Christopher Parrish

Empirical Comparison of Full-Waveform Lidar Algorithms: Range Extraction and Discrimination Performance

As third-party lidar software manufacturers are increasingly adding support for full-waveform data, a common question is which algorithm(s) to implement. To this end, a new approach is needed to compare and contrast various lidar waveform processing strategies from a practical, operational perspective. Quality and type of information output, processing speed, suitability for particular applications, robustness against poor parameter selection, and more subjective measures related to user experience are of interest. This paper describes a new empirical method of comparing range extraction and discrimination performance of different algorithms, based on a ranging-lab setup with multiple, adjustable screen targets, with precisely-measured separations. We present the results of comparing three different algorithms described in the scientific literature. The results show distinct differences and also indicate that there is no “one-size-fits-all” approach: the choice of a specific algorithm and adjustable parameter settings are highly application-dependent.

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.

Satellite Remote Sensing as a Reconnaissance Tool for Assessing Nautical Chart Adequacy and Completeness

National hydrographic offices need a better means of assessing the adequacy of existing nautical charts in order to plan and prioritize future hydrographic surveys. The ability to derive bathymetry from multispectral satellite imagery is a topic that has received considerable attention in scientific literature. However, published studies have not addressed the ability of satellite-derived bathymetry to meet specific hydrographic survey requirements. Specifically, the bathymetry needs to be referenced to a chart datum and statistical uncertainty estimates of the bathymetry should be provided. Ideally, the procedure should be based on readily-available, low-cost software, tools, and data. This paper describes the development and testing of a procedure using publicly-available, multispectral satellite imagery to map and portray shallow-water bathymetry in a GIS environment for three study sites: Northeast United States, Nigeria, and Belize. Landsat imagery and published algorithms were used to derive estimates of the bathymetry in shallow waters, and uncertainty of the satellite-derived bathymetry was then assessed using a Monte Carlo method. Results indicate that the practical procedures developed in this study are suitable for use by national hydrographic offices.

Assessment of Waveform Features for Lidar Uncertainty Modeling in a Coastal Salt Marsh Environment

There is currently great interest in lidar surveys of salt marshes to support coastal management and decision making. However, vertical uncertainty of lidar elevations is generally higher in salt marshes than in upland areas, and it can be difficult to empirically quantify due to the challenges of obtaining ground control in marshes. Assuming that most of the component uncertainties in the lidar geolocation equation will remain essentially constant over a relatively small location, it is posited that vertical uncertainty in a marsh will vary mostly as a function of surface and cover characteristics. These, in turn, should affect lidar waveforms recorded during the survey, and therefore, analysis of the waveform shapes may allow for prediction of vertical uncertainty variation. Waveforms at three test sites were used to compute 16 computationally efficient features that describe the shapes; and simple, multilinear, and principal component regressions were used to evaluate their ability to predict elevation differences between lidar and Global Positioning System ground control. The results show that a simple estimate of waveform width can explain over 50% of the total variability in elevation differences but that multilinear regression does not significantly improve the performance. Somewhat surprisingly, skewness of the waveform does not appear to be a good predictor of elevation differences in these cases.

Retrieval of Substrate Bearing Strength from Hyperspectral Imagery during the Virginia Coast Reserve (VCR’07) Multi-Sensor Campaign

Hyperspectral imagery (HSI) derived from remote sensing can delineate surface properties of substrates such as type, moisture, and grain size. These are critical parameters that determine the substrate bearing strength. Although HSI only sees the surface layer, statistics can be derived that relate surface properties to the likely bearing strength of soils in particular regions. This information can be used to provide an initial map estimate on large scales of potential bearing strength. We describe an initial validation study at the Virginia Coast Reserve relating airborne HSI to in situ spectral and geotechnical measurements through a spectral-geotechnical lookup table (LUT).

