all data for plant performance monitoring.« less

Constraining lidar stand-alone retrievals with lunar photometry measurements

NASA Astrophysics Data System (ADS)

Ortiz-Amezcua, Pablo; Luis Guerrero-Rascado, Juan; Antonio Benavent-Oltra, Jose; Román, Roberto; Böckmann, Christine; Alados-Arboledas, Lucas

2018-04-01

This study combines atmospheric optical information measured with lidar and nocturnal photometers in order to find configurations that allow for the retrieval of particle microphysical properties without "3+2" lidar setups. It has been carried out using data measured at the EARLINET Granada station during the experimental campaign SLOPE in the framework of ACTRIS-2 project.

The use of LiDAR-derived high-resolution DSM and intensity data to support modelling of urban flooding

NASA Astrophysics Data System (ADS)

Aktaruzzaman, Md.; Schmitt, Theo G.

2011-11-01

This paper addresses the issue of a detailed representation of an urban catchment in terms of hydraulic and hydrologic attributes. Modelling of urban flooding requires a detailed knowledge of urban surface characteristics. The advancement in spatial data acquisition technology such as airborne LiDAR (Light Detection and Ranging) has greatly facilitated the collection of high-resolution topographic information. While the use of the LiDAR-derived Digital Surface Model (DSM) has gained popularity over the last few years as input data for a flood simulation model, the use of LiDAR intensity data has remained largely unexplored in this regard. LiDAR intensity data are acquired along with elevation data during the data collection mission by an aircraft. The practice of using of just aerial images with RGB (Red, Green and Blue) wavebands is often incapable of identifying types of surface under the shadow. On the other hand, LiDAR intensity data can provide surface information independent of sunlight conditions. The focus of this study is the use of intensity data in combination with aerial images to accurately map pervious and impervious urban areas. This study presents an Object-Based Image Analysis (OBIA) framework for detecting urban land cover types, mainly pervious and impervious surfaces in order to improve the rainfall-runoff modelling. Finally, this study shows the application of highresolution DSM and land cover maps to flood simulation software in order to visualize the depth and extent of urban flooding phenomena.

Leveraging North Carolina's QL2 Lidar to Quantify Sensitivity of National Water Model Derived Flood Inundation Extent to DEM Resolution

NASA Astrophysics Data System (ADS)

Lovette, J. P.; Lenhardt, W. C.; Blanton, B.; Duncan, J. M.; Stillwell, L.

2017-12-01

The National Water Model (NWM) has provided a novel framework for near real time flood inundation mapping across CONUS at a 10m resolution. In many regions, this spatial scale is quickly being surpassed through the collection of high resolution lidar (1 - 3m). As one of the leading states in data collection for flood inundation mapping, North Carolina is currently improving their previously available 20 ft statewide elevation product to a Quality Level 2 (QL2) product with a nominal point spacing of 0.7 meters. This QL2 elevation product increases the ground points by roughly ten times over the previous statewide lidar product, and by over 250 times when compared to the 10m NED elevation grid. When combining these new lidar data with the discharge estimates from the NWM, we can further improve statewide flood inundation maps and predictions of at-risk areas. In the context of flood risk management, these improved predictions with higher resolution elevation models consistently represent an improvement on coarser products. Additionally, the QL2 lidar also includes coarse land cover classification data for each point return, opening the possibility for expanding analysis beyond the use of only digital elevation models (e.g. improving estimates of surface roughness, identifying anthropogenic features in floodplains, characterizing riparian zones, etc.). Using the NWM Height Above Nearest Drainage approach, we compare flood inundation extents derived from multiple lidar-derived grid resolutions to assess the tradeoff between precision and computational load in North Carolina's coastal river basins. The elevation data distributed through the state's new lidar collection program provide spatial resolutions ranging from 5-50 feet, with most inland areas also including a 3 ft product. Data storage increases by almost two orders of magnitude across this range, as does processing load. In order to further assess the validity of the higher resolution elevation products on flood inundation, we examine the NWM outputs from Hurricane Matthew, which devastated southeastern North Carolina in October 2016. When compared with numerous surveyed high water marks across the coastal plain, this assessment provides insight on the impacts of grid resolution on flood inundation extent.

Monitoring of the Polar Stratospheric Clouds formation and evolution in Antarctica in August 2007 during IPY with the MATCH method applied to lidar data

NASA Astrophysics Data System (ADS)

Montoux, Nadege; David, Christine; Klekociuk, Andrew; Pitts, Michael; di Liberto, Luca; Snels, Marcel; Jumelet, Julien; Bekki, Slimane; Larsen, Niels

2010-05-01

The project ORACLE-O3 ("Ozone layer and UV RAdiation in a changing CLimate Evaluated during IPY") is one of the coordinated international proposals selected for the International Polar Year (IPY). As part of this global project, LOLITA-PSC ("Lagrangian Observations with Lidar Investigations and Trajectories in Antarctica and Arctic, of PSC") is devoted to Polar Stratospheric Clouds (PSC) studies. Indeed, understanding the formation and evolution of PSC is an important issue to quantify the impact of climate changes on their frequency of formation and, further, on chlorine activation and subsequent ozone depletion. In this framework, three lidar stations performed PSC observations in Antarctica during the 2006, 2007, and 2008 winters: Davis (68.58°S, 77.97°E), McMurdo (77.86°S, 166.48°E) and Dumont D'Urville (66.67°S, 140.01°E). The data are completed with the lidar data from CALIOP ("Cloud-Aerosol Lidar with Orthogonal Polarization") onboard the CALIPSO ("Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation") satellite. Lagrangian trajectory calculations are used to identify air masses with PSCs sounded by several ground-based lidar stations with the same method, called MATCH, applied for the first time in Arctic to study the ozone depletion with radiosoundings. The evolution of the optical properties of the PSCs and thus the type of PSCs formed (supercooled ternary solution, nitric acid trihydrate particles or ice particles) could thus be linked to the thermodynamical evolution of the air mass deduced from the trajectories. A modeling with the microphysical model of the Danish Meteorological Institute allows assessing our ability to predict PSCs for various environmental conditions. Indeed, from pressure and temperature evolution, the model allows retrieving the types of particles formed as well as their mean radii, their concentrations and could also simulate the lidar signals. In a first step, a case in August 2007 around 17-18 km, involving the three ground-based lidar stations and CALIOP has been selected. Trajectories with different models (gscf and ecmwf), grids and initializations have been computed to test the robustness of the MATCH. Then the DMI model has been used with these different trajectories to test its ability to reproduce the observations. For a same case, the temperature differences (~2-3 K) between the trajectories have a strong impact on the number density of the particles formed (factor 1000). This case is presented here in detail and a statistical comparison is planned with the numerous MATCH cases identified during the three winters and which involve most of the time two ground-based lidar stations with CALIOP.

A graph signal filtering-based approach for detection of different edge types on airborne lidar data

NASA Astrophysics Data System (ADS)

Bayram, Eda; Vural, Elif; Alatan, Aydin

2017-10-01

Airborne Laser Scanning is a well-known remote sensing technology, which provides a dense and highly accurate, yet unorganized point cloud of earth surface. During the last decade, extracting information from the data generated by airborne LiDAR systems has been addressed by many studies in geo-spatial analysis and urban monitoring applications. However, the processing of LiDAR point clouds is challenging due to their irregular structure and 3D geometry. In this study, we propose a novel framework for the detection of the boundaries of an object or scene captured by LiDAR. Our approach is motivated by edge detection techniques in vision research and it is established on graph signal filtering which is an exciting and promising field of signal processing for irregular data types. Due to the convenient applicability of graph signal processing tools on unstructured point clouds, we achieve the detection of the edge points directly on 3D data by using a graph representation that is constructed exclusively to answer the requirements of the application. Moreover, considering the elevation data as the (graph) signal, we leverage aerial characteristic of the airborne LiDAR data. The proposed method can be employed both for discovering the jump edges on a segmentation problem and for exploring the crease edges on a LiDAR object on a reconstruction/modeling problem, by only adjusting the filter characteristics.

Estimating Aboveground Forest Carbon Stock of Major Tropical Forest Land Uses Using Airborne Lidar and Field Measurement Data in Central Sumatra

NASA Astrophysics Data System (ADS)

Thapa, R. B.; Watanabe, M.; Motohka, T.; Shiraishi, T.; shimada, M.

2013-12-01

Tropical forests are providing environmental goods and services including carbon sequestration, energy regulation, water fluxes, wildlife habitats, fuel, and building materials. Despite the policy attention, the tropical forest reserve in Southeast Asian region is releasing vast amount of carbon to the atmosphere due to deforestation. Establishing quality forest statistics and documenting aboveground forest carbon stocks (AFCS) are emerging in the region. Airborne and satellite based large area monitoring methods are developed to compliment conventional plot based field measurement methods as they are costly, time consuming, and difficult to implement for large regions. But these methods still require adequate ground measurements for calibrating accurate AFCS model. Furthermore, tropical region comprised of varieties of natural and plantation forests capping higher variability of forest structures and biomass volumes. To address this issue and the needs for ground data, we propose the systematic collection of ground data integrated with airborne light detection and ranging (LiDAR) data. Airborne LiDAR enables accurate measures of vertical forest structure, including canopy height and volume demanding less ground measurement plots. Using an appropriate forest type based LiDAR sampling framework, structural properties of forest can be quantified and treated similar to ground measurement plots, producing locally relevant information to use independently with satellite data sources including synthetic aperture radar (SAR). In this study, we examined LiDAR derived forest parameters with field measured data and developed general and specific AFCS models for tropical forests in central Sumatra. The general model is fitted for all types of natural and plantation forests while the specific model is fitted to the specific forest type. The study region consists of natural forests including peat swamp and dry moist forests, regrowth, and mangrove and plantation forests including rubber, acacia, oil palm, and coconut. To cover these variations of forest type, eight LiDAR transacts crossing 60 (1-ha size) field plots were acquired for calibrating the models. The field plots consisted of AFCS ranging from 4 - 161 Mg /ha. The calibrated LiDAR to AFCS general model enabled to predict the AFCS with R2 = 0.87 and root mean square errors (RMSE) = 17.4 Mg /ha. The specific AFCS models provided carbon estimates, varied by forest types, with R2 ranging from 0.72 - 0.97 and uncertainty (RMSE) ranging from 1.4 - 10.7 Mg /ha. Using these models, AFCS maps were prepared for the LiDAR coverage that provided AFCS estimates for 8,000 ha offering larger ground sampling measurements for calibration of SAR based carbon mapping model to wider region of Sumatra.

Assessing Surface Fuel Hazard in Coastal Conifer Forests through the Use of LiDAR Remote Sensing

NASA Astrophysics Data System (ADS)

Koulas, Christos

The research problem that this thesis seeks to examine is a method of predicting conventional fire hazards using data drawn from specific regions, namely the Sooke and Goldstream watershed regions in coastal British Columbia. This thesis investigates whether LiDAR data can be used to describe conventional forest stand fire hazard classes. Three objectives guided this thesis: to discuss the variables associated with fire hazard, specifically the distribution and makeup of fuel; to examine the relationship between derived LiDAR biometrics and forest attributes related to hazard assessment factors defined by the Capitol Regional District (CRD); and to assess the viability of the LiDAR biometric decision tree in the CRD based on current frameworks for use. The research method uses quantitative datasets to assess the optimal generalization of these types of fire hazard data through discriminant analysis. Findings illustrate significant LiDAR-derived data limitations, and reflect the literature in that flawed field application of data modelling techniques has led to a disconnect between the ways in which fire hazard models have been intended to be used by scholars and the ways in which they are used by those tasked with prevention of forest fires. It can be concluded that a significant trade-off exists between computational requirements for wildfire simulation models and the algorithms commonly used by field teams to apply these models with remote sensing data, and that CRD forest management practices would need to change to incorporate a decision tree model in order to decrease risk.

DOE ASR Final Report on “Use of ARM Observations to Investigate the Role of Tropical Radiative Processes and Cloud Radiative Effects in Climate Simulations”

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fu, Qiang; Comstock, Jennifer

The overall objective of this ASR funded project is to investigate the role of cloud radiative effects, especially those associated with tropical thin cirrus clouds in the tropical tropopause layer, by analyzing the ARM observations combined with numerical models. In particular, we have processed and analyzed the observations from the Raman lidar at the ARM SGP and TWP sites. In the tenure of the project (8/15/2013 – 8/14/2016 and with a no-cost extension to 8/14/2017), we have been concentrating on (i) developing an automated feature detection scheme of clouds and aerosols for the ARM Raman lidar; (ii) developing an automatedmore » retrieval of cloud and aerosol extinctions for the ARM Raman lidar; (iii) investigating cloud radiative effects based on the observations on the simulated temperatures in the tropical tropopause layer using a radiative-convective model; and (iv) examining the effect of changes of atmospheric composition on the tropical lower-stratospheric temperatures. In addition, we have examined the biases in the CALIPSO-inferred aerosol direct radiative effects using ground-based Raman lidars at the ARM SGP and TWP sites, and estimated the impact of lidar detection sensitivity on assessing global aerosol direct radiative effects. We have also investigated the diurnal cycle of clouds and precipitation at the ARM site using the cloud radar observations along with simulations from the multiscale modeling framework. The main results of our research efforts are reported in the six referred journal publications that acknowledge the DOE Grant DE-SC0010557.« less

Dynamo: A Model Transition Framework for Dynamic Stability Control and Body Mass Manipulation

DTIC Science & Technology

2011-11-01

driving at high speed, and you turn the steering wheel hard to the right and slam on the brakes, then you will end up in the oversteer regime. At the...sensors (GPS, IMU, LIDAR ) for vehicle control. Figure 17: Dynamo high-speed small UGV hardware platform We will perform experiments to measure the MTC

Seasonal variability of dust in the eastern Mediterranean (Athens, Greece), through lidar measurements in the frame of EARLINET (2002-2012)

NASA Astrophysics Data System (ADS)

Kokkalis, Panos; Papayannis, Alex; Tsaknakis, George; Mamouri, RodElise; Argyrouli, Athina

2013-04-01

Aerosols play an important role in earth's atmospheric radiation balance, which is enhanced in areas where dust is mostly present (e.g. the Mediterranean region), as in the case of the city of Athens. The focus of this paper is to provide a comprehensive analysis of the seasonal variability of optical and geometrical properties, as well as the mass concentration of Saharan dust over the city of Athens, Greece, for a 10-years time period: 2002-2012 based on the laser remote sensing (lidar) technique. More specifically, the aerosol optical properties concern the extinction and the backscatter coefficient, as well as the lidar ratio, while the geometrical properties concern the dust layer thickness and center of mass. The calculations of the aerosol extinction coefficient and of the so-called lidar ratio (defined as the ratio of the aerosol extinction coefficient over the aerosol backscatter coefficient) are made by using the Raman lidar technique, only under cloud-free conditions. The calculation of the dust mass concentration was retrieved by a applying a conversion factor (the so-called dust extinction cross section; mean value of the order of 0.64 m2g-1) and by combining sun photometric measurements and modeled dust loading values. Our data analysis was based on monthly-mean values, and only in time periods under cloud-free conditions and for lidar signals with signal to noise ratios (SNR) greater than 1.5 under dusty conditions. The mean value of the lidar ratio at 355 nm was found to be 62±20sr, while the mean dust mass concentration was of the order of 240 μgm-3. The data analyzed were obtained by systematic aerosol lidar measurements performed by the EOLE Raman lidar system of the National Technical University of Athens (NTUA), in the frame of the European Aerosol Research Lidar network (EARLINET). EOLE is able to provide the vertical profiles of the aerosol backscatter (at 355, 532, 1064 nm) and extinction coefficients (at 355 and 532 nm), as well as the water vapor mixing ratio, from about 700 m up to 10000 m, with high temporal (< 5 min.) and spatial (7.5 m) resolution. Acknowledgements: This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Heracleitus II - Investing in knowledge society through the European Social Fund. This research was also financially supported by ITARS (www.itars.net), European Union Seventh Framework Programme (FP7/2007-2013): People, ITN Marie Curie Actions Programme (2012-2016) under grant agreement no 289923.

Automatic registration of Iphone images to LASER point clouds of the urban structures using shape features

NASA Astrophysics Data System (ADS)

Sirmacek, B.; Lindenbergh, R. C.; Menenti, M.

2013-10-01

Fusion of 3D airborne laser (LIDAR) data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different sensor sources. As we use iPhone camera images which are taken in front of the interested urban structure by the application user and the high resolution LIDAR point clouds of the acquired by an airborne laser sensor. After finding the photo capturing position and orientation from the iPhone photograph metafile, we automatically select the area of interest in the point cloud and transform it into a range image which has only grayscale intensity levels according to the distance from the image acquisition position. We benefit from local features for registering the iPhone image to the generated range image. In this article, we have applied the registration process based on local feature extraction and graph matching. Finally, the registration result is used for facade texture mapping on the 3D building surface mesh which is generated from the LIDAR point cloud. Our experimental results indicate possible usage of the proposed algorithm framework for 3D urban map updating and enhancing purposes.

A pose estimation method for unmanned ground vehicles in GPS denied environments

NASA Astrophysics Data System (ADS)

Tamjidi, Amirhossein; Ye, Cang

2012-06-01

This paper presents a pose estimation method based on the 1-Point RANSAC EKF (Extended Kalman Filter) framework. The method fuses the depth data from a LIDAR and the visual data from a monocular camera to estimate the pose of a Unmanned Ground Vehicle (UGV) in a GPS denied environment. Its estimation framework continuy updates the vehicle's 6D pose state and temporary estimates of the extracted visual features' 3D positions. In contrast to the conventional EKF-SLAM (Simultaneous Localization And Mapping) frameworks, the proposed method discards feature estimates from the extended state vector once they are no longer observed for several steps. As a result, the extended state vector always maintains a reasonable size that is suitable for online calculation. The fusion of laser and visual data is performed both in the feature initialization part of the EKF-SLAM process and in the motion prediction stage. A RANSAC pose calculation procedure is devised to produce pose estimate for the motion model. The proposed method has been successfully tested on the Ford campus's LIDAR-Vision dataset. The results are compared with the ground truth data of the dataset and the estimation error is ~1.9% of the path length.

Using small area estimation and Lidar-derived variables for multivariate prediction of forest attributes

Treesearch

F. Mauro; Vicente Monleon; H. Temesgen

2015-01-01

Small area estimation (SAE) techniques have been successfully applied in forest inventories to provide reliable estimates for domains where the sample size is small (i.e. small areas). Previous studies have explored the use of either Area Level or Unit Level Empirical Best Linear Unbiased Predictors (EBLUPs) in a univariate framework, modeling each variable of interest...

Investigating wind turbine impacts on near-wake flow using profiling Lidar data and large-eddy simulations with an actuator disk model

DOE PAGES

Mirocha, Jeffrey D.; Rajewski, Daniel A.; Marjanovic, Nikola; ...

2015-08-27

In this study, wind turbine impacts on the atmospheric flow are investigated using data from the Crop Wind Energy Experiment (CWEX-11) and large-eddy simulations (LESs) utilizing a generalized actuator disk (GAD) wind turbine model. CWEX-11 employed velocity-azimuth display (VAD) data from two Doppler lidar systems to sample vertical profiles of flow parameters across the rotor depth both upstream and in the wake of an operating 1.5 MW wind turbine. Lidar and surface observations obtained during four days of July 2011 are analyzed to characterize the turbine impacts on wind speed and flow variability, and to examine the sensitivity of thesemore » changes to atmospheric stability. Significant velocity deficits (VD) are observed at the downstream location during both convective and stable portions of four diurnal cycles, with large, sustained deficits occurring during stable conditions. Variances of the streamwise velocity component, σ u, likewise show large increases downstream during both stable and unstable conditions, with stable conditions supporting sustained small increases of σ u , while convective conditions featured both larger magnitudes and increased variability, due to the large coherent structures in the background flow. Two representative case studies, one stable and one convective, are simulated using LES with a GAD model at 6 m resolution to evaluate the compatibility of the simulation framework with validation using vertically profiling lidar data in the near wake region. Virtual lidars were employed to sample the simulated flow field in a manner consistent with the VAD technique. Simulations reasonably reproduced aggregated wake VD characteristics, albeit with smaller magnitudes than observed, while σu values in the wake are more significantly underestimated. The results illuminate the limitations of using a GAD in combination with coarse model resolution in the simulation of near wake physics, and validation thereof using VAD data.« less

Improving lidar turbulence estimates for wind energy

NASA Astrophysics Data System (ADS)

Newman, J. F.; Clifton, A.; Churchfield, M. J.; Klein, P.

2016-09-01

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.

Evaluation of performance of silicon photomultipliers in lidar applications

NASA Astrophysics Data System (ADS)

Vinogradov, Sergey L.

2017-05-01

Silicon Photomultipliers (SiPMs) are a well-recognized new generation of photon number resolving avalanche photodetectors. Many advantages - a high gain with an ultra-low excess noise of multiplication, multi-pixel architecture, relatively low operating voltage - make SiPMs very competitive in a growing number of applications. Challenging demands of LIDAR applications for a receiver having high sensitivity starting from single photons, superior time-offlight resolution, robustness including surviving at bright light flashes, solid-state compactness and more, are expected to be feasible for the SiPMs. Despite some known drawbacks, namely crosstalk, afterpulsing, dark noise, limited dynamic range, SiPMs are already considered as promising substitutes for conventional APDs and PMTs in LIDAR applications. However, these initial considerations are based on a rather simplified representation of the SiPM as a generic LIDAR receiver described by generic expressions. This study is focused on a comprehensive evaluation of a SiPM potential considering essential features of this new technology, which could affect applicability and performance of SiPMs as LIDAR receivers. Namely, an excess noise due to correlated processes of crosstalk and afterpulsing, are included into account utilizing the well-established framework of analytical probabilistic models. The analysis of SiPM performance in terms of a photon number and time resolution clarifies their competitiveness over conventional APD and PMT and anticipates the development of next SiPM generations.

Characterization of particle hygroscopicity by Raman lidar: Selected case studies from the convective and orographically-induced precipitation study

NASA Astrophysics Data System (ADS)

Stelitano, Dario; Di Girolamo, Paolo; Summa, Donato

2013-05-01

The characterization of particle hygroscopicity has primary importance for climate monitoring and prediction. Model studies have demonstrated that relative humidity (RH) has a critical influence on aerosol climate forcing. Hygroscopic properties of aerosols influence particle size distribution and refractive index and hence their radiative effects. Aerosol particles tend to grow at large relative humidity values as a result of their hygroscopicity. Raman lidars with aerosol, water vapor and temperature measurement capability are potentially attractive tools for studying aerosol hygroscopicity as in fact they can provide continuous altitude-resolved measurements of particle optical, size and microphysical properties, as well as relative humidity, without perturbing the aerosols or their environment. Specifically, the University of Basilicata Raman lidar system (BASIL) considered for the present study, has the capability to perform all-lidar measurements of relative humidity based on the application of both the rotational and the vibrational Raman lidar techniques in the UV. BASIL was operational in Achern (Black Forest, Lat: 48.64° N, Long: 8.06° E, Elev.: 140 m) between 25 May and 30 August 2007 in the framework of the Convective and Orographically-induced Precipitation Study (COPS). The present analysis is focused on selected case studies characterized by the presence of different aerosol types with different hygroscopic behavior. The observed behavior, dependent upon aerosol composition, may range from hygrophobic to strongly hygroscopic.

A geospatial framework for improving the vertical accuracy of elevation models in Florida's coastal Everglades

NASA Astrophysics Data System (ADS)

Cooper, H.; Zhang, C.; Sirianni, M.

2016-12-01

South Florida relies upon the health of the Everglades, the largest subtropical wetland in North America, as a vital source of water. Since the late 1800's, this imperiled ecosystem has been highly engineered to meet human needs of flood control and water use. The Comprehensive Everglades Restoration Plan (CERP) was initiated in 2000 to restore original water flows to the Everglades and improve overall ecosystem health, while also aiming to achieve balance with human water usage. Due to subtle changes in the Everglades terrain, better vertical accuracy elevation data are needed to model groundwater and surface water levels that are integral to monitoring the effects of restoration under impacts such as sea-level rise. The current best available elevation datasets for the coastal Everglades include High Accuracy Elevation Data (HAED) and Florida Department of Emergency Management (FDEM) Light Detection and Ranging (LiDAR). However, the horizontal resolution of the HAED data is too coarse ( 400 m) for fine scale mapping, and the LiDAR data does not contain an accuracy assessment for coastal Everglades' vegetation communities. The purpose of this study is to develop a framework for generating better vertical accuracy and horizontal resolution Digital Elevation Models in the Flamingo District of Everglades National Park. In the framework, field work is conducted to collect RTK GPS and total station elevation measurements for mangrove swamp, coastal prairies, and freshwater marsh, and the proposed accuracy assessment and elevation modeling methodology is integrated with a Geographical Information System (GIS). It is anticipated that this study will provide more accurate models of the soil substrate elevation that can be used by restoration planners to better predict the future state of the Everglades ecosystem.

Improving Lidar Turbulence Estimates for Wind Energy: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Newman, Jennifer; Clifton, Andrew; Churchfield, Matthew

2016-10-01

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidarsmore » were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.« less

Improving Lidar Turbulence Estimates for Wind Energy

DOE PAGES

Newman, Jennifer F.; Clifton, Andrew; Churchfield, Matthew J.; ...

2016-10-03

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidarsmore » were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.« less

Simultaneous polarimeter retrievals of microphysical aerosol and ocean color parameters from the "MAPP" algorithm with comparison to high-spectral-resolution lidar aerosol and ocean products.

PubMed

Stamnes, S; Hostetler, C; Ferrare, R; Burton, S; Liu, X; Hair, J; Hu, Y; Wasilewski, A; Martin, W; van Diedenhoven, B; Chowdhary, J; Cetinić, I; Berg, L K; Stamnes, K; Cairns, B

2018-04-01

We present an optimal-estimation-based retrieval framework, the microphysical aerosol properties from polarimetry (MAPP) algorithm, designed for simultaneous retrieval of aerosol microphysical properties and ocean color bio-optical parameters using multi-angular total and polarized radiances. Polarimetric measurements from the airborne NASA Research Scanning Polarimeter (RSP) were inverted by MAPP to produce atmosphere and ocean products. The RSP MAPP results are compared with co-incident lidar measurements made by the NASA High-Spectral-Resolution Lidar HSRL-1 and HSRL-2 instruments. Comparisons are made of the aerosol optical depth (AOD) at 355 and 532 nm, lidar column-averaged measurements of the aerosol lidar ratio and Ångstrøm exponent, and lidar ocean measurements of the particulate hemispherical backscatter coefficient and the diffuse attenuation coefficient. The measurements were collected during the 2012 Two-Column Aerosol Project (TCAP) campaign and the 2014 Ship-Aircraft Bio-Optical Research (SABOR) campaign. For the SABOR campaign, 73% RSP MAPP retrievals fall within ±0.04 AOD at 532 nm as measured by HSRL-1, with an R value of 0.933 and root-mean-square deviation of 0.0372. For the TCAP campaign, 53% of RSP MAPP retrievals are within 0.04 AOD as measured by HSRL-2, with an R value of 0.927 and root-mean-square deviation of 0.0673. Comparisons with HSRL-2 AOD at 355 nm during TCAP result in an R value of 0.959 and a root-mean-square deviation of 0.0694. The RSP retrievals using the MAPP optimal estimation framework represent a key milestone on the path to a combined lidar + polarimeter retrieval using both HSRL and RSP measurements.

Simultaneous polarimeter retrievals of microphysical aerosol and ocean color parameters from the “MAPP” algorithm with comparison to high-spectral-resolution lidar aerosol and ocean products

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stamnes, S.; Hostetler, C.; Ferrare, R.

We present an optimal estimation based retrieval framework, the Microphysical Aerosol Properties from Polarimetry (MAPP) algorithm, designed for simultaneous retrieval of aerosol microphysical properties and ocean color bio-optical parameters using multi-angular polarized radiances. Polarimetric measurements from the airborne NASA Research Scanning Polarimeter (RSP) were inverted by MAPP to produce atmosphere and ocean products. The RSP MAPP results are compared with co-incident lidar measurements made by the NASA High Spectral Resolution Lidar HSRL-1 and HSRL-2 instruments. Comparisons are made of the aerosol optical depth (AOD) at 355, 532, and 1064 nm, lidar column-averaged measurements of the aerosol lidar ratio and Ã…ngstrømmore » exponent, and lidar ocean measurements of the particulate hemispherical backscatter coefficient and the diffuse attenuation coefficient. The measurements were collected during the 2012 Two-Column Aerosol Project (TCAP) campaign and the 2014 Ship-Aircraft Bio- Optical Research (SABOR) campaign. For the SABOR campaign, 71% RSP MAPP retrievals fall within 0.04 AOD at 532 nm as measured by HSRL-1, with an R value of 0.925 and root-mean-square deviation of 0.04. For the TCAP campaign, 55% of RSP MAPP retrievals are within 0.04 AOD as measured by HSRL-2, with an R value of 0.925 and root-mean-square deviation of 0.07. Comparisons with HSRL-2 AOD at 355 nm during TCAP result in an R value of 0.96 and a root-mean-square deviation of also 0.07. The RSP retrievals using the MAPP optimal estimation framework represent a key milestone on the path to a combined lidar+polarimeter retrieval using both HSRL and RSP measurements.« less

Achieving Accuracy Requirements for Forest Biomass Mapping: A Data Fusion Method for Estimating Forest Biomass and LiDAR Sampling Error with Spaceborne Data

NASA Technical Reports Server (NTRS)

Montesano, P. M.; Cook, B. D.; Sun, G.; Simard, M.; Zhang, Z.; Nelson, R. F.; Ranson, K. J.; Lutchke, S.; Blair, J. B.

2012-01-01

The synergistic use of active and passive remote sensing (i.e., data fusion) demonstrates the ability of spaceborne light detection and ranging (LiDAR), synthetic aperture radar (SAR) and multispectral imagery for achieving the accuracy requirements of a global forest biomass mapping mission. This data fusion approach also provides a means to extend 3D information from discrete spaceborne LiDAR measurements of forest structure across scales much larger than that of the LiDAR footprint. For estimating biomass, these measurements mix a number of errors including those associated with LiDAR footprint sampling over regional - global extents. A general framework for mapping above ground live forest biomass (AGB) with a data fusion approach is presented and verified using data from NASA field campaigns near Howland, ME, USA, to assess AGB and LiDAR sampling errors across a regionally representative landscape. We combined SAR and Landsat-derived optical (passive optical) image data to identify forest patches, and used image and simulated spaceborne LiDAR data to compute AGB and estimate LiDAR sampling error for forest patches and 100m, 250m, 500m, and 1km grid cells. Forest patches were delineated with Landsat-derived data and airborne SAR imagery, and simulated spaceborne LiDAR (SSL) data were derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (L VIS). At both the patch and grid scales, we evaluated differences in AGB estimation and sampling error from the combined use of LiDAR with both SAR and passive optical and with either SAR or passive optical alone. This data fusion approach demonstrates that incorporating forest patches into the AGB mapping framework can provide sub-grid forest information for coarser grid-level AGB reporting, and that combining simulated spaceborne LiDAR with SAR and passive optical data are most useful for estimating AGB when measurements from LiDAR are limited because they minimized forest AGB sampling errors by 15 - 38%. Furthermore, spaceborne global scale accuracy requirements were achieved. At least 80% of the grid cells at 100m, 250m, 500m, and 1km grid levels met AGB density accuracy requirements using a combination of passive optical and SAR along with machine learning methods to predict vegetation structure metrics for forested areas without LiDAR samples. Finally, using either passive optical or SAR, accuracy requirements were met at the 500m and 250m grid level, respectively.

LABVIEW graphical user interface for precision multichannel alignment of Raman lidar at Jet Propulsion Laboratory, Table Mountain Facility.

PubMed

Aspey, R A; McDermid, I S; Leblanc, T; Howe, J W; Walsh, T D

2008-09-01

The Jet Propulsion Laboratory operates lidar systems at Table Mountain Facility (TMF), California (34.4 degrees N, 117.7 degrees W) and Mauna Loa Observatory, Hawaii (19.5 degrees N, 155.6 degrees W) under the framework of the Network for the Detection of Atmospheric Composition Change. To complement these systems a new Raman lidar has been developed at TMF with particular attention given to optimizing water vapor profile measurements up to the tropopause and lower stratosphere. The lidar has been designed for accuracies of 5% up to 12 km in the free troposphere and a detection capability of <5 ppmv. One important feature of the lidar is a precision alignment system using range resolved data from eight Licel transient recorders, allowing fully configurable alignment via a LABVIEW/C++ graphical user interface (GUI). This allows the lidar to be aligned on any channel while simultaneously displaying signals from other channels at configurable altitude/bin combinations. The general lidar instrumental setup and the details of the alignment control system, data acquisition, and GUI alignment software are described. Preliminary validation results using radiosonde and lidar intercomparisons are briefly presented.

The GAW Aerosol Lidar Observation Network (GALION) as a source of near-real time aerosol profile data for model evaluation and assimilation

NASA Astrophysics Data System (ADS)

Hoff, R. M.; Pappalardo, G.

2010-12-01

In 2007, the WMO Global Atmospheric Watch’s Science Advisory Group on Aerosols described a global network of lidar networks called GAW Aerosol Lidar Observation Network (GALION). GALION has a purpose of providing expanded coverage of aerosol observations for climate and air quality use. Comprised of networks in Asia (AD-NET), Europe (EARLINET and CIS-LINET), North America (CREST and CORALNET), South America (ALINE) and with contribution from global networks such as MPLNET and NDACC, the collaboration provides a unique capability to define aerosol profiles in the vertical. GALION is designed to supplement existing ground-based and column profiling (AERONET, PHOTONS, SKYNET, GAWPFR) stations. In September 2010, GALION held its second workshop and one component of discussion focussed how the network would integrate into model needs. GALION partners have contributed to the Sand and Dust Storm Warning and Analysis System (SDS-WAS) and to assimilation in models such as DREAM. This paper will present the conclusions of those discussions and how these observations can fit into a global model analysis framework. Questions of availability, latency, and aerosol parameters that might be ingested into models will be discussed. An example of where EARLINET and GALION have contributed in near-real time observations was the suite of measurements during the Eyjafjallajokull eruption in Iceland and its impact on European air travel. Lessons learned from this experience will be discussed.

Performance Modeling of an Airborne Raman Water Vapor Lidar

NASA Technical Reports Server (NTRS)

Whiteman, D. N.; Schwemmer, G.; Berkoff, T.; Plotkin, H.; Ramos-Izquierdo, L.; Pappalardo, G.

2000-01-01

A sophisticated Raman lidar numerical model had been developed. The model has been used to simulate the performance of two ground-based Raman water vapor lidar systems. After tuning the model using these ground-based measurements, the model is used to simulate the water vapor measurement capability of an airborne Raman lidar under both day-and night-time conditions for a wide range of water vapor conditions. The results indicate that, under many circumstances, the daytime measurements possess comparable resolution to an existing airborne differential absorption water vapor lidar while the nighttime measurement have higher resolution. In addition, a Raman lidar is capable of measurements not possible using a differential absorption system.

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

USGS Publications Warehouse

Ackers, Steven H.; Davis, Raymond J.; Olsen, K.; Dugger, Catherine

2015-01-01

Wildlife habitat mapping has evolved at a rapid pace over the last few decades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls (Strix occidentalis caurina), whose geographic covers about 23 million ha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as predictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map.Recent increases in the use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. Instead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetation height for use in SDMs. We expect a SDM habitat map produced from directly measured predictor variables to be more accurate than one produced from modeled predictors.We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and estimates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo-interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC = 0.809 ± 0.011), but the performance of the Landsat-based map was within acceptable limits (AUC = 0.717 ± 0.021). As is common with photo-interpreted maps, there was no accuracy assessment available for comparison. The photo-interpreted map produced the highest and lowest estimates of habitat area, depending on which habitat classes were included (nesting, roosting, and foraging habitat = 9962 ha, nesting habitat only = 6036 ha). The Landsat-based map produced an estimate of habitat area that was within this range (95% CI: 6679–9592 ha), while the lidar-based map produced an area estimate similar to what was interpreted by local wildlife biologists as nesting (i.e., high quality) habitat using aerial imagery (95% CI: 5453–7216). Confidence intervals of habitat area estimates from the SDMs based on Landsat and lidar overlapped.We concluded that both Landsat- and lidar-based SDMs produced reasonable maps and area estimates for northern spotted owl habitat within the study area. The lidar-based map was more precise and spatially similar to what local wildlife biologists considered spotted owl nesting habitat. The Landsat-based map provided a less precise spatial representation of habitat within the relatively small geographic confines of the study area, but habitat area estimates were similar to both the photo-interpreted and lidar-based maps.Photo-interpreted maps are time consuming to produce, subjective in nature, and difficult to replicate. SDMs provide a framework for efficiently producing habitat maps that can be replicated as habitat conditions change over time, provided that comparable remotely sensed data are available. When the SDM uses predictor variables extracted from lidar data, it can produce a habitat map that is both accurate and useful at large and small spatial scales. In comparison, SDMs using Landsat-based data are more appropriate for large scale analyses of amounts and general spatial patterns of habitat at regional scales.

Modelling submerged coastal environments: Remote sensing technologies, techniques, and comparative analysis

NASA Astrophysics Data System (ADS)

Dillon, Chris

Built upon remote sensing and GIS littoral zone characterization methodologies of the past decade, a series of loosely coupled models aimed to test, compare and synthesize multi-beam SONAR (MBES), Airborne LiDAR Bathymetry (ALB), and satellite based optical data sets in the Gulf of St. Lawrence, Canada, eco-region. Bathymetry and relative intensity metrics for the MBES and ALB data sets were run through a quantitative and qualitative comparison, which included outputs from the Benthic Terrain Modeller (BTM) tool. Substrate classification based on relative intensities of respective data sets and textural indices generated using grey level co-occurrence matrices (GLCM) were investigated. A spatial modelling framework built in ArcGIS(TM) for the derivation of bathymetric data sets from optical satellite imagery was also tested for proof of concept and validation. Where possible, efficiencies and semi-automation for repeatable testing was achieved using ArcGIS(TM) ModelBuilder. The findings from this study could assist future decision makers in the field of coastal management and hydrographic studies. Keywords: Seafloor terrain characterization, Benthic Terrain Modeller (BTM), Multi-beam SONAR, Airborne LiDAR Bathymetry, Satellite Derived Bathymetry, ArcGISTM ModelBuilder, Textural analysis, Substrate classification.

The Early Detection of the Emerald Ash Borer (eab) Using Multi-Source Remotely Sensed Data

NASA Astrophysics Data System (ADS)

Hu, B.; Naveed, F.; Tasneem, F.; Xing, C.

2018-04-01

The objectives of this study were to exploit the synergy of hyperspectral imagery, Light Detection And Ranging (LiDAR) and high spatial resolution data and their synergy in the early detection of the EAB (Emerald Ash Borer) presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, an advanced individual tree delineation method was developed to delineate individual trees using the combined high-spatial resolution worldview-3 imagery was used together with LiDAR data. Individual trees were then classified to ash and non-ash trees using spectral and spatial information. In order to characterize the health state of individual ash trees, leaves from ash trees with various health states were sampled and measured using a field spectrometer. Based on the field measurements, the best indices that sensitive to leaf chlorophyll content were selected. The developed framework and methods were tested using worldview-3, airborne LiDAR data over the Keele campus of York University Toronto Canada. Satisfactory results in terms of individual tree crown delineation, ash tree identification and characterization of the health state of individual ash trees. Quantitative evaluations is being carried out.

An historically consistent and broadly applicable MRV system based on LiDAR sampling and Landsat time-series

Treesearch

W. Cohen; H. Andersen; S. Healey; G. Moisen; T. Schroeder; C. Woodall; G. Domke; Z. Yang; S. Stehman; R. Kennedy; C. Woodcock; Z. Zhu; J. Vogelmann; D. Steinwand; C. Huang

2014-01-01

The authors are developing a REDD+ MRV system that tests different biomass estimation frameworks and components. Design-based inference from a costly fi eld plot network was compared to sampling with LiDAR strips and a smaller set of plots in combination with Landsat for disturbance monitoring. Biomass estimation uncertainties associated with these different data sets...

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

NASA Technical Reports Server (NTRS)

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.

Development and Experimental Verification of a High Resolution, Tunable LIDAR Computer Simulation Model for Atmospheric Laser Remote Sensing

NASA Astrophysics Data System (ADS)

Wilcox, William Edward, Jr.

1995-01-01

A computer program (LIDAR-PC) and associated atmospheric spectral databases have been developed which accurately simulate the laser remote sensing of the atmosphere and the system performance of a direct-detection Lidar or tunable Differential Absorption Lidar (DIAL) system. This simulation program allows, for the first time, the use of several different large atmospheric spectral databases to be coupled with Lidar parameter simulations on the same computer platform to provide a real-time, interactive, and easy to use design tool for atmospheric Lidar simulation and modeling. LIDAR -PC has been used for a range of different Lidar simulations and compared to experimental Lidar data. In general, the simulations agreed very well with the experimental measurements. In addition, the simulation offered, for the first time, the analysis and comparison of experimental Lidar data to easily determine the range-resolved attenuation coefficient of the atmosphere and the effect of telescope overlap factor. The software and databases operate on an IBM-PC or compatible computer platform, and thus are very useful to the research community for Lidar analysis. The complete Lidar and atmospheric spectral transmission modeling program uses the HITRAN database for high-resolution molecular absorption lines of the atmosphere, the BACKSCAT/LOWTRAN computer databases and models for the effects of aerosol and cloud backscatter and attenuation, and the range-resolved Lidar equation. The program can calculate the Lidar backscattered signal-to-noise for a slant path geometry from space and simulate the effect of high resolution, tunable, single frequency, and moderate line width lasers on the Lidar/DIAL signal. The program was used to model and analyze the experimental Lidar data obtained from several measurements. A fixed wavelength, Ho:YSGG aerosol Lidar (Sugimoto, 1990) developed at USF and a tunable Ho:YSGG DIAL system (Cha, 1991) for measuring atmospheric water vapor at 2.1 μm were analyzed. The simulations agreed very well with the measurements, and also yielded, for the first time, the ability to easily deduce the atmospheric attentuation coefficient, alpha, from the Lidar data. Simulations and analysis of other Lidar measurements included that of a 1.57 mu m OPO aerosol Lidar system developed at USF (Harrell, 1995) and of the NASA LITE (Laser-in-Space Technology Experiment) Lidar recently flown in the Space shuttle. Finally, an extensive series of laboratory experiments were made with the 1.57 μm OPO Lidar system to test calculations of the telescope/laser overlap and the effect of different telescope sizes and designs. The simulations agreed well with the experimental data for the telescope diameter and central obscuration test cases. The LIDAR-PC programs are available on the Internet from the USAF Lidar Home Page Web site, http://www.cas.usf.edu/physics/lidar.html/.

A multi-resolution analysis of lidar-DTMs to identify geomorphic processes from characteristic topographic length scales

NASA Astrophysics Data System (ADS)

Sangireddy, H.; Passalacqua, P.; Stark, C. P.

2013-12-01

Characteristic length scales are often present in topography, and they reflect the driving geomorphic processes. The wide availability of high resolution lidar Digital Terrain Models (DTMs) allows us to measure such characteristic scales, but new methods of topographic analysis are needed in order to do so. Here, we explore how transitions in probability distributions (pdfs) of topographic variables such as (log(area/slope)), defined as topoindex by Beven and Kirkby[1979], can be measured by Multi-Resolution Analysis (MRA) of lidar DTMs [Stark and Stark, 2001; Sangireddy et al.,2012] and used to infer dominant geomorphic processes such as non-linear diffusion and critical shear. We show this correlation between dominant geomorphic processes to characteristic length scales by comparing results from a landscape evolution model to natural landscapes. The landscape evolution model MARSSIM Howard[1994] includes components for modeling rock weathering, mass wasting by non-linear creep, detachment-limited channel erosion, and bedload sediment transport. We use MARSSIM to simulate steady state landscapes for a range of hillslope diffusivity and critical shear stresses. Using the MRA approach, we estimate modal values and inter-quartile ranges of slope, curvature, and topoindex as a function of resolution. We also construct pdfs at each resolution and identify and extract characteristic scale breaks. Following the approach of Tucker et al.,[2001], we measure the average length to channel from ridges, within the GeoNet framework developed by Passalacqua et al.,[2010] and compute pdfs for hillslope lengths at each scale defined in the MRA. We compare the hillslope diffusivity used in MARSSIM against inter-quartile ranges of topoindex and hillslope length scales, and observe power law relationships between the compared variables for simulated landscapes at steady state. We plot similar measures for natural landscapes and are able to qualitatively infer the dominant geomorphic processes. Also, we explore the variability in hillslope length scales as a function of hillslope diffusivity coefficients and critical shear stress in natural landscapes and show that we can infer signatures of dominant geomorphic processes by analyzing characteristic topographic length scales present in topography. References: Beven, K. and Kirkby, M. J.: A physically based variable contributing area model of basin hydrology, Hydrol. Sci. Bull., 24, 43-69, 1979 Howard, A. D. (1994). A detachment-limited model of drainage basin evolution.Water resources research, 30(7), 2261-2285. Passalacqua, P., Do Trung, T., Foufoula Georgiou, E., Sapiro, G., & Dietrich, W. E. (2010). A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths. Journal of Geophysical. Research: Earth Surface (2003-2012), 115(F1). Sangireddy, H., Passalacqua, P., Stark, C.P.(2012). Multi-resolution estimation of lidar-DTM surface flow metrics to identify characteristic topographic length scales, EP13C-0859: AGU Fall meeting 2012. Stark, C. P., & Stark, G. J. (2001). A channelization model of landscape evolution. American Journal of Science, 301(4-5), 486-512. Tucker, G. E., Catani, F., Rinaldo, A., & Bras, R. L. (2001). Statistical analysis of drainage density from digital terrain data. Geomorphology, 36(3), 187-202.

Hydrologic enforcement of lidar DEMs

USGS Publications Warehouse

Poppenga, Sandra K.; Worstell, Bruce B.; Danielson, Jeffrey J.; Brock, John C.; Evans, Gayla A.; Heidemann, H. Karl

2014-01-01

Hydrologic-enforcement (hydro-enforcement) of light detection and ranging (lidar)-derived digital elevation models (DEMs) modifies the elevations of artificial impediments (such as road fills or railroad grades) to simulate how man-made drainage structures such as culverts or bridges allow continuous downslope flow. Lidar-derived DEMs contain an extremely high level of topographic detail; thus, hydro-enforced lidar-derived DEMs are essential to the U.S. Geological Survey (USGS) for complex modeling of riverine flow. The USGS Coastal and Marine Geology Program (CMGP) is integrating hydro-enforced lidar-derived DEMs (land elevation) and lidar-derived bathymetry (water depth) to enhance storm surge modeling in vulnerable coastal zones.

Lidar-Based Estimates of Above-Ground Biomass in the Continental US and Mexico Using Ground, Airborne, and Satellite Observations

NASA Technical Reports Server (NTRS)

Nelson, Ross; Margolis, Hank; Montesano, Paul; Sun, Guoqing; Cook, Bruce; Corp, Larry; Andersen, Hans-Erik; DeJong, Ben; Pellat, Fernando Paz; Fickel, Thaddeus;

Urban forest ecosystem analysis using fused airborne hyperspectral and lidar data

NASA Astrophysics Data System (ADS)

Alonzo, Mike Gerard

Urban trees are strategically important in a city's effort to mitigate their carbon footprint, heat island effects, air pollution, and stormwater runoff. Currently, the most common method for quantifying urban forest structure and ecosystem function is through field plot sampling. However, taking intensive structural measurements on private properties throughout a city is difficult, and the outputs from sample inventories are not spatially explicit. The overarching goal of this dissertation is to develop methods for mapping urban forest structure and function using fused hyperspectral imagery and waveform lidar data at the individual tree crown scale. Urban forest ecosystem services estimated using the USDA Forest Service's i-Tree Eco (formerly UFORE) model are based largely on tree species and leaf area index (LAI). Accordingly, tree species were mapped in my Santa Barbara, California study area for 29 species comprising >80% of canopy. Crown-scale discriminant analysis methods were introduced for fusing Airborne Visible Infrared Imaging Spectrometry (AVIRIS) data with a suite of lidar structural metrics (e.g., tree height, crown porosity) to maximize classification accuracy in a complex environment. AVIRIS imagery was critical to achieving an overall species-level accuracy of 83.4% while lidar data was most useful for improving the discrimination of small and morphologically unique species. LAI was estimated at both the field-plot scale using laser penetration metrics and at the crown scale using allometry. Agreement of the former with photographic estimates of gap fraction and the latter with allometric estimates based on field measurements was examined. Results indicate that lidar may be used reasonably to measure LAI in an urban environment lacking in continuous canopy and characterized by high species diversity. Finally, urban ecosystem services such as carbon storage and building energy-use modification were analyzed through combination of aforementioned methods and the i-Tree Eco modeling framework. The remote sensing methods developed in this dissertation will allow researchers to more precisely constrain urban ecosystem spatial analyses and equip cities to better manage their urban forest resource.

A kite-based approach for water-vapor lidar calibration and application to multi-platform intercomparison in the Western Mediterranean during ChArMEx/ADRIMED

NASA Astrophysics Data System (ADS)

Totems, Julien; Chazette, Patrick; Dulac, François; Hassanzadeh, Sahar

2015-04-01

In the framework of the ADRIMED campaign included in the ChArMEx (Chemistry Aerosol Mediterranean Experiment) research program, performed in June 2013 in the western Mediterranean, the mobile Water vapor Aerosol LIdar (WALI) developed by LSCE was deployed at Cap d'en Font on the island of Menorca (Spain). Alongside an elastic backscatter channel, it features depolarization, N2- and H2O-Raman channels, the two latter yielding profiles of atmospheric water vapor mixing ratio (WVMR). The water content thus provided by the lidar is essential to validate models or satellite water vapor products for meteorological purposes. It also proved to be very helpful in characterizing particle types and sources, especially for the multi-layer situations observed during the ChArMEx/ADRIMED special observation period. Beforehand, however, a precise calibration of the WVMR had to be done on-site. Balloon rawindsoundings performed by CNES were available about 10 km off-site on Saint-Lluis aerodrome or 100 km away on Majorca for this purpose, but strong inhomogeneities in the WVMR observed under 2 km altitude prevent an accurate calibration and the determination of the lidar overlap factor, which biases WVMR retrieval under 300 m. Instead, we propose the use of a lightweight Pressure-Temperature-Relative Humidity (PTU) sound carried under a simple kite to perform a co-localized sounding. Modern kites indeed combine the advantages of an easy deployment and the possibility of longer, more precise soundings in the low troposphere. After showing that this approach leads to calibration with less than 2% error from 80 m altitude, we validate it against rawindsounding WVMR profiles, with very good agreement at high altitude. We also present further comparisons between the lidar-derived WVMR and the one given by meteorological model reanalyses (AROME, ECMWF) or satellite inversion products (IASI).

Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables

Treesearch

Jacob Strunk; Hailemariam Temesgen; Hans-Erik Andersen; James P. Flewelling; Lisa Madsen

2012-01-01

Using lidar in an area-based model-assisted approach to forest inventory has the potential to increase estimation precision for some forest inventory variables. This study documents the bias and precision of a model-assisted (regression estimation) approach to forest inventory with lidar-derived auxiliary variables relative to lidar pulse density and the number of...

Simulated full-waveform lidar compared to Riegl VZ-400 terrestrial laser scans

NASA Astrophysics Data System (ADS)

Kim, Angela M.; Olsen, Richard C.; Béland, Martin

2016-05-01

A 3-D Monte Carlo ray-tracing simulation of LiDAR propagation models the reflection, transmission and ab- sorption interactions of laser energy with materials in a simulated scene. In this presentation, a model scene consisting of a single Victorian Boxwood (Pittosporum undulatum) tree is generated by the high-fidelity tree voxel model VoxLAD using high-spatial resolution point cloud data from a Riegl VZ-400 terrestrial laser scanner. The VoxLAD model uses terrestrial LiDAR scanner data to determine Leaf Area Density (LAD) measurements for small volume voxels (20 cm sides) of a single tree canopy. VoxLAD is also used in a non-traditional fashion in this case to generate a voxel model of wood density. Information from the VoxLAD model is used within the LiDAR simulation to determine the probability of LiDAR energy interacting with materials at a given voxel location. The LiDAR simulation is defined to replicate the scanning arrangement of the Riegl VZ-400; the resulting simulated full-waveform LiDAR signals compare favorably to those obtained with the Riegl VZ-400 terrestrial laser scanner.

Proceedings of the 20th Annual Conference on Atmospheric Transmission Models, 10-12 June 1997

DTIC Science & Technology

1998-05-21

which models multi - wavelength or tunable system performance over slant paths in the atmosphere. Both hard target and aerosol LIDAR systems may be... extinction due to aerosols is a Raman lidar. Raman lidars also have the capability to measure atmospheric temperature and humidity. The lidars we...ozone absorption. The present lidar system provides extinction measurements at UV and visible wavelengths. The tech- nique has been verified over a

NASA's DESDynI in Alaska

NASA Astrophysics Data System (ADS)

Sauber, J. M.; Hofton, M. A.; Bruhn, R. L.; Forster, R. R.; Burgess, E. W.; Cotton, M. M.

2010-12-01

In 2007 the National Research Council Earth Science Decadal Survey, Earth Science Applications from Space, recommended an integrated L-band InSAR and multibeam Lidar mission called DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice) and it is scheduled for launch in 2017. The NASA InSAR and Lidar mission is optimized for studying geohazards and global environmental change. The complex plate boundary in southern coastal Alaska provides an excellent setting for testing DESDynI capabilities to recover fundamental parameters of glacio-seismotectonic processes. Also, aircraft and satellites acquisitions of Lidar and L-band SAR have been made in this region in the last decade that can be used for DESDynI performance simulations. Since the Lidar observations would penetrate most vegetation, the accurate bald Earth elevation profiles will give new elevation information beyond the standard 30-m digital elevation models (DEM) and the Lidar-derived elevations will provide an accurate georeferenced surface for local and regional scale studies. In an earlier study we demonstrated how the Lidar observations could be used in combination with SAR to generate an improved InSAR derived DEM in the Barrow, Alaska region [Atwood et al., 2007]; here we discuss how Lidar could be fused with L-band SAR in more rugged, vegetated terrane. Based on simulations of multi-beam Lidar instrument performance over uplifted marine terraces, active faults and folds, uplift associated with the 1899 Yakataga seismic event (M=8), and elevation change on the glaciers in southern, coastal Alaska, we report on the significance of the DESDynI Lidar contiguous 25 m footprint elevation profiles for EarthScope related studies in Alaska. We are using the morphology and dynamics of glaciers derived from L-band SAR ice velocities to infer the large scale sub-ice structures that form the structural framework of the Seward-Bagley Basins. Using primarily winter acquisitions of L-band SAR data from ALOS/PALSAR (Mode: Fine beam, HH) we have been able to estimate ice velocities from offset-tracking in the Upper and Lower Seward Basin even though the acquisitions are 46 days apart. We anticipate with the shorter repeat time for DESDynI-SAR acquisitions that we will be able to estimate seasonal ice velocities over a larger range of regions within both the ablation and accumulation zones.

An Approach for Forest Inventory in Canada's Northern Boreal region, Northwest Territories

NASA Astrophysics Data System (ADS)

Mahoney, C.; Hopkinson, C.; Hall, R.; Filiatrault, M.

2017-12-01

The northern extent of Canada's northern boreal forest is largely inaccessible resulting in logistical, financial, and human challenges with respect to obtaining concise and accurate forest resource inventory (FRI) attributes such as stand height, aboveground biomass and forest carbon stocks. This challenge is further exacerbated by mandated government resource management and reporting of key attributes with respect to assessing impacts of natural disturbances, monitoring wildlife habitat and establishing policies to mitigate effects of climate change. This study presents a framework methodology utilized to inventory canopy height and crown closure over a 420,000 km2 area in Canada's Northwest Territories (NWT) by integrating field, LiDAR and satellite remote sensing data. Attributes are propagated from available field to coincident airborne LiDAR thru to satellite laser altimetry footprints. A quality controlled form of the latter are then submitted to a k-nearest neighbor (kNN) imputation algorithm to produce a continuous map of each attribute on a 30 m grid. The resultant kNN stand height (r=0.62, p=0.00) and crown closure (r=0.64, p=0.00) products were identified as statistically similar to a comprehensive independent airborne LiDAR source. Regional uncertainty can be produced with each attribute to identify areas of potential improvement through future strategic data acquisitions or the fine tuning of model parameters. This study's framework concept was developed to inform Natural Resources Canada - Canadian Forest Service's Multisource Vegetation Inventory and update vast regions of Canada's northern forest inventories, however, its applicability can be generalized to any environment. Not only can such a framework approach incorporate other data sources (such as Synthetic Aperture Radar) to potentially better characterize forest attributes, but it can also utilize future Earth observation mission data (for example ICESat-2) to monitor forest dynamics and the status, health and sustainability of Canada's northern boreal regions as areas where detailed inventory information is typically not available.

Improving Lidar-based Aboveground Biomass Estimation with Site Productivity for Central Hardwood Forests, USA

NASA Astrophysics Data System (ADS)

Shao, G.; Gallion, J.; Fei, S.

2016-12-01

Sound forest aboveground biomass estimation is required to monitor diverse forest ecosystems and their impacts on the changing climate. Lidar-based regression models provided promised biomass estimations in most forest ecosystems. However, considerable uncertainties of biomass estimations have been reported in the temperate hardwood and hardwood-dominated mixed forests. Varied site productivities in temperate hardwood forests largely diversified height and diameter growth rates, which significantly reduced the correlation between tree height and diameter at breast height (DBH) in mature and complex forests. It is, therefore, difficult to utilize height-based lidar metrics to predict DBH-based field-measured biomass through a simple regression model regardless the variation of site productivity. In this study, we established a multi-dimension nonlinear regression model incorporating lidar metrics and site productivity classes derived from soil features. In the regression model, lidar metrics provided horizontal and vertical structural information and productivity classes differentiated good and poor forest sites. The selection and combination of lidar metrics were discussed. Multiple regression models were employed and compared. Uncertainty analysis was applied to the best fit model. The effects of site productivity on the lidar-based biomass model were addressed.

The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies

NASA Astrophysics Data System (ADS)

Hu, B.; Li, J.; Wang, J.; Hall, B.

2014-11-01

The objectives of this study were to exploit Light Detection And Ranging (LiDAR) and very high spatial resolution (VHR) data and their synergy with hyperspectral imagery in the early detection of the EAB presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, individual trees were first extracted and then classified into different species based on their spectral information derived from hyperspectral imagery, spatial information from VHR imagery, and for each ash tree its health state and EAB infestation stage were determined based on hyperspectral imagery. The developed framework and methods were demonstrated to be effective according to the results obtained on two study sites in the city of Toronto, Ontario Canada. The individual tree delineation method provided satisfactory results with an overall accuracy of 78 % and 19 % commission and 23 % omission errors when used on the combined very high-spatial resolution imagery and LiDAR data. In terms of the identification of ash trees, given sufficient representative training data, our classification model was able to predict tree species with above 75 % overall accuracy, and mis-classification occurred mainly between ash and maple trees. The hypothesis that a strong correlation exists between general tree stress and EAB infestation was confirmed. Vegetation indices sensitive to leaf chlorophyll content derived from hyperspectral imagery can be used to predict the EAB infestation levels for each ash tree.

Estimation of aerosol direct radiative forcing in Lecce during the 2013 ADRIMED campaign

NASA Astrophysics Data System (ADS)

Barragan, Ruben; Romano, Salvatore; Sicard, Michaël.; Burlizzi, Pasquale; Perrone, Maria-Rita; Comeron, Adolfo

2015-10-01

In the framework of the ChArMEx (Chemistry-Aerosol Mediterranean Experiment, http://charmex.lsce.ipsl.fr/) initiative, a field campaign took place in the western Mediterranean Basin between 10 June and 5 July 2013 within the ADRIMED (Aerosol Direct Radiative Impact on the regional climate in the MEDiterranean region) project. The scientific objectives of ADRIMED are the characterization of the typical "Mediterranean aerosol" and its direct radiative forcing (column closure and regional scale). This work is focused on the multi-intrusion Saharan dust transport period of moderate intensity that occurred over the western and central Mediterranean Basin during the period 14 - 27 June. The dust plumes were detected by the EARLINET/ACTRIS (European Aerosol Research Lidar Network / Aerosols, Clouds, and Trace gases Research InfraStructure Network, http://www.actris.net/) lidar stations of Barcelona (16 and 17 June) and Lecce (22 June). First, two well-known and robust radiative transfer models, parametrized by lidar profiles for the aerosol vertical distribution, are validated both in the shortwave and longwave spectral range 1) at the surface with down- and up-ward flux measurements from radiometers and 2) at the top of the atmosphere with upward flux measurements from the CERES (Clouds and the Earth's Radiant Energy System) radiometers on board the AQUA and TERRA satellites. The differences between models and their limitations are discussed. The instantaneous and clear-sky direct radiative forcing of mineral dust is then estimated using lidar data for parametrizing the particle vertical distribution at Lecce. The difference between the obtained forcings is discussed in regard to the mineralogy and vertical structure of the dust plume.

Water vapour intercomparison effort in the frame of HyMeX-SOP1

NASA Astrophysics Data System (ADS)

Summa, Donato; Di Girolamo, Paolo; Stelitano, Dario; Cacciani, Marco; Flamant, Cyrille; Chazette, Patrick; Ducrocq, Véronique; Nuret, Mathieu; Fourié, Nadia; Richard, Evelyne

2014-05-01

A water vapour intercomparison effort, involving airborne and ground-based water vapour lidar systems and mesoscale models, was carried out in the framework of the international HyMeX (Hydrological cycle in the Mediterranean Experiment) dedicated to the hydrological cycle and related high-impact events. Within HyMeX, a major field campaign was dedicated to heavy precipitation and flash flood events from 5 September to 6 November 2012. The 2 month field campaign took place over the Northwestern Mediterranean Sea and its surrounding coastal regions in France, Italy, and Spain. The main objective of this work is to provide accurate error estimates for the lidar systems i.e. the ground-based Raman lidar BASIL and the CNRS DIAL Leandre 2 on board the ATR42, as well as use BASIL data to validate mesoscale model results from the MESO NH and Arome WMED. The effort will benefit from the few dedicated ATR42 flights in the frame of the EUFAR Project "WaLiTemp". In the present work our attention was focused on two specific case studies: 13 September and 2 October in the altitude region 0.5 - 5.5 km. Comparisons between the ground-based Raman lidar BASIL and the airborne CNRS DIAL indicate a mean relative bias between the two sensors of 6.5%, while comparisons between BASIL and CNRS DIAL vs. the radiosondes indicate a bias of 2.6 and -3.5 %, respectively. The bias of BASIL vs. the ATR insitu sensor indicate a bias of -20.4 %. Specific attention will also be dedicated to the WALI/BASIL intercomparison effort which took place in Candillargues on 30 October 2012. Specific results from this intercomparison effort and from the intercomparison between BASIL and Meso-NH/AROME-WMed will be illustrated and discussed at the Conference.

Characterization of Saharan dust ageing over the western Mediterranean Basin during a multi-intrusion event in June 2013 in the framework of the ADRIMED/ChArMEx campaign

NASA Astrophysics Data System (ADS)

Barragan, Ruben; Sicard, Michaël; Totems, Julien; François Léon, Jean; Baptiste Renard, Jean; Dulac, François; Mallet, Marc; Pelon, Jacques; Alados-Arboledas, Lucas; Amodeo, Aldo; José Granados-Muñoz, María; Boselli, Antonella; Bravo-Aranda, Juan Antonio; Muñoz-Porcar, Constantino; Chazette, Patrick; Comerón, Adolfo; D'Amico, Giuseppe; Wang, Xuan; Mona, Lucia; Pappalardo, Gelsomina

2015-04-01

In the framework of the ChArMEx (Chemistry-Aerosol Mediterranean Experiment, http://charmex.lsce.ipsl.fr/) initiative, a field campaign took place in the western Mediterranean Basin between 10 June and 5 July 2013 within the ADRIMED (Aerosol Direct Radiative Impact on the regional climate in the MEDiterranean region) project. The scientific objectives of the campaign were the characterization of the different aerosol types found over the Mediterranean Sea and the calculation of their direct radiative forcing (column closure and regional scale). Two super-sites (Ersa, Corsica Island, France, and Lampedusa Island, Italy) were equipped with a complete set of instruments to measure in-situ aerosol physical, chemical and optical properties, as well as aerosol mixing state and vertical distribution and radiative fluxes. Four secondary sites were operated in Granada (Spain), Menorca Island (Spain), Rome (Italy) and Lecce (Italy). All sites were equipped with AERONET sunphotometers. The ground observations were supported by airborne measurements including 2 SAFIRE aircraft (ATR-42 equipped with in situ measurements (10 June - 5 July) and Falcon-20 (17 June - 5 July) with the LNG aerosol lidar) and sounding and drifting balloons launched by CNES from Menorca Island and carrying the LOAC particle counter/sizer (16 June - 4 July). Satellite products from MODIS, MSG/SEVIRI and CALIOP provided additional observations. In several occasions corresponding to aerosol loads of different types, the aircraft flew near EARLINET/ACTRIS (European Aerosol Research Lidar Network / Aerosols, Clouds, and Trace gases Research InfraStructure Network, http://www.actris.net/) lidar stations. This work is focused on a moderate multi-intrusion Saharan dust event occurred over the western Mediterranean Basin (WMB) during the period 14 - 27 June. The dust plumes were detected by the EARLINET stations of Granada, Barcelona, Naples, Potenza, Lecce and Serra la Nave (Sicily) and by the ChArMEx lidar stations of Menorca, Ersa and Lampedusa. The dust origin is chronologically identified from northern Morocco, center Algeria and center Tunisia. The multi-intrusion aspect of the event results in aerosol optical depth peaks higher in the eastern part of the WMB (maximum of 0.45 at 440 nm detected in Lecce) than in the western part of the WMB where the event starts (maximum of 0.29 at 440 nm detected in Granada). The spatio-temporal evolution of the plumes during their transport and the differences due to the different dust origins are investigated with multi-wavelength ground-based lidars, sun-photometers, the airborne lidar and balloon-borne aerosol counters. Acknowledgments: EARLINET lidar measurements are supported by the 7th Framework Programme under the project ACTRIS (Aerosols, Clouds, and Trace Gases Research Infrastructure Network; grant agreement no. 262254). The field campaign was performed in the framework of work package 4 on aerosol-radiation-climate interactions of the coordinated programme MISTRALS/ChArMEx) and was also supported by ANR.

Improving snow density estimation for mapping SWE with Lidar snow depth: assessment of uncertainty in modeled density and field sampling strategies in NASA SnowEx

NASA Astrophysics Data System (ADS)

Raleigh, M. S.; Smyth, E.; Small, E. E.

2017-12-01

The spatial distribution of snow water equivalent (SWE) is not sufficiently monitored with either remotely sensed or ground-based observations for water resources management. Recent applications of airborne Lidar have yielded basin-wide mapping of SWE when combined with a snow density model. However, in the absence of snow density observations, the uncertainty in these SWE maps is dominated by uncertainty in modeled snow density rather than in Lidar measurement of snow depth. Available observations tend to have a bias in physiographic regime (e.g., flat open areas) and are often insufficient in number to support testing of models across a range of conditions. Thus, there is a need for targeted sampling strategies and controlled model experiments to understand where and why different snow density models diverge. This will enable identification of robust model structures that represent dominant processes controlling snow densification, in support of basin-scale estimation of SWE with remotely-sensed snow depth datasets. The NASA SnowEx mission is a unique opportunity to evaluate sampling strategies of snow density and to quantify and reduce uncertainty in modeled snow density. In this presentation, we present initial field data analyses and modeling results over the Colorado SnowEx domain in the 2016-2017 winter campaign. We detail a framework for spatially mapping the uncertainty in snowpack density, as represented across multiple models. Leveraging the modular SUMMA model, we construct a series of physically-based models to assess systematically the importance of specific process representations to snow density estimates. We will show how models and snow pit observations characterize snow density variations with forest cover in the SnowEx domains. Finally, we will use the spatial maps of density uncertainty to evaluate the selected locations of snow pits, thereby assessing the adequacy of the sampling strategy for targeting uncertainty in modeled snow density.

A Backscatter-Lidar Forward-Operator

NASA Astrophysics Data System (ADS)

Geisinger, Armin; Behrendt, Andreas; Wulfmeyer, Volker; Vogel, Bernhard; Mattis, Ina; Flentje, Harald; Förstner, Jochen; Potthast, Roland

2015-04-01

We have developed a forward-operator which is capable of calculating virtual lidar profiles from atmospheric state simulations. The operator allows us to compare lidar measurements and model simulations based on the same measurement parameter: the lidar backscatter profile. This method simplifies qualitative comparisons and also makes quantitative comparisons possible, including statistical error quantification. Implemented into an aerosol-capable model system, the operator will act as a component to assimilate backscatter-lidar measurements. As many weather services maintain already networks of backscatter-lidars, such data are acquired already in an operational manner. To estimate and quantify errors due to missing or uncertain aerosol information, we started sensitivity studies about several scattering parameters such as the aerosol size and both the real and imaginary part of the complex index of refraction. Furthermore, quantitative and statistical comparisons between measurements and virtual measurements are shown in this study, i.e. applying the backscatter-lidar forward-operator on model output.

Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests.

PubMed

Jaime-González, Carlos; Acebes, Pablo; Mateos, Ana; Mezquida, Eduardo T

2017-01-01

LiDAR technology has firmly contributed to strengthen the knowledge of habitat structure-wildlife relationships, though there is an evident bias towards flying vertebrates. To bridge this gap, we investigated and compared the performance of LiDAR and field data to model habitat preferences of wood mouse (Apodemus sylvaticus) in a Mediterranean high mountain pine forest (Pinus sylvestris). We recorded nine field and 13 LiDAR variables that were summarized by means of Principal Component Analyses (PCA). We then analyzed wood mouse's habitat preferences using three different models based on: (i) field PCs predictors, (ii) LiDAR PCs predictors; and (iii) both set of predictors in a combined model, including a variance partitioning analysis. Elevation was also included as a predictor in the three models. Our results indicate that LiDAR derived variables were better predictors than field-based variables. The model combining both data sets slightly improved the predictive power of the model. Field derived variables indicated that wood mouse was positively influenced by the gradient of increasing shrub cover and negatively affected by elevation. Regarding LiDAR data, two LiDAR PCs, i.e. gradients in canopy openness and complexity in forest vertical structure positively influenced wood mouse, although elevation interacted negatively with the complexity in vertical structure, indicating wood mouse's preferences for plots with lower elevations but with complex forest vertical structure. The combined model was similar to the LiDAR-based model and included the gradient of shrub cover measured in the field. Variance partitioning showed that LiDAR-based variables, together with elevation, were the most important predictors and that part of the variation explained by shrub cover was shared. LiDAR derived variables were good surrogates of environmental characteristics explaining habitat preferences by the wood mouse. Our LiDAR metrics represented structural features of the forest patch, such as the presence and cover of shrubs, as well as other characteristics likely including time since perturbation, food availability and predation risk. Our results suggest that LiDAR is a promising technology for further exploring habitat preferences by small mammal communities.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Newman, Jennifer F.; Clifton, Andrew

Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidarsmore » in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices. L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physics-based corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-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.« less

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

DOE PAGES

Newman, Jennifer F.; Clifton, Andrew

2017-02-10

Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidarsmore » in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices. L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physics-based corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-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.« less

Development and application of a backscatter lidar forward operator for quantitative validation of aerosol dispersion models and future data assimilation

NASA Astrophysics Data System (ADS)

Geisinger, Armin; Behrendt, Andreas; Wulfmeyer, Volker; Strohbach, Jens; Förstner, Jochen; Potthast, Roland

2017-12-01

A new backscatter lidar forward operator was developed which is based on the distinct calculation of the aerosols' backscatter and extinction properties. The forward operator was adapted to the COSMO-ART ash dispersion simulation of the Eyjafjallajökull eruption in 2010. While the particle number concentration was provided as a model output variable, the scattering properties of each individual particle type were determined by dedicated scattering calculations. Sensitivity studies were performed to estimate the uncertainties related to the assumed particle properties. Scattering calculations for several types of non-spherical particles required the usage of T-matrix routines. Due to the distinct calculation of the backscatter and extinction properties of the models' volcanic ash size classes, the sensitivity studies could be made for each size class individually, which is not the case for forward models based on a fixed lidar ratio. Finally, the forward-modeled lidar profiles have been compared to automated ceilometer lidar (ACL) measurements both qualitatively and quantitatively while the attenuated backscatter coefficient was chosen as a suitable physical quantity. As the ACL measurements were not calibrated automatically, their calibration had to be performed using satellite lidar and ground-based Raman lidar measurements. A slight overestimation of the model-predicted volcanic ash number density was observed. Major requirements for future data assimilation of data from ACL have been identified, namely, the availability of calibrated lidar measurement data, a scattering database for atmospheric aerosols, a better representation and coverage of aerosols by the ash dispersion model, and more investigation in backscatter lidar forward operators which calculate the backscatter coefficient directly for each individual aerosol type. The introduced forward operator offers the flexibility to be adapted to a multitude of model systems and measurement setups.

