K.C. Slatton

Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests

Measuring individual trees can provide valuable information about forests, and airborne light detection and ranging (LiDAR) sensors have been used recently to identify individual trees and measure structural tree parameters. Past results, however, have been mixed because of reliance on interpolated (image) versions of the LiDAR measurements and search methods that do not adapt to variations in canopies. In this work, an adaptive clustering method is developed using airborne LiDAR data acquired over two distinctly different managed pine forests in North-Central Florida, USA. A crucial issue in isolating individual trees is determining the appropriate size of the moving window (search radius) when locating seed points. The proposed approach works directly on the three-dimensional (3D) ‘cloud’ of LiDAR points and adapts to irregular canopies sizes. The region growing step yields collectively exhaustive sets in an initial segmentation of tree canopies. An agglomerative clustering step is then used to merge clusters that represent parts of whole canopies using locally varying height distribution. The overall tree detection accuracy achieved is 95.1% with no significant bias. The tree detection enables subsequent estimation of tree height and vertical crown length to an accuracy better than 0.8 and 1.5 m, respectively.

Prediction of forest canopy light interception using three-dimensional airborne LiDAR data

The amount of light intercepted by forest canopies plays a crucial role in forest primary production. However, the photosynthetically active part of this intercepted solar radiation (IPAR) is difficult to measure using traditional ground‐based techniques. In situ measurement of IPAR requires labour‐intensive field work, often resulting in limited datasets, especially when collected over extensive areas. Remote sensing methods have been applied to the estimation of light interception in forests, but until recently have been restricted to two‐dimensional image data. These approaches do not directly account for the three‐dimensional structure of forested canopies, and therefore predicting IPAR for arbitrary sun positions is problematic. We utilized a 3D point cloud dataset acquired via an airborne laser ranging (LiDAR) system to predict in situ measured IPAR. This was achieved by defining a field‐of‐view (scope) function between observer points just above the forest floor and the sun, which relate IPAR to the LiDAR data over southern pine experimental plots containing a wide range of standing biomass. A conical scope function with an angular divergence from the centreline of ±7° provided the best agreement with the in situ measurements. This scope function yielded remarkably consistent IPAR estimates for different pine species and growing conditions. IPAR for loblolly stands, which have diffuse canopy architecture, was slightly underestimated.

Predicting Small Target Detection Performance of Low-SNR Airborne Lidar

Recent technological advances in the performance of small micro-lasers and multi-channel multi-event photo-detectors have enabled the development of experimental airborne lidar (light detection and ranging) systems based on a low-SNR (LSNR) paradigm. Due to dense point spacing (tens of points per square meter) and sub-decimeter range resolution, LSNR lidar can likely enable detection of meter-scale targets that would go unnoticed by traditional lidar technology. Small vehicle obstructions and other similar targets in the beach and littoral zones are of particular interest, because of LSNR lidars applicability to the near-shore environment and the general desire to improve detection of antivehicle and antipersonnel obstacles in the coastal zone. A target detection procedure is presented that exploits the detailed information available from LSNR lidar data while diminishing the effect of spurious noise events. Consideration is given to detection in both topographic and bathymetric scenarios. Data sets for target detection analysis are supplied by a numerical sensor simulator developed at the University of Florida. Target detection performance is evaluated as a function of environmental characteristics, such as water clarity and depth, and system parameters, specifically transmitted pulse energy and laser pulse repetition frequency. Analysis of results with regards to consideration for future system design is discussed.

Predicting Topographic and Bathymetric Measurement Performance for Low-SNR Airborne Lidar

Government and commercial airborne light detection and ranging (lidar) systems have enabled extensive measurements of the Earths surface and land cover over the past decade. There is much interest, however, in employing smaller lidar systems that require less power to enable sensing from small unmanned aerial vehicles or satellites. Technological advances in the performance of small microlasers and photodetector sensitivity have recently enabled the development of experimental airborne lidar systems with low signal-to-noise ratios (LSNRs). Recent government and academic prototypes have indicated that LSNR airborne lidars could significantly increase the fidelity of terrain reconstruction over what is possible with existing conventional lidars. Thus, there is a need to build up a modeling capability for such systems in order to aid in future system and mission design. A numerical sensor simulator has been developed to model the expected returns from LSNR microlaser altimeter systems and predict their performance. Both optical and signal processing system components are considered, along with other factors, including atmospheric effects and surface conditions. Topographic (solid Earth) and bathymetric (littoral zone) measurement scenarios are considered. The analysis of topographic simulation data focuses on the effect of solar noise on SNR and elevation accuracy while bathymetric performance is evaluated with regard to water depth and scan angle for different water clarities. The mission conditions chiefly responsible for limiting the performance of LSNR lidar are discussed in detail, along with suggestions for further algorithm development and system performance evaluation.

