Lori A. Magruder

Landcover classification of small-footprint, full-waveform lidar data

Full-waveform lidar data are emerging into the commercial sector and provide a unique ability to characterize the landscape. The returned laser waveforms indicate specific reflectors within the footprint (vertical structure), while the shape of the return convolves surface reflectance and physical topography. These data are especially effective in vegetative regions with respect to canopy structure characterization. The objective of this research is to evaluate the performance of waveform-derived parameters as input into a supervised classifier. Extracted waveform metrics include Gaussian amplitude, Gaussian standard deviation, canopy energy, ground energy, total waveform energy, ratio between canopy and ground energy, rise time to the first peak, fall time of the last peak, and height of median energy (HOME). The classifier utilizes a feature selection methodology which provides information on the value of waveform parameters for discriminating between class pairs. For this study area, energy ratio and Gaussian amplitude were selected most frequently, but rise time and fall time were also important for discriminating different tree types and densities. The lidar classification accuracy for this study area was 85.8% versus 71.2% for Quickbird imagery. Since the lidar-based input data are structural parameters derived from the waveforms, the classification is improved for classes that are spectrally similar but structurally different.

ICESat Altimetry Data Product Verification at White Sands Space Harbor

Three unique techniques have been developed to validate the Ice, Cloud, and Land Elevation Satellite (ICESat) mission altimetry data product and implemented at White Sands Space Harbor (WSSH) in New Mexico. One specific technique at WSSH utilizes zenith-pointed sensors to detect the laser on the surface and enable geolocation determination of the altimeter footprint that is independent of the data product generation. The system of detectors also registers the laser light time of arrival, which is related to the data product time tag. Several overflights of the WSSH have validated these time tags to less than 3plusmn1 mus. The ground-based detector system also verified the laser illuminated spot geolocation to 10.6 m (3.5 arcsec) plusmn4.5 m on one occasion, which is consistent with the requirement of 3.5 m (1sigma). A third technique using corner cube retroreflector signatures in the altimeter echo waveforms was also shown to provide an assessment of the laser spot geolocation. Although the accuracy of this technique is not equal to the other methodologies, it does offer position determination for comparison to the spacecraft altimetry data product. In addition, elevation verifications were made using the comparison of the ICESat elevation products at WSSH to those acquired with an airborne light detection and ranging. The elevation comparisons show an agreement to within plusmn34 cm (plusmn6.7 cm under best conditions) which indicate no significant errors associated with the pointing knowledge of the altimeter

Terrain classification of ladar data over Haitian urban environments using a lower envelope follower and adaptive gradient operator

In response to the 2010 Haiti earthquake, the ALIRT ladar system was tasked with collecting surveys to support disaster relief efforts. Standard methodologies to classify the ladar data as ground, vegetation, or man-made features failed to produce an accurate representation of the underlying terrain surface. The majority of these methods rely primarily on gradient- based operations that often perform well for areas with low topographic relief, but often fail in areas of high topographic relief or dense urban environments. An alternative approach based on a adaptive lower envelope follower (ALEF) with an adaptive gradient operation for accommodating local slope and roughness was investigated for recovering the ground surface from the ladar data. This technique was successful for classifying terrain in the urban and rural areas of Haiti over which the ALIRT data had been acquired.

ICESat Geolocation Validation Using Airborne Photography

NASAs ICESat satellite launched in January of 2003, carrying the Geoscience Laser Altimeter. During the initial phase of this mission, many validation procedures were implemented to verify the accuracy associated with a variety of altimetry-derived data products. Of specific interest was the need to validate the geodetic position of the ICESat footprints, which is a convolution of laser-pointing determination, satellite position, and ranging measurements. This paper describes the methodology and implementation of one effort using aerial photography to image the laser spots on the surface during a satellite overflight. The spot locations are determined based on the relative positions of accurately placed geodetic infrared-emitting markers within the overflight area and apparent in the aerial photograph. One specific overflight opportunity captured six successive ICESat footprints with the airborne camera system. The mean geolocation predictions of those spots using the ground fiducial placement in the image provide a data product validation to better than 3.1-m rms on the surface with an estimated accuracy of ±3.6 m when compared to the ICESat solution. These results are within the ICESat mission requirement of 4.5 m on the surface (1.5-arcsecond pointing knowledge) for geodetic position determination.