Raman lidar observations of particle hygroscopicity during COPS

NASA Astrophysics Data System (ADS)

Stelitano, D.; Di Girolamo, P.; Summa, D.

2012-04-01

The characterization of particle hygroscopicity has primary importance for climate monitoring and prediction. Model studies have demonstrated that relative humidity (RH) has a critical influence on aerosol climate forcing. The relationship between aerosol backscattering and relative humidity has been investigated in numerous studies (among others, Pahlow et al., 2006; Wulfmeyer and Feingold, 2000; Veselovskii et al., 2009). Hygroscopic properties of aerosols influence particle size distribution and refractive index and hence their radiative effects. Aerosol particles tend to grow at large relative humidity values as a result of their hygroscopicity. Raman lidars with aerosol, water vapour and temperature measurement capability are potentially attractive tools for studying aerosol hygroscopicity as in fact they can provide continuous altitude-resolved measurements of particle optical, size and microphysical properties, as well as relative humidity, without perturbing the aerosols or their environment. Specifically, the University of Basilicata Raman lidar system (BASIL) considered for the present study, has the capability to perform all-lidar measurements of relative humidity based on the application of both the rotational and the vibrational Raman lidar techniques in the UV. BASIL was operational in Achern (Black Forest, Lat: 48.64 ° N, Long: 8.06 ° E, Elev.: 140 m) between 25 May and 30 August 2007 in the framework of the Convective and Orographically-induced Precipitation Study (COPS). During COPS, BASIL collected more than 500 hours of measurements, distributed over 58 measurement days and 34 intensive observation periods (IOPs). The present analysis is focused on selected case studies characterized by the presence of different aerosol types with different hygroscopic behaviour. The observed behaviour, dependent upon aerosol composition, may range from hygrophobic to strongly hygroscopic. Results from the different case studies will be illustrated and discussed at the Conference.

Contribution of corner reflections from oriented ice crystals to backscattering and depolarization characteristics for off-zenith lidar profiling

NASA Astrophysics Data System (ADS)

Borovoi, Anatoli G.; Konoshonkin, Alexander V.; Kustova, Natalia V.; Veselovskii, Igor A.

2018-06-01

Backscattering Mueller matrix and the depolarization and color ratios for quasi-horizontally oriented hexagonal ice plates have been calculated within the framework of the physical optics approximation. In the case of a tilted lidar, the dependence of the color and depolarization ratios on polarization of the incident light has been analyzed. It is shown that the corner reflection effect inherent to the pristine hexagonal ice crystals results in sharp peaks of both the backscattering cross section and depolarization ratio at the lidar tilts of about 30° off zenith. The experimental results obtained recently by Veselovskii et al. [13] at the lidar tilt of 43° have been interpreted as a partial manifestation of the corner reflection effect. The retrieval of the vertical profile of the ice crystal fraction consisting of quasi-horizontally oriented hexagonal plates has been demonstrated.

Combining High Spatial Resolution Optical and LIDAR Data for Object-Based Image Classification

NASA Astrophysics Data System (ADS)

Li, R.; Zhang, T.; Geng, R.; Wang, L.

2018-04-01

In order to classify high spatial resolution images more accurately, in this research, a hierarchical rule-based object-based classification framework was developed based on a high-resolution image with airborne Light Detection and Ranging (LiDAR) data. The eCognition software is employed to conduct the whole process. In detail, firstly, the FBSP optimizer (Fuzzy-based Segmentation Parameter) is used to obtain the optimal scale parameters for different land cover types. Then, using the segmented regions as basic units, the classification rules for various land cover types are established according to the spectral, morphological and texture features extracted from the optical images, and the height feature from LiDAR respectively. Thirdly, the object classification results are evaluated by using the confusion matrix, overall accuracy and Kappa coefficients. As a result, a method using the combination of an aerial image and the airborne Lidar data shows higher accuracy.

Improving Lidar Turbulence Estimates for Wind Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Newman, Jennifer F.; Clifton, Andrew; Churchfield, Matthew J.

2016-10-06

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidarsmore » were collocated with meteorological towers. This presentation primarily focuses on the physics-based corrections, which include corrections for instrument noise, volume averaging, and variance contamination. As different factors affect TI under different stability conditions, the combination of physical corrections applied in L-TERRA changes depending on the atmospheric stability during each 10-minute time period. This stability-dependent version of L-TERRA performed well at both sites, reducing TI error and bringing lidar TI estimates closer to estimates from instruments on towers. However, there is still scatter evident in the lidar TI estimates, indicating that there are physics that are not being captured in the current version of L-TERRA. Two options are discussed for modeling the remainder of the TI error physics in L-TERRA: machine learning and lidar simulations. Lidar simulations appear to be a better approach, as they can help improve understanding of atmospheric effects on TI error and do not require a large training data set.« less

Error Sources in Proccessing LIDAR Based Bridge Inspection

NASA Astrophysics Data System (ADS)

Bian, H.; Chen, S. E.; Liu, W.

2017-09-01

Bridge inspection is a critical task in infrastructure management and is facing unprecedented challenges after a series of bridge failures. The prevailing visual inspection was insufficient in providing reliable and quantitative bridge information although a systematic quality management framework was built to ensure visual bridge inspection data quality to minimize errors during the inspection process. The LiDAR based remote sensing is recommended as an effective tool in overcoming some of the disadvantages of visual inspection. In order to evaluate the potential of applying this technology in bridge inspection, some of the error sources in LiDAR based bridge inspection are analysed. The scanning angle variance in field data collection and the different algorithm design in scanning data processing are the found factors that will introduce errors into inspection results. Besides studying the errors sources, advanced considerations should be placed on improving the inspection data quality, and statistical analysis might be employed to evaluate inspection operation process that contains a series of uncertain factors in the future. Overall, the development of a reliable bridge inspection system requires not only the improvement of data processing algorithms, but also systematic considerations to mitigate possible errors in the entire inspection workflow. If LiDAR or some other technology can be accepted as a supplement for visual inspection, the current quality management framework will be modified or redesigned, and this would be as urgent as the refine of inspection techniques.

Aerosol backscatter lidar calibration and data interpretation

NASA Technical Reports Server (NTRS)

Kavaya, M. J.; Menzies, R. T.

1984-01-01

A treatment of the various factors involved in lidar data acquisition and analysis is presented. This treatment highlights sources of fundamental, systematic, modeling, and calibration errors that may affect the accurate interpretation and calibration of lidar aerosol backscatter data. The discussion primarily pertains to ground based, pulsed CO2 lidars that probe the troposphere and are calibrated using large, hard calibration targets. However, a large part of the analysis is relevant to other types of lidar systems such as lidars operating at other wavelengths; continuous wave (CW) lidars; lidars operating in other regions of the atmosphere; lidars measuring nonaerosol elastic or inelastic backscatter; airborne or Earth-orbiting lidar platforms; and lidars employing combinations of the above characteristics.

Mapping and monitoring Mount Graham red squirrel habitat with Lidar and Landsat imagery

USGS Publications Warehouse

Hatten, James R.

2014-01-01

The Mount Graham red squirrel (Tamiasciurus hudsonicus grahamensis) is an endemic subspecies located in the Pinaleño Mountains of southeast Arizona. Living in a conifer forest on a sky-island surrounded by desert, the Mount Graham red squirrel is one of the rarest mammals in North America. Over the last two decades, drought, insect infestations, and fire destroyed much of its habitat. A federal recovery team is working on a plan to recover the squirrel and detailed information is necessary on its habitat requirements and population dynamics. Toward that goal I developed and compared three probabilistic models of Mount Graham red squirrel habitat with a geographic information system and logistic regression. Each model contained the same topographic variables (slope, aspect, elevation), but the Landsat model contained a greenness variable (Normalized Difference Vegetation Index) extracted from Landsat, the Lidar model contained three forest-inventory variables extracted from lidar, while the Hybrid model contained Landsat and lidar variables. The Hybrid model produced the best habitat classification accuracy, followed by the Landsat and Lidar models, respectively. Landsat-derived forest greenness was the best predictor of habitat, followed by topographic (elevation, slope, aspect) and lidar (tree height, canopy bulk density, and live basal area) variables, respectively. The Landsat model's probabilities were significantly correlated with all 12 lidar variables, indicating its utility for habitat mapping. While the Hybrid model produced the best classification results, only the Landsat model was suitable for creating a habitat time series or habitat–population function between 1986 and 2013. The techniques I highlight should prove valuable in the development of Landsat- or lidar-based habitat models range wide.

Vertical profiles of fine and coarse aerosol particles over Cyprus: Comparison between in-situ drone measurements and remote sensing observations

NASA Astrophysics Data System (ADS)

Mamali, Dimitra; Marinou, Eleni; Pikridas, Michael; Kottas, Michael; Binietoglou, Ioannis; Kokkalis, Panagiotis; Tsekeri, Aleksandra; Amiridis, Vasilis; Sciare, Jean; Keleshis, Christos; Engelmann, Ronny; Ansmann, Albert; Russchenberg, Herman W. J.; Biskos, George

2017-04-01

Vertical profiles of the aerosol mass concentration derived from light detection and ranging (lidar) measurements were compared to airborne dried optical particle counter (OPC MetOne; Model 212) measurements during the INUIT-BACCHUS-ACTRIS campaign. The campaign took place in April 2016 and its main focus was the study of aerosol dust particles. During the campaign the NOA Polly-XT Raman lidar located at Nicosia (35.08° N, 33.22° E) was providing round-the-clock vertical profiles of aerosol optical properties. In addition, an unmanned aerial vehicle (UAV) carrying an OPC flew on 7 days during the first morning hours. The flights were performed at Orounda (35.1018° N, 33.0944° E) reaching altitudes of 2.5 km a.s.l, which allows comparison with a good fraction of the recorded lidar data. The polarization lidar photometer networking method (POLIPHON) was used for the estimation of the fine (non-dust) and coarse (dust) mode aerosol mass concentration profiles. This method uses as input the particle backscatter coefficient and the particle depolarization profiles of the lidar at 532 nm wavelength and derives the aerosol mass concentration. The first step in this approach makes use of the lidar observations to separate the backscatter and extinction contributions of the weakly depolarizing non-dust aerosol components from the contributions of the strongly depolarizing dust particles, under the assumption of an externally mixed two-component aerosol. In the second step, sun photometer retrievals of the fine and the coarse modes aerosol optical thickness (AOT) and volume concentration are used to calculate the associated concentrations from the extinction coefficients retrieved from the lidar. The estimated aerosol volume concentrations were converted into mass concentration with an assumption for the bulk aerosol density, and compared with the OPC measurements. The first results show agreement within the experimental uncertainty. This project received funding from the European Union's Seventh Framework Programme (FP7) project BACCHUS under grant agreement no. 603445, and the European Union's Horizon 2020 research and innovation programme ACTRIS-2 under grant agreement No 654109.

A New 3D Object Pose Detection Method Using LIDAR Shape Set

PubMed Central

Kim, Jung-Un

2018-01-01

In object detection systems for autonomous driving, LIDAR sensors provide very useful information. However, problems occur because the object representation is greatly distorted by changes in distance. To solve this problem, we propose a LIDAR shape set that reconstructs the shape surrounding the object more clearly by using the LIDAR point information projected on the object. The LIDAR shape set restores object shape edges from a bird’s eye view by filtering LIDAR points projected on a 2D pixel-based front view. In this study, we use this shape set for two purposes. The first is to supplement the shape set with a LIDAR Feature map, and the second is to divide the entire shape set according to the gradient of the depth and density to create a 2D and 3D bounding box proposal for each object. We present a multimodal fusion framework that classifies objects and restores the 3D pose of each object using enhanced feature maps and shape-based proposals. The network structure consists of a VGG -based object classifier that receives multiple inputs and a LIDAR-based Region Proposal Networks (RPN) that identifies object poses. It works in a very intuitive and efficient manner and can be extended to other classes other than vehicles. Our research has outperformed object classification accuracy (Average Precision, AP) and 3D pose restoration accuracy (3D bounding box recall rate) based on the latest studies conducted with KITTI data sets. PMID:29547551

A New 3D Object Pose Detection Method Using LIDAR Shape Set.

PubMed

Kim, Jung-Un; Kang, Hang-Bong

2018-03-16

In object detection systems for autonomous driving, LIDAR sensors provide very useful information. However, problems occur because the object representation is greatly distorted by changes in distance. To solve this problem, we propose a LIDAR shape set that reconstructs the shape surrounding the object more clearly by using the LIDAR point information projected on the object. The LIDAR shape set restores object shape edges from a bird's eye view by filtering LIDAR points projected on a 2D pixel-based front view. In this study, we use this shape set for two purposes. The first is to supplement the shape set with a LIDAR Feature map, and the second is to divide the entire shape set according to the gradient of the depth and density to create a 2D and 3D bounding box proposal for each object. We present a multimodal fusion framework that classifies objects and restores the 3D pose of each object using enhanced feature maps and shape-based proposals. The network structure consists of a VGG -based object classifier that receives multiple inputs and a LIDAR-based Region Proposal Networks (RPN) that identifies object poses. It works in a very intuitive and efficient manner and can be extended to other classes other than vehicles. Our research has outperformed object classification accuracy (Average Precision, AP) and 3D pose restoration accuracy (3D bounding box recall rate) based on the latest studies conducted with KITTI data sets.

Voxel-Based 3-D Tree Modeling from Lidar Images for Extracting Tree Structual Information

NASA Astrophysics Data System (ADS)

Hosoi, F.

2014-12-01

Recently, lidar (light detection and ranging) has been used to extracting tree structural information. Portable scanning lidar systems can capture the complex shape of individual trees as a 3-D point-cloud image. 3-D tree models reproduced from the lidar-derived 3-D image can be used to estimate tree structural parameters. We have proposed the voxel-based 3-D modeling for extracting tree structural parameters. One of the tree parameters derived from the voxel modeling is leaf area density (LAD). We refer to the method as the voxel-based canopy profiling (VCP) method. In this method, several measurement points surrounding the canopy and optimally inclined laser beams are adopted for full laser beam illumination of whole canopy up to the internal. From obtained lidar image, the 3-D information is reproduced as the voxel attributes in the 3-D voxel array. Based on the voxel attributes, contact frequency of laser beams on leaves is computed and LAD in each horizontal layer is obtained. This method offered accurate LAD estimation for individual trees and woody canopy trees. For more accurate LAD estimation, the voxel model was constructed by combining airborne and portable ground-based lidar data. The profiles obtained by the two types of lidar complemented each other, thus eliminating blind regions and yielding more accurate LAD profiles than could be obtained by using each type of lidar alone. Based on the estimation results, we proposed an index named laser beam coverage index, Ω, which relates to the lidar's laser beam settings and a laser beam attenuation factor. It was shown that this index can be used for adjusting measurement set-up of lidar systems and also used for explaining the LAD estimation error using different types of lidar systems. Moreover, we proposed a method to estimate woody material volume as another application of the voxel tree modeling. In this method, voxel solid model of a target tree was produced from the lidar image, which is composed of consecutive voxels that filled the outer surface and the interior of the stem and large branches. From the model, the woody material volume of any part of the target tree can be directly calculated easily by counting the number of corresponding voxels and multiplying the result by the per-voxel volume.

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

NASA Technical Reports Server (NTRS)

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.

Optimum efficiency lidar sensing of multilayer hydrometeors through a turbid atmosphere

NASA Astrophysics Data System (ADS)

Evgenieva, Ts T.; Gurdev, L. L.

2018-03-01

The detected lidar return power is a basic factor determining the brightness of the detected lidar images and the signal-to-noise ratio (SNR) of a given measurement. At equal other characteristics, the laser radiation wavelength should influence the lidar return signal and assume an optimum value depending on the specificity of the objects investigated. As such a problem had not been considered systematically, we recently began developing a modeling approach to solving it, based on evaluating the mean and the noisy lidar profiles and the SNR profile of the measurement along the lidar line of sight by using the lidar equation and well known realistic models of the atmospheric objects and background. The main purpose of the present work is to estimate by numerical modeling the detectability of the lidar return from different distances and multilayer cirrus clouds, depending on the laser radiation wavelengths. The results obtained confirm the expectations that at a higher atmospheric turbidity, a relatively higher sensing efficiency (return power) is achievable by longer-wavelength laser radiation, within the NIR range.

A Combined Retrieval of Aerosol Microphysical Properties using active HSRL and Passive Polarimeter Multi-sensor Data

NASA Astrophysics Data System (ADS)

Liu, X.; Stamnes, S.; Ferrare, R. A.; Hostetler, C. A.; Burton, A. S.; Chemyakin, E.; Sawamura, P.; Mueller, D.

2017-12-01

Vertically resolved measurements of aerosol optical, microphysical, and macrophysical parameters are required to better understand the influence of aerosols on climate and air quality. We will describe an Optimal Estimation (OE) retrieval framework which can perform aerosol property retrievals in three modes: 1) lidar-only, 2) polarimeter-only, and 3) combined lidar-polarimeter muti-sensor system. The lidar data can be profile measurements by any high spectral resolution lidar (HSRL) and/or Raman lidar with multiple wavelengths of aerosol backscattering (β) and extinction (α). The polarimeter data can be any multi-angle and multi-wavelength measurements with 2 or 3 polarization components. We will show aerosol microphysical retrieval results from the HSRL-2 data measured from various NASA airborne field campaigns including the recent ORACLES mission. We will also show the OE retrieval results from the polarimeter-only mode. Finally, we will demonstrate how the information content of the aerosol microphysical retrieval is increased by combining the active HSRL and passive polarimeter data in our simultaneous OE retrieval system.

Automated Mounting Bias Calibration for Airborne LIDAR System

NASA Astrophysics Data System (ADS)

Zhang, J.; Jiang, W.; Jiang, S.

2012-07-01

Mounting bias is the major error source of Airborne LIDAR system. In this paper, an automated calibration method for estimating LIDAR system mounting parameters is introduced. LIDAR direct geo-referencing model is used to calculate systematic errors. Due to LIDAR footprints discretely sampled, the real corresponding laser points are hardly existence among different strips. The traditional corresponding point methodology does not seem to apply to LIDAR strip registration. We proposed a Virtual Corresponding Point Model to resolve the corresponding problem among discrete laser points. Each VCPM contains a corresponding point and three real laser footprints. Two rules are defined to calculate tie point coordinate from real laser footprints. The Scale Invariant Feature Transform (SIFT) is used to extract corresponding points in LIDAR strips, and the automatic flow of LIDAR system calibration based on VCPM is detailed described. The practical examples illustrate the feasibility and effectiveness of the proposed calibration method.

Profiling of Saharan dust from the Caribbean to western Africa - Part 1: Layering structures and optical properties from shipborne polarization/Raman lidar observations

NASA Astrophysics Data System (ADS)

Rittmeister, Franziska; Ansmann, Albert; Engelmann, Ronny; Skupin, Annett; Baars, Holger; Kanitz, Thomas; Kinne, Stefan

2017-11-01

We present final and quality-assured results of multiwavelength polarization/Raman lidar observations of the Saharan air layer (SAL) over the tropical Atlantic. Observations were performed aboard the German research vessel R/V Meteor during the 1-month transatlantic cruise from Guadeloupe to Cabo Verde over 4500 km from 61.5 to 20° W at 14-15° N in April-May 2013. First results of the shipborne lidar measurements, conducted in the framework of SALTRACE (Saharan Aerosol Long-range Transport and Aerosol-Cloud Interaction Experiment), were reported by Kanitz et al.(2014). Here, we present four observational cases representing key stages of the SAL evolution between Africa and the Caribbean in detail in terms of layering structures and optical properties of the mixture of predominantly dust and aged smoke in the SAL. We discuss to what extent the lidar results confirm the validity of the SAL conceptual model which describes the dust long-range transport and removal processes over the tropical Atlantic. Our observations of a clean marine aerosol layer (MAL, layer from the surface to the SAL base) confirm the conceptual model and suggest that the removal of dust from the MAL, below the SAL, is very efficient. However, the removal of dust from the SAL assumed in the conceptual model to be caused by gravitational settling in combination with large-scale subsidence is weaker than expected. To explain the observed homogenous (height-independent) dust optical properties from the SAL base to the SAL top, from the African coast to the Caribbean, we have to assume that the particle sedimentation strength is reduced and dust vertical mixing and upward transport mechanisms must be active in the SAL. Based on lidar observations on 20 nights at different longitudes in May 2013, we found, on average, MAL and SAL layer mean values (at 532 nm) of the extinction-to-backscatter ratio (lidar ratio) of 17±5 sr (MAL) and 43±8 sr (SAL), of the particle linear depolarization ratio of 0.025±0.015 (MAL) and 0.19±0.09 (SAL), and of the particle extinction coefficient of 67±45 Mm-1 (MAL) and 68±37 Mm-1 (SAL). The 532 nm optical depth of the lofted SAL was found to be, on average, 0.15±0.13 during the ship cruise. The comparably low values of the SAL mean lidar ratio and depolarization ratio (compared to typical pure dust values of 50-60 sr and 0.3, respectively) in combination with backward trajectories indicate a smoke contribution to light extinction of the order of 20 % during May 2013, at the end of the burning season in central-western Africa.

TOLNet ozone lidar intercomparison during the discover-aq and frappé campaigns

NASA Astrophysics Data System (ADS)

Newchurch, Michael J.; Alvarez, Raul J.; Berkoff, Timothy A.; Carrion, William; DeYoung, Russell J.; Ganoe, Rene; Gronoff, Guillaume; Kirgis, Guillaume; Kuang, Shi; Langford, Andy O.; Leblanc, Thierry; McGee, Thomas J.; Pliutau, Denis; Senff, Christoph; Sullivan, John T.; Sumnicht, Grant; Twigg, Laurence W.; Wang, Lihua

2018-04-01

The Tropospheric Ozone Lidar Network (TOLNet) is a unique network of lidar systems that measure atmospheric profiles of ozone and aerosols, to contribute to air-quality studies, atmospheric modeling, and satellite validation efforts. The accurate characterization of these lidars is of critical interest, and is necessary to determine cross-instrument calibration uniformity. From July to August 2014, three lidars, the TROPospheric OZone (TROPOZ) lidar, the Tunable Optical Profiler for Aerosol and oZone (TOPAZ) lidar, and the Langley Mobile Ozone Lidar (LMOL), of TOLNet participated in the "Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality" (DISCOVER-AQ) mission and the "Front Range Air Pollution and Photochemistry Éxperiment" (FRAPPÉ) to measure sub-hourly ozone variations from near the surface to the top of the troposphere. Although large differences occur at few individual altitudes in the near field and far field range, the TOLNet lidars agree with each other within ±4%. These results indicate excellent measurement accuracy for the TOLNet lidars that is suitable for use in air-quality and ozone modeling efforts.

Integrated Path Differential Absorption Lidar Optimizations Based on Pre-Analyzed Atmospheric Data for ASCENDS Mission Applications

NASA Technical Reports Server (NTRS)

Pliutau, Denis; Prasad, Narasimha S.

2012-01-01

In this paper a modeling method based on data reductions is investigated which includes pre analyzed MERRA atmospheric fields for quantitative estimates of uncertainties introduced in the integrated path differential absorption methods for the sensing of various molecules including CO2. This approach represents the extension of our existing lidar modeling framework previously developed and allows effective on- and offline wavelength optimizations and weighting function analysis to minimize the interference effects such as those due to temperature sensitivity and water vapor absorption. The new simulation methodology is different from the previous implementation in that it allows analysis of atmospheric effects over annual spans and the entire Earth coverage which was achieved due to the data reduction methods employed. The effectiveness of the proposed simulation approach is demonstrated with application to the mixing ratio retrievals for the future ASCENDS mission. Independent analysis of multiple accuracy limiting factors including the temperature, water vapor interferences, and selected system parameters is further used to identify favorable spectral regions as well as wavelength combinations facilitating the reduction in total errors in the retrieved XCO2 values.

Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery

NASA Astrophysics Data System (ADS)

Wu, Qiusheng; Lane, Charles R.

2017-07-01

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling-spilling-merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow simulation and hydrologic connectivity analysis.

Modelling rating curves using remotely sensed LiDAR data

USGS Publications Warehouse

Nathanson, Marcus; Kean, Jason W.; Grabs, Thomas J.; Seibert, Jan; Laudon, Hjalmar; Lyon, Steve W.

2012-01-01

Accurate stream discharge measurements are important for many hydrological studies. In remote locations, however, it is often difficult to obtain stream flow information because of the difficulty in making the discharge measurements necessary to define stage-discharge relationships (rating curves). This study investigates the feasibility of defining rating curves by using a fluid mechanics-based model constrained with topographic data from an airborne LiDAR scanning. The study was carried out for an 8m-wide channel in the boreal landscape of northern Sweden. LiDAR data were used to define channel geometry above a low flow water surface along the 90-m surveyed reach. The channel topography below the water surface was estimated using the simple assumption of a flat streambed. The roughness for the modelled reach was back calculated from a single measurment of discharge. The topographic and roughness information was then used to model a rating curve. To isolate the potential influence of the flat bed assumption, a 'hybrid model' rating curve was developed on the basis of data combined from the LiDAR scan and a detailed ground survey. Whereas this hybrid model rating curve was in agreement with the direct measurements of discharge, the LiDAR model rating curve was equally in agreement with the medium and high flow measurements based on confidence intervals calculated from the direct measurements. The discrepancy between the LiDAR model rating curve and the low flow measurements was likely due to reduced roughness associated with unresolved submerged bed topography. Scanning during periods of low flow can help minimize this deficiency. These results suggest that combined ground surveys and LiDAR scans or multifrequency LiDAR scans that see 'below' the water surface (bathymetric LiDAR) could be useful in generating data needed to run such a fluid mechanics-based model. This opens a realm of possibility to remotely sense and monitor stream flows in channels in remote locations.

A GUI visualization system for airborne lidar image data to reconstruct 3D city model

NASA Astrophysics Data System (ADS)

Kawata, Yoshiyuki; Koizumi, Kohei

2015-10-01

A visualization toolbox system with graphical user interfaces (GUIs) was developed for the analysis of LiDAR point cloud data, as a compound object oriented widget application in IDL (Interractive Data Language). The main features in our system include file input and output abilities, data conversion capability from ascii formatted LiDAR point cloud data to LiDAR image data whose pixel value corresponds the altitude measured by LiDAR, visualization of 2D/3D images in various processing steps and automatic reconstruction ability of 3D city model. The performance and advantages of our graphical user interface (GUI) visualization system for LiDAR data are demonstrated.

Ozone Lidar Observations for Air Quality Studies

NASA Technical Reports Server (NTRS)

Wang, Lihua; Newchurch, Mike; Kuang, Shi; Burris, John F.; Huang, Guanyu; Pour-Biazar, Arastoo; Koshak, William; Follette-Cook, Melanie B.; Pickering, Kenneth E.; McGee, Thomas J.;

Understanding the Laminar Distribution of Tropospheric Ozone from Ground-Based, Airborne, Spaceborne, and Modeling Perspectives

NASA Technical Reports Server (NTRS)

Newchurch, Mike; Johnson, Matthew S.; Huang, Guanyu; Kuang, Shi; Wang, Lihua; Chance, Kelly; Liu, Xiong

2016-01-01

Laminar ozone structure is a ubiquitous feature of tropospheric-ozone distributions resulting from dynamic and chemical atmospheric processes. Understanding the characteristics of these ozone laminae and the mechanisms responsible for producing them is important to outline the transport pathways of trace gases and to quantify the impact of different sources on tropospheric background ozone. In this study, we present a new method to detect ozone laminae to understand their climatological characteristics of occurrence frequency in terms of thickness and altitude. We employ both ground-based and airborne ozone lidar measurements and other synergistic observations and modeling to investigate the sources and mechanisms such as biomass burning transport, stratospheric intrusion, lightning-generated NOx, and nocturnal low-level jets that are responsible for depleted or enhanced tropospheric ozone layers. Spaceborne (e.g., OMI (Ozone Monitoring Instrument), TROPOMI (Tropospheric Monitoring Instrument), TEMPO (Tropospheric Emissions: Monitoring of Pollution)) measurements of these laminae will observe greater horizontal extent and lower vertical resolution than balloon-borne or lidar measurements will quantify. Using integrated ground-based, airborne, and spaceborne observations in a modeling framework affords insight into how to gain knowledge of both the vertical and horizontal evolution of these ubiquitous ozone laminae.

Lidar aboveground vegetation biomass estimates in shrublands: Prediction, uncertainties and application to coarser scales

USGS Publications Warehouse

Li, Aihua; Dhakal, Shital; Glenn, Nancy F.; Spaete, Luke P.; Shinneman, Douglas; Pilliod, David S.; Arkle, Robert; McIlroy, Susan

2017-01-01

Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the RF regression models with a pseudo R2 of 0.76 and RMSE of 125 g/m2 for shrub biomass and a pseudo R2 of 0.74 and RMSE of 141 g/m2 for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77–79% of the variance, with RMSE ranging from 120 to 129 g/m2 for shrub and total biomass, respectively. We further explored the computational efficiency and relative accuracies of using point cloud and raster Lidar metrics at different resolutions (1 m to 1 ha). Metrics derived from the Lidar point cloud processing led to improved biomass estimates at nearly all resolutions in comparison to raster-derived Lidar metrics. Only at 1 m were the results from the point cloud and raster products nearly equivalent. The best Lidar prediction models of biomass at the plot-level (1 ha) were achieved when Lidar metrics were derived from an average of fine resolution (1 m) metrics to minimize boundary effects and to smooth variability. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure and statistical measures of this structure (e.g., standard deviation of height was a strong predictor of biomass). Using our model results, we developed spatially-explicit Lidar estimates of total and shrub biomass across our study site in the Great Basin, U.S.A., for monitoring and planning in this imperiled ecosystem.

Mapping Above- and Below-Ground Carbon Pools in Boreal Forests: The Case for Airborne Lidar

PubMed Central

Kristensen, Terje; Næsset, Erik; Ohlson, Mikael; Bolstad, Paul V.; Kolka, Randall

2015-01-01

A large and growing body of evidence has demonstrated that airborne scanning light detection and ranging (lidar) systems can be an effective tool in measuring and monitoring above-ground forest tree biomass. However, the potential of lidar as an all-round tool for assisting in assessment of carbon (C) stocks in soil and non-tree vegetation components of the forest ecosystem has been given much less attention. Here we combine the use airborne small footprint scanning lidar with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools within and among multilayered Norway spruce (Picea abies) stands. Predictor variables from lidar derived metrics delivered precise models of above- and below-ground tree C, which comprised the largest C pool in our study stands. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock, consisting mainly of ericaceous dwarf shrubs and herbaceous plants. However, lidar metrics derived directly from understory echoes did not yield significant models. Furthermore, our results indicate that the variation in both the mosses and soil organic layer C stock plots appears less influenced by differences in stand structure properties than topographical gradients. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. Increasing the topographical resolution from plot averages (~2000 m2) towards individual grid cells (1 m2) did not yield consistent models. Our study demonstrates a connection between the size and distribution of different forest C pools and models derived from airborne lidar data, providing a foundation for future research concerning the use of lidar for assessing and monitoring boreal forest C. PMID:26426532

Mapping Above- and Below-Ground Carbon Pools in Boreal Forests: The Case for Airborne Lidar.

PubMed

Kristensen, Terje; Næsset, Erik; Ohlson, Mikael; Bolstad, Paul V; Kolka, Randall

2015-01-01

A large and growing body of evidence has demonstrated that airborne scanning light detection and ranging (lidar) systems can be an effective tool in measuring and monitoring above-ground forest tree biomass. However, the potential of lidar as an all-round tool for assisting in assessment of carbon (C) stocks in soil and non-tree vegetation components of the forest ecosystem has been given much less attention. Here we combine the use airborne small footprint scanning lidar with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools within and among multilayered Norway spruce (Picea abies) stands. Predictor variables from lidar derived metrics delivered precise models of above- and below-ground tree C, which comprised the largest C pool in our study stands. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock, consisting mainly of ericaceous dwarf shrubs and herbaceous plants. However, lidar metrics derived directly from understory echoes did not yield significant models. Furthermore, our results indicate that the variation in both the mosses and soil organic layer C stock plots appears less influenced by differences in stand structure properties than topographical gradients. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. Increasing the topographical resolution from plot averages (~2000 m2) towards individual grid cells (1 m2) did not yield consistent models. Our study demonstrates a connection between the size and distribution of different forest C pools and models derived from airborne lidar data, providing a foundation for future research concerning the use of lidar for assessing and monitoring boreal forest C.

Tracking aerosol plumes: lidar, modeling, and in situ measurement

NASA Astrophysics Data System (ADS)

Calhoun, Ron J.; Heap, Robert; Sommer, Jeffrey; Princevac, Marko; Peccia, Jordan; Fernando, H.

2004-09-01

The authors report on recent progress of on-going research at Arizona State University for tracking aerosol plumes using remote sensing and modeling approaches. ASU participated in a large field experiment, Joint Urban 2003, focused on urban and suburban flows and dispersion phenomena which took place in Oklahoma City during summer 2003. A variety of instruments were deployed, including two Doppler-lidars. ASU deployed one lidar and the Army Research deployed the other. Close communication and collaboration has produced datasets which will be available for dual Doppler analysis. The lidars were situated in a way to provide insight into dynamical flow structures caused by the urban core. Complementary scanning by the two lidars during the July 4 firework display in Oklahoma City demonstrated that smoke plumes could be tracked through the atmosphere above the urban area. Horizontal advection and dispersion of the smoke plumes were tracked on two horizontal planes by the ASU lidar and in two vertical planes with a similar lidar operated by the Army Research Laboratory. A number of plume dispersion modeling systems are being used at ASU for the modeling of plumes in catastrophic release scenarios. Progress using feature tracking techniques and data fusion approaches is presented for utilizing single and dual radial velocity fields from coherent Doppler lidar to improve dispersion modeling. The possibility of producing sensor/computational tools for civil and military defense applications appears worth further investigation. An experiment attempting to characterize bioaerosol plumes (using both lidar and in situ biological measurements) associated with the application of biosolids on agricultural fields is in progress at the time of writing.

Vertical distribution of Saharan dust over Rome (Italy): Comparison between 3-year model predictions and lidar soundings

NASA Astrophysics Data System (ADS)

Kishcha, P.; Barnaba, F.; Gobbi, G. P.; Alpert, P.; Shtivelman, A.; Krichak, S. O.; Joseph, J. H.

2005-03-01

Mineral dust particles loaded into the atmosphere from the Sahara desert represent one major factor affecting the Earth's radiative budget. Regular model-based forecasts of 3-D dust fields can be used in order to determine the dust radiative effect in climate models, in spite of the large gaps in observations of dust vertical profiles. In this study, dust forecasts by the Tel Aviv University (TAU) dust prediction system were compared to lidar observations to better evaluate the model's capabilities. The TAU dust model was initially developed at the University of Athens and later modified at Tel Aviv University. Dust forecasts are initialized with the aid of the Total Ozone Mapping Spectrometer aerosol index (TOMS AI) measurements. The lidar soundings employed were collected at the outskirts of Rome, Italy (41.84°N, 12.64°E) during the high-dust activity season from March to June of the years 2001, 2002, and 2003. The lidar vertical profiles collected in the presence of dust were used for obtaining statistically significant reference parameters of dust layers over Rome and for model versus lidar comparison. The Barnaba and Gobbi (2001) approach was used in the current study to derive height-resolved dust volumes from lidar measurements of backscatter. Close inspection of the juxtaposed vertical profiles, obtained from lidar and model data near Rome, indicates that the majority (67%) of the cases under investigation can be classified as good or acceptable forecasts of the dust vertical distribution. A more quantitative comparison shows that the model predictions are mainly accurate in the middle part of dust layers. This is supported by high correlation (0.85) between lidar and model data for forecast dust volumes greater than the threshold of 1 × 10-12 cm3/cm3. In general, however, the model tends to underestimate the lidar-derived dust volume profiles. The effect of clouds in the TOMS detection of AI is supposed to be the main factor responsible for this effect. Moreover, some model assumptions on dust sources and particle size and the accuracy of model-simulated meteorological parameters are also likely to affect the dust forecast quality.

Recent developments with the asian dust and aerosol lidar observation network (AD-NET)

NASA Astrophysics Data System (ADS)

Sugimoto, Nobuo; Shimizu, Atsushi; Nishizawa, Tomoaki; Jin, Yoshitaka

2018-04-01

Recent developments of lidars and data analysis methods for AD-Net, and the studies using ADNet are presented. Continuous observation was started in 2001 at three stations using polarizationsensitive Mie-scattering lidars. Currently, lidars, including three multi-wavelength Raman lidars and one high-spectral-resolution lidar, are operated at 20 stations. Recent studies on validation/assimilation of chemical transport models, climatology, and epidemiology of Asian dust are also described.

Lidar performance analysis

NASA Technical Reports Server (NTRS)

Spiers, Gary D.

1994-01-01

Section 1 details the theory used to build the lidar model, provides results of using the model to evaluate AEOLUS design instrument designs, and provides snapshots of the visual appearance of the coded model. Appendix A contains a Fortran program to calculate various forms of the refractive index structure function. This program was used to determine the refractive index structure function used in the main lidar simulation code. Appendix B contains a memo on the optimization of the lidar telescope geometry for a line-scan geometry. Appendix C contains the code for the main lidar simulation and brief instruction on running the code. Appendix D contains a Fortran code to calculate the maximum permissible exposure for the eye from the ANSI Z136.1-1992 eye safety standards. Appendix E contains a paper on the eye safety analysis of a space-based coherent lidar presented at the 7th Coherent Laser Radar Applications and Technology Conference, Paris, France, 19-23 July 1993.

Modeling the Performance of Direct-Detection Doppler Lidar Systems in Real Atmospheres

NASA Technical Reports Server (NTRS)

McGill, Matthew J.; Hart, William D.; McKay, Jack A.; Spinhirne, James D.

1999-01-01

Previous modeling of the performance of spaceborne direct-detection Doppler lidar systems has assumed extremely idealized atmospheric models. Here we develop a technique for modeling the performance of these systems in a more realistic atmosphere, based on actual airborne lidar observations. The resulting atmospheric model contains cloud and aerosol variability that is absent in other simulations of spaceborne Doppler lidar instruments. To produce a realistic simulation of daytime performance, we include solar radiance values that are based on actual measurements and are allowed to vary as the viewing scene changes. Simulations are performed for two types of direct-detection Doppler lidar systems: the double-edge and the multi-channel techniques. Both systems were optimized to measure winds from Rayleigh backscatter at 355 nm. Simulations show that the measurement uncertainty during daytime is degraded by only about 10-20% compared to nighttime performance, provided a proper solar filter is included in the instrument design.

Modeling the performance of direct-detection Doppler lidar systems including cloud and solar background variability.

PubMed

McGill, M J; Hart, W D; McKay, J A; Spinhirne, J D

1999-10-20

Previous modeling of the performance of spaceborne direct-detection Doppler lidar systems assumed extremely idealized atmospheric models. Here we develop a technique for modeling the performance of these systems in a more realistic atmosphere, based on actual airborne lidar observations. The resulting atmospheric model contains cloud and aerosol variability that is absent in other simulations of spaceborne Doppler lidar instruments. To produce a realistic simulation of daytime performance, we include solar radiance values that are based on actual measurements and are allowed to vary as the viewing scene changes. Simulations are performed for two types of direct-detection Doppler lidar system: the double-edge and the multichannel techniques. Both systems were optimized to measure winds from Rayleigh backscatter at 355 nm. Simulations show that the measurement uncertainty during daytime is degraded by only approximately 10-20% compared with nighttime performance, provided that a proper solar filter is included in the instrument design.

Surface characteristics modeling and performance evaluation of urban building materials using LiDAR data.

PubMed

Li, Xiaolu; Liang, Yu

2015-05-20

Analysis of light detection and ranging (LiDAR) intensity data to extract surface features is of great interest in remote sensing research. One potential application of LiDAR intensity data is target classification. A new bidirectional reflectance distribution function (BRDF) model is derived for target characterization of rough and smooth surfaces. Based on the geometry of our coaxial full-waveform LiDAR system, the integration method is improved through coordinate transformation to establish the relationship between the BRDF model and intensity data of LiDAR. A series of experiments using typical urban building materials are implemented to validate the proposed BRDF model and integration method. The fitting results show that three parameters extracted from the proposed BRDF model can distinguish the urban building materials from perspectives of roughness, specular reflectance, and diffuse reflectance. A comprehensive analysis of these parameters will help characterize surface features in a physically rigorous manner.

Estimating Above-Ground Carbon Biomass in a Newly Restored Coastal Plain Wetland Using Remote Sensing

PubMed Central

Riegel, Joseph B.; Bernhardt, Emily; Swenson, Jennifer

2013-01-01

Developing accurate but inexpensive methods for estimating above-ground carbon biomass is an important technical challenge that must be overcome before a carbon offset market can be successfully implemented in the United States. Previous studies have shown that LiDAR (light detection and ranging) is well-suited for modeling above-ground biomass in mature forests; however, there has been little previous research on the ability of LiDAR to model above-ground biomass in areas with young, aggrading vegetation. This study compared the abilities of discrete-return LiDAR and high resolution optical imagery to model above-ground carbon biomass at a young restored forested wetland site in eastern North Carolina. We found that the optical imagery model explained more of the observed variation in carbon biomass than the LiDAR model (adj-R2 values of 0.34 and 0.18 respectively; root mean squared errors of 0.14 Mg C/ha and 0.17 Mg C/ha respectively). Optical imagery was also better able to predict high and low biomass extremes than the LiDAR model. Combining both the optical and LiDAR improved upon the optical model but only marginally (adj-R2 of 0.37). These results suggest that the ability of discrete-return LiDAR to model above-ground biomass may be rather limited in areas with young, small trees and that high spatial resolution optical imagery may be the better tool in such areas. PMID:23840837

LiDAR - An emerging tool for geological applications

USGS Publications Warehouse

Stoker, Jason M.

2012-01-01

Over the past five to ten years the use and applicability of light detection and ranging (LiDAR) technology has increased dramatically. As a result, more and more LiDAR data now are being collected across the country for a wide range of applications, and LiDAR currently is the technology of choice for high resolution terrain model creation, 3-D city and infrastructure modeling, forestry, and a wide range of scientific applications. LiDAR is a key technology for geological applications both within and outside the U.S. Geological Survey, and efforts are underway to try to collect high resolution LiDAR data for the entire United States (https://pubs.usgs.gov/fs/2012/3089/pdf/fs2012-3089.pdf).

Making lidar more photogenic: creating band combinations from lidar information

USGS Publications Warehouse

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).

On the impact of a refined stochastic model for airborne LiDAR measurements

NASA Astrophysics Data System (ADS)

Bolkas, Dimitrios; Fotopoulos, Georgia; Glennie, Craig

2016-09-01

Accurate topographic information is critical for a number of applications in science and engineering. In recent years, airborne light detection and ranging (LiDAR) has become a standard tool for acquiring high quality topographic information. The assessment of airborne LiDAR derived DEMs is typically based on (i) independent ground control points and (ii) forward error propagation utilizing the LiDAR geo-referencing equation. The latter approach is dependent on the stochastic model information of the LiDAR observation components. In this paper, the well-known statistical tool of variance component estimation (VCE) is implemented for a dataset in Houston, Texas, in order to refine the initial stochastic information. Simulations demonstrate the impact of stochastic-model refinement for two practical applications, namely coastal inundation mapping and surface displacement estimation. Results highlight scenarios where erroneous stochastic information is detrimental. Furthermore, the refined stochastic information provides insights on the effect of each LiDAR measurement in the airborne LiDAR error budget. The latter is important for targeting future advancements in order to improve point cloud accuracy.

`Dhara': An Open Framework for Critical Zone Modeling

NASA Astrophysics Data System (ADS)

Le, P. V.; Kumar, P.

2016-12-01

Processes in the Critical Zone, which sustain terrestrial life, are tightly coupled across hydrological, physical, biological, chemical, pedological, geomorphological and ecological domains over both short and long timescales. Observations and quantification of the Earth's surface across these domains using emerging high resolution measurement technologies such as light detection and ranging (lidar) and hyperspectral remote sensing are enabling us to characterize fine scale landscape attributes over large spatial areas. This presents a unique opportunity to develop novel approaches to model the Critical Zone that can capture fine scale intricate dependencies across the different processes in 3D. The development of interdisciplinary tools that transcend individual disciplines and capture new levels of complexity and emergent properties is at the core of Critical Zone science. Here we introduce an open framework for high-performance computing model (`Dhara') for modeling complex processes in the Critical Zone. The framework is designed to be modular in structure with the aim to create uniform and efficient tools to facilitate and leverage process modeling. It also provides flexibility to maintain, collaborate, and co-develop additional components by the scientific community. We show the essential framework that simulates ecohydrologic dynamics, and surface - sub-surface coupling in 3D using hybrid parallel CPU-GPU. We demonstrate that the open framework in Dhara is feasible for detailed, multi-processes, and large-scale modeling of the Critical Zone, which opens up exciting possibilities. We will also present outcomes from a Modeling Summer Institute led by Intensively Managed Critical Zone Observatory (IMLCZO) with representation from several CZOs and international representatives.

Aerosol properties computed from aircraft-based observations during the ACE- Asia campaign. 2; A case study of lidar ratio closure and aerosol radiative effects

NASA Technical Reports Server (NTRS)

Kuzmanoski, Maja; Box, M. A.; Schmid, B.; Box, G. P.; Wang, J.; Russell, P. B.; Bates, D.; Jonsson, H. H.; Welton, Ellsworth J.; Flagan, R. C.

2005-01-01

For a vertical profile with three distinct layers (marine boundary, pollution and dust), observed during the ACE-Asia campaign, we carried out a comparison between the modeled lidar ratio vertical profile and that obtained from collocated airborne NASA AATS-14 sunphotometer and shipborne Micro-Pulse Lidar (MPL) measurements. Vertically resolved lidar ratio was calculated from two size distribution vertical profiles - one obtained by inversion of sunphotometer-derived extinction spectra, and one measured in-situ - combined with the same refractive index model based on aerosol chemical composition. The aerosol model implies single scattering albedos of 0.78 - 0.81 and 0.93 - 0.96 at 0.523 microns (the wavelength of the lidar measurements), in the pollution and dust layers, respectively. The lidar ratios calculated from the two size distribution profiles have close values in the dust layer; they are however, significantly lower than the lidar ratios derived from combined lidar and sunphotometer measurements, most probably due to the use of a simple nonspherical model with a single particle shape in our calculations. In the pollution layer, the two size distribution profiles yield generally different lidar ratios. The retrieved size distributions yield a lidar ratio which is in better agreement with that derived from lidar/sunphotometer measurements in this layer, with still large differences at certain altitudes (the largest relative difference was 46%). We explain these differences by non-uniqueness of the result of the size distribution retrieval and lack of information on vertical variability of particle refractive index. Radiative transfer calculations for this profile showed significant atmospheric radiative forcing, which occurred mainly in the pollution layer. We demonstrate that if the extinction profile is known then information on the vertical structure of absorption and asymmetry parameter is not significant for estimating forcing at TOA and the surface, while it is of importance for estimating vertical profiles of radiative forcing and heating rates.

Registration of Laser Scanning Point Clouds: A Review.

PubMed

Cheng, Liang; Chen, Song; Liu, Xiaoqiang; Xu, Hao; Wu, Yang; Li, Manchun; Chen, Yanming

2018-05-21

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

Registration of Laser Scanning Point Clouds: A Review

PubMed Central

Cheng, Liang; Chen, Song; Xu, Hao; Wu, Yang; Li, Manchun

2018-01-01

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles. PMID:29883397

Comparison of tropospheric ozone profiles measured by lidars simultaneously over land and water during the 2017 NASA OWLETS campaign

NASA Astrophysics Data System (ADS)

Gronoff, G.; Sullivan, J.; Berkoff, T.; Carrion, W.; Farris, B.

2017-12-01

The NASA Langley Mobile Ozone Lidar (LMOL) and NASA Goddard's lidar (TROPOZ) have routinely measured tropospheric ozone profiles in support of various NASA campaigns and local field studies since 2013 (e.g. DISCOVER-AQ 2014). They are both charter members of the NASA Tropospheric Lidar Network (TOLNet) and were constructed within transportable containers, allowing for observations directly within a variety of complex environments. To gain a better understanding of ozone's interactions close to the surface, both of these instruments have recently designed and optimized near field optical elements for ozone detection. One of the major difficulties for the modeling and satellite communities are the sharp transition regions, both horizontal and vertical, such as the land-water gradients in O3 near coastal/urban regions that are driven by differences in surface deposition, boundary layer height, and cloud coverage.To better understand these gradients, both lidars were deployed in the Hampton Roads / Tidewater region, in Virginia, in July-August 2017, in the context of the OWLETS (Ozone Water Land Environment Transition Study) campaign. The TROPOZ lidar was deployed above land at NASA LaRC, while the LMOL lidar was deployed on the Chesapeake Bay Bridge Tunnel third island, being de-facto an over-water lidar. The distance between the two lidars was approximately 30 km. Strong differences between the two lidars measurements were observed. Some influence of the ship traffic can be seen over water, but does not affect the observations above 300m. Overall, some important discrepancies between the modeling and the lidar observations over water were found. These results shows the importance of making more measurements over water to better constrain pollution models.

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

USGS Publications Warehouse

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.

An Error-Reduction Algorithm to Improve Lidar Turbulence Estimates for Wind Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Newman, Jennifer F.; Clifton, Andrew

2016-08-01

Currently, cup anemometers on meteorological (met) towers are used to measure wind speeds and turbulence intensity to make decisions about wind turbine class and site suitability. However, as modern turbine hub heights increase and wind energy expands to complex and remote sites, it becomes more difficult and costly to install met towers at potential sites. As a result, remote sensing devices (e.g., lidars) are now commonly used by wind farm managers and researchers to estimate the flow field at heights spanned by a turbine. While lidars can accurately estimate mean wind speeds and wind directions, there is still a largemore » amount of uncertainty surrounding the measurement of turbulence with lidars. This uncertainty in lidar turbulence measurements is one of the key roadblocks that must be overcome in order to replace met towers with lidars for wind energy applications. In this talk, a model for reducing errors in lidar turbulence estimates is presented. Techniques for reducing errors from instrument noise, volume averaging, and variance contamination are combined in the model to produce a corrected value of the turbulence intensity (TI), a commonly used parameter in wind energy. In the next step of the model, machine learning techniques are used to further decrease the error in lidar TI estimates.« less

Improving salt marsh digital elevation model accuracy with full-waveform lidar and nonparametric predictive modeling

NASA Astrophysics Data System (ADS)

Rogers, Jeffrey N.; Parrish, Christopher E.; Ward, Larry G.; Burdick, David M.

2018-03-01

Salt marsh vegetation tends to increase vertical uncertainty in light detection and ranging (lidar) derived elevation data, often causing the data to become ineffective for analysis of topographic features governing tidal inundation or vegetation zonation. Previous attempts at improving lidar data collected in salt marsh environments range from simply computing and subtracting the global elevation bias to more complex methods such as computing vegetation-specific, constant correction factors. The vegetation specific corrections can be used along with an existing habitat map to apply separate corrections to different areas within a study site. It is hypothesized here that correcting salt marsh lidar data by applying location-specific, point-by-point corrections, which are computed from lidar waveform-derived features, tidal-datum based elevation, distance from shoreline and other lidar digital elevation model based variables, using nonparametric regression will produce better results. The methods were developed and tested using full-waveform lidar and ground truth for three marshes in Cape Cod, Massachusetts, U.S.A. Five different model algorithms for nonparametric regression were evaluated, with TreeNet's stochastic gradient boosting algorithm consistently producing better regression and classification results. Additionally, models were constructed to predict the vegetative zone (high marsh and low marsh). The predictive modeling methods used in this study estimated ground elevation with a mean bias of 0.00 m and a standard deviation of 0.07 m (0.07 m root mean square error). These methods appear very promising for correction of salt marsh lidar data and, importantly, do not require an existing habitat map, biomass measurements, or image based remote sensing data such as multi/hyperspectral imagery.

Simultaneous lidar and EOS MLS measurements, and modeling, of a rare polar ozone filament event over Mauna Loa Observatory, Hawaii

NASA Astrophysics Data System (ADS)

Leblanc, Thierry; Tripathi, Om Prakash; McDermid, I. Stuart; Froidevaux, Lucien; Livesey, N. J.; Read, W. G.; Waters, J. W.

2006-08-01

In mid-March 2005, a rare lower stratospheric polar vortex filamentation event was observed simultaneously by the JPL lidar at Mauna Loa Observatory, Hawaii, and by the EOS MLS instrument onboard the Aura satellite. The event coincided with the beginning of the spring 2005 final warming. On 16 March, the filament was observed by lidar around 0600 UT between 415 K and 455 K, and by MLS six hours earlier. It was seen on both the lidar and MLS profiles as a layer of enhanced ozone, peaking at 1.7 ppmv in a region where the climatological values are usually around or below 1 ppmv. Ozone profiles measured by lidar and MLS were compared to profiles from the Chemical Transport Model MIMOSA-CHIM. The agreement between lidar, MLS, and the model is excellent considering the difference in the sampling techniques. MLS was also able to identify the filament at another location north of Hawaii.

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

NASA Astrophysics Data System (ADS)

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 < 20-30 % below 3 km and larger above. The variability of the CL51 calibration constant is within ±46 %. For the CS135, the 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 explain the dependency of the ceilometer calibration constant on the environmental temperature hypothesized during INTERACT.

Estimation of the fraction of absorbed photosynthetically active radiation (fPAR) in maize canopies using LiDAR data and hyperspectral imagery.

PubMed

Qin, Haiming; Wang, Cheng; Zhao, Kaiguang; Xi, Xiaohuan

2018-01-01

Accurate estimation of the fraction of absorbed photosynthetically active radiation (fPAR) for maize canopies are important for maize growth monitoring and yield estimation. The goal of this study is to explore the potential of using airborne LiDAR and hyperspectral data to better estimate maize fPAR. This study focuses on estimating maize fPAR from (1) height and coverage metrics derived from airborne LiDAR point cloud data; (2) vegetation indices derived from hyperspectral imagery; and (3) a combination of these metrics. Pearson correlation analyses were conducted to evaluate the relationships among LiDAR metrics, hyperspectral metrics, and field-measured fPAR values. Then, multiple linear regression (MLR) models were developed using these metrics. Results showed that (1) LiDAR height and coverage metrics provided good explanatory power (i.e., R2 = 0.81); (2) hyperspectral vegetation indices provided moderate interpretability (i.e., R2 = 0.50); and (3) the combination of LiDAR metrics and hyperspectral metrics improved the LiDAR model (i.e., R2 = 0.88). These results indicate that LiDAR model seems to offer a reliable method for estimating maize fPAR at a high spatial resolution and it can be used for farmland management. Combining LiDAR and hyperspectral metrics led to better performance of maize fPAR estimation than LiDAR or hyperspectral metrics alone, which means that maize fPAR retrieval can benefit from the complementary nature of LiDAR-detected canopy structure characteristics and hyperspectral-captured vegetation spectral information.

Lidar-based mapping of flood control levees in south Louisiana

USGS Publications Warehouse

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.

General Model Study of Scour at Proposed Pier Extensions - Santa Ana River at BNSF Bridge, Corona, California

DTIC Science & Technology

2017-11-01

model of the bridge piers, other related structures, and the adjacent channel. Data from the model provided a qualitative and quantitative evaluation of...minus post-test lidar survey . ......................... 42 Figure 38. Test 1 (30,000 cfs existing conditions) pre- minus post-test lidar survey ...43 Figure 39. Test 7 (15,000 cfs original proposed conditions) pre- minus post-test lidar survey

Automated retrieval of cloud and aerosol properties from the ARM Raman lidar, part 1: feature detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thorsen, Tyler J.; Fu, Qiang; Newsom, Rob K.

A Feature detection and EXtinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement (ARM) program’s Raman lidar (RL) has been developed. Presented here is part 1 of the FEX algorithm: the detection of features including both clouds and aerosols. The approach of FEX is to use multiple quantities— scattering ratios derived using elastic and nitro-gen channel signals from two fields of view, the scattering ratio derived using only the elastic channel, and the total volume depolarization ratio— to identify features using range-dependent detection thresholds. FEX is designed to be context-sensitive with thresholds determined for each profile by calculating the expectedmore » clear-sky signal and noise. The use of multiple quantities pro-vides complementary depictions of cloud and aerosol locations and allows for consistency checks to improve the accuracy of the feature mask. The depolarization ratio is shown to be particularly effective at detecting optically-thin features containing non-spherical particles such as cirrus clouds. Improve-ments over the existing ARM RL cloud mask are shown. The performance of FEX is validated against a collocated micropulse lidar and observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the ARM Darwin, Australia site. While we focus on a specific lidar system, the FEX framework presented here is suitable for other Raman or high spectral resolution lidars.« less

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

PubMed Central

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 mission) with the goal of improving wildlife modeling for more locations across the globe. PMID:24324655

Where’s the Ground Surface? – Elevation Bias in LIDAR-derived Digital Elevation Models Due to Dense Vegetation in Oregon Tidal Marshes

EPA Science Inventory

Light Detection and Ranging (LIDAR) is a powerful resource for coastal and wetland managers and its use is increasing. Vegetation density and other land cover characteristics influence the accuracy of LIDAR-derived ground surface digital elevation models; however the degree to wh...

Lidar cross-sections of soot fractal aggregates: Assessment of equivalent-sphere models

NASA Astrophysics Data System (ADS)

Ceolato, Romain; Gaudfrin, Florian; Pujol, Olivier; Riviere, Nicolas; Berg, Matthew J.; Sorensen, Christopher M.

2018-06-01

This work assesses the ability of equivalent-sphere models to reproduce the optical properties of soot aggregates relevant for lidar remote sensing, i.e. the backscattering and extinction cross sections. Lidar cross-sections are computed with a spectral discrete dipole approximation model over the visible-to-infrared (400-5000 nm) spectrum and compared with equivalent-sphere approximations. It is shown that the equivalent-sphere approximation, applied to fractal aggregates, has a limited ability to calculate such cross-sections well. The approximation should thus be used with caution for the computation of broadband lidar cross-sections, especially backscattering, at small and intermediate wavelengths (e.g. UV to visible).

Polarization effects on hard target calibration of lidar systems

NASA Technical Reports Server (NTRS)

Kavaya, Michael J.

1987-01-01

The theory of hard target calibration of lidar backscatter data, including laboratory measurements of the pertinent target reflectance parameters, is extended to include the effects of polarization of the transmitted and received laser radiation. The bidirectional reflectance-distribution function model of reflectance is expanded to a 4 x 4 matrix allowing Mueller matrix and Stokes vector calculus to be employed. Target reflectance parameters for calibration of lidar backscatter data are derived for various lidar system polarization configurations from integrating sphere and monostatic reflectometer measurements. It is found that correct modeling of polarization effects is mandatory for accurate calibration of hard target reflectance parameters and, therefore, for accurate calibration of lidar backscatter data.

Measurements of aerosol layer height and vertical profiles by lidar over Jinhua City

NASA Astrophysics Data System (ADS)

Yu, Siqi; Liu, Dong; Wang, Zhenzhu; Xu, Jiwei; Tian, Xiaomin; Wu, Decheng; Xie, Chenbo; Wang, Yingjian

2018-03-01

The vertical distribution of the aerosol layers is depicted by using the lidar data in Jinhua city from 2013 to 2014. The lidar installed in Jinhua is a dual-wavelength Mie polarization Raman lidar. Aerosol layers are searched through gradient method. At the same time, HYSPLIT model is used to tracing the aerosol trajectories. The results show that different heights of aerosol layers have different transportation route. By a case study, the lidar data on December 30, 2013 and May 1, 2014 reveal several vertical aerosol layers. According to the 24-hour backward trajectory of HYSPLIT model, different aerosol layers comes from different places, and this may relate to the winter monsoon in China.

USGS lidar science strategy—Mapping the technology to the science

USGS Publications Warehouse

Stoker, Jason M.; Brock, John C.; Soulard, Christopher E.; Ries, Kernell G.; Sugarbaker, Larry J.; Newton, Wesley E.; Haggerty, Patricia K.; Lee, Kathy E.; Young, John A.

2016-01-11

The U.S. Geological Survey (USGS) utilizes light detection and ranging (lidar) and enabling technologies to support many science research activities. Lidar-derived metrics and products have become a fundamental input to complex hydrologic and hydraulic models, flood inundation models, fault detection and geologic mapping, topographic and land-surface mapping, landslide and volcano hazards mapping and monitoring, forest canopy and habitat characterization, coastal and fluvial erosion mapping, and a host of other research and operational activities. This report documents the types of lidar being used by the USGS, discusses how lidar technology facilitates the achievement of individual mission area goals within the USGS, and offers recommendations and suggested changes in direction in terms of how a mission area could direct work using lidar as it relates to the mission area goals that have already been established.

LIDAR vs. GEODAS land elevation data in hurricane induced inundation modelling

NASA Astrophysics Data System (ADS)

Peng, M.; Pietrafesa, L. J.; Bao, S.; Liu, H.; Xia, M.; Yan, T.

2007-04-01

LIDAR (Light Detection and Ranging) and GEODAS (GEOphysical DAta System) are respectively taken as the land elevation data for a 3-D storm surge and inundation model to investigate the subsequent inundation differences. Hilton Head, South Carolina, and Croatan-Albemarle-Pamlico Estuary System (CAPES), North Carolina, are the two investigated regions. Significant inundation differences with LIDAR versus GEODAS are found in both regions. The modeled inundation area with GEODAS is larger than with LIDAR. For Category 2-3 hypothetical hurricanes, the maximum inundation difference in Hilton Head region is 67%, while the difference in the CAPES is 156%. Generally, vertical precision difference of the two databases is the major reason for the inundation difference. Recently constructed man-made structures, not included in the GEODAS, but included in the LIDAR data sets may be another contributing reason.

High-Resolution Forest Carbon Monitoring and Modeling: Continued Prototype Development and Deployment Across The Tri-state Area (MD, PA, DE), USA

NASA Astrophysics Data System (ADS)

Hurtt, G. C.; Birdsey, R.; Campbell, E.; Dolan, K. A.; Dubayah, R.; Escobar, V. M.; Finley, A. O.; Flanagan, S.; Huang, W.; Johnson, K.; Lister, A.; ONeil-Dunne, J.; Sepulveda Carlo, E.; Zhao, M.

2017-12-01

Local, national and international programs have increasing need for precise and accurate estimates of forest carbon and structure to support greenhouse gas reduction plans, climate initiatives, and other international climate treaty frameworks. In 2010 Congress directed NASA to initiate research towards the development of Carbon Monitoring Systems (CMS). In response, our team has worked to develop a robust, replicable framework to produce maps of high-resolution carbon stocks and future carbon sequestration potential. High-resolution (30m) maps of carbon stocks and uncertainty were produced by linking national 1m-resolution imagery and existing wall-to-wall airborne lidar to spatially explicit in-situ field observations such as the USFS Forest Inventory and Analysis (FIA) network. These same data, characterizing forest extent and vertical structure, were used to drive a prognostic ecosystem model to predict carbon fluxes and carbon sequestration potential at unprecedented spatial resolution and scale (90m), more than 100,000 times the spatial resolution of standard global models. Through project development, the domain of this research has expanded from two counties in MD (2,181 km2), to the entire state (32,133 km2), to the tri-state region of MD, PA, and DE (157,868 km2), covering forests in four major USDA ecological providences (Eastern Broadleaf, Northeastern Mixed, Outer Coastal Plain, and Central Appalachian). Across the region, we estimate 694 Tg C (14 DE, 113 MD, 567 PA) in above ground biomass, and estimate a carbon sequestration potential more than twice that amount. Empirical biomass products enhance existing approaches though high resolution accounting for trees outside traditional forest maps. Modeling products move beyond traditional MRV, and map future afforestation and reforestation potential for carbon at local actionable spatial scales. These products are relevant to multiple stakeholder needs in the region as discussed through the Tri-sate Working Group, and are actively being used to inform the state of MD's Greenhouse Gas Reduction Act. The approach is scalable, and provides a protoype framework for application in other domains and for future spaceborne lidar missions.

Airborne lidar observations of Saharan dust during FENNEC

NASA Astrophysics Data System (ADS)

Marenco, Franco; Garcia-Carreras, Luis; Rosenberg, Phil; McQuaid, Jim

2013-04-01

In June 2011 and June 2012, the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft took part in the Fennec campaign. The main purpose was to quantify and model boundary layer and aerosol processes over the Saharan "heat low" region, the greatest dust region during summer. Although the central Sahara is extremely remote, the meteorology of this region is vital in driving the West African monsoon, and the dry and dusty air layers are closely related to the formation of Atlantic tropical cyclones. In this presentation, we shall characterise these air layers using data collected with the on-board lidar together with dropsondes. The interpretation of lidar signals in this particular geometry represents a challenge (nadir observations of thick layers), but we shall show that a suitable data inversion framework is possible under certain assumptions. The quality of the lidar data will be assessed using in-situ data from the nephelometer and optical particle counters. Deep air layers containing dust have been observed up to altitude of 5-6 km above mean sea level. The analysis of temperature and dew point profiles are used to identify the boundary layer and residual layer tops, and in conjunction with lidar observations this serves to quantify the dust content of both layers. An aerosol-laden residual layer is usually found during the campaign at an altitude of 2-6 km in the morning hours, with little aerosol below. The aerosol in the boundary layer is seen to develop later when solar heating of the surface induces turbulence until in the late afternoon the top of the boundary layer reaches up to ~ 6 km. Clouds embedded in aerosol layers and aerosol-cloud interactions have also been revealed. Dust aerosol has been observed in most cases, but a thin polluted non-dusty layer has been observed during one flight.

The Potential of The Synergy of Sunphotometer and Lidar Data to Validate Vertical Profiles of The Aerosol Mass Concentration Estimated by An Air Quality Model

NASA Astrophysics Data System (ADS)

Siomos, N.; Filioglou, M.; Poupkou, A.; Liora, N.; Dimopoulos, S.; Melas, D.; Chaikovsky, A.; Balis, D. S.

2016-06-01

Vertical profiles of the aerosol mass concentration derived by the Lidar/Radiometer Inversion Code (LIRIC), that uses combined sunphotometer and lidar data, were used in order to validate the aerosol mass concentration profiles estimated by the air quality model CAMx. Lidar and CIMEL measurements performed at the Laboratory of Atmospheric Physics of the Aristotle University of Thessaloniki, Greece (40.5N, 22.9E) from the period 2013-2014 were used in this study.

Grid occupancy estimation for environment perception based on belief functions and PCR6

NASA Astrophysics Data System (ADS)

Moras, Julien; Dezert, Jean; Pannetier, Benjamin

2015-05-01

In this contribution, we propose to improve the grid map occupancy estimation method developed so far based on belief function modeling and the classical Dempster's rule of combination. Grid map offers a useful representation of the perceived world for mobile robotics navigation. It will play a major role for the security (obstacle avoidance) of next generations of terrestrial vehicles, as well as for future autonomous navigation systems. In a grid map, the occupancy of each cell representing a small piece of the surrounding area of the robot must be estimated at first from sensors measurements (typically LIDAR, or camera), and then it must also be classified into different classes in order to get a complete and precise perception of the dynamic environment where the robot moves. So far, the estimation and the grid map updating have been done using fusion techniques based on the probabilistic framework, or on the classical belief function framework thanks to an inverse model of the sensors. Mainly because the latter offers an interesting management of uncertainties when the quality of available information is low, and when the sources of information appear as conflicting. To improve the performances of the grid map estimation, we propose in this paper to replace Dempster's rule of combination by the PCR6 rule (Proportional Conflict Redistribution rule #6) proposed in DSmT (Dezert-Smarandache) Theory. As an illustrating scenario, we consider a platform moving in dynamic area and we compare our new realistic simulation results (based on a LIDAR sensor) with those obtained by the probabilistic and the classical belief-based approaches.

A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments

Treesearch

Jeffrey S. Evans; Andrew T. Hudak

2007-01-01

One prerequisite to the use of light detection and ranging (LiDAR) across disciplines is differentiating ground from nonground returns. The objective was to automatically and objectively classify points within unclassified LiDAR point clouds, with few model parameters and minimal postprocessing. Presented is an automated method for classifying LiDAR returns as ground...

Gravity Waves and Mesospheric Clouds in the Summer Middle Atmosphere: A Comparison of Lidar Measurements and Ray Modeling of Gravity Waves Over Sondrestrom, Greenland

NASA Technical Reports Server (NTRS)

Gerrard, Andrew J.; Kane, Timothy J.; Eckermann, Stephen D.; Thayer, Jeffrey P.

2004-01-01

We conducted gravity wave ray-tracing experiments within an atmospheric region centered near the ARCLITE lidar system at Sondrestrom, Greenland (67N, 310 deg E), in efforts to understand lidar observations of both upper stratospheric gravity wave activity and mesospheric clouds during August 1996 and the summer of 2001. The ray model was used to trace gravity waves through realistic three-dimensional daily-varying background atmospheres in the region, based on forecasts and analyses in the troposphere and stratosphere and climatologies higher up. Reverse ray tracing based on upper stratospheric lidar observations at Sondrestrom was also used to try to objectively identify wave source regions in the troposphere. A source spectrum specified by reverse ray tracing experiments in early August 1996 (when atmospheric flow patterns produced enhanced transmission of waves into the upper stratosphere) yielded model results throughout the remainder of August 1996 that agreed best with the lidar observations. The model also simulated increased vertical group propagation of waves between 40 km and 80 km due to intensifying mean easterlies, which allowed many of the gravity waves observed at 40 km over Sondrestrom to propagate quasi-vertically from 40-80 km and then interact with any mesospheric clouds at 80 km near Sondrestrom, supporting earlier experimentally-inferred correlations between upper stratospheric gravity wave activity and mesospheric cloud backscatter from Sondrestrom lidar observations. A pilot experiment of real-time runs with the model in 2001 using weather forecast data as a low-level background produced less agreement with lidar observations. We believe this is due to limitations in our specified tropospheric source spectrum, the use of climatological winds and temperatures in the upper stratosphere and mesosphere, and missing lidar data from important time periods.

1.5 μm lidar anemometer for true air speed, angle of sideslip, and angle of attack measurements on-board Piaggio P180 aircraft

NASA Astrophysics Data System (ADS)

Augere, B.; Besson, B.; Fleury, D.; Goular, D.; Planchat, C.; Valla, M.

2016-05-01

Lidar (light detection and ranging) is a well-established measurement method for the prediction of atmospheric motions through velocity measurements. Recent advances in 1.5 μm Lidars show that the technology is mature, offers great ease of use, and is reliable and compact. A 1.5 μm airborne Lidar appears to be a good candidate for airborne in-flight measurement systems. It allows measurements remotely, outside aircraft aerodynamic disturbance, and absolute air speed (no need for calibration) with great precision in all aircraft flight domains. In the framework of the EU AIM2 project, the ONERA task has consisted of developing and testing a 1.5 μm anemometer sensor for in-flight airspeed measurements. The objective of this work is to demonstrate that the 1.5 μm Lidar sensor can increase the quality of the data acquisition procedure for aircraft flight test certification. This article presents the 1.5 μm anemometer sensor dedicated to in-flight airspeed measurements and describes the flight tests performed successfully on-board the Piaggio P180 aircraft. Lidar air data have been graphically compared to the air data provided by the aircraft flight test instrumentation (FTI) in the reference frame of the Lidar sensor head. Very good agreement of true air speed (TAS) by a fraction of ms-1, angle of sideslip (AOS), and angle of attack (AOA) by a fraction of degree were observed.

