Glenn Newnham

Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna ® )

A prototype upward-scanning, under-canopy, near-infrared light detection and ranging (lidar) system, the Echidna® validation instrument (EVI), built by CSIRO Australia, retrieves forest stand structural parameters, including mean diameter at breast height (DBH), stand height, distance to tree, stem count density (stems/area), leaf-area index (LAI), and stand foliage profile (LAI with height) with very good accuracy in early trials. We validated retrievals with ground-truth data collected from two sites near Tumbarumba, New South Wales, Australia. In a ponderosa pine plantation, LAI values of 1.84 and 2.18 retrieved by two different methods using a single EVI scan bracketed a value of 1.98 estimated by allometric equations. In a natural, but managed, Eucalypus stand, eight scans provided mean LAI values of 2.28–2.47, depending on the method, which compare favorably with a value of 2.4 from hemispherical photography. The retrieved foliage profile clearly showed two canopy layers. A “find-trunks” algorithm processed the EVI scans at both sites to identify stems, determine their diameters, and measure their distances from the scan center. Distances were retrieved very accurately (r2 = 0.99). The accuracy of EVI diameter retrieval decreased somewhat with distance as a function of angular resolution of the instrument but remained unbiased. We estimated stand basal area, mean diameter, and stem count density using the Relaskop method of variable radius plot sampling and found agreement with manual Relaskop values within about 2% after correcting for the obscuring of far trunks by near trunks (occlusion). These early trials prove the potential of under-canopy, upward-scanning lidar to retrieve forest structural parameters quickly and accurately.

Nondestructive estimates of above‐ground biomass using terrestrial laser scanning

Summary: Allometric equations are currently used to estimate above-ground biomass (AGB) based on the indirect relationship with tree parameters. Terrestrial laser scanning (TLS) can measure the canopy structure in 3D with high detail. In this study, we develop an approach to estimate AGB from TLS data, which does not need any prior information about allometry. We compare these estimates against destructively harvested AGB estimates and AGB derived from allometric equations. We also evaluate tree parameters, diameter at breast height (DBH) and tree height, estimated from traditional field inventory and TLS data. Tree height, DBH and AGB data are collected through traditional forest inventory, TLS and destructive sampling of 65 trees in a native Eucalypt Open Forest in Victoria, Australia. Single trees are extracted from the TLS data and quantitative structure models are used to estimate the tree volume directly from the point cloud data. AGB is inferred from these volumes and basic density information and is then compared with the estimates derived from allometric equations and destructive sampling. AGB estimates derived from TLS show a high agreement with the reference values from destructive sampling, with a concordance correlation coefficient (CCC) of 0·98. The agreement between AGB estimates from allometric equations and the reference is lower (CCC = 0·68-0·78). Our TLS approach shows a total AGB overestimation of 9·68% compared to an underestimation of 36·57-29·85% for the allometric equations. The error for AGB estimates using allometric equations increases exponentially with increasing DBH, whereas the error for AGB estimates from TLS is not dependent on DBH. The TLS method does not rely on indirect relationships with tree parameters or calibration data and shows better agreement with the reference data compared to estimates from allometric equations. Using 3D data also enables us to look at the height distributions of AGB, and we demonstrate that 80% of the AGB at plot level is located in the lower 60% of the trees for a Eucalypt Open Forest. This method can be applied in many forest types and can assist in the calibration and validation of broad-scale biomass maps.s

Status and prospects for LiDAR remote sensing of forested ecosystems

The science associated with the use of airborne and satellite Light Detection and Ranging (LiDAR) to remotely sense forest structure has rapidly progressed over the past decade. LiDAR has evolved from being a poorly understood, potentially useful tool to an operational technology in a little over a decade, and these instruments have become a major success story in terms of their application to the measurement, mapping, or monitoring of forests worldwide. Invented in 1960, the laser and, a short time later, LiDAR, were found in research and military laboratories. Since the early 2000s, commercial technological developments coupled with an improved understanding of how to manipulate and analyze large amounts of collected data enabled notable scientific and application developments. A diversity of rapidly developing fields especially benefit from communications offered through conferences such as SilviLaser, and LiDAR has been no different. In 2002 the SilviLaser conference series was initiated to bring together those interested in the development and application of LiDAR for forested environments. Now, a little over a decade later, commercial use of LiDAR is common. In this paper – using the deliberations of SilviLaser 2012 as a source of information – we aim to capture aspects of importance to LiDAR users in the forest ecosystems community and to also point to key emerging issues as well as some remaining challenges.

