Exploiting Full-Waveform Lidar Data and Multiresolution Wavelet Analysis for Vertical Object Detection and Recognition

A current challenge in performing airport obstruction surveys using airborne lidar is lack of reliable, automated methods for extracting and attributing vertical objects from the lidar data. This paper presents a new approach to solving this problem, taking advantage of the additional data provided byfull-waveform systems. The procedure entails first deconvolving and georeferencing the lidar waveformdata to create dense, detailed point clouds in which the vertical structure of objects, such as trees, towers, and buildings, is well characterized. The point clouds are then voxelized to produce high-resolution volumes of lidar intensity values, and a 3D wavelet decomposition is computed. Verticalobject detection and recognition is performed in the wavelet domain using a multiresolution template matching approach. The method was tested using lidar waveform data and ground truth collected for project areas in Madison,Wisconsin. Preliminary results demonstrate the potential of the approach

Investigating Full-Waveform Lidar Data for Detection and Recognition of Vertical Objects

A recent innovation in commercially-available topographic lidar systems is the ability to record return waveforms at high sampling frequencies. These “full-waveform” systems provide up to two orders of magnitude more data than “discrete-return” systems. However, due to the relatively limited capabilities of current processing and analysis software, more data does not always translate into more or better information for object extraction applications. In this paper, we describe a new approach for exploiting full waveform data to improve detection and recognition of vertical objects, such as trees, poles, buildings, towers, and antennas. Each waveform is first deconvolved using an expectation-maximization (EM) algorithm to obtain a train of spikes in time, where each spike corresponds to an individual laser reflection. The output is then georeferenced to create extremely dense, detailed X,Y,Z,I point clouds, where I denotes intensity. A tunable parameter is used to control the number of spikes in the deconvolved waveform, and, hence, the point density of the output point cloud. Preliminary results indicate that the average number of points on vertical objects using this method is several times higher than using discrete-return lidar data. The next steps in this ongoing research will involve voxelizing the lidar point cloud to obtain a high-resolution volume of intensity values and computing a 3D wavelet representation. The final step will entail performing vertical object detection/recognition in the wavelet domain using a multiresolution template matching approach

Extracting More Data from LiDAR in Forested Areas by Analyzing Waveform Shape

Light Detection And Ranging (LiDAR) in forested areas is used for constructing Digital Terrain Models (DTMs), estimating biomass carbon and timber volume and estimating foliage distribution as an indicator of tree growth and health. All of these purposes are hindered by the inability to distinguish the source of returns as foliage, stems, understorey and the ground except by their relative positions. The ability to separate these returns would improve all analyses significantly. Furthermore, waveform metrics providing information on foliage density could improve forest health and growth estimates. In this study, the potential to use waveform LiDAR was investigated. Aerial waveform LiDAR data were acquired for a New Zealand radiata pine plantation forest, and Leaf Area Density (LAD) was measured in the field. Waveform peaks with a good signal-to-noise ratio were analyzed and each described with a Gaussian peak height, half-height width, and an exponential decay constant. All parameters varied substantially across all surface types, ruling out the potential to determine source characteristics for individual returns, particularly those with a lower signal-to-noise ratio. However, pulses on the ground on average had a greater intensity, decay constant and a narrower peak than returns from coniferous foliage. When spatially averaged, canopy foliage density (measured as LAD) varied significantly, and was found to be most highly correlated with the volume-average exponential decay rate. A simple model based on the Beer-Lambert law is proposed to explain this relationship, and proposes waveform decay rates as a new metric that is less affected by shadowing than intensity-based metrics. This correlation began to fail when peaks with poorer curve fits were included

Another dimension from LiDAR – Obtaining foliage density from full waveform data

LiDAR tells the user where surfaces are, not what they are. In this study we investigate the potential for waveform LiDAR to provide more information on the nature of the returns over forestry. Waveform LiDAR was acquired for ten Pinus radiata plots in a New Zealand plantation, along with comprehensive leaf area sampling in 2m vertical bands. The decay rate of each waveform peak was shown to be a useful tool for estimating foliage density, and has potential for identifying regions containing ground and understorey. Leaf Area Density (LAD) is an expression of foliage density per unit height, and a relationship between waveform decay rate and LAD was developed with an R2 of 56%. Incorporating the proportion of discrete LiDAR that fell in that band (which itself has an R2 of 50%) improves this model to explain 69% of the variation in LAD. This is a good result, especially given the costs and difficulties in measuring leaf area directly. As foliage density varies dramatically on a fine scale it was not possible to differentiate the nature of every single LiDAR return – but by averaging over a small area local variation in LAD could be easily mapped. Ground returns could be distinguished as having short decays, and broad leafed understorey typically had values between those of the canopy and ground, although surface roughness and slope make it impossible to robustly identify single returns. This study produced a useful model for estimating LAD in Pinus radiata which could easily be extended to other coniferous species

Satellite-Derived Bathymetry a Reconnaissance Tool for Hydrography

In some developing countries, the information available to plan and prioritise hydrographic surveys is typically based on visual inspection of existing nautical charts. However, due to the age of many existing charts and lack of availability of the original source data from which they were compiled (e.g., smooth sheets) this type of analysis is often quite limited. A study was conducted to evaluate the use of a satellite-derived bathymetry (SDB) procedure to map shallow-water bathymetry in a GIS environment, and to identify areas that require a new hydrographic survey. Publically available, multispectral satellite imagery and published algorithms are used to derive estimates of the bathymetry. The study results indicate a potential use of the procedure by national hydrographic offices as a reconnaissance too