Finite element modal analysis of a vehicle-borne lidar cabin

NASA Astrophysics Data System (ADS)

Chen, Yafeng; Liu, Qiuwu; Wang, Jie; Hu, Shunxing; Huang, Jian

2018-02-01

Using SolidWorks software, the finite element modal analysis of a vehicle-borne pollution monitoring lidar cabin is carried out. The lidar cabin for the integrated lidar can ensure that the lidar system has good maneuverability and can effectively monitor the emission of air pollution. Since lidar is an integrated system of optics, mechanism, electricity and calculation, the performance of the cabin is directly related to the safety of the equipment and the lidar to work properly. Firstly, the cubic structure is modeled to simulate the cubic structure. Then, the model of the cabin model is analyzed by using the simulation plug-in, and the first 10 modes and natural frequencies are analyzed and recorded. The calculation results show that the cabin is dominated by bending vibration, and the amplitude area is concentrated in the opening of some windows and doors on each board. Therefore, we should increase the number of reinforcement bars or the strength of the skeleton in the vicinity of the door and window. At the same time, to avoid the resonance and ensure the precision of the optical elements and the electrical components and avoid structural damage of the cabin, the incentive frequency should be keep away from the natural frequency of the cabin. The vehicle-borne lidar system has been put into operation, and the analysis results have direct meaning to the transport of the cabin and the normal work.

Using satellite and airborne LiDAR to model woodpecker habitat occupancy at the landscape scale

Treesearch

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

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...

High-Fidelity Flash Lidar Model Development

NASA Technical Reports Server (NTRS)

Hines, Glenn D.; Pierrottet, Diego F.; Amzajerdian, Farzin

2014-01-01

NASA's Autonomous Landing and Hazard Avoidance Technologies (ALHAT) project is currently developing the critical technologies to safely and precisely navigate and land crew, cargo and robotic spacecraft vehicles on and around planetary bodies. One key element of this project is a high-fidelity Flash Lidar sensor that can generate three-dimensional (3-D) images of the planetary surface. These images are processed with hazard detection and avoidance and hazard relative navigation algorithms, and then are subsequently used by the Guidance, Navigation and Control subsystem to generate an optimal navigation solution. A complex, high-fidelity model of the Flash Lidar was developed in order to evaluate the performance of the sensor and its interaction with the interfacing ALHAT components on vehicles with different configurations and under different flight trajectories. The model contains a parameterized, general approach to Flash Lidar detection and reflects physical attributes such as range and electronic noise sources, and laser pulse temporal and spatial profiles. It also provides the realistic interaction of the laser pulse with terrain features that include varying albedo, boulders, craters slopes and shadows. This paper gives a description of the Flash Lidar model and presents results from the Lidar operating under different scenarios.

SRS-lidar for 13C/12C isotops measurements environmental and food

NASA Astrophysics Data System (ADS)

Grishkanich, Alexsandr; Chubchenko, Yan; Elizarov, Valentin; Zhevlakov, Aleksandr; Konopelko, Leonid

2017-09-01

The possibilities of the Raman method of radiocarbon measurements in the field of gas analysis are investigated. With the help of veneer gas mixtures of carbon monoxide, carbon dioxide-12, carbon dioxide-13, methane, formaldehyde, the micrometric characteristics of Raman lidars were found, which in most cases coincided with the claimed ones. When gas mixtures are supplied, the diluent gas in which differs from air, the broadening of the spectral lines associated with the interactions between the particles, the results, to significant errors in the measured concentration. These effects, which negate the advantages of the measurement method, are investigated in the framework of this paper. The results of determining the coefficients for correcting the readings of gas analyzers with the achievement of inaccuracies from various diluent gases, as well as the data of the prototype Raman lidar.

Towards Camera-LIDAR Fusion-Based Terrain Modelling for Planetary Surfaces: Review and Analysis

PubMed Central

Shaukat, Affan; Blacker, Peter C.; Spiteri, Conrad; Gao, Yang

2016-01-01

In recent decades, terrain modelling and reconstruction techniques have increased research interest in precise short and long distance autonomous navigation, localisation and mapping within field robotics. One of the most challenging applications is in relation to autonomous planetary exploration using mobile robots. Rovers deployed to explore extraterrestrial surfaces are required to perceive and model the environment with little or no intervention from the ground station. Up to date, stereopsis represents the state-of-the art method and can achieve short-distance planetary surface modelling. However, future space missions will require scene reconstruction at greater distance, fidelity and feature complexity, potentially using other sensors like Light Detection And Ranging (LIDAR). LIDAR has been extensively exploited for target detection, identification, and depth estimation in terrestrial robotics, but is still under development to become a viable technology for space robotics. This paper will first review current methods for scene reconstruction and terrain modelling using cameras in planetary robotics and LIDARs in terrestrial robotics; then we will propose camera-LIDAR fusion as a feasible technique to overcome the limitations of either of these individual sensors for planetary exploration. A comprehensive analysis will be presented to demonstrate the advantages of camera-LIDAR fusion in terms of range, fidelity, accuracy and computation. PMID:27879625

Forest structures retrieval from LiDAR onboard ULA

NASA Astrophysics Data System (ADS)

Shang, Xiaoxia; Chazette, Patrick; Totems, Julien; Marnas, Fabien; Sanak, Joseph

2013-04-01

Following the United Nations Framework Convention on Climate Change, the assessment of forest carbon stock is one of the main elements for a better understanding of the carbon cycle and its evolution following the climate change. The forests sequester 80% of the continental biospheric carbon and this efficiency is a function of the tree species and the tree health. The airborne backscatter LiDAR onboard the ultra light aircraft (ULA) can provide the key information on the forest vertical structures and evolution in the time. The most important structural parameter is the tree top height, which is directly linked to the above-ground biomass using non-linear relationships. In order to test the LiDAR capability for retrieving the tree top height, the LiDAR ULICE (Ultraviolet LIdar for Canopy Experiment) has been used over different forest types, from coniferous (maritime pins) to deciduous (oaks, hornbeams ...) trees. ULICE works at the wavelength of 355 nm with a sampling along the line-of-sight between 15 and 75 cm. According to the LiDAR signal to noise ratio (SNR), two different algorithms have been used in our study. The first algorithm is a threshold method directly based on the comparison between the LiDAR signal and the noise distributions, while the second one used a low pass filter by fitting a Gaussian curve family. In this paper, we will present these two algorithms and their evolution as a function of the SNR. The main error sources will be also discussed and assessed for each algorithm. The results show that these algorithms have great potential for ground-segment of future space borne LiDAR missions dedicated to the forest survey at the global scale. Acknowledgements: the canopy LiDAR system ULICE has been developed by CEA (Commissariat à l'Energie Atomique). It has been deployed with the support of CNES (Centre National d'Etude Spariales) and ANR (Agence Nationale de la Recherche). We acknowledge the ULA pilots Franck Toussaint for logistical help during the ULA campaign.

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

PubMed

Cui, Tingting; Ji, Shunping; Shan, Jie; Gong, Jianya; Liu, Kejian

2016-12-31

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.

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

PubMed Central

Cui, Tingting; Ji, Shunping; Shan, Jie; Gong, Jianya; Liu, Kejian

2016-01-01

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. PMID:28042855

Lidar observations of the planetary boundary layer

NASA Technical Reports Server (NTRS)

Melfi, S. H.; Spinhirne, J. D.; Palm, S. P.

1985-01-01

The application of an airborne downward-looking lidar to the study of organized cellular convection in the planetary boundary layer (PBL) over the ocean is described. The lidar consisted of a frequency doubled Nd-YAG 530 mm-wavelength laser whose axis was aligned colinearly with the optical axis of an all-reflecting 40 mm-diameter Newtonian telescope. The airborne lidar provided a unique observation of both microscale and mesoscale variations of the PBL top. The lidar data, presented as constant backscatter isopleth soundings, provide a visual indication of the presence of vertically organized convection cells. Comparisons of the lidar-derived PBL structure with both a conceptual model of the PBL and laboratory simulations of Deardorf et al. (1980) of a developing convective PBL showed that the observations are consistent with a model of mixing in the PBL, which involves a field of organized updrafts separated by downdrafts.

Airborne Lidar-Based Estimates of Tropical Forest Structure in Complex Terrain: Opportunities and Trade-Offs for REDD+

NASA Technical Reports Server (NTRS)

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

2015-01-01

Background: 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. Results: 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 (approx. 20 returns/sq m) data was highly accurate (mean signed error of 0.19 +/-0.97 m), while those derived from reduced-density datasets (8/sq m, 4/sq m, 2/sq m and 1/sq m) 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/sq m, the bias in height estimates translated into errors of 80-125 Mg/ha in predicted aboveground biomass. Conclusions: 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.

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

PubMed

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.

Improving Measurement of Forest Structural Parameters by Co-Registering of High Resolution Aerial Imagery and Low Density LiDAR Data

PubMed Central

Huang, Huabing; Gong, Peng; Cheng, Xiao; Clinton, Nick; Li, Zengyuan

2009-01-01

Forest structural parameters, such as tree height and crown width, are indispensable for evaluating forest biomass or forest volume. LiDAR is a revolutionary technology for measurement of forest structural parameters, however, the accuracy of crown width extraction is not satisfactory when using a low density LiDAR, especially in high canopy cover forest. We used high resolution aerial imagery with a low density LiDAR system to overcome this shortcoming. A morphological filtering was used to generate a DEM (Digital Elevation Model) and a CHM (Canopy Height Model) from LiDAR data. The LiDAR camera image is matched to the aerial image with an automated keypoints search algorithm. As a result, a high registration accuracy of 0.5 pixels was obtained. A local maximum filter, watershed segmentation, and object-oriented image segmentation are used to obtain tree height and crown width. Results indicate that the camera data collected by the integrated LiDAR system plays an important role in registration with aerial imagery. The synthesis with aerial imagery increases the accuracy of forest structural parameter extraction when compared to only using the low density LiDAR data. PMID:22573971

Application of a multiple scattering model to estimate optical depth, lidar ratio and ice crystal effective radius of cirrus clouds observed with lidar.

NASA Astrophysics Data System (ADS)

Gouveia, Diego; Baars, Holger; Seifert, Patric; Wandinger, Ulla; Barbosa, Henrique; Barja, Boris; Artaxo, Paulo; Lopes, Fabio; Landulfo, Eduardo; Ansmann, Albert

2018-04-01

Lidar measurements of cirrus clouds are highly influenced by multiple scattering (MS). We therefore developed an iterative approach to correct elastic backscatter lidar signals for multiple scattering to obtain best estimates of single-scattering cloud optical depth and lidar ratio as well as of the ice crystal effective radius. The approach is based on the exploration of the effect of MS on the molecular backscatter signal returned from above cloud top.

Airborne lidar sensing of massive stony coral colonies on patch reefs in the northern Florida reef tract

USGS Publications Warehouse

Brock, J.C.; Wright, C.W.; Kuffner, I.B.; Hernandez, R.; Thompson, P.

2006-01-01

In this study we examined the ability of the NASA Experimental Advanced Airborne Research Lidar (EAARL) to discriminate cluster zones of massive stony coral colonies on northern Florida reef tract (NFRT) patch reefs based on their topographic complexity (rugosity). Spatially dense EAARL laser submarine topographic soundings acquired in August 2002 were used to create a 1-m resolution digital rugosity map for adjacent NFRT study areas characterized by patch reefs (Region A) and diverse substratums (Region B). In both regions, sites with lidar-sensed rugosities above 1.2 were imaged by an along-track underwater videography system that incorporated the acquisition of instantaneous GPS positions. Subsequent manual interpretation of videotape segments was performed to identify substratum types that caused elevated lidar-sensed rugosity. Our study determined that massive coral colony formation, modified by subsequent physical and biological processes that breakdown patch reef framework, was the primary source of topographic complexity sensed by the EAARL in the NFRT. Sites recognized by lidar scanning to be topographically complex preferentially occurred around the margins of patch reefs, constituted a minor fraction of the reef system, and usually reflected the presence of massive coral colonies in cluster zones, or their derivatives created by mortality, bioerosion, and physical breakdown.

Urban boundary-layer height determination from lidar measurements over the paris area.

PubMed

Menut, L; Flamant, C; Pelon, J; Flamant, P H

1999-02-20

The Paris area is strongly urbanized and is exposed to atmospheric pollution events. To understand the chemical and physical processes that are taking place in this area it is necessary to describe correctly the atmospheric boundary-layer (ABL) dynamics and the ABL height evolution. During the winter of 1994-1995, within the framework of the Etude de la Couche Limite Atmosphérique en Agglomération Parisienne (ECLAP) experiment, the vertical structure of the ABL over Paris and its immediate suburbs was extensively documented by means of lidar measurements. We present methods suited for precise determination of the ABL structure's temporal evolution in a dynamic environment as complex as the Paris area. The purpose is to identify a method that can be used on a large set of lidar data. We compare commonly used methods that permit ABL height retrievals from backscatter lidar signals under different meteorological conditions. Incorrect tracking of the ABL depth's diurnal cycle caused by limitations in the methods is analyzed. The study uses four days of the ECLAP experiment characterized by different meteorological and synoptic conditions.

EARLINET: potential operationality of a research network

NASA Astrophysics Data System (ADS)

Sicard, M.; D'Amico, G.; Comerón, A.; Mona, L.; Alados-Arboledas, L.; Amodeo, A.; Baars, H.; Belegante, L.; Binietoglou, I.; Bravo-Aranda, J. A.; Fernández, A. J.; Fréville, P.; García-Vizcaíno, D.; Giunta, A.; Granados-Muñoz, M. J.; Guerrero-Rascado, J. L.; Hadjimitsis, D.; Haefele, A.; Hervo, M.; Iarlori, M.; Kokkalis, P.; Lange, D.; Mamouri, R. E.; Mattis, I.; Molero, F.; Montoux, N.; Muñoz, A.; Muñoz Porcar, C.; Navas-Guzmán, F.; Nicolae, D.; Nisantzi, A.; Papagiannopoulos, N.; Papayannis, A.; Pereira, S.; Preißler, J.; Pujadas, M.; Rizi, V.; Rocadenbosch, F.; Sellegri, K.; Simeonov, V.; Tsaknakis, G.; Wagner, F.; Pappalardo, G.

2015-07-01

In the framework of ACTRIS summer 2012 measurement campaign (8 June-17 July 2012), EARLINET organized and performed a controlled exercise of feasibility to demonstrate its potential to perform operational, coordinated measurements and deliver products in near-real time. Eleven lidar stations participated to the exercise which started on 9 July 2012 at 06:00 UT and ended 72 h later on 12 July at 06:00 UT. For the first time the Single-Calculus Chain (SCC), the common calculus chain developed within EARLINET for the automatic evaluation of lidar data from raw signals up to the final products, was used. All stations sent in real time measurements of 1 h of duration to the SCC server in a predefined netcdf file format. The pre-processing of the data was performed in real time by the SCC while the optical processing was performed in near-real time after the exercise ended. 98 and 84 % of the files sent to SCC were successfully pre-processed and processed, respectively. Those percentages are quite large taking into account that no cloud screening was performed on lidar data. The paper shows time series of continuous and homogeneously obtained products retrieved at different levels of the SCC: range-square corrected signals (pre-processing) and daytime backscatter and nighttime extinction coefficient profiles (optical processing), as well as combined plots of all direct and derived optical products. The derived products include backscatter- and extinction-related Ångström exponents, lidar ratios and color ratios. The combined plots reveal extremely valuable for aerosol classification. The efforts made to define the measurements protocol and to configure properly the SCC pave the way for applying this protocol for specific applications such as the monitoring of special events, atmospheric modelling, climate research and calibration/validation activities of spaceborne observations.

Three atmospheric dispersion experiments involving oil fog plumes measured by lidar

NASA Technical Reports Server (NTRS)

Eberhard, W. L.; Mcnice, G. T.; Troxel, S. W.

1986-01-01

The Wave Propagation Lab. participated with the U.S. Environmental Protection Agency in a series of experiments with the goal of developing and validating dispersion models that perform substantially better that models currently available. The lidar systems deployed and the data processing procedures used in these experiments are briefly described. Highlights are presented of conclusions drawn thus far from the lidar data.

Pulsed Lidar Performance/Technical Maturity Assessment

NASA Technical Reports Server (NTRS)

Gimmestad, Gary G.; West, Leanne L.; Wood, Jack W.; Frehlich, Rod

2004-01-01

This report describes the results of investigations performed by the Georgia Tech Research Institute (GTRI) and the National Center for Atmospheric Research (NCAR) under a task entitled 'Pulsed Lidar Performance/Technical Maturity Assessment' funded by the Crew Systems Branch of the Airborne Systems Competency at the NASA Langley Research Center. The investigations included two tasks, 1.1(a) and 1.1(b). The Tasks discussed in this report are in support of the NASA Virtual Airspace Modeling and Simulation (VAMS) program and are designed to evaluate a pulsed lidar that will be required for active wake vortex avoidance solutions. The Coherent Technologies, Inc. (CTI) WindTracer LIDAR is an eye-safe, 2-micron, coherent, pulsed Doppler lidar with wake tracking capability. The actual performance of the WindTracer system was to be quantified. In addition, the sensor performance has been assessed and modeled, and the models have been included in simulation efforts. The WindTracer LIDAR was purchased by the Federal Aviation Administration (FAA) for use in near-term field data collection efforts as part of a joint NASA/FAA wake vortex research program. In the joint research program, a minimum common wake and weather data collection platform will be defined. NASA Langley will use the field data to support wake model development and operational concept investigation in support of the VAMS project, where the ultimate goal is to improve airport capacity and safety. Task 1.1(a), performed by NCAR in Boulder, Colorado to analyze the lidar system to determine its performance and capabilities based on results from simulated lidar data with analytic wake vortex models provided by NASA, which were then compared to the vendor's claims for the operational specifications of the lidar. Task 1.1(a) is described in Section 3, including the vortex model, lidar parameters and simulations, and results for both detection and tracking of wake vortices generated by Boeing 737s and 747s. Task 1.1(b) was performed by GTRI in Atlanta, Georgia and is described in Section 4. Task 1.1(b) includes a description of the St. Louis Airport (STL) field test being conducted by the Volpe National Transportation Systems Center, and it also addresses the development of a test plan to validate simulation studies conducted as part of Task 1.1(a). Section 4.2 provides a description of the Volpe STL field tests, and Section 4.3 describes 3 possible ways to validate the WindTracer lidar simulations performed in Task 1.1(a).

Simulation Studies of Forest Structure using 3D Lidar and Radar Models

NASA Technical Reports Server (NTRS)

Sun, Guoqing; Ranson, K. Jon; Koetz, Benjamin; Liu, Dawei

2007-01-01

The use of lidars and radars to measure forest structure attributes such as height and biomass are being considered for future Earth Observation missions. Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield information about the vertical profile of the canopy. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and is useful for estimating biomass. Interferometric SAR (InSAR) has been shown to yield forest canopy height information. For example, the height of scattering phase retrieved from InSAR data is considered to be correlated with the three height and the spatial structure of the forest stand. There is much interest in exploiting these technologies separately and together to get important information for carbon cycle and ecosystem science. More detailed information of the electromagnetic radiation interactions within forest canopies is needed. And backscattering models can be of much utility here. As part of a NASA funded project to explore data fusion, a three-dimensional (3D) coherent radar backscattering model and a 3D lidar backscatter models were used to investigate the use of large footprint lidar, SAR and InSAR for characterizing realistic forest scenes. For this paper, we use stem maps and other forest measurements to develop a realistic spatial structure of a spruce-hemlock forest canopy found in Maine, USA. The radar and lidar models used measurements of the 3D forest scene as input and simulated the coherent radar backscattering signature and 1064nm energy backscatter, respectively. The relationships of backscatter derived forest structure were compared with field measurements. In addition, we also had detailed airborne lidar (Laser Imaging Vegetation Sensor, LVIS) data available over the stem map sites that was used to study the accuracies of tree height derived from modeled SAR backscatter and the scattering phase center retrieved from the simulated InSAR data will be compared with the height indices, or other structure parameters derived from the lidar data. These results will address the possible synergies between lidar and radar in data in terms of forest structural information.

Quantification of LiDAR measurement uncertainty through propagation of errors due to sensor sub-systems and terrain morphology

NASA Astrophysics Data System (ADS)

Goulden, T.; Hopkinson, C.

2013-12-01

The quantification of LiDAR sensor measurement uncertainty is important for evaluating the quality of derived DEM products, compiling risk assessment of management decisions based from LiDAR information, and enhancing LiDAR mission planning capabilities. Current quality assurance estimates of LiDAR measurement uncertainty are limited to post-survey empirical assessments or vendor estimates from commercial literature. Empirical evidence can provide valuable information for the performance of the sensor in validated areas; however, it cannot characterize the spatial distribution of measurement uncertainty throughout the extensive coverage of typical LiDAR surveys. Vendor advertised error estimates are often restricted to strict and optimal survey conditions, resulting in idealized values. Numerical modeling of individual pulse uncertainty provides an alternative method for estimating LiDAR measurement uncertainty. LiDAR measurement uncertainty is theoretically assumed to fall into three distinct categories, 1) sensor sub-system errors, 2) terrain influences, and 3) vegetative influences. This research details the procedures for numerical modeling of measurement uncertainty from the sensor sub-system (GPS, IMU, laser scanner, laser ranger) and terrain influences. Results show that errors tend to increase as the laser scan angle, altitude or laser beam incidence angle increase. An experimental survey over a flat and paved runway site, performed with an Optech ALTM 3100 sensor, showed an increase in modeled vertical errors of 5 cm, at a nadir scan orientation, to 8 cm at scan edges; for an aircraft altitude of 1200 m and half scan angle of 15°. In a survey with the same sensor, at a highly sloped glacial basin site absent of vegetation, modeled vertical errors reached over 2 m. Validation of error models within the glacial environment, over three separate flight lines, respectively showed 100%, 85%, and 75% of elevation residuals fell below error predictions. Future work in LiDAR sensor measurement uncertainty must focus on the development of vegetative error models to create more robust error prediction algorithms. To achieve this objective, comprehensive empirical exploratory analysis is recommended to relate vegetative parameters to observed errors.

[Estimating individual tree aboveground biomass of the mid-subtropical forest using airborne LiDAR technology].

PubMed

Liu, Feng; Tan, Chang; Lei, Pi-Feng

2014-11-01

Taking Wugang forest farm in Xuefeng Mountain as the research object, using the airborne light detection and ranging (LiDAR) data under leaf-on condition and field data of concomitant plots, this paper assessed the ability of using LiDAR technology to estimate aboveground biomass of the mid-subtropical forest. A semi-automated individual tree LiDAR cloud point segmentation was obtained by using condition random fields and optimization methods. Spatial structure, waveform characteristics and topography were calculated as LiDAR metrics from the segmented objects. Then statistical models between aboveground biomass from field data and these LiDAR metrics were built. The individual tree recognition rates were 93%, 86% and 60% for coniferous, broadleaf and mixed forests, respectively. The adjusted coefficients of determination (R(2)adj) and the root mean squared errors (RMSE) for the three types of forest were 0.83, 0.81 and 0.74, and 28.22, 29.79 and 32.31 t · hm(-2), respectively. The estimation capability of model based on canopy geometric volume, tree percentile height, slope and waveform characteristics was much better than that of traditional regression model based on tree height. Therefore, LiDAR metrics from individual tree could facilitate better performance in biomass estimation.

Processing LiDAR Data to Predict Natural Hazards

NASA Technical Reports Server (NTRS)

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.

Electro-optical characterization system develped for ATLIDCAS AIV: flat field and collimated beam injections

NASA Astrophysics Data System (ADS)

Ramos, G.; Laguna, H.; Torres, J.; Belenguer, T.

2017-11-01

In the framework of the ESA EarthCare Mission, an atmospheric LIDAR (ATLID) was included as a payload. CAS is the co-alignment system of such a LIDAR instrument, the system responsible of guaranteeing the proper alignment of the projected laser beam and the reflected light collected. Within CAS, in which a consortium leaded by ASTRIUM France is working in, as well as CRISA (electronics) and LIDAX (mechanical engineering), INTA is in charge of the development of the instrumentation to be used on ground (on ground support equipments, OGSEs) needed for the proper electro-optical characterization.

No Substitute for Going to the Field: Correcting Lidar DEMs in Salt Marshes

NASA Astrophysics Data System (ADS)

Renken, K.; Morris, J. T.; Lynch, J.; Bayley, H.; Neil, A.; Rasmussen, S.; Tyrrell, M.; Tanis, M.

2016-12-01

Models that forecast the response of salt marshes to current and future trends in sea level rise increasingly are used to guide management of these vulnerable ecosystems. Lidar-derived DEMs serve as the foundation for modeling landform change. However, caution is advised when using these DEMs as the starting point for models of salt marsh evolution. While broad vegetation class (i.e., young forest, old forest, grasslands, desert, etc.) has proven to be a significant predictor of vertical displacement error in terrestrial environments, differentiating error among different species or community types within the same ecosystem has received less attention. Salt marshes are dominated by monocultures of grass species and thus are an ideal environment to examine the within-species effect on lidar DEM error. We analyzed error of lidar DEMs using elevations from real-time kinematic (RTK) surveys in saltmarshes in multiple national parks and wildlife refuge areas from the mouth of the Chesapeake Bay to Massachusetts. Error of the lidar DEMs was sometimes large, on the order of 0.25 m, and varied significantly between sites because vegetation cover varies seasonally and lidar data was not always collected in the same season for each park. Vegetation cover and composition were used to explain differences between RTK elevations and lidar DEMs. This research underscores the importance of collecting RTK elevation data and vegetation cover data coincident with lidar data to produce correction factors specific to individual salt marsh sites.

Modeling Diurnal and Seasonal 3D Light Profiles in Amazon Forests

NASA Astrophysics Data System (ADS)

Morton, D. C.; Rubio, J.; Gastellu-Etchegorry, J.; Cook, B. D.; Hunter, M. O.; Yin, T.; Nagol, J. R.; Keller, M. M.

2013-12-01

The complex horizontal and vertical structure in tropical forests generates a diversity of light environments for canopy and understory trees. These 3D light profiles are dynamic on diurnal and seasonal time scales based on changes in solar illumination and the fraction of diffuse light. Understanding this variability is critical for improving ecosystem models and interpreting optical and LiDAR remote sensing data from tropical forests. Here, we initialized the Discrete Anisotropic Radiative Transfer (DART) model using dense airborne LiDAR data (>20 returns m2) from three forest sites in the central and eastern Amazon. Forest scenes derived from airborne LiDAR data were tested using modeled and observed large-footprint LiDAR data from the ICESat-GLAS sensor. Next, diurnal and seasonal profiles of photosynthetically active radiation (PAR) for each forest site were simulated under clear sky and cloudy conditions using DART. Incident PAR was summarized for canopy, understory, and ground levels. Our study illustrates the importance of realistic canopy models for accurate representation of LiDAR and optical radiative transfer. In particular, canopy rugosity and ground topography information from airborne LiDAR data provided critical 3D information that cannot be recreated using stem maps and allometric relationships for crown dimensions. The spatial arrangement of canopy trees altered PAR availability, even for dominant individuals, compared to downwelling measurements from nearby eddy flux towers. Pseudo-realistic branch and leaf architecture was also essential for recreating multiple scattering within canopies at near-infrared wavelengths commonly used for LiDAR remote sensing and quantifying PAR attenuation from shading within and between canopies. These findings point to the need for more spatial information on forest structure to improve the representation of light availability in models of tropical forest productivity.

Lidar configurations for wind turbine control

NASA Astrophysics Data System (ADS)

Mirzaei, Mahmood; Mann, Jakob

2016-09-01

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 configuration of an inexpensive lidar in terms of number of measurement points, the measurement distance and the opening angle is the subject of this study. In order to solve the problem, a lidar model is developed and used to measure wind speed in a turbulence box. The effective wind speed measured 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.

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

USGS Publications Warehouse

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.

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

NASA Astrophysics Data System (ADS)

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.

Optimizing variable radius plot size and LiDAR resolution to model standing volume in conifer forests

Treesearch

Ram Kumar Deo; Robert E. Froese; Michael J. Falkowski; Andrew T. Hudak

2016-01-01

The conventional approach to LiDAR-based forest inventory modeling depends on field sample data from fixed-radius plots (FRP). Because FRP sampling is cost intensive, combining variable-radius plot (VRP) sampling and LiDAR data has the potential to improve inventory efficiency. The overarching goal of this study was to evaluate the integration of LiDAR and VRP data....

Estimate of rain evaporation rates from dual-wavelength lidar measurements: comparison against a model analytical solution

NASA Astrophysics Data System (ADS)

Lolli, Simone; Di Girolamo, Paolo; Demoz, Belay; Li, Xiaowen; Welton, Ellsworth J.

2018-04-01

Rain evaporation significantly contributes to moisture and heat cloud budgets. In this paper, we illustrate an approach to estimate the median volume raindrop diameter and the rain evaporation rate profiles from dual-wavelength lidar measurements. These observational results are compared with those provided by a model analytical solution. We made use of measurements from the multi-wavelength Raman lidar BASIL.

Fish habitat characterization and quantification using lidar and conventional topographic information in river survey

NASA Astrophysics Data System (ADS)

Marchamalo, Miguel; Bejarano, María-Dolores; García de Jalón, Diego; Martínez Marín, Rubén

2007-10-01

This study presents the application of LIDAR data to the evaluation and quantification of fluvial habitat in river systems, coupling remote sensing techniques with hydrological modeling and ecohydraulics. Fish habitat studies depend on the quality and continuity of the input topographic data. Conventional fish habitat studies are limited by the feasibility of field survey in time and budget. This limitation results in differences between the level of river management and the level of models. In order to facilitate upscaling processes from modeling to management units, meso-scale methods were developed (Maddock & Bird, 1996; Parasiewicz, 2001). LIDAR data of regulated River Cinca (Ebro Basin, Spain) were acquired in the low flow season, maximizing the recorded instream area. DTM meshes obtained from LIDAR were used as the input for hydraulic simulation for a range of flows using GUAD2D software. Velocity and depth outputs were combined with gradient data to produce maps reflecting the availability of each mesohabitat unit type for each modeled flow. Fish habitat was then estimated and quantified according to the preferences of main target species as brown trout (Salmo trutta). LIDAR data combined with hydraulic modeling allowed the analysis of fluvial habitat in long fluvial segments which would be time-consuming with traditional survey. LIDAR habitat assessment at mesoscale level avoids the problems of time efficiency and upscaling and is a recommended approach for large river basin management.

Volumetric visualization of multiple-return LIDAR data: Using voxels

USGS Publications Warehouse

Stoker, Jason M.

2009-01-01

Elevation data are an important component in the visualization and analysis of geographic information. The creation and display of 3D models representing bare earth, vegetation, and surface structures have become a major focus of light detection and ranging (lidar) remote sensing research in the past few years. Lidar is an active sensor that records the distance, or range, of a laser usually fi red from an airplane, helicopter, or satellite. By converting the millions of 3D lidar returns from a system into bare ground, vegetation, or structural elevation information, extremely accurate, high-resolution elevation models can be derived and produced to visualize and quantify scenes in three dimensions. These data can be used to produce high-resolution bare-earth digital elevation models; quantitative estimates of vegetative features such as canopy height, canopy closure, and biomass; and models of urban areas such as building footprints and 3D city models.

Spatially explicit modelling of forest structure and function using airborne lidar and hyperspectral remote sensing data combined with micrometeorological measurements

NASA Astrophysics Data System (ADS)

Thomas, Valerie Anne

This research models canopy-scale photosynthesis at the Groundhog River Flux Site through the integration of high-resolution airborne remote sensing data and micrometeorological measurements collected from a flux tower. Light detection and ranging (lidar) data are analysed to derive models of tree structure, including: canopy height, basal area, crown closure, and average aboveground biomass. Lidar and hyperspectral remote sensing data are used to model canopy chlorophyll (Chl) and carotenoid concentrations (known to be good indicators of photosynthesis). The integration of lidar and hyperspectral data is applied to derive spatially explicit models of the fraction of photosynthetically active radiation (fPAR) absorbed by the canopy as well as a species classification for the site. These products are integrated with flux tower meteorological measurements (i.e., air temperature and global solar radiation) collected on a continuous basis over 2004 to apply the C-Fix model of carbon exchange to the site. Results demonstrate that high resolution lidar and lidar-hyperspectral integration techniques perform well in the boreal mixedwood environment. Lidar models are well correlated with forest structure, despite the complexities introduced in the mixedwood case (e.g., r2=0.84, 0.89, 0.60, and 0.91, for mean dominant height, basal area, crown closure, and average aboveground biomass). Strong relationships are also shown for canopy scale chlorophyll/carotenoid concentration analysis using integrated lidar-hyperspectral techniques (e.g., r2=0.84, 0.84, and 0.82 for Chl(a), Chl(a+b), and Chl(b)). Examination of the spatially explicit models of fPAR reveal distinct spatial patterns which become increasingly apparent throughout the season due to the variation in species groupings (and canopy chlorophyll concentration) within the 1 km radius surrounding the flux tower. Comparison of results from the modified local-scale version of the C-Fix model to tower gross ecosystem productivity (GEP) demonstrate a good correlation to flux tower measured GEP (r2=0.70 for 10 day averages), with the largest deviations occurring in June-July. This research has direct benefits for forest inventory mapping and management practices; mapping of canopy physiology and biochemical constituents related to forest health; and scaling and direct comparison to large resolution satellite models to help bridge the gap between the local-scale measurements at flux towers and predictions derived from continental-scale carbon models.

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

NASA Astrophysics Data System (ADS)

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 models whereby the active channel is simulated in 1-dimension and the floodplain in 2-dimensions provided the best means of utilizing the LiDAR data to evaluate existing conditions and develop alternative flood hazard mitigation and habitat restoration strategies. Such an approach recognizes the limitations of the LiDAR data within active channel areas with dense riparian cover and is cost-effective in that it allows field survey efforts to focus primarily on characterizing active stream channel areas. The multi-dimensional modeling approach also conforms well to the physical realties of the stream system whereby in-channel flows can generally be well-described as a one-dimensional flow problem and floodplain flows are often characterized by multiple and often poorly understood flowpaths. The multi-dimensional modeling approach has the additional advantages of allowing for accurate simulation of the effects of hydraulic structures using well-tested one-dimensional formulae and minimizing the computational burden of the models by not requiring the small spatial resolutions necessary to resolve the geometries of small stream channels in two-dimensions.

Initial Performance Assessment of CALIOP

NASA Technical Reports Server (NTRS)

Winker, David; Hunt, Bill; McGill, Matthew

2007-01-01

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, pronounced the same as "calliope") is a spaceborne two-wavelength polarizatio n lidar that has been acquiring global data since June 2006. CALIOP p rovides high resolution vertical profiles of clouds and aerosols, and has been designed with a very large linear dynamic range to encompas s the full range of signal returns from aerosols and clouds. CALIOP is the primary instrument carried by the Cloud-Aerosol Lidar and Infrar ed Pathfinder Satellite Observations (CALIPSO) satellite, which was l aunched on April, 28 2006. CALIPSO was developed within the framework of a collaboration between NASA and the French space agency, CNES. I nitial data analysis and validation intercomparisons indicate the qua lity of data from CALIOP meets or exceeds expectations. This paper presents a description of the CALIPSO mission, the CALIOP instrument, an d an initial assessment of on-orbit measurement performance.

Quantifying TOLNet Ozone Lidar Accuracy During the 2014 DISCOVER-AQ and FRAPPE Campaigns

NASA Technical Reports Server (NTRS)

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;

Quantifying TOLNet ozone lidar accuracy during the 2014 DISCOVER-AQ and FRAPPÉ campaigns

NASA Astrophysics Data System (ADS)

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; Kirgis, Guillaume; Kuang, Shi; Langford, Andrew O.; Leblanc, Thierry; McDuffie, Erin E.; McGee, Thomas J.; Pliutau, Denis; Senff, Christoph J.; Sullivan, John T.; Sumnicht, Grant; Twigg, Laurence W.; Weinheimer, Andrew J.

2017-10-01

The Tropospheric Ozone Lidar Network (TOLNet) is a unique network of lidar systems that measure high-resolution atmospheric profiles of ozone. The accurate characterization of these lidars is necessary to determine the uniformity of the network calibration. From July to August 2014, three lidars, the TROPospheric OZone (TROPOZ) lidar, the Tunable Optical Profiler for Aerosol and oZone (TOPAZ) lidar, and the Langley Mobile Ozone Lidar (LMOL), of TOLNet participated in the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission and the Front Range Air Pollution and Photochemistry Éxperiment (FRAPPÉ) to measure ozone variations from the boundary layer to the top of the troposphere. This study presents the analysis of the intercomparison between the TROPOZ, TOPAZ, and LMOL lidars, along with comparisons between the lidars and other in situ ozone instruments including ozonesondes and a P-3B airborne chemiluminescence sensor. The TOLNet lidars measured vertical ozone structures with an accuracy generally better than ±15 % within the troposphere. Larger differences occur at some individual altitudes in both the near-field and far-field range of the lidar systems, largely as expected. In terms of column average, the TOLNet lidars measured ozone with an accuracy better than ±5 % for both the intercomparison between the lidars and between the lidars and other instruments. These results indicate that these three TOLNet lidars are suitable for use in air quality, satellite validation, and ozone modeling efforts.

Winners and losers in the competition for space in tropical forest canopies.

PubMed

Kellner, James R; Asner, Gregory P

2014-05-01

Trees compete for space in the canopy, but where and how individuals or their component parts win or lose is poorly understood. We developed a stochastic model of three-dimensional dynamics in canopies using a hierarchical Bayesian framework, and analysed 267,533 positive height changes from 1.25 m pixels using data from airborne LiDAR within 43 ha on the windward flank of Mauna Kea. Model selection indicates a strong resident's advantage, with 97.9% of positions in the canopy retained by their occupants over 2 years. The remaining 2.1% were lost to a neighbouring contender. Absolute height was a poor predictor of success, but short stature greatly raised the risk of being overtopped. Growth in the canopy was exponentially distributed with a scaling parameter of 0.518. These findings show how size and spatial proximity influence the outcome of competition for space, and provide a general framework for the analysis of canopy dynamics. © 2014 John Wiley & Sons Ltd/CNRS.

Retrievals of aerosol microphysics from simulations of spaceborne multiwavelength lidar measurements

NASA Astrophysics Data System (ADS)

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

2018-01-01

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 μm 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.

Retrievals of Aerosol Microphysics from Simulations of Spaceborne Multiwavelength Lidar Measurements

NASA Technical Reports Server (NTRS)

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.

Development of a regional LiDAR field plot strategy for Oregon and Washington

Treesearch

Arvind Bhuta; Leah Rathbun

2015-01-01

The National Forest System (NFS) Pacific Northwest Region (R6) has been flying LiDAR on a per project basis. Additional field data was also collected in situ to many of these LiDAR projects to aid in the development of predictive models and estimate values which are unattainable through LiDAR data alone (e.g. species composition, tree volume, and downed woody material...

Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data

NASA Astrophysics Data System (ADS)

Luo, Shezhou; Wang, Cheng; Xi, Xiaohuan; Pan, Feifei; Qian, Mingjie; Peng, Dailiang; Nie, Sheng; Qin, Haiming; Lin, Yi

2017-06-01

Wetland biomass is essential for monitoring the stability and productivity of wetland ecosystems. Conventional field methods to measure or estimate wetland biomass are accurate and reliable, but expensive, time consuming and labor intensive. This research explored the potential for estimating wetland reed biomass using a combination of airborne discrete-return Light Detection and Ranging (LiDAR) and hyperspectral data. To derive the optimal predictor variables of reed biomass, a range of LiDAR and hyperspectral metrics at different spatial scales were regressed against the field-observed biomasses. The results showed that the LiDAR-derived H_p99 (99th percentile of the LiDAR height) and hyperspectral-calculated modified soil-adjusted vegetation index (MSAVI) were the best metrics for estimating reed biomass using the single regression model. Although the LiDAR data yielded a higher estimation accuracy compared to the hyperspectral data, the combination of LiDAR and hyperspectral data produced a more accurate prediction model for reed biomass (R2 = 0.648, RMSE = 167.546 g/m2, RMSEr = 20.71%) than LiDAR data alone. Thus, combining LiDAR data with hyperspectral data has a great potential for improving the accuracy of aboveground biomass estimation.

Shuttle atmospheric lidar research program

NASA Technical Reports Server (NTRS)

1979-01-01

The Shuttle atmospheric lidar program is discussed in relation to an understanding of the processes governing the Earth's atmosphere and in the capacity to evaluate the atmospheric susceptibility to manmade and natural perturbations. Applications of the lidar which are discussed are the determination of the global flow of water vapor and pollutants in the troposphere, improvement of chemical and transport models of the stratosphere and mesosphere, evaluation of radiative models of the atmosphere, investigation of chemistry and transport of thermospheric atomic species, and investigation of magnetospheric aspects of sun/weather relationships. The features of the lidar measurements discussed are the high spatial resolution, control of the source wavelength and intensity, and high measurement specificity.

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

NASA Astrophysics Data System (ADS)

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 quantify the sensitivity of calibration equations relating GEDI lidar structure measurements and AGBD to these factors at a range of spatial scales (0.0625-1ha) and discuss the implications for the expanding use of existing in situ ground plot data by GEDI.

From LIDAR Scanning to 3d FEM Analysis for Complex Surface and Underground Excavations

NASA Astrophysics Data System (ADS)

Chun, K.; Kemeny, J.

2017-12-01

Light detection and ranging (LIDAR) has been a prevalent remote-sensing technology applied in the geological fields due to its high precision and ease to use. One of the major applications is to use the detailed geometrical information of underground structures as a basis for the generation of three-dimensional numerical model that can be used in FEM analysis. To date, however, straightforward techniques in reconstructing numerical model from the scanned data of underground structures have not been well established or tested. In this paper, we propose a comprehensive approach integrating from LIDAR scanning to finite element numerical analysis, specifically converting LIDAR 3D point clouds of object containing complex surface geometry into finite element model. This methodology has been applied to the Kartchner Caverns in Arizona for the stability analysis. Numerical simulations were performed using the finite element code ABAQUS. The results indicate that the highlights of our technologies obtained from LIDAR is effective and provide reference for other similar engineering project in practice.

In-Flight Thermal Performance of the Lidar In-Space Technology Experiment

NASA Technical Reports Server (NTRS)

Roettker, William

1995-01-01

The Lidar In-Space Technology Experiment (LITE) was developed at NASA s Langley Research Center to explore the applications of lidar operated from an orbital platform. As a technology demonstration experiment, LITE was developed to gain experience designing and building future operational orbiting lidar systems. Since LITE was the first lidar system to be flown in space, an important objective was to validate instrument design principles in such areas as thermal control, laser performance, instrument alignment and control, and autonomous operations. Thermal and structural analysis models of the instrument were developed during the design process to predict the behavior of the instrument during its mission. In order to validate those mathematical models, extensive engineering data was recorded during all phases of LITE's mission. This inflight engineering data was compared with preflight predictions and, when required, adjustments to the thermal and structural models were made to more accurately match the instrument s actual behavior. The results of this process for the thermal analysis and design of LITE are presented in this paper.

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

NASA Astrophysics Data System (ADS)

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 vigor or senescence. Additionally, the strength of hyperspectral data for vegetation classification suggests these data have additional utility for modeling carbon flux dynamics by allowing more accurate plant functional type mapping.

Effects of LiDAR point density and landscape context on estimates of urban forest biomass

NASA Astrophysics Data System (ADS)

Singh, Kunwar K.; Chen, Gang; McCarter, James B.; Meentemeyer, Ross K.

2015-03-01

Light Detection and Ranging (LiDAR) data is being increasingly used as an effective alternative to conventional optical remote sensing to accurately estimate aboveground forest biomass ranging from individual tree to stand levels. Recent advancements in LiDAR technology have resulted in higher point densities and improved data accuracies accompanied by challenges for procuring and processing voluminous LiDAR data for large-area assessments. Reducing point density lowers data acquisition costs and overcomes computational challenges for large-area forest assessments. However, how does lower point density impact the accuracy of biomass estimation in forests containing a great level of anthropogenic disturbance? We evaluate the effects of LiDAR point density on the biomass estimation of remnant forests in the rapidly urbanizing region of Charlotte, North Carolina, USA. We used multiple linear regression to establish a statistical relationship between field-measured biomass and predictor variables derived from LiDAR data with varying densities. We compared the estimation accuracies between a general Urban Forest type and three Forest Type models (evergreen, deciduous, and mixed) and quantified the degree to which landscape context influenced biomass estimation. The explained biomass variance of the Urban Forest model, using adjusted R2, was consistent across the reduced point densities, with the highest difference of 11.5% between the 100% and 1% point densities. The combined estimates of Forest Type biomass models outperformed the Urban Forest models at the representative point densities (100% and 40%). The Urban Forest biomass model with development density of 125 m radius produced the highest adjusted R2 (0.83 and 0.82 at 100% and 40% LiDAR point densities, respectively) and the lowest RMSE values, highlighting a distance impact of development on biomass estimation. Our evaluation suggests that reducing LiDAR point density is a viable solution to regional-scale forest assessment without compromising the accuracy of biomass estimates, and these estimates can be further improved using development density.

CLICK: The new USGS center for LIDAR information coordination and knowledge

USGS Publications Warehouse

Stoker, Jason M.; Greenlee, Susan K.; Gesch, Dean B.; Menig, Jordan C.

2006-01-01

Elevation data is rapidly becoming an important tool for the visualization and analysis of geographic information. The creation and display of three-dimensional models representing bare earth, vegetation, and structures have become major requirements for geographic research in the past few years. Light Detection and Ranging (lidar) has been increasingly accepted as an effective and accurate technology for acquiring high-resolution elevation data for bare earth, vegetation, and structures. Lidar is an active remote sensing system that records the distance, or range, of a laser fi red from an airborne or space borne platform such as an airplane, helicopter or satellite to objects or features on the Earth’s surface. By converting lidar data into bare ground topography and vegetation or structural morphologic information, extremely accurate, high-resolution elevation models can be derived to visualize and quantitatively represent scenes in three dimensions. In addition to high-resolution digital elevation models (Evans et al., 2001), other lidar-derived products include quantitative estimates of vegetative features such as canopy height, canopy closure, and biomass (Lefsky et al., 2002), and models of urban areas such as building footprints and three-dimensional city models (Maas, 2001).

lidar change detection using building models

NASA Astrophysics Data System (ADS)

Kim, Angela M.; Runyon, Scott C.; Jalobeanu, Andre; Esterline, Chelsea H.; Kruse, Fred A.

2014-06-01

Terrestrial LiDAR scans of building models collected with a FARO Focus3D and a RIEGL VZ-400 were used to investigate point-to-point and model-to-model LiDAR change detection. LiDAR data were scaled, decimated, and georegistered to mimic real world airborne collects. Two physical building models were used to explore various aspects of the change detection process. The first model was a 1:250-scale representation of the Naval Postgraduate School campus in Monterey, CA, constructed from Lego blocks and scanned in a laboratory setting using both the FARO and RIEGL. The second model at 1:8-scale consisted of large cardboard boxes placed outdoors and scanned from rooftops of adjacent buildings using the RIEGL. A point-to-point change detection scheme was applied directly to the point-cloud datasets. In the model-to-model change detection scheme, changes were detected by comparing Digital Surface Models (DSMs). The use of physical models allowed analysis of effects of changes in scanner and scanning geometry, and performance of the change detection methods on different types of changes, including building collapse or subsistence, construction, and shifts in location. Results indicate that at low false-alarm rates, the point-to-point method slightly outperforms the model-to-model method. The point-to-point method is less sensitive to misregistration errors in the data. Best results are obtained when the baseline and change datasets are collected using the same LiDAR system and collection geometry.

Utilization of volume correlation filters for underwater mine identification in LIDAR imagery

NASA Astrophysics Data System (ADS)

Walls, Bradley

2008-04-01

Underwater mine identification persists as a critical technology pursued aggressively by the Navy for fleet protection. As such, new and improved techniques must continue to be developed in order to provide measurable increases in mine identification performance and noticeable reductions in false alarm rates. In this paper we show how recent advances in the Volume Correlation Filter (VCF) developed for ground based LIDAR systems can be adapted to identify targets in underwater LIDAR imagery. Current automated target recognition (ATR) algorithms for underwater mine identification employ spatial based three-dimensional (3D) shape fitting of models to LIDAR data to identify common mine shapes consisting of the box, cylinder, hemisphere, truncated cone, wedge, and annulus. VCFs provide a promising alternative to these spatial techniques by correlating 3D models against the 3D rendered LIDAR data.

EARLINET Single Calculus Chain - overview on methodology and strategy

NASA Astrophysics Data System (ADS)

D'Amico, G.; Amodeo, A.; Baars, H.; Binietoglou, I.; Freudenthaler, V.; Mattis, I.; Wandinger, U.; Pappalardo, G.

2015-11-01

In this paper we describe the EARLINET Single Calculus Chain (SCC), a tool for the automatic analysis of lidar measurements. The development of this tool started in the framework of EARLINET-ASOS (European Aerosol Research Lidar Network - Advanced Sustainable Observation System); it was extended within ACTRIS (Aerosol, Clouds and Trace gases Research InfraStructure Network), and it is continuing within ACTRIS-2. The main idea was to develop a data processing chain that allows all EARLINET stations to retrieve, in a fully automatic way, the aerosol backscatter and extinction profiles starting from the raw lidar data of the lidar systems they operate. The calculus subsystem of the SCC is composed of two modules: a pre-processor module which handles the raw lidar data and corrects them for instrumental effects and an optical processing module for the retrieval of aerosol optical products from the pre-processed data. All input parameters needed to perform the lidar analysis are stored in a database to keep track of all changes which may occur for any EARLINET lidar system over the time. The two calculus modules are coordinated and synchronized by an additional module (daemon) which makes the whole analysis process fully automatic. The end user can interact with the SCC via a user-friendly web interface. All SCC modules are developed using open-source and freely available software packages. The final products retrieved by the SCC fulfill all requirements of the EARLINET quality assurance programs on both instrumental and algorithm levels. Moreover, the manpower needed to provide aerosol optical products is greatly reduced and thus the near-real-time availability of lidar data is improved. The high-quality of the SCC products is proven by the good agreement between the SCC analysis, and the corresponding independent manual retrievals. Finally, the ability of the SCC to provide high-quality aerosol optical products is demonstrated for an EARLINET intense observation period.

Developing a portable, autonomous aerosol backscatter lidar for network or remote operations

NASA Astrophysics Data System (ADS)

Strawbridge, K. B.

2013-03-01

Lidar has the ability to detect the complex vertical structure of the atmosphere and can therefore identify the existence and extent of aerosols with high spatial and temporal resolution, making it well suited for understanding atmospheric dynamics and transport processes. Environment Canada has developed a portable, autonomous lidar system that can be monitored remotely and operated continuously except during precipitation events. The lidar, housed in a small trailer, simultaneously emits two wavelengths of laser light (1064 nm and 532 nm) at energies of approximately 150 mJ/pulse/wavelength and detects the backscatter signal at 1064 nm and both polarizations at 532 nm. For laser energies of this magnitude, the challenge resides in designing a system that meets the airspace safety requirements for autonomous operations. Through the combination of radar technology, beam divergence, laser cavity interlocks and using computer log files, this risk was mitigated. A Continuum Inlite small footprint laser is the backbone of the system because of three design criteria: requiring infrequent flash lamp changes compared to previous Nd : YAG Q-switch lasers, complete software control capability and a built-in laser energy monitoring system. A computer-controlled interface was designed to monitor the health of the system, adjust operational parameters and maintain a climate-controlled environment. Through an Internet connection, it also transmitted the vital performance indicators and data stream to allow the lidar profile data for multiple instruments from near ground to 15 km, every 10 s, to be viewed, in near real-time via a website. The details of the system design and calibration will be discussed and the success of the instrument as tested within the framework of a national lidar network dubbed CORALNet (Canadian Operational Research Aerosol Lidar Network). In addition, the transport of a forest fire plume across the country will be shown as evidenced by the lidar network, HYSPLIT back trajectories, MODIS imagery and CALIPSO overpasses.

Developing a portable, autonomous aerosol backscatter lidar for network or remote operations

NASA Astrophysics Data System (ADS)

Strawbridge, K. B.

2012-11-01

Lidar has the ability to detect the complex vertical structure of the atmosphere and can therefore identify the existence and extent of aerosols with high spatial and temporal resolution, making it well-suited for understanding atmospheric dynamics and transport processes. Environment Canada has developed a portable, autonomous lidar system that can be monitored remotely and operate continuously except during precipitation events. The lidar, housed in a small trailer, simultaneously emits two wavelengths of laser light (1064 nm and 532 nm) at energies of approximately 150 mJ/pulse/wavelength and detects the backscatter signal at 1064 nm and both polarizations at 532 nm. For laser energies of this magnitude, the challenge resides in designing a system that meets the airspace safety requirements for autonomous operations. Through the combination of radar technology, beam divergence, laser cavity interlocks and using computer log files, this risk was mitigated. A Continuum Inlite small footprint laser is the backbone of the system because of three design criteria: requiring infrequent flash lamp changes compared to previous Nd:YAG Q-switch lasers, complete software control capability and a built-in laser energy monitoring system. A computer-controlled interface was designed to monitor the health of the system, adjust operational parameters and maintain a climate-controlled environment. Through an internet connection, it also transmitted the vital performance indicators and data stream to allow the lidar profile data for multiple instruments from near ground to 15 km, every 10 s, to be viewed, in near real-time via a website. The details of the system design and calibration will be discussed and the success of the instrument as tested within the framework of a national lidar network dubbed CORALNet (Canadian Operational Research Aerosol Lidar Network). In addition, the transport of a forest fire plume across the country will be shown as evidenced by the lidar network, HYSPLIT back trajectories, MODIS imagery and CALIPSO overpasses.

TOLNet - A Tropospheric Ozone Lidar Profiling Network for Satellite Continuity and Process Studies

NASA Technical Reports Server (NTRS)

Newchurch, Michael J.; Kuang, Shi; Wang, Lihua; LeBlanc, Thierry; Alvarez II, Raul J.; Langford, Andrew O.; Senff, Christoph J.; Brown, Steve; Johnson, Bryan; Burris, John F.;

Characterizing Urban Volumetry Using LIDAR Data

NASA Astrophysics Data System (ADS)

Santos, T.; Rodrigues, A. M.; Tenedório, J. A.

2013-05-01

Urban indicators are efficient tools designed to simplify, quantify and communicate relevant information for land planners. Since urban data has a strong spatial representation, one can use geographical data as the basis for constructing information regarding urban environments. One important source of information about the land status is imagery collected through remote sensing. Afterwards, using digital image processing techniques, thematic detail can be extracted from those images and used to build urban indicators. Most common metrics are based on area (2D) measurements. These include indicators like impervious area per capita or surface occupied by green areas, having usually as primary source a spectral image obtained through a satellite or airborne camera. More recently, laser scanning data has become available for large-scale applications. Such sensors acquire altimetric information and are used to produce Digital Surface Models (DSM). In this context, LiDAR data available for the city is explored along with demographic information, and a framework to produce volumetric (3D) urban indexes is proposed, and measures like Built Volume per capita, Volumetric Density and Volumetric Homogeneity are computed.

DTMs Assessment to the Definition of Shallow Landslides Prone Areas

NASA Astrophysics Data System (ADS)

Martins, Tiago D.; Oka-Fiori, Chisato; Carvalho Vieira, Bianca; Montgomery, David R.

2017-04-01

Predictive methods have been developed, especially since the 1990s, to identify landslide prone areas. One of the examples it is the physically based model SHALSTAB (Shallow Landsliding Stability Model), that calculate the potential instability for shallow landslides based on topography and physical soil properties. Normally, in such applications in Brazil, the Digital Terrain Model (DTM), is obtained mainly from conventional contour lines. However, recently the LiDAR (Light Detection and Ranging) system has been largely used in Brazil. Thus, this study aimed to evaluate different DTM's, generated from conventional data and LiDAR, and their influence in generating susceptibility maps to shallow landslides using SHALSTAB model. For that were analyzed the physical properties of soil, the response of the model when applying conventional topographical data and LiDAR's in the generation of DTM, and the shallow landslides susceptibility maps based on different topographical data. The selected area is in the urban perimeter of the municipality of Antonina (PR), affected by widespread landslides in March 2011. Among the results, it was evaluated different LiDAR data interpolation, using GIS tools, wherein the Triangulation/Natural Neighbor presented the best performance. It was also found that in one of evaluation indexes (Scars Concentration), the LiDAR derived DTM presented the best performance when compared with the one originated from contour lines, however, the Landslide Potential index, has presented a small increase. Consequently, it was possible to assess the DTM's, and the one derived from LiDAR improved very little the certitude percentage. It is also noted a gap in researches carried out in Brazil on the use of products generated from LiDAR data on geomorphological analysis.

Effects of Forest Disturbances on Forest Structural Parameters Retrieval from Lidar Waveform Data

NASA Technical Reports Server (NTRS)

Ranson, K, Lon; Sun, G.

2011-01-01

The effect of forest disturbance on the lidar waveform and the forest biomass estimation was demonstrated by model simulation. The results show that the correlation between stand biomass and the lidar waveform indices changes when the stand spatial structure changes due to disturbances rather than the natural succession. This has to be considered in developing algorithms for regional or global mapping of biomass from lidar waveform data.

Integrating LIDAR and forest inventories to fill the trees outside forests data gap.

PubMed

Johnson, Kristofer D; Birdsey, Richard; Cole, Jason; Swatantran, Anu; O'Neil-Dunne, Jarlath; Dubayah, Ralph; Lister, Andrew

2015-10-01

Forest inventories are commonly used to estimate total tree biomass of forest land even though they are not traditionally designed to measure biomass of trees outside forests (TOF). The consequence may be an inaccurate representation of all of the aboveground biomass, which propagates error to the outputs of spatial and process models that rely on the inventory data. An ideal approach to fill this data gap would be to integrate TOF measurements within a traditional forest inventory for a parsimonious estimate of total tree biomass. In this study, Light Detection and Ranging (LIDAR) data were used to predict biomass of TOF in all "nonforest" Forest Inventory and Analysis (FIA) plots in the state of Maryland. To validate the LIDAR-based biomass predictions, a field crew was sent to measure TOF on nonforest plots in three Maryland counties, revealing close agreement at both the plot and county scales between the two estimates. Total tree biomass in Maryland increased by 25.5 Tg, or 15.6%, when biomass of TOF were included. In two counties (Carroll and Howard), there was a 47% increase. In contrast, counties located further away from the interstate highway corridor showed only a modest increase in biomass when TOF were added because nonforest conditions were less common in those areas. The advantage of this approach for estimating biomass of TOF is that it is compatible with, and explicitly separates TOF biomass from, forest biomass already measured by FIA crews. By predicting biomass of TOF at actual FIA plots, this approach is directly compatible with traditionally reported FIA forest biomass, providing a framework for other states to follow, and should improve carbon reporting and modeling activities in Maryland.

New approaches to observation and modeling of fast-moving glaciers and ice streams

NASA Astrophysics Data System (ADS)

Herzfeld, U. C.; Trantow, T.; Markle, M. J.; Medley, G.; Markus, T.; Neumann, T.

2016-12-01

In this paper, we will give an overview of several new approaches to remote-sensing observations and analysis and to modeling of fast glacier flow. The approaches will be applied in case studies of different types of fast-moving glaciers: (1) The Bering-Bagley Glacier System, Alaska (a surge-type glacier system), (2) Jakobshavn Isbræ, Greenland (a tide-water terminating fjord glacier and outlet of the Greenland Inland Ice), and (3) Icelandic Ice Caps (manifestations of the interaction of volcanic and glaciologic processes). On the observational side, we will compare the capabilities of lidar and radar altimeters, including ICESat's Geoscience Laser Altimeter System (GLAS), CryoSat-2's Synthetic Aperture Interferometric Radar Altimeter (SIRAL) and the future ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS), especially regarding retrieval of surface heights over crevassed regions as typical of spatial and temporal acceleration. Properties that can be expected from ICESat-2 ATLAS data will be illustrated based on analyses of data from ICESat-2 simulator instruments: the Slope Imaging Multi-polarization Photon-counting Lidar (SIMPL) and the Multiple Altimeter Beam Experimental Lidar (MABEL). Information from altimeter data will be augmented by an automated surface classification based on image data, which includes satellite imagery such as LANDSAT and WorldView as well as airborne video imagery of ice surfaces. Numerical experiments using Elmer/Ice will be employed to link parameters derived in observations to physical processes during the surge of the Bering Bagley Glacier System. This allows identification of processes that can be explained in an existing framework and processes that may require new concepts for glacier evolution. Topics include zonation of surge progression in a complex glacier system and crevassing as an indication, storage of glacial water, influence of basal topography and the role of friction laws.

Performance modelling of miniaturized flash-imaging lidars for future mars exploration missions

NASA Astrophysics Data System (ADS)

Mitev, V.; Pollini, A.; Haesler, J.; Pereira do Carmo, João.

2017-11-01

Future planetary exploration missions require the support of 3D vision in the GN&C during key spacecraft's proximity phases, namely: i) spacecraft precision and soft Landing on the planet's surface; ii) Rendezvous and Docking (RVD) between a Sample Canister (SC) and an orbiter spacecraft; iii) Rover Navigation (RN) on planetary surface. The imaging LiDARs are among the best candidate for such tasks [1-3]. The combination of measurement requirements and environmental conditions seems to find its optimum in the flash 3D LiDAR architecture. Here we present key steps is the evaluation of novelty light detectors and MOEMS (Micro-Opto- Electro-Mechanical Systems) technologies with respect to LiDAR system performance and miniaturization. The objectives of the project MILS (Miniaturized Imaging LiDAR System, Phase 1) concentrated on the evaluation of novel detection and scanning technologies for the miniaturization of 3D LiDARs intended for planetary mission. Preliminary designs for an elegant breadboard (EBB) for the three tasks stated above (Landing, RVD and RN) were proposed, based on results obtained with a numerical model developed in the project and providing the performances evaluation of imaging LiDARs.

Lidar and radar measurements of the melting layer in the frame of the Convective and Orographically-induced Precipitation Study: observations of dark and bright band phenomena

NASA Astrophysics Data System (ADS)

di Girolamo, P.; Summa, D.; Bhawar, R.; di Iorio, T.; Norton, E. G.; Peters, G.; Dufournet, Y.

2011-11-01

During the Convective and Orographically-induced Precipitation Study (COPS), lidar dark and bright bands were observed by the University of BASILicata Raman lidar system (BASIL) during several intensive (IOPs) and special (SOPs) observation periods (among others, 23 July, 15 August, and 17 August 2007). Lidar data were supported by measurements from the University of Hamburg cloud radar MIRA 36 (36 GHz), the University of Hamburg dual-polarization micro rain radars (24.1 GHz) and the University of Manchester UHF wind profiler (1.29 GHz). Results from BASIL and the radars for 23 July 2007 are illustrated and discussed to support the comprehension of the microphysical and scattering processes responsible for the appearance of the lidar and radar dark and bright bands. Simulations of the lidar dark and bright band based on the application of concentric/eccentric sphere Lorentz-Mie codes and a melting layer model are also provided. Lidar and radar measurements and model results are also compared with measurements from a disdrometer on ground and a two-dimensional cloud (2DC) probe on-board the ATR42 SAFIRE.

Applicability Analysis of Cloth Simulation Filtering Algorithm for Mobile LIDAR Point Cloud

NASA Astrophysics Data System (ADS)

Cai, S.; Zhang, W.; Qi, J.; Wan, P.; Shao, J.; Shen, A.

2018-04-01

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.

Sequential Modelling of Building Rooftops by Integrating Airborne LIDAR Data and Optical Imagery: Preliminary Results

NASA Astrophysics Data System (ADS)

Sohn, G.; Jung, J.; Jwa, Y.; Armenakis, C.

2013-05-01

This paper presents a sequential rooftop modelling method to refine initial rooftop models derived from airborne LiDAR data by integrating it with linear cues retrieved from single imagery. A cue integration between two datasets is facilitated by creating new topological features connecting between the initial model and image lines, with which new model hypotheses (variances to the initial model) are produced. We adopt Minimum Description Length (MDL) principle for competing the model candidates and selecting the optimal model by considering the balanced trade-off between the model closeness and the model complexity. Our preliminary results, combined with the Vaihingen data provided by ISPRS WGIII/4 demonstrate the image-driven modelling cues can compensate the limitations posed by LiDAR data in rooftop modelling.

Characterization of smoke and dust episode over West Africa: comparison of MERRA-2 modeling with multiwavelength Mie-Raman lidar observations

NASA Astrophysics Data System (ADS)

Veselovskii, Igor; Goloub, Philippe; Podvin, Thierry; Tanre, Didier; da Silva, Arlindo; Colarco, Peter; Castellanos, Patricia; Korenskiy, Mikhail; Hu, Qiaoyun; Whiteman, David N.; Pérez-Ramírez, Daniel; Augustin, Patrick; Fourmentin, Marc; Kolgotin, Alexei

2018-02-01

Observations of multiwavelength Mie-Raman lidar taken during the SHADOW field campaign are used to analyze a smoke-dust episode over West Africa on 24-27 December 2015. For the case considered, the dust layer extended from the ground up to approximately 2000 m while the elevated smoke layer occurred in the 2500-4000 m range. The profiles of lidar measured backscattering, extinction coefficients, and depolarization ratios are compared with the vertical distribution of aerosol parameters provided by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The MERRA-2 model simulated the correct location of the near-surface dust and elevated smoke layers. The values of modeled and observed aerosol extinction coefficients at both 355 and 532 nm are also rather close. In particular, for the episode reported, the mean value of difference between the measured and modeled extinction coefficients at 355 nm is 0.01 km-1 with SD of 0.042 km-1. The model predicts significant concentration of dust particles inside the elevated smoke layer, which is supported by an increased depolarization ratio of 15 % observed in the center of this layer. The modeled at 355 nm the lidar ratio of 65 sr in the near-surface dust layer is close to the observed value (70 ± 10) sr. At 532 nm, however, the simulated lidar ratio (about 40 sr) is lower than measurements (55 ± 8 sr). The results presented demonstrate that the lidar and model data are complimentary and the synergy of observations and models is a key to improve the aerosols characterization.

Assesment of CALIPSO's level 3 climatological product

NASA Astrophysics Data System (ADS)

Papagiannopoulos, Nikolaos; Mona, Lucia; Pappalardo, Gelsomina

2015-04-01

Since December 2011 has been released the latest CALIPSO Level 3 (CL3) monthly product and is subject to calibration/validation studies. EARLINET as the unique European lidar network on a continental scale is the key candidate for these kind of studies. CALIPSO Level 3 data were compared against EARLINET monthly averages obtained by profiles during satellite overpasses. Data from stations of Potenza, Naples, Granada, Évora and Leipzig equipped with advanced multi-wavelength Raman lidars were used for this study. EARLINET monthly profiles yielded higher extinction values comparing to CALIPSO ones. In order to mitigate uncertainties due to spatial and temporal differences, we reproduced the CL3 filtering rubric onto the CALIPSO Level 2 data. Only grid CALIPSO overflights during EARLINET correlative measurements were used. From these data, monthly averages on 2x5 grid are reconstructed. The CALIPSO monthly mean profiles following the new approach are called CALIPSOLevel 3*,CL3*. This offers the possibility to achieve direct comparable datasets, even if greatly reduces the number of satellite grid overflights. Moreover, the comparison of matched observations reduces uncertainties from spatial variability that affects the sampled volumes. The agreement typically improved, in particular above the areas directly affected by the anthropogenic activities within the planetary boundary layer. In contrast to CL3 product, CL3* data offers the possibility to assess also the CALIPSO performance in terms of the backscatter coefficient keeping the same quality assurance criteria applied to extinction coefficient. Lastly, the typing capabilities of CALIPSO were assessed outlining the importance of the correct aerosol type assessment to the CALIPSO aerosol properties retrieval. This work is the first in-depth assessment to evaluate the aerosol optical properties reported in the CL 3 data product. The outcome will assist the establishment of independently derived uncertainty estimates that can be used to create more reliable model forecasts based on CALIPSO data. Moreover, the presented work can contribute to current and future studies that use space-based lidar data. Acknowledgments: The financial support for EARLINET provided by the European Union under grant RICA 025991 within the framework of the Sixth Framework Programme is gratefully acknowledged. Since 2011 EARLINET has been integrated in the ACTRIS Research Infrastructure Project supported by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 262254.

Creating high-resolution bare-earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for lidar point cloud filtering

USGS Publications Warehouse

DeWitt, Jessica D.; Warner, Timothy A.; Chirico, Peter G.; Bergstresser, Sarah E.

2017-01-01

For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.

An Adjoint-Based Analysis of the Sampling Footprints of Tall Tower, Aircraft and Potential Future Lidar Observations of CO2

NASA Technical Reports Server (NTRS)

Andrews, Arlyn; Kawa, Randy; Zhu, Zhengxin; Burris, John; Abshire, Jim

2004-01-01

A detailed mechanistic understanding of the sources and sinks of CO2 will be required to reliably predict future CO2 levels and climate. A commonly used technique for deriving information about CO2 exchange with surface reservoirs is to solve an 'inverse problem', where CO2 observations are used with an atmospheric transport model to find the optimal distribution of sources and sinks. Synthesis inversion methods are powerful tools for addressing this question, but the results are disturbingly sensitive to the details of the calculation. Studies done using different atmospheric transport models and combinations of surface station data have produced substantially different distributions of surface fluxes. Adjoint methods are now being developed that will more effectively incorporate diverse datasets in estimates of surface fluxes of CO2. In an adjoint framework, it will be possible to combine CO2 concentration data from longterm surface and aircraft monitoring stations with data from intensive field campaigns and with proposed future satellite observations. We have recently developed an adjoint for the GSFC 3-D Parameterized Chemistry and Transport Model (PCTM). Here, we will present results from a PCTM Adjoint study comparing the sampling footprints of tall tower, aircraft and potential future lidar observations of CO2. The vertical resolution and extent of the profiles and the observation frequency will be considered for several sites in North America.

The Escompte - Marseille 2001 International Field Experiment: Ground Based and Lidar Results Obtained At St. Chamas By The Epfl Mobile Laboratory

NASA Astrophysics Data System (ADS)

Balin, I.; Jimenez, R.; Simeonov, V.; Ristori, P.; Navarette, M.; van den Bergh, H.; Calpini, B.

The assessment of the air pollution problems in term of understanding of the non- linear chemical mechanisms, the transport or the meteorological processes, and the choice of the abatement strategies could be based on the air pollution models. Nowa- days, very few of these models were validated due to the lack of 3D measurements. The goal of the ESCOMPTE experiment was to provide such of 3D database in order to constrain the air pollution models. The EPFL-LPA mobile laboratory was part of the ESCOMPTE extensive network and was located on the northern side of the Berre Lake at St.Chamas. In this framework, measurements of the air pollutants (O3, SO2, NOx, polycyclic aromatic hydrocarbons, black carbon and particulate matter of less than 10 microns mean diameter) and meteorological parameters (wind, temperature, pressure and relative humidity) were continuously performed from June 10 to July 13, 2001. They were combined with ground based lidar observations for ozone and aerosol estimation from 100m above ground level up to the free troposphere at ca.7 km agl. This paper will present an overview of the results obtained and will highlight one of the intensive observation period (IOP) during which clean air conditions were initially observed followed by highly polluted air masses during the second half of the IOP.

Underwater lidar system: design challenges and application in pollution detection

NASA Astrophysics Data System (ADS)

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.

Effects of LiDAR point density and landscape context on the retrieval of urban forest biomass

NASA Astrophysics Data System (ADS)

Singh, K. K.; Chen, G.; McCarter, J. B.; Meentemeyer, R. K.