Finding Leaves in the Forest: The Dual-Wavelength Echidna Lidar

The dual-wavelength Echidna lidar is a portable ground-based full-waveform terrestrial scanning lidar for characterization of fine-scale forest structure and biomass content. While scanning, the instrument records the full time series of returns at a half-nanosecond rate from two coaligned 5-ns pulsed lasers at 1064 and 1548 nm wavelengths. Leaves absorb more strongly at 1548 nm compared to stems, allowing discrimination of forest composition at milliradian scales from the ground to the forest canopy. This work describes the instrument design and data products and demonstrates the power of two wavelength lidar to clearly distinguish leaves from woody material with preliminary field data from the Sierra Nevada National Forest.

DWEL: A Dual-Wavelength Echidna Lidar for ground-based forest scanning

The Dual-Wavelength Echidna® Lidar (DWEL), a ground-based, full-waveform lidar scanner designed for automated retrieval of forest structure, uses simultaneously-pulsing, 1064 nm and 1548 nm lasers to separate scattering by leaves from scattering by trunks, branches, and ground materials. Leaf hits are separated from others by a reduced response at 1548 nm due to water absorption by leaf cellular contents. By digitizing the full return-pulse waveform (full-width half maximum, 1.5 m) at 7.5 cm intervals, the scanner can identify the type of scattering event, as well as identify and separate multiple scattering events along the pulse path to reconstruct multiple hits at distances of up to 100 m from the scanner. Scanning covers zenith angles of 0-119° and 360 azimuth with pulse centers spaced at 4, 2, and 1 mrad intervals, providing spatial resolutions of 4-40, 2-20, and 1-10 cm respectively at 10 and 100 m distances. The instrument is currently undergoing integration and testing for field deployment in July-August, 2012.

A simple technique for co-registration of terrestrial LiDAR observations for forestry applications

Light detection and ranging (LiDAR) from terrestrial platforms provides unprecedented detail about the three-dimensional structure of forest canopies. Although airborne laser scanning is designed to yield a relatively homogeneous distribution of returns, the radial perspective of terrestrial laser scanning (TLS) results in a rapid decrease of number of returns with increasing distance from the instrument. Additionally, when used in forested environments, significant parts of the area under investigation may be obscured by tree trunks and understorey. A possible approach to mitigate this effect is to combine TLS observations acquired at different locations to obtain multiple perspectives of an area under investigation. The denser and more evenly distributed observations then allow a spatially explicit and more comprehensive study of forest characteristics. This study demonstrates a simple approach to combine TLS observations made at multiple locations using bright reference targets as tie-points. Results show this technique was able to accurately combine the different TLS data sets (root mean square error (RMSE): 0.04–0.7 m, coefficient of determination (R 2): 0.70–0.99). Terrain elevations from TLS system were highly correlated with field-measured terrain heights (R 2: 0.70–0.98).

Automated In-Situ Laser Scanner for Monitoring Forest Leaf Area Index

An automated laser rangefinding instrument was developed to characterize overstorey and understorey vegetation dynamics over time. Design criteria were based on information needs within the statewide forest monitoring program in Victoria, Australia. The ground-based monitoring instrument captures the key vegetation structural information needed to overcome ambiguity in the estimation of forest Leaf Area Index (LAI) from satellite sensors. The scanning lidar instrument was developed primarily from low cost, commercially accessible components. While the 635 nm wavelength lidar is not ideally suited to vegetation studies, there was an acceptable trade-off between cost and performance. Tests demonstrated reliable range estimates to live foliage up to a distance of 60 m during night-time operation. Given the instruments scan angle of 57.5 degrees zenith, the instrument is an effective tool for monitoring LAI in forest canopies up to a height of 30 m. An 18 month field trial of three co-located instruments showed consistent seasonal trends and mean LAI of between 1.32 to 1.56 and a temporal LAI variation of 8 to 17% relative to the mean.