ALB Evaluation for NOAA charting requirements

The National Oceanic and Atmospheric Administration (NOAA) acquires hydrographic data around the coasts of the US and its territories using in-house surveys and contracting resources. Hydrographic data are primarily collected using sonar systems, while a small percent is acquired via Airborne Lidar Bathymetry (ALB) for nearshore areas. NOAA has an ongoing requirement, as per the Coast and Geodetic Survey Act of 1947, to survey nearshore areas as part of its coastal mapping activities, including updating nautical charts, creating hydrodynamic models and supporting coastal planning and habitat mapping. NOAA has initiated a project to investigate the potential use of ALB data from non-hydrographic survey programmes (i.e., programmes designed to support objectives other than nautical charting and with specifications and requirements that differ from those of NOAA hydrographic surveys) in order to increase the amount of data available to meet these nearshore mapping requirements. THIS PAPER PRESENTS AN evaluation of ALB data from the US Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP) for use by NOAA’s Offi ce of Coast Survey (OCS). Th ese NCMP datasets were evaluated through a statistical comparison to bathymetric surfaces derived from hydrographic NOAA surveys. Th e objectives of the analysis were: 1. to assess the level of agreement between the NCMP and OCS data in areas of overlap in a variety of coastal environments and 2. to determine whether NCMP ALB survey data can be compiled with NOAA OCS hydrographic data to generate seamless shallowbathymetry digital elevation modes (DEMs)

Develping a Methodology for the Mapping and Characterization of the Nigerian Coastline Using Remote Sensing

Coastline delineation is important in maritime boundary determination, as well as for analyzing coastline change rates due to coastal erosion, sea level change, storms, and other causes. Coastline change rate estimates depend on the uncertainty of the current and historical coastlines used in the analysis, which, in turn, depend on the surveying technologies and techniques that were originally used. Current techniques for coastline mapping include photogrammetric delineation using tide-coordinated aerial imagery. However, in many developing countries, the charted coastlines may have been inadequately and inconsistently mapped largely due to inadequate resources. This paper describes the use of an automated technique for coastline mapping and classification that is based on satellite imagery. A spectral analysis using different image bands can be used to define the land/water boundary and characterize the coastal area around the coastline. A first-order uncertainty analysis was also performed. The satellite-derived coastlines were compared to charted coastlines to evaluate the adequacy and consistency of the charted coastlines. The satellite-derived coastlines were also compared to coastlines derived from historical maps to assess changes and change rates. The results of the coastline uncertainty analysis were then used to compute propagated uncertainties in coastline change rate estimates and to gain greater insight into actual changes. The procedure was developed in a GIS environment using study sites along the Nigerian coastline. However, this procedure can be applied to other poorly charted/mapped coastal areas as well

Empirical Analysis of Aerial Camera Filters for Shoreline Mapping

Accurate, up-to-date national shoreline is critical in defining the territorial limits of the Unites States, updating nautical charts, and managing coastal resources. The National Oceanic and Atmospheric Administration (NOAA) delineates the interpreted shoreline photogrammetrically using tide-coordinated stereo photography acquired with black-and-white infrared emulsion. In this paper, we present the results of a two-phased study aimed at quantifying the effect of camera filter selection on the interpreted shoreline when utilizing this method of shoreline mapping

Determining how stressors effect the onset of substance abuse in runaways

In America, it is estimated that between 500,000 and two million children run away each year. A majority of these runaways become involved with illegal substance abuse. This study questions whether children experience substance abuse prior to their running away or if their substance abuse is an attempt to cope with the new stressors created by street life. Data collection will include having 50 volunteers complete a questionnaire, with consideration of race and gender. The findings suggested that no relationship exist between runaways engaging in drug use and the amount of stress experienced at home or during the runaway

Buoyancy Instabilities in a Weakly Collisional Intracluster Medium

The intracluster medium of galaxy clusters is a weakly collisional, high-beta plasma in which the transport of heat and momentum occurs primarily along magnetic-field lines. Anisotropic heat conduction allows convective instabilities to be driven by temperature gradients of either sign, the magnetothermal instability (MTI) in the outskirts of non-isothermal clusters and the heat-flux buoyancy-driven instability (HBI) in their cooling cores. We employ the Athena MHD code to investigate the nonlinear evolution of these instabilities, self-consistently including the effects of anisotropic viscosity (i.e. Braginskii pressure anisotropy), anisotropic conduction, and radiative cooling. We highlight the importance of the microscale instabilities that inevitably accompany and regulate the pressure anisotropies generated by the HBI and MTI. We find that, in all but the innermost regions of cool-core clusters, anisotropic viscosity significantly impairs the ability of the HBI to reorient magnetic-field lines orthogonal to the temperature gradient. Thus, while radio-mode feedback appears necessary in the central few tens of kpc, conduction may be capable of offsetting radiative losses throughout most of a cool core over a significant fraction of the Hubble time. Magnetically-aligned cold filaments are then able to form by local thermal instability. Viscous dissipation during the formation of a cold filament produces accompanying hot filaments, which can be searched for in deep Chandra observations of nearby cool-core clusters. In the case of the MTI, anisotropic viscosity maintains the coherence of magnetic-field lines over larger distances than in the inviscid case, providing a natural lower limit for the scale on which the field can fluctuate freely. In the nonlinear state, the magnetic field exhibits a folded structure in which the field-line curvature and field strength are anti-correlated.Comment: 20 pages, 20 figures, submitted to ApJ; Abstract abridge