2014-12-01

Light Detection and Ranging (LiDAR), as an alternative to conventional optical remote sensing, is being increasingly used to accurately estimate aboveground forest biomass ranging from individual tree to stand levels. Recent advancements in LiDAR technology have resulted in higher point densities and better data accuracies, which however pose challenges to the procurement and processing of LiDAR data for large-area assessments. Reducing point density cuts data acquisition costs and overcome computational challenges for broad-scale forest management. However, how does that impact the accuracy of biomass estimation in an urban environment containing a great level of anthropogenic disturbances? The main goal of this study is to evaluate the effects of LiDAR point density on the biomass estimation of remnant forests in the rapidly urbanizing regions of Charlotte, North Carolina, USA. We used multiple linear regression to establish the statistical relationship between field-measured biomass and predictor variables (PVs) derived from LiDAR point clouds with varying densities. We compared the estimation accuracies between the general Urban Forest models (no discrimination of forest type) and the Forest Type models (evergreen, deciduous, and mixed), which was followed by quantifying the degree to which landscape context influenced biomass estimation. The explained biomass variance of Urban Forest models, adjusted R2, was fairly consistent across the reduced point densities with the highest difference of 11.5% between the 100% and 1% point densities. The combined estimates of Forest Type biomass models outperformed the Urban Forest models using two representative point densities (100% and 40%). The Urban Forest biomass model with development density of 125 m radius produced the highest adjusted R2 (0.83 and 0.82 at 100% and 40% LiDAR point densities, respectively) and the lowest RMSE values, signifying the distance impact of development on biomass estimation. Our evaluation suggests that reducing LiDAR point density is a viable solution to regional-scale forest biomass assessment without compromising the accuracy of estimation, which may further be improved using development density.

A 3D radiative transfer model based on lidar data and its application on hydrological and ecosystem modeling

NASA Astrophysics Data System (ADS)

Li, W.; Su, Y.; Harmon, T. C.; Guo, Q.

2013-12-01

Light Detection and Ranging (lidar) is an optical remote sensing technology that measures properties of scattered light to find range and/or other information of a distant object. Due to its ability to generate 3-dimensional data with high spatial resolution and accuracy, lidar technology is being increasingly used in ecology, geography, geology, geomorphology, seismology, remote sensing, and atmospheric physics. In this study we construct a 3-dimentional (3D) radiative transfer model (RTM) using lidar data to simulate the spatial distribution of solar radiation (direct and diffuse) on the surface of water and mountain forests. The model includes three sub-models: a light model simulating the light source, a sensor model simulating the camera, and a scene model simulating the landscape. We use ground-based and airborne lidar data to characterize the 3D structure of the study area, and generate a detailed 3D scene model. The interactions between light and object are simulated using the Monte Carlo Ray Tracing (MCRT) method. A large number of rays are generated from the light source. For each individual ray, the full traveling path is traced until it is absorbed or escapes from the scene boundary. By locating the sensor at different positions and directions, we can simulate the spatial distribution of solar energy at the ground, vegetation and water surfaces. These outputs can then be incorporated into meteorological drivers for hydrologic and energy balance models to improve our understanding of hydrologic processes and ecosystem functions.

Performance modeling of ultraviolet Raman lidar systems for daytime profiling of atmospheric water vapor

NASA Technical Reports Server (NTRS)

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

1991-01-01

We describe preliminary results from a comprehensive computer model developed to guide optimization of a Raman lidar system for measuring daytime profiles of atmospheric water vapor, emphasizing an ultraviolet, solar-blind approach.

Identification of long-range transport of aerosols over Austria using EARLINET lidar measurements

NASA Astrophysics Data System (ADS)

Camelia, Talianu

2018-04-01

The aims of the study is to identify the paths of the long-range transported aerosols over Austria and their potential origin, and to estimate their properties, using lidar measurements from EARLINET stations closest to Austria from Germany and Romania and aerosol transport models. As of now, there is no lidar station in Austria. The study is part of a project to estimate the usefulness of a lidar station located in Vienna, Austria.

Enhancements to the caliop aerosol subtyping and lidar ratio selection algorithms for level II version 4

NASA Astrophysics Data System (ADS)

Omar, A.; Tackett, J.; Kim, M.-H.; Vaughan, M.; Kar, J.; Trepte, C.; Winker, D.

2018-04-01

Several enhancements have been implemented for the version 4 aerosol subtyping and lidar ratio selection algorithms of Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP). Version 4 eliminates the confusion between smoke and clean marine aerosols seen in version 3 by modifications to the elevated layer flag definitions used to identify smoke aerosols over the ocean. To differentiate between mixtures of dust and smoke, and dust and marine aerosols, a new aerosol type will be added in the version 4 data products. In the marine boundary layer, moderately depolarizing aerosols are no longer modeled as mixtures of dust and smoke (polluted dust) but rather as mixtures of dust and seasalt (dusty marine). Some lidar ratios have been updated in the version 4 algorithms. In particular, the dust lidar ratios have been adjusted to reflect the latest measurements and model studies.

Automatic 3d Building Model Generations with Airborne LiDAR Data

NASA Astrophysics Data System (ADS)

Yastikli, N.; Cetin, Z.

2017-11-01

LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D building models can be generated successfully using raw LiDAR point cloud data.

On the factors governing water vapor turbulence mixing in the convective boundary layer over land: Concept and data analysis technique using ground-based lidar measurements.

PubMed

Pal, Sandip

2016-06-01

The convective boundary layer (CBL) turbulence is the key process for exchanging heat, momentum, moisture and trace gases between the earth's surface and the lower part of the troposphere. The turbulence parameterization of the CBL is a challenging but important component in numerical models. In particular, correct estimation of CBL turbulence features, parameterization, and the determination of the contribution of eddy diffusivity are important for simulating convection initiation, and the dispersion of health hazardous air pollutants and Greenhouse gases. In general, measurements of higher-order moments of water vapor mixing ratio (q) variability yield unique estimates of turbulence in the CBL. Using the high-resolution lidar-derived profiles of q variance, third-order moment, and skewness and analyzing concurrent profiles of vertical velocity, potential temperature, horizontal wind and time series of near-surface measurements of surface flux and meteorological parameters, a conceptual framework based on bottom up approach is proposed here for the first time for a robust characterization of the turbulent structure of CBL over land so that our understanding on the processes governing CBL q turbulence could be improved. Finally, principal component analyses will be applied on the lidar-derived long-term data sets of q turbulence statistics to identify the meteorological factors and the dominant physical mechanisms governing the CBL turbulence features. Copyright © 2016 Elsevier B.V. All rights reserved.

Backscatter Modeling at 2.1 Micron Wavelength for Space-Based and Airborne Lidars Using Aerosol Physico-Chemical and Lidar Datasets

NASA Technical Reports Server (NTRS)

Srivastava, V.; Rothermel, J.; Jarzembski, M. A.; Clarke, A. D.; Cutten, D. R.; Bowdle, D. A.; Spinhirne, J. D.; Menzies, R. T.

1999-01-01

Space-based and airborne coherent Doppler lidars designed for measuring global tropospheric wind profiles in cloud-free air rely on backscatter, beta from aerosols acting as passive wind tracers. Aerosol beta distribution in the vertical can vary over as much as 5-6 orders of magnitude. Thus, the design of a wave length-specific, space-borne or airborne lidar must account for the magnitude of 8 in the region or features of interest. The SPAce Readiness Coherent Lidar Experiment under development by the National Aeronautics and Space Administration (NASA) and scheduled for launch on the Space Shuttle in 2001, will demonstrate wind measurements from space using a solid-state 2 micrometer coherent Doppler lidar. Consequently, there is a critical need to understand variability of aerosol beta at 2.1 micrometers, to evaluate signal detection under varying aerosol loading conditions. Although few direct measurements of beta at 2.1 micrometers exist, extensive datasets, including climatologies in widely-separated locations, do exist for other wavelengths based on CO2 and Nd:YAG lidars. Datasets also exist for the associated microphysical and chemical properties. An example of a multi-parametric dataset is that of the NASA GLObal Backscatter Experiment (GLOBE) in 1990 in which aerosol chemistry and size distributions were measured concurrently with multi-wavelength lidar backscatter observations. More recently, continuous-wave (CW) lidar backscatter measurements at mid-infrared wavelengths have been made during the Multicenter Airborne Coherent Atmospheric Wind Sensor (MACAWS) experiment in 1995. Using Lorenz-Mie theory, these datasets have been used to develop a method to convert lidar backscatter to the 2.1 micrometer wavelength. This paper presents comparison of modeled backscatter at wavelengths for which backscatter measurements exist including converted beta (sub 2.1).

Lidar and radar measurements of the melting layer: observations of dark and bright band phenomena

NASA Astrophysics Data System (ADS)

Di Girolamo, P.; Summa, D.; Cacciani, M.; Norton, E. G.; Peters, G.; Dufournet, Y.

2012-05-01

Multi-wavelength lidar measurements in the melting layer revealing the presence of dark and bright bands have been performed by the University of BASILicata Raman lidar system (BASIL) during a stratiform rain event. Simultaneously radar measurements have been also performed from the same site by the University of Hamburg cloud radar MIRA 36 (35.5 GHz), the University of Hamburg dual-polarization micro rain radar (24.15 GHz) and the University of Manchester UHF wind profiler (1.29 GHz). Measurements from BASIL and the radars are illustrated and discussed in this paper for a specific case study on 23 July 2007 during the Convective and Orographically-induced Precipitation Study (COPS). Simulations of the lidar dark and bright band based on the application of concentric/eccentric sphere Lorentz-Mie codes and a melting layer model are also provided. Lidar and radar measurements and model results are also compared with measurements from a disdrometer on ground and a two-dimensional cloud (2DC) probe on-board the ATR42 SAFIRE. Measurements and model results are found to confirm and support the conceptual microphysical/scattering model elaborated by Sassen et al. (2005).

LiDAR error estimation with WAsP engineering

NASA Astrophysics Data System (ADS)

Bingöl, F.; Mann, J.; Foussekis, D.

2008-05-01

The LiDAR measurements, vertical wind profile in any height between 10 to 150m, are based on assumption that the measured wind is a product of a homogenous wind. In reality there are many factors affecting the wind on each measurement point which the terrain plays the main role. To model LiDAR measurements and predict possible error in different wind directions for a certain terrain we have analyzed two experiment data sets from Greece. In both sites LiDAR and met, mast data have been collected and the same conditions are simulated with RisØ/DTU software, WAsP Engineering 2.0. Finally measurement data is compared with the model results. The model results are acceptable and very close for one site while the more complex one is returning higher errors at higher positions and in some wind directions.

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

PubMed Central

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

2017-01-01

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 (R2 = 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 R2 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 (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns. PMID:28106819

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

PubMed

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 (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns.

Atmospheric Turbulence Estimates from a Pulsed Lidar

NASA Technical Reports Server (NTRS)

Pruis, Matthew J.; Delisi, Donald P.; Ahmad, Nash'at N.; Proctor, Fred H.

2013-01-01

Estimates of the eddy dissipation rate (EDR) were obtained from measurements made by a coherent pulsed lidar and compared with estimates from mesoscale model simulations and measurements from an in situ sonic anemometer at the Denver International Airport and with EDR estimates from the last observation time of the trailing vortex pair. The estimates of EDR from the lidar were obtained using two different methodologies. The two methodologies show consistent estimates of the vertical profiles. Comparison of EDR derived from the Weather Research and Forecast (WRF) mesoscale model with the in situ lidar estimates show good agreement during the daytime convective boundary layer, but the WRF simulations tend to overestimate EDR during the nighttime. The EDR estimates from a sonic anemometer located at 7.3 meters above ground level are approximately one order of magnitude greater than both the WRF and lidar estimates - which are from greater heights - during the daytime convective boundary layer and substantially greater during the nighttime stable boundary layer. The consistency of the EDR estimates from different methods suggests a reasonable ability to predict the temporal evolution of a spatially averaged vertical profile of EDR in an airport terminal area using a mesoscale model during the daytime convective boundary layer. In the stable nighttime boundary layer, there may be added value to EDR estimates provided by in situ lidar measurements.

An Improved Calibration Method for a Rotating 2D LIDAR System.

PubMed

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.

An Improved Calibration Method for a Rotating 2D LIDAR System

PubMed Central

Zeng, Yadan; Yu, Heng; Song, Shuang; Lin, Mingqiang; Sun, Bo; Jiang, Wei; Meng, Max Q.-H.

2018-01-01

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. PMID:29414885

Studies on mineral dust using airborne lidar, ground-based remote sensing, and in situ instrumentation

NASA Astrophysics Data System (ADS)

Marenco, Franco; Ryder, Claire; Estellés, Victor; Segura, Sara; Amiridis, Vassilis; Proestakis, Emmanouil; Marinou, Eleni; Tsekeri, Alexandra; Smith, Helen; Ulanowski, Zbigniew; O'Sullivan, Debbie; Brooke, Jennifer; Pradhan, Yaswant; Buxmann, Joelle

2018-04-01

In August 2015, the AER-D campaign made use of the FAAM research aircraft based in Cape Verde, and targeted mineral dust. First results will be shown here. The campaign had multiple objectives: (1) lidar dust mapping for the validation of satellite and model products; (2) validation of sunphotometer remote sensing with airborne measurements; (3) coordinated measurements with the CATS lidar on the ISS; (4) radiative closure studies; and (5) the validation of a new model of dustsonde.

Assessment and Optimization of Lidar Measurement Availability for Wind Turbine Control: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Davoust, S.; Jehu, A.; Bouillet, M.

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 Lidarmore » 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.« less

Derivation of Sky-View Factors from LIDAR Data

NASA Technical Reports Server (NTRS)

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.

Modeling Lidar Multiple Scattering

NASA Astrophysics Data System (ADS)

Sato, Kaori; Okamoto, Hajime; Ishimoto, Hiroshi

2016-06-01

A practical model to simulate multiply scattered lidar returns from inhomogeneous cloud layers are developed based on Backward Monte Carlo (BMC) simulations. The estimated time delay of the backscattered intensities returning from different vertical grids by the developed model agreed well with that directly obtained from BMC calculations. The method was applied to the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite data to improve the synergetic retrieval of cloud microphysics with CloudSat radar data at optically thick cloud grids. Preliminary results for retrieving mass fraction of co-existing cloud particles and drizzle size particles within lowlevel clouds are demonstrated.

Organic carbon stock modelling for the quantification of the carbon sinks in terrestrial ecosystems

NASA Astrophysics Data System (ADS)

Durante, Pilar; Algeet, Nur; Oyonarte, Cecilio

2017-04-01

Given the recent environmental policies derived from the serious threats caused by global change, practical measures to decrease net CO2 emissions have to be put in place. Regarding this, carbon sequestration is a major measure to reduce atmospheric CO2 concentrations within a short and medium term, where terrestrial ecosystems play a basic role as carbon sinks. Development of tools for quantification, assessment and management of organic carbon in ecosystems at different scales and management scenarios, it is essential to achieve these commitments. The aim of this study is to establish a methodological framework for the modeling of this tool, applied to a sustainable land use planning and management at spatial and temporal scale. The methodology for carbon stock estimation in ecosystems is based on merger techniques between carbon stored in soils and aerial biomass. For this purpose, both spatial variability map of soil organic carbon (SOC) and algorithms for calculation of forest species biomass will be created. For the modelling of the SOC spatial distribution at different map scales, it is necessary to fit in and screen the available information of soil database legacy. Subsequently, SOC modelling will be based on the SCORPAN model, a quantitative model use to assess the correlation among soil-forming factors measured at the same site location. These factors will be selected from both static (terrain morphometric variables) and dynamic variables (climatic variables and vegetation indexes -NDVI-), providing to the model the spatio-temporal characteristic. After the predictive model, spatial inference techniques will be used to achieve the final map and to extrapolate the data to unavailable information areas (automated random forest regression kriging). The estimated uncertainty will be calculated to assess the model performance at different scale approaches. Organic carbon modelling of aerial biomass will be estimate using LiDAR (Light Detection And Ranging) algorithms. The available LiDAR databases will be used. LiDAR statistics (which describe the LiDAR cloud point data to calculate forest stand parameters) will be correlated with different canopy cover variables. The regression models applied to the total area will produce a continuous geo-information map to each canopy variable. The CO2 estimation will be calculated by dry-mass conversion factors for each forest species (C kg-CO2 kg equivalent). The result is the organic carbon modelling at spatio-temporal scale with different levels of uncertainty associated to the predictive models and diverse detailed scales. However, one of the main expected problems is due to the heterogeneous spatial distribution of the soil information, which influences on the prediction of the models at different spatial scales and, consequently, at SOC map scale. Besides this, the variability and mixture of the forest species of the aerial biomass decrease the accuracy assessment of the organic carbon.

Analysis and interpretation of lidar observations of the stratospheric aerosol

NASA Technical Reports Server (NTRS)

Hamill, P.; Swissler, T. J.; Osborn, M.; Mccormick, M. P.

1980-01-01

Data obtained with a 48 in. telescope lidar system are compared with results obtained using a one-dimensional stratospheric aerosol model to analyze various microphysical processes influencing the formation of this aerosol. Special attention is given to the following problems: (1) how lidar data can help determine the composition of the aerosol particles and (2) how the layer corresponds to temperature profile variations. The lidar record during the period 1974 to 1979 shows a considerable decrease of the peak value of the backscatter ratio. Seasonal variations in the aerosol layer and a gradual decrease in stratospheric loading are observed. The aerosol model simulates a background stratospheric aerosol layer, and it predicts stratospheric aerosol concentrations and compositions. Numerical experiments are carried out by using the model and by comparing the theoretical results with the experimentally obtained lidar record. Comparisons show that the backscatter profile is consistent with the composition when the particles are sulfuric acid and water; it is not consistent with an ammonium sulfate composition. It is shown that the backscatter ratio is not sensitive to the composition or stratospheric loading of condensation nuclei such as meteoritic debris.

Assessing the influence of return density on estimation of lidar-based aboveground biomass in tropical peat swamp forests of Kalimantan, Indonesia

NASA Astrophysics Data System (ADS)

Manuri, Solichin; Andersen, Hans-Erik; McGaughey, Robert J.; Brack, Cris

2017-04-01

The airborne lidar system (ALS) provides a means to efficiently monitor the status of remote tropical forests and continues to be the subject of intense evaluation. However, the cost of ALS acquisition can vary significantly depending on the acquisition parameters, particularly the return density (i.e., spatial resolution) of the lidar point cloud. This study assessed the effect of lidar return density on the accuracy of lidar metrics and regression models for estimating aboveground biomass (AGB) and basal area (BA) in tropical peat swamp forests (PSF) in Kalimantan, Indonesia. A large dataset of ALS covering an area of 123,000 ha was used in this study. This study found that cumulative return proportion (CRP) variables represent a better accumulation of AGB over tree heights than height-related variables. The CRP variables in power models explained 80.9% and 90.9% of the BA and AGB variations, respectively. Further, it was found that low-density (and low-cost) lidar should be considered as a feasible option for assessing AGB and BA in vast areas of flat, lowland PSF. The performance of the models generated using reduced return densities as low as 1/9 returns per m2 also yielded strong agreement with the original high-density data. The use model-based statistical inferences enabled relatively precise estimates of the mean AGB at the landscape scale to be obtained with a fairly low-density of 1/4 returns per m2, with less than 10% standard error (SE). Further, even when very low-density lidar data was used (i.e., 1/49 returns per m2) the bias of the mean AGB estimates were still less than 10% with a SE of approximately 15%. This study also investigated the influence of different DTM resolutions for normalizing the elevation during the generation of forest-related lidar metrics using various return densities point cloud. We found that the high-resolution digital terrain model (DTM) had little effect on the accuracy of lidar metrics calculation in PSF. The accuracy of low-density lidar metrics in PSF was more influenced by the density of aboveground returns, rather than the last return. This is due to the flat topography of the study area. The results of this study will be valuable for future economical and feasible assessments of forest metrics over large areas of tropical peat swamp ecosystems.

The Uncertainty of Biomass Estimates from Modeled ICESat-2 Returns Across a Boreal Forest Gradient

NASA Technical Reports Server (NTRS)

Montesano, P. M.; Rosette, J.; Sun, G.; North, P.; Nelson, R. F.; Dubayah, R. O.; Ranson, K. J.; Kharuk, V.

2014-01-01

The Forest Light (FLIGHT) radiative transfer model was used to examine the uncertainty of vegetation structure measurements from NASA's planned ICESat-2 photon counting light detection and ranging (LiDAR) instrument across a synthetic Larix forest gradient in the taiga-tundra ecotone. The simulations demonstrate how measurements from the planned spaceborne mission, which differ from those of previous LiDAR systems, may perform across a boreal forest to non-forest structure gradient in globally important ecological region of northern Siberia. We used a modified version of FLIGHT to simulate the acquisition parameters of ICESat-2. Modeled returns were analyzed from collections of sequential footprints along LiDAR tracks (link-scales) of lengths ranging from 20 m-90 m. These link-scales traversed synthetic forest stands that were initialized with parameters drawn from field surveys in Siberian Larix forests. LiDAR returns from vegetation were compiled for 100 simulated LiDAR collections for each 10 Mg · ha(exp -1) interval in the 0-100 Mg · ha(exp -1) above-ground biomass density (AGB) forest gradient. Canopy height metrics were computed and AGB was inferred from empirical models. The root mean square error (RMSE) and RMSE uncertainty associated with the distribution of inferred AGB within each AGB interval across the gradient was examined. Simulation results of the bright daylight and low vegetation reflectivity conditions for collecting photon counting LiDAR with no topographic relief show that 1-2 photons are returned for 79%-88% of LiDAR shots. Signal photons account for approximately 67% of all LiDAR returns, while approximately 50% of shots result in 1 signal photon returned. The proportion of these signal photon returns do not differ significantly (p greater than 0.05) for AGB intervals greater than 20 Mg · ha(exp -1). The 50m link-scale approximates the finest horizontal resolution (length) at which photon counting LiDAR collection provides strong model fits and minimizes forest structure uncertainty in the synthetic Larix stands. At this link-scale AGB greater than 20 Mg · ha(exp -1) has AGB error from 20-50% at the 95% confidence level. These results suggest that the theoretical sensitivity of ICESat-2 photon counting LiDAR measurements alone lack the ability to consistently discern differences in inferred AGB at 10 Mg · ha(exp -1) intervals in sparse forests characteristic of the taiga-tundra ecotone.

Modelling lidar volume-averaging and its significance to wind turbine wake measurements

NASA Astrophysics Data System (ADS)

Meyer Forsting, A. R.; Troldborg, N.; Borraccino, A.

2017-05-01

Lidar velocity measurements need to be interpreted differently than conventional in-situ readings. A commonly ignored factor is “volume-averaging”, which refers to lidars not sampling in a single, distinct point but along its entire beam length. However, especially in regions with large velocity gradients, like the rotor wake, can it be detrimental. Hence, an efficient algorithm mimicking lidar flow sampling is presented, which considers both pulsed and continous-wave lidar weighting functions. The flow-field around a 2.3 MW turbine is simulated using Detached Eddy Simulation in combination with an actuator line to test the algorithm and investigate the potential impact of volume-averaging. Even with very few points discretising the lidar beam is volume-averaging captured accurately. The difference in a lidar compared to a point measurement is greatest at the wake edges and increases from 30% one rotor diameter (D) downstream of the rotor to 60% at 3D.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Newman, Jennifer F.; Clifton, Andrew

Currently, cup anemometers on meteorological towers are used to measure wind speeds and turbulence intensity to make decisions about wind turbine class and site suitability; however, as modern turbine hub heights increase and wind energy expands to complex and remote sites, it becomes more difficult and costly to install meteorological towers at potential sites. As a result, remote-sensing devices (e.g., lidars) are now commonly used by wind farm managers and researchers to estimate the flow field at heights spanned by a turbine. Although lidars can accurately estimate mean wind speeds and wind directions, there is still a large amount ofmore » uncertainty surrounding the measurement of turbulence using these devices. Errors in lidar turbulence estimates are caused by a variety of factors, including instrument noise, volume averaging, and variance contamination, in which the magnitude of these factors is highly dependent on measurement height and atmospheric stability. As turbulence has a large impact on wind power production, errors in turbulence measurements will translate into errors in wind power prediction. The impact of using lidars rather than cup anemometers for wind power prediction must be understood if lidars are to be considered a viable alternative to cup anemometers.In this poster, the sensitivity of power prediction error to typical lidar turbulence measurement errors is assessed. Turbulence estimates from a vertically profiling WINDCUBE v2 lidar are compared to high-resolution sonic anemometer measurements at field sites in Oklahoma and Colorado to determine the degree of lidar turbulence error that can be expected under different atmospheric conditions. These errors are then incorporated into a power prediction model to estimate the sensitivity of power prediction error to turbulence measurement error. Power prediction models, including the standard binning method and a random forest method, were developed using data from the aeroelastic simulator FAST for a 1.5 MW turbine. The impact of lidar turbulence error on the predicted power from these different models is examined to determine the degree of turbulence measurement accuracy needed for accurate power prediction.« less

Demonstration of synchronised scanning Lidar measurements of 2D velocity fields in a boundary-layer wind tunnel

NASA Astrophysics Data System (ADS)

van Dooren, M. F.; Kühn, M.; PetroviĆ, V.; Bottasso, C. L.; Campagnolo, F.; Sjöholm, M.; Angelou, N.; Mikkelsen, T.; Croce, A.; Zasso, A.

2016-09-01

This paper combines the currently relevant research methodologies of scaled wind turbine model experiments in wind tunnels with remote-sensing short-range WindScanner Lidar measurement technology. The wind tunnel of the Politecnico di Milano was equipped with three wind turbine models and two short-range WindScanner Lidars to demonstrate the benefits of synchronised scanning Lidars in such experimental surroundings for the first time. The dual- Lidar system can provide fully synchronised trajectory scans with sampling time scales ranging from seconds to minutes. First, staring mode measurements were compared to hot wire probe measurements commonly used in wind tunnels. This yielded goodness of fit coefficients of 0.969 and 0.902 for the 1 Hz averaged u- and v-components of the wind speed, respectively, validating the 2D measurement capability of the Lidar scanners. Subsequently, the measurement of wake profiles on a line as well as wake area scans were executed to illustrate the applicability of Lidar scanning to measuring small scale wind flow effects. The downsides of Lidar with respect to the hot wire probes are the larger measurement probe volume and the loss of some measurements due to moving blades. In contrast, the benefits are the high flexibility in conducting both point measurements and area scanning, and the fact that remote sensing techniques do not disturb the flow while measuring. The research campaign revealed a high potential for using short-range WindScanner Lidar for accurately measuring small scale flow structures in a wind tunnel.

Automated integration of lidar into the LANDFIRE product suite

USGS Publications Warehouse

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.

A new method to derive middle atmospheric temperature profiles using a combination of Rayleigh lidar and O2 airglow temperatures measurements

NASA Astrophysics Data System (ADS)

Taori, A.; Jayaraman, A.; Raghunath, K.; Kamalakar, V.

2012-01-01

The vertical temperature profiles in a typical Rayleigh lidar system depends on the backscatter photon counts and the CIRA-86 model inputs. For the first time, we show that, by making simultaneous measurements of Rayleigh lidar and upper mesospheric O2 temperatures, the lidar capability can be enhanced to obtain mesospheric temperature profile up to about 95 km altitudes. The obtained results are compared with instantaneous space-borne SABER measurements for a validation.

Lidar Remote Sensing of Forests: New Instruments and Modeling Capabilities

NASA Technical Reports Server (NTRS)

Cook, Bruce D.

2012-01-01

Lidar instruments provide scientists with the unique opportunity to characterize the 3D structure of forest ecosystems. This information allows us to estimate properties such as wood volume, biomass density, stocking density, canopy cover, and leaf area. Structural information also can be used as drivers for photosynthesis and ecosystem demography models to predict forest growth and carbon sequestration. All lidars use time-in-flight measurements to compute accurate ranging measurements; however, there is a wide range of instruments and data types that are currently available, and instrument technology continues to advance at a rapid pace. This seminar will present new technologies that are in use and under development at NASA for airborne and space-based missions. Opportunities for instrument and data fusion will also be discussed, as Dr. Cook is the PI for G-LiHT, Goddard's LiDAR, Hyperspectral, and Thermal airborne imager. Lastly, this talk will introduce radiative transfer models that can simulate interactions between laser light and forest canopies. Developing modeling capabilities is important for providing continuity between observations made with different lidars, and to assist the design of new instruments. Dr. Bruce Cook is a research scientist in NASA's Biospheric Sciences Laboratory at Goddard Space Flight Center, and has more than 25 years of experience conducting research on ecosystem processes, soil biogeochemistry, and exchange of carbon, water vapor and energy between the terrestrial biosphere and atmosphere. His research interests include the combined use of lidar, hyperspectral, and thermal data for characterizing ecosystem form and function. He is Deputy Project Scientist for the Landsat Data Continuity Mission (LDCM); Project Manager for NASA s Carbon Monitoring System (CMS) pilot project for local-scale forest biomass; and PI of Goddard's LiDAR, Hyperspectral, and Thermal (G-LiHT) airborne imager.

Application of randomly oriented spheroids for retrieval of dust particle parameters from multiwavelength lidar measurements

NASA Astrophysics Data System (ADS)

Veselovskii, I.; Dubovik, O.; Kolgotin, A.; Lapyonok, T.; di Girolamo, P.; Summa, D.; Whiteman, D. N.; Mishchenko, M.; Tanré, D.

2010-11-01

Multiwavelength (MW) Raman lidars have demonstrated their potential to profile particle parameters; however, until now, the physical models used in retrieval algorithms for processing MW lidar data have been predominantly based on the Mie theory. This approach is applicable to the modeling of light scattering by spherically symmetric particles only and does not adequately reproduce the scattering by generally nonspherical desert dust particles. Here we present an algorithm based on a model of randomly oriented spheroids for the inversion of multiwavelength lidar data. The aerosols are modeled as a mixture of two aerosol components: one composed only of spherical and the second composed of nonspherical particles. The nonspherical component is an ensemble of randomly oriented spheroids with size-independent shape distribution. This approach has been integrated into an algorithm retrieving aerosol properties from the observations with a Raman lidar based on a tripled Nd:YAG laser. Such a lidar provides three backscattering coefficients, two extinction coefficients, and the particle depolarization ratio at a single or multiple wavelengths. Simulations were performed for a bimodal particle size distribution typical of desert dust particles. The uncertainty of the retrieved particle surface, volume concentration, and effective radius for 10% measurement errors is estimated to be below 30%. We show that if the effect of particle nonsphericity is not accounted for, the errors in the retrieved aerosol parameters increase notably. The algorithm was tested with experimental data from a Saharan dust outbreak episode, measured with the BASIL multiwavelength Raman lidar in August 2007. The vertical profiles of particle parameters as well as the particle size distributions at different heights were retrieved. It was shown that the algorithm developed provided substantially reasonable results consistent with the available independent information about the observed aerosol event.

Lidar-revised geologic map of the Poverty Bay 7.5' quadrangle, King and Pierce Counties, Washington

USGS Publications Warehouse

Tabor, Rowland W.; Booth, Derek B.; Troost, Kathy Goetz

2014-01-01

In 2003, the Puget Sound Lidar Consortium obtained a lidar-derived digital elevation model (DEM) for the Puget Sound region including all of the Poverty Bay 7.5' quadrangle. For a brief description of lidar (LIght Detection And Ranging) and this data acquisition program, see Haugerud and others (2003). This new DEM has a horizontal resolution and accuracy of 6 ft (2 m) and vertical accuracy of approximately 1 ft (0.3 m). The greater resolution and accuracy of the lidar DEM have facilitated a new interpretation of the geology, especially the distribution and relative age of some surficial deposits.

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.

PubMed

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.

Helios: a Multi-Purpose LIDAR Simulation Framework for Research, Planning and Training of Laser Scanning Operations with Airborne, Ground-Based Mobile and Stationary Platforms

NASA Astrophysics Data System (ADS)

Bechtold, S.; Höfle, B.

2016-06-01

In many technical domains of modern society, there is a growing demand for fast, precise and automatic acquisition of digital 3D models of a wide variety of physical objects and environments. Laser scanning is a popular and widely used technology to cover this demand, but it is also expensive and complex to use to its full potential. However, there might exist scenarios where the operation of a real laser scanner could be replaced by a computer simulation, in order to save time and costs. This includes scenarios like teaching and training of laser scanning, development of new scanner hardware and scanning methods, or generation of artificial scan data sets to support the development of point cloud processing and analysis algorithms. To test the feasibility of this idea, we have developed a highly flexible laser scanning simulation framework named Heidelberg LiDAR Operations Simulator (HELIOS). HELIOS is implemented as a Java library and split up into a core component and multiple extension modules. Extensible Markup Language (XML) is used to define scanner, platform and scene models and to configure the behaviour of modules. Modules were developed and implemented for (1) loading of simulation assets and configuration (i.e. 3D scene models, scanner definitions, survey descriptions etc.), (2) playback of XML survey descriptions, (3) TLS survey planning (i.e. automatic computation of recommended scanning positions) and (4) interactive real-time 3D visualization of simulated surveys. As a proof of concept, we show the results of two experiments: First, a survey planning test in a scene that was specifically created to evaluate the quality of the survey planning algorithm. Second, a simulated TLS scan of a crop field in a precision farming scenario. The results show that HELIOS fulfills its design goals.

Point Cloud Refinement with a Target-Free Intrinsic Calibration of a Mobile Multi-Beam LIDAR System

NASA Astrophysics Data System (ADS)

Nouiraa, H.; Deschaud, J. E.; Goulettea, F.

2016-06-01

LIDAR sensors are widely used in mobile mapping systems. The mobile mapping platforms allow to have fast acquisition in cities for example, which would take much longer with static mapping systems. The LIDAR sensors provide reliable and precise 3D information, which can be used in various applications: mapping of the environment; localization of objects; detection of changes. Also, with the recent developments, multi-beam LIDAR sensors have appeared, and are able to provide a high amount of data with a high level of detail. A mono-beam LIDAR sensor mounted on a mobile platform will have an extrinsic calibration to be done, so the data acquired and registered in the sensor reference frame can be represented in the body reference frame, modeling the mobile system. For a multibeam LIDAR sensor, we can separate its calibration into two distinct parts: on one hand, we have an extrinsic calibration, in common with mono-beam LIDAR sensors, which gives the transformation between the sensor cartesian reference frame and the body reference frame. On the other hand, there is an intrinsic calibration, which gives the relations between the beams of the multi-beam sensor. This calibration depends on a model given by the constructor, but the model can be non optimal, which would bring errors and noise into the acquired point clouds. In the litterature, some optimizations of the calibration parameters are proposed, but need a specific routine or environment, which can be constraining and time-consuming. In this article, we present an automatic method for improving the intrinsic calibration of a multi-beam LIDAR sensor, the Velodyne HDL-32E. The proposed approach does not need any calibration target, and only uses information from the acquired point clouds, which makes it simple and fast to use. Also, a corrected model for the Velodyne sensor is proposed. An energy function which penalizes points far from local planar surfaces is used to optimize the different proposed parameters for the corrected model, and we are able to give a confidence value for the calibration parameters found. Optimization results on both synthetic and real data are presented.

CALIPSO Satellite Lidar Identification Of Elevated Dust Over Australia Compared With Air Quality Model PM60 Forecasts

NASA Technical Reports Server (NTRS)

Young, Stuart A.; Vaughan, Mark; Omar, Ali; Liu, Zhaoyan; Lee, Sunhee; Hu, Youngxiang; Cope, Martin

2008-01-01

Global measurements of the vertical distribution of clouds and aerosols have been recorded by the lidar on board the CALIPSO (Cloud Aerosol Lidar Infrared Pathfinder Satellite Observations) satellite since June 2006. Such extensive, height-resolved measurements provide a rare and valuable opportunity for developing, testing and validating various atmospheric models, including global climate, numerical weather prediction, chemical transport and air quality models. Here we report on the initial results of an investigation into the performance of the Australian Air Quality Forecast System (AAQFS) model in forecasting the distribution of elevated dust over the Australian region. The model forecasts of PM60 dust distribution are compared with the CALIPSO lidar Vertical Feature Mask (VFM) data product. The VFM classifies contiguous atmospheric regions of enhanced backscatter as either cloud or aerosols. Aerosols are further classified into six subtypes. By comparing forecast PM60 concentration profiles to the spatial distribution of dust reported in the CALIPSO VFM, we can assess the model s ability to predict the occurrence and the vertical and horizontal extents of dust events within the study area.

In-situ, sunphotometer and Raman lidar observations of aerosol transport events in the western Mediterranean during the June 2013 ChArMEx campaign

NASA Astrophysics Data System (ADS)

Totems, Julien; Sicard, Michael; Bertolin, Santi; Boytard, Mai-Lan; Chazette, Patrick; Comeron, Adolfo; Dulac, Francois; Hassanzadeh, Sahar; Lange, Diego; Marnas, Fabien; Munoz, Constantino; Shang, Xiaoxia

2014-05-01

We present a preliminary analysis of aerosol observations performed in June 2013 in the western Mediterranean at two stations set up in Barcelona and Menorca (Spain) in the framework of the ChArMEx (Chemistry Aerosol Mediterranean Experiment) project. The Barcelona station was equipped with the following fixed instruments belonging to the Universitat Politècnica de Catalunya (UPC): an AERONET (Aerosol Robotic Network) sun-photometer, an MPL (Micro Pulse Lidar) lidar and the UPC multi-wavelength lidar. The MPL lidar works at 532 nm and has a depolarization channel, while the UPC lidar works at 355, 532 and 1064 nm, and also includes two N2- (at 387 and 607 nm) and one H2O-Raman (at 407 nm) channels. The MPL system works continuously 24 hour/day. The UPC system was operated on alert in coordination with the research aircrafts plans involved in the campaign. In Cap d'en Font, Menorca, the mobile laboratory of the Laboratoire des Sciences du Climat et de l'Environnement hosted an automated (AERONET) and a manual (Microtops) 5-lambda sunphotometer, a 3-lambda nephelometer, a 7-lambda aethalometer, as well as the LSCE Water vapor Aerosol LIdar (WALI). This mini Raman lidar, first developed and validated for the HyMEX (Hydrological cycle in the Mediterranean eXperiment) campaign in 2012, works at 355 nm for eye safety and is designed with a short overlap distance (<300m) to probe the lower troposphere. It includes depolarization, N2- and H2O-Raman channels. H2O observations have been calibrated on-site by different methods and show good agreement with balloon measurements. Observations at Cap d'en Font were quasi-continuous from June 10th to July 3rd, 2013. The lidar data at both stations helped direct the research aircrafts and balloon launches to interesting plumes of particles in real time for in-situ measurements. Among some light pollution background from the European continent, a typical Saharan dust event and an unusual American dust/biomass burning event are highlighted in our measurements. The lidar ratio, depolarization ratio and water content, as well as the usual aerosol vertical distribution and extinction properties provided by the Raman lidars, and the size distributions provided by AERONET, prove very helpful in characterizing particle types and sources, especially for the multi-layer situations observed. Further on, the study of parameters extracted during this campaign will allow us an assessment of the local direct aerosol radiative forcing.

Full-waveform and discrete-return lidar in salt marsh environments: An assessment of biophysical parameters, vertical uncertatinty, and nonparametric dem correction

NASA Astrophysics Data System (ADS)

Rogers, Jeffrey N.

High-resolution and high-accuracy elevation data sets of coastal salt marsh environments are necessary to support restoration and other management initiatives, such as adaptation to sea level rise. Lidar (light detection and ranging) data may serve this need by enabling efficient acquisition of detailed elevation data from an airborne platform. However, previous research has revealed that lidar data tend to have lower vertical accuracy (i.e., greater uncertainty) in salt marshes than in other environments. The increase in vertical uncertainty in lidar data of salt marshes can be attributed primarily to low, dense-growing salt marsh vegetation. Unfortunately, this increased vertical uncertainty often renders lidar-derived digital elevation models (DEM) ineffective for analysis of topographic features controlling tidal inundation frequency and ecology. This study aims to address these challenges by providing a detailed assessment of the factors influencing lidar-derived elevation uncertainty in marshes. The information gained from this assessment is then used to: 1) test the ability to predict marsh vegetation biophysical parameters from lidar-derived metrics, and 2) develop a method for improving salt marsh DEM accuracy. Discrete-return and full-waveform lidar, along with RTK GNSS (Real-time Kinematic Global Navigation Satellite System) reference data, were acquired for four salt marsh systems characterized by four major taxa (Spartina alterniflora, Spartina patens, Distichlis spicata, and Salicornia spp.) on Cape Cod, Massachusetts. These data were used to: 1) develop an innovative combination of full-waveform lidar and field methods to assess the vertical distribution of aboveground biomass as well as its light blocking properties; 2) investigate lidar elevation bias and standard deviation using varying interpolation and filtering methods; 3) evaluate the effects of seasonality (temporal differences between peak growth and senescent conditions) using lidar data flown in summer and spring; 4) create new products, called Relative Uncertainty Surfaces (RUS), from lidar waveform-derived metrics and determine their utility; and 5) develop and test five nonparametric regression model algorithms (MARS -- Multivariate Adaptive Regression, CART -- Classification and Regression Trees, TreeNet, Random Forests, and GPSM -- Generalized Path Seeker) with 13 predictor variables derived from both discrete and full waveform lidar sources in order to develop a method of improving lidar DEM quality. Results of this study indicate strong correlations for Spartina alterniflora (r > 0.9) between vertical biomass (VB), the distribution of vegetation biomass by height, and vertical obscuration (VO), the measure of the vertical distribution of the ratio of vegetation to airspace. It was determined that simple, feature-based lidar waveform metrics, such as waveform width, can provide new information to estimate salt marsh vegetation biophysical parameters such as vegetation height. The results also clearly illustrate the importance of seasonality, species, and lidar interpolation and filtering methods on elevation uncertainty in salt marshes. Relative uncertainty surfaces generated from lidar waveform features were determined useful in qualitative/visual assessment of lidar elevation uncertainty and correlate well with vegetation height and presence of Spartina alterniflora. Finally, DEMs generated using full-waveform predictor models produced corrections (compared to ground based RTK GNSS elevations) with R2 values of up to 0.98 and slopes within 4% of a perfect 1:1 correlation. The findings from this research have strong potential to advance tidal marsh mapping, research and management initiatives.

Comparison of LIDAR system performance for alternative single-mode receiver architectures: modeling and experimental validation

NASA Astrophysics Data System (ADS)

Toliver, Paul; Ozdur, Ibrahim; Agarwal, Anjali; Woodward, T. K.

2013-05-01

In this paper, we describe a detailed performance comparison of alternative single-pixel, single-mode LIDAR architectures including (i) linear-mode APD-based direct-detection, (ii) optically-preamplified PIN receiver, (iii) PINbased coherent-detection, and (iv) Geiger-mode single-photon-APD counting. Such a comparison is useful when considering next-generation LIDAR on a chip, which would allow one to leverage extensive waveguide-based structures and processing elements developed for telecom and apply them to small form-factor sensing applications. Models of four LIDAR transmit and receive systems are described in detail, which include not only the dominant sources of receiver noise commonly assumed in each of the four detection limits, but also additional noise terms present in realistic implementations. These receiver models are validated through the analysis of detection statistics collected from an experimental LIDAR testbed. The receiver is reconfigurable into four modes of operation, while transmit waveforms and channel characteristics are held constant. The use of a diffuse hard target highlights the importance of including speckle noise terms in the overall system analysis. All measurements are done at 1550 nm, which offers multiple system advantages including less stringent eye safety requirements and compatibility with available telecom components, optical amplification, and photonic integration. Ultimately, the experimentally-validated detection statistics can be used as part of an end-to-end system model for projecting rate, range, and resolution performance limits and tradeoffs of alternative integrated LIDAR architectures.

A Numerical Model of the Performance of the Howard University Raman Lidar System

NASA Astrophysics Data System (ADS)

Connell, Rasheen M.; Adam, Mariana; Venable, Demetrius

2009-07-01

At the Howard University Atmospheric Observatory in Beltsville, MD, a Raman Lidar system was developed to provide both daytime and nighttime measurements of water vapor, aerosols, and cirrus clouds with 1 min temporal and 7.5 m spatial resolution in the lower troposphere. Signals at three wavelengths associated with Rayleigh/Mie scattering for aerosols and cirrus clouds at 354.7 nm, Raman scattering for nitrogen at 386.7 nm, and water vapor at 407.5 nm are analyzed. The transmitter is a triple harmonic Nd: YAG solid state laser. The receiver is a 40 cm Cassegrain telescope. Our detector system consists of a multi-channel wavelength separator unit and data acquisition system. We are developing a numerical model to provide a realistic representation of the system behavior. The variants of the lidar equation in the model use system parameters and are solved to determine the return signals for our lidar system. In this paper, we report on two of the five case studies being investigated: clear sky and cirrus cloud covered molecular atmosphere. The first simulations are based on a standard atmosphere, which assumes an unpolluted (aerosol-free) dry air atmosphere. The second set of simulations is based on a cloudy atmosphere, where cirrus clouds are added to the conditions in case study I. Lidar signals are simulated over the altitude range covered by our measurements (up to 14 km). Results will show comparisons between the simulated and actual measurements when varying lidar and atmospheric optical parameters in the model.

Registration of Panoramic/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features

PubMed Central

Zhu, Ningning; Jia, Yonghong; Ji, Shunping

2018-01-01

We propose utilizing a rigorous registration model and a skyline-based method for automatic registration of LiDAR points and a sequence of panoramic/fish-eye images in a mobile mapping system (MMS). This method can automatically optimize original registration parameters and avoid the use of manual interventions in control point-based registration methods. First, the rigorous registration model between the LiDAR points and the panoramic/fish-eye image was built. Second, skyline pixels from panoramic/fish-eye images and skyline points from the MMS’s LiDAR points were extracted, relying on the difference in the pixel values and the registration model, respectively. Third, a brute force optimization method was used to search for optimal matching parameters between skyline pixels and skyline points. In the experiments, the original registration method and the control point registration method were used to compare the accuracy of our method with a sequence of panoramic/fish-eye images. The result showed: (1) the panoramic/fish-eye image registration model is effective and can achieve high-precision registration of the image and the MMS’s LiDAR points; (2) the skyline-based registration method can automatically optimize the initial attitude parameters, realizing a high-precision registration of a panoramic/fish-eye image and the MMS’s LiDAR points; and (3) the attitude correction values of the sequences of panoramic/fish-eye images are different, and the values must be solved one by one. PMID:29883431

Modeling marbled murrelet (Brachyramphus marmoratus) habitat using LiDAR-derived canopy data

USGS Publications Warehouse

Hagar, Joan C.; Eskelson, Bianca N.I.; Haggerty, Patricia K.; Nelson, S. Kim; Vesely, David G.

2014-01-01

LiDAR (Light Detection And Ranging) is an emerging remote-sensing tool that can provide fine-scale data describing vertical complexity of vegetation relevant to species that are responsive to forest structure. We used LiDAR data to estimate occupancy probability for the federally threatened marbled murrelet (Brachyramphus marmoratus) in the Oregon Coast Range of the United States. Our goal was to address the need identified in the Recovery Plan for a more accurate estimate of the availability of nesting habitat by developing occupancy maps based on refined measures of nest-strand structure. We used murrelet occupancy data collected by the Bureau of Land Management Coos Bay District, and canopy metrics calculated from discrete return airborne LiDAR data, to fit a logistic regression model predicting the probability of occupancy. Our final model for stand-level occupancy included distance to coast, and 5 LiDAR-derived variables describing canopy structure. With an area under the curve value (AUC) of 0.74, this model had acceptable discrimination and fair agreement (Cohen's κ = 0.24), especially considering that all sites in our sample were regarded by managers as potential habitat. The LiDAR model provided better discrimination between occupied and unoccupied sites than did a model using variables derived from Gradient Nearest Neighbor maps that were previously reported as important predictors of murrelet occupancy (AUC = 0.64, κ = 0.12). We also evaluated LiDAR metrics at 11 known murrelet nest sites. Two LiDAR-derived variables accurately discriminated nest sites from random sites (average AUC = 0.91). LiDAR provided a means of quantifying 3-dimensional canopy structure with variables that are ecologically relevant to murrelet nesting habitat, and have not been as accurately quantified by other mensuration methods.

Mapping the spatial pattern of temperate forest above ground biomass by integrating airborne lidar with Radarsat-2 imagery via geostatistical models

NASA Astrophysics Data System (ADS)

Li, Wang; Niu, Zheng; Gao, Shuai; Wang, Cheng

2014-11-01

Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR) are two competitive active remote sensing techniques in forest above ground biomass estimation, which is important for forest management and global climate change study. This study aims to further explore their capabilities in temperate forest above ground biomass (AGB) estimation by emphasizing the spatial auto-correlation of variables obtained from these two remote sensing tools, which is a usually overlooked aspect in remote sensing applications to vegetation studies. Remote sensing variables including airborne LiDAR metrics, backscattering coefficient for different SAR polarizations and their ratio variables for Radarsat-2 imagery were calculated. First, simple linear regression models (SLR) was established between the field-estimated above ground biomass and the remote sensing variables. Pearson's correlation coefficient (R2) was used to find which LiDAR metric showed the most significant correlation with the regression residuals and could be selected as co-variable in regression co-kriging (RCoKrig). Second, regression co-kriging was conducted by choosing the regression residuals as dependent variable and the LiDAR metric (Hmean) with highest R2 as co-variable. Third, above ground biomass over the study area was estimated using SLR model and RCoKrig model, respectively. The results for these two models were validated using the same ground points. Results showed that both of these two methods achieved satisfactory prediction accuracy, while regression co-kriging showed the lower estimation error. It is proved that regression co-kriging model is feasible and effective in mapping the spatial pattern of AGB in the temperate forest using Radarsat-2 data calibrated by airborne LiDAR metrics.

TLSpy: An Open-Source Addition to Terrestrial Lidar Workflows

NASA Astrophysics Data System (ADS)

Frechette, J. D.; Weissmann, G. S.; Wawrzyniec, T. F.

2008-12-01

Terrestrial lidar scanners (TLS) that capture three dimensional (3D) geometry with cm scale precision present many new opportunities in the Earth Sciences and related fields. However, the lack of domain specific tools impedes full and efficient utilization of the information contained in these datasets. Most processing and analysis is performed using a variety of manufacturing, surveying, airborne lidar, and GIS software. Although much overlap exists, inevitably some needs are not addressed by these applications. TLSpy provides a plugin driven framework with 3D visualization capabilities that encourages researchers to fill these gaps. The goal is to free researchers from the intellectual overhead imposed by user and data interface design, enabling rapid development of TLS specific processing and analysis algorithms. We present two plugins as examples of problems that TLSpy is being applied to. The first plugin corrects for the strong influence of target orientation on TLS measured reflectance intensities. It calculates the distribution of incidence angles and intensities in an input scan and assists the user in fitting a reflectance model to the distribution. The model is then used to normalize input intensities, minimizing the impact of surface orientation and simplifying the extraction of quantitative data from reflectance measurements. Although reasonable default models can be determined the large number of factors influencing reflectance values require that the plugin be designed for maximum flexibility, allowing the user to adjust all model parameters and define new reflectance models as needed. The second plugin helps eliminate multipath reflections from water surfaces. Characterized by a lower intensity mirror image of the subaerial bank appearing below the water surface, these reflections are a common problem in scans containing water. These erroneous reflections can be removed by manually selecting points that lie on the waterline, fitting a plane to the points, and deleting points below that plane. This plugin simplifies the process by automatically identifying waterline points using characteristic changes in geometry and intensity. Automatic identification is often faster and more reliable than manual identification, however, manual control is retained as a fallback for degenerate cases.

Retrieval of Methane Source Strengths in Europe Using a Simple Modeling Approach to Assess the Potential of Spaceborne Lidar Observations

NASA Technical Reports Server (NTRS)

Weaver, C.; Kiemle, C.; Kawa, S. R.; Aalto, T.; Necki, J.; Steinbacher, M.; Arduini, J.; Apadula, F.; Berkhout, H.; Hatakka, J.

2014-01-01

We investigate the sensitivity of future spaceborne lidar measurements to changes in surface methane emissions. We use surface methane observations from nine European ground stations and a Lagrangian transport model to infer surface methane emissions for 2010. Our inversion shows the strongest emissions from the Netherlands, the coal mines in Upper Silesia, Poland, and wetlands in southern Finland. The simulated methane surface concentrations capture at least half of the daily variability in the observations, suggesting that the transport model is correctly simulating the regional transport pathways over Europe. With this tool we can test whether proposed methane lidar instruments will be sensitive to changes in surface emissions. We show that future lidar instruments should be able to detect a 50% reduction in methane emissions from the Netherlands and Germany, at least during summer.

A case history of using high-resolution LiDAR data to support archaeological prediction models in a low-relief area

NASA Astrophysics Data System (ADS)

Pacskó, Vivien; Székely, Balázs; Stibrányi, Máté; Koma, Zsófia

2016-04-01

Hungary is situated in the crossroad of several large-scale infrastructural pathways like transnational pipelines and transcontinental motorways. At the same time the country is rich in known and potential archaeological sites. Archaeological prediction techniques aided by remote sensing are intended to help increase preparedness for archaeological surveying and rescue activities in response to planned new infrastructural developments (e.g., a new pipeline), as they try to estimate the number of potential archaeological sites, area to be surveyed, potential cost and time needed for these activities. In very low-relief areas microtopographic forms may indicate sites, high-resolution LiDAR DTMs are suitable for their detection. Main sources of archaeological prediction models are known archaeological sites, where optimal environmental conditions of settling down existed at historic ages. Hydrological characteristics, relief, geology, vegetation cover and soil are considered to be as most important natural factors. Sorting of the factors and accuracy of the sampling differentiate our models. Resolution of an inductive model depends on the spatial properties of the integrated data: a raster data set can be generated that contains probability values and the reliability of the estimation. The information content of the predictive model is highly influenced by the resolution of the used digital terrain model (DTM): its derivatives (slope, aspect, topographic features) are important inputs of the modelling. The quality of the DTM is even more important in low-relief areas as microtopographic features may indicate archaeological sites. The conventional digital elevation models (SRTM, ASTER GDEM) provide unsatisfying resolution (both in horizontal and vertical senses) as they are rather digital surface models containing the vegetation and the built-up structures. Processed multiecho LiDAR data can be used instead. Our study area is situated in the foothills of the Transdanubian Range characterized by NNW-SSE directed valleys. One of the largest valleys is a conspicuously straight valley section of the River Sárvíz between Székesfehérvár and Szekszárd. Archaeological surveys revealed various settlement remains since the Neolithic. LiDAR data acquisition has been carried out in the framework of an EUFAR project supported by the European Union. Although the weather conditions were not optimal during the flight, sophisticated processing (carried out with of OPALS software) removed most of the artifacts. The resulting 1 m resolution digital terrain model (DTM) has been used to out. This DTM and the known archaeological site locations were integrated in a GIS system for qualitative and quantitative analysis. The processing aimed at analyzing elevation patterns of archaeological sites: local microtopographic features have been outlined and local low-relief elevation data have been extracted and analysed along the Sárvíz valley. Sites have been grouped according to the age of the artifacts identified by the quick-look archaeological walkthrough surveys. The topographic context of these elevation patterns were compared to the relative relief positions of the sites. Some ages groups have confined elevation ranges that may indicate hydrological/climate dependency of the settlement site selection, whereas some long-lived sites can also be identified, typically further away from the local erosional base. Extremely low-relief areas are supposed to have had swampy or partly inundated environmental conditions in ancient times; these areas were unsuitable for human settlement for long time periods. Such areas can be typically attributed by low predictive probabilities, whereas small mounds, patches of topographic unevenness would get higher model probabilities. The final the models can be used for focused field surveys that can improve our archaeological knowledge of the area. The data used were acquired in the framework of the EUFAR ARMSRACE project (to MS), the studies were carried out in project OTKA NK83400 financed by the Hungarian Scientific Research Fund. BS contributed as an Alexander von Humboldt Research Fellow.

Analysis of spatial correlation in predictive models of forest variables that use LiDAR auxiliary information

Treesearch

F. Mauro; Vicente J. Monleon; H. Temesgen; L.A. Ruiz

2017-01-01

Accounting for spatial correlation of LiDAR model errors can improve the precision of model-based estimators. To estimate spatial correlation, sample designs that provide close observations are needed, but their implementation might be prohibitively expensive. To quantify the gains obtained by accounting for the spatial correlation of model errors, we examined (

LIDAR Investigation Of The 2004 Niigata Ken Chuetsu, Japan, Earthquake

NASA Astrophysics Data System (ADS)

Kayen, R.; Pack, R. T.; Sugimoto, S.; Tanaka, H.

2005-12-01

The 23 October 2004 Niigata Ken Chuetsu, Japan, Mw 6.6 earthquake was the most significant earthquake to affect Japan since the 1995 Kobe earthquake. Forty people were killed, almost 3,000 injured, and numerous landslides destroyed entire upland villages. Landslides and permanent ground deformation caused extensive damage to roads, rail lines and other lifelines, resulting in major economic disruption. The cities and towns most significantly affected by the earthquake were Nagaoka, Ojiya, and the mountainous rural areas of Yamakoshi village and Kawaguchi town. Our EERI team traveled with a tripod mounted LIDAR (Light Detection and Ranging) unit, a scanning-laser that creates ultra high-resolution 3-D digital terrain models of the earthquake damaged surfaces the ground, structures, and life-lines. This new technology allows for rapid and remote sensing of damaged terrain. Ground-based LIDAR has an accuracy range of 0.5-2.5 cm, and can illuminate targets up to 400m away from the sensor. During a single tripod-mounted LIDAR scan of 10 minutes, several million survey points are collected and processed into an ultra-high resolution terrain model of the damaged ground or structure. There are several benefits in acquiring these LIDAR data in the initial reconnaissance effort after the earthquake. First, we record the detailed failure morphologies of damaged ground and structures in order to make measurements that are either impractical or impossible by conventional survey means. The digital terrain models allow us to enlarge, enhance and rotate data in order to visualize damage in orientations and scales not previously possible. This ability to visualize damage allows us to better understand failure modes. Finally, LIDAR allows us to archive 3-D terrain models so that the engineering community can evaluate analytical and numerical models of deformation potential against detailed field measurements. Here, we discuss the findings of this 2004 Niigata Chuetsu Earthquake (M6.6) reconnaissance presented with LIDAR examples for damage-visualization.

New calibration technique for water-vapor Raman lidar combined with the GNSS precipitable water vapor and the Meso-Scale Model

NASA Astrophysics Data System (ADS)

Kakihara, H.; Yabuki, M.; Kitafuji, F.; Tsuda, T.; Tsukamoto, M.; Hasegawa, T.; Hashiguchi, H.; Yamamoto, M.

2017-12-01

Atmospheric water vapor plays an important role in atmospheric chemistry and meteorology, with implications for climate change and severe weather. The Raman lidar technique is useful for observing water-vapor with high spatiotemporal resolutions. However, the calibration factor must be determined before observations. Because the calibration factor is generally evaluated by comparing Raman-signal results with those of independent measurement techniques (e.g., radiosonde), it is difficult to apply this technique to lidar sites where radiosonde observation cannot be carried out. In this study, we propose a new calibration technique for water-vapor Raman lidar using global navigation satellite system (GNSS)-derived precipitable water vapor (PWV) and Japan Meteorological Agency meso-scale model (MSM). The analysis was accomplished by fitting the GNSS-PWV to integrated water-vapor profiles combined with the MSM and the results of the lidar observations. The maximum height of the lidar signal applicable to this method was determined within 2.0 km by considering the signal noise mainly caused by low clouds. The MSM data was employed at higher regions that cannot apply the lidar data. This method can be applied to lidar signals lower than a limited height range due to weather conditions and lidar specifications. For example, Raman lidar using a laser operating in the ultraviolet C (UV-C) region has the advantage of daytime observation since there is no solar background radiation in the system. The observation range is, however, limited at altitudes lower than 1-3 km because of strong ozone absorption at the UV-C region. The new calibration technique will allow the utilization of various types of Raman lidar systems and provide many opportunities for calibration. We demonstrated the potential of this method by using the UV-C Raman lidar and GNSS observation data at the Shigaraki MU radar observatory (34°51'N, 136°06'E; 385m a.s.l.) of the Research Institute for Sustainable Humanosphere (RISH, Kyoto University, Japan, in June 2016. Differences of the calibration factor between the proposed method and the conventional method were 0.7% under optimal conditions such as clear skies and low ozone concentrations.

Combining satellite photographs and raster lidar data for channel connectivity in tidal marshes.

NASA Astrophysics Data System (ADS)

Li, Zhi; Hodges, Ben

2017-04-01

High resolution airborne lidar is capable of providing topographic detail down to the 1 x 1 m scale or finer over large tidal marshes of a river delta. Such data sets can be challenging to develop and ground-truth due to the inherent complexities of the environment, the relatively small changes in elevation throughout a marsh, and practical difficulties in accessing the variety of flooded, dry, and muddy regions. Standard lidar point-cloud processing techniques (as typically applied in large lidar data collection program) have a tendency to mis-identify narrow channels and water connectivity in a marsh, which makes it difficult to directly use such data for modeling marsh flows. Unfortunately, it is not always practical, or even possible, to access the point cloud and re-analyze the raw lidar data when discrepancies have been found in a raster work product. Faced with this problem in preparing a model of the Trinity River delta (Texas, USA), we developed an approach to integrating analysis of a lidar-based raster with satellite images. Our primary goal was to identify the clear land/water boundaries needed to identify channelization in the available rasterized lidar data. The channel extraction method uses pixelized satellite photographs that are stretched/distorted with image-processing techniques to match identifiable control features in both lidar and photographic data sets. A kmeans clustering algorithm was applied cluster pixels based on their colors, which is effective in separating land and water in a satellite photograph. The clustered image was matched to the lidar data such that the combination shows the channel network. In effect, we are able to use the fact that the satellite photograph is higher resolution than the lidar data, and thus provides connectivity in the clustering at a finer scale. The principal limitation of the method is the where the satellite image and lidar suffer from similar problems For example, vegetation overhanging a narrow channel might show up as higher-elevation land in the lidar data an also as a non-water cluster color in the satellite photo.

Building Damage Assessment after Earthquake Using Post-Event LiDAR Data

NASA Astrophysics Data System (ADS)

Rastiveis, H.; Eslamizade, F.; Hosseini-Zirdoo, E.

2015-12-01

After an earthquake, damage assessment plays an important role in leading rescue team to help people and decrease the number of mortality. Damage map is a map that demonstrates collapsed buildings with their degree of damage. With this map, finding destructive buildings can be quickly possible. In this paper, we propose an algorithm for automatic damage map generation after an earthquake using post-event LiDAR Data and pre-event vector map. The framework of the proposed approach has four main steps. To find the location of all buildings on LiDAR data, in the first step, LiDAR data and vector map are registered by using a few number of ground control points. Then, building layer, selected from vector map, are mapped on the LiDAR data and all pixels which belong to the buildings are extracted. After that, through a powerful classifier all the extracted pixels are classified into three classes of "debris", "intact building" and "unclassified". Since textural information make better difference between "debris" and "intact building" classes, different textural features are applied during the classification. After that, damage degree for each candidate building is estimated based on the relation between the numbers of pixels labelled as "debris" class to the whole building area. Calculating the damage degree for each candidate building, finally, building damage map is generated. To evaluate the ability proposed method in generating damage map, a data set from Port-au-Prince, Haiti's capital after the 2010 Haiti earthquake was used. In this case, after calculating of all buildings in the test area using the proposed method, the results were compared to the damage degree which estimated through visual interpretation of post-event satellite image. Obtained results were proved the reliability of the proposed method in damage map generation using LiDAR data.

A mini backscatter lidar for airborne measurements in the framework of DACCIWA

NASA Astrophysics Data System (ADS)

Chazette, Patrick; Totems, Julien; Flamant, Cyrille; Shang, Xiaoxia; Denjean, Cyrielle; Meynadier, Rémi; Perrin, Thierry; Laurens, Marc

2017-04-01

During the international campaign of the European program Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA), investigating the relationship between weather, climate and air pollution in southern West Africa, a mini backscatter lidar was embedded on the French research aircraft (ATR42) of the Service des Avions Français Instrumentés pour la Recherche en Environnement (SAFIRE). This implementation was made possible thanks to the support of the Centre National d'Etude Spatial (CNES), with the aim of assessing the relative relevance of airborne or spaceborne (e.g. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, CALIPSO) remote sensing instruments. The lidar complemented the various in-situ observations carried out on the plane, by identifying the aerosol layers in the atmospheric column below the aircraft, and bringing strong constraints for the validation of other measurements. The field campaign took place from 27 to 16 July 2016 from Lomé, Togo. The aircraft conducted flights between 1 km and 5 km above the mean sea level (amsl), allowing the coupling of in situ and remote sensing data to assess the properties of the aerosol layers. Aerosol plumes of different origins were identified using the coupling between the lidar cross-polarized channels, satellite observations and a set of back trajectories analyses. During several flights, depolarizing aerosol layers from the northeast were observed between 2.5 and 4 km amsl, which highlight the significant contribution of dust-like particles to the aerosol load in the coastal region. Conversely, air masses originating from the east-southeast were loaded with a mixing of biomass burning and pollution aerosols. The former originated from Central Africa and the latter from human activities in and around large cities (Lomé). The flight sampling strategy and related lidar investigations will be presented and discussed.

Dynamic Shade and Irradiance Simulation of Aquatic ...

EPA Pesticide Factsheets

Penumbra is a landscape shade and irradiance simulation model that simulates how solar energy spatially and temporally interacts within dynamic ecosystems such as riparian zones, forests, and other terrain that cast topological shadows. Direct and indirect solar energy accumulates across landscapes and is the main energy driver for increasing aquatic and landscape temperatures at both local and holistic scales. Landscape disturbances such as landuse change, clear cutting, and fire can cause significant variations in the resulting irradiance reaching particular locations. Penumbra can simulate solar angles and irradiance at definable temporal grains as low as one minute while simulating landscape shadowing up to an entire year. Landscapes can be represented at sub-meter resolutions with appropriate spatial data inputs, such as field data or elevation and surface object heights derived from light detection and ranging (LiDAR) data. This work describes Penumbra’s framework and methodology, external model integration capability, and appropriate model application for a variety of watershed restoration project types. First, an overview of Penumbra’s framework reveals what this model adds to the existing ecological modeling domain. Second, Penumbra’s stand-alone and integration modes are explained and demonstrated. Stand-alone modeling results are showcased within the 3-D visualization tool VISTAS (VISualizing Terrestrial-Aquatic Systems), which fluently summariz

Helicopter-borne observations of the continental background aerosol in combination with remote sensing and ground-based measurements

NASA Astrophysics Data System (ADS)

Düsing, Sebastian; Wehner, Birgit; Seifert, Patric; Ansmann, Albert; Baars, Holger; Ditas, Florian; Henning, Silvia; Ma, Nan; Poulain, Laurent; Siebert, Holger; Wiedensohler, Alfred; Macke, Andreas

2018-01-01

This paper examines the representativeness of ground-based in situ measurements for the planetary boundary layer (PBL) and conducts a closure study between airborne in situ and ground-based lidar measurements up to an altitude of 2300 m. The related measurements were carried out in a field campaign within the framework of the High-Definition Clouds and Precipitation for Advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE) in September 2013 in a rural background area of central Europe.The helicopter-borne probe ACTOS (Airborne Cloud and Turbulence Observation System) provided measurements of the aerosol particle number size distribution (PNSD), the aerosol particle number concentration (PNC), the number concentration of cloud condensation nuclei (CCN-NC), and meteorological atmospheric parameters (e.g., temperature and relative humidity). These measurements were supported by the ground-based 3+2 wavelength polarization lidar system PollyXT, which provided profiles of the particle backscatter coefficient (σbsc) for three wavelengths (355, 532, and 1064 nm). Particle extinction coefficient (σext) profiles were obtained by using a fixed backscatter-to-extinction ratio (also lidar ratio, LR). A new approach was used to determine profiles of CCN-NC for continental aerosol. The results of this new approach were consistent with the airborne in situ measurements within the uncertainties.In terms of representativeness, the PNSD measurements on the ground showed a good agreement with the measurements provided with ACTOS for lower altitudes. The ground-based measurements of PNC and CCN-NC are representative of the PBL when the PBL is well mixed. Locally isolated new particle formation events on the ground or at the top of the PBL led to vertical variability in the cases presented here and ground-based measurements are not entirely representative of the PBL. Based on Mie theory (Mie, 1908), optical aerosol properties under ambient conditions for different altitudes were determined using the airborne in situ measurements and were compared with the lidar measurements. The investigation of the optical properties shows that on average the airborne-based particle light backscatter coefficient is 50.1 % smaller for 1064 nm, 27.4 % smaller for 532 nm, and 29.5 % smaller for 355 nm than the measurements of the lidar system. These results are quite promising, since in situ measurement-based Mie calculations of the particle light backscattering are scarce and the modeling is quite challenging. In contrast, for the particle light extinction coefficient we found a good agreement. The airborne-based particle light extinction coefficient was just 8.2 % larger for 532 nm and 3 % smaller for 355 nm, for an assumed LR of 55 sr. The particle light extinction coefficient for 1064 nm was derived with a LR of 30 sr. For this wavelength, the airborne-based particle light extinction coefficient is 5.2 % smaller than the lidar measurements. For the first time, the lidar ratio of 30 sr for 1064 nm was determined on the basis of in situ measurements and the LR of 55 sr for 355 and 532 nm wavelength was reproduced for European continental aerosol on the basis of this comparison. Lidar observations and the in situ based aerosol optical properties agree within the uncertainties. However, our observations indicate that a determination of the PNSD for a large size range is important for a reliable modeling of aerosol particle backscattering.

Highway 3D model from image and lidar data

NASA Astrophysics Data System (ADS)

Chen, Jinfeng; Chu, Henry; Sun, Xiaoduan

2014-05-01

We present a new method of highway 3-D model construction developed based on feature extraction in highway images and LIDAR data. We describe the processing road coordinate data that connect the image frames to the coordinates of the elevation data. Image processing methods are used to extract sky, road, and ground regions as well as significant objects (such as signs and building fronts) in the roadside for the 3D model. LIDAR data are interpolated and processed to extract the road lanes as well as other features such as trees, ditches, and elevated objects to form the 3D model. 3D geometry reasoning is used to match the image features to the 3D model. Results from successive frames are integrated to improve the final model.

Integrated multi-sensor fusion for mapping and localization in outdoor environments for mobile robots

NASA Astrophysics Data System (ADS)

Emter, Thomas; Petereit, Janko

2014-05-01

An integrated multi-sensor fusion framework for localization and mapping for autonomous navigation in unstructured outdoor environments based on extended Kalman filters (EKF) is presented. The sensors for localization include an inertial measurement unit, a GPS, a fiber optic gyroscope, and wheel odometry. Additionally a 3D LIDAR is used for simultaneous localization and mapping (SLAM). A 3D map is built while concurrently a localization in a so far established 2D map is estimated with the current scan of the LIDAR. Despite of longer run-time of the SLAM algorithm compared to the EKF update, a high update rate is still guaranteed by sophisticatedly joining and synchronizing two parallel localization estimators.

Investigation of feasibility of wind turbulence measurement by a pulsed coherent doppler lidar in the atmospheric boundary layer

NASA Astrophysics Data System (ADS)

Smalikho, Igor; Banakh, Viktor

2018-04-01

Feasibilities of determination of the wind turbulence parameters from data measured by the Stream Line coherent Doppler lidar under different atmospheric conditions have been studied experimentally. It has been found that the spatial structure of the turbulence is described well by the von Karman model in the layer of intensive mixing. From the lidar measurements at night under stable conditions the estimation of the outer scale of turbulence with the use of the von Karman model is not possible.

Analysis of intrapulse chirp in CO2 oscillators

NASA Technical Reports Server (NTRS)

Moody, Stephen E.; Berger, Russell G.; Thayer, William J., III

1987-01-01

Pulsed single-frequency CO2 laser oscillators are often used as transmitters for coherent lidar applications. These oscillators suffer from intrapulse chirp, or dynamic frequency shifting. If excessive, such chirp can limit the signal-to-noise ratio of the lidar (by generating excess bandwidth), or limit the velocity resolution if the lidar is of the Doppler type. This paper describes a detailed numerical model that considers all known sources of intrapulse chirp. Some typical predictions of the model are shown, and simple design rules to minimize chirp are proposed.

The evaluation of a shuttle borne lidar experiment to measure the global distribution of aerosols and their effect on the atmospheric heat budget

NASA Technical Reports Server (NTRS)

Shipley, S. T.; Joseph, J. H.; Trauger, J. T.; Guetter, P. J.; Eloranta, E. W.; Lawler, J. E.; Wiscombe, W. J.; Odell, A. P.; Roesler, F. L.; Weinman, J. A.

1975-01-01

A shuttle-borne lidar system is described, which will provide basic data about aerosol distributions for developing climatological models. Topics discussed include: (1) present knowledge of the physical characteristics of desert aerosols and the absorption characteristics of atmospheric gas, (2) radiative heating computations, and (3) general circulation models. The characteristics of a shuttle-borne radar are presented along with some laboratory studies which identify schemes that permit the implementation of a high spectral resolution lidar system.