Lidar waveform stacking techniques for faint ground return extraction

Innovative algorithm development for small-footprint full-waveform lidar data processing extends this technologys capabilities to more complicated acquisition scenarios then previously determined, namely success of surveys over obscured areas. Waveform decomposition and the extraction of waveform metrics provide a straightforward approach to identifying vertical structure within each laser measurement. However, there are some limitations in this approach as faint returns within the waveform go undetected within the classical processing chain. These faint returns are the result of reduced energy levels due to obscurant scattering, attenuation and absorption. Lidar surveys over non-homogeneous wooded regions indicate that there are meaningful ground returns within dense tree coverage if extracted correctly from the data. By using a waveform stacking technique with appropriate waveforms in near geospatial proximity to the original, these faint returns can be augmented and detected during data processing. In comparison to the traditional approach, the waveform stacking technique provides up to a 60% increase in perceived ground returns with the faint signal extraction for the particular datasets analyzed over a broadleaf forest in Mississippi. The enhanced capability in the presence of foliage provides a decrease in operational effort associated with data density, dwell or targeting techniques, in addition to required survey expense.

Automated bare earth extraction technique for complex topography in light detection and ranging surveys

Abstract Bare earth extraction is an important component to light detection and ranging (LiDAR) data analysis in terms of terrain classification. The challenge in providing accurate digital surface models is augmented when there is diverse topography within the data set or complex combinations of vegetation and built structures. Few existing algorithms can handle substantial terrain diversity without significant editing or user interaction. This effort presents a newly developed methodology that provides a flexible, adaptable tool capable of integrating multiple LiDAR data attributes for an accurate terrain assessment. The terrain extraction and segmentation (TEXAS) approach uses a third-order spatial derivative for each point in the digital surface model to determine the curvature of the terrain rather than rely solely on the slope. The utilization of the curvature has shown to successfully preserve ground points in areas of steep terrain as they typically exhibit low curvature. Within the framework of TEXAS, the contiguous sets of points with low curvatures are grouped into regions using an edge-based segmentation method. The process does not require any user inputs and is completely data driven. This technique was tested on a variety of existing LiDAR surveys, each with varying levels of topographic complexity.

Signal Processing Techniques for Feature Extraction and Classification using Small-Footprint Full-Waveform Airborne LIDAR

Full-waveform digitizer hardware integration within discrete return LIDAR systems provides an enhanced capability to resolve vertical structure within the laser line of sight and potentially classify specific surfaces or objects. However, the subject of waveform signal processing as it applies to surface classification is fairly underdeveloped. This research includes the examination of LIDAR waveform pulse characteristics for known targets and vegetation types. Using these data, a number of signal processing techniques were investigated as precursors to classification engines without prior knowledge of surface slope, or obscuration density. Relevant waveform features were extracted using both Gaussian decomposition method and raw waveform features revealing surface classification distinctions. Preliminary results from this data set indicate that the total integrated waveform energy provides an efficient and rapid methodology for discrimination of vegetation from built surfaces. These results indicate that metrics derived from the full waveforms can be utilized to characterize and classify (to a limited degree) an environment without prior knowledge.