Retrieval of Gap Fraction and Effective Plant Area Index from Phase-Shift Terrestrial Laser Scans

The characterization of canopy structure is crucial for modeling eco-physiological processes. Two commonly used metrics for characterizing canopy structure are the gap fraction and the effective Plant Area Index (PAIe). Both have been successfully retrieved with terrestrial laser scanning. However, a systematic assessment of the influence of the laser scan properties on the retrieval of these metrics is still lacking. This study investigated the effects of resolution, measurement speed, and noise compression on the retrieval of gap fraction and PAIe from phase-shift FARO Photon 120 laser scans. We demonstrate that FARO’s noise compression yields gap fractions and PAIe that deviate significantly from those based on scans without noise compression and strongly overestimate Leaf Area Index (LAI) estimates based on litter trap measurements. Scan resolution and measurement speed were also shown to impact gap fraction and PAIe, but this depended on leaf development phase, stand structure, and LAI calculation method. Nevertheless, PAIe estimates based on various scan parameter combinations without noise compression proved to be quite stable.

Study of bat flight behavior by combining thermal image analysis with a LiDAR forest reconstruction

The nature of forest structure plays an important role in the study of foraging behaviors of bats. In this study, we demonstrate a new combined methodology that uses both thermal imaging technology and a ground-based LiDAR system to record and reconstruct Eptesicus fuscus (big brown bats) flight trajectories in three-dimensional (3-D) space. The combination of the two 3-D datasets provided a fine-scale reconstruction of the flight characteristics adjacent to and within the forests. A 3-D forest reconstruction, assembled from nine Echidna Validation Instrument LiDAR scans over the 1 ha site area, provided the essential environmental variables for the study of bat foraging behaviors, such as the canopy height, terrain, location of the obstacles, and canopy openness at a bat roosting and maternity site in Petersham, Massachusetts. Flight trajectories of 24 bats were recorded over the 25 m × 37.5 m region within the LiDAR forest reconstruction area. The trajectories were reconstructed using imaging data from multiple FLIR ThermoVision SC8000 cameras and were co-registered to the 3-D forest reconstruction. Twenty-four of these flight trajectories were categorized into four different behavior groups according to velocity and altitude analysis of the flight trajectories. Initial results showed that although all bats were guided by echolocation and avoided hitting a tree that was in all of their flight paths, different bats chose different flight routes. This study is an initial demonstration of the power of coupling thermal image analysis and LiDAR forest reconstructions. Our goal was to break ground for future ecological studies, where more extensive flight trajectories of bats can be coupled with the canopy reconstructions to better establish responses of bats to different habitat characteristics and clutter, which includes both static (trees) and dynamic (other bats) obstacles.

Separating leaves from trunks and branches with dual-wavelength terrestrial lidar scanning

Terrestrial laser scanning combining both near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths can readily distinguish broad leaves from trunks, branches, and ground surfaces. Merging data from the 1548 nm SWIR laser in the Dual-Wavelength Echidna® Lidar (DWEL) instrument in engineering trials with data from the 1064 nm NIR laser in the Echidna® Validation Instrument (EVI), we imaged a deciduous forest scene at the Harvard Forest, Petersham, Massachusetts, and showed that trunks are about twice as bright as leaves at 1548 nm, while they have about equal brightness at 1064 nm. The reduced return of leaves in the SWIR is also evident in merged point clouds constructed from the two laser scans. This distinctive difference between leaf and trunk reflectance in the two wavelengths validates the principle of effective discrimination of leaves from other targets using the new dual-wavelength instrument.