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

The potential of LiDAR in ecohydrology is significant as characterising catchment vegetation is crucial to accurate estimation of evapotranspiration (ET). While this may be done at large scales for model parameterisation, stand-scale applications are equally appropriate where traditional methods of measurement of LAI or sapwood areas are time consuming and reliant on assumptions of representative sampling. This is particularly challenging in mountain forests where aspect, soil properties and energy budgets can vary significantly, reflected in the vegetation or where there are changes in the spatial distribution of structural attributes following disturbance. Recent research has investigated the spatial distribution of ET in a eucalypt forest in SE Australia using plot-scale sapflow, interception and forest floor ET measurements. LiDAR was used scale up these measurements. LiDAR (0.16 m scanner footprint) canopy indices were correlated via stepwise regression with 4 water use scalars: basal area (BA), sapwood area (SA), leaf area index (LAI) and canopy coverage (C), with Hmed, Hmean, H80, H95 the best predictors. Combining these indices with empirical relationships between SA and BA, and SA and transpiration (T), and inventory plot 'ground truthing' transpiration was estimated across the 1.3 km2 catchment. Interception was scaled via the Gash model with LiDAR derived inputs. The up-scaling showed a significant variability in the spatial distribution of ET, related to the distribution of SA. The use of LiDAR meant scaling could be achieved at an appropriate spatial scale (20 x 20 m) to the measurements. The second example is the use of airborne LiDAR in developing growth forest models for hydrologic modeling. LiDAR indices were used to stratify multilayered forests using mixed-effect models with a wide range of theoretical distribution functions. When combined with historical plot-scale inventory data we show demonstrated improved growth modeling over traditional 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.

Evaluating cloudiness in an AGCM with Cloud Vertical Structure classes and their radiative effects

NASA Astrophysics Data System (ADS)

Lee, D.; Cho, N.; Oreopoulos, L.; Barahona, D.

2017-12-01

Clouds are recognized not only as the main modulator of Earth's Radiation Budget but also as the atmospheric constituent carrying the largest uncertainty in future climate projections. The presentation will showcase a new framework for evaluating clouds and their radiative effects in Atmospheric Global Climate Models (AGCMs) using Cloud Vertical Structure (CVS) classes. We take advantage of a new CVS reference dataset recently created from CloudSat's 2B-CLDCLASS-LIDAR product and which assigns observed cloud vertical configurations to nine simplified CVS classes based on cloud co-occurrence in three standard atmospheric layers. These CVS classes can also be emulated in GEOS-5 using the subcolumn cloud generator currently paired with the RRTMG radiation package as an implementation of the McICA scheme. Comparisons between the observed and modeled climatologies of the frequency of occurrence of the various CVS classes provide a new vantage point for assessing the realism of GEOS-5 clouds. Furthermore, a comparison between observed and modeled cloud radiative effects according to their CVS is also possible thanks to the availability of CloudSat's 2B-FLXHR-LIDAR product and our ability to composite radiative fluxes by CVS class - both in the observed and modeled realm. This latter effort enables an investigation of whether the contribution of the various CVS classes to the Earth's radiation budget is represented realistically in GEOS-5. Making this new pathway of cloud evaluation available to the community is a major step towards the improved representation of clouds in climate models.

Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

DOE PAGES

Annoni, Jennifer; Fleming, Paul; Scholbrock, Andrew; ...

2018-02-08

Wind turbines in a wind farm operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Wind farm controls can be used to increase power production or reduce overall structural loads by properly coordinating turbines. One wind farm control strategy that is addressed in literature is known as wake steering, wherein upstream turbines operate in yaw misaligned conditions to redirect their wakes away from downstream turbines. The National Renewable Energy Laboratory (NREL) in Golden, CO conducted a demonstration of wake steering on a single utility-scale turbine. In this study, the turbine was operatedmore » at various yaw misalignment setpoints while a lidar mounted on the nacelle scanned five downstream distances. The lidar measurements were combined with turbine data, as well as measurements of the inflow made by a highly instrumented meteorological mast upstream. The full-scale measurements are used to validate controls-oriented tools, including wind turbine wake models, used for wind farm controls and optimization. This paper presents a quantitative comparison of the lidar data and controls-oriented wake models under different atmospheric conditions and turbine operation. The results show good agreement between the lidar data and the models under these different conditions.« less

Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Annoni, Jennifer; Fleming, Paul; Scholbrock, Andrew

Wind turbines in a wind farm operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Wind farm controls can be used to increase power production or reduce overall structural loads by properly coordinating turbines. One wind farm control strategy that is addressed in literature is known as wake steering, wherein upstream turbines operate in yaw misaligned conditions to redirect their wakes away from downstream turbines. The National Renewable Energy Laboratory (NREL) in Golden, CO conducted a demonstration of wake steering on a single utility-scale turbine. In this study, the turbine was operatedmore » at various yaw misalignment setpoints while a lidar mounted on the nacelle scanned five downstream distances. The lidar measurements were combined with turbine data, as well as measurements of the inflow made by a highly instrumented meteorological mast upstream. The full-scale measurements are used to validate controls-oriented tools, including wind turbine wake models, used for wind farm controls and optimization. This paper presents a quantitative comparison of the lidar data and controls-oriented wake models under different atmospheric conditions and turbine operation. The results show good agreement between the lidar data and the models under these different conditions.« less

Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data

USGS Publications Warehouse

Li, Ainong; Huang, Chengquan; Sun, Guoqing; Shi, Hua; Toney, Chris; Zhu, Zhiliang; Rollins, Matthew G.; Goward, Samuel N.; Masek, Jeffery G.

2011-01-01

Many forestry and earth science applications require spatially detailed forest height data sets. Among the various remote sensing technologies, lidar offers the most potential for obtaining reliable height measurement. However, existing and planned spaceborne lidar systems do not have the capability to produce spatially contiguous, fine resolution forest height maps over large areas. This paper describes a Landsat–lidar fusion approach for modeling the height of young forests by integrating historical Landsat observations with lidar data acquired by the Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud, and land Elevation (ICESat) satellite. In this approach, “young” forests refer to forests reestablished following recent disturbances mapped using Landsat time-series stacks (LTSS) and a vegetation change tracker (VCT) algorithm. The GLAS lidar data is used to retrieve forest height at sample locations represented by the footprints of the lidar data. These samples are used to establish relationships between lidar-based forest height measurements and LTSS–VCT disturbance products. The height of “young” forest is then mapped based on the derived relationships and the LTSS–VCT disturbance products. This approach was developed and tested over the state of Mississippi. Of the various models evaluated, a regression tree model predicting forest height from age since disturbance and three cumulative indices produced by the LTSS–VCT method yielded the lowest cross validation error. The R2 and root mean square difference (RMSD) between predicted and GLAS-based height measurements were 0.91 and 1.97 m, respectively. Predictions of this model had much higher errors than indicated by cross validation analysis when evaluated using field plot data collected through the Forest Inventory and Analysis Program of USDA Forest Service. Much of these errors were due to a lack of separation between stand clearing and non-stand clearing disturbances in current LTSS–VCT products and difficulty in deriving reliable forest height measurements using GLAS samples when terrain relief was present within their footprints. In addition, a systematic underestimation of about 5 m by the developed model was also observed, half of which could be explained by forest growth that occurred between field measurement year and model target year. The remaining difference suggests that tree height measurements derived using waveform lidar data could be significantly underestimated, especially for young pine forests. Options for improving the height modeling approach developed in this study were discussed.

Voxel-Based LIDAR Analysis and Applications

NASA Astrophysics Data System (ADS)

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 show the voxel model's advantage, we apply it to several outstanding problems in remote sensing: LIDAR quality metrics, line-of-sight mapping, and multi-model fusion. Each of these applications is derived, validated, and examined in detail, and our results compared with other state-of-the-art methods. In most cases the voxel-based methods demonstrate superior results and are able to derive information not available to existing methods. Realizing these improvements requires only a shift away from traditional 3D model generation, and our results give a small indicator of what is possible. Many examples of possible areas for future improvement and expansion of algorithms beyond the scope of our work are also noted.

Modeling the Height of Young Forests Regenerating from Recent Disturbances in Mississippi using Landsat and ICESat data

NASA Technical Reports Server (NTRS)

Li, Ainong; Huang, Chengquan; Sun, Guoqing; Shi, Hua; Toney, Chris; Zhu, Zhiliang; Rollins, Matthew G.; Goward, Samuel N.; Masek, Jeffrey G.

2011-01-01

Many forestry and earth science applications require spatially detailed forest height data sets. Among the various remote sensing technologies, lidar offers the most potential for obtaining reliable height measurement. However, existing and planned spaceborne lidar systems do not have the capability to produce spatially contiguous, fine resolution forest height maps over large areas. This paper describes a Landsat-lidar fusion approach for modeling the height of young forests by integrating historical Landsat observations with lidar data acquired by the Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud, and land Elevation (ICESat) satellite. In this approach, "young" forests refer to forests reestablished following recent disturbances mapped using Landsat time-series stacks (LTSS) and a vegetation change tracker (VCT) algorithm. The GLAS lidar data is used to retrieve forest height at sample locations represented by the footprints of the lidar data. These samples are used to establish relationships between lidar-based forest height measurements and LTSS-VCT disturbance products. The height of "young" forest is then mapped based on the derived relationships and the LTSS-VCT disturbance products. This approach was developed and tested over the state of Mississippi. Of the various models evaluated, a regression tree model predicting forest height from age since disturbance and three cumulative indices produced by the LTSS-VCT method yielded the lowest cross validation error. The R(exp 2) and root mean square difference (RMSD) between predicted and GLAS-based height measurements were 0.91 and 1.97 m, respectively. Predictions of this model had much higher errors than indicated by cross validation analysis when evaluated using field plot data collected through the Forest Inventory and Analysis Program of USDA Forest Service. Much of these errors were due to a lack of separation between stand clearing and non-stand clearing disturbances in current LTSS-VCT products and difficulty in deriving reliable forest height measurements using GLAS samples when terrain relief was present within their footprints. In addition, a systematic underestimation of about 5 m by the developed model was also observed, half of which could be explained by forest growth that occurred between field measurement year and model target year. The remaining difference suggests that tree height measurements derived using waveform lidar data could be significantly underestimated, especially for young pine forests. Options for improving the height modeling approach developed in this study were discussed.

Lidar observations of the planetary boundary layer during FASINEX

NASA Technical Reports Server (NTRS)

Melfi, S. H.; Boers, R.; Palm, S. P.

1988-01-01

Data are presented on the planetary boundary layer (PBL) over the ocean acquired with an airborne downward-looking lidar during the Frontal Air-Sea Interaction Experiment (FASINEX) with the purpose of studying the impact of an ocean front on air-sea interactions. No changes in the PBL structure were detected by lidar. Lidar data were then used along with other readily available remotely-sensed data and a one-dimensional boundary-layer-growth model to infer the mean PBL moisture and temperature structure and to estimate the surface fluxes of heat and moisture.

Implementation of spaceborne lidar-retrieved canopy height in the WRF model

NASA Astrophysics Data System (ADS)

Lee, Junhong; Hong, Jinkyu

2016-06-01

Canopy height is closely related to biomass and aerodynamic properties, which regulate turbulent transfer of energy and mass at the soil-vegetation-atmosphere continuum. However, this key information has been prescribed as a constant value in a fixed plant functional type in atmospheric models. This paper is the first to report impacts of using realistic forest canopy height, retrieved from spaceborne lidar, on regional climate simulation by using the canopy height data in the Weather Research and Forecasting (WRF) model's land surface model. Numerical simulations were conducted over the Amazon Basin during summer season. Over this region, the lidar-retrieved canopy heights were higher than the default values used in the WRF, which are dependent only on plant functional type. By modifying roughness length and zero-plane displacement height, the change of canopy height resulted in changes in surface energy balance by regulating aerodynamic conductances and vertical temperature gradient, thus modifying the lifting condensation level and equivalent potential temperature in the atmospheric boundary layer. Our analysis also showed that the WRF model better reproduced the observed precipitation when lidar-retrieved canopy height was used over the Amazon Basin.

Inspiration from drones, Lidar measurements and 3D models in undergraduate teaching

NASA Astrophysics Data System (ADS)

Blenkinsop, Thomas; Ellis, Jennifer

2017-04-01

Three-dimensional models, photogrammetry and remote sensing are increasingly common techniques used in structural analysis. We have found that using drones and Lidar on undergraduate field trips has piqued interest in fieldwork, provided data for follow-up laboratory exercises, and inspired undergraduates to attempt 3D modelling in independent mapping projects. The scale of structures visible in cliff and sea shore exposures in South Wales is ideal for using drones to capture images for 3D models. Fault scarps in the South Wales coalfield were scanned by Lidar and drone. Our experience suggests that the drone data were much easier to acquire and process than the Lidar data, and adequate for most teaching purposes. In the lab, we used the models to show the structure in 3D, and as the basis for an introduction to geological modelling software. Now that tools for photogrammetry, drones, and processing software are widely available and affordable, they can be readily integrated into teaching. An additional benefit from the images and models is that they may be used for exercises that can be substituted for fieldwork to achieve some (but not all) of the learning outcomes in the case that field access is prevented.

Lidar-based wake tracking for closed-loop wind farm control

NASA Astrophysics Data System (ADS)

Raach, Steffen; Schlipf, David; Cheng, Po Wen

2016-09-01

This work presents two advancements towards closed-loop wake redirecting of a wind turbine. First, a model-based estimation approach is presented which uses a nacelle-based lidar system facing downwind to obtain information about the wake. A reduced order wake model is described which is then used in the estimation to track the wake. The tracking is demonstrated with lidar measurement data from an offshore campaign and with simulated lidar data from a SOWFA simulation. Second, a controller for closed-loop wake steering is presented. It uses the wake tracking information to set the yaw actuator of the wind turbine to redirect the wake to a desired position. Altogether, this paper aims to present the concept of closed-loop wake redirecting and gives a possible solution to it.

Characterizing the Vertical Distribution of Aerosols Over the ARM SGP Site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richard Ferrare, Connor Flynn, David Turner

This project focused on: 1) evaluating the performance of the DOE ARM SGP Raman lidar system in measuring profiles of water vapor and aerosols, and 2) the use of the Raman lidar measurements of aerosol and water vapor profiles for assessing the vertical distribution of aerosols and water vapor simulated by global transport models and examining diurnal variability of aerosols and water vapor. The highest aerosol extinction was generally observed close to the surface during the nighttime just prior to sunrise. The high values of aerosol extinction are most likely associated with increased scattering by hygroscopic aerosols, since the correspondingmore » average relative humidity values were above 70%. After sunrise, relative humidity and aerosol extinction below 500 m decreased with the growth in the daytime convective boundary layer. The largest aerosol extinction for altitudes above 1 km occurred during the early afternoon most likely as a result of the increase in relative humidity. The water vapor mixing ratio profiles generally showed smaller variations with altitude between day and night. We also compared simultaneous measurements of relative humidity, aerosol extinction, and aerosol optical thickness derived from the ARM SGP Raman lidar and in situ instruments on board a small aircraft flown routinely over the ARM SGP site. In contrast, the differences between the CARL and IAP aerosol extinction measurements are considerably larger. Aerosol extinction derived from the IAP measurements is, on average, about 30-40% less than values derived from the Raman lidar. The reasons for this difference are not clear, but may be related to the corrections for supermicron scattering and relative humidity that were applied to the IAP data. The investigators on this project helped to set up a major field mission (2003 Aerosol IOP) over the DOE ARM SGP site. One of the goals of the mission was to further evaluate the aerosol and water vapor retrievals from this lidar system. Analysis of the aerosol and water vapor data collected by the Raman lidar during the 2003 Aerosol IOP indicated that the sensitivity of the lidar was significantly lower than when the lidar was initially deployed. A detailed analysis after the IOP of the long-term dataset demonstrated that the lidar began degrading in early 2002, and that it lost approximately a factor of 4 in sensitivity between 2002 and 2004. We participated in the development of the remediation plan for the system to restore its initial performance. We conducted this refurbishment and upgrade from May- September 2004. This remediation lead to an increase in the signal-to-noise ratio of 10 and 30 for the Raman lidar's water vapor mixing ratio and aerosol backscatter coefficient data, respectively as compared to the signal strengths when the system was first deployed. The DOE ARM Aerosol Lidar Validation Experiment (ALIVE), which was conducted during September 2005, evaluated the impact of these modifications and upgrades on the SGP Raman lidar measurements of aerosol extinction and optical thickness. The CARL modifications significantly improved the accuracy and temporal resolution of the aerosol measurements. Aerosol extinction profiles measured by the Raman lidar were also used to evaluate aerosol extinction profiles and aerosol optical thickness (AOT) simulated by aerosol models as part of the Aerosol module inter-Comparison in global models (AEROCOM) (http://nansen.ipsl.jussieu.fr/AEROCOM/aerocomhome.html) project. There was a wide range in how the models represent the aerosol extinction profiles over the ARM SGP site, even though the average annual AOT represented by the various models and measured by CARL and the Sun photometer were in general agreement, at least within the standard deviations of the averages. There were considerable differences in the average vertical distributions among the models, even among models that had similar average aerosol optical thickness. Deviations between mean aerosol extinction profiles were generally small (~20-30%) for altitudes above 2 km, and grew considerably larger below 2 km.« less

Ceilometer-based Rainfall Rate estimates in the framework of VORTEX-SE campaign: A discussion

NASA Astrophysics Data System (ADS)

Barragan, Ruben; Rocadenbosch, Francesc; Waldinger, Joseph; Frasier, Stephen; Turner, Dave; Dawson, Daniel; Tanamachi, Robin

2017-04-01

During Spring 2016 the first season of the Verification of the Origins of Rotation in Tornadoes EXperiment-Southeast (VORTEX-SE) was conducted in the Huntsville, AL environs. Foci of VORTEX-SE include the characterization of the tornadic environments specific to the Southeast US as well as societal response to forecasts and warnings. Among several experiments, a research team from Purdue University and from the University of Massachusetts Amherst deployed a mobile S-band Frequency-Modulated Continuous-Wave (FMCW) radar and a co-located Vaisala CL31 ceilometer for a period of eight weeks near Belle Mina, AL. Portable disdrometers (DSDs) were also deployed in the same area by Purdue University, occasionally co-located with the radar and lidar. The NOAA National Severe Storms Laboratory also deployed the Collaborative Lower Atmosphere Mobile Profiling System (CLAMPS) consisting of a Doppler lidar, a microwave radiometer, and an infrared spectrometer. The purpose of these profiling instruments was to characterize the atmospheric boundary layer evolution over the course of the experiment. In this paper we focus on the lidar-based retrieval of rainfall rate (RR) and its limitations using observations from intensive observation periods during the experiment: 31 March and 29 April 2016. Departing from Lewandowski et al., 2009, the RR was estimated by the Vaisala CL31 ceilometer applying the slope method (Kunz and Leeuw, 1993) to invert the extinction caused by the rain. Extinction retrievals are fitted against RR estimates from the disdrometer in order to derive a correlation model that allows us to estimate the RR from the ceilometer in similar situations without a disdrometer permanently deployed. The problem of extinction retrieval is also studied from the perspective of Klett-Fernald-Sasano's (KFS) lidar inversion algorithm (Klett, 1981; 1985), which requires the assumption of an aerosol extinction-to-backscatter ratio (the so-called lidar ratio) and calibration in a molecular reference range. The latter is hampered by the limited dynamic range of the ceilometer under rain events, which usually makes it difficult to properly record the reference-range interval. The RR is also compared to estimates by the FMCW radar, which provides vertical profiles of reflectivity and Doppler spectra, from which DSDs and rainfall rates can be inferred more directly. Ceilometer-derived RRs are compared with RR radar estimates for the same days in order to identify pros and cons of the proposed approach. Following Westbrook et al. (2010), we also consider the estimation of rain rates using two-color lidar, which is limited to drizzle and low rain rates. The key to this method is that the Doppler lidar's wavelength (1.5 µm) is partially absorbed by the liquid, and thus it is a differential absorption technique. This work was supported by NOAA under contracts NA1501R4590232 and NA16OAR4590209 and by the Purdue University Dept. of Earth, Atmospheric, and Planetary Sciences. UPC collaborated via Spanish Government - European Regional Development Funds, TEC2015-63832-P project and EU H2020 ACTRIS-2 (GA-654109) project. Klett J.D., 1981: Stable analytical inversion solution for processing lidar returns. Appl. Opt. 20, 211-220. doi: 10.1364/AO.20.000211. Klett J.D., 1985: Lidar inversion with variable backscatter/extinction ratios. Appl. Opt. 24, 1638-1643. doi: 10.1364/AO.24.001638. Kunz G.J., de Leeuv G., 1993: Inversion of lidar signals with the slope method. Appl Opt. 32(18):3249-56. doi: 10.1364/AO.32.003249. Lewandowski P.A., Eichinger W.E., Kruger A., Krajewski W.F., 2008: Lidar-Based Estimation of Small-Scale Rainfall: Empirical Evidence. Journal of Atmospheric and Oceanic Technology, 26, 656-664. doi: 10.1175/2008JTECHA1122.1. Westbrook C.D., Hogan R.J., O'Connor E.J., Illinworth A.J., 2010: Estimating drizzle drop size and precipitation rate using two-colour lidar measurements. Atmos. Meas. Tech., 3, 671-681. doi: 10.5194/amt-3-671-2010.

Long-range transport of Siberian forest fire smoke to Canada's west coast identified by Lidar observations

NASA Astrophysics Data System (ADS)

Strawbridge, Kevin; Cottle, Paul; McKendry, Ian

2014-05-01

During the summer of 2012, forest fire smoke wasdetected by two CORALNet lidar systems operated by Environment Canada along Canada's west coast. Based on satellite, model and back trajectory analysis it is thought the smoke originated in Boreal Asia as a result of unusually large amounts of Siberian wildfire activity. The CORALNet lidar systems operate autonomously, measuring the vertical profile of aerosols from near ground to 18 km at a vertical resolution of 3 m and 7.5 m and a temporal resolution of 10 s and 60 s at 1064 nm and 532 nm wavelengths respectively. The lidar also measures the depolarization ratio at 532 nm: and indicator of particle shape. The lidars, located at the University of British Columbia in Vancouver and in the village of Whistler, British Columbia observed an increase in the aerosol backscatter ratio in the free troposphere as the Siberian forest fire smoke was transported across the Pacific Ocean into the region. Of particular importance was the increase in ground level particulate due to the mixing of the smoke into the boundary layer, impacting the air quality in southwestern British Columbia. Lidar depolarization ratios in the boundary layer and the free troposphere were consistent with high concentrations of smoke. Detailed lidar observations will be presented along with supporting satellite, model and ground observations revealing the magnitude of the impact on the region.

Radiometric Calibration of a Dual-Wavelength, Full-Waveform Terrestrial Lidar.

PubMed

Li, Zhan; Jupp, David L B; Strahler, Alan H; Schaaf, Crystal B; Howe, Glenn; Hewawasam, Kuravi; Douglas, Ewan S; Chakrabarti, Supriya; Cook, Timothy A; Paynter, Ian; Saenz, Edward J; Schaefer, Michael

2016-03-02

Radiometric calibration of the Dual-Wavelength Echidna(®) Lidar (DWEL), a full-waveform terrestrial laser scanner with two simultaneously-pulsing infrared lasers at 1064 nm and 1548 nm, provides accurate dual-wavelength apparent reflectance (ρ(app)), a physically-defined value that is related to the radiative and structural characteristics of scanned targets and independent of range and instrument optics and electronics. The errors of ρ(app) are 8.1% for 1064 nm and 6.4% for 1548 nm. A sensitivity analysis shows that ρ(app) error is dominated by range errors at near ranges, but by lidar intensity errors at far ranges. Our semi-empirical model for radiometric calibration combines a generalized logistic function to explicitly model telescopic effects due to defocusing of return signals at near range with a negative exponential function to model the fall-off of return intensity with range. Accurate values of ρ(app) from the radiometric calibration improve the quantification of vegetation structure, facilitate the comparison and coupling of lidar datasets from different instruments, campaigns or wavelengths and advance the utilization of bi- and multi-spectral information added to 3D scans by novel spectral lidars.

Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density

DOE PAGES

Medeiros, Stephen; Hagen, Scott; Weishampel, John; ...

2015-03-25

Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three-class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer tomore » true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.« less

Radiometric Calibration of a Dual-Wavelength, Full-Waveform Terrestrial Lidar

PubMed Central

Li, Zhan; Jupp, David L. B.; Strahler, Alan H.; Schaaf, Crystal B.; Howe, Glenn; Hewawasam, Kuravi; Douglas, Ewan S.; Chakrabarti, Supriya; Cook, Timothy A.; Paynter, Ian; Saenz, Edward J.; Schaefer, Michael

2016-01-01

Radiometric calibration of the Dual-Wavelength Echidna® Lidar (DWEL), a full-waveform terrestrial laser scanner with two simultaneously-pulsing infrared lasers at 1064 nm and 1548 nm, provides accurate dual-wavelength apparent reflectance (ρapp), a physically-defined value that is related to the radiative and structural characteristics of scanned targets and independent of range and instrument optics and electronics. The errors of ρapp are 8.1% for 1064 nm and 6.4% for 1548 nm. A sensitivity analysis shows that ρapp error is dominated by range errors at near ranges, but by lidar intensity errors at far ranges. Our semi-empirical model for radiometric calibration combines a generalized logistic function to explicitly model telescopic effects due to defocusing of return signals at near range with a negative exponential function to model the fall-off of return intensity with range. Accurate values of ρapp from the radiometric calibration improve the quantification of vegetation structure, facilitate the comparison and coupling of lidar datasets from different instruments, campaigns or wavelengths and advance the utilization of bi- and multi-spectral information added to 3D scans by novel spectral lidars. PMID:26950126

Characterization of convection-related parameters by Raman lidar: Selected case studies from the convective and orographically-induced precipitation study

NASA Astrophysics Data System (ADS)

Di Girolamo, Paolo; Summa, Donato; Stelitano, Dario

2013-05-01

An approach to determine the convective available potential energy (CAPE) and the convective inhibition (CIN) based on the use of data from a Raman lidar system is illustrated in this work. The use of Raman lidar data allows to provide high temporal resolution measurements (5 min) of CAPE and CIN and follow their evolution over extended time periods covering the full cycle of convective activity. Lidar-based measurements of CAPE and CIN are obtained from Raman lidar measurements of the temperature and water vapor mixing ratio profiles and the surface measurements of temperature, pressure and dew point temperature provided by a surface weather station. The approach is applied to the data collected by the Raman lidar system BASIL in the frame of COPS. Attention was focused on 15 July and 25-26 July 2007. Lidar-based measurements are in good agreement with simultaneous measurements from radiosondes and with estimates from different mesoscale models.

Towards an operational lidar network across the UK

NASA Astrophysics Data System (ADS)

Adam, Mariana; Horseman, Andrew; Turp, Myles; Buxmann, Joelle; Sugier, Jacqueline

2015-04-01

The Met Office has been operating a ceilometer network since 2012. This network consists of 11 Jenoptik Nimbus ceilometers (operating at 1064 nm) and 32 Vaisala ceilometers (25 CL31, operating at 910 nm and 7 CT25 operating at 905 nm). The data are available in near real time (NRT) (15 min for Jenoptik and 1 h for Vaisala). In 2014, six additional stations from Met Éireann (Ireland) were added to the network (5 CL31 and 1 CT25). Visualisation of attenuated backscatter and cloud base height are available from http://www.metoffice.gov.uk/public/lidarnet/lcbr-network.html. The main customers are the Met Office Hazard Centre which provides a quick response to customers requiring forecast information to manage a wide variety of environmental incidents and the London Volcanic Ash Advisory Centre (VAAC), also based at the Met Office, which monitor volcanic ash events. As a response to the strong impact of the Eyjafjallajökull eruption in 2010, the UK Civil Aviation Authority (CAA) financed a lidar - sunphotometer network for NRT monitoring of the volcanic ash. This new network will consist of nine fixed sites and one mobile unit, each equipped with a lidar and a sunphotometer. The sunphotometers were acquired from Cimel Electronique (CE318-NE DPS9). The lidars were acquired from Raymetrics. They operate at 355 nm and have receiving channels at 355 nm (parallel and perpendicular polarization) and 387 nm (N2 Raman). The first two lidar systems were deployed in November 2014 at Camborne (SW England) and the data are under evaluation. The network is planned to be operational in 2016. Initially, the NRT data will consist of quick look plots of the total range corrected signal and volume depolarization ratio from lidar and aerosol optical depth from sunphotometer (including 355nm, through interpolation). During EGU presentation, the following features will be emphasized: IT considerations for the operational network, data quality assurance (including error estimates) for the ceilometer network on one hand and for sunphotometer and lidar network on the other hand, technical presentation of the lidar, first results from lidars and sunphotometer, future considerations about other potential NRT data products (aerosol extinction and backscatter coefficients, particles linear depolarization ratio), NRT ceilometer data within the Hazard Centre and VAAC framework.

Using Lidar and Radar measurements to constrain predictions of forest ecosystem structure and function.

PubMed

Antonarakis, Alexander S; Saatchi, Sassan S; Chazdon, Robin L; Moorcroft, Paul R

2011-06-01

Insights into vegetation and aboveground biomass dynamics within terrestrial ecosystems have come almost exclusively from ground-based forest inventories that are limited in their spatial extent. Lidar and synthetic-aperture Radar are promising remote-sensing-based techniques for obtaining comprehensive measurements of forest structure at regional to global scales. In this study we investigate how Lidar-derived forest heights and Radar-derived aboveground biomass can be used to constrain the dynamics of the ED2 terrestrial biosphere model. Four-year simulations initialized with Lidar and Radar structure variables were compared against simulations initialized from forest-inventory data and output from a long-term potential-vegtation simulation. Both height and biomass initializations from Lidar and Radar measurements significantly improved the representation of forest structure within the model, eliminating the bias of too many large trees that arose in the potential-vegtation-initialized simulation. The Lidar and Radar initializations decreased the proportion of larger trees estimated by the potential vegetation by approximately 20-30%, matching the forest inventory. This resulted in improved predictions of ecosystem-scale carbon fluxes and structural dynamics compared to predictions from the potential-vegtation simulation. The Radar initialization produced biomass values that were 75% closer to the forest inventory, with Lidar initializations producing canopy height values closest to the forest inventory. Net primary production values for the Radar and Lidar initializations were around 6-8% closer to the forest inventory. Correcting the Lidar and Radar initializations for forest composition resulted in improved biomass and basal-area dynamics as well as leaf-area index. Correcting the Lidar and Radar initializations for forest composition and fine-scale structure by combining the remote-sensing measurements with ground-based inventory data further improved predictions, suggesting that further improvements of structural and carbon-flux metrics will also depend on obtaining reliable estimates of forest composition and accurate representation of the fine-scale vertical and horizontal structure of plant canopies.

Narrow-field-of-view bathymetrical lidar: theory and field test

NASA Astrophysics Data System (ADS)

Feygels, Viktor I.; Wright, C. Wayne; Kopilevich, Yuri I.; Surkov, Alexey I.

2003-11-01

The purpose of this paper is to derive a reliable theory to predict the performance of a narrow-FOV bathymetric lidar. A fundamental discrepancy between the theoretical estimate and experimental results was the inspiration for the work presented here Meeting oceanographic mapping requirements is a critically important goal for littoral laser bathymetry. In contrast to traditional airborne lidar system which are optimized for recovering signals from the deepest possible waters , the above challenge may be met with a radical narrowing to the lidar transmit beam and receiver field of view (FOV) employed in EAARL (Experimental Advanced Airborne Research Lidar, NASA). In this paper we discuss theoretical analysis carried out on the basis of a sophisticated "multiple-forward scattering and single-backscattering model" for lidar return signals allows a quantitative estimation of the advantages of a narrow-FOV system over traditional bathymetric lidars (SHOALS-400, SHOALS-100, LADS Mk II) when used in clear shallow-water cases. Some of those advantages are: ¸ Increase in bottom definition (or reduced false-alarm probability) due to the enhanced contrast of the bottom return over the background backscatter from the water column, ¸ Enhancement in depth measurement accuracy resulting from narrower bottom return pulse width, ¸ Reduction of post-surface return effects in the lidar photo-multiplier detector due to a more rapid decay of water column backscatter, ¸ Greatly improved rejection of ambient light permitting lidar operations in all zenith sun angles and flight directions. The model computations make it possible to estimate the maximal operational depth for the system under consideration by the implementation of statistical theory of detectability. These computations depend on the prevailing seawater optical properties and lidar parameters. The theoretical predictions are compared with results obtained in the field test of the EAARL system carried out in Florida Keys in 2001.

Modeling lidar waveforms with time-dependent stochastic radiative transfer theory for remote estimations of forest structure

NASA Astrophysics Data System (ADS)

Kotchenova, Svetlana Y.; Shabanov, Nikolay V.; Knyazikhin, Yuri; Davis, Anthony B.; Dubayah, Ralph; Myneni, Ranga B.

2003-08-01

Large footprint waveform-recording laser altimeters (lidars) have demonstrated a potential for accurate remote sensing of forest biomass and structure, important for regional and global climate studies. Currently, radiative transfer analyses of lidar data are based on the simplifying assumption that only single scattering contributes to the return signal, which may lead to errors in the modeling of the lower portions of recorded waveforms in the near-infrared spectrum. In this study we apply time-dependent stochastic radiative transfer (RT) theory to model the propagation of lidar pulses through forest canopies. A time-dependent stochastic RT equation is formulated and solved numerically. Such an approach describes multiple scattering events, allows for realistic representation of forest structure including foliage clumping and gaps, simulates off-nadir and multiangular observations, and has the potential to provide better approximations of return waveforms. The model was tested with field data from two conifer forest stands (southern old jack pine and southern old black spruce) in central Canada and two closed canopy deciduous forest stands (with overstory dominated by tulip poplar) in eastern Maryland. Model-simulated signals were compared with waveforms recorded by the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) over these regions. Model simulations show good agreement with SLICER signals having a slow decay of the waveform. The analysis of the effects of multiple scattering shows that multiply scattered photons magnify the amplitude of the reflected signal, especially that originating from the lower portions of the canopy.

The carbon debt from Amazon forest degradation: integrating airborne lidar, field measurements, and an ecosystem demography model.

NASA Astrophysics Data System (ADS)

Longo, M.; Keller, M. M.; dos-Santos, M. N.; Scaranello, M. A., Sr.; Pinagé, E. R.; Leitold, V.; Morton, D. C.

2016-12-01

Amazon deforestation has declined over the last decade, yet forest degradation from logging, fire, and fragmentation continue to impact forest carbon stocks and fluxes. The magnitude of this impact remains uncertain, and observation-based studies are often limited by short time intervals or small study areas. To better understand the long-term impact of forest degradation and recovery, we have been developing a framework that integrates field plot measurements and airborne lidar surveys into an individual- and process-based model (Ecosystem Demography model, ED). We modeled forest dynamics for three forest landscapes in the Amazon with diverse degradation histories: conventional and reduced-impact logging, logging and burning, and multiple burns. Based on the initialization with contemporary forest structure and composition, model results suggest that degraded forests rapidly recover (30 years) water and energy fluxes compared with old-growth, even at sites that were affected by multiple fires. However, degraded forests maintained different carbon stocks and fluxes even after 100 years without further disturbances, because of persistent differences in forest structure and composition. Recurrent disturbances may hinder the recovery of degraded forests. Simulations using a simple fire model entirely dependent on environmental controls indicate that the most degraded forests would take much longer to reach biomass typical of old-growth forests, because drier conditions near the ground make subsequent fires more intense and more recurrent. Fires in tropical forests are also closely related to nearby human activities; while results suggest an important feedback between fires and the microenvironment, additional work is needed to improve how the model represents the human impact on current and future fire regimes. Our study highlights that recovery of degraded forests may act as an important carbon sink, but efficient recovery depends on controlling future disturbances.

Exploitation of an atmospheric lidar network node in single-shot mode for the classification of aerofauna

NASA Astrophysics Data System (ADS)

Jansson, Samuel; Brydegaard, Mikkel; Papayannis, Alexandros; Tsaknakis, Georgios; Åkesson, Susanne

2017-07-01

The migration of aerofauna is a seasonal phenomenon of global scale, engaging billions of individuals in long-distance movements every year. Multiband lidar systems are commonly employed for the monitoring of aerosols and atmospheric gases, and a number of systems are operated regularly across Europe in the framework of the European Aerosol Lidar Network (EARLINET). This work examines the feasibility of utilizing EARLINET for the monitoring and classification of migratory fauna based on their pigmentation. An EARLINET Raman lidar system in Athens transmits laser pulses in three bands. By installing a four-channel digital oscilloscope on the system, the backscattered light from single-laser shots is measured. Roughly 100 h of data were gathered in the summer of 2013. The data were examined for aerofauna observations, and a total of 1735 observations interpreted as airborne organisms intercepting the laser beam were found during the study period in July to August 2013. The properties of the observations were analyzed spectrally and intercompared. A spectral multimodality that could be related to different observed species is shown. The system used in this pilot study is located in Athens, Greece. It is concluded that monitoring aerial migration using it and other similar systems is feasible with minor modifications, and that in-flight species classification could be possible.

Using LiDAR to Estimate Total Aboveground Biomass of Redwood Stands in the Jackson Demonstration State Forest, Mendocino, California

NASA Astrophysics Data System (ADS)

Rao, M.; Vuong, H.

2013-12-01

The overall objective of this study is to develop a method for estimating total aboveground biomass of redwood stands in Jackson Demonstration State Forest, Mendocino, California using airborne LiDAR data. LiDAR data owing to its vertical and horizontal accuracy are increasingly being used to characterize landscape features including ground surface elevation and canopy height. These LiDAR-derived metrics involving structural signatures at higher precision and accuracy can help better understand ecological processes at various spatial scales. Our study is focused on two major species of the forest: redwood (Sequoia semperirens [D.Don] Engl.) and Douglas-fir (Pseudotsuga mensiezii [Mirb.] Franco). Specifically, the objectives included linear regression models fitting tree diameter at breast height (dbh) to LiDAR derived height for each species. From 23 random points on the study area, field measurement (dbh and tree coordinate) were collected for more than 500 trees of Redwood and Douglas-fir over 0.2 ha- plots. The USFS-FUSION application software along with its LiDAR Data Viewer (LDV) were used to to extract Canopy Height Model (CHM) from which tree heights would be derived. Based on the LiDAR derived height and ground based dbh, a linear regression model was developed to predict dbh. The predicted dbh was used to estimate the biomass at the single tree level using Jenkin's formula (Jenkin et al 2003). The linear regression models were able to explain 65% of the variability associated with Redwood's dbh and 80% of that associated with Douglas-fir's dbh.

Systematic Relationships Between Lidar Observables and Sizes And Mineral Composition Of Dust Aerosols

NASA Technical Reports Server (NTRS)

Van Diedenhoven, Bastiaan; Stangl, Alexander; Perlwitz, Jan; Fridlind, Ann M.; Chowdhary, Jacek; Cairns, Brian

2015-01-01

The physical and chemical properties of soil dust aerosol particles fundamentally affect their interaction with climate, including shortwave absorption and radiative forcing, nucleation of cloud droplets and ice crystals, heterogeneous formation of sulfates and nitrates on the surface of dust particles, and atmospheric processing of iron into bioavailable forms that increase the productivity of marine phytoplankton. Lidar measurements, such as extinction-to-backscatter, color and depolarization ratios, are frequently used to distinguish between aerosol types with different physical and chemical properties. The chemical composition of aerosol particles determines their complex refractive index, hence affecting their backscattering properties. Here we present a study on how dust aerosol backscattering and depolarization properties at wavelengths of 355, 532 and 1064 nm are related to size and complex refractive index, which varies with the mineral composition of the dust. Dust aerosols are represented by collections of spheroids with a range of prolate and oblate aspect ratios and their optical properties are obtained using T-matrix calculations. We find simple, systematic relationships between lidar observables and the dust size and complex refractive index that may aid the use of space-based or airborne lidars for direct retrieval of dust properties or for the evaluation of chemical transport models using forward simulated lidar variables. In addition, we present first results on the spatial variation of forward-simulated lidar variables based on a dust model that accounts for the atmospheric cycle of eight different mineral types plus internal mixtures of seven mineral types with iron oxides, which was recently implemented in the NASA GISS Earth System ModelE2.

Determining Surface Roughness in Urban Areas Using Lidar Data

NASA Technical Reports Server (NTRS)

Holland, Donald

2009-01-01

An automated procedure has been developed to derive relevant factors, which can increase the ability to produce objective, repeatable methods for determining aerodynamic surface roughness. Aerodynamic surface roughness is used for many applications, like atmospheric dispersive models and wind-damage models. For this technique, existing lidar data was used that was originally collected for terrain analysis, and demonstrated that surface roughness values can be automatically derived, and then subsequently utilized in disaster-management and homeland security models. The developed lidar-processing algorithm effectively distinguishes buildings from trees and characterizes their size, density, orientation, and spacing (see figure); all of these variables are parameters that are required to calculate the estimated surface roughness for a specified area. By using this algorithm, aerodynamic surface roughness values in urban areas can then be extracted automatically. The user can also adjust the algorithm for local conditions and lidar characteristics, like summer/winter vegetation and dense/sparse lidar point spacing. Additionally, the user can also survey variations in surface roughness that occurs due to wind direction; for example, during a hurricane, when wind direction can change dramatically, this variable can be extremely significant. In its current state, the algorithm calculates an estimated surface roughness for a square kilometer area; techniques using the lidar data to calculate the surface roughness for a point, whereby only roughness elements that are upstream from the point of interest are used and the wind direction is a vital concern, are being investigated. This technological advancement will improve the reliability and accuracy of models that use and incorporate surface roughness.

New generation of airborne lidar for forest canopy sampling

NASA Astrophysics Data System (ADS)

Cuesta, J.; Chazette, P.; Allouis, T.; Sanak, J.; Genou, P.; Flamant, P. H.; Durrieu, S.; Toussaint, F.

2009-04-01

Cuesta J. (1,2), Chazette P. (1,3), Allouis T. (4), Sanak J. (1,3), Genau P. (2), Flamant P.H. (1), Durrieu S. (4) and Toussaint F. Biomass in forest cover is an essential actor in climate regulation. It is one of the principal sinks of atmospheric CO2 and a major water cycle regulator. In the coming years, climate change may generate an increase in the frequency of fires in the ecosystems, which are already affected in regions as southern Europe, near the Mediterranean basin. For a better understanding and prevention of the risks created by the propagation and intensity of fires, one requires a detailed characterization of the structural parameters of the forest canopy. Such description is as well essential for a proper management and sustainable use of forest resources and the characterization of the evolution of bio-diversity. These environmental and socio-economical issues motivate the development of new remote sensing instruments and methodology, particularly active remote sensing by lidar. These tools should be evaluated in order to achieve a global survey of the forest cover by satellite observation. In this framework, a French effort of the Institut Pierre Simon Laplace (LMD, LSCE and LATMOS) and the CEMAGREF has led to the deployment of a new airborne lidar prototype to study the vertical distribution of the forest canopy in the Landes region in France, around the Arcachon basin and Mimizan. The measuring system is the ultra-violet new generation lidar LAUVA (Lidar Aérosol UtraViolet (Aéroporté), Chazette et al., EST 2007), onboard an Ultra-Light Airplane (ULA). This system was developed by the Comissarait pour l'Energie Atomique and the Centre National de Recherches Scientifiques, originally for atmospheric applications, and it was successfully used in West Africa in the framework of the African Monsoon Multidisciplinary Analyses. After a proper adaptation, this compact and polyvalent lidar onboard an ULA is capable of measuring the forest canopy with an unequal malleability, both in terms of adaptability of instrumental parameters (divergence, field of view, sensitivity, pointing angle) and the flight plan (measuring range and field exploration). The use of a ultra-violet wavelength at 355 nm enables eye-safe emission of energetic laser pulses (16 mJ at 20 Hz). Besides the lidar and geo-referencing instruments, the ULA payload has been completed by two cameras operating at three bands (visible, near infrared and ultra-violet) to retrieve the canopy tri-dimensional structure by stereoscopy. During this experience, the vegetation vertical structure (tree height and crowns, bushes and underbrush) of tree parcels were statistically characterized. A total of three parcels of approximately 500 x 500 m2 composed principally by maritime pines of several ages were sampled following difference experimental configurations. Observations at two flight altitudes at 300 and 500 m were performed, obtaining lidar footprints of 2.4 and 4 m of diameter, respectively. These comparisons will be presented as well as measurements pointing at nadir and 30°. New experiences are planned for 2009 to sample different types of forest cover (leaf and conifers) and optimize the lidar instrument and the associated methodology, in order to achieve a multifunction tool to measure both the forest canopy and the atmospheric components.

Simultaneous lidar observations of the water vapor and ozone signatures of a stratospheric intrusion during the MOHAVE-2009 campaign

NASA Astrophysics Data System (ADS)

Leblanc, T.; McDermid, I. S.; Pérot, K.

2010-12-01

Ozone and water vapor signatures of a stratospheric intrusion were simultaneously observed by the Jet Propulsion Laboratory lidars located at Table Mountain Facility, California (TMF, 34.4N, 117.7W) during the Measurements of Humidity in the Atmosphere and Validation Experiments (MOHAVE-2009) campaign in October 2009. These observations are placed in the context of the meridional displacement and folding of the tropopause, and resulting contrast in the properties of the air masses sampled by lidar. The lidar observations are supported by model data, specifically potential vorticity fields advected by the high-resolution transport model MIMOSA, and by 10-day backward isentropic trajectories. The ozone and water vapor anomalies measured by lidar were largely anti-correlated, and consistent with the assumption of a wet and ozone-poor subtropical upper troposphere, and a dry and ozone-rich extra-tropical lowermost stratosphere. However, it is shown that this anti-correlation relation collapsed just after the stratospheric intrusion event of October 20, suggesting mixed air embedded along the subtropical jet stream and sampled by lidar during its displacement south of TMF (tropopause fold). The ozone-PV expected positive correlation relation held strongly throughout the measurement period, including when a lower polar stratospheric filament passed over TMF just after the stratospheric intrusion. The numerous highly-correlated signatures observed during this event demonstrate the strong capability of the water vapor and ozone lidars at TMF, and provide new confidence in the future detection by lidar of long-term variability of water vapor and ozone in the Upper Troposphere-Lower Stratosphere (UTLS).

LiDAR Applications in Resource Geology and Benefits for Land Management

NASA Astrophysics Data System (ADS)

Mikulovsky, R. P.; De La Fuente, J. A.

2013-12-01

The US Forest Service (US Department of Agriculture) manages a broad range of geologic resources and hazards on National Forests and Grass Lands throughout the United States. Resources include rock and earth materials, groundwater, caves and paleontological resources, minerals, energy resources, and unique geologic areas. Hazards include landslides, floods, earthquakes, volcanic eruptions, and naturally hazardous materials (e.g., asbestos, radon). Forest Service Geologists who address these issues are Resource Geologists. They have been exploring LiDAR as a revolutionary tool to efficiently manage all of these hazards and resources. However, most LiDAR applications for management have focused on timber and fuels management, rather than landforms. This study shows the applications and preliminary results of using LiDAR for managing geologic resources and hazards on public lands. Applications shown include calculating sediment budgets, mapping and monitoring landslides, mapping and characterizing borrow pits or mines, determining landslide potential, mapping faults, and characterizing groundwater dependent ecosystems. LiDAR can be used to model potential locations of groundwater dependent ecosystems with threatened or endangered plant species such as Howellia aquatilis. This difficult to locate species typically exists on the Mendocino National Forest within sag ponds on landslide benches. LiDAR metrics of known sites are used to model potential habitat. Thus LiDAR can link the disciplines of geology, hydrology, botany, archaeology and others for enhanced land management. As LiDAR acquisition costs decrease and it becomes more accessible, land management organizations will find a wealth of applications with potential far-reaching benefits for managing geologic resources and hazards.

Results of the Simulation and Assimilation of Doppler Wind Lidar Observations in Preparation for European Space Agency's Aeolus Mission

NASA Technical Reports Server (NTRS)

McCarty, Will

2011-01-01

With the launch of the European Space Agency's Aeolus Mission in 2013, direct spaceborne measurements of vertical wind profiles are imminent via Doppler wind lidar technology. Part of the preparedness for such missions is the development of the proper data assimilation methodology for handling such observations. Since no heritage measurements exist in space, the Joint Observing System Simulation Experiment (Joint OSSE) framework has been utilized to generate a realistic proxy dataset as a precursor to flight. These data are being used for the development of the Gridpoint Statistical Interpolation (GSI) data assimilation system utilized at a number of centers through the United States including the Global Modeling and Assimilation Office (GMAO) at NASA/Goddard Space Flight Center and at the National Centers for Environmental Prediction (NOAA/NWS/NCEP) as an activity through the Joint Center for Satellite Data Assimilation. An update of this ongoing effort will be presented, including the methodology of proxy data generation, the limitations of the proxy data, the handling of line-of-sight wind measurements within the GSI, and the impact on both analyses and forecasts with the addition of the new data type.

Modeling forest biomass and growth: Coupling long-term inventory and LiDAR data

Treesearch

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

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...

Effects of bathymetric lidar errors on flow properties predicted with a multi-dimensional hydraulic model

Treesearch

J. McKean; D. Tonina; C. Bohn; C. W. Wright

2014-01-01

New remote sensing technologies and improved computer performance now allow numerical flow modeling over large stream domains. However, there has been limited testing of whether channel topography can be remotely mapped with accuracy necessary for such modeling. We assessed the ability of the Experimental Advanced Airborne Research Lidar, to support a multi-dimensional...

Progress in interpreting CO2 lidar signatures to obtain cirrus microphysical and optical properties

NASA Technical Reports Server (NTRS)

Eberhard, Wynn L.

1993-01-01

One cloud/radiation issue at FIRE 2 that has been addressed by the CO2 lidar team is the zenith-enhanced backscatter (ZEB) signature from oriented crystals. A second topic is narrow-beam optical depth measurements using CO2 lidar. This paper describes the theoretical models we have developed for these phenomena and the data-processing algorithms derived from them.

Predicting live and dead tree basal area of bark beetle affected forests from discrete-return lidar

Treesearch

Benjamin C. Bright; Andrew T. Hudak; Robert McGaughey; Hans-Erik Andersen; Jose Negron

2013-01-01

Bark beetle outbreaks have killed large numbers of trees across North America in recent years. Lidar remote sensing can be used to effectively estimate forest biomass, but prediction of both live and dead standing biomass in beetle-affected forests using lidar alone has not been demonstrated. We developed Random Forest (RF) models predicting total, live, dead, and...

Wavelength dependence of coherent and incoherent satellite-based lidar measurements of wind velocity and aerosol backscatter

NASA Technical Reports Server (NTRS)

Kavaya, M. J.; Huffaker, R. M.

1986-01-01

The results are presented of a capability study of Earth orbiting lidar systems, at various wavelengths from 1.06 to 10.6 microns, for the measurement of wind velocity and aerosol backscatter, and for the detection of clouds. Both coherent and incoherent lidar systems were modeled and compared for the aerosol backscatter and cloud detection applications.

Mapping Understory Trees Using Airborne Discrete-Return LIDAR Data

NASA Astrophysics Data System (ADS)

Korpela, I.; Hovi, A.; Morsdorf, F.

2011-09-01

Understory trees in multi-layer stands are often ignored in forest inventories. Information about them would benefit silviculture, wood procurement and biodiversity management. Cost-efficient inventory methods for the assessment of the presence, density, species- and size-distributions are called for. LiDAR remote sensing is a promising addition to field work. Unlike in passive image data, in which the signals from multiple layers mix, the 3D position of each hot-spot reflection is known in LiDAR data. The overstory however prevents from obtaining a wall-to-wall sample of understory, and measurements are subject to transmission losses. Discriminating between the crowns of dominant and suppressed trees can also be challenging. We examined the potential of LiDAR for the mapping of the understory trees in Scots pine stands (62°N, 24°E), using carefully georeferenced reference data and several LiDAR data sets. We present results that highlight differences in echo-triggering between sensors that affect the near-ground height data. A conceptual model for the transmission losses in the overstory was created and formulated into simple compensation models that reduced the intensity variation in second- and third return data. The task is highly ill-posed in discrete-return LiDAR data, and our models employed the geometry of the overstory as well as the intensity of previous returns. We showed that even first-return data in the understory is subject to losses in the overstory that did not trigger an echo. Even with compensation of the losses, the intensity data was deemed of low value in species discrimination. Area-based LiDAR height metrics that were derived from the data belonging to the crown volume of the understory showed reasonable correlation with the density and mean height of the understory trees. Assessment of the species seems out of reach in discrete-return LiDAR data, which is a drastic drawback.

Lidar Penetration Depth Observations for Constraining Cloud Longwave Feedbacks

NASA Astrophysics Data System (ADS)

Vaillant de Guelis, T.; Chepfer, H.; Noel, V.; Guzman, R.; Winker, D. M.; Kay, J. E.; Bonazzola, M.

2017-12-01

Satellite-borne active remote sensing Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations [CALIPSO; Winker et al., 2010] and CloudSat [Stephens et al., 2002] provide direct measurements of the cloud vertical distribution, with a very high vertical resolution. The penetration depth of the laser of the lidar Z_Opaque is directly linked to the LongWave (LW) Cloud Radiative Effect (CRE) at Top Of Atmosphere (TOA) [Vaillant de Guélis et al., in review]. In addition, this measurement is extremely stable in time making it an excellent observational candidate to verify and constrain the cloud LW feedback mechanism [Chepfer et al., 2014]. In this work, we present a method to decompose the variations of the LW CRE at TOA using cloud properties observed by lidar [GOCCP v3.0; Guzman et al., 2017]. We decompose these variations into contributions due to changes in five cloud properties: opaque cloud cover, opaque cloud altitude, thin cloud cover, thin cloud altitude, and thin cloud emissivity [Vaillant de Guélis et al., in review]. We apply this method, in the real world, to the CRE variations of CALIPSO 2008-2015 record, and, in climate model, to LMDZ6 and CESM simulations of the CRE variations of 2008-2015 period and of the CRE difference between a warm climate and the current climate. In climate model simulations, the same cloud properties as those observed by CALIOP are extracted from the CFMIP Observation Simulator Package (COSP) [Bodas-Salcedo et al., 2011] lidar simulator [Chepfer et al., 2008], which mimics the observations that would be performed by the lidar on board CALIPSO satellite. This method, when applied on multi-model simulations of current and future climate, could reveal the altitude of cloud opacity level observed by lidar as a strong constrain for cloud LW feedback, since the altitude feedback mechanism is physically explainable and the altitude of cloud opacity accurately observed by lidar.

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

NASA Astrophysics Data System (ADS)

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 aim to improve the data consistency and comparability (D'Amico et al., 2016; Mattis et al., 2016). A first statistical analysis of simultaneous observations performed by all the instruments during the campaign reveals that ceilometers have fairly good performances in aerosol profiling in the lower troposphere, up to an altitude of about 2000 m above the ground, but they are limited at higher altitudes. Among the considered devices, the mini-MPL shows the best performances with discrepancies limited to 10 % throughout the troposphere. Further analysis is ongoing also to assess the stability of the considered lidar technologies with respect to variation of working and environment temperature, aerosol loading, laser operation.

Temporal evolution of the planetary boundary layer over Athens, Greece - Statistical analysis based on coincident lidar and radiosonde data in the frame of EARLINET (2002-2012)

NASA Astrophysics Data System (ADS)

Kokkalis, Panos; Papayannis, Alex; Tsaknakis, George; Mamouri, RodElise

2013-04-01

The focus of this paper is to study the temporal evolution of the Planetary Boundary Layer (PBL) height over the basin of the megacity of Athens, Greece for a 10-years period: 2002-2012. This study is based on a statistical analysis of PBL heights derived from coincident laser remote sensing (lidar) and radiosonde data, obtained in the frame of the European Aerosol Research Lidar network (EARLINET). To this end, the systematically obtained data (in terms of the lidar signals) by the EOLE Raman-elastic lidar system of the Laser Remote Sensing Unit (LRSU) of the National Technical University of Athens (NTUA), in conjunction with the radiosonde data obtained by the Hellenic National Meteorological Service (HNMS), have been statistically analyzed. The NTUA EOLE lidar system is able to provide the vertical aerosol backscatter (at 355, 532, 1064 nm), aerosol extinction (at 355 and 532 nm), as well as water vapor mixing ratio profiles, from about 700 m up to 10000 m, with high temporal (< 5 min.) and spatial (7.5 m) resolution. The calculation of the first and second derivative of the Range-Corrected Lidar Signal (RCLS) permits the calculation of the PBL height, with a spatial resolution of about 15-30 m, in the range height 700-10000 m, respectively. Radiosonde data are collected daily by HNMS radiosoundings at midnight (00:00 UTC) and midday (12:00 UTC) at the site of Hellenikon, approximately 10 km SW from the NTUA lidar station. The atmospheric parameters calculated from the radiosonde data to provide the PBL height are the potential temperature and the Richardson number. Our data analysis was based on hourly-averaged lidar RCLS measurements, obtained in a time window starting 30 min before and ending 30 minutes after the radiosounding launching time. A good correlation coefficient value (R2 > 0.8) between the aforementioned lidar - radiosonde dataset ensured the accurate derivation of the PBL height. A statistical analysis based on the spatial and temporal variation of PBL height was also introduced, as the PBL height differentiates on a diurnal and seasonal scale. Our results have been compared with previous studies and conclusions are finally drawn. Acknowledgements: This research has been financed by ITARS (www.itars.net), European Union Seventh Framework Programme (FP7/2007-2013): People, ITN Marie Curie Actions Programme (2012-2016) under grant agreement no 289923.

Atmospheric lidar co-alignment sensor: flight model electro-optical characterization campaign

NASA Astrophysics Data System (ADS)

Valverde Guijarro, Ángel Luis; Belenguer Dávila, Tomás.; Laguna Hernandez, Hugo; Ramos Zapata, Gonzalo

2017-10-01

Due to the difficulty in studying the upper layer of the troposphere by using ground-based instrumentation, the conception of a space-orbit atmospheric LIDAR (ATLID) becomes necessary. ATLID born in the ESA's EarthCare Programme framework as one of its payloads, being the first instrument of this kind that will be in the Space. ATLID will provide vertical profiles of aerosols and thin clouds, separating the relative contribution of aerosol and molecular scattering to know aerosol optical depth. It operates at a wavelength of 355 nm and has a high spectral resolution receiver and depolarization channel with a vertical resolution up to 100m from ground to an altitude of 20 km and, and up to 500m from 20km to 40km. ATLID measurements will be done from a sun-synchronous orbit at 393 km altitude, and an alignment (co-alignment) sensor (CAS) is revealed as crucial due to the way in which LIDAR analyses the troposphere. As in previous models, INTA has been in charge of part of the ATLID instrument co-alignment sensor (ATLID-CAS) electro-optical characterization campaign. CAS includes a set of optical elements to take part of the useful signal, to direct it onto the memory CCD matrix (MCCD) used for the co-alignment determination, and to focus the selected signal on the MCCD. Several tests have been carried out for a proper electro-optical characterization: CAS line of sight (LoS) determination and stability, point spread function (PSF), absolute response (AbsRes), pixel response non uniformity (PRNU), response linearity (ResLin) and spectral response. In the following lines, a resume of the flight model electrooptical characterization campaign is reported on. In fact, results concerning the protoflight model (CAS PFM) will be summarized. PFM requires flight-level characterization, so most of the previously mentioned tests must be carried out under simulated working conditions, i.e., the vacuum level (around 10-5 mbar) and temperature range (between 50°C and -30°C) that are expected during ATLID Space operation.

Assessment of MFLL column CO2 measurements obtained during the ACT-America field campaigns

NASA Astrophysics Data System (ADS)

Lin, B.; Browell, E. V.; Kooi, S. A.; Dobler, J. T.; Campbell, J.; Fan, T. F.; Pal, S.; O'Dell, C. W.; Obland, M. D.; Erxleben, W. H.; McGregor, D.; Kochanov, R. V.; DiGangi, J. P.; Davis, K. J.; Choi, Y.

2017-12-01

Accurate observations of atmospheric CO2 with airborne and space-based lidar systems such as those used during the Atmospheric Carbon and Transport - America (ACT-America) field campaigns and proposed for the NASA ASCENDS mission would improve our knowledge of CO2 distributions and variations on both regional and global scales, reduce the uncertainties in atmospheric CO2 transport and fluxes, and increase confidence in predictions of future climate changes. To reach these scientific goals, atmospheric column CO2 (XCO2) measurements of the Harris Corporation's Multifunctional Fiber Laser Lidar (MFLL) obtained during the first two ACT-America flight campaigns have been thoroughly investigated by the ACT-America lidar measurement group. MFLL is an intensity-modulated continuous-wave lidar operating in the 1.57-mm CO2 absorption band. Atmospheric XCO2 amounts are retrieved based on the integrated path differential absorption of the lidar signals at online and offline wavelengths between the aircraft and the ground. NASA Langley Research Center and Harris have been collaborating in the development and evaluation of this CO2 lidar approach for a number of years. To gain insights into the lidar performance, the measurement group has collected all possible lidar measurements with corresponding in-situ atmospheric profile information from the first two ACT-America field campaigns, including the data from several flight legs dedicated to lidar calibration. Initially large differences (-1 to 2 %) were found between lidar measured CO2 optical depths and those derived from in-situ observations and spectroscopy from HITRAN2008. When an improved spectroscopic model (Pre-HITRAN2016) was applied, the large systematic errors were much more consistent leading to the development of an empirical linear correction of measured optical depth based on the calibration flight data. This correction accounts for remaining uncertainties in spectroscopic models, environmental conditions, such as temperature, pressure, and water vapor, and possible instrumental issues like optical cross-talk between wavelengths introduced in the power amplifier. Results from a flight of Gulf of Mexico with a very homogenous environment showed that the precision of lidar XCO2 measurements was as high as about 0.5ppm for 10-s averages.

Lidar observations of vertically organized convection in the planetary boundary layer over the ocean

NASA Technical Reports Server (NTRS)

Melfi, S. H.; Spinhirne, J. D.; Chou, S.-H.; Palm, S. P.

1985-01-01

Observations of a convective planetary boundary layer (PBL) were made with an airborne, downward-looking lidar system over the Atlantic Ocean during a cold air outbreak. The lidar data revealed well-organized, regularly spaced cellular convection with dominant spacial scales between two and four times the height of the boundary layer. It is demonstrated that the lidar can accurately measure the structure of the PBL with high vertical and horizontal resolution. Parameters important for PBL modeling such as entrainment zone thickness, entrainment rate, PBL height and relative heat flux can be inferred from the lidar data. It is suggested that wind shear at the PBL top may influence both entrainment and convective cell size.

Statistical modeling of the long-range-dependent structure of barrier island framework geology and surface geomorphology

NASA Astrophysics Data System (ADS)

Weymer, Bradley A.; Wernette, Phillipe; Everett, Mark E.; Houser, Chris

2018-06-01

Shorelines exhibit long-range dependence (LRD) and have been shown in some environments to be described in the wave number domain by a power-law characteristic of scale independence. Recent evidence suggests that the geomorphology of barrier islands can, however, exhibit scale dependence as a result of systematic variations in the underlying framework geology. The LRD of framework geology, which influences island geomorphology and its response to storms and sea level rise, has not been previously examined. Electromagnetic induction (EMI) surveys conducted along Padre Island National Seashore (PAIS), Texas, United States, reveal that the EMI apparent conductivity (σa) signal and, by inference, the framework geology exhibits LRD at scales of up to 101 to 102 km. Our study demonstrates the utility of describing EMI σa and lidar spatial series by a fractional autoregressive integrated moving average (ARIMA) process that specifically models LRD. This method offers a robust and compact way of quantifying the geological variations along a barrier island shoreline using three statistical parameters (p, d, q). We discuss how ARIMA models that use a single parameter d provide a quantitative measure for determining free and forced barrier island evolutionary behavior across different scales. Statistical analyses at regional, intermediate, and local scales suggest that the geologic framework within an area of paleo-channels exhibits a first-order control on dune height. The exchange of sediment amongst nearshore, beach, and dune in areas outside this region are scale independent, implying that barrier islands like PAIS exhibit a combination of free and forced behaviors that affect the response of the island to sea level rise.

Novel Methods for Measuring LiDAR

NASA Astrophysics Data System (ADS)

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 tested against one another using 28 different sites and over 42 different LiDAR acquisitions. The optimal model will then be used to generate regional wall-to-wall forest inventories at a 10 m resolution.

Lidar-revised geologic map of the Des Moines 7.5' quadrangle, King County, Washington

USGS Publications Warehouse

Tabor, Rowland W.; Booth, Derek B.

2017-11-06

This map is an interpretation of a modern lidar digital elevation model combined with the geology depicted on the Geologic Map of the Des Moines 7.5' Quadrangle, King County, Washington (Booth and Waldron, 2004). Booth and Waldron described, interpreted, and located the geology on the 1:24,000-scale topographic map of the Des Moines 7.5' quadrangle. The base map that they used was originally compiled in 1943 and revised using 1990 aerial photographs; it has 25-ft contours, nominal horizontal resolution of about 40 ft (12 m), and nominal mean vertical accuracy of about 10 ft (3 m). Similar to many geologic maps, much of the geology in the Booth and Waldron (2004) map was interpreted from landforms portrayed on the topographic map. In 2001, the Puget Sound Lidar Consortium obtained a lidar-derived digital elevation model (DEM) for much of the Puget Sound area, including the entire Des Moines 7.5' quadrangle. This new DEM has a horizontal resolution of about 6 ft (2 m) and a mean vertical accuracy of about 1 ft (0.3 m). The greater resolution and accuracy of the lidar DEM compared to topography constructed from air-photo stereo models have much improved the interpretation of geology, even in this heavily developed area, especially the distribution and relative age of some surficial deposits. For a brief description of the light detection and ranging (lidar) remote sensing method and this data acquisition program, see Haugerud and others (2003). 

Development of a LiDAR derived digital elevation model (DEM) as Input to a METRANS geographic information system (GIS).

DOT National Transportation Integrated Search

2011-05-01

This report describes an assessment of digital elevation models (DEMs) derived from : LiDAR data for a subset of the Ports of Los Angeles and Long Beach. A methodology : based on Monte Carlo simulation was applied to investigate the accuracy of DEMs ...

A New Background Stratospheric Aerosol Model for Use in Atmospheric Radiation Models

DTIC Science & Technology

1988-07-30

I. Soufriere 1979 event, J. Atmos. Terr. Phy, 43:1127- 1131. 19. Hirono, M., Fujiwara, M.,and Shibata, T. (1982) Lidar observation of sudden... Soufriere 1979 event, J. Atmos. Terr. Phy, 43:1127- 1131. 19. Hirono, M., Fujiwara, M.,and Shibata, T. (1982) Lidar observation of sudden increases

Impact of Lidar Wind Sounding on Mesoscale Forecast

NASA Technical Reports Server (NTRS)

Miller, Timothy L.; Chou, Shih-Hung; Goodman, H. Michael (Technical Monitor)

2001-01-01

An Observing System Simulation Experiment (OSSE) was conducted to study the impact of airborne lidar wind sounding on mesoscale weather forecast. A wind retrieval scheme, which interpolates wind data from a grid data system, simulates the retrieval of wind profile from a satellite lidar system. A mesoscale forecast system based on the PSU/NCAR MM5 model is developed and incorporated the assimilation of the retrieved line-of-sight wind. To avoid the "identical twin" problem, the NCEP reanalysis data is used as our reference "nature" atmosphere. The simulated space-based lidar wind observations were retrieved by interpolating the NCEP values to the observation locations. A modified dataset obtained by smoothing the NCEP dataset was used as the initial state whose forecast was sought to be improved by assimilating the retrieved lidar observations. Forecasts using wind profiles with various lidar instrument parameters has been conducted. The results show that to significantly improve the mesoscale forecast the satellite should fly near the storm center with large scanning radius. Increasing lidar firing rate also improves the forecast. Cloud cover and lack of aerosol degrade the quality of the lidar wind data and, subsequently, the forecast.

Applying Lidar and High-Resolution Multispectral Imagery for Improved Quantification and Mapping of Tundra Vegetation Structure and Distribution in the Alaskan Arctic

NASA Astrophysics Data System (ADS)

Greaves, Heather E.

Climate change is disproportionately affecting high northern latitudes, and the extreme temperatures, remoteness, and sheer size of the Arctic tundra biome have always posed challenges that make application of remote sensing technology especially appropriate. Advances in high-resolution remote sensing continually improve our ability to measure characteristics of tundra vegetation communities, which have been difficult to characterize previously due to their low stature and their distribution in complex, heterogeneous patches across large landscapes. In this work, I apply terrestrial lidar, airborne lidar, and high-resolution airborne multispectral imagery to estimate tundra vegetation characteristics for a research area near Toolik Lake, Alaska. Initially, I explored methods for estimating shrub biomass from terrestrial lidar point clouds, finding that a canopy-volume based algorithm performed best. Although shrub biomass estimates derived from airborne lidar data were less accurate than those from terrestrial lidar data, algorithm parameters used to derive biomass estimates were similar for both datasets. Additionally, I found that airborne lidar-based shrub biomass estimates were just as accurate whether calibrated against terrestrial lidar data or harvested shrub biomass--suggesting that terrestrial lidar potentially could replace destructive biomass harvest. Along with smoothed Normalized Differenced Vegetation Index (NDVI) derived from airborne imagery, airborne lidar-derived canopy volume was an important predictor in a Random Forest model trained to estimate shrub biomass across the 12.5 km2 covered by our lidar and imagery data. The resulting 0.80 m resolution shrub biomass maps should provide important benchmarks for change detection in the Toolik area, especially as deciduous shrubs continue to expand in tundra regions. Finally, I applied 33 lidar- and imagery-derived predictor layers in a validated Random Forest modeling approach to map vegetation community distribution at 20 cm resolution across the data collection area, creating maps that will enable validation of coarser maps, as well as study of fine-scale ecological processes in the area. These projects have pushed the limits of what can be accomplished for vegetation mapping using airborne remote sensing in a challenging but important region; it is my hope that the methods explored here will illuminate potential paths forward as landscapes and technologies inevitably continue to change.

Aggregating pixel-level basal area predictions derived from LiDAR data to industrial forest stands in North-Central Idaho

Treesearch

Andrew T. Hudak; Jeffrey S. Evans; Nicholas L. Crookston; Michael J. Falkowski; Brant K. Steigers; Rob Taylor; Halli Hemingway

2008-01-01

Stand exams are the principal means by which timber companies monitor and manage their forested lands. Airborne LiDAR surveys sample forest stands at much finer spatial resolution and broader spatial extent than is practical on the ground. In this paper, we developed models that leverage spatially intensive and extensive LiDAR data and a stratified random sample of...

Retrieving microphysics of cirrus clouds from data measured with raman lidar ramses and a tilted ceilometer

NASA Astrophysics Data System (ADS)

Borovoi, Anatoli; Reichardt, Jens; Görsdorf, Ulrich; Wolf, Veronika; Konoshonkin, Alexander; Shishko, Victor; Kustova, Natalia

2018-04-01

To develop a microphysical model of cirrus clouds, data obtained by Raman lidar RAMSES and a tilted ceilometer are studied synergistically. The measurements are interpreted by use of a data archive containing the backscattering matrixes as well as the depolarization, color and lidar ratios of ice crystals of different shapes, sizes and spatial orientations calculated within the physical-optics approximation.

Fractal properties and denoising of lidar signals from cirrus clouds

NASA Astrophysics Data System (ADS)

van den Heuvel, J. C.; Driesenaar, M. L.; Lerou, R. J. L.

2000-02-01

Airborne lidar signals of cirrus clouds are analyzed to determine the cloud structure. Climate modeling and numerical weather prediction benefit from accurate modeling of cirrus clouds. Airborne lidar measurements of the European Lidar in Space Technology Experiment (ELITE) campaign were analyzed by combining shots to obtain the backscatter at constant altitude. The signal at high altitude was analyzed for horizontal structure of cirrus clouds. The power spectrum and the structure function show straight lines on a double logarithmic plot. This behavior is characteristic for a Brownian fractal. Wavelet analysis using the Haar wavelet confirms the fractal aspects. It is shown that the horizontal structure of cirrus can be described by a fractal with a dimension of 1.8 over length scales that vary 4 orders of magnitude. We use the fractal properties in a new denoising method. Denoising is required for future lidar measurements from space that have a low signal to noise ratio. Our wavelet denoising is based on the Haar wavelet and uses the statistical fractal properties of cirrus clouds in a method based on the maximum a posteriori (MAP) probability. This denoising based on wavelets is tested on airborne lidar signals from ELITE using added Gaussian noise. Superior results with respect to averaging are obtained.