Bathymetry Retrieval from Hyperspectral Imagery in the Very Shallow Water Limit: A Case Study from the 2007 Virginia Coast Reserve (VCR'07) Multi-Sensor Campaign

We focus on the validation of a simplified approach to bathymetry retrieval from hyperspectral imagery (HSI) in the very shallow water limit (less than 1–2 m), where many existing bathymetric LIDAR sensors perform poorly. In this depth regime, near infra-red (NIR) reflectance depends primarily on water depth (water absorption) and bottom type, with suspended constituents playing a secondary role. Our processing framework exploits two optimal regions where a simple model depending on bottom type and water depth can be applied in the very shallow limit. These two optimal spectral regions are at a local maximum in the near infra-red reflectance near 810 nm, corresponding to a local minimum in absorption, and a maximum in the first derivative of the reflectance near 720 nm. These two regions correspond to peaks in spectral correlation with bathymetry at these depths.

CONFIGURING AN AIRBORNE LASER SCANNER FOR DETECTING AIRPORT OBSTRUCTIONS

To ensure the safety of the national airspace system, the Federal Aviation Administration (FAA) oversees surveying programs with the goal of geolocating vertical features that penetrate 3D Obstruction Identification Surfaces (OIS) around airfields. These OIS are defined mathematically and are based on the layout of the runways, the types of electronic navigation equipment used for each runway, and other factors. Under a series of interagency agreements, the National Geodetic Survey (NGS) is tasked with supplying obstruction survey data to the FAA. Airport obstruction surveys have traditionally been conducted using a combination of photogrammetry and conventional field survey techniques. In this study, we investigate the use of airborne laser scanning technology for this application. Specifically, we examine the effects of varying the forward look angle, laser footprint, flying height, flying speed, scan angle, scan frequency, transmitted power, and receiver sensitivity, as these parameters relate to the detection and measurement of vertical obstructions. We present results of test flights over the Gainesville Regional Airport (GNV) using fourteen different configurations of an Optech ALTM 2050. The results are compared to field-surveyed obstruction data obtained by NGS. Our analysis shows that certain configurations of the airborne laser scanning system notably improve the detection of obstructions. In this study, the best results were obtained using a 20° forward tilt angle and a 0.2 milliradian beam divergence from a flying height of 750 m. With this configuration, all obstructions were detected and the vertical accuracy (RMSE) was 0.88 m when compared to the field-surveyed obstructions.

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 by full-waveform systems. The procedure entails first deconvolving and georeferencing the lidar waveform data 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. Vertical object 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.

A dual-spectrometer approach to reflectance measurements under sub-optimal sky conditions

This paper presents a practical method for the development of spectral reflectance libraries under sub-optimal sky conditions. Although there are commercially available spectrometers which simultaneously measure both downwelling and upwelling radiance to mitigate the impact of sub-optimal sky conditions, these spectrometers only record in the visible and near infra-red. There are presently no commercially available spectrometers with this capability that can record the visible through short-wave infra-red. This paper presents a practical method of recording and processing data using coordinated measurements from two full-range spectrometers and discusses potential pitfalls and solutions required to achieve accurate reflectance spectra. Results demonstrate that high-quality spectral reflectance libraries can be developed with this approach.

Field calibration and validation of remote-sensing surveys

The Optical Collection Suite (OCS) is a ground-truth sampling system designed to perform in situ measurements that help calibrate and validate optical remote-sensing and swath-sonar surveys for mapping and monitoring coastal ecosystems and ocean planning. The OCS system enables researchers to collect underwater imagery with real-time feedback, measure the spectral response, and quantify the water clarity with simple and relatively inexpensive instruments that can be hand-deployed from a small vessel. This article reviews the design and performance of the system, based on operational and logistical considerations, as well as the data requirements to support a number of coastal science and management projects. The OCS system has been operational since 2009 and has been used in several ground-truth missions that overlapped with airborne lidar bathymetry (ALB), hyperspectral imagery (HSI), and swath-sonar bathymetric surveys in the Gulf of Maine, southwest Alaska, and the US Virgin Islands (USVI). Research projects that have used the system include a comparison of backscatter intensity derived from acoustic (multibeam/interferometric sonars) versus active optical (ALB) sensors, ALB bottom detection, and seafloor characterization using HSI and ALB.