Analysis of spatial and temporal stability of airborne laser swath mapping data in feature space

Several features extracted from airborne laser swath mapping (ALSM) data are examined to determine their effectiveness in separating buildings from trees across geographically and temporally diverse landscapes. These two classes are often spatially mixed in urban and suburban areas and can be quite difficult to separate based solely on geometric information due to the discrete sampling of ALSM. New median-based distance measures are used to quantify the separability of the classes using the different features. Information-based measures are also applied to the same data. For each of the test cases, it is possible to identify a common feature space in which the distance between the two classes is large. This distance information is an indication of the separability between classes and is therefore indicative of the potential success likely when trying to classify ALSM data. This analysis provides new insights into the richness of simple two-return ALSM data and to the spatial and temporal stability of ALSM features when discriminating between classes.

Shallow Bathymetric Mapping via Multistop Single Photoelectron Sensitivity Laser Ranging

We discuss the optimization of components in a single-wavelength airborne laser bathymeter that is intended for a low-power unmanned aerial vehicle platform. The theoretical minimum energy requirement to detect the submerged sea floor in shallow (<; 5 m) water using a low signal-to-noise ratio (LSNR) detection methodology is calculated. Results are presented from tests of a prototype light detection and ranging (LiDAR) instrument that was developed by the University of Florida, Gainesville. A green wavelength (532 nm), 100-beamlet, low-energy (35-nJ/beamlet), short-pulse (480 ps) laser ranging system was operated from a low-altitude (500-m) aircraft, with a multichannel sensor that is capable of single photoelectron sensitivity and multiple stops. Data that were collected during tests display vertical structure in shallow-water areas based on fixed threshold crossings at a single-photon sensitivity level. A major concern for the binary detection strategy is the reliable identification and removal of noise events. Potential causes of ranging errors related to photomultiplier tube afterpulsing, impedance mismatching, and gain block overdrive are described. Data collection/processing solutions based on local density estimation are explored. Previous studies on LSNR performance metrics showed that short (15-cm) dead time could be expected in the case of multiple scattering objects, indicating the possibility of seamless topographic/bathymetric mapping with minimal discontinuity at the waterline. LiDAR depth estimates from airborne profiles are compared to on-site measurements, and near-shore submerged feature identification is presented.

Predicting L-band Microwave Attenuation through Forest Canopy using Directional Structuring Elements and Airborne Lidar

The L-band signals broadcast by GPS satellites are attenuated by vegetation, making it problematic, if not impossible, to predict the performance of the system in forested areas without some quantitative measure of the structure and density of the local forest canopy. Airborne laser swath mapping (ALSM) observations can be used to rapidly and remotely sample the structure and density of forested areas. We report here the results of a study performed to determine the attenuation of GPS signals in forests, by correlating changes in the signal-to-noise ratio (SNR) of the received GPS signals under different canopies, using three dimensional structure and density information about each canopy derived from ALSM observations. The results of this study verify that the loss of signal is strongly correlated with the local structure and density of the forest, and we demonstrate how the ALSM point cloud can be used to better predict the attenuation of the GPS signals. The results of this research also pertain to other modes of microwave transmission in forested areas, including satellite and cellular telephony, and the estimation of biomass from L-band radar.

Morphological segmentation of Lidar Digital Elevation Models to extract stream channels in forested terrain

Our paper proposes an approach for the extraction of stream channels from Airborne Laser Swath Mapping (ALSM) data. Recent advances in technology have led to high-resolution topographic data acquisition by means of airborne lidar (i.e. ALSM), which can yield Digital Elevation Model (DEM) datasets with horizontal resolutions of 1 m and vertical rms errors in the range of 10 - 15 cm. The extraction of a stream network from a DEM plays a fundamental role in modeling spatially distributed hydrological processes and flow routing. We apply morphological filtering to an ALSM DEM to detect and characterize stream channels in forested terrain. Since the size and shape of morphological Structuring Elements (SEs) is known to strongly affect filtered results, we test for accuracy by developing a set of error measures over simulated terrain. We subsequently apply the filter to actual ALSM data. For linking disconnected stream segments, a measure of pixel connectedness known as the Connectivity Number is used. The method presented is shown to enable systematic characterization and comparisons of streams, even in heavily forested terrain.

Extraction of Stream Channels in High-Resolution Digital Terrain Images Using Morphology

Our paper proposes an approach for the extraction of stream channels from airborne laser swath mapping (ALSM) data. Recent advances in technology have led to high-resolution topographic data acquisition by means of ALSM yielding digital elevation model (DEM) datasets with horizontal resolutions of lm and a vertical accuracy of 0.15 m. We apply morphological operations on an ALSM DEM to detect stream channels. The results are compared with an existing terrain analysis tool known as TauDEM. Tools like TauDEM use morphology over large-scale areas but they often fail in detecting stream networks over small-scale areas. The proposed method uses small-scale morphology to provide complementary results for streamline locations over a small catchment area.

Segmentation of ALSM point data and the prediction of subcanopy sunlight distribution

Sunlight flux in forest canopies is an important source of energy for the productivity of forests and is therefore an important input to biophysical process models. High-resolution three-dimensional estimates of sunlight flux are derived from directional foliage density measured from airborne laser swath mapping data. Forest edges are shown to exhibit measurable differences in flux. A method for determining appropriate in situ sampling intervals is also presented.