Building block extraction and classification by means of aerial images fused with super-resolution reconstructed elevation data

NASA Astrophysics Data System (ADS)

Panagiotopoulou, Antigoni; Bratsolis, Emmanuel; Charou, Eleni; Perantonis, Stavros

2017-10-01

The detailed three-dimensional modeling of buildings utilizing elevation data, such as those provided by light detection and ranging (LiDAR) airborne scanners, is increasingly demanded today. There are certain application requirements and available datasets to which any research effort has to be adapted. Our dataset includes aerial orthophotos, with a spatial resolution 20 cm, and a digital surface model generated from LiDAR, with a spatial resolution 1 m and an elevation resolution 20 cm, from an area of Athens, Greece. The aerial images are fused with LiDAR, and we classify these data with a multilayer feedforward neural network for building block extraction. The innovation of our approach lies in the preprocessing step in which the original LiDAR data are super-resolution (SR) reconstructed by means of a stochastic regularized technique before their fusion with the aerial images takes place. The Lorentzian estimator combined with the bilateral total variation regularization performs the SR reconstruction. We evaluate the performance of our approach against that of fusing unprocessed LiDAR data with aerial images. We present the classified images and the statistical measures confusion matrix, kappa coefficient, and overall accuracy. The results demonstrate that our approach predominates over that of fusing unprocessed LiDAR data with aerial images.

ELF and ALEX SURF WINTER WAVES: Lidar Intercomparison of Aerosol and Water Vapor Measurements in the Baltimore-Washington Metropolitan Area During the Winter Water Vapor Validation Experiments (WAVES) 2008 campaign.

NASA Astrophysics Data System (ADS)

Delgado, R.; Weldegaber, M.; Wilson, R. C.; McMillan, W.; McCann, K. J.; Woodman, M.; Demoz, B.; Adam, M.; Connell, R.; Venable, D.; Joseph, E.; Rabenhorst, S.; Twigg, L.; McGee, T.; Whiteman, D. N.; Hoff, R. M.

2008-12-01

Elastic and Raman lidar measurements were conducted to measure the vertical distribution of aerosols and water vapor during the Water Vapor Validation Experiments (WAVES) 2008 campaign by the University of Maryland Baltimore County (UMBC) Atmospheric Lidar Group at UMBC, at the same time as measurements at Howard University's Beltsville Research Station (26.5 km distant). The lidar profiles of atmospheric water vapor and aerosols allowed comparison for AURA/Aqua retrieval studies, by performing instrument accuracy assessments and data, generated by various independent active and passive remote sensing instruments for case studies of regional water vapor and aerosol sub-pixel variability. Integration of the lidar water vapor mixing ratios has been carried out to generate a column precipitable water vapor timeseries that can be compared to UMBC's SUOMINET station and Baltimore Bomem Atmospheric Emitted Radiance Interferometer (BBAERI). Changes in atmospheric aerosol concentration and water vapor mixing ratios due to meteorological events observed in the lidar timeseries have been correlated to the vertical temperature timeseries of BBAERI and to modeling of the air mass over the Baltimore-Washington metro area with the Weather Research and Forecasting (WRF) model.

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

NASA Astrophysics Data System (ADS)

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 from various remote sensing scenarios will also be presented.

Random forest regression modelling for forest aboveground biomass estimation using RISAT-1 PolSAR and terrestrial LiDAR data

NASA Astrophysics Data System (ADS)

Mangla, Rohit; Kumar, Shashi; Nandy, Subrata

2016-05-01

SAR and LiDAR remote sensing have already shown the potential of active sensors for forest parameter retrieval. SAR sensor in its fully polarimetric mode has an advantage to retrieve scattering property of different component of forest structure and LiDAR has the capability to measure structural information with very high accuracy. This study was focused on retrieval of forest aboveground biomass (AGB) using Terrestrial Laser Scanner (TLS) based point clouds and scattering property of forest vegetation obtained from decomposition modelling of RISAT-1 fully polarimetric SAR data. TLS data was acquired for 14 plots of Timli forest range, Uttarakhand, India. The forest area is dominated by Sal trees and random sampling with plot size of 0.1 ha (31.62m*31.62m) was adopted for TLS and field data collection. RISAT-1 data was processed to retrieve SAR data based variables and TLS point clouds based 3D imaging was done to retrieve LiDAR based variables. Surface scattering, double-bounce scattering, volume scattering, helix and wire scattering were the SAR based variables retrieved from polarimetric decomposition. Tree heights and stem diameters were used as LiDAR based variables retrieved from single tree vertical height and least square circle fit methods respectively. All the variables obtained for forest plots were used as an input in a machine learning based Random Forest Regression Model, which was developed in this study for forest AGB estimation. Modelled output for forest AGB showed reliable accuracy (RMSE = 27.68 t/ha) and a good coefficient of determination (0.63) was obtained through the linear regression between modelled AGB and field-estimated AGB. The sensitivity analysis showed that the model was more sensitive for the major contributed variables (stem diameter and volume scattering) and these variables were measured from two different remote sensing techniques. This study strongly recommends the integration of SAR and LiDAR data for forest AGB estimation.

CO2 laser modeling

NASA Technical Reports Server (NTRS)

Johnson, Barry

1992-01-01

The topics covered include the following: (1) CO2 laser kinetics modeling; (2) gas lifetimes in pulsed CO2 lasers; (3) frequency chirp and laser pulse spectral analysis; (4) LAWS A' Design Study; and (5) discharge circuit components for LAWS. The appendices include LAWS Memos, computer modeling of pulsed CO2 lasers for lidar applications, discharge circuit considerations for pulsed CO2 lidars, and presentation made at the Code RC Review.

Applying inventory methods to estimate aboveground biomass from satellite light detection and ranging (LiDAR) forest height data

Treesearch

Sean P. Healey; Paul L. Patterson; Sassan Saatchi; Michael A. Lefsky; Andrew J. Lister; Elizabeth A. Freeman; Gretchen G. Moisen

2012-01-01

Light Detection and Ranging (LiDAR) returns from the spaceborne Geoscience Laser Altimeter (GLAS) sensor may offer an alternative to solely field-based forest biomass sampling. Such an approach would rely upon model-based inference, which can account for the uncertainty associated with using modeled, instead of field-collected, measurements. Model-based methods have...

Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral data

Treesearch

Andrew T. Hudak; Nicholas L. Crookston; Jeffrey S. Evans; Michael K. Falkowski; Alistair M. S. Smith; Paul E. Gessler; Penelope Morgan

2006-01-01

We compared the utility of discrete-return light detection and ranging (lidar) data and multispectral satellite imagery, and their integration, for modeling and mapping basal area and tree density across two diverse coniferous forest landscapes in north-central Idaho. We applied multiple linear regression models subset from a suite of 26 predictor variables derived...

Topographic lidar survey of the Chandeleur Islands, Louisiana, February 6, 2012

USGS Publications Warehouse

Guy, Kristy K.; Plant, Nathaniel G.; Bonisteel-Cormier, Jamie M.

2014-01-01

This Data Series Report contains lidar elevation data collected February 6, 2012, for Chandeleur Islands, Louisiana. Point cloud data in lidar data exchange format (LAS) and bare earth digital elevation models (DEMs) in ERDAS Imagine raster format (IMG) are available as downloadable files. The point cloud data—data points described in three dimensions—were processed to extract bare earth data; therefore, the point cloud data are organized into the following classes: 1– and 17–unclassified, 2–ground, 9–water, and 10–breakline proximity. Digital Aerial Solutions, LLC, (DAS) was contracted by the U.S. Geological Survey (USGS) to collect and process these data. The lidar data were acquired at a horizontal spacing (or nominal pulse spacing) of 0.5 meters (m) or less. The USGS conducted two ground surveys in small areas on the Chandeleur Islands on February 5, 2012. DAS calculated a root mean square error (RMSEz) of 0.034 m by comparing the USGS ground survey point data to triangulated irregular network (TIN) models built from the lidar elevation data. This lidar survey was conducted to document the topography and topographic change of the Chandeleur Islands. The survey supports detailed studies of Louisiana, Mississippi and Alabama barrier islands that resolve annual and episodic changes in beaches, berms and dunes associated with processes driven by storms, sea-level rise, and even human restoration activities. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

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

USGS Publications Warehouse

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.

NASA Goddards LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager

NASA Technical Reports Server (NTRS)

Cook, Bruce D.; Corp, Lawrence A.; Nelson, Ross F.; Middleton, Elizabeth M.; Morton, Douglas C.; McCorkel, Joel T.; Masek, Jeffrey G.; Ranson, Kenneth J.; Ly, Vuong; Montesano, Paul M.

2013-01-01

The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (approximately 1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT's data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA's Data and Information policy.

Capability of simultaneous Rayleigh LiDAR and O2 airglow measurements in exploring the short period wave characteristics

NASA Astrophysics Data System (ADS)

Taori, Alok; Raghunath, Karnam; Jayaraman, Achuthan

We use combination of simultaneous measurements made with Rayleigh lidar and O2 airglow monitoring to improve lidar investigation capability to cover a higher altitude range. We feed instantaneous O2 airglow temperatures instead the model values at the top altitude for subsequent integration method of temperature retrieval using Rayleigh lidar back scattered signals. Using this method, errors in the lidar temperature estimates converges at higher altitudes indicating better altitude coverage compared to regular methods where model temperatures are used instead of real-time measurements. This improvement enables the measurements of short period waves at upper mesospheric altitudes (~90 km). With two case studies, we show that above 60 km the few short period wave amplitude drastically increases while, some of the short period wave show either damping or saturation. We claim that by using such combined measurements, a significant and cost effective progress can be made in the understanding of short period wave processes which are important for the coupling across the different atmospheric regions.

An Overview of Ocean Lidar Studies At NRL Stennis, NOAA ESRL and NASA LaRC (Invited)

NASA Astrophysics Data System (ADS)

Hu, Y.; Arnone, R. A.; Churnside, J. H.

2009-12-01

(Author List: Robert A Arnone, James H Churnside, Yongxiang Hu) Naval interests in ocean LIDAR systems has several areas which include the use of LIDAR for bathymetry mapping, underwater imaging systems and characterizing the vertical bio-optical scattering layers. Ocean LIDAR provides a new capability of extending surface bio-optical properties retrieved form ocean color imagery into the subsurface. The relationships between bio-optical scattering layers and physical properties such as mixed layer depth are being developed and LIDAR profiles can provide a significant contribution. These techniques for combining the vertical bio-optical structure with physical models and satellite ocean color using data assimilation are providing new capability in characterize the 3d ocean volume. The LIDAR capability in resolving the vertical ocean structure can provide the next generation remote sensing information for ocean characterization. Over the last ten years, NOAA has been developing and testing lidar for aerial surveys of fish schools and plankton aggregations. We have flown the lidar on numerous aircraft, ranging in size from a four-seat Cessna to a Casa twin-engine cargo plane. Surveys have covered both inland and offshore waters on both coasts of North America and the Atlantic waters of Europe. The species of interest have generally been near surface schooling fishes like sardines, anchovies, herring, mackerel, and menhaden. Plankton studies have included both zooplankton and phytoplankton. There are several general conclusions that can be drawn from the results of this work. The penetration depth of the lidar varies from about 15 m in very turbid inland waters to over 50 m in blue offshore waters. Reliable detection of fish schools and plankton layers requires filtering and application of a threshold to remove background scattering levels. The correlation between the results of a lidar survey and traditional acoustic or net surveys generally depends on the time delay between the two surveys. With no time delay the correlation is well above 0.9, and the correlation is essentially zero after about four days. The ocean lidar studies at NASA Langley Research Center started from the launch of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. The lidar, designed for cloud and aerosol observation, also provides information about (1) ocean subsurface particulate backscatter, and (2) high resolution ocean surface wind speeds / mean square wave slopes. More than three years of global ocean measurements from CALIPSO are analyzed and available to the community. Ocean and atmospheric seasonal and inter-annual variations are studied using the CALIPSO observations combined with measurements from other A-train satellites. Future studies at NASA Langley Research Center includes: (1) Improvement of CALIPSO ocean product; (2) Theoretical radiative transfer modeling, such as lidar multiple scatter and coupled ocean-atmospheric Stokes vector, and new retrieval concepts with combined lidar and multi-angle, multi-spectral polarimeter; (3) Aircraft based measurements of next generation lidar, polarimeter and hyperspectral measurements.

Rockfall hazard analysis using LiDAR and spatial modeling

NASA Astrophysics Data System (ADS)

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.

Wave optics simulation of atmospheric turbulence and reflective speckle effects in carbon dioxide lidar

NASA Astrophysics Data System (ADS)

Nelson, Douglas Harold

Laser speckle can influence lidar measurements from a diffuse hard target. Atmospheric optical turbulence will also affect the lidar return signal. This investigation develops a numerical simulation that models the propagation of a lidar beam and accounts for both reflective speckle and atmospheric turbulence effects. The simulation, previously utilized to simulate the effects of atmospheric optical turbulence alone, is based on implementing a Huygens-Fresnel approximation to laser propagation. A series of phase screens, with the appropriate atmospheric statistical characteristics, is used to simulate the effect of atmospheric optical turbulence. A single random phase screen is used to simulate scattering of the entire beam from a rough surface. These investigations compare the output of the numerical model with separate CO2 lidar measurements of atmospheric turbulence and reflective speckle. This work also compares the output of the model with separate analytical predictions for atmospheric turbulence and reflective speckle. Good agreement is found between the model and the experimental data. Good agreement is also found with analytical predictions. Additionally, results of simulation of the combined effects on a finite aperture lidar system show agreement with experimental observations of increasing RMS noise with increasing turbulence level and the behavior of the experimental integrated intensity probability distribution. Simulation studies are included that demonstrate the usefulness of the model, examine its limitations and provide greater insight into the process of combined atmospheric optical turbulence and reflective speckle. One highlight of these studies is examination of the limitations of the simulation that shows, in general, precision increases with increasing grid size. The study of the backscatter intensity enhancement predicted by analytical theory show it to behave as a multi-path effect, like scintillation, with the highest contributions from atmospheric optical turbulence weighted at the middle of the propagation path. Aperture geometry also affects the signal-to-noise ratio with thin annular apertures exhibiting lower RMS noise than circular apertures of the same active area. The simulation is capable of studying a variety of lidar schemes including varying atmospheric optical turbulence along the propagation path as well as diverse transmitter and receiver geometries.

Application of High-resolution Aerial LiDAR Data in Calibration of a Two-dimensional Urban Flood Simulation

NASA Astrophysics Data System (ADS)

Piotrowski, J.; Goska, R.; Chen, B.; Krajewski, W. F.; Young, N.; Weber, L.

2009-12-01

In June 2008, the state of Iowa experienced an unprecedented flood event which resulted in an economic loss of approximately $2.88 billion. Flooding in the Iowa River corridor, which exceeded the previous flood of record by 3 feet, devastated several communities, including Coralville and Iowa City, home to the University of Iowa. Recognizing an opportunity to capture a unique dataset detailing the impacts of the historic flood, the investigators contacted the National Center for Airborne Laser Mapping (NCALM), which performed an aerial Light Detection and Ranging (LiDAR) survey along the Iowa River. The survey, conducted immediately following the flood peak, provided coverage of a 60-mile reach. The goal of the present research is to develop a process by which flood extents and water surface elevations can be accurately extracted from the LiDAR data set and to evaluate the benefit of such data in calibrating one- and two-dimensional hydraulic models. Whereas data typically available for model calibration include sparsely distributed point observations and high water marks, the LiDAR data used in the present study provide broad-scale, detailed, and continuous information describing the spatial extent and depth of flooding. Initial efforts were focused on a 10-mile, primarily urban reach of the Iowa River extending from Coralville Reservoir, a United States Army Corps of Engineers flood control project, downstream through the Coralville and Iowa City. Spatial extent and depth of flooding were estimated from the LiDAR data. At a given cross-sectional location, river channel and floodplain measurements were compared. When differences between floodplain and river channel measurements were less than a standard deviation of the vertical uncertainty in the LiDAR survey, floodplain measurements were classified as flooded. A flood water surface DEM was created using measurements classified as flooded. A two-dimensional, depth-averaged numerical model of a 10-mile reach of the Iowa River corridor was developed using the United States Bureau of Reclamation SRH-2D hydraulic modeling software. The numerical model uses an unstructured numerical mesh and variable surface roughness, assigned according to observed land use and cover. The numerical model was calibrated using inundation extents and water surface elevations derived from the LiDAR data. It was also calibrated using high water marks and land survey data collected daily during the 2008 flood. The investigators compared the two calibrations to evaluate the benefit of high-resolution LiDAR data in improving the accuracy of a two-dimensional urban flood simulation.

Application of a Terrestrial LIDAR System for Elevation Mapping in Terra Nova Bay, Antarctica.

PubMed

Cho, Hyoungsig; Hong, Seunghwan; Kim, Sangmin; Park, Hyokeun; Park, Ilsuk; Sohn, Hong-Gyoo

2015-09-16

A terrestrial Light Detection and Ranging (LIDAR) system has high productivity and accuracy for topographic mapping, but the harsh conditions of Antarctica make LIDAR operation difficult. Low temperatures cause malfunctioning of the LIDAR system, and unpredictable strong winds can deteriorate data quality by irregularly shaking co-registration targets. For stable and efficient LIDAR operation in Antarctica, this study proposes and demonstrates the following practical solutions: (1) a lagging cover with a heating pack to maintain the temperature of the terrestrial LIDAR system; (2) co-registration using square planar targets and two-step point-merging methods based on extracted feature points and the Iterative Closest Point (ICP) algorithm; and (3) a georeferencing module consisting of an artificial target and a Global Navigation Satellite System (GNSS) receiver. The solutions were used to produce a topographic map for construction of the Jang Bogo Research Station in Terra Nova Bay, Antarctica. Co-registration and georeferencing precision reached 5 and 45 mm, respectively, and the accuracy of the Digital Elevation Model (DEM) generated from the LIDAR scanning data was ±27.7 cm.

Low-SWAP Lidar Instrument for Arctic Ice Sheet Mass Balance Monitoring Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, George; Barsic, David

To meet the need to obtain statistically significant data in the North Slope of Alaska (NSA) in support of climate models, Voxtel is developing an nmanned-aircraft-system (UAS)-optimized lidar focal plane array (FPA) and lidar instrument design that integrates the most recent developments in optics, electronics, and computing. Bound by the size, weight, and power (SWAP) budget of low altitude/long endurance (LALE) small UAS (SUAS) platforms—a design tradeoff study was conducted. The class of SUAS considered typically: operates at altitudes between 150 meters and 2,000 meters; accommodates payloads weighing less than 5 kg; encompasses no more than 4,000 cm3 of space;more » and consumes no more than 50 watts of power. To address the SWAP constraints, a lowpower standalone strap-down (gimbal-less) lidar was developed based on single-photon-counting silicon avalanche photodiodes. To reduce SWAP, a lidar FPA design capable of simultaneous imaging and lidar was developed. The 532-nm-optimized FPA modular design was developed for easy integration, as a lidar payload, in any of a variety of SUAS platforms.« less

Simulations of the observation of clouds and aerosols with the Experimental Lidar in Space Equipment system.

PubMed

Liu, Z; Voelger, P; Sugimoto, N

2000-06-20

We carried out a simulation study for the observation of clouds and aerosols with the Japanese Experimental Lidar in Space Equipment (ELISE), which is a two-wavelength backscatter lidar with three detection channels. The National Space Development Agency of Japan plans to launch the ELISE on the Mission Demonstrate Satellite 2 (MDS-2). In the simulations, the lidar return signals for the ELISE are calculated for an artificial, two-dimensional atmospheric model including different types of clouds and aerosols. The signal detection processes are simulated realistically by inclusion of various sources of noise. The lidar signals that are generated are then used as input for simulations of data analysis with inversion algorithms to investigate retrieval of the optical properties of clouds and aerosols. The results demonstrate that the ELISE can provide global data on the structures and optical properties of clouds and aerosols. We also conducted an analysis of the effects of cloud inhomogeneity on retrievals from averaged lidar profiles. We show that the effects are significant for space lidar observations of optically thick broken clouds.

Supporting Indonesia's National Forest Monitoring System with LiDAR Observations

NASA Astrophysics Data System (ADS)

Hagen, S. C.

2015-12-01

Scientists at Applied GeoSolutions, Jet Propulsion Laboratory, Winrock International, and the University of New Hampshire are working with the government of Indonesia to enhance the National Forest Monitoring System in Kalimantan, Indonesia. The establishment of a reliable, transparent, and comprehensive NFMS has been limited by a dearth of relevant data that are accurate, low-cost, and spatially resolved at subnational scales. In this NASA funded project, we are developing, evaluating, and validating several critical components of a NFMS in Kalimantan, Indonesia, focusing on the use of LiDAR and radar imagery for improved carbon stock and forest degradation information. Applied GeoSolutions and the University of New Hampshire have developed an Open Source Software package to process large amounts LiDAR data quickly, easily, and accurately. The Open Source project is called lidar2dems and includes the classification of raw LAS point clouds and the creation of Digital Terrain Models (DTMs), Digital Surface Models (DSMs), and Canopy Height Models (CHMs). Preliminary estimates of forest structure and forest damage from logging from these data sets support the idea that comprehensive, well documented, freely available software for processing LiDAR data can enable countries such as Indonesia to cost effectively monitor their forests with high precision.

Retrieval method of aerosol extinction coefficient profile by an integral lidar system and case study

NASA Astrophysics Data System (ADS)

Shan, Huihui; Zhang, Hui; Liu, Junjian; Wang, Shenhao; Ma, Xiaomin; Zhang, Lianqing; Liu, Dong; Xie, Chenbo; Tao, Zongming

2018-02-01

Aerosol extinction coefficient profile is an essential parameter for atmospheric radiation model. But it is difficult to get the full aerosol extinction profile from the ground to the tropopause especially in near ground precisely using backscattering lidar. A combined measurement of side-scattering, backscattering and Raman-scattering lidar is proposed to retrieve the aerosol extinction coefficient profile from the surface to the tropopause which covered a dynamic range of 5 orders. The side-scattering technique solves the dead zone and the overlap problem caused by the traditional lidar in the near range. Using the Raman-scattering the aerosol lidar ratio (extinction to backscatter ratio) can be obtained. The cases studies in this paper show the proposed method is reasonable and feasible.

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

NASA Technical Reports Server (NTRS)

Basu, Saikat; Ganguly, Sangram; Michaelis, Andrew; Votava, Petr; Roy, Anshuman; Mukhopadhyay, Supratik; Nemani, Ramakrishna

2015-01-01

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery using a Probabilistic Learning Framework

NASA Astrophysics Data System (ADS)

Basu, S.; Ganguly, S.; Michaelis, A.; Votava, P.; Roy, A.; Mukhopadhyay, S.; Nemani, R. R.

2015-12-01

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

Raster Vs. Point Cloud LiDAR Data Classification

NASA Astrophysics Data System (ADS)

El-Ashmawy, N.; Shaker, A.

2014-09-01

Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surface. These effects may lead to uneven distribution of point cloud or some gaps that may affect the classification process. Researchers have investigated the conversion of LiDAR range point data to raster image for terrain modelling. Interpolation techniques have been used to achieve the best representation of surfaces, and to fill the gaps between the LiDAR footprints. Interpolation methods are also investigated to generate LiDAR range and intensity image data for land cover classification applications. In this paper, different approach has been followed to classifying the LiDAR data (range and intensity) for land cover mapping. The methodology relies on the classification of the point cloud data based on their range and intensity and then converted the classified points into raster image. The gaps in the data are filled based on the classes of the nearest neighbour. Land cover maps are produced using two approaches using: (a) the conventional raster image data based on point interpolation; and (b) the proposed point data classification. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to compare the results of the two approaches. Five different land cover classes can be distinguished in that area: buildings, roads and parking areas, trees, low vegetation (grass), and bare soil. The results show that an improvement of around 10 % in the classification results can be achieved by using the proposed approach.

The impact of lidar elevation uncertainty on mapping intertidal habitats on barrier islands

USGS Publications Warehouse

Enwright, Nicholas M.; Wang, Lei; Borchert, Sinéad M.; Day, Richard H.; Feher, Laura C.; Osland, Michael J.

2018-01-01

While airborne lidar data have revolutionized the spatial resolution that elevations can be realized, data limitations are often magnified in coastal settings. Researchers have found that airborne lidar can have a vertical error as high as 60 cm in densely vegetated intertidal areas. The uncertainty of digital elevation models is often left unaddressed; however, in low-relief environments, such as barrier islands, centimeter differences in elevation can affect exposure to physically demanding abiotic conditions, which greatly influence ecosystem structure and function. In this study, we used airborne lidar elevation data, in situ elevation observations, lidar metadata, and tide gauge information to delineate low-lying lands and the intertidal wetlands on Dauphin Island, a barrier island along the coast of Alabama, USA. We compared three different elevation error treatments, which included leaving error untreated and treatments that used Monte Carlo simulations to incorporate elevation vertical uncertainty using general information from lidar metadata and site-specific Real-Time Kinematic Global Position System data, respectively. To aid researchers in instances where limited information is available for error propagation, we conducted a sensitivity test to assess the effect of minor changes to error and bias. Treatment of error with site-specific observations produced the fewest omission errors, although the treatment using the lidar metadata had the most well-balanced results. The percent coverage of intertidal wetlands was increased by up to 80% when treating the vertical error of the digital elevation models. Based on the results from the sensitivity analysis, it could be reasonable to use error and positive bias values from literature for similar environments, conditions, and lidar acquisition characteristics in the event that collection of site-specific data is not feasible and information in the lidar metadata is insufficient. The methodology presented in this study should increase efficiency and enhance results for habitat mapping and analyses in dynamic, low-relief coastal environments.

Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS)

NASA Technical Reports Server (NTRS)

Rhothermel, Jeffry; Jones, W. D.; Dunkin, J. A.; Mccaul, E. W., Jr.

1993-01-01

This effort involves development of a calibrated, pulsed coherent CO2 Doppler lidar, followed by a carefully-planned and -executed program of multi-dimensional wind velocity and aerosol backscatter measurements from the NASA DC-8 research aircraft. The lidar, designated as the Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS), will be applicable to two research areas. First, MACAWS will enable specialized measurements of atmospheric dynamical processes in the planetary boundary layer and free troposphere in geographic locations and over scales of motion not routinely or easily accessible to conventional sensors. The proposed observations will contribute fundamentally to a greater understanding of the role of the mesoscale, helping to improve predictive capabilities for mesoscale phenomena and to provide insights into improving model parameterizations of sub-grid scale processes within large-scale circulation models. As such, it has the potential to contribute uniquely to major, multi-institutional field programs planned for the mid 1990's. Second, MACAWS measurements can be used to reduce the degree of uncertainty in performance assessments and algorithm development for NASA's prospective Laser Atmospheric Wind Sounder (LAWS), which has no space-based instrument heritage. Ground-based lidar measurements alone are insufficient to address all of the key issues. To minimize costs, MACAWS is being developed cooperatively by the lidar remote sensing groups of the Jet Propulsion Laboratory, NOAA Wave Propagation Laboratory, and MSFC using existing lidar hardware and manpower resources. Several lidar components have already been exercised in previous airborne lidar programs (for example, MSFC Airborne Doppler Lidar System (ADLS) used in 1981,4 Severe Storms Wind Measurement Program; JPL Airborne Backscatter Lidar Experiment (ABLE) used in 1989,90 Global Backscatter Experiment Survey Missions). MSFC has been given responsibility for directing the overall program of instrument development and scientific measurement. The focus of current research and plans for next year are presented.

Linking rainforest ecophysiology and microclimate through fusion of airborne LiDAR and hyperspectral imagery

Treesearch

Eben N. Broadbent; Angélica M. Almeyda Zambrano; Gregory P. Asner; Christopher B. Field; Brad E. Rosenheim; Ty Kennedy-Bowdoin; David E. Knapp; David Burke; Christian Giardina; Susan Cordell

2014-01-01

We develop and validate a high-resolution three-dimensional model of light and air temperature for a tropical forest interior in Hawaii along an elevation gradient varying greatly in structure but maintaining a consistent species composition. Our microclimate models integrate high-resolution airborne waveform light detection and ranging data (LiDAR) and hyperspectral...

Cordilleran forest scaling dynamics and disturbance regimes quantified by aerial lidar

NASA Astrophysics Data System (ADS)

Swetnam, Tyson L.

Semi-arid forests are in a period of rapid transition as a result of unprecedented landscape scale fires, insect outbreaks, drought, and anthropogenic land use practices. Understanding how historically episodic disturbances led to coherent forest structural and spatial patterns that promoted resilience and resistance is a critical part of addressing change. Here my coauthors and I apply metabolic scaling theory (MST) to examine scaling behavior and structural patterns of semi-arid conifer forests in Arizona and New Mexico. We conceptualize a linkage to mechanistic drivers of forest assembly that incorporates the effects of low-intensity disturbance, and physiologic and resource limitations as an extension of MST. We use both aerial LiDAR data and field observations to quantify changes in forest structure from the sub-meter to landscape scales. We found: (1) semi-arid forest structure exhibits MST-predicted behaviors regardless of disturbance and that MST can help to quantitatively measure the level of disturbance intensity in a forest, (2) the application of a power law to a forest overstory frequency distribution can help predict understory presence/absence, (3) local indicators of spatial association can help to define first order effects (e.g. topographic changes) and map where recent disturbances (e.g. logging and fire) have altered forest structure. Lastly, we produced a comprehensive set of above-ground biomass and carbon models for five distinct forest types and ten common species of the southwestern US that are meant for use in aerial LiDAR forest inventory projects. This dissertation presents both a conceptual framework and applications for investigating local scales (stands of trees) up to entire ecosystems for diagnosis of current carbon balances, levels of departure from historical norms, and ecological stability. These tools and models will become more important as we prepare our ecosystems for a future characterized by increased climatic variability with an associated increase in frequency and severity of ecological disturbances.

EARLINET: potential operationality of a research network

NASA Astrophysics Data System (ADS)

Sicard, M.; D'Amico, G.; Comerón, A.; Mona, L.; Alados-Arboledas, L.; Amodeo, A.; Baars, H.; Baldasano, J. M.; Belegante, L.; Binietoglou, I.; Bravo-Aranda, J. A.; Fernández, A. J.; Fréville, P.; García-Vizcaíno, D.; Giunta, A.; Granados-Muñoz, M. J.; Guerrero-Rascado, J. L.; Hadjimitsis, D.; Haefele, A.; Hervo, M.; Iarlori, M.; Kokkalis, P.; Lange, D.; Mamouri, R. E.; Mattis, I.; Molero, F.; Montoux, N.; Muñoz, A.; Muñoz Porcar, C.; Navas-Guzmán, F.; Nicolae, D.; Nisantzi, A.; Papagiannopoulos, N.; Papayannis, A.; Pereira, S.; Preißler, J.; Pujadas, M.; Rizi, V.; Rocadenbosch, F.; Sellegri, K.; Simeonov, V.; Tsaknakis, G.; Wagner, F.; Pappalardo, G.

2015-11-01

In the framework of ACTRIS (Aerosols, Clouds, and Trace Gases Research Infrastructure Network) summer 2012 measurement campaign (8 June-17 July 2012), EARLINET organized and performed a controlled exercise of feasibility to demonstrate its potential to perform operational, coordinated measurements and deliver products in near-real time. Eleven lidar stations participated in the exercise which started on 9 July 2012 at 06:00 UT and ended 72 h later on 12 July at 06:00 UT. For the first time, the single calculus chain (SCC) - the common calculus chain developed within EARLINET for the automatic evaluation of lidar data from raw signals up to the final products - was used. All stations sent in real-time measurements of a 1 h duration to the SCC server in a predefined netcdf file format. The pre-processing of the data was performed in real time by the SCC, while the optical processing was performed in near-real time after the exercise ended. 98 and 79 % of the files sent to SCC were successfully pre-processed and processed, respectively. Those percentages are quite large taking into account that no cloud screening was performed on the lidar data. The paper draws present and future SCC users' attention to the most critical parameters of the SCC product configuration and their possible optimal value but also to the limitations inherent to the raw data. The continuous use of SCC direct and derived products in heterogeneous conditions is used to demonstrate two potential applications of EARLINET infrastructure: the monitoring of a Saharan dust intrusion event and the evaluation of two dust transport models. The efforts made to define the measurements protocol and to configure properly the SCC pave the way for applying this protocol for specific applications such as the monitoring of special events, atmospheric modeling, climate research and calibration/validation activities of spaceborne observations.

Studies of midlatitude mesospheric temperature variability and its relationship to gravity waves, tides, and planetary waves

NASA Astrophysics Data System (ADS)

Beissner, Kenneth C.

1997-10-01

Temperature observations of the middle atmosphere have been carried out from September 1993 through July 1995 using a Rayleigh backscatter lidar located at Utah State University (42oN, 111oW). Data have been analyzed to obtain absolute temperature profiles from 40 to 90 km. Various sources of error were reviewed in order to ensure the quality of the measurements. This included conducting a detailed examination of the data reduction procedure, integration methods, and averaging techniques, eliminating errors of 1-3%. The temperature structure climatology has been compared with several other mid-latitude data sets, including those from the French lidars, the SME spacecraft, the sodium lidars at Ft. Collins and Urbana, the MSISe90 model, and a high- latitude composite set from Andenes, Norway. In general, good agreement occurs at mid-latitudes, but areas of disagreement do exist. Among these, the Utah temperatures are significantly warmer than the MSISe90 temperatures above approximately 80 km, they are lower below 80 km than any of the others in summer, they show major year- to-year variability in the winter profiles, and they differ from the sodium lidar data at the altitudes where the temperature profiles should overlap. Also, comparisons between observations and a physics based global circulation model, the TIME-GCM, were conducted for a mid-latitude site. A photo-chemical model was developed to predict airglow intensity of OH based on output from the TIME-GCM. Many discrepancies between the model and observations were found, including a modeled summer mesopause too high, a stronger summer inversion not normally observed by lidar, a fall-spring asymmetry in the OH winds and lidar temperatures but not reproduced in the TIME-GCM equinoctial periods, larger winter seasonal wind tide than observed by the FPI, and a failure of the model to reverse the summertime mesospheric jet. It is our conclusion these discrepancies are due to a gravity wave parameterization in the model that is too weak and an increase will effectively align the model calculations with our observations.

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

NASA Astrophysics Data System (ADS)

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.

Evaluating and improving cloud phase in the Community Atmosphere Model version 5 using spaceborne lidar observations

NASA Astrophysics Data System (ADS)

Kay, Jennifer E.; Bourdages, Line; Miller, Nathaniel B.; Morrison, Ariel; Yettella, Vineel; Chepfer, Helene; Eaton, Brian

2016-04-01

Spaceborne lidar observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite are used to evaluate cloud amount and cloud phase in the Community Atmosphere Model version 5 (CAM5), the atmospheric component of a widely used state-of-the-art global coupled climate model (Community Earth System Model). By embedding a lidar simulator within CAM5, the idiosyncrasies of spaceborne lidar cloud detection and phase assignment are replicated. As a result, this study makes scale-aware and definition-aware comparisons between model-simulated and observed cloud amount and cloud phase. In the global mean, CAM5 has insufficient liquid cloud and excessive ice cloud when compared to CALIPSO observations. Over the ice-covered Arctic Ocean, CAM5 has insufficient liquid cloud in all seasons. Having important implications for projections of future sea level rise, a liquid cloud deficit contributes to a cold bias of 2-3°C for summer daily maximum near-surface air temperatures at Summit, Greenland. Over the midlatitude storm tracks, CAM5 has excessive ice cloud and insufficient liquid cloud. Storm track cloud phase biases in CAM5 maximize over the Southern Ocean, which also has larger-than-observed seasonal variations in cloud phase. Physical parameter modifications reduce the Southern Ocean cloud phase and shortwave radiation biases in CAM5 and illustrate the power of the CALIPSO observations as an observational constraint. The results also highlight the importance of using a regime-based, as opposed to a geographic-based, model evaluation approach. More generally, the results demonstrate the importance and value of simulator-enabled comparisons of cloud phase in models used for future climate projection.

Geomorphic Map of Worcester County, Maryland, Interpreted from a LIDAR-Based, Digital Elevation Model

USGS Publications Warehouse

Newell, Wayne L.; Clark, Inga

2008-01-01

A recently compiled mosaic of a LIDAR-based digital elevation model (DEM) is presented with geomorphic analysis of new macro-topographic details. The geologic framework of the surficial and near surface late Cenozoic deposits of the central uplands, Pocomoke River valley, and the Atlantic Coast includes Cenozoic to recent sediments from fluvial, estuarine, and littoral depositional environments. Extensive Pleistocene (cold climate) sandy dune fields are deposited over much of the terraced landscape. The macro details from the LIDAR image reveal 2 meter-scale resolution of details of the shapes of individual dunes, and fields of translocated sand sheets. Most terrace surfaces are overprinted with circular to elliptical rimmed basins that represent complex histories of ephemeral ponds that were formed, drained, and overprinted by younger basins. The terrains of composite ephemeral ponds and the dune fields are inter-shingled at their margins indicating contemporaneous erosion, deposition, and re-arrangement and possible internal deformation of the surficial deposits. The aggregate of these landform details and their deposits are interpreted as the products of arid, cold climate processes that were common to the mid-Atlantic region during the Last Glacial Maximum. In the Pocomoke valley and its larger tributaries, erosional remnants of sandy flood plains with anastomosing channels indicate the dynamics of former hydrology and sediment load of the watershed that prevailed at the end of the Pleistocene. As the climate warmed and precipitation increased during the transition from late Pleistocene to Holocene, dune fields were stabilized by vegetation, and the stream discharge increased. The increased discharge and greater local relief of streams graded to lower sea levels stimulated down cutting and created the deeply incised valleys out onto the continental shelf. These incised valleys have been filling with fluvial to intertidal deposits that record the rising sea level and warmer, more humid climate in the mid-Atlantic region throughout the Holocene. Thus, the geomorphic details provided by the new LIDAR DEM actually record the response of the landscape to abrupt climate change. Holocene trends and land-use patterns from Colonial to modern times can also be interpreted from the local macro- scale details of the landscape. Beyond the obvious utility of these data for land-use planning and assessments of resources and hazards, the new map presents new details on the impact of climate changes on a mid-latitude, outer Coastal plain landscape.

Implicit Shape Models for Object Detection in 3d Point Clouds

NASA Astrophysics Data System (ADS)

Velizhev, A.; Shapovalov, R.; Schindler, K.

2012-07-01

We present a method for automatic object localization and recognition in 3D point clouds representing outdoor urban scenes. The method is based on the implicit shape models (ISM) framework, which recognizes objects by voting for their center locations. It requires only few training examples per class, which is an important property for practical use. We also introduce and evaluate an improved version of the spin image descriptor, more robust to point density variation and uncertainty in normal direction estimation. Our experiments reveal a significant impact of these modifications on the recognition performance. We compare our results against the state-of-the-art method and get significant improvement in both precision and recall on the Ohio dataset, consisting of combined aerial and terrestrial LiDAR scans of 150,000 m2 of urban area in total.

Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device

PubMed Central

He, Xiang; Aloi, Daniel N.; Li, Jia

2015-01-01

Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design. PMID:26694387

Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device.

PubMed

He, Xiang; Aloi, Daniel N; Li, Jia

2015-12-14

Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design.

On the instrumental characterization of a 3-λ scanning lidar to monitor industrial flames and its application for retrieving optical and microphysical properties

NASA Astrophysics Data System (ADS)

Guerrero-Rascado, Juan Luis; da Costa, Renata; Esteban Bedoya, Andrés; Guardani, Roberto; Alados-Arboledas, Lucas; Efrain Bastidas, Álvaro; Landulfo, Eduardo

2015-04-01

The emission of pollutants in megacities and industrial areas can have strong impact, not only from an environmental point of view, but also for human health. Cubatão (23° 53' S, 46° 26' W, 10 m asl) has been one of the most industrialized city in Brazil (located at São Paulo state coast) during the last decades. This work deals with the recent advances made on a 3-λ scanning lidar placed at this industrial region. Special attention has been paid to the characterization of the electronic performance of this lidar system. For this goal, the quality assurance tests, regularly applied in well-established lidar networks such as LALINET [Guerrero-Rascado et al., 2014] and EARLINET [Pappalardo et al. 2014], were applied to the Cubatão scanning lidar in order to improve the knowledge of its performing itself and to design protocols for correcting lidar signal for undesirable instrumental effects. The application of the results derived from these quality assurance tests together with the state-of-the-art methodologies to map the particle optical and microphysical properties inside industrial flares demonstrate the potential of this lidar for the study and measurement of industrial emissions. References: J. L. Guerrero-Rascado, E. Landulfo, J. C. Antuña, H. M. J. Barbosa, B. Barja, A. E. Bastidas, A. E. Bedoya, R. da Costa, R. Estevan, R. N. Forno, D. A. Gouveia, C. Jiménez, E. G. Larroza, F. J. S. Lopes, E. Montilla-Rosero, G. A. Moreira, W. M. Nakaema, D. Nisperuza, L. Otero, J. V. Pallotta, S. Papandrea, E. Pawelko, E. J. Quel, P. Ristori, P. F. Rodrigues, J. Salvador, M. F. Sánchez, and A. Silva, "Towards an instrumental harmonization in the framework of LAINET: dataset of technical specifications", Proceedings of SPIE 2014, vol. 9246, 92460O-1 -- 92460O-14, doi: 10.1117/12.2066873 (2014) G. Pappalardo, A. Amodeo, A. Apituley, A. Comerón, V. Freudenthaler, H. Linné, A. Ansmann, J. Bösenberg, G. D'Amico, I. Mattis, L. Mona, U. Wandinger, V. Amiridis, L. Alados-Arboledas, D. Nicolae, and M. Wiegner, "EARLINET: towards an advanced sustainable European aerosol lidar network," Atmos. Meas. Tech. 7(8), 2389-2409 (2014)

The Registration and Segmentation of Heterogeneous Laser Scanning Data

NASA Astrophysics Data System (ADS)

Al-Durgham, Mohannad M.

Light Detection And Ranging (LiDAR) mapping has been emerging over the past few years as a mainstream tool for the dense acquisition of three dimensional point data. Besides the conventional mapping missions, LiDAR systems have proven to be very useful for a wide spectrum of applications such as forestry, structural deformation analysis, urban mapping, and reverse engineering. The wide application scope of LiDAR lead to the development of many laser scanning technologies that are mountable on multiple platforms (i.e., airborne, mobile terrestrial, and tripod mounted), this caused variations in the characteristics and quality of the generated point clouds. As a result of the increased popularity and diversity of laser scanners, one should address the heterogeneous LiDAR data post processing (i.e., registration and segmentation) problems adequately. Current LiDAR integration techniques do not take into account the varying nature of laser scans originating from various platforms. In this dissertation, the author proposes a methodology designed particularly for the registration and segmentation of heterogeneous LiDAR data. A data characterization and filtering step is proposed to populate the points' attributes and remove non-planar LiDAR points. Then, a modified version of the Iterative Closest Point (ICP), denoted by the Iterative Closest Projected Point (ICPP) is designed for the registration of heterogeneous scans to remove any misalignments between overlapping strips. Next, a region-growing-based heterogeneous segmentation algorithm is developed to ensure the proper extraction of planar segments from the point clouds. Validation experiments show that the proposed heterogeneous registration can successfully align airborne and terrestrial datasets despite the great differences in their point density and their noise level. In addition, similar testes have been conducted to examine the heterogeneous segmentation and it is shown that one is able to identify common planar features in airborne and terrestrial data without resampling or manipulating the data in any way. The work presented in this dissertation provides a framework for the registration and segmentation of airborne and terrestrial laser scans which has a positive impact on the completeness of the scanned feature. Therefore, the derived products from these point clouds have higher accuracy as seen in the full manuscript.

(Re)Considering Foucault for Science Education Research: Considerations of Truth, Power and Governance

ERIC Educational Resources Information Center

Bazzul, Jesse; Carter, Lyn

2017-01-01

This article is a response to Anna Danielsonn, Maria Berge, and Malena Lidar's paper, "Knowledge and power in the technology classroom: a framework for studying teachers and students in action," and an appeal to science educators of all epistemological orientations to (re)consider the work of Michel Foucault for research in science…

Optimal contrast elastic lidar sensing of clear and aerosol-loaded atmosphere

NASA Astrophysics Data System (ADS)

Evgenieva, Tsvetina T.; Gurdev, Ljuan L.

2016-01-01

The sensing laser radiation wavelength is one of the most significant factors conditioning the elastic lidar efficiency. Nevertheless, its role in the process of lidar sensing has not been investigated systematically so far. Therefore, the main purpose of the present work is to develop and perform an initial examination of an approach to solve this problem based on modeling the profile of the lidar return signal (the lidar profile) and evaluating, in a specific way, the corresponding profile of the measurement signal-to-noise ratio (SNR). The measurement fluctuations are considered as mainly due to the Poisson shot noise that is intrinsic to the dark current and the photocurrent induced by the useful signal itself and the atmospheric background. The initial results obtained show for instance that for ground-based lidar facilities the maximum Rayleigh return signal is obtainable at wavelengths about 350nm. The roles are changed when sensing clouds using wavelength from 400nm to 1000-2000nm. Then, the longer wavelengths provide higher return power from clouds, and the effect is magnified in aerosol-loaded (and especially hazy) atmosphere. The results of such investigations are useful when selecting optimal lidar-design characteristics ensuring maximum brightness and contrast of the lidar-acquired images of specific aerosol strata and objects in the atmosphere.

Forest Biomass Mapping From Lidar and Radar Synergies

NASA Technical Reports Server (NTRS)

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 potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.

Lidar data assimilation for improved analyses of volcanic aerosol events

NASA Astrophysics Data System (ADS)

Lange, Anne Caroline; Elbern, Hendrik

2014-05-01

Observations of hazardous events with release of aerosols are hardly analyzable by today's data assimilation algorithms, without producing an attenuating bias. Skillful forecasts of unexpected aerosol events are essential for human health and to prevent an exposure of infirm persons and aircraft with possibly catastrophic outcome. Typical cases include mineral dust outbreaks, mostly from large desert regions, wild fires, and sea salt uplifts, while the focus aims for volcanic eruptions. In general, numerical chemistry and aerosol transport models cannot simulate such events without manual adjustments. The concept of data assimilation is able to correct the analysis, as long it is operationally implemented in the model system. Though, the tangent-linear approximation, which describes a substantial precondition for today's cutting edge data assimilation algorithms, is not valid during unexpected aerosol events. As part of the European COPERNICUS (earth observation) project MACC II and the national ESKP (Earth System Knowledge Platform) initiative, we developed a module that enables the assimilation of aerosol lidar observations, even during unforeseeable incidences of extreme emissions of particulate matter. Thereby, the influence of the background information has to be reduced adequately. Advanced lidar instruments comprise on the one hand the aspect of radiative transfer within the atmosphere and on the other hand they can deliver a detailed quantification of the detected aerosols. For the assimilation of maximal exploited lidar data, an appropriate lidar observation operator is constructed, compatible with the EURAD-IM (European Air Pollution and Dispersion - Inverse Model) system. The observation operator is able to map the modeled chemical and physical state on lidar attenuated backscatter, transmission, aerosol optical depth, as well as on the extinction and backscatter coefficients. Further, it has the ability to process the observed discrepancies with lidar data in a variational data assimilation algorithm. The implemented method is tested by the assimilation of CALIPSO attenuated backscatter data that were taken during the eruption of the Eyjafjallajökull volcano in April 2010. It turned out that the implemented module is fully capable to integrate unexpected aerosol events in an automatic way into reasonable analyses. The estimations of the aerosol mass concentrations showed promising properties for the application of observations that are taken by lidar systems with both, higher and lower sophistication than CALIOP.

Wind Ressources in Complex Terrain investigated with Synchronized Lidar Measurements

NASA Astrophysics Data System (ADS)

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.

Object-based habitat mapping using very high spatial resolution multispectral and hyperspectral imagery with LiDAR data

NASA Astrophysics Data System (ADS)

Onojeghuo, Alex Okiemute; Onojeghuo, Ajoke Ruth

2017-07-01

This study investigated the combined use of multispectral/hyperspectral imagery and LiDAR data for habitat mapping across parts of south Cumbria, North West England. The methodology adopted in this study integrated spectral information contained in pansharp QuickBird multispectral/AISA Eagle hyperspectral imagery and LiDAR-derived measures with object-based machine learning classifiers and ensemble analysis techniques. Using the LiDAR point cloud data, elevation models (such as the Digital Surface Model and Digital Terrain Model raster) and intensity features were extracted directly. The LiDAR-derived measures exploited in this study included Canopy Height Model, intensity and topographic information (i.e. mean, maximum and standard deviation). These three LiDAR measures were combined with spectral information contained in the pansharp QuickBird and Eagle MNF transformed imagery for image classification experiments. A fusion of pansharp QuickBird multispectral and Eagle MNF hyperspectral imagery with all LiDAR-derived measures generated the best classification accuracies, 89.8 and 92.6% respectively. These results were generated with the Support Vector Machine and Random Forest machine learning algorithms respectively. The ensemble analysis of all three learning machine classifiers for the pansharp QuickBird and Eagle MNF fused data outputs did not significantly increase the overall classification accuracy. Results of the study demonstrate the potential of combining either very high spatial resolution multispectral or hyperspectral imagery with LiDAR data for habitat mapping.

Aerosol classification using EARLINET measurements for an intensive observational period

NASA Astrophysics Data System (ADS)

Papagiannopoulos, Nikolaos; Mona, Lucia; Pappalardo, Gelsomina

2016-04-01

ACTRIS (Aerosols, Clouds and Trace gases Research Infrastructure Network) organized an intensive observation period during summer 2012. This campaign aimed at the provision of advanced observations of physical and chemical aerosol properties, at the delivery of information about the 3D distribution of European atmospheric aerosols, and at the monitoring of Saharan dust intrusions events. EARLINET (European Aerosol Research Lidar Network) participated in the ACTRIS campaign through the addition of measurements according to the EARLINET schedule as well as daily lidar-profiling measurements around sunset by 11 selected lidar stations for the period from 8 June - 17 July. EARLINET observations during this almost two-month period are used to characterize the optical properties and vertical distribution of long-range transported aerosol over the broader area of Mediterranean basin. The lidar measurements of aerosol intensive parameters (lidar ratio, depolarization, Angstrom exponents) are shown to vary with location and aerosol type. A methodology based on EARLINET observations of frequently observed aerosol types is used to classify aerosols into seven separate types. The summertime Mediterranean basin is prone to African dust aerosols. Two major dust events were studied. The first episode occurred from the 18 to 21 of the June and the second one lasted from 28 June to 6 July. The lidar ratio within the dust layer was found to be wavelength independent with mean values of 58±14 sr at 355 nm and 57±11 sr at 532 nm. For the particle linear depolarization ratio, mean values of 0.27±0.04 at 532 nm have been found. Acknowledgements. The financial support for EARLINET in the ACTRIS Research Infrastructure Project by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 654169 and previously under grant agreement no. 262254 in the Seventh Framework Programme (FP7/2007-2013) is gratefully acknowledged.

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

NASA Astrophysics Data System (ADS)

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.

Lidar for Lateral Mixing (LATMIX)

DTIC Science & Technology

2013-09-30

overflew, a strong reflection front he float could be detected in the lidar elastic backscatter channel. Initial searches have detected several liar...grid scale mixing and stirring in numerical models. Ultimately our research will also enhance modeling and understanding of upper ocean ecosystems...km. A second objective is to determine whether slow but persistent vortices enhance the stirring attributable to shear dispersion. We also share

Lidar for Lateral Mixing (LATMIX)

DTIC Science & Technology

2012-09-30

strong reflection front he float could be detected in the lidar elastic backscatter channel. Initial searches have detected several liar float returns...stirring in numerical models. Ultimately our research will also enhance modeling and understanding of upper ocean ecosystems, since the flow of...is to determine whether slow but persistent vortices enhance the stirring attributable to shear dispersion. We also share the overall objectives of

Landscape-scale parameterization of a tree-level forest growth model: a k-nearest neighbor imputation approach incorporating LiDAR data

Treesearch

Michael J. Falkowski; Andrew T. Hudak; Nicholas L. Crookston; Paul E. Gessler; Edward H. Uebler; Alistair M. S. Smith

2010-01-01

Sustainable forest management requires timely, detailed forest inventory data across large areas, which is difficult to obtain via traditional forest inventory techniques. This study evaluated k-nearest neighbor imputation models incorporating LiDAR data to predict tree-level inventory data (individual tree height, diameter at breast height, and...

Lalinet status - station expansion and lidar ratio systematic measurements

NASA Astrophysics Data System (ADS)

Landulfo, Eduardo; Lopes, Fabio; Moreira, Gregori Arruda; da Silva, Jonatan; Ristori, Pablo; Quel, Eduardo; Otero, Lidia; Pallota, Juan Vicente; Herrera, Milagros; Salvador, Jacobo; Bali, Juan Lucas; Wolfram, Eliam; Etala, Paula; Barbero, Albane; Forno, Ricardo; Sanchez, Maria Fernanda; Barbosa, Henrique; Gouveia, Diego; Santos, Amanda Vieira; Hoelzemann, Judith; Fernandez, Jose Henrique; Guedes, Anderson; Silva, Antonieta; Barja, Boris; Zamorano, Felix; Legue, Raul Perez; Bastidas, Alvaro; Zabala, Maribel Vellejo; Velez, Juan; Nisperuza, Daniel; Montilla, Elena; Arredondo, Rene Estevam; Marrero, Juan Carlos Antuña; Vega, Alberth Rodriguez; Alados-Arboledas, Lucas; Guerrero-Rascado, Juan Luis; Sugimoto, Nobuo; Yoshitaka, Jin

2018-04-01

LALINET is expanding regionally to guarantee spatial coverage over South and Central Americas. One of the network goals is to obtain a set of regional representative aerosol optical properties such as particle backscatter, extinction and lidar ratio. Given the North-South extension and influence of distinct airmass circulation patterns it is paramount to distinguish these optical parameters in order to gain better perfomance in radiation transfer models. A set of lidar ratio data is presented.

Diffuse sunlight based calibration of the water vapor channel in the upc raman lidar

NASA Astrophysics Data System (ADS)

Muñoz-Porcar, Constantino; Comeron, Adolfo; Sicard, Michaël; Barragan, Ruben; Garcia-Vizcaino, David; Rodríguez-Gómez, Alejandro; Rocadenbosch, Francesc

2018-04-01

A method for determining the calibration factor of the water vapor channel of a Raman lidar, based on zenith measurements of diffuse sunlight and on assumptions regarding some system parameters and Raman scattering models, has been applied to the lidar system of Universitat Politècnica de Catalunya (UPC; Technical University of Catalonia, Spain). Results will be analyzed in terms of stability and comparison with typical methods relying on simultaneous radiosonde measurements.

Evaluation of the MODIS LAI product using independent lidar-derived LAI: A case study in mixed conifer forest

Treesearch

Jennifer L. R. Jensen; Karen S. Humes; Andrew T. Hudak; Lee A. Vierling; Eric Delmelle

2011-01-01

This study presents an alternative assessment of the MODIS LAI product for a 58,000 ha evergreen needleleaf forest located in the western Rocky Mountain range in northern Idaho by using lidar data to model (R2=0.86, RMSE=0.76) and map LAI at higher resolution across a large number of MODIS pixels in their entirety. Moderate resolution (30 m) lidar-based LAI estimates...

The uk Lidar-sunphotometer operational volcanic ash monitoring network

NASA Astrophysics Data System (ADS)

Adam, Mariana; Buxmann, Joelle; Freeman, Nigel; Horseman, Andrew; Salmon, Christopher; Sugier, Jacqueline; Bennett, Richard

2018-04-01

The Met Office completed the deployment of ten lidars (UV Raman and depolarization), each accompanied by a sunphotometer (polarized model), to provide quantitative monitoring of volcanic ash over UK for VAAC London. The lidars provide range corrected signal and volume depolarization ratio in near-real time. The sunphotometers deliver aerosol optical depth, Ångstrom exponent and degree of linear polarization. Case study analyses of Saharan dust events (as a proxy for volcanic ash) are presented.

Aerosols Observations with a new lidar station in Punta Arenas, Chile

NASA Astrophysics Data System (ADS)

Barja, Boris; Zamorano, Felix; Ristori, Pablo; Otero, Lidia; Quel, Eduardo; Sugimoto, Nobuo; Shimizu, Atsushi; Santana, Jorge

2018-04-01

A tropospheric lidar system was installed in Punta Arenas, Chile (53.13°S, 70.88°W) in September 2016 under the collaboration project SAVERNET (Chile, Japan and Argentina) to monitor the atmosphere. Statistical analyses of the clouds and aerosols behavior and some cases of dust detected with lidar, at these high southern latitude and cold environment regions during three months (austral spring) are discussed using information from satellite, modelling and solar radiation ground measurements.

Intercomparison of Pulsed Lidar Data with Flight Level CW Lidar Data and Modeled Backscatter from Measured Aerosol Microphysics Near Japan and Hawaii

NASA Technical Reports Server (NTRS)

Cutten, D. R.; Spinhirne, J. D.; Menzies, R. T.; Bowdle, D. A.; Srivastava, V.; Pueschel, R. F.; Clarke, A. D.; Rothermel, J.

1998-01-01

Aerosol backscatter coefficient data were examined from two nights near Japan and Hawaii undertaken during NASA's Global Backscatter Experiment (GLOBE) in May-June 1990. During each of these two nights the aircraft traversed different altitudes within a region of the atmosphere defined by the same set of latitude and longitude coordinates. This provided an ideal opportunity to allow flight level focused continuous wave (CW) lidar backscatter measured at 9.11-micron wavelength and modeled aerosol backscatter from two aerosol optical counters to be compared with pulsed lidar aerosol backscatter data at 1.06- and 9.25-micron wavelengths. The best agreement between all sensors was found in the altitude region below 7 km, where backscatter values were moderately high at all three wavelengths. Above this altitude the pulsed lidar backscatter data at 1.06- and 9.25-micron wavelengths were higher than the flight level data obtained from the CW lidar or derived from the optical counters, suggesting sample volume effects were responsible for this. Aerosol microphysics analysis of data near Japan revealed a strong sea-salt aerosol plume extending upward from the marine boundary layer. On the basis of sample volume differences, it was found that large particles were of different composition compared with the small particles for low backscatter conditions.

Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data.

PubMed

Nie, Sheng; Wang, Cheng; Xi, Xiaohuan; Luo, Shezhou; Li, Guoyuan; Tian, Jinyan; Wang, Hongtao

2018-05-14

The upcoming space-borne LiDAR satellite Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is scheduled to launch in 2018. Different from the waveform LiDAR system onboard the ICESat, ICESat-2 will use a micro-pulse photon-counting LiDAR system. Thus new data processing algorithms are required to retrieve vegetation canopy height from photon-counting LiDAR data. The objective of this paper is to develop and validate an automated approach for better estimating vegetation canopy height. The new proposed method consists of three key steps: 1) filtering out the noise photons by an effective noise removal algorithm based on localized statistical analysis; 2) separating ground returns from canopy returns using an iterative photon classification algorithm, and then determining ground surface; 3) generating canopy-top surface and calculating vegetation canopy height based on canopy-top and ground surfaces. This automatic vegetation height estimation approach was tested to the simulated ICESat-2 data produced from Sigma Space LiDAR data and Multiple Altimeter Beam Experimental LiDAR (MABEL) data, and the retrieved vegetation canopy heights were validated by canopy height models (CHMs) derived from airborne discrete-return LiDAR data. Results indicated that the estimated vegetation canopy heights have a relatively strong correlation with the reference vegetation heights derived from airborne discrete-return LiDAR data (R 2 and RMSE values ranging from 0.639 to 0.810 and 4.08 m to 4.56 m respectively). This means our new proposed approach is appropriate for retrieving vegetation canopy height from micro-pulse photon-counting LiDAR data.

Use of an airborne lidar system to model plant species composition and diversity of Mediterranean oak forests.

PubMed

Simonson, William D; Allen, Harriet D; Coomes, David A

2012-10-01

Airborne lidar is a remote-sensing tool of increasing importance in ecological and conservation research due to its ability to characterize three-dimensional vegetation structure. If different aspects of plant species diversity and composition can be related to vegetation structure, landscape-level assessments of plant communities may be possible. We examined this possibility for Mediterranean oak forests in southern Portugal, which are rich in biological diversity but also threatened. We compared data from a discrete, first-and-last return lidar data set collected for 31 plots of cork oak (Quercus suber) and Algerian oak (Quercus canariensis) forest with field data to test whether lidar can be used to predict the vertical structure of vegetation, diversity of plant species, and community type. Lidar- and field-measured structural data were significantly correlated (up to r= 0.85). Diversity of forest species was significantly associated with lidar-measured vegetation height (R(2) = 0.50, p < 0.001). Clustering and ordination of the species data pointed to the presence of 2 main forest classes that could be discriminated with an accuracy of 89% on the basis of lidar data. Lidar can be applied widely for mapping of habitat and assessments of habitat condition (e.g., in support of the European Species and Habitats Directive [92/43/EEC]). However, particular attention needs to be paid to issues of survey design: density of lidar points and geospatial accuracy of ground-truthing and its timing relative to acquisition of lidar data. ©2012 Society for Conservation Biology.

An Intercomparison of Lidar Ozone and Temperature Measurements From the SOLVE Mission With Predicted Model Values

NASA Technical Reports Server (NTRS)

Burris, John; McGee, Thomas J.; Hoegy, Walt; Lait, Leslie; Sumnicht, Grant; Twigg, Larry; Heaps, William

2000-01-01

Temperature profiles acquired by Goddard Space Flight Center's AROTEL lidar during the SOLVE mission onboard NASA's DC-8 are compared with predicted values from several atmospheric models (DAO, NCEP and UKMO). The variability in the differences between measured and calculated temperature fields was approximately 5 K. Retrieved temperatures within the polar vortex showed large regions that were significantly colder than predicted by the atmospheric models.

Evaluating Mesoscale Simulations of the Coastal Flow Using Lidar Measurements

NASA Astrophysics Data System (ADS)

Floors, R.; Hahmann, A. N.; Peña, A.

2018-03-01

The atmospheric flow in the coastal zone is investigated using lidar and mast measurements and model simulations. Novel dual-Doppler scanning lidars were used to investigate the flow over a 7 km transect across the coast, and vertically profiling lidars were used to study the vertical wind profile at offshore and onshore positions. The Weather, Research and Forecasting model is set up in 12 different configurations using 2 planetary boundary layer schemes, 3 horizontal grid spacings and varied sources of land use, and initial and lower boundary conditions. All model simulations describe the observed mean wind profile well at different onshore and offshore locations from the surface up to 500 m. The simulated mean horizontal wind speed gradient across the shoreline is close to that observed, although all simulations show wind speeds that are slightly higher than those observed. Inland at the lowest observed height, the model has the largest deviations compared to the observations. Taylor diagrams show that using ERA-Interim data as boundary conditions improves the model skill scores. Simulations with 0.5 and 1 km horizontal grid spacing show poorer model performance compared to those with a 2 km spacing, partially because smaller resolved wave lengths degrade standard error metrics. Modeled and observed velocity spectra were compared and showed that simulations with the finest horizontal grid spacing resolved more high-frequency atmospheric motion.

Speckle noise in satellite based lidar systems

NASA Technical Reports Server (NTRS)

Gardner, C. S.

1977-01-01

The lidar system model was described, and the statistics of the signal and noise at the receiver output were derived. Scattering media effects were discussed along with polarization and atmospheric turbulence. The major equations were summarized and evaluated for some typical parameters.

Modelling the subsurface geomorphology of an active landslide using LIDAR.

DOT National Transportation Integrated Search

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. ...

NASA/MSFC FY-85 Atmospheric Processes Research Review

NASA Technical Reports Server (NTRS)

Vaughan, W. W. (Compiler); Porter, F. (Compiler)

1985-01-01

The two main areas of focus for the research program are global scale processes and mesoscale processes. Geophysical fluid processes, satellite doppler lidar, satellite data analysis, atmospheric electricity, doppler lidar wind research, and mesoscale modeling are among the topics covered.

Experimental feasibility of the airborne measurement of absolute oil fluorescence spectral conversion efficiency

NASA Technical Reports Server (NTRS)

Hoge, F. E.; Swift, R. N.

1983-01-01

Airborne lidar oil spill experiments carried out to determine the practicability of the AOFSCE (absolute oil fluorescence spectral conversion efficiency) computational model are described. The results reveal that the model is suitable over a considerable range of oil film thicknesses provided the fluorescence efficiency of the oil does not approach the minimum detection sensitivity limitations of the lidar system. Separate airborne lidar experiments to demonstrate measurement of the water column Raman conversion efficiency are also conducted to ascertain the ultimate feasibility of converting such relative oil fluorescence to absolute values. Whereas the AOFSCE model is seen as highly promising, further airborne water column Raman conversion efficiency experiments with improved temporal or depth-resolved waveform calibration and software deconvolution techniques are thought necessary for a final determination of suitability.

Estimation of stable boundary-layer height using variance processing of backscatter lidar data

NASA Astrophysics Data System (ADS)

Saeed, Umar; Rocadenbosch, Francesc

2017-04-01

Stable boundary layer (SBL) is one of the most complex and less understood topics in atmospheric science. The type and height of the SBL is an important parameter for several applications such as understanding the formation of haze fog, and accuracy of chemical and pollutant dispersion models, etc. [1]. This work addresses nocturnal Stable Boundary-Layer Height (SBLH) estimation by using variance processing and attenuated backscatter lidar measurements, its principles and limitations. It is shown that temporal and spatial variance profiles of the attenuated backscatter signal are related to the stratification of aerosols in the SBL. A minimum variance SBLH estimator using local minima in the variance profiles of backscatter lidar signals is introduced. The method is validated using data from HD(CP)2 Observational Prototype Experiment (HOPE) campaign at Jülich, Germany [2], under different atmospheric conditions. This work has received funding from the European Union Seventh Framework Programme, FP7 People, ITN Marie Curie Actions Programme (2012-2016) in the frame of ITaRS project (GA 289923), H2020 programme under ACTRIS-2 project (GA 654109), the Spanish Ministry of Economy and Competitiveness - European Regional Development Funds under TEC2015-63832-P project, and from the Generalitat de Catalunya (Grup de Recerca Consolidat) 2014-SGR-583. [1] R. B. Stull, An Introduction to Boundary Layer Meteorology, chapter 12, Stable Boundary Layer, pp. 499-543, Springer, Netherlands, 1988. [2] U. Löhnert, J. H. Schween, C. Acquistapace, K. Ebell, M. Maahn, M. Barrera-Verdejo, A. Hirsikko, B. Bohn, A. Knaps, E. O'Connor, C. Simmer, A. Wahner, and S. Crewell, "JOYCE: Jülich Observatory for Cloud Evolution," Bull. Amer. Meteor. Soc., vol. 96, no. 7, pp. 1157-1174, 2015.

Study of the Integration of LIDAR and Photogrammetric Datasets by in Situ Camera Calibration and Integrated Sensor Orientation

NASA Astrophysics Data System (ADS)

Mitishita, E.; Costa, F.; Martins, M.

2017-05-01

Photogrammetric and Lidar datasets should be in the same mapping or geodetic frame to be used simultaneously in an engineering project. Nowadays direct sensor orientation is a common procedure used in simultaneous photogrammetric and Lidar surveys. Although the direct sensor orientation technologies provide a high degree of automation process due to the GNSS/INS technologies, the accuracies of the results obtained from the photogrammetric and Lidar surveys are dependent on the quality of a group of parameters that models accurately the user conditions of the system at the moment the job is performed. This paper shows the study that was performed to verify the importance of the in situ camera calibration and Integrated Sensor Orientation without control points to increase the accuracies of the photogrammetric and LIDAR datasets integration. The horizontal and vertical accuracies of photogrammetric and Lidar datasets integration by photogrammetric procedure improved significantly when the Integrated Sensor Orientation (ISO) approach was performed using Interior Orientation Parameter (IOP) values estimated from the in situ camera calibration. The horizontal and vertical accuracies, estimated by the Root Mean Square Error (RMSE) of the 3D discrepancies from the Lidar check points, increased around of 37% and 198% respectively.

Design validation of an eye-safe scanning aerosol lidar with the Center for Lidar and Atmospheric Sciences Students (CLASS) at Hampton University

NASA Astrophysics Data System (ADS)

Richter, Dale A.; Higdon, N. S.; Ponsardin, Patrick L.; Sanchez, David; Chyba, Thomas H.; Temple, Doyle A.; Gong, Wei; Battle, Russell; Edmondson, Mika; Futrell, Anne; Harper, David; Haughton, Lincoln; Johnson, Demetra; Lewis, Kyle; Payne-Baggott, Renee S.

2002-01-01

ITTs Advanced Engineering and Sciences Division and the Hampton University Center for Lidar and Atmospheric Sciences Students (CLASS) team have worked closely to design, fabricate and test an eye-safe, scanning aerosol-lidar system that can be safely deployed and used by students form a variety of disciplines. CLASS is a 5-year undergraduate- research training program funded by NASA to provide hands-on atmospheric-science and lidar-technology education. The system is based on a 1.5 micron, 125 mJ, 20 Hz eye-safe optical parametric oscillator (OPO) and will be used by the HU researchers and students to evaluate the biological impact of aerosols, clouds, and pollution a variety of systems issues. The system design tasks we addressed include the development of software to calculate eye-safety levels and to model lidar performance, implementation of eye-safety features in the lidar transmitter, optimization of the receiver using optical ray tracing software, evaluation of detectors and amplifiers in the near RI, test of OPO and receiver technology, development of hardware and software for laser and scanner control and video display of the scan region.

Raman Lidar Profiling of Aerosols Over the Central US; Diurnal Variability and Comparisons with the GOCART Model

NASA Technical Reports Server (NTRS)

Ferrare, R. A.; Chin, M.; Clayton, M.; Turner, D.

2002-01-01

We use profiles of aerosol extinction, water vapor mixing ratio, and relative humidity measured by the ARM SGP Raman lidar in northern Oklahoma to show how the vertical distributions of aerosol extinction and water vapor vary throughout the diurnal cycle. While significant (20-30%) variations in aerosol extinction occurred near the surface as well as aloft, smaller (approximately 10%) variations were observed in the diurnal variability of aerosol optical thickness (AOT). The diurnal variations in aerosol extinction profiles are well correlated with corresponding variations in the average relative humidity profiles. The water vapor mixing ratio profiles and integrated water vapor amounts generally show less diurnal variability. The Raman lidar profiles are also used to evaluate the aerosol optical thickness and aerosol extinction profiles simulated by the GOCART global aerosol model. Initial comparisons show that the AOT simulated by GOCART was in closer agreement with the AOT derived from the Raman lidar and Sun photometer measurements during November 2000 than during September 2000. For both months, the vertical variability in average aerosol extinction profiles simulated by GOCART is less than the variability in the corresponding Raman lidar profiles.

Investigation of Space Based Solid State Coherent Lidar

NASA Technical Reports Server (NTRS)

Amzajerdian, Farzin

2002-01-01

This report describes the work performed over the period of October 1, 1997 through March 31, 2001. Under this contract, UAH/CAO participated in defining and designing the SPAce Readiness Coherent Lidar Experiment (SPARCLE) mission, and developed the instrument's optical subsystem. This work was performed in collaborative fashion with NASA/MSFC engineers at both UAH/CAO and NASA/MSFC facilities. Earlier work by the UAH/CAO had produced a preliminary top-level system design for the Shuttle lidar instrument meeting the proposed mission performance requirements and the Space Shuttle Hitchhiker canister volume constraints. The UAH/CAO system design efforts had concentrated on the optical and mechanical designs of the instrument. The instrument electronics were also addressed, and the major electronic components and their interfaces defined. The instrument design concept was mainly based on the state of the transmitter and local oscillator laser development at NASA Langley Research Center and Jet Propulsion Laboratory, and utilized several lidar-related technologies that were either developed or evaluated by the NASA/MSFC and UAH/CAO scientists. UAH/CAO has developed a comprehensive coherent lidar numerical model capable of analyzing the performance of different instrument and mission concepts. This model uses the instrument configuration, atmospheric conditions and current velocity estimation theory to provide prediction of instrument performance during different phases of operation. This model can also optimize the design parameters of the instrument.