The Potential Impact of Vertical Sampling Uncertainty on ICESat-2/ATLAS Terrain and Canopy Height Retrievals for Multiple Ecosystems

With a planned launch no later than September 2018, the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) will provide a global distribution of geodetic elevation measurements for both the terrain surface and relative canopy heights. The Advanced Topographic Laser Altimeter System (ATLAS) instrument on-board ICESat-2 is a LiDAR system sensitive to the photon level. The photon-counting technology has many advantages for space-based altimetry, but also has challenges, particularly with delineating the signal from background noise. As such, a current unknown facing the ecosystem community is the performance of ICESat-2 for terrain and canopy height retrievals. This paper aims to provide the science user community of ICESat-2 land/vegetation data products with a realistic understanding of the performance characteristics and potential uncertainties related to the vertical sampling error, which includes the error in the perceived height value and the measurement precision. Terrain and canopy heights from simulated ICESat-2 data are evaluated against the airborne LiDAR ground truth values to provide a baseline performance uncertainty for multiple ecosystems. Simulation results for wooded savanna and boreal forest result in a mean bias error and error uncertainty (precision) for terrain height retrievals at 0.06 m (0.24 m RMSE) and −0.13 m (0.77 m RMSE). In contrast, results over ecosystems with dense vegetation show terrain errors of 1.93 m (1.66 m RMSE) and 2.52 m (3.18 m RMSE), indicating problems extracting terrain height due to diminished ground returns. Simulated top of canopy heights from ICESat-2 underestimated true top of canopy returns for all types analyzed with errors ranging from 0.28 m (1.39 m RMSE) to 1.25 m (2.63 m RMSE). These results comprise a first step in a comprehensive evaluation of ICESat-2 anticipated performance. Future steps will include solar noise impact analysis and investigation into performance discrepancy between visible and near-infrared wavelengths.

Using dual-wavelength, full-waveform airborne lidar for surface classification and vegetation characterization

Abstract. This study examines the utility of cocollected, dual-wavelength, full-waveform lidar data to characterize vegetation and landscapes through the extraction of waveform features, such as total waveform energy, canopy energy distribution, and foliage penetration metrics. Assessments are performed using data collected in May 2014 over Monterey, California, using the Chiroptera dual-laser lidar mapping system from Airborne Hydrography AB. Both full-waveform and discrete return data were collected simultaneously at green (532 nm) and near-infrared (NIR) (1064 nm) wavelengths; however, the two channels are operated independently at different pulse repetition frequencies, thus measurements are not spatially coincident. A voxelization approach is employed to generate pseudowaveforms for each wavelength along vertical columns in a regularly spaced grid, such that spectral waveform properties can be evaluated independently of spatial variations resulting from instrumentation configuration and collection scenario. The pseudowaveforms are parameterized and extracted parameters are mapped to raster layers, which are then used as inputs to a random forest classifier to predict land cover classifications across the survey area. In comparison to independent classification results for the two wavelength channels, the combination of the NIR and green response provided an improvement in overall classification accuracy of up to 6%. This effort presents the methodology associated with the voxelization approach and the exploitation of the pseudowaveform features, while illustrating a potential utility for geospatial classification using multiple wavelengths.

ICESat laser altimeter measurement time validation system

NASA launched its Ice, Cloud and Land Elevation Satellite (ICESat) in January 2003. The primary goal of this laser altimeter mission is to provide determination of volumetric changes in the ice sheets, specifically in Antarctica and Greenland. The instrument performance requirements are driven by the scientific goal of determining a change in elevation on the centimetre level over the course of a years time. One important aspect of the altimeter data is the time of measurement, or bounce time, associated with each laser shot, as it is an important factor that assists in revealing the temporal changes in the surface (land/ice/sea) characteristics. In order to provide verification that the laser bounce time is accurately being determined, a ground-based detector system has been developed. The ground-based system methodology time-tags the arrival of the transmitted photons on the surface of the Earth with an accuracy of 0.1 ms. The timing software and hardware that will be used in the ground-based system has been developed and extensively tested. One particular test utilized an airborne laser equipped to produce a similar signal to that of ICESat. The overflight of the detectors by the aircraft was successful in that the signals were detected by the electro-optical devices and appropriately time-tagged with the timing hardware/software. There are many calibration and validation activities planned with the intention to help resolve the validity of the ICESat data, but pre-launch analysis suggests the ground-based system will provide the most accurate recovery of timing bias.