The 1996 Leonid shower as studied with a potassium lidar: Observations and inferred meteoroid sizes

NASA Astrophysics Data System (ADS)

Höffner, Josef; von Zahn, Ulf; McNeil, William J.; Murad, Edmond

1999-02-01

We report on the observation and analysis of meteor trails that are detected by ground-based lidar tuned to the D1 fine structure line of K. The lidar is located at Kühlungsborn, Germany. The echo profiles are analyzed with a temporal resolution of about 1 s and altitude resolution of 200 m. Identification of meteor trails in the large archive of raw data is performed with help of an automated computer search code. During the peak of the Lenoid meteor shower on the morning of November 17, 1996, we observed seven meteor trails between 0245 and 0445 UT. Their mean altitude was 89.0 km. The duration of observation of individual trails ranges from 3 s to ~30 min. We model the probability of observing a meteor trail by ground-based lidar as a function of both altitude distribution and duration of the trails. These distributions depend on the mass distribution, entry velocity, and entry angle of the meteoroids, on the altitude-dependent chemical and dynamical lifetimes of the released K atom, and on the absolute detection sensitivity of our lidar experiment. From the modeling, we derive the statistical likelihood of detection of trails from meteoroids of a particular size. These bracket quite well the observed trails. The model also gives estimates of the probable size of the meteoroids based on characteristics of individual trails.

Initial Results from CALIPSO

NASA Technical Reports Server (NTRS)

Winker, David M.; Pelon, Jacques; McCormick, M. Patrick

2006-01-01

CALIPSO will carry the first polarization lidar in orbit, along with infrared and visible passive imagers, and will fly in formation as part of the Afternoon Constellation (A-train). The acquisition of observations which are simultaneous and coincident with observations from other instruments of the A-train will allow numerous synergies to be realized from combining CALIPSO observations with observations from other platforms. In particular, cloud observations from the CALIPSO lidar and the CloudSat radar will complement each other, together encompassing the variety of clouds found in the atmosphere, from thin cirrus to deep convective clouds. CALIPSO has been developed within the framework of a collaboration between NASA and CNES and is currently scheduled to launch, along with the CloudSat satellite, in spring 2006. This paper will present an overview of the CALIPSO mission, including initial results.

Automatic drawing for traffic marking with MMS LIDAR intensity

NASA Astrophysics Data System (ADS)

Takahashi, G.; Takeda, H.; Shimano, Y.

2014-05-01

Upgrading the database of CYBER JAPAN has been strategically promoted because the "Basic Act on Promotion of Utilization of Geographical Information", was enacted in May 2007. In particular, there is a high demand for road information that comprises a framework in this database. Therefore, road inventory mapping work has to be accurate and eliminate variation caused by individual human operators. Further, the large number of traffic markings that are periodically maintained and possibly changed require an efficient method for updating spatial data. Currently, we apply manual photogrammetry drawing for mapping traffic markings. However, this method is not sufficiently efficient in terms of the required productivity, and data variation can arise from individual operators. In contrast, Mobile Mapping Systems (MMS) and high-density Laser Imaging Detection and Ranging (LIDAR) scanners are rapidly gaining popularity. The aim in this study is to build an efficient method for automatically drawing traffic markings using MMS LIDAR data. The key idea in this method is extracting lines using a Hough transform strategically focused on changes in local reflection intensity along scan lines. However, also note that this method processes every traffic marking. In this paper, we discuss a highly accurate and non-human-operator-dependent method that applies the following steps: (1) Binarizing LIDAR points by intensity and extracting higher intensity points; (2) Generating a Triangulated Irregular Network (TIN) from higher intensity points; (3) Deleting arcs by length and generating outline polygons on the TIN; (4) Generating buffers from the outline polygons; (5) Extracting points from the buffers using the original LIDAR points; (6) Extracting local-intensity-changing points along scan lines using the extracted points; (7) Extracting lines from intensity-changing points through a Hough transform; and (8) Connecting lines to generate automated traffic marking mapping data.

Investigating the constraint imposed by column averaged PBL CO2 data within an atmospheric inversion framework

NASA Astrophysics Data System (ADS)

Schuh, A. E.; Kawa, S. R.; Denning, A. S.; Baker, D. F.; Ramanathan, A. K.

2014-12-01

It was initially hoped that the proposed Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) NASA mission could rectify diurnal fluxes through it's ability to measure during both days and nights. However, initial simulation results (Kawa et al 2010) showed limited skill at identifying diurnal differences in fluxes. We investigate the possibility of (1) supplementing ASCENDS with well chosen in-situ surface sites and/or (2) adding distinct column measurements for the PBL and free troposphere into the inversion framework to determine the impact on recovering net ecosystem exchange (NEE), as well as distinct gross primary production (GPP) and respiration fluxes. In particular, we run forward simulations and inversions with distinct respiration and GPP fluxes calculated from the SiB model (Baker et al 2008) and test the ability of an EnKF based inversion framework to recover a hypothetical tropical CO2 fertilization effect resulting in enhanced GPP. Baker, I. T.; Prihodko, L.; Denning, A. S.; Goulden, M.; Miller, S. & da Rocha, H. R. (2008), 'Seasonal drought stress in the Amazon: Reconciling 3 models and observations', Journal of Geophysical Research 113. Kawa, S. R.; MAO, J.; ABSHIRE, J. B.; J., C. G.; SUN, X. & WEAVER, C. J. (2010), 'Simulation studies for a space-based CO2 lidar mission.', Tellus B 62, 759-769.

Airborne lidar detection and mapping of invasive lake trout in Yellowstone Lake.

PubMed

Roddewig, Michael R; Churnside, James H; Hauer, F Richard; Williams, Jacob; Bigelow, Patricia E; Koel, Todd M; Shaw, Joseph A

2018-05-20

The use of airborne lidar to survey fisheries has not yet been extensively applied in freshwater environments. In this study, we investigated the applicability of this technology to identify invasive lake trout (Salvelinus namaycush) in Yellowstone Lake, Yellowstone National Park, USA. Results of experimental trials conducted in 2004 and in 2015-16 provided lidar data that identified groups of fish coherent with current knowledge and models of lake trout spawning sites, and one identified site was later confirmed to have lake trout.

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

Treesearch

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

2017-01-01

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...

Effects of multiple scattering on time- and depth-resolved signals in airborne lidar systems

NASA Technical Reports Server (NTRS)

Punjabi, A.; Venable, D. D.

1986-01-01

A semianalytic Monte Carlo radiative transfer model (SALMON) is employed to probe the effects of multiple-scattering events on the time- and depth-resolved lidar signals from homogeneous aqueous media. The effective total attenuation coefficients in the single-scattering approximation are determined as functions of dimensionless parameters characterizing the lidar system and the medium. Results show that single-scattering events dominate when these parameters are close to their lower bounds and that when their values exceed unity multiple-scattering events dominate.

Development of a 9.3 micrometer CW LIDAR for the study of atmospheric aerosol

NASA Technical Reports Server (NTRS)

Whiteside, B. N.; Schotland, R. M.

1993-01-01

This report provides a brief summary of the basic requirements to obtain coherent or heterodyne mixing of the optical radiation backscattered by atmospheric aerosols with that from a fixed frequency source. The continuous wave (CW) mode of operation for a coherent lidar is reviewed along with the associated lidar transfer equation. A complete optical design of the three major subsystems of a CW, coherent lidar is given. Lens design software is implemented to model and optimize receiver performance. Techniques for the opto-mechanical assembly and some of the critical tolerances of the coherent lidar are provided along with preliminary tests of the subsystems. Included in these tests is a comparison of the experimental and the theoretical average power signal-to-noise ratio. The analog to digital software used to evaluate the power spectrum of the backscattered signal is presented in the Appendix of this report.

Modeling of microjoule and millijoule energy LIDARs with PMT/SiPM/APD detectors: a sensitivity analysis.

PubMed

Agishev, Ravil

2018-05-10

This paper demonstrates a renewed concept and applications of the generalized methodology for atmospheric light detection and ranging (LIDAR) capability prediction as a continuation of a series of our previous works, where the dimensionless parameterization appeared as a tool for comparing systems of a different scale, design, and applications. The modernized concept applied to microscale and milliscale LIDARs with relatively new silicon photomultiplier detectors and traditional photomultiplier tube and avalanche photodiode detectors allowed prediction of the remote sensing instruments' performance and limitations. Such a generalized, uniform, and objective concept is applied for evaluation of the increasingly popular class of limited-energy LIDARs using the best optical detectors, operating on different targets (back-scatter or topographic, static or dynamic) and under intense sky background conditions. It can be used in the LIDAR community to compare different instruments and select the most suitable and effective ones for specific applications.

Influence of daylight and noise current on cloud and aerosol observations by spaceborne elastic scattering lidar.

PubMed

Nakajima, T Y; Imai, T; Uchino, O; Nagai, T

1999-08-20

The influence of daylight and noise current on cloud and aerosol observations by realistic spaceborne lidar was examined by computer simulations. The reflected solar radiations, which contaminate the daytime return signals of lidar operations, were strictly and explicitly estimated by accurate radiative transfer calculations. It was found that the model multilayer cirrus clouds and the boundary layer aerosols could be observed during the daytime and the nighttime with only a few laser shots. However, high background noise and noise current make it difficult to observe volcanic aerosols in middle and upper atmospheric layers. Optimal combinations of the laser power and receiver field of view are proposed to compensate for the negative influence that is due to these noises. For the computer simulations, we used a realistic set of lidar parameters similar to the Experimental Lidar in-Space Equipment of the National Space Development Agency of Japan.

Accuracy of a high-resolution lidar terrain model under a conifer forest canopy

Treesearch

S.E. Reutebuch; R.J. McGaughey; H.-E. Andersen; W.W. Carson

2003-01-01

Airborne laser scanning systems can provide terrain elevation data for open areas with a vertical accuracy of 15 cm. In this study, a high-resolution digital terrain model (DTM) was produced from high-density lidar data. Vegetation in the 500-ha mountainous study area varied from bare ground to dense 70-year-old conifer forest. Conventional ground survey methods were...

A bayesian hierarchical model for spatio-temporal prediction and uncertainty assessment using repeat LiDAR acquisitions for the Kenai Peninsula, AK, USA

Treesearch

Chad Babcock; Hans Andersen; Andrew O. Finley; Bruce D. Cook

2015-01-01

Models leveraging repeat LiDAR and field collection campaigns may be one possible mechanism to monitor carbon flux in remote forested regions. Here, we look to the spatio-temporally data-rich Kenai Peninsula in Alaska, USA to examine the potential for Bayesian spatio-temporal mapping of terrestrial forest carbon storage and uncertainty.

Quantifying the Spatio-temporal Impacts of Sea Level Rise on Carbon Storage Using Repeat Lidar Surveys and Multispectral Satellite Imagery

NASA Astrophysics Data System (ADS)

Smart, L.; Taillie, P. J.; Smith, J. W.; Meentemeyer, R. K.

2017-12-01

Sound coastal land-use policy and management decisions to mitigate or adapt to sea level rise impacts depend on understanding vegetation responses to sea level rise over large extents. Accurate methodologies to quantify these changes are necessary to understand the continued production of the ecosystem services upon which human health and well-being depend. This research quantifies spatio-temporal changes in aboveground biomass altered by sea level rise across North Carolina's coastal plain using a combination of repeat-acquisition lidar data and multi-temporal satellite imagery. Using field data from across the study area, we evaluated the reliability of multi-temporal lidar data with disparate densities and accuracies to detect changes along a coastal vegetation gradient from marsh to forested wetland. Despite an 18 fold increase in lidar point density between survey years (2001, 2014), the relationships between lidar-derived heights and field-measured heights were similar (adjusted r2; 0.6 -0.7). Random Forest, a machine learning algorithm, was used to separately predict above-ground biomass pools at the landscape-scale for the two time periods using the 98 field plots as reference data. Models performed well for both years (adjusted r2; 0.67-0.85). The 2001 model required the addition of Landsat spectral indices to meet the same adjusted r2 values as the 2014 model, which utilized lidar-derived metrics alone. Of the many potential lidar-derived predictor metrics, median and mean vegetation height were the best predictors in both time periods. To measure the spatial patterns of biomass change across the landscape, we subtracted the 2001 biomass model from the 2014 model and found significant spatial heterogeneity in biomass change across both the vegetation gradient and across the peninsula over the 12-year time period. In forested areas, we found a mean increase in aboveground biomass whereas in transition zones, marshes and freshwater emergent wetlands we found overall decreases in aboveground biomass. These changes were correlated with distance to estuarine shoreline - areas closest to the shoreline exhibiting the strongest biomass declines. Results from this study have allowed us to better understand climate change-related vegetation dynamics in a sensitive coastal region.

A universal airborne LiDAR approach for tropical forest carbon mapping.

PubMed

Asner, Gregory P; Mascaro, Joseph; Muller-Landau, Helene C; Vieilledent, Ghislain; Vaudry, Romuald; Rasamoelina, Maminiaina; Hall, Jefferson S; van Breugel, Michiel

2012-04-01

Airborne light detection and ranging (LiDAR) is fast turning the corner from demonstration technology to a key tool for assessing carbon stocks in tropical forests. With its ability to penetrate tropical forest canopies and detect three-dimensional forest structure, LiDAR may prove to be a major component of international strategies to measure and account for carbon emissions from and uptake by tropical forests. To date, however, basic ecological information such as height-diameter allometry and stand-level wood density have not been mechanistically incorporated into methods for mapping forest carbon at regional and global scales. A better incorporation of these structural patterns in forests may reduce the considerable time needed to calibrate airborne data with ground-based forest inventory plots, which presently necessitate exhaustive measurements of tree diameters and heights, as well as tree identifications for wood density estimation. Here, we develop a new approach that can facilitate rapid LiDAR calibration with minimal field data. Throughout four tropical regions (Panama, Peru, Madagascar, and Hawaii), we were able to predict aboveground carbon density estimated in field inventory plots using a single universal LiDAR model (r ( 2 ) = 0.80, RMSE = 27.6 Mg C ha(-1)). This model is comparable in predictive power to locally calibrated models, but relies on limited inputs of basal area and wood density information for a given region, rather than on traditional plot inventories. With this approach, we propose to radically decrease the time required to calibrate airborne LiDAR data and thus increase the output of high-resolution carbon maps, supporting tropical forest conservation and climate mitigation policy.

Identification of Lightning Gaps in Mangrove Forests Using Airborne LIDAR Measurements

NASA Astrophysics Data System (ADS)

Zhang, K.

2006-12-01

Mangrove forests are highly dynamic ecosystems and change frequently due to tropical storms, frost, and lightning. These factors can cause gaps in mangrove forests by damaging trees. Compared to gaps generated by storms and frost, gaps caused by lightning strikes are small, ranging from 50 to 300 m2. However, these small gaps may play a critical role in mangrove forest dynamics because of the frequent occurrence of lightning in tropical areas. It has been hypothesized that the turnover of mangrove forests is mainly due to the death and regeneration of trees in lightning gaps. However, there is a lack of data for gap occurrence in mangrove forests to verify this hypothesis. It is impractical to measure gaps through a field survey on a large scale because of the logistic difficulties of muddy mangrove forests. Airborne light detection and ranging (LIDAR) technology is an effective alternative because it provides direct measurements of ground and canopy elevations remotely. This study developed a method to identify lightning gaps in mangrove forests in terms of LIDAR measurements. First, LIDAR points are classified into vegetation and ground measurements using the progressive morphological filter. Second, a digital canopy model (DCM) is generated by subtracting a digital terrain model (DTM) from a digital surface model (DSM). The DSM is generated by interpolating raw LIDAR measurements, and DTM is produced by interpolating ground measurements. Third, a black top-hat mathematical morphological transformation is used to identify canopy gaps. Comparison of identified gap polygons with raw LIDAR measurements and field surveys shows that the proposed method identifies lightning gaps in mangrove forests successfully. The area of lightning gaps in mangrove forests in Everglades National Park is about 3% of total forest area, which verifies that lightning gaps play a critical role in mangrove forest turnover.

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

NASA Astrophysics Data System (ADS)

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.

Multi-Scale Observation and Modelling of Energy and Matter Exchange in the Atmospheric Boundary-Layer (ScaleX Campaigns)

NASA Astrophysics Data System (ADS)

Zeeman, M. J.; Wolz, K.; Adler, B.; Brenner, C.; De Roo, F.; Emeis, S.; Kalthoff, N.; Mauder, M.; Schäfer, K.; Wohlfahrt, G.; Zhao, P.

2016-12-01

We investigated biosphere-atmosphere exchange processes in relation to the atmospheric boundary-layer (ABL) flow in a shallow valley. Land-use heterogeneity and topography can force local atmospheric flow patterns, including local circulations. Such flow patterns can impair current techniques for the quantification and source attribution of surface-exchange fluxes due to flux-divergence, advection and decoupling. Wind field, temperature and humidity structures in the ABL were observed in high resolution with spatially distributed observations in a 1 km3 experimental domain. Remote-sensing observations of wind, temperature and particles in the ABL (Raman-lidar; RASS; ceilometer; microwave radiometer; 3D Doppler-lidar) were combined with a high-resolution network of in-situ observations that included vertical and horizontal profiles of wind, temperature, carbon dioxide, methane and water vapor concentrations. The experiments were co-located with the long-term eddy covariance (EC) observatory Fendt (DE-Fen; ICOS, TERENO) and were part of international cooperative efforts in 2015 and 2016 (the ScaleX campaigns). The gathered experimental data offers a scale-transcending insight in local flow patterns in mountainous terrain and their influence on surface-exchange fluxes of energy and matter as observed by EC and flux-gradient methodology. In addition, the data is used for validation of Large-Eddy Simulations in complex terrain using PALM-LES. Within this modelling framework, virtual measurements are conducted to further assess the importance of three-dimensional advective and horizontal turbulent transport terms.

Hierarchical Higher Order Crf for the Classification of Airborne LIDAR Point Clouds in Urban Areas

NASA Astrophysics Data System (ADS)

Niemeyer, J.; Rottensteiner, F.; Soergel, U.; Heipke, C.

2016-06-01

We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the distance and the orientation of a segment with respect to the closest road. It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification.

A long Saharan dust event over the western Mediterranean: Lidar, Sun photometer observations, and regional dust modeling

NASA Astrophysics Data System (ADS)

PéRez, C.; Nickovic, S.; Baldasano, J. M.; Sicard, M.; Rocadenbosch, F.; Cachorro, V. E.

2006-08-01

A long Saharan dust event affected the western Mediterranean in the period 12-28 June 2002. Dust was present mainly between 1- and 5-km height affecting most parts of the Iberian Peninsula and reaching western/central Europe. Intensive backscatter lidar observations over Barcelona (Spain) and Sun photometer data from two stations (El Arenosillo, Spain, and Avignon, France) are used to evaluate different configurations the Dust Regional Atmospheric Modeling (DREAM) system. DREAM currently operates dust forecasts over the Mediterranean region (http://www.bsc.es/projects/earthscience/DREAM/) considering four particle size bins while only the first two are relevant for long-range transport analysis since their life time is larger than 12 hours. A more detailed bin method is implemented, and two different dust distributions at sources are compared to the operational version. Evaluations are performed at two wavelengths (532 and 1064 nm). The dust horizontal and vertical structure simulated by DREAM shows very good qualitative agreement when compared to SeaWIFS satellite images and lidar height-time displays over Barcelona. When evaluating the modeled aerosol optical depth (AOD) against Sun photometer data, significant improvements are achieved with the use of the new detailed bin method. In general, the model underpredicts the AOD for increasing Ångström exponents because of the influence of anthropogenic pollution in the boundary layer. In fact, the modeled AOD is highly anticorrelated with the observed Ångström exponents. Avignon shows higher influence of small anthropogenic aerosols which explains the better results of the model at the wavelength of 1064 nm over this location. The uncertainties of backscatter lidar inversions (20-30%) are in the same order of magnitude as the differences between the model experiments. Better model results are obtained when comparing to lidar because most of the anthropogenic effect is removed.

Promoting scientific collaboration and research through integrated social networking capabilities within the OpenTopography Portal

NASA Astrophysics Data System (ADS)

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

2009-04-01

LiDAR (Light Distance And Ranging) topography data offer earth scientists the opportunity to study the earth's surface at very high resolutions. As a result, the popularity of these data is growing dramatically. However, the management, distribution, and analysis of community LiDAR data sets is a challenge due to their massive size (multi-billion point, mutli-terabyte). We have also found that many earth science users of these data sets lack the computing resources and expertise required to process these data. We have developed the OpenTopography Portal to democratize access to these large and computationally challenging data sets. The OpenTopography Portal uses cyberinfrastructure technology developed by the GEON project to provide access to LiDAR data in a variety of formats. LiDAR data products available range from simple Google Earth visualizations of LiDAR-derived hillshades to 1 km2 tiles of standard digital elevation model (DEM) products as well as LiDAR point cloud data and user generated custom-DEMs. We have found that the wide spectrum of LiDAR users have variable scientific applications, computing resources and technical experience and thus require a data system with multiple distribution mechanisms and platforms to serve a broader range of user communities. Because the volume of LiDAR topography data available is rapidly expanding, and data analysis techniques are evolving, there is a need for the user community to be able to communicate and interact to share knowledge and experiences. To address this need, the OpenTopography Portal enables social networking capabilities through a variety of collaboration tools, web 2.0 technologies and customized usage pattern tracking. Fundamentally, these tools offer users the ability to communicate, to access and share documents, participate in discussions, and to keep up to date on upcoming events and emerging technologies. The OpenTopography portal achieves the social networking capabilities by integrating various software technologies and platforms. These include the Expression Engine Content Management System (CMS) that comes with pre-packaged collaboration tools like blogs and wikis, the Gridsphere portal framework that contains the primary GEON LiDAR System portlet with user job monitoring capabilities and a java web based discussion forum (Jforums) application all seamlessly integrated under one portal. The OpenTopography Portal also provides integrated authentication mechanism between the various CMS collaboration tools and the core gridsphere based portlets. The integration of these various technologies allows for enhanced user interaction capabilities within the portal. By integrating popular collaboration tools like discussion forums and blogs we can promote conversation and openness among users. The ability to ask question and share expertise in forum discussions allows users to easily find information and interact with users facing similar challenges. The OpenTopography Blog enables our domain experts to post ideas, news items, commentary, and other resources in order to foster discussion and information sharing. The content management capabilities of the portal allow for easy updates to information in the form of publications, documents, and news articles. Access to the most current information fosters better decision-making. As has become the standard for web 2.0 technologies, the OpenTopography Portal is fully RSS enabled to allow users of the portal to keep track of news items, forum discussions, blog updates, and system outages. We are currently exploring how the information captured by user and job monitoring components of the Gridsphere based GEON LiDAR System can be harnessed to provide a recommender system that will help users to identify appropriate processing parameters and to locate related documents and data. By seamlessly integrating the various platforms and technologies under one single portal, we can take advantage of popular online collaboration tools that are either stand alone or software platform restricted. The availability of these collaboration tools along with the data will foster more community interaction and increase the strength and vibrancy of the LiDAR topography user community.

Potential and range of application of elastic backscatter lidar systems using polarization selection to minimize detected skylight noise

NASA Astrophysics Data System (ADS)

Ahmed, S. A.; Hassebo, Y. Y.; Gross, B.; Oo, M.; Moshary, F.

2006-09-01

We examine the potential, range of application, and limiting factors of a polarization selection technique, recently devised by us, which takes advantage of naturally occurring polarization properties of scattered sky light to minimize the detected sky background signal and which can be used in conjunction with linearly polarized elastic backscatter lidars to maximize lidar receiver SNR. In this approach, a polarization selective lidar receiver is aligned to minimize detected skylight, while the polarization of the transmitted lidar signal is rotated to maintain maximum lidar backscatter signal throughput to the receiver detector, consequently maximizing detected signal to noise ratio. Results presented include lidar elastic backscatter measurements, at 532 nm which show as much as a factor of √10 improvement in signal-to-noise ratio over conventional un-polarized schemes. For vertically pointing lidars, the largest improvements are limited to symmetric early morning and late afternoon hours. For non-vertical scanning lidars, significant improvements are achievable over much more extended time periods, depending on the specific angle between the lidar and solar axes. A theoretical model that simulates the background skylight within the single scattering approximation showed good agreement with measured SNR improvement factors. Diurnally asymmetric improvement factors, sometimes observed, are explained by measured increases in PWV and subsequent modification of aerosol optical depth by dehydration from morning to afternoon. Finally, since the polarization axis follows the solar azimuth angle even for high aerosol loading, as demonstrated using radiative transfer simulations, it is possible to conceive automation of the technique. In addition, it is shown that while multiple scattering reduces the SNR improvement, the orientation of the minimum noise state remains the same.

Remote sensing based detection of forested wetlands: An evaluation of LiDAR, aerial imagery, and their data fusion

NASA Astrophysics Data System (ADS)

Suiter, Ashley Elizabeth

Multi-spectral imagery provides a robust and low-cost dataset for assessing wetland extent and quality over broad regions and is frequently used for wetland inventories. However in forested wetlands, hydrology is obscured by tree canopy making it difficult to detect with multi-spectral imagery alone. Because of this, classification of forested wetlands often includes greater errors than that of other wetlands types. Elevation and terrain derivatives have been shown to be useful for modelling wetland hydrology. But, few studies have addressed the use of LiDAR intensity data detecting hydrology in forested wetlands. Due the tendency of LiDAR signal to be attenuated by water, this research proposed the fusion of LiDAR intensity data with LiDAR elevation, terrain data, and aerial imagery, for the detection of forested wetland hydrology. We examined the utility of LiDAR intensity data and determined whether the fusion of Lidar derived data with multispectral imagery increased the accuracy of forested wetland classification compared with a classification performed with only multi-spectral image. Four classifications were performed: Classification A -- All Imagery, Classification B -- All LiDAR, Classification C -- LiDAR without Intensity, and Classification D -- Fusion of All Data. These classifications were performed using random forest and each resulted in a 3-foot resolution thematic raster of forested upland and forested wetland locations in Vermilion County, Illinois. The accuracies of these classifications were compared using Kappa Coefficient of Agreement. Importance statistics produced within the random forest classifier were evaluated in order to understand the contribution of individual datasets. Classification D, which used the fusion of LiDAR and multi-spectral imagery as input variables, had moderate to strong agreement between reference data and classification results. It was found that Classification A performed using all the LiDAR data and its derivatives (intensity, elevation, slope, aspect, curvatures, and Topographic Wetness Index) was the most accurate classification with Kappa: 78.04%, indicating moderate to strong agreement. However, Classification C, performed with LiDAR derivative without intensity data had less agreement than would be expected by chance, indicating that LiDAR contributed significantly to the accuracy of Classification B.

Detecting understory plant invasion in urban forests using LiDAR

NASA Astrophysics Data System (ADS)

Singh, Kunwar K.; Davis, Amy J.; Meentemeyer, Ross K.

2015-06-01

Light detection and ranging (LiDAR) data are increasingly used to measure structural characteristics of urban forests but are rarely used to detect the growing problem of exotic understory plant invaders. We explored the merits of using LiDAR-derived metrics alone and through integration with spectral data to detect the spatial distribution of the exotic understory plant Ligustrum sinense, a rapidly spreading invader in the urbanizing region of Charlotte, North Carolina, USA. We analyzed regional-scale L. sinense occurrence data collected over the course of three years with LiDAR-derived metrics of forest structure that were categorized into the following groups: overstory, understory, topography, and overall vegetation characteristics, and IKONOS spectral features - optical. Using random forest (RF) and logistic regression (LR) classifiers, we assessed the relative contributions of LiDAR and IKONOS derived variables to the detection of L. sinense. We compared the top performing models developed for a smaller, nested experimental extent using RF and LR classifiers, and used the best overall model to produce a predictive map of the spatial distribution of L. sinense across our country-wide study extent. RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming IKONOS data by 17.5% and the integration of LiDAR and IKONOS data by 5.3%. The top performing model from the RF classifier produced the highest kappa of 64.8%, improving on the parsimonious LR model kappa by 31.1% with a moderate gain of 6.2% over the county extent model. Our results demonstrate the superiority of LiDAR-derived metrics over spectral data and fusion of LiDAR and spectral data for accurately mapping the spatial distribution of the forest understory invader L. sinense.

Modelling above-ground carbon dynamics using multi-temporal airborne lidar: insights from a Mediterranean woodland

NASA Astrophysics Data System (ADS)

Simonson, W.; Ruiz-Benito, P.; Valladares, F.; Coomes, D.

2015-09-01

Woodlands represent highly significant carbon sinks globally, though could lose this function under future climatic change. Effective large-scale monitoring of these woodlands has a critical role to play in mitigating for, and adapting to, climate change. Mediterranean woodlands have low carbon densities, but represent important global carbon stocks due to their extensiveness and are particularly vulnerable because the region is predicted to become much hotter and drier over the coming century. Airborne lidar is already recognized as an excellent approach for high-fidelity carbon mapping, but few studies have used multi-temporal lidar surveys to measure carbon fluxes in forests and none have worked with Mediterranean woodlands. We use a multi-temporal (five year interval) airborne lidar dataset for a region of central Spain to estimate above-ground biomass (AGB) and carbon dynamics in typical mixed broadleaved/coniferous Mediterranean woodlands. Field calibration of the lidar data enabled the generation of grid-based maps of AGB for 2006 and 2011, and the resulting AGB change were estimated. There was a close agreement between the lidar-based AGB growth estimate (1.22 Mg ha-1 year-1) and those derived from two independent sources: the Spanish National Forest Inventory, and a~tree-ring based analysis (1.19 and 1.13 Mg ha-1 year-1, respectively). We parameterised a simple simulator of forest dynamics using the lidar carbon flux measurements, and used it to explore four scenarios of fire occurrence. Under undisturbed conditions (no fire occurrence) an accelerating accumulation of biomass and carbon is evident over the next 100 years with an average carbon sequestration rate of 1.95 Mg C ha-1 year-1. This rate reduces by almost a third when fire probability is increased to 0.01, as has been predicted under climate change. Our work shows the power of multi-temporal lidar surveying to map woodland carbon fluxes and provide parameters for carbon dynamics models. Space deployment of lidar instruments in the near future could open the way for rolling out wide-scale forest carbon stock monitoring to inform management and governance responses to future environmental change.

Modelling above-ground carbon dynamics using multi-temporal airborne lidar: insights from a Mediterranean woodland

NASA Astrophysics Data System (ADS)

Simonson, W.; Ruiz-Benito, P.; Valladares, F.; Coomes, D.

2016-02-01

Woodlands represent highly significant carbon sinks globally, though could lose this function under future climatic change. Effective large-scale monitoring of these woodlands has a critical role to play in mitigating for, and adapting to, climate change. Mediterranean woodlands have low carbon densities, but represent important global carbon stocks due to their extensiveness and are particularly vulnerable because the region is predicted to become much hotter and drier over the coming century. Airborne lidar is already recognized as an excellent approach for high-fidelity carbon mapping, but few studies have used multi-temporal lidar surveys to measure carbon fluxes in forests and none have worked with Mediterranean woodlands. We use a multi-temporal (5-year interval) airborne lidar data set for a region of central Spain to estimate above-ground biomass (AGB) and carbon dynamics in typical mixed broadleaved and/or coniferous Mediterranean woodlands. Field calibration of the lidar data enabled the generation of grid-based maps of AGB for 2006 and 2011, and the resulting AGB change was estimated. There was a close agreement between the lidar-based AGB growth estimate (1.22 Mg ha-1 yr-1) and those derived from two independent sources: the Spanish National Forest Inventory, and a tree-ring based analysis (1.19 and 1.13 Mg ha-1 yr-1, respectively). We parameterised a simple simulator of forest dynamics using the lidar carbon flux measurements, and used it to explore four scenarios of fire occurrence. Under undisturbed conditions (no fire) an accelerating accumulation of biomass and carbon is evident over the next 100 years with an average carbon sequestration rate of 1.95 Mg C ha-1 yr-1. This rate reduces by almost a third when fire probability is increased to 0.01 (fire return rate of 100 years), as has been predicted under climate change. Our work shows the power of multi-temporal lidar surveying to map woodland carbon fluxes and provide parameters for carbon dynamics models. Space deployment of lidar instruments in the near future could open the way for rolling out wide-scale forest carbon stock monitoring to inform management and governance responses to future environmental change.

Lidar characterizations of atmospheric aerosols and clouds

NASA Astrophysics Data System (ADS)

Ferrare, R. A.; Hostetler, C. A.; Hair, J. W.; Burton, S. P.

2017-12-01

Knowledge of the vertical profile, composition, concentration, and size distribution of aerosols is required to quantify the impacts of aerosols on human health, global and regional climate, clouds and precipitation. In particular, radiative forcing due to anthropogenic aerosols is the most uncertain part of anthropogenic radiative forcing, with aerosol-cloud interactions (ACI) as the largest source of uncertainty in current estimates of global radiative forcing. Improving aerosol transport model predictions of the vertical profile of aerosol optical and microphysical characteristics is crucial for improving assessments of aerosol radiative forcing. Understanding how aerosols and clouds interact is essential for investigating the aerosol indirect effect and ACI. Through its ability to provide vertical profiles of aerosol and cloud distributions as well as important information regarding the optical and physical properties of aerosols and clouds, lidar is a crucial tool for addressing these science questions. This presentation describes how surface, airborne, and satellite lidar measurements have been used to address these questions, and in particular how High Spectral Resolution Lidar (HSRL) measurements provide profiles of aerosol properties (backscatter, extinction, depolarization, concentration, size) important for characterizing radiative forcing. By providing a direct measurement of aerosol extinction, HSRL provides more accurate aerosol measurement profiles and more accurate constraints for models than standard retrievals from elastic backscatter lidar, which loses accuracy and precision at lower altitudes due to attenuation from overlying layers. Information regarding particle size and abundance from advanced lidar retrievals provides better proxies for cloud-condensation-nuclei (CCN), which are required for assessing aerosol-cloud interactions. When combined with data from other sensors, advanced lidar measurements can provide information on aerosol and cloud properties for addressing both direct and indirect radiative forcing.

The impact of water vapor assimilation on quantitative precipitation forecast over the Washington, DC metropolitan area

NASA Astrophysics Data System (ADS)

Walford, Segayle Cereta

Forecasting subtle, small-scale convective cases in both winter and summer time is an ongoing challenge in weather forecasting. Recent studies have shown that better structure of moisture within the boundary layer is crucial for improving forecasting skills, particularly quantitative precipitation forecasting (QPF). Lidars, which take high temporal observations of moisture, are able to capture very detailed structures, especially within the boundary layer where convection often begins. This study first investigates the extent to which an aerosol and a water vapor lidar are able to capture key boundary layer processes necessary for the development of convection. The results of this preliminary study show that the water vapor lidar is best able to capture the small scale water vapor variability that is necessary for the development of convection. These results are then used to investigate impacts of assimilating moisture from the Howard University Raman Lidar (HURL) for one mesoscale convective case, July 27-28, 2006. The data for this case is from the Water Vapor Validation Experiment-Satellite and Sondes (WAVES) field campaign located at the Howard University Beltsville Site (HUBS) in Beltsville, MD. Specifically, lidar-based water vapor mixing ratio profiles are assimilated into the Weather Research and Forecasting (WRF) regional model over a 4 km grid resolution over Washington, DC. Model verification is conducted using the Meteorological Evaluation Tool (MET) and the results from the lidar run are then compared to a control (no assimilation) run. The findings indicate that quantitatively conclusions cannot be draw from this one case study. However, qualitatively, the assimilation of the lidar observations improved the equivalent potential temperature, and water vapor distribution of the region. This difference changed location, strength and spatial coverage of the convective system over the HUBS region.

Airborne lidar mapping of vertical ozone distributions in support of the 1990 Clean Air Act Amendments

NASA Technical Reports Server (NTRS)

Uthe, Edward E.; Nielsen, Norman B.; Livingston, John M.

1992-01-01

The 1990 Clean Air Act Amendments mandated attainment of the ozone standard established by the U.S. Environmental Protection Agency. Improved photochemical models validated by experimental data are needed to develop strategies for reducing near surface ozone concentrations downwind of urban and industrial centers. For more than 10 years, lidar has been used on large aircraft to provide unique information on ozone distributions in the atmosphere. However, compact airborne lidar systems are needed for operation on small aircraft of the type typically used on regional air quality investigations to collect data with which to develop and validate air quality models. Data presented in this paper will consist of a comparison between airborne differential absorption lidar (DIAL) and airborne in-situ ozone measurements. Also discussed are future plans to improve the airborne ultraviolet-DIAL for ozone and other gas observations and addition of a Fourier Transform Infrared (FTIR) emission spectrometer to investigate the effects of other gas species on vertical ozone distribution.

Estimating forest and woodland aboveground biomass using active and passive remote sensing

USGS Publications Warehouse

Wu, Zhuoting; Dye, Dennis G.; Vogel, John M.; Middleton, Barry R.

2016-01-01

Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R2 = 0.98). At the plot-level, lidar-derived height and intensity metrics provided the most robust estimate for aboveground biomass, producing dominant species-based aboveground models with errors ranging from 4 to 14Mg ha –1 across all woodland and forest species. Landsat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha –1. Thus, airborne lidar allowed for estimates for fine-scale aboveground biomass mapping with low uncertainty, while Landsat-8 seems best suited for broader spatial scale products such as a national biomass essential climate variable (ECV) based on land cover types for the United States.

Tropospheric Ozone Source Attribution in Southern California during Summer 2014 Based on Lidar Measurements and Model Simulations

NASA Technical Reports Server (NTRS)

Granados Munoz, Maria Jose; Johnson, Matthew S.; Leblanc, Thierry

2016-01-01

In the past decades, significant efforts have been made to increase tropospheric ozone long-term monitoring. A large number of ground-based, airborne and space-borne instruments are currently providing valuable data to contribute to better understand tropospheric ozone budget and variability. Nonetheless, most of these instruments provide in-situ surface and column-integrated data, whereas vertically resolved measurements are still scarce. Besides ozonesondes and aircraft, lidar measurements have proven to be valuable tropospheric ozone profilers. Using the measurements from the tropospheric ozone differential absorption lidar (DIAL) located at the JPL Table Mountain Facility, California, and the GEOS-Chem and GEOS-5 model outputs, the impact of the North American monsoon on tropospheric ozone during summer 2014 is investigated. The influence of the Monsoon lightning-induced NOx will be evaluated against other sources (e.g. local anthropogenic emissions and the stratosphere) using also complementary data such as backward-trajectories analysis, coincident water vapor lidar measurements, and surface ozone in-situ measurements.

Computational modeling of river flow using bathymetry collected with an experimental, water-penetrating, green LiDAR

NASA Astrophysics Data System (ADS)

Kinzel, P. J.; Legleiter, C. J.; Nelson, J. M.

2009-12-01

Airborne bathymetric Light Detection and Ranging (LiDAR) systems designed for coastal and marine surveys are increasingly being deployed in fluvial environments. While the adaptation of this technology to rivers and streams would appear to be straightforward, currently technical challenges remain with regard to achieving high levels of vertical accuracy and precision when mapping bathymetry in shallow fluvial settings. Collectively these mapping errors have a direct bearing on hydraulic model predictions made using these data. We compared channel surveys conducted along the Platte River, Nebraska, and the Trinity River, California, using conventional ground-based methods with those made with the hybrid topographic/bathymetric Experimental Advanced Airborne Research LiDAR (EAARL). In the turbid and braided Platte River, a bathymetric-waveform processing algorithm was shown to enhance the definition of thalweg channels over a more simplified, first-surface waveform processing algorithm. Consequently flow simulations using data processed with the shallow bathymetric algorithm resulted in improved prediction of wetted area relative to the first-surface algorithm, when compared to the wetted area in concurrent aerial imagery. However, when compared to using conventionally collected data for flow modeling, the inundation extent was over predicted with the EAARL topography due to higher bed elevations measured by the LiDAR. In the relatively clear, meandering Trinity River, bathymetric processing algorithms were capable of defining a 3 meter deep pool. However, a similar bias in depth measurement was observed, with the LiDAR measuring the elevation of the river bottom above its actual position, resulting in a predicted water surface higher than that measured by field data. This contribution addresses the challenge of making bathymetric measurements with the EAARL in different environmental conditions encountered in fluvial settings, explores technical issues related to reliably detecting the water surface and river bottom, and illustrates the impact of using LiDAR data and current processing techniques to produce above and below water topographic surfaces for hydraulic modeling and habitat applications.

Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests

USGS Publications Warehouse

Hall, S. A.; Burke, I.C.; Box, D. O.; Kaufmann, M. R.; Stoker, Jason M.

2005-01-01

The ponderosa pine forests of the Colorado Front Range, USA, have historically been subjected to wildfires. Recent large burns have increased public interest in fire behavior and effects, and scientific interest in the carbon consequences of wildfires. Remote sensing techniques can provide spatially explicit estimates of stand structural characteristics. Some of these characteristics can be used as inputs to fire behavior models, increasing our understanding of the effect of fuels on fire behavior. Others provide estimates of carbon stocks, allowing us to quantify the carbon consequences of fire. Our objective was to use discrete-return lidar to estimate such variables, including stand height, total aboveground biomass, foliage biomass, basal area, tree density, canopy base height and canopy bulk density. We developed 39 metrics from the lidar data, and used them in limited combinations in regression models, which we fit to field estimates of the stand structural variables. We used an information–theoretic approach to select the best model for each variable, and to select the subset of lidar metrics with most predictive potential. Observed versus predicted values of stand structure variables were highly correlated, with r2 ranging from 57% to 87%. The most parsimonious linear models for the biomass structure variables, based on a restricted dataset, explained between 35% and 58% of the observed variability. Our results provide us with useful estimates of stand height, total aboveground biomass, foliage biomass and basal area. There is promise for using this sensor to estimate tree density, canopy base height and canopy bulk density, though more research is needed to generate robust relationships. We selected 14 lidar metrics that showed the most potential as predictors of stand structure. We suggest that the focus of future lidar studies should broaden to include low density forests, particularly systems where the vertical structure of the canopy is important, such as fire prone forests.

Quantifying post-fire fallen trees using multi-temporal lidar

NASA Astrophysics Data System (ADS)

Bohlin, Inka; Olsson, Håkan; Bohlin, Jonas; Granström, Anders

2017-12-01

Massive tree-felling due to root damage is a common fire effect on burnt areas in Scandinavia, but has so far not been analyzed in detail. Here we explore if pre- and post-fire lidar data can be used to estimate the proportion of fallen trees. The study was carried out within a large (14,000 ha) area in central Sweden burnt in August 2014, where we had access to airborne lidar data from both 2011 and 2015. Three data-sets of predictor variables were tested: POST (post-fire lidar metrics), DIF (difference between post- and pre-fire lidar metrics) and combination of those two (POST_DIF). Fractional logistic regression was used to predict the proportion of fallen trees. Training data consisted of 61 plots, where the number of fallen and standing trees was calculated both in the field and with interpretation of drone images. The accuracy of the best model was tested based on 100 randomly selected validation plots with a size of 25 × 25 m. Our results showed that multi-temporal lidar together with field-collected training data can be used for quantifying post-fire tree felling over large areas. Several height-, density- and intensity metrics correlated with the proportion of fallen trees. The best model combined metrics from both datasets (POST_DIF), resulting in a RMSE of 0.11. Results were slightly poorer in the validation plots with RMSE of 0.18 using pixel size of 12.5 m and RMSE of 0.15 using pixel size of 6.25 m. Our model performed least well for stands that had been exposed to high-intensity crown fire. This was likely due to the low amount of echoes from the standing black tree skeletons. Wall-to-wall maps produced with this model can be used for landscape level analysis of fire effects and to explore the relationship between fallen trees and forest structure, soil type, fire intensity or topography.

Selection Algorithm for the CALIPSO Lidar Aerosol Extinction-to-Backscatter Ratio

NASA Technical Reports Server (NTRS)

Omar, Ali H.; Winker, David M.; Vaughan, Mark A.

2006-01-01

The extinction-to-backscatter ratio (S(sub a)) is an important parameter used in the determination of the aerosol extinction and subsequently the optical depth from lidar backscatter measurements. We outline the algorithm used to determine Sa for the Cloud and Aerosol Lidar and Infrared Pathfinder Spaceborne Observations (CALIPSO) lidar. S(sub a) for the CALIPSO lidar will either be selected from a look-up table or calculated using the lidar measurements depending on the characteristics of aerosol layer. Whenever suitable lofted layers are encountered, S(sub a) is computed directly from the integrated backscatter and transmittance. In all other cases, the CALIPSO observables: the depolarization ratio, delta, the layer integrated attenuated backscatter, beta, and the mean layer total attenuated color ratio, gamma, together with the surface type, are used to aid in aerosol typing. Once the type is identified, a look-up-table developed primarily from worldwide observations, is used to determine the S(sub a) value. The CALIPSO aerosol models include desert dust, biomass burning, background, polluted continental, polluted dust, and marine aerosols.

Modelling vertical error in LiDAR-derived digital elevation models

NASA Astrophysics Data System (ADS)

Aguilar, Fernando J.; Mills, Jon P.; Delgado, Jorge; Aguilar, Manuel A.; Negreiros, J. G.; Pérez, José L.

2010-01-01

A hybrid theoretical-empirical model has been developed for modelling the error in LiDAR-derived digital elevation models (DEMs) of non-open terrain. The theoretical component seeks to model the propagation of the sample data error (SDE), i.e. the error from light detection and ranging (LiDAR) data capture of ground sampled points in open terrain, towards interpolated points. The interpolation methods used for infilling gaps may produce a non-negligible error that is referred to as gridding error. In this case, interpolation is performed using an inverse distance weighting (IDW) method with the local support of the five closest neighbours, although it would be possible to utilize other interpolation methods. The empirical component refers to what is known as "information loss". This is the error purely due to modelling the continuous terrain surface from only a discrete number of points plus the error arising from the interpolation process. The SDE must be previously calculated from a suitable number of check points located in open terrain and assumes that the LiDAR point density was sufficiently high to neglect the gridding error. For model calibration, data for 29 study sites, 200×200 m in size, belonging to different areas around Almeria province, south-east Spain, were acquired by means of stereo photogrammetric methods. The developed methodology was validated against two different LiDAR datasets. The first dataset used was an Ordnance Survey (OS) LiDAR survey carried out over a region of Bristol in the UK. The second dataset was an area located at Gador mountain range, south of Almería province, Spain. Both terrain slope and sampling density were incorporated in the empirical component through the calibration phase, resulting in a very good agreement between predicted and observed data (R2 = 0.9856 ; p < 0.001). In validation, Bristol observed vertical errors, corresponding to different LiDAR point densities, offered a reasonably good fit to the predicted errors. Even better results were achieved in the more rugged morphology of the Gador mountain range dataset. The findings presented in this article could be used as a guide for the selection of appropriate operational parameters (essentially point density in order to optimize survey cost), in projects related to LiDAR survey in non-open terrain, for instance those projects dealing with forestry applications.

KML-Based Access and Visualization of High Resolution LiDAR Topography

NASA Astrophysics Data System (ADS)

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 the user as a KML groundoverlay. The KML product enables users to quickly and easily visualize the DEMs in Google Earth. By combining internet-based LiDAR data processing with KML visualization products, users are able to execute computationally intensive data sub-setting, processing and visualization without having local access to computing resources, GIS software, or data processing expertise. Finally, GEON has partnered with the US Geological Survey to generate region-dependant network linked KML visualizations for large volumes of LiDAR derived hillshades of the Northern San Andreas fault system. These data, acquired by the NSF-funded GeoEarthScope project, offer an unprecedented look at active faults in the northern portion of the San Andreas system. Through the region-dependant network linked KML, users can seamlessly access 1 meter hillshades (both 315 and 45 degree sun angles) for the full 1400 square kilometer dataset, without downloading huge volumes of data. This type of data access has great utility for users ranging from earthquake scientists to K-12 educators who wish to introduce cutting edge real world data into their earth science lessons.

Model of lidar range-Doppler signatures of solid rocket fuel plumes

NASA Astrophysics Data System (ADS)

Bankman, Isaac N.; Giles, John W.; Chan, Stephen C.; Reed, Robert A.

2004-09-01

The analysis of particles produced by solid rocket motor fuels relates to two types of studies: the effect of these particles on the Earth's ozone layer, and the dynamic flight behavior of solid fuel boosters used by the NASA Space Shuttle. Since laser backscatter depends on the particle size and concentration, a lidar system can be used to analyze the particle distributions inside a solid rocket plume in flight. We present an analytical model that simulates the lidar returns from solid rocket plumes including effects of beam profile, spot size, polarization and sensing geometry. The backscatter and extinction coefficients of alumina particles are computed with the T-matrix method that can address non-spherical particles. The outputs of the model include time-resolved return pulses and range-Doppler signatures. Presented examples illustrate the effects of sensing geometry.

Technique to separate lidar signal and sunlight.

PubMed

Sun, Wenbo; Hu, Yongxiang; MacDonnell, David G; Weimer, Carl; Baize, Rosemary R

2016-06-13

Sunlight contamination dominates the backscatter noise in space-based lidar measurements during daytime. The background scattered sunlight is highly variable and dependent upon the surface and atmospheric albedo. The scattered sunlight contribution to noise increases over land and snow surfaces where surface albedos are high and thus overwhelm lidar backscatter from optically thin atmospheric constituents like aerosols and thin clouds. In this work, we developed a novel lidar remote sensing concept that potentially can eliminate sunlight induced noise. The new lidar concept requires: (1) a transmitted laser light that carries orbital angular momentum (OAM); and (2) a photon sieve (PS) diffractive filter that separates scattered sunlight from laser light backscattered from the atmosphere, ocean and solid surfaces. The method is based on numerical modeling of the focusing of Laguerre-Gaussian (LG) laser beam and plane-wave light by a PS. The model results show that after passing through a PS, laser light that carries the OAM is focused on a ring (called "focal ring" here) on the focal plane of the PS filter, very little energy arrives at the center of the focal plane. However, scattered sunlight, as a plane wave without the OAM, focuses at the center of the focal plane and thus can be effectively blocked or ducted out. We also find that the radius of the "focal ring" increases with the increase of azimuthal mode (L) of LG laser light, thus increasing L can more effectively separate the lidar signal away from the sunlight noise.

Localized Segment Based Processing for Automatic Building Extraction from LiDAR Data

NASA Astrophysics Data System (ADS)

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.

Change detection of riverbed movements using river cross-sections and LiDAR data

NASA Astrophysics Data System (ADS)

Vetter, Michael; Höfle, Bernhard; Mandlburger, Gottfried; Rutzinger, Martin

2010-05-01

Today, Airborne LiDAR derived digital terrain models (DTMs) are used for several aspects in different scientific disciplines, such as hydrology, geomorphology or archaeology. In the field of river geomorphology, LiDAR data sets can provide information on the riverine vegetation, the level and boundary of the water body, the elevation of the riparian foreland and their roughness. The LiDAR systems in use for topographic data acquisition mainly operate with wavelengths of at least 1064nm and, thus, are not able to penetrate water. LiDAR sensors with two wavelengths are available (bathymetric LiDAR), but they can only provide elevation information of riverbeds or lakes, if the water is clear and the minimum water depth exceeds 1.5m. In small and shallow rivers it is impossible to collect information of the riverbed, regardless of the used LiDAR sensor. In this article, we present a method to derive a high-resolution DTM of the riverbed and to combine it with the LiDAR DTM resulting in a watercourse DTM (DTM-W) as a basis for calculating the changes in the riverbed during several years. To obtain such a DTM-W we use river cross-sections acquired by terrestrial survey or echo-sounding. First, a differentiation between water and land has to be done. A highly accurate water surface can be derived by using a water surface delineation algorithm, which incorporates the amplitude information of the LiDAR point cloud and additional geometrical features (e.g. local surface roughness). The second step is to calculate a thalweg line, which is the lowest flow path in the riverbed. This is achieved by extracting the lowest point of each river cross section and by fitting a B-spline curve through those points. In the next step, the centerline of the river is calculated by applying a shrinking algorithm of the water boundary polygon. By averaging the thalweg line and the centerline, a main flow path line can be computed. Subsequently, a dense array of 2D-profiles perpendicular to the flow path line is defined and the heights are computed by linear interpolation of the original cross sections. Thus, a very dense 3D point cloud of the riverbed is obtained from which a grid model of the river bed can be calculated applying any suitable interpolation technique like triangulation, linear prediction or inverse distance mapping. In a final step, the river bed model and the LiDAR DTM are combined resulting in a watercourse DTM. By computing different DTM-Ws from multiple cross section data sets, the volume and the magnitude of changes in the riverbed can be estimated. Hence, the erosion or accumulation areas and their volume changes during several years can be quantified.

Optical, microphysical, mass and geometrical properties of aged volcanic particles observed over Athens, Greece, during the Eyjafjallajökull eruption in April 2010 through synergy of Raman lidar and sunphotometer measurements

NASA Astrophysics Data System (ADS)

Kokkalis, P.; Papayannis, A.; Amiridis, V.; Mamouri, R. E.; Veselovskii, I.; Kolgotin, A.; Tsaknakis, G.; Kristiansen, N. I.; Stohl, A.; Mona, L.

2013-09-01

Vertical profiles of the optical (extinction and backscatter coefficients, lidar ratio and Ångström exponent), microphysical (mean effective radius, mean refractive index, mean number concentration) and geometrical properties as well as the mass concentration of volcanic particles from the Eyjafjallajökull eruption were retrieved at selected heights over Athens, Greece, using multi-wavelength Raman lidar measurements performed during the period 21-24 April 2010. Aerosol Robotic Network (AERONET) particulate columnar measurements along with inversion schemes were initialized together with lidar observations to deliver the aforementioned products. The well-known FLEXPART (FLEXible PARTicle dispersion model) model used for volcanic dispersion simulations is initiated as well in order to estimate the horizontal and vertical distribution of volcanic particles. Compared with the lidar measurements within the planetary boundary layer over Athens, FLEXPART proved to be a useful tool for determining the state of mixing of ash with other, locally emitted aerosol types. The major findings presented in our work concern the identification of volcanic particles layers in the form of filaments after 7-day transport from the volcanic source (approximately 4000 km away from our site) from the surface and up to 10 km according to the lidar measurements. Mean hourly averaged lidar signals indicated that the layer thickness of volcanic particles ranged between 1.5 and 2.2 km. The corresponding aerosol optical depth was found to vary from 0.01 to 0.18 at 355 nm and from 0.02 up to 0.17 at 532 nm. Furthermore, the corresponding lidar ratios (S) ranged between 60 and 80 sr at 355 nm and 44 and 88 sr at 532 nm. The mean effective radius of the volcanic particles estimated by applying inversion scheme to the lidar data found to vary within the range 0.13-0.38 μm and the refractive index ranged from 1.39+0.009i to 1.48+0.006i. This high variability is most probably attributed to the mixing of aged volcanic particles with other aerosol types of local origin. Finally, the LIRIC (LIdar/Radiometer Inversion Code) lidar/sunphotometric combined inversion algorithm has been applied in order to retrieve particle concentrations. These have been compared with FLEXPART simulations of the vertical distribution of ash showing good agreement concerning not only the geometrical properties of the volcanic particles layers but also the particles mass concentration.

A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales

NASA Astrophysics Data System (ADS)

Ghosh, Aniruddha; Fassnacht, Fabian Ewald; Joshi, P. K.; Koch, Barbara

2014-02-01

Knowledge of tree species distribution is important worldwide for sustainable forest management and resource evaluation. The accuracy and information content of species maps produced using remote sensing images vary with scale, sensor (optical, microwave, LiDAR), classification algorithm, verification design and natural conditions like tree age, forest structure and density. Imaging spectroscopy reduces the inaccuracies making use of the detailed spectral response. However, the scale effect still has a strong influence and cannot be neglected. This study aims to bridge the knowledge gap in understanding the scale effect in imaging spectroscopy when moving from 4 to 30 m pixel size for tree species mapping, keeping in mind that most current and future hyperspectral satellite based sensors work with spatial resolution around 30 m or more. Two airborne (HyMAP) and one spaceborne (Hyperion) imaging spectroscopy dataset with pixel sizes of 4, 8 and 30 m, respectively were available to examine the effect of scale over a central European forest. The forest under examination is a typical managed forest with relatively homogenous stands featuring mostly two canopy layers. Normalized digital surface model (nDSM) derived from LiDAR data was used additionally to examine the effect of height information in tree species mapping. Six different sets of predictor variables (reflectance value of all bands, selected components of a Minimum Noise Fraction (MNF), Vegetation Indices (VI) and each of these sets combined with LiDAR derived height) were explored at each scale. Supervised kernel based (Support Vector Machines) and ensemble based (Random Forest) machine learning algorithms were applied on the dataset to investigate the effect of the classifier. Iterative bootstrap-validation with 100 iterations was performed for classification model building and testing for all the trials. For scale, analysis of overall classification accuracy and kappa values indicated that 8 m spatial resolution (reaching kappa values of over 0.83) slightly outperformed the results obtained from 4 m for the study area and five tree species under examination. The 30 m resolution Hyperion image produced sound results (kappa values of over 0.70), which in some areas of the test site were comparable with the higher spatial resolution imagery when qualitatively assessing the map outputs. Considering input predictor sets, MNF bands performed best at 4 and 8 m resolution. Optical bands were found to be best for 30 m spatial resolution. Classification with MNF as input predictors produced better visual appearance of tree species patches when compared with reference maps. Based on the analysis, it was concluded that there is no significant effect of height information on tree species classification accuracies for the present framework and study area. Furthermore, in the examined cases there was no single best choice among the two classifiers across scales and predictors. It can be concluded that tree species mapping from imaging spectroscopy for forest sites comparable to the one under investigation is possible with reliable accuracies not only from airborne but also from spaceborne imaging spectroscopy datasets.

Predicting diameter at breast height from total height and crown length

Treesearch

Quang V. Cao; Thomas J. Dean

2013-01-01

Tree diameter at breast height (d.b.h.) is often predicted from total height (model 1a) or both total height and number of trees per acre (model 1b). These approaches are useful when Light Detection and Ranging (LiDAR) data are available. LiDAR height data can be employed to predict tree d.b.h., and consequently individual tree volumes and volume/ ha can be obtained...

Modelling Sensor and Target effects on LiDAR Waveforms

NASA Astrophysics Data System (ADS)

Rosette, J.; North, P. R.; Rubio, J.; Cook, B. D.; Suárez, J.

2010-12-01

The aim of this research is to explore the influence of sensor characteristics and interactions with vegetation and terrain properties on the estimation of vegetation parameters from LiDAR waveforms. This is carried out using waveform simulations produced by the FLIGHT radiative transfer model which is based on Monte Carlo simulation of photon transport (North, 1996; North et al., 2010). The opportunities for vegetation analysis that are offered by LiDAR modelling are also demonstrated by other authors e.g. Sun and Ranson, 2000; Ni-Meister et al., 2001. Simulations from the FLIGHT model were driven using reflectance and transmittance properties collected from the Howland Research Forest, Maine, USA in 2003 together with a tree list for a 200m x 150m area. This was generated using field measurements of location, species and diameter at breast height. Tree height and crown dimensions of individual trees were calculated using relationships established with a competition index determined for this site. Waveforms obtained by the Laser Vegetation Imaging Sensor (LVIS) were used as validation of simulations. This provided a base from which factors such as slope, laser incidence angle and pulse width could be varied. This has enabled the effect of instrument design and laser interactions with different surface characteristics to be tested. As such, waveform simulation is relevant for the development of future satellite LiDAR sensors, such as NASA’s forthcoming DESDynI mission (NASA, 2010), which aim to improve capabilities of vegetation parameter estimation. ACKNOWLEDGMENTS We would like to thank scientists at the Biospheric Sciences Branch of NASA Goddard Space Flight Center, in particular to Jon Ranson and Bryan Blair. This work forms part of research funded by the NASA DESDynI project and the UK Natural Environment Research Council (NE/F021437/1). REFERENCES NASA, 2010, DESDynI: Deformation, Ecosystem Structure and Dynamics of Ice. http://desdyni.jpl.nasa.gov/ (accessed May 2010). NI-MEISTER, W., JUPP, D. L. B. and DUBAYAH, R., 2001, Modeling Lidar Waveforms in Heterogeneous and Discrete Canopies. IEEE Transactions on Geoscience and Remote Sensing, 39 (9): 1943-1958. NORTH, P. R. J., 1996, Three-Dimensional Forest Light Interaction Model Using a Monte Carlo Method. IEEE Transactions on Geoscience and Remote Sensing, 34 (4): 946-956. NORTH, P. R. J., ROSETTE, J. A. B., SUÁREZ, J. C. and LOS, S. O., 2010, A Monte Carlo radiative transfer model of satellite waveform lidar. International Journal of Remote Sensing, 31 (5): 1343-1358. SUN, G. and RANSON, K. J., 2000, Modeling lidar returns from forest canopies. IEEE Transactions on Geoscience and Remote Sensing, 38 (6): 2617-2626.

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

NASA Astrophysics Data System (ADS)

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.

Lidar method to estimate emission rates from extended sources

USDA-ARS?s Scientific Manuscript database

Currently, point measurements, often combined with models, are the primary means by which atmospheric emission rates are estimated from extended sources. However, these methods often fall short in their spatial and temporal resolution and accuracy. In recent years, lidar has emerged as a suitable to...

Mars Atmospheric Characterization Using Advanced 2-Micron Orbiting Lidar

NASA Technical Reports Server (NTRS)

Singh, U.; Engelund, W.; Refaat, T.; Kavaya, M.; Yu, J.; Petros, M.

2015-01-01

Mars atmospheric characterization is critical for exploring the planet. Future Mars missions require landing massive payloads to the surface with high accuracy. The accuracy of entry, descent and landing (EDL) of a payload is a major technical challenge for future Mars missions. Mars EDL depends on atmospheric conditions such as density, wind and dust as well as surface topography. A Mars orbiting 2-micron lidar system is presented in this paper. This advanced lidar is capable of measuring atmospheric pressure and temperature profiles using the most abundant atmospheric carbon dioxide (CO2) on Mars. In addition Martian winds and surface altimetry can be mapped, independent of background radiation or geographical location. This orbiting lidar is a valuable tool for developing EDL models for future Mars missions.

Lidar sprectroscopy instrument (LISSI): An infrastructure facility for chemical aerosol profiling at the University of Hertfordshire

NASA Astrophysics Data System (ADS)

Tesche, Matthias; Tatarov, Boyan; Noh, Youngmin; Müller, Detlef

2018-04-01

The lidar development at the University of Hertfordshire explores the feasibility of using Raman backscattering for chemical aerosol profiling. This paper provides an overview of the new facility. A high-power Nd:YAG/OPO setup is used to excite Raman backscattering at a wide range of wavelengths. The receiver combines a spectrometer with a 32-channel detector or an ICCD camera to resolve Raman signals of various chemical compounds. The new facility will open new avenues for chemical profiling of aerosol pollution from measurements of Raman scattering by selected chemical compounds, provide data that allow to close the gap between optical and microphysical aerosol profiling with lidar and enables connecting lidar measurements to parameters used in atmospheric modelling.

Impact of varying lidar measurement and data processing techniques in evaluating cirrus cloud and aerosol direct radiative effects

NASA Astrophysics Data System (ADS)

Lolli, Simone; Madonna, Fabio; Rosoldi, Marco; Campbell, James R.; Welton, Ellsworth J.; Lewis, Jasper R.; Gu, Yu; Pappalardo, Gelsomina

2018-03-01

In the past 2 decades, ground-based lidar networks have drastically increased in scope and relevance, thanks primarily to the advent of lidar observations from space and their need for validation. Lidar observations of aerosol and cloud geometrical, optical and microphysical atmospheric properties are subsequently used to evaluate their direct radiative effects on climate. However, the retrievals are strongly dependent on the lidar instrument measurement technique and subsequent data processing methodologies. In this paper, we evaluate the discrepancies between the use of Raman and elastic lidar measurement techniques and corresponding data processing methods for two aerosol layers in the free troposphere and for two cirrus clouds with different optical depths. Results show that the different lidar techniques are responsible for discrepancies in the model-derived direct radiative effects for biomass burning (0.05 W m-2 at surface and 0.007 W m-2 at top of the atmosphere) and dust aerosol layers (0.7 W m-2 at surface and 0.85 W m-2 at top of the atmosphere). Data processing is further responsible for discrepancies in both thin (0.55 W m-2 at surface and 2.7 W m-2 at top of the atmosphere) and opaque (7.7 W m-2 at surface and 11.8 W m-2 at top of the atmosphere) cirrus clouds. Direct radiative effect discrepancies can be attributed to the larger variability of the lidar ratio for aerosols (20-150 sr) than for clouds (20-35 sr). For this reason, the influence of the applied lidar technique plays a more fundamental role in aerosol monitoring because the lidar ratio must be retrieved with relatively high accuracy. In contrast, for cirrus clouds, with the lidar ratio being much less variable, the data processing is critical because smoothing it modifies the aerosol and cloud vertically resolved extinction profile that is used as input to compute direct radiative effect calculations.

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

NASA Astrophysics Data System (ADS)

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 radius and concentration retrieved from the CHARMS data and compare column-average aerosol properties derived from the multiwavelength lidar aerosol retrievals to corresponding values retrieved from AERONET measurements.

Hardware in the Loop Performance Assessment of LIDAR-Based Spacecraft Pose Determination

PubMed Central

Fasano, Giancarmine; Grassi, Michele

2017-01-01

In this paper an original, easy to reproduce, semi-analytic calibration approach is developed for hardware-in-the-loop performance assessment of pose determination algorithms processing point cloud data, collected by imaging a non-cooperative target with LIDARs. The laboratory setup includes a scanning LIDAR, a monocular camera, a scaled-replica of a satellite-like target, and a set of calibration tools. The point clouds are processed by uncooperative model-based algorithms to estimate the target relative position and attitude with respect to the LIDAR. Target images, acquired by a monocular camera operated simultaneously with the LIDAR, are processed applying standard solutions to the Perspective-n-Points problem to get high-accuracy pose estimates which can be used as a benchmark to evaluate the accuracy attained by the LIDAR-based techniques. To this aim, a precise knowledge of the extrinsic relative calibration between the camera and the LIDAR is essential, and it is obtained by implementing an original calibration approach which does not need ad-hoc homologous targets (e.g., retro-reflectors) easily recognizable by the two sensors. The pose determination techniques investigated by this work are of interest to space applications involving close-proximity maneuvers between non-cooperative platforms, e.g., on-orbit servicing and active debris removal. PMID:28946651

Hardware in the Loop Performance Assessment of LIDAR-Based Spacecraft Pose Determination.

PubMed

Opromolla, Roberto; Fasano, Giancarmine; Rufino, Giancarlo; Grassi, Michele

2017-09-24

In this paper an original, easy to reproduce, semi-analytic calibration approach is developed for hardware-in-the-loop performance assessment of pose determination algorithms processing point cloud data, collected by imaging a non-cooperative target with LIDARs. The laboratory setup includes a scanning LIDAR, a monocular camera, a scaled-replica of a satellite-like target, and a set of calibration tools. The point clouds are processed by uncooperative model-based algorithms to estimate the target relative position and attitude with respect to the LIDAR. Target images, acquired by a monocular camera operated simultaneously with the LIDAR, are processed applying standard solutions to the Perspective- n -Points problem to get high-accuracy pose estimates which can be used as a benchmark to evaluate the accuracy attained by the LIDAR-based techniques. To this aim, a precise knowledge of the extrinsic relative calibration between the camera and the LIDAR is essential, and it is obtained by implementing an original calibration approach which does not need ad-hoc homologous targets (e.g., retro-reflectors) easily recognizable by the two sensors. The pose determination techniques investigated by this work are of interest to space applications involving close-proximity maneuvers between non-cooperative platforms, e.g., on-orbit servicing and active debris removal.

Terrestrial hyperspectral image shadow restoration through fusion with terrestrial lidar

NASA Astrophysics Data System (ADS)

Hartzell, Preston J.; Glennie, Craig L.; Finnegan, David C.; Hauser, Darren L.

2017-05-01

Recent advances in remote sensing technology have expanded the acquisition and fusion of active lidar and passive hyperspectral imagery (HSI) from exclusively airborne observations to include terrestrial modalities. In contrast to airborne collection geometry, hyperspectral imagery captured from terrestrial cameras is prone to extensive solar shadowing on vertical surfaces leading to reductions in pixel classification accuracies or outright removal of shadowed areas from subsequent analysis tasks. We demonstrate the use of lidar spatial information for sub-pixel HSI shadow detection and the restoration of shadowed pixel spectra via empirical methods that utilize sunlit and shadowed pixels of similar material composition. We examine the effectiveness of radiometrically calibrated lidar intensity in identifying these similar materials in sun and shade conditions and further evaluate a restoration technique that leverages ratios derived from the overlapping lidar laser and HSI wavelengths. Simulations of multiple lidar wavelengths, i.e., multispectral lidar, indicate the potential for 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 of shadowed HSI pixels is quantified for imagery of a geologic outcrop through improvements in spectral shape, spectral scale, and HSI band correlation.

Development of LiDAR aware allometrics for Abies grandis: A Case Study

NASA Astrophysics Data System (ADS)

Stone, G. A.; Tinkham, W. T.; Smith, A. M.; Hudak, A. T.; Falkowski, M. J.; Keefe, R.

2012-12-01

Forest managers rely increasingly on accurate allometric relationships to inform decisions regarding stand rotations, silvilcultural treatments, timber harvesting, and biometric modeling. At the same time, advances in remote sensing techniques like LiDAR (light detection and ranging) have brought about opportunities to advance how we assess forest growth, and thus are contributing to the need for more accurate allometries. Past studies have attempted to relate LiDAR data to both plot and individual tree measures of forest biomass. However, many of these studies have been limited by the accuracy of their coincident observations. In this study, 24 Abies grandis were measured, felled, and dissected for the explicit objective of developing LiDAR aware allometrics. The analysis predicts spatial variables of competition, growth potential (e.g, trees per acre, aspect, elevation, etc.) and common statistical distributional metrics (e.g., mean, mode, percentiles, variance, skewness, kurtosis, etc.) derived from LiDAR point cloud returns to coincident in situ measures of Abies grandis stem biomass. The resulting allometries exemplify a new approach for predicting structural attributes of interest (biomass, basal area, volume, etc.) directly from LiDAR point cloud data, precluding the measurement errors that are propogated by indirectly predicting these structure attributes of interest from LiDAR data using traditional plot-based measurements.

Forest height Mapping using the fusion of Lidar and MULTI-ANGLE spectral data

NASA Astrophysics Data System (ADS)

Pang, Y.; Li, Z.

2016-12-01

Characterizing the complexity of forest ecosystem over large area is highly complex. Light detection and Ranging (LIDAR) approaches have demonstrated a high capacity to accurately estimate forest structural parameters. A number of satellite mission concepts have been proposed to fuse LiDAR with other optical imagery allowing Multi-angle spectral observations to be captured using the Bidirectional Reflectance Distribution Function (BRDF) characteristics of forests. China is developing the concept of Chinese Terrestrial Carbon Mapping Satellite. A multi-beam waveform Lidar is the main sensor. A multi-angle imagery system is considered as the spatial mapping sensor. In this study, we explore the fusion potential of Lidar and multi-angle spectral data to estimate forest height across different scales. We flew intensive airborne Lidar and Multi-angle hyperspectral data in Genhe Forest Ecological Research Station, Northeast China. Then extended the spatial scale with some long transect flights to cover more forest structures. Forest height data derived from airborne lidar data was used as reference data and the multi-angle hyperspectral data was used as model inputs. Our results demonstrate that the multi-angle spectral data can be used to estimate forest height with the RMSE of 1.1 m with an R2 approximately 0.8.

Standardization of the Definitions of Vertical Resolution and Uncertainty in the NDACC-archived Ozone and Temperature Lidar Measurements

NASA Technical Reports Server (NTRS)

Leblanc, T.; Godin-Beekmann, S.; Payen, Godin-Beekmann; Gabarrot, Franck; vanGijsel, Anne; Bandoro, J.; Sica, R.; Trickl, T.

2012-01-01

The international Network for the Detection of Atmospheric Composition Change (NDACC) is a global network of high-quality, remote-sensing research stations for observing and understanding the physical and chemical state of the Earth atmosphere. As part of NDACC, over 20 ground-based lidar instruments are dedicated to the long-term monitoring of atmospheric composition and to the validation of space-borne measurements of the atmosphere from environmental satellites such as Aura and ENVISAT. One caveat of large networks such as NDACC is the difficulty to archive measurement and analysis information consistently from one research group (or instrument) to another [1][2][3]. Yet the need for consistent definitions has strengthened as datasets of various origin (e.g., satellite and ground-based) are increasingly used for intercomparisons, validation, and ingested together in global assimilation systems.In the framework of the 2010 Call for Proposals by the International Space Science Institute (ISSI) located in Bern, Switzerland, a Team of lidar experts was created to address existing issues in three critical aspects of the NDACC lidar ozone and temperature data retrievals: signal filtering and the vertical filtering of the retrieved profiles, the quantification and propagation of the uncertainties, and the consistent definition and reporting of filtering and uncertainties in the NDACC- archived products. Additional experts from the satellite and global data standards communities complement the team to help address issues specific to the latter aspect.

Semantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery

NASA Astrophysics Data System (ADS)

Dechesne, Clément; Mallet, Clément; Le Bris, Arnaud; Gouet-Brunet, Valérie

2017-04-01

Forest stands are the basic units for forest inventory and mapping. Stands are defined as large forested areas (e.g., ⩾ 2 ha) of homogeneous tree species composition and age. Their accurate delineation is usually performed by human operators through visual analysis of very high resolution (VHR) infra-red images. This task is tedious, highly time consuming, and should be automated for scalability and efficient updating purposes. In this paper, a method based on the fusion of airborne lidar data and VHR multispectral images is proposed for the automatic delineation of forest stands containing one dominant species (purity superior to 75%). This is the key preliminary task for forest land-cover database update. The multispectral images give information about the tree species whereas 3D lidar point clouds provide geometric information on the trees and allow their individual extraction. Multi-modal features are computed, both at pixel and object levels: the objects are individual trees extracted from lidar data. A supervised classification is then performed at the object level in order to coarsely discriminate the existing tree species in each area of interest. The classification results are further processed to obtain homogeneous areas with smooth borders by employing an energy minimum framework, where additional constraints are joined to form the energy function. The experimental results show that the proposed method provides very satisfactory results both in terms of stand labeling and delineation (overall accuracy ranges between 84 % and 99 %).

Boundary Layer Aerosol Composition over Sierra Nevada Mountains using 9.11- and 10.59-micron CW Lidars and Modeled Backscatter from Size Distribution Data

NASA Technical Reports Server (NTRS)

Cutten, D. R.; Jarzembski, M. A.; Srivastava, V.; Pueschel, R. F.; Howard, S. D.; McCaul, E. W., Jr.

2003-01-01

An inversion technique has been developed to determine volume fractions of an atmospheric aerosol composed primarily of ammonium sulfate and ammonium nitrate and water combined with fixed concentration of elemental and organic carbon. It is based on measured aerosol backscatter obtained with 9.11 - and 10.59-micron wavelength continuous wave CO2 lidars and modeled backscatter from aerosol size distribution data. The technique is demonstrated during a flight of the NASA DC-8 aircraft over the Sierra Nevada Mountain Range, California on 19 September, 1995. Volume fraction of each component and effective complex refractive index of the composite particle were determined assuming an internally mixed composite aerosol model. The volume fractions were also used to re-compute aerosol backscatter, providing good agreement with the lidar-measured data. The robustness of the technique for determining volume fractions was extended with a comparison of calculated 2.1,-micron backscatter from size distribution data with the measured lidar data converted to 2.1,-micron backscatter using an earlier derived algorithm, verifying the algorithm as well as the backscatter calculations.

Algorithms used in the Airborne Lidar Processing System (ALPS)

USGS Publications Warehouse

Nagle, David B.; Wright, C. Wayne

2016-05-23

The Airborne Lidar Processing System (ALPS) analyzes Experimental Advanced Airborne Research Lidar (EAARL) data—digitized laser-return waveforms, position, and attitude data—to derive point clouds of target surfaces. A full-waveform airborne lidar system, the EAARL seamlessly and simultaneously collects mixed environment data, including submerged, sub-aerial bare earth, and vegetation-covered topographies.ALPS uses three waveform target-detection algorithms to determine target positions within a given waveform: centroid analysis, leading edge detection, and bottom detection using water-column backscatter modeling. The centroid analysis algorithm detects opaque hard surfaces. The leading edge algorithm detects topography beneath vegetation and shallow, submerged topography. The bottom detection algorithm uses water-column backscatter modeling for deeper submerged topography in turbid water.The report describes slant range calculations and explains how ALPS uses laser range and orientation measurements to project measurement points into the Universal Transverse Mercator coordinate system. Parameters used for coordinate transformations in ALPS are described, as are Interactive Data Language-based methods for gridding EAARL point cloud data to derive digital elevation models. Noise reduction in point clouds through use of a random consensus filter is explained, and detailed pseudocode, mathematical equations, and Yorick source code accompany the report.

Lidar-revised geologic map of the Wildcat Lake 7.5' quadrangle, Kitsap and Mason Counties, Washington

USGS Publications Warehouse

Tabor, Rowland W.; Haugerud, Ralph A.; Haeussler, Peter J.; Clark, Kenneth P.

2011-01-01

This map is an interpretation of a 6-ft-resolution (2-m-resolution) lidar (light detection and ranging) digital elevation model combined with the geology depicted on the Geologic Map of the Wildcat Lake 7.5' quadrangle, Kitsap and Mason Counties, Washington (Haeussler and Clark, 2000). Haeussler and Clark described, interpreted, and located the geology on the 1:24,000-scale topographic map of the Wildcat Lake 7.5' quadrangle. This map, derived from 1951 aerial photographs, has 20-ft contours, nominal horizontal resolution of approximately 40 ft (12 m), and nominal mean vertical accuracy of approximately 10 ft (3 m). Similar to many geologic maps, much of the geology in the Haeussler and Clark (2000) map-especially the distribution of surficial deposits-was interpreted from landforms portrayed on the topographic map. In 2001, the Puget Sound lidar Consortium obtained a lidar-derived digital elevation model (DEM) for Kitsap Peninsula including all of the Wildcat Lake 7.5' quadrangle. This new DEM has a horizontal resolution of 6 ft (2 m) and a mean vertical accuracy of about 1 ft (0.3 m). The greater resolution and accuracy of the lidar DEM compared to topography constructed from air photo stereo models have much improved the interpretation of geology in this heavily vegetated landscape, especially the distribution and relative age of some surficial deposits. Many contacts of surficial deposits are adapted unmodified or slightly modified from Haugerud (2009).

Estimating Stand Height and Tree Density in Pinus taeda plantations using in-situ data, airborne LiDAR and k-Nearest Neighbor Imputation.

PubMed

Silva, Carlos Alberto; Klauberg, Carine; Hudak, Andrew T; Vierling, Lee A; Liesenberg, Veraldo; Bernett, Luiz G; Scheraiber, Clewerson F; Schoeninger, Emerson R

2018-01-01

Accurate forest inventory is of great economic importance to optimize the entire supply chain management in pulp and paper companies. The aim of this study was to estimate stand dominate and mean heights (HD and HM) and tree density (TD) of Pinus taeda plantations located in South Brazil using in-situ measurements, airborne Light Detection and Ranging (LiDAR) data and the non- k-nearest neighbor (k-NN) imputation. Forest inventory attributes and LiDAR derived metrics were calculated at 53 regular sample plots and we used imputation models to retrieve the forest attributes at plot and landscape-levels. The best LiDAR-derived metrics to predict HD, HM and TD were H99TH, HSD, SKE and HMIN. The Imputation model using the selected metrics was more effective for retrieving height than tree density. The model coefficients of determination (adj.R2) and a root mean squared difference (RMSD) for HD, HM and TD were 0.90, 0.94, 0.38m and 6.99, 5.70, 12.92%, respectively. Our results show that LiDAR and k-NN imputation can be used to predict stand heights with high accuracy in Pinus taeda. However, furthers studies need to be realized to improve the accuracy prediction of TD and to evaluate and compare the cost of acquisition and processing of LiDAR data against the conventional inventory procedures.

Processing and utilization of LiDAR data as a support for a good management of DDBR

NASA Astrophysics Data System (ADS)

Nichersu, I.; Grigoras, I.; Constantinescu, A.; Mierla, M.; Tifanov, C.

2012-04-01

Danube Delta Biosphere Reserve (DDBR) has 5,800 km2 as surface and it is situated in the South-East of Europe, in the East of Romania. The paper is taking into account the data related to the elevation surfaces of the DDBR (Digital Terrain Model DTM and Digital Surface Model DSM). To produce such kind of models of elevation for the entire area of DDBR it was used the most modern method that utilizes the Light Detection And Ranging (LiDAR). The raw LiDAR data (x, y, z) for each point were transformed into grid formats for DTM and DSM. Based on these data multiple GIS analyses can be done for management purposes : hydraulic modeling 1D2D scenarios, flooding regime and protection, biomass volume estimation, GIS biodiversity processing. These analyses are very useful in the management planning process. The hydraulic modeling 1D2D scenarios are used by the administrative authority to predict the sense of the fluvial water flow and also to predict the places where the flooding could occur. Also it can be predicted the surface of the terrain that will be occupied by the water from floods. Flooding regime gives information about the frequency of the floods and also the intensity of these. In the same time it could be predicted the time of water remanence period. The protection face of the flooding regime is in direct relation with the socio-cultural communities and all their annexes those that are in risk of being flooded. This raises the problem of building dykes and other flooding protection systems. The biomass volume contains information derived from the LiDAR cloud points that describes only the vegetation. The volume of biomass is an important item in the management of a Biosphere Reserve. Also the LiDAR cloud points that refer to vegetation could help in identifying the groups of vegetal association. All these information corroborated with other information build good premises for a good management. Keywords: Danube Delta Biosphere Reserve, LiDAR data, DTM, DSM, flooding, management

Estimation of desert-dust-related ice nuclei profiles from polarization lidar

NASA Astrophysics Data System (ADS)

Mamouri, Rodanthi-Elisavet; Nisantzi, Argyro; Hadjimitsis, Diofantos; Ansmann, Albert

2015-04-01

This paper presents a methodology based on the use of active remote sensing techniques for the estimation of ice nuclei concentrations (INC) for desert dust plumes. Although this method can be applied to other aerosol components, in this study we focus on desert dust. The method makes use of the polarization lidar technique for the separation of dust and non-dust contributions to the particle backscatter and extinction coefficients. The profile of the dust extinction coefficient is converted to APC280 (dust particles with radius larger than 280 nm) and, in a second step, APC280 is converted to INC by means of an APC-INC relationship from the literature. The observed close relationship between dust extinction at 500 nm and APC280 is the key to a successful INC retrieval. The correlation between dust extinction coefficient and APC280 is studied by means of AERONET sun/sky photometer at Morocco, Cape Verde, Barbados, and Cyprus, during situations dominated by desert dust outbreaks. In the present study, polarization lidar observations of the EARLINET (European Aerosol Research Lidar Network) lidar at the Cyprus University of Technology (CUT), Limassol (34.7o N, 33o E), Cyprus were used together with spaceborne lidar observations during CALIPSO satellite overpasses to demonstrate the potential of the new INC retrieval method. A good agreement between the CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization) and our CUT lidar observations regarding the retrieval of dust extinction coefficient, APC280, and INC profiles were found and corroborate the potential of CALIOP to provide 3-D global desert-dust-related INC data sets. In the next step, efforts should be undertaken towards the establishment of a global, height-resolved INC climatology for desert dust plumes. Realistic global INC distributions are required for an improved estimation of aerosol effects on cloud formation and the better quantification of the indirect aerosol effect on climate. Acknowledgements. The authors thank the CUT Remote Sensing Laboratory for their support. The research leading to these results has also received scientific support from the European Union Seventh Framework Programme (FP7/2011-2015) under grant agreement no. 262254 (ACTRIS project). We acknowledge funding from the EU FP7-ENV-2013 programme "impact of Biogenic vs. Anthropogenic emissions on Clouds and Climate: towards a Holistic UnderStanding" (BACCHUS), project no. 603445. We are grateful to AERONET for high-quality sun/sky photometer measurements in Cyprus, Morocco, Cape Verde, and Barbados. We thank the NASA Langley Research Center and the CALIPSO science team for the constant effort and improvement of then CALIPSO data.

Tracking atmospheric boundary layer in tehran using combined lidar remote sensing and ground base measurements

NASA Astrophysics Data System (ADS)

Panahifar, Hossein; Khalesifard, Hamid

2018-04-01

The vertical structure of the atmospheric boundary layer (ABL) has been studied by use of a depolarized LiDAR over Tehran, Iran. The boundary layer height (BLH) remains under 1km, and its retrieval from LiDAR have been compared with sonding measurements and meteorological model outputs. It is also shown that the wind speed and direction as well as topography lead to the persistence of air pollution in Tehran. The situation aggravate in fall and winter due to temperature inversion.

Automated integration of lidar into the LANDFIRE product suite

Treesearch

Birgit Peterson; Kurtis J. Nelson; Carl Seielstad; Jason Stoker; W. Matt Jolly; Russell Parsons

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...

Application of Lidar Data to the Performance Evaluations of CMAQ Model

EPA Science Inventory

The Tropospheric Ozone (O3) Lidar Network (TOLNet) provides time/height O3 measurements from near the surface to the top of the troposphere to describe in high-fidelity spatial-temporal distributions, which is uniquely useful to evaluate the temporal evolution of O3 profiles in a...

Development of a quantitative model for the mechanism of raveling failure in highway rock slopes using LIDAR.

DOT National Transportation Integrated Search

2013-03-01

Rock falls on highways while dangerous are unpredictable. Most rock falls are of the raveling type and not conducive to stability : calculations, and even the failure mechanisms are not well understood. LIDAR (LIght Detection And Ranging) has been sh...

Using LiDAR datasets to improve HSPF water quality modeling in the Red River of the North Basin

NASA Astrophysics Data System (ADS)

Burke, M. P.; Foreman, C. S.

2013-12-01

The Red River of the North Basin (RRB), located in the lakebed of ancient glacial Lake Agassiz, comprises one of the flattest landscapes in North America. The topography of the basin, coupled with the Red River's direction of flow from south to north results in a system that is highly susceptible to flooding. The magnitude and frequency of flood events in the RRB has prompted several multijurisdictional projects and mitigation efforts. In response to the devastating 1997 flood, an International Joint Commission sponsored task force established the need for accurate elevation data to help improve flood forecasting and better understand risks. This led to the International Water Institute's Red River Basin Mapping Initiative, and the acquisition LiDAR Data for the entire US portion of the RRB. The resulting 1 meter bare earth digital elevation models have been used to improve hydraulic and hydrologic modeling within the RRB, with focus on flood prediction and mitigation. More recently, these LiDAR datasets have been incorporated into Hydrological Simulation Program-FORTRAN (HSPF) model applications to improve water quality predictions in the MN portion of the RRB. RESPEC is currently building HSPF model applications for five of MN's 8-digit HUC watersheds draining to the Red River, including: the Red Lake River, Clearwater River, Sandhill River, Two Rivers, and Tamarac River watersheds. This work is being conducted for the Minnesota Pollution Control Agency (MPCA) as part of MN's statewide watershed approach to restoring and protecting water. The HSPF model applications simulate hydrology (discharge, stage), as well as a number of water quality constituents (sediment, temperature, organic and inorganic nitrogen, total ammonia, organic and inorganic phosphorus, dissolved oxygen and biochemical oxygen demand, and algae) continuously for the period 1995-2009 and are formulated to provide predictions at points of interest within the watersheds, such as observation gages, management boundaries, compliance points, and impaired water body endpoints. Incorporation of the LiDAR datasets has been critical to representing the topographic characteristics that impact hydrologic and water quality processes in the extremely flat, heavily drained sub-basins of the RRB. Beyond providing more detailed elevation and slope measurements, the high resolution LiDAR datasets have helped to identify drainage alterations due to agricultural practices, as well as improve representation of channel geometry. Additionally, when available, LiDAR based hydraulic models completed as part of the RRB flood mitigation efforts, are incorporated to further improve flow routing. The MPCA will ultimately use these HSPF models to aid in Total Maximum Daily Load (TMDL) development, permit development/compliance, analysis of Best Management Practice (BMP) implementation scenarios, and other watershed planning and management objectives. LiDAR datasets are an essential component of the water quality models build for the watersheds within the RRB and would greatly benefit water quality modeling efforts in similarly characterized areas.

Creating Digital Elevation Model Using a Mobile Device

NASA Astrophysics Data System (ADS)

Durmaz, A. İ.

2017-11-01

DEM (Digital Elevation Models) is the best way to interpret topography on the ground. In recent years, lidar technology allows to create more accurate elevation models. However, the problem is this technology is not common all over the world. Also if Lidar data are not provided by government agencies freely, people have to pay lots of money to reach these point clouds. In this article, we will discuss how we can create digital elevation model from less accurate mobile devices' GPS data. Moreover, we will evaluate these data on the same mobile device which we collected data to reduce cost of this modeling.

Erosion and Channel Incision Analysis with High-Resolution Lidar

NASA Astrophysics Data System (ADS)

Potapenko, J.; Bookhagen, B.

2013-12-01

High-resolution LiDAR (LIght Detection And Ranging) provides a new generation of sub-meter topographic data that is still to be fully exploited by the Earth science communities. We make use of multi-temporal airborne and terrestrial lidar scans in the south-central California and Santa Barbara area. Specifically, we have investigated the Mission Canyon and Channel Islands regions from 2009-2011 to study changes in erosion and channel incision on the landscape. In addition to gridding the lidar data into digital elevation models (DEMs), we also make use of raw lidar point clouds and triangulated irregular networks (TINs) for detailed analysis of heterogeneously spaced topographic data. Using recent advancements in lidar point cloud processing from information technology disciplines, we have employed novel lidar point cloud processing and feature detection algorithms to automate the detection of deeply incised channels and gullies, vegetation, and other derived metrics (e.g. estimates of eroded volume). Our analysis compares topographically-derived erosion volumes to field-derived cosmogenic radionuclide age and in-situ sediment-flux measurements. First results indicate that gully erosion accounts for up to 60% of the sediment volume removed from the Mission Canyon region. Furthermore, we observe that gully erosion and upstream arroyo propagation accelerated after fires, especially in regions where vegetation was heavily burned. The use of high-resolution lidar point cloud data for topographic analysis is still a novel method that needs more precedent and we hope to provide a cogent example of this approach with our research.

Influence of vegetation structure on lidar-derived canopy height and fractional cover in forested riparian buffers during leaf-off and leaf-on conditions.

PubMed

Wasser, Leah; Day, Rick; Chasmer, Laura; Taylor, Alan

2013-01-01

Estimates of canopy height (H) and fractional canopy cover (FC) derived from lidar data collected during leaf-on and leaf-off conditions are compared with field measurements from 80 forested riparian buffer plots. The purpose is to determine if existing lidar data flown in leaf-off conditions for applications such as terrain mapping can effectively estimate forested riparian buffer H and FC within a range of riparian vegetation types. Results illustrate that: 1) leaf-off and leaf-on lidar percentile estimates are similar to measured heights in all plots except those dominated by deciduous compound-leaved trees where lidar underestimates H during leaf off periods; 2) canopy height models (CHMs) underestimate H by a larger margin compared to percentile methods and are influenced by vegetation type (conifer needle, deciduous simple leaf or deciduous compound leaf) and canopy height variability, 3) lidar estimates of FC are within 10% of plot measurements during leaf-on periods, but are underestimated during leaf-off periods except in mixed and conifer plots; and 4) depth of laser pulse penetration lower in the canopy is more variable compared to top of the canopy penetration which may influence within canopy vegetation structure estimates. This study demonstrates that leaf-off lidar data can be used to estimate forested riparian buffer canopy height within diverse vegetation conditions and fractional canopy cover within mixed and conifer forests when leaf-on lidar data are not available.

A Raman Lidar as Operational Tool for Long-Term Water Vapor, Temperature and Aerosol Profiling in the Swiss Meteorological Office

NASA Astrophysics Data System (ADS)

Simeonov, Dr; Dinoev, Dr; Serikov, Dr; Calpini, Dr; Bobrovnikov, Dr; Arshinov, Dr; Ristori, Dr; van den Bergh, Dr; Parlange, Dr

2010-09-01

To satisfy the rising demands on the quality and frequency of atmospheric water vapor, temperature and aerosol measurements used for numerical weather prediction models, climate change observations and special events (volcanoes, dust and smoke transport) monitoring, MeteoSwiss decided to implement a lidar at his main aerological station in Payerne. The instrument is narrow field of view, narrowband UV Raman lidar designed for continuous day and night operational profiling of tropospheric water vapor, aerosol and temperature The lidar was developed and built by the Swiss Federal Institute of Technology- Lausanne (EPFL) within a joint project with MeteoSwiss. To satisfy the requirements for operational exploitation in a meteorological network the lidar had to satisfy a number of criteria, the most important of which are: accuracy and precision, traceability of the measurement, long-term data consistency, long-term system stability, automated operation, requiring minimal maintenance by a technician, and eye safety. All this requirements were taken into account during the design phase of the lidar. After a ten months test phase of the lidar at Payerne it has been in regular operation since August 2008. Selected data illustrating interesting atmospheric phenomena captured by the lidar as well as long-term intercomparison with collocated microwave radiometer, GPS, radiosonding and an airborne DIAL will be presented and discussed. The talk will address also the technical availability, alignment and calibration stabilities of the instrument.

First Simultaneous and Common-Volume Lidar Observations of Na and Fe Metals, Temperatures, and Vertical Winds in Antarctica

NASA Astrophysics Data System (ADS)

Chu, X.

2017-12-01

A new STAR Na Doppler lidar will be installed to Arrival Heights near McMurdo Station, Antarctica in October 2017. This new lidar will be operated next to an existing Fe Boltzmann lidar to make simultaneous and common-volume measurements of metal Na and Fe layers, neutral temperatures, and vertical winds in the mesosphere and thermosphere, up to nearly 200 km. These measurements will be used to study a variety of science topics, e.g., the meteoric metal layers, wave dynamics, polar mesospheric clouds, constituent and heat fluxes, and cosmic dust. The discoveries of thermospheric neutral Fe layers and persistent gravity waves by the Fe Boltzmann lidar observations has opened a new door to explore the space-atmosphere interactions with ground-based instruments, especially in the least understood but crucially important altitude range of 100-200 km. These neutral metal layers provide excellent tracers for modern resonance lidars to measure the neutral wind and temperature directly. Even more exciting, the neutral metal layers in the thermosphere provide a natural laboratory to test our fundamental understandings of the atmosphere-ionosphere-magnetosphere coupling and processes. This paper will report the first summer results from the simultaneous Na and Fe lidar observations from Antarctica, and highlight important discoveries made by the Fe lidar during its first seven years of campaign at McMurdo. A thermosphere-ionosphere Fe/Fe+ (TIFe) model will be introduced to explain the TIFe layers in Antarctica.

Influence of Vegetation Structure on Lidar-derived Canopy Height and Fractional Cover in Forested Riparian Buffers During Leaf-Off and Leaf-On Conditions

PubMed Central

Wasser, Leah; Day, Rick; Chasmer, Laura; Taylor, Alan

2013-01-01

Estimates of canopy height (H) and fractional canopy cover (FC) derived from lidar data collected during leaf-on and leaf-off conditions are compared with field measurements from 80 forested riparian buffer plots. The purpose is to determine if existing lidar data flown in leaf-off conditions for applications such as terrain mapping can effectively estimate forested riparian buffer H and FC within a range of riparian vegetation types. Results illustrate that: 1) leaf-off and leaf-on lidar percentile estimates are similar to measured heights in all plots except those dominated by deciduous compound-leaved trees where lidar underestimates H during leaf off periods; 2) canopy height models (CHMs) underestimate H by a larger margin compared to percentile methods and are influenced by vegetation type (conifer needle, deciduous simple leaf or deciduous compound leaf) and canopy height variability, 3) lidar estimates of FC are within 10% of plot measurements during leaf-on periods, but are underestimated during leaf-off periods except in mixed and conifer plots; and 4) depth of laser pulse penetration lower in the canopy is more variable compared to top of the canopy penetration which may influence within canopy vegetation structure estimates. This study demonstrates that leaf-off lidar data can be used to estimate forested riparian buffer canopy height within diverse vegetation conditions and fractional canopy cover within mixed and conifer forests when leaf-on lidar data are not available. PMID:23382966

Landslide stability analysis on basis of LIDAR data extraction

NASA Astrophysics Data System (ADS)

Hu, Hui; Fernandez-Steeger, Tomas M.; Dong, Mei; Azzam, Rafig

2010-05-01

Currently, existing contradictory between remediation and acquisition from natural resource induces a series of divergences. With regard to open pit mining, legal regulation requires human to fill back the open pit area with water or recreate new landscape by other materials; on the other hand, human can not help excavating the mining area due to the shortage of power resource. However, to engineering geologists, one coincident problem which takes place not only in filling but also in mining operation should be paid more attention to, i.e. the slope stability analysis within these areas. There are a number of construction activities during remediation or mining process which can directly or indirectly cause slope failure. Lives can be endangered since local failure either while or after remediation; for mining process, slope failure in a bench, which carries a main haul road or is adjacent to human activity area, would be significant catastrophe to the whole mining program. The stability of an individual bench or slope is controlled by several factors, which are geological condition, morphology, climate, excavation techniques and transportation approach. The task which takes the longest time is to collect the morphological data. Consequently, it is one of the most dangerous tasks due to the time consuming in mining field. LIDAR scanning for morphological data collecting can help to skip this obstacle since advantages of LIDAR techniques as follows: • Dynamic range available on the market: from 3 m to beyond 1 km, • Ruggedly designed for demanding field applications, • Compact, easily hand-carried and deployed by a single operator. In 2009, scanning campaigns for 2 open pit quarry have been carried out. The aim for these LIDAR detections is to construct a detailed 3D quarry model and analyze the bench stability to support the filling planning. The 3D quarry surface was built up by using PolyWorks 10.1 on basis of LIDAR data. LIDAR data refining takes an important role during surface construction for further more precise analysis purpose. 3D geological model can be built based on the connection between surface model and geological data like borehole data in GOCAD. Regarding the bench stability analysis, LEM (Limit Equilibrium Method) analysis using Janbu and FEM (Finite Element Method) have been adopted during this analyzing task. A program was developed to convert GOCAD 2D section data directly into the FEM software. The meshed model is then used for stability analysis. In one quarry, 3 cross sections have been extracted on basis of LIDAR original data (original 3 cross sections). To evaluate the advantages of LIDAR data for slope analysis, the results of safety factor (SF) were compared to simplified slope models as they are used normally. The comparison showed that variations of the SF reach up to 9%. Additionally, conservative evaluation demonstrated by SF results based on simplified model is not adaptive for decision making of filling.

Comparing Individual Tree Segmentation Based on High Resolution Multispectral Image and Lidar Data

NASA Astrophysics Data System (ADS)

Xiao, P.; Kelly, M.; Guo, Q.

2014-12-01

This study compares the use of high-resolution multispectral WorldView images and high density Lidar data for individual tree segmentation. The application focuses on coniferous and deciduous forests in the Sierra Nevada Mountains. The tree objects are obtained in two ways: a hybrid region-merging segmentation method with multispectral images, and a top-down and bottom-up region-growing method with Lidar data. The hybrid region-merging method is used to segment individual tree from multispectral images. It integrates the advantages of global-oriented and local-oriented region-merging strategies into a unified framework. The globally most-similar pair of regions is used to determine the starting point of a growing region. The merging iterations are constrained within the local vicinity, thus the segmentation is accelerated and can reflect the local context. The top-down region-growing method is adopted in coniferous forest to delineate individual tree from Lidar data. It exploits the spacing between the tops of trees to identify and group points into a single tree based on simple rules of proximity and likely tree shape. The bottom-up region-growing method based on the intensity and 3D structure of Lidar data is applied in deciduous forest. It segments tree trunks based on the intensity and topological relationships of the points, and then allocate other points to exact tree crowns according to distance. The accuracies for each method are evaluated with field survey data in several test sites, covering dense and sparse canopy. Three types of segmentation results are produced: true positive represents a correctly segmented individual tree, false negative represents a tree that is not detected and assigned to a nearby tree, and false positive represents that a point or pixel cluster is segmented as a tree that does not in fact exist. They respectively represent correct-, under-, and over-segmentation. Three types of index are compared for segmenting individual tree from multispectral image and Lidar data: recall, precision and F-score. This work explores the tradeoff between the expensive Lidar data and inexpensive multispectral image. The conclusion will guide the optimal data selection in different density canopy areas for individual tree segmentation, and contribute to the field of forest remote sensing.

Application of Lidar Data to the Performance Evaluations of ...

EPA Pesticide Factsheets

The Tropospheric Ozone (O3) Lidar Network (TOLNet) provides time/height O3 measurements from near the surface to the top of the troposphere to describe in high-fidelity spatial-temporal distributions, which is uniquely useful to evaluate the temporal evolution of O3 profiles in air quality models. This presentation describes the application of the Lidar data to the performance evaluation of CMAQ simulated O3 vertical profiles during the summer, 2014. Two-way coupled WRF-CMAQ simulations with 12km and 4km domains centered over Boulder, Colorado were performed during this time period. The analysis on the time series of observed and modeled O3 mixing ratios at different vertical layers indicates that the model frequently underestimated the observed values, and the underestimation was amplified in the middle model layers (~1km above the ground). When the lightning strikes detected by the National Lightning Detection Network (NLDN) were analyzed along with the observed O3 time series, it was found that the daily maximum O3 mixing ratios correlated well with the lightning strikes in the vicinity of the Lidar station. The analysis on temporal vertical profiles of both observed and modeled O3 mixing ratios on episodic days suggests that the model resolutions (12km and 4km) do not make any significant difference for this analysis (at this specific location and simulation period), but high O3 levels in the middle layers were linked to lightning activity that occurred in t

Investigating the relationship between tree heights derived from SIBBORK forest model and remote sensing measurements

NASA Astrophysics Data System (ADS)

Osmanoglu, B.; Feliciano, E. A.; Armstrong, A. H.; Sun, G.; Montesano, P.; Ranson, K.

2017-12-01

Tree heights are one of the most commonly used remote sensing parameters to measure biomass of a forest. In this project, we investigate the relationship between remotely sensed tree heights (e.g. G-LiHT lidar and commercially available high resolution satellite imagery, HRSI) and the SIBBORK modeled tree heights. G-LiHT is a portable, airborne imaging system that simultaneously maps the composition, structure, and function of terrestrial ecosystems using lidar, imaging spectroscopy and thermal mapping. Ground elevation and canopy height models were generated using the lidar data acquired in 2012. A digital surface model was also generated using the HRSI technique from the commercially available WorldView data in 2016. The HRSI derived height and biomass products are available at the plot (10x10m) level. For this study, we parameterized the SIBBORK individual-based gap model for Howland forest, Maine. The parameterization was calibrated using field data for the study site and results show that the simulated forest reproduces the structural complexity of Howland old growth forest, based on comparisons of key variables including, aboveground biomass, forest height and basal area. Furthermore carbon cycle and ecosystem observational capabilities will be enhanced over the next 6 years via the launch of two LiDAR (NASA's GEDI and ICESAT 2) and two SAR (NASA's ISRO NiSAR and ESA's Biomass) systems. Our aim is to present the comparison of canopy height models obtained with SIBBORK forest model and remote sensing techniques, highlighting the synergy between individual-based forest modeling and high-resolution remote sensing.

Remote measurement of surface roughness, surface reflectance, and body reflectance with LiDAR.

PubMed

Li, Xiaolu; Liang, Yu

2015-10-20

Light detection and ranging (LiDAR) intensity data are attracting increasing attention because of the great potential for use of such data in a variety of remote sensing applications. To fully investigate the data potential for target classification and identification, we carried out a series of experiments with typical urban building materials and employed our reconstructed built-in-lab LiDAR system. Received intensity data were analyzed on the basis of the derived bidirectional reflectance distribution function (BRDF) model and the established integration method. With an improved fitting algorithm, parameters involved in the BRDF model can be obtained to depict the surface characteristics. One of these parameters related to surface roughness was converted to a most used roughness parameter, the arithmetical mean deviation of the roughness profile (R_a), which can be used to validate the feasibility of the BRDF model in surface characterizations and performance evaluations.

Investigating the size, shape and surface roughness dependence of polarization lidars with light-scattering computations on real mineral dust particles: Application to dust particles' external mixtures and dust mass concentration retrievals

NASA Astrophysics Data System (ADS)

Mehri, Tahar; Kemppinen, Osku; David, Grégory; Lindqvist, Hannakaisa; Tyynelä, Jani; Nousiainen, Timo; Rairoux, Patrick; Miffre, Alain

2018-05-01

Our understanding of the contribution of mineral dust to the Earth's radiative budget is limited by the complexity of these particles, which present a wide range of sizes, are highly-irregularly shaped, and are present in the atmosphere in the form of particle mixtures. To address the spatial distribution of mineral dust and atmospheric dust mass concentrations, polarization lidars are nowadays frequently used, with partitioning algorithms allowing to discern the contribution of mineral dust in two or three-component particle external mixtures. In this paper, we investigate the dependence of the retrieved dust backscattering (βd) vertical profiles with the dust particle size and shape. For that, new light-scattering numerical simulations are performed on real atmospheric mineral dust particles, having determined mineralogy (CAL, DOL, AGG, SIL), derived from stereogrammetry (stereo-particles), with potential surface roughness, which are compared to the widely-used spheroidal mathematical shape model. For each dust shape model (smooth stereo-particles, rough stereo-particles, spheroids), the dust depolarization, backscattering Ångström exponent, lidar ratio are computed for two size distributions representative of mineral dust after long-range transport. As an output, two Saharan dust outbreaks involving mineral dust in two, then three-component particle mixtures are studied with Lyon (France) UV-VIS polarization lidar. If the dust size matters most, under certain circumstances, βd can vary by approximately 67% when real dust stereo-particles are used instead of spheroids, corresponding to variations in the dust backscattering coefficient as large as 2 Mm- 1·sr- 1. Moreover, the influence of surface roughness in polarization lidar retrievals is for the first time discussed. Finally, dust mass-extinction conversion factors (ηd) are evaluated for each assigned shape model and dust mass concentrations are retrieved from polarization lidar measurements. From spheroids to stereo-particles, ηd increases by about 30%. We believe these results may be useful for our understanding of the spatial distribution of mineral dust contained in an aerosol external mixture and to better quantify dust mass concentrations from polarization lidar experiments.

Estimating Volume, Biomass, and Carbon in Hedmark County, Norway Using a Profiling LiDAR

NASA Technical Reports Server (NTRS)

Nelson, Ross; Naesset, Erik; Gobakken, T.; Gregoire, T.; Stahl, G.

2009-01-01

A profiling airborne LiDAR is used to estimate the forest resources of Hedmark County, Norway, a 27390 square kilometer area in southeastern Norway on the Swedish border. One hundred five profiling flight lines totaling 9166 km were flown over the entire county; east-west. The lines, spaced 3 km apart north-south, duplicate the systematic pattern of the Norwegian Forest Inventory (NFI) ground plot arrangement, enabling the profiler to transit 1290 circular, 250 square meter fixed-area NFI ground plots while collecting the systematic LiDAR sample. Seven hundred sixty-three plots of the 1290 plots were overflown within 17.8 m of plot center. Laser measurements of canopy height and crown density are extracted along fixed-length, 17.8 m segments closest to the center of the ground plot and related to basal area, timber volume and above- and belowground dry biomass. Linear, nonstratified equations that estimate ground-measured total aboveground dry biomass report an R(sup 2) = 0.63, with an regression RMSE = 35.2 t/ha. Nonstratified model results for the other biomass components, volume, and basal area are similar, with R(sup 2) values for all models ranging from 0.58 (belowground biomass, RMSE = 8.6 t/ha) to 0.63. Consistently, the most useful single profiling LiDAR variable is quadratic mean canopy height, h (sup bar)(sub qa). Two-variable models typically include h (sup bar)(sub qa) or mean canopy height, h(sup bar)(sub a), with a canopy density or a canopy height standard deviation measure. Stratification by productivity class did not improve the nonstratified models, nor did stratification by pine/spruce/hardwood. County-wide profiling LiDAR estimates are reported, by land cover type, and compared to NFI estimates.

Typical Applications of Airborne LIDAR Technolagy in Geological Investigation

NASA Astrophysics Data System (ADS)

Zheng, X.; Xiao, C.

2018-05-01

The technology of airborne light detection and ranging (LiDAR), also referred to as Airborne Laser Scanning, is widely used for high-resolution topographic data acquisition (even under forest cover) with sub-meter planimetric and vertical accuracy. This contribution constructs the real digital terrain model to provide the direct observation data for the landscape analysis in geological domains. Based on the advantage of LiDAR, the authors mainly deal with the applications of LiDAR data to such fields as surface land collapse, landslide and fault structure extraction. The review conclusion shows that airborne LiDAR technology is becoming an indispensable tool for above mentioned issues, especially in the local and large scale investigations of micro-topography. The technology not only can identify the surface collapse, landslide boundary and subtle faulted landform, but also be able to extract the filling parameters of collapsed surface, the geomorphic parameters of landslide stability evaluation and cracks. This technology has extensive prospect of applications in geological investigation.

Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters.

PubMed

Luo, Shezhou; Chen, Jing M; Wang, Cheng; Xi, Xiaohuan; Zeng, Hongcheng; Peng, Dailiang; Li, Dong

2016-05-30

Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R² = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m²), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.

Retrieval of Urban Boundary Layer Structures from Doppler Lidar Data. Part I: Accuracy Assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xia, Quanxin; Lin, Ching Long; Calhoun, Ron

2008-01-01

Two coherent Doppler lidars from the US Army Research Laboratory (ARL) and Arizona State University (ASU) were deployed in the Joint Urban 2003 atmospheric dispersion field experiment (JU2003) held in Oklahoma City. The dual lidar data are used to evaluate the accuracy of the four-dimensional variational data assimilation (4DVAR) method and identify the coherent flow structures in the urban boundary layer. The objectives of the study are three-fold. The first objective is to examine the effect of eddy viscosity models on the quality of retrieved velocity data. The second objective is to determine the fidelity of single-lidar 4DVAR and evaluatemore » the difference between single- and dual-lidar retrievals. The third objective is to correlate the retrieved flow structures with the ground building data. It is found that the approach of treating eddy viscosity as part of control variables yields better results than the approach of prescribing viscosity. The ARL single-lidar 4DVAR is able to retrieve radial velocity fields with an accuracy of 98% in the along-beam direction and 80-90% in the cross-beam direction. For the dual-lidar 4DVAR, the accuracy of retrieved radial velocity in the ARL cross-beam direction improves to 90-94%. By using the dual-lidar retrieved data as a reference, the single-lidar 4DVAR is able to recover fluctuating velocity fields with 70-80% accuracy in the along-beam direction and 60-70% accuracy in the cross-beam direction. Large-scale convective roll structures are found in the vicinity of downtown airpark and parks. Vortical structures are identified near the business district. Strong updrafts and downdrafts are also found above a cluster of restaurants.« less

A Depolarisation Lidar Based Method for the Determination of Liquid-Cloud Microphysical Properties.

NASA Astrophysics Data System (ADS)

Donovan, D. P.; Klein Baltink, H.; Henzing, J. S.; De Roode, S. R.; Siebesma, P.

2014-12-01

The fact that polarisation lidars measure a multiple-scattering induced depolarisation signal in liquid clouds is well-known. The depolarisation signal depends on the lidar characteristics (e.g. wavelength and field-of-view) as well as the cloud properties (e.g. liquid water content (LWC) and cloud droplet number concentration (CDNC)). Previous efforts seeking to use depolarisation information in a quantitative manner to retrieve cloud properties have been undertaken with, arguably, limited practical success. In this work we present a retrieval procedure applicable to clouds with (quasi-)linear LWC profiles and (quasi-)constant CDNC in the cloud base region. Limiting the applicability of the procedure in this manner allows us to reduce the cloud variables to two parameters (namely liquid water content lapse-rate and the CDNC). This simplification, in turn, allows us to employ a robust optimal-estimation inversion using pre-computed look-up-tables produced using lidar Monte-Carlo multiple-scattering simulations. Here, we describe the theory behind the inversion procedure and apply it to simulated observations based on large-eddy simulation model output. The inversion procedure is then applied to actual depolarisation lidar data covering to a range of cases taken from the Cabauw measurement site in the central Netherlands. The lidar results were then used to predict the corresponding cloud-base region radar reflectivities. In non-drizzling condition, it was found that the lidar inversion results can be used to predict the observed radar reflectivities with an accuracy within the radar calibration uncertainty (2-3 dBZ). This result strongly supports the accuracy of the lidar inversion results. Results of a comparison between ground-based aerosol number concentration and lidar-derived CDNC are also presented. The results are seen to be consistent with previous studies based on aircraft-based in situ measurements.

Application of backpack Lidar to geological cross-section measurement

NASA Astrophysics Data System (ADS)

Lin, Jingyu; Wang, Ran; Xiao, Zhouxuan; Li, Lu; Yao, Weihua; Han, Wei; Zhao, Baolin

2017-11-01

As the traditional geological cross section measurement, the artificial traverse method was recently substituted by using point coordinates data. However, it is still the crux of the matter that how to acquire the high-precision point coordinates data quickly and economically. Thereby, the backpack Lidar is presented on the premise of the principle of using point coordinates in this issue. Undoubtedly, Lidar technique, one of booming and international active remote sensing techniques, is a powerful tool in obtaining precise topographic information, high-precision 3-D coordinates and building a real 3-D model. With field practice and date processing indoors, it is essentially accomplished that geological sections maps could be generated simply, accurately and automatically in the support of relevant software such as ArcGIS and LiDAR360.

Lidar-Based Rock-Fall Hazard Characterization of Cliffs

USGS Publications Warehouse

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.

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

USGS Publications Warehouse

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.

Fusing enhanced radar precipitation, in-situ hydrometeorological measurements and airborne LIDAR snowpack estimates in a hyper-resolution hydrologic model to improve seasonal water supply forecasts

NASA Astrophysics Data System (ADS)

Gochis, D. J.; Busto, J.; Howard, K.; Mickey, J.; Deems, J. S.; Painter, T. H.; Richardson, M.; Dugger, A. L.; Karsten, L. R.; Tang, L.

2015-12-01

Scarcity of spatially- and temporally-continuous observations of precipitation and snowpack conditions in remote mountain watersheds results in fundamental limitations in water supply forecasting. These limitationsin observational capabilities can result in strong biases in total snowmelt-driven runoff amount, the elevational distribution of runoff, river basin tributary contributions to total basin runoff and, equally important for water management, the timing of runoff. The Upper Rio Grande River basin in Colorado and New Mexico is one basin where observational deficiencies are hypothesized to have significant adverse impacts on estimates of snowpack melt-out rates and on water supply forecasts. We present findings from a coordinated observational-modeling study within Upper Rio Grande River basin whose aim was to quanitfy the impact enhanced precipitation, meteorological and snowpack measurements on the simulation and prediction of snowmelt driven streamflow. The Rio Grande SNOwpack and streamFLOW (RIO-SNO-FLOW) Prediction Project conducted enhanced observing activities during the 2014-2015 water year. Measurements from a gap-filling, polarimetric radar (NOXP) and in-situ meteorological and snowpack measurement stations were assimilated into the WRF-Hydro modeling framework to provide continuous analyses of snowpack and streamflow conditions. Airborne lidar estimates of snowpack conditions from the NASA Airborne Snow Observatory during mid-April and mid-May were used as additional independent validations against the various model simulations and forecasts of snowpack conditions during the melt-out season. Uncalibrated WRF-Hydro model performance from simulations and forecasts driven by enhanced observational analyses were compared against results driven by currently operational data inputs. Precipitation estimates from the NOXP research radar validate significantly better against independent in situ observations of precipitation and snow-pack increases. Correcting the operational NLDAS2 forcing data with the experimental observations led to significant improvements in the seasonal accumulation and ablation of mountain snowpack and ultimately led to marked improvement in model simulated streamflow as compared with streamflow observations.

Reading Ombrone river delta evolution through beach ridges morphology

NASA Astrophysics Data System (ADS)

Mammi, Irene; Piccardi, Marco; Pranzini, Enzo; Rossi, Lorenzo

2017-04-01

The present study focuses on the evolution of the Ombrone River delta (Southern Tuscany, Italy) in the last five centuries, when fluvial sediment input was huge also as a consequence of the deforestation performed on the watershed. The aim of this study is to find a correlation between river input and beach ridges morphology and to explain the different distribution of wetlands and sand deposits on the two sides of the delta. Visible, NIR and TIR satellite images were processed to retrieve soil wetness associated to sand ridges and interdune silty deposits. High resolution LiDAR data were analysed using vegetation filter and GIS enhancement algorithms in order to highlight small morphological variations, especially in areas closer to the river where agriculture has almost deleted these morphologies. A topographic survey and a very high resolution 3D model obtained from a set of images acquired by an Unmanned Aerial Vehicle (UAV) were carried out in selected sites, both to calibrate satellite LiDAR 3D data, and to map low relief areas. Historical maps, aerial photography and written documents were analysed for dating ancient shorelines associated to specific beach ridges. Thus allowing the reconstruction of erosive and accretive phases of the delta. Seventy beach ridges were identified on the two wings of the delta. On the longer down-drift side (Northern wing) beach ridges are more spaced at the apex and gradually converge to the extremity, where the Bruna River runs and delimits the sub aerial depositional area of the Ombrone River. On the shorter up-drift lobe (Southern wing), beach ridges are closer, but run almost parallel each other. In this case, a rocky headland called Collelungo promontory closes and cuts the beach ridges sequence but shallow water depth allows sediment by pass. One kilometre to the south a more pronounced promontory encloses a small pocket beach (Cala di Forno) and identifies the limit of the subaerial depositionary area. Beach ridges heights were analysed through LiDAR data and some of them were found higher than average. Conceptual models in literature allowed us to explain higher beach ridges as periods of stability or a very initial erosion stage interesting the beach. The high resolution DTM produced from LiDAR and UAV data permitted a better reconstruction of the last five centuries of delta evolution and to characterize the difference of beach ridges morphology of the up-drift and the down-drift sides of the delta. Within this framework the presence of interdune swales in the down-drift side has been explained.

Volumetric LiDAR scanning of a wind turbine wake and comparison with a 3D analytical wake model

NASA Astrophysics Data System (ADS)

Carbajo Fuertes, Fernando; Porté-Agel, Fernando

2016-04-01

A correct estimation of the future power production is of capital importance whenever the feasibility of a future wind farm is being studied. This power estimation relies mostly on three aspects: (1) a reliable measurement of the wind resource in the area, (2) a well-established power curve of the future wind turbines and, (3) an accurate characterization of the wake effects; the latter being arguably the most challenging one due to the complexity of the phenomenon and the lack of extensive full-scale data sets that could be used to validate analytical or numerical models. The current project addresses the problem of obtaining a volumetric description of a full-scale wake of a 2MW wind turbine in terms of velocity deficit and turbulence intensity using three scanning wind LiDARs and two sonic anemometers. The characterization of the upstream flow conditions is done by one scanning LiDAR and two sonic anemometers, which have been used to calculate incoming vertical profiles of horizontal wind speed, wind direction and an approximation to turbulence intensity, as well as the thermal stability of the atmospheric boundary layer. The characterization of the wake is done by two scanning LiDARs working simultaneously and pointing downstream from the base of the wind turbine. The direct LiDAR measurements in terms of radial wind speed can be corrected using the upstream conditions in order to provide good estimations of the horizontal wind speed at any point downstream of the wind turbine. All this data combined allow for the volumetric reconstruction of the wake in terms of velocity deficit as well as turbulence intensity. Finally, the predictions of a 3D analytical model [1] are compared to the 3D LiDAR measurements of the wind turbine. The model is derived by applying the laws of conservation of mass and momentum and assuming a Gaussian distribution for the velocity deficit in the wake. This model has already been validated using high resolution wind-tunnel measurements and large-eddy simulation (LES) data of miniature wind turbine wakes, as well as LES data of real-scale wind-turbine wakes, but not yet with full-scale wind turbine wake measurements. [1] M. Bastankhah and F. Porté-Agel. A New Analytical Model For Wind-Turbine Wakes, in Renewable Energy, vol. 70, p. 116-123, 2014.

Divergences and estimating tight bounds on Bayes error with applications to multivariate Gaussian copula and latent Gaussian copula

NASA Astrophysics Data System (ADS)

Thelen, Brian J.; Xique, Ismael J.; Burns, Joseph W.; Goley, G. Steven; Nolan, Adam R.; Benson, Jonathan W.

2017-04-01

In Bayesian decision theory, there has been a great amount of research into theoretical frameworks and information- theoretic quantities that can be used to provide lower and upper bounds for the Bayes error. These include well-known bounds such as Chernoff, Battacharrya, and J-divergence. Part of the challenge of utilizing these various metrics in practice is (i) whether they are "loose" or "tight" bounds, (ii) how they might be estimated via either parametric or non-parametric methods, and (iii) how accurate the estimates are for limited amounts of data. In general what is desired is a methodology for generating relatively tight lower and upper bounds, and then an approach to estimate these bounds efficiently from data. In this paper, we explore the so-called triangle divergence which has been around for a while, but was recently made more prominent in some recent research on non-parametric estimation of information metrics. Part of this work is motivated by applications for quantifying fundamental information content in SAR/LIDAR data, and to help in this, we have developed a flexible multivariate modeling framework based on multivariate Gaussian copula models which can be combined with the triangle divergence framework to quantify this information, and provide approximate bounds on Bayes error. In this paper we present an overview of the bounds, including those based on triangle divergence and verify that under a number of multivariate models, the upper and lower bounds derived from triangle divergence are significantly tighter than the other common bounds, and often times, dramatically so. We also propose some simple but effective means for computing the triangle divergence using Monte Carlo methods, and then discuss estimation of the triangle divergence from empirical data based on Gaussian Copula models.

Computing Risk to West Coast Intertidal Rocky Habitat due to Sea Level Rise using LiDAR Topobathy

EPA Science Inventory

Compared to marshes, little information is available on the potential for rocky intertidal habitats to migrate upward in response to sea level rise (SLR). To address this gap, we utilized topobathy LiDAR digital elevation models (DEMs) downloaded from NOAA’s Digital Coast G...

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter.

PubMed

Liu, Wanli

2017-03-08

The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated.

Lidar observations of low-level wind reversals over the Gulf of Lion and characterization of their impact on the water vapour variability

NASA Astrophysics Data System (ADS)

Di Girolamo, Paolo; Flamant, Cyrille; Cacciani, Marco; Summa, Donato; Stelitano, Dario; Richard, Evelyne; Ducrocq, Véronique; Fourrie, Nadia; Said, Frédérique

2017-02-01

Water vapour measurements from a ground-based Raman lidar and an airborne differential absorption lidar, complemented by high resolution numerical simulations from two mesoscale models (Arome-WMED and MESO-NH), are considered to investigate transition events from Mistral/Tramontane to southerly marine flow taking place over the Gulf of Lion in Southern France in the time frame September-October 2012, during the Hydrological Cycle in the Mediterranean Experiment (HyMeX) Special Observation Period 1 (SOP1). Low-level wind reversals associated with these transitions are found to have a strong impact on water vapour transport, leading to a large variability of the water vapour vertical and horizontal distribution. The high spatial and temporal resolution of the lidar data allow to monitor the time evolution of the three-dimensional water vapour field during these transitions from predominantly northerly Mistral/Tramontane flow to a predominantly southerly flow, allowing to identify the quite sharp separation between these flows, which is also quite well captured by the mesoscale models.

The Multi-Center Airborne Coherent Atmospheric Wind Sensor: Recent Measurements and Future Applications

NASA Technical Reports Server (NTRS)

Rothermel, Jeffry; Cutten, Dean R.; Hardesty, R. Michael; Howell, James N.; Darby, Lisa S.; Tratt, David M.; Menzies, Robert T.

1999-01-01

The coherent Doppler lidar, when operated from an airborne platform, offers a unique measurement capability for study of atmospheric dynamical and physical properties. This is especially true for scientific objectives requiring measurements in optically-clear air, where other remote sensing technologies such as Doppler radar are at a disadvantage in terms of spatial resolution and coverage. Recent experience suggests airborne coherent Doppler lidar can yield unique wind measurements of--and during operation within--extreme weather phenomena. This paper presents the first airborne coherent Doppler lidar measurements of hurricane wind fields. The lidar atmospheric remote sensing groups of National Aeronautics and Space Administration (NASA) Marshall Space Flight Center, National Oceanic and Atmospheric Administration (NOAA) Environmental Technology Laboratory, and Jet Propulsion Laboratory jointly developed an airborne lidar system, the Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS). The centerpiece of MACAWS is the lidar transmitter from the highly successful NOAA Windvan. Other field-tested lidar components have also been used, when feasible, to reduce costs and development time. The methodology for remotely sensing atmospheric wind fields with scanning coherent Doppler lidar was demonstrated in 1981; enhancements were made and the system was reflown in 1984. MACAWS has potentially greater scientific utility, compared to the original airborne scanning lidar system, owing to a factor of approx. 60 greater energy-per-pulse from the NOAA transmitter. MACAWS development was completed and the system was first flown in 1995. Following enhancements to improve performance, the system was re-flown in 1996 and 1998. The scientific motivation for MACAWS is three-fold: obtain fundamental measurements of subgrid scale (i.e., approx. 2-200 km) processes and features which may be used to improve parameterizations in hydrological, climate, and general/regional circulation models; obtain similar datasets to improve understanding and predictive capabilities for similarly-scaled processes and features; and simulate and validate the performance of prospective satellite Doppler lidars for global tropospheric wind measurement.

Applications of High-Resolution LiDAR Data for the Christina River Basin CZO

NASA Astrophysics Data System (ADS)

Hicks, N. S.; Aufdenkampe, A. K.; Hicks, S. D.

2011-12-01

High-resolution LiDAR data allows for fine scale geomorphic assessment over relatively large spatial extents. Previously available DEMs with a resolution of ten meters or more did not provide adequate resolution for geomorphic characterization of small streams and watersheds or the identification of changes in stream morphology over time. High-resolution LiDAR data for a portion of the Christina River Basin Critical Zone Observatory (CRB-CZO) was obtained during both leaf-off and leaf-on time periods in 2010. Topographic data from these flights is being analyzed with the intent of geomorphic applications such as stream morphology, sediment transport studies, and the evaluation of alluvial deposits. These data and resultant products will also be used in hydrologic and biogeochemical modeling and in biologic and biogeochemical studies of these streams, which are long-term study sites. The LiDAR data also facilitate informed instrument placement and will be used for vegetation studies. The LiDAR data for the CRB-CZO has been used to create a variety of LiDAR based topographic data products including TINs and 0.5-m DEMs. LiDAR derived slope and elevation products were combined with LiDAR intensity images to identify stream channel boundaries and stream centerlines for third through first-order streams. High-resolution slope data also aided in floodplain characterization of these small streams. These high precision stream channel and floodplain characterizations would not have been otherwise possible without extensive field surveying. Future LiDAR flights will allow for the identification of changes in channel morphology over time in low order basins. These characterizations are of particular interest in comparisons between forested and meadow reaches, and in studying the effects of changes in land-use on channel morphology. High-resolution LiDAR data allow for the generation of surface characterizations of importance to a wide range of interdisciplinary researchers.

Direct measurements of vertical heat flux and Na flux in the mesosphere and lower thermosphere by lidar at Boulder (40°N, 105°W), Colorado

NASA Astrophysics Data System (ADS)

Huang, W.; Chu, X.; Gardner, C. S.; Barry, I. F.; Smith, J. A.; Fong, W.; Yu, Z.; Chen, C.

2014-12-01

The vertical transport of heat and constituent by gravity waves and tides plays a fundamental role in establishing the thermal and constituent structures of the mesosphere and lower thermosphere (MLT), but has not been thoroughly investigated by observations. In particular, direct measurements of vertical heat flux and metal constituent flux caused by dissipating waves are extremely rare, which demand precise measurements with high spatial and temporal resolutions over a long period. Such requirements are necessary to overcome various uncertainties to reveal the small quantities of the heat and constituent fluxes induced by dissipating waves. So far such direct observations have only been reported for vertical heat and Na fluxes using a Na Doppler lidar at Starfire Optical Range (SOR) in Albuquerque, New Mexico. Furthermore, estimate of eddy heat and constituent fluxes from the turbulent mixing generated by breaking waves is even more challenging due to the even smaller temporal and spatial scales of the eddy. Consequently, the associated coefficients of thermal (kH) and constituent (kzz) diffusion have not been well characterized and remain as large uncertainties in models. We attempt to address these issues with direct measurements by a Na Doppler lidar with exceptional high-resolution measurement capabilities. Since summer 2010, we have been operating a Na Doppler lidar at Boulder, Colorado. The efficiency of the lidar has been greatly improved in summer of 2011 and achieved generally over 1000 counts of Na signal per lidar pulse in winter. In 2013, we made extensive Na lidar observations in 98 nights. These data covering each month of a full year will be used to characterize the seasonal variations of heat and Na fluxes and to be compared with the pioneering observations at SOR. In November 2013, we further upgraded the lidar with two new frequency shifters and a new data acquisition scheme, which are optimized for estimating eddy fluxes and reducing the measurement bias. Since then, we have been making observations in order to directly measure the eddy heat and Na fluxes for the first time. Such lidar observations at Boulder will certainly help advance the understanding on the vertical transport in the MLT region and provide crucial observational references to the models.

Spatial and temporal analyses for multiscale monitoring of landslides: Examples from Northern Ireland

NASA Astrophysics Data System (ADS)

Bell, Andrew; McKinley, Jennifer; Hughes, David

2013-04-01

Landslides in the form of debris flows, large scale rotational features and composite mudflows impact transport corridors cutting off local communities and in some instances result in loss of life. This study presents landslide monitoring methods used for predicting and characterising landslide activity along transport corridors. A variety of approaches are discussed: desk based risk assessment of slopes using Geographical Information Systems (GIS); Aerial LiDAR surveys and Terrestrial LiDAR monitoring and field instrumentation of selected sites. A GIS based case study is discussed which provides risk assessment for the potential of slope stability issues. Layers incorporated within the system include Digital Elevation Model (DEM), slope, aspect, solid and drift geology and groundwater conditions. Additional datasets include consequence of failure. These are combined within a risk model, presented as likelihoods of failure. This integrated spatial approach for slope risk assessment provides the user with a preliminary risk assessment of sites. An innovative "Flexviewer" web-based server interface allows users to view data without needing advanced GIS techniques to gather information about selected areas. On a macro landscape scale, Aerial LiDAR (ALS) surveys are used for the characterisation of landslides from the surrounding terrain. DEMs are generated along with terrain derivatives: slope, curvature and various measures of terrain roughness. Spatial analysis of terrain morphological parameters allow characterisation of slope stability issues and are used to predict areas of potential failure or recently failure terrain. On a local scale ground monitoring approaches are employed for the monitoring of changes in selected slopes using ALS and risk assessment approaches. Results are shown from on-going bimonthly Terrestrial LiDAR (TLS) monitoring of the slope within a site specific geodectically referenced network. This has allowed a classification of changes in the slopes with DEMs of difference showing areas of recent movement, erosion and deposition. In addition, changes in the structure of the slope characterised by DEM of difference and morphological parameters in the form of roughness, slope and curvature measures are progressively linked to failures indicated from temporal DEM monitoring. Preliminary results are presented for a case site at Straidkilly Point, Glenarm, Co. Antrim, Northern Ireland, illustrating multiple approaches to the spatial and temporal monitoring of landslides. These indicate how spatial morphological approaches and risk assessment frameworks coupled with TLS monitoring and field instrumentation enable characterisation and prediction of potential areas of slope stability issues. On site weather instrumentation and piezometers document changes in pore water pressures resulting in site-specific information with geotechnical observations parameterised within the temporal LiDAR monitoring. This provides a multifaceted approach to the characterisation and analysis of slope stability issues. The presented methodology of multiscale datasets and surveying approaches utilising spatial parameters and risk index mapping enables a more comprehensive and effective prediction of landslides resulting in effective characterisation and remediation strategies.

Observing System Simulations for ASCENDS: Synthesizing Science Measurement Requirements (Invited)

NASA Astrophysics Data System (ADS)

Kawa, S. R.; Baker, D. F.; Schuh, A. E.; Crowell, S.; Rayner, P. J.; Hammerling, D.; Michalak, A. M.; Wang, J. S.; Eluszkiewicz, J.; Ott, L.; Zaccheo, T.; Abshire, J. B.; Browell, E. V.; Moore, B.; Crisp, D.

2013-12-01

The measurement of atmospheric CO2 from space using active (lidar) sensing techniques has several potentially significant advantages in comparison to current and planned passive CO2 instruments. Application of this new technology aims to advance CO2 measurement capability and carbon cycle science into the next decade. The NASA Active Sensing of Carbon Emissions, Nights, Days, and Seasons (ASCENDS) mission has been recommended by the US National Academy of Sciences Decadal Survey for the next generation of space-based CO2 observing systems. ASCENDS is currently planned for launch in 2022. Several possible lidar instrument approaches have been demonstrated in airborne campaigns and the results indicate that such sensors are quite feasible. Studies are now underway to evaluate performance requirements for space mission implementation. Satellite CO2 observations must be highly precise and unbiased in order to accurately infer global carbon source/sink fluxes. Measurement demands are likely to further increase in the wake of GOSAT, OCO-2, and enhanced ground-based in situ and remote sensing CO2 data. The objective of our work is to quantitatively and consistently evaluate the measurement capabilities and requirements for ASCENDS in the context of advancing our knowledge of carbon flux distributions and their dependence on underlying physical processes. Considerations include requirements for precision, relative accuracy, spatial/temporal coverage and resolution, vertical information content, interferences, and possibly the tradeoffs among these parameters, while at the same time framing a mission that can be implemented within a constrained budget. Here, we attempt to synthesize the results of observing system simulation studies, commissioned by the ASCENDS Science Requirements Definition Team, into a coherent set of mission performance guidelines. A variety of forward and inverse model frameworks are employed to reduce the potential dependence of the results on model specifics. Sensitivity to key instrument design variables is explored and quantified. Global random error measurement scenarios show significant improvement in resolving CO2 fluxes and reducing uncertainties for expected lidar instrument error levels. The improvement beyond that expected for OCO-2 with random errors only, however, is limited for regions where passive sampling is not limited by lack of sunlight or heavy cloud cover. Simulations including prospective systematic (bias) errors, which are expected to be lesser for the lidar system, provide guidance for instrument design requirements as well as reinforcing the priority for a comprehensive calibration/validation component to the mission. The necessity of including coincident lidar measurements of the O2 column, in order to normalize the CO2 column to dry air mole fraction, will also be discussed. The results indicate that within reasonable technological assumptions for the system performance, high measurement quality and quantity can be obtained that will fulfill the nominal ASCENDS objectives and provide substantial improvement in our knowledge of global carbon cycle processes.

The impact of forest structure and light utilization on carbon cycling in tropical forests

NASA Astrophysics Data System (ADS)

Morton, D. C.; Longo, M.; Leitold, V.; Keller, M. M.

2015-12-01

Light competition is a fundamental organizing principle of forest ecosystems, and interactions between forest structure and light availability provide an important constraint on forest productivity. Tropical forests maintain a dense, multi-layered canopy, based in part on abundant diffuse light reaching the forest understory. Climate-driven changes in light availability, such as more direct illumination during drought conditions, therefore alter the potential productivity of forest ecosystems during such events. Here, we used multi-temporal airborne lidar data over a range of Amazon forest conditions to explore the influence of forest structure on gross primary productivity (GPP). Our analysis combined lidar-based observations of canopy illumination and turnover in the Ecosystem Demography model (ED, version 2.2). The ED model was updated to specifically account for regional differences in canopy and understory illumination using lidar-derived measures of canopy light environments. Model simulations considered the influence of forest structure on GPP over seasonal to decadal time scales, including feedbacks from differential productivity between illuminated and shaded canopy trees on mortality rates and forest composition. Finally, we constructed simple scenarios with varying diffuse and direct illumination to evaluate the potential for novel plant-climate interactions under scenarios of climate change. Collectively, the lidar observations and model simulations underscore the need to account for spatial heterogeneity in the vertical structure of tropical forests to constrain estimates of tropical forest productivity under current and future climate conditions.

An Online 3D Database System for Endangered Architectural and Archaeological Heritage in the South-Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Abate, D.; Avgousti, A.; Faka, M.; Hermon, S.; Bakirtzis, N.; Christofi, P.

2017-10-01

This study compares performance of aerial image based point clouds (IPCs) and light detection and ranging (LiDAR) based point clouds in detection of thinnings and clear cuts in forests. IPCs are an appealing method to update forest resource data, because of their accuracy in forest height estimation and cost-efficiency of aerial image acquisition. We predicted forest changes over a period of three years by creating difference layers that displayed the difference in height or volume between the initial and subsequent time points. Both IPCs and LiDAR data were used in this process. The IPCs were constructed with the Semi-Global Matching (SGM) algorithm. Difference layers were constructed by calculating differences in fitted height or volume models or in canopy height models (CHMs) from both time points. The LiDAR-derived digital terrain model (DTM) was used to scale heights to above ground level. The study area was classified in logistic regression into the categories ClearCut, Thinning or NoChange with the values from the difference layers. We compared the predicted changes with the true changes verified in the field, and obtained at best a classification accuracy for clear cuts 93.1 % with IPCs and 91.7 % with LiDAR data. However, a classification accuracy for thinnings was only 8.0 % with IPCs. With LiDAR data 41.4 % of thinnings were detected. In conclusion, the LiDAR data proved to be more accurate method to predict the minor changes in forests than IPCs, but both methods are useful in detection of major changes.

3D turbulence measurements in inhomogeneous boundary layers with three wind LiDARs

NASA Astrophysics Data System (ADS)

Carbajo Fuertes, Fernando; Valerio Iungo, Giacomo; Porté-Agel, Fernando

2014-05-01

One of the most challenging tasks in atmospheric anemometry is obtaining reliable turbulence measurements of inhomogeneous boundary layers at heights or in locations where is not possible or convenient to install tower-based measurement systems, e.g. mountainous terrain, cities, wind farms, etc. Wind LiDARs are being extensively used for the measurement of averaged vertical wind profiles, but they can only successfully accomplish this task under the limiting conditions of flat terrain and horizontally homogeneous flow. Moreover, it has been shown that common scanning strategies introduce large systematic errors in turbulence measurements, regardless of the characteristics of the flow addressed. From the point of view of research, there exist a variety of techniques and scanning strategies to estimate different turbulence quantities but most of them rely in the combination of raw measurements with atmospheric models. Most of those models are only valid under the assumption of horizontal homogeneity. The limitations stated above can be overcome by a new triple LiDAR technique which uses simultaneous measurements from three intersecting Doppler wind LiDARs. It allows for the reconstruction of the three-dimensional velocity vector in time as well as local velocity gradients without the need of any turbulence model and with minimal assumptions [EGU2013-9670]. The triple LiDAR technique has been applied to the study of the flow over the campus of EPFL in Lausanne (Switzerland). The results show the potential of the technique for the measurement of turbulence in highly complex boundary layer flows. The technique is particularly useful for micrometeorology and wind engineering studies.

LIDAR as an alternative to passive collectors to measure pesticide spray drift

NASA Astrophysics Data System (ADS)

Gregorio, Eduard; Rosell-Polo, Joan R.; Sanz, Ricardo; Rocadenbosch, Francesc; Solanelles, Francesc; Garcerá, Cruz; Chueca, Patricia; Arnó, Jaume; del Moral, Ignacio; Masip, Joan; Camp, Ferran; Viana, Rafael; Escolà, Alexandre; Gràcia, Felip; Planas, Santiago; Moltó, Enrique

2014-01-01

Pesticide spray drift entails a series of risks and costs in terms of human, animal and environmental well-being. A proper understanding of this phenomenon is essential to minimise these risks. However, most conventional methods used in drift measurement are based on point collectors which are unable to obtain information concerning the temporal or spatial evolution of the pesticide cloud. Such methods are also costly, labour-intensive, and require a considerable amount of time. The aim of this paper is to propose a method to measure the spray drift based on lidar (LIght Detection And Ranging) and to prove that it can be an alternative to passive collectors. An analytical model is proposed to relate the measurements obtained through passive collectors and those obtained with lidar systems considering several spray application and meteorological parameters. The model was tested through an experimental campaign involving multiple ground spray tests. A lidar system and two types of passive collectors (nylon strings and water-sensitive paper) were used simultaneously to measure the drift. The results showed for each test a high coefficient of determination (R2 ≈ 0.90) between the lidar signal and the tracer mass captured by the nylon strings. This coefficient decreased (R2 = 0.77) when all tests were considered together. Lidar measurements were also used to study the evolution of the pesticide cloud with high range (1.5 m) and temporal resolution (1 s) and to estimate its velocity. Furthermore, a very satisfactory adjustment (R2 = 0.89) was observed between the tracer mass collected by the nylon lines and the coverage on water-sensitive paper sheets. These results are in accordance with the proposed analytical model and allow the conclusion that the application and meteorological parameters can be considered spatially invariant for a given test but are not invariant for different tests.

Retrieval of Vertical LAI Profiles Over Tropical Rain Forests using Waveform Lidar at La Selva, Costa Rica

NASA Technical Reports Server (NTRS)

Tang, Hao; Dubayah, Ralph; Swatantra, Anu; Hofton, Michelle; Sheldon, Sage; Clark, David B.; Blair, Bryan

2012-01-01

This study explores the potential of waveform lidar in mapping the vertical and spatial distributions of leaf area index (LAI) over the tropical rain forest of La Selva Biological Station in Costa Rica. Vertical profiles of LAI were derived at 0.3 m height intervals from the Laser Vegetation Imaging Sensor (LVIS) data using the Geometric Optical and Radiative Transfer (GORT) model. Cumulative LAI profiles obtained from LVIS were validated with data from 55 ground to canopy vertical transects using a modular field tower to destructively sample all vegetation. Our results showed moderate agreement between lidar and field derived LAI (r2=0.42, RMSE=1.91, bias=-0.32), which further improved when differences between lidar and tower footprint scales (r2=0.50, RMSE=1.79, bias=0.27) and distance of field tower from lidar footprint center (r2=0.63, RMSE=1.36, bias=0.0) were accounted for. Next, we mapped the spatial distribution of total LAI across the landscape and analyzed LAI variations over different land cover types. Mean values of total LAI were 1.74, 5.20, 5.41 and 5.62 over open pasture, secondary forests, regeneration forests after selective-logging and old-growth forests respectively. Lastly, we evaluated the sensitivities of our LAI retrieval model to variations in canopy/ground reflectance ratio and to waveform noise such as induced by topographic slopes. We found for both, that the effects were not significant for moderate LAI values (about 4). However model derivations of LAI might be inaccurate in areas of high-slope and high LAI (about 8) if ground return energies are low. This research suggests that large footprint waveform lidar can provide accurate vertical LAI profile estimates that do not saturate even at the high LAI levels in tropical rain forests and may be a useful tool for understanding the light transmittance within these canopies.

Topographic lidar survey of Dauphin Island, Alabama and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana, July 12-14, 2013

USGS Publications Warehouse

Guy, Kristy K.; Plant, Nathaniel G.

2014-01-01

This Data Series Report contains lidar elevation data collected on July 12 and 14, 2013, for Dauphin Island, Alabama, and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana. Classified point cloud data—data points described in three dimensions—in lidar data exchange format (LAS) and bare earth digital elevation models (DEMs) in ERDAS Imagine raster format (IMG) are available as downloadable files. Photo Science, Inc., was contracted by the U.S. Geological Survey (USGS) to collect and process these data. The lidar data were acquired at a horizontal spacing (or nominal pulse spacing) of 1 meter (m) or less. The USGS surveyed points within the project area from July 14–23, 2013, for use in ground control and accuracy assessment. Photo Science, Inc., calculated a vertical root mean square error (RMSEz) of 0.012 m by comparing 10 surveyed points to an interpolated elevation surface of unclassified lidar data. The USGS also checked the data using 80 surveyed points and unclassified lidar point elevation data and found an RMSEz of 0.073 m. The project specified an RMSEz of 0.0925 m or less. The lidar survey was acquired to document the short- and long-term changes of several different barrier island systems. Specifically, this survey supports detailed studies of Chandeleur and Dauphin Islands that resolve annual changes in beaches, berms and dunes associated with processes driven by storms, sea-level rise, and even human restoration activities. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.