Zhan Li

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.

Capabilities and performance of dual-wavelength Echidna ® lidar

Abstract. We describe the capabilities and performance of a terrestrial laser scanning instrument built for the purpose of recording and retrieving the three-dimensional structure of forest vegetation. The dual-wavelength Echidna® lidar characterizes the forest structure at an angular resolution as fine as 1 mrad while distinguishing between leaves and trunks by exploiting their differential reflectances at two wavelengths: 1 and 1.5 μm. The instrument records the full waveforms of return signals from 5 ns laser pulses at half-nanosecond time resolution; obtains ±117 deg zenith and 360 deg azimuth coverage out to a radius of more than 70 m; provides single-target range resolution of 4.8 and 2.3 cm for the 1 and 1.5 μm channels, respectively (1σ); and separates adjacent pulse returns in the same waveform at a distance of 52.0 and 63.8 cm apart for the 1 and 1.5 μm channels, respectively. The angular resolution is in part controlled by user-selectable divergence optics and is shown to be <2 mrad for the instrument’s standard resolution mode, while the signal-to-noise ratio is 10 at 70 m range for targets with leaf-like reflectance for both channels. The portability and target differentiation make the instrument an ideal ground-based lidar suited for vegetation sensing.

Radiometric Calibration of a Dual-Wavelength, Full-Waveform Terrestrial Lidar

Radiometric calibration of the Dual-Wavelength Echidna® Lidar (DWEL), a full-waveform terrestrial laser scanner with two simultaneously-pulsing infrared lasers at 1064 nm and 1548 nm, provides accurate dual-wavelength apparent reflectance (ρapp), a physically-defined value that is related to the radiative and structural characteristics of scanned targets and independent of range and instrument optics and electronics. The errors of ρapp are 8.1% for 1064 nm and 6.4% for 1548 nm. A sensitivity analysis shows that ρapp error is dominated by range errors at near ranges, but by lidar intensity errors at far ranges. Our semi-empirical model for radiometric calibration combines a generalized logistic function to explicitly model telescopic effects due to defocusing of return signals at near range with a negative exponential function to model the fall-off of return intensity with range. Accurate values of ρapp from the radiometric calibration improve the quantification of vegetation structure, facilitate the comparison and coupling of lidar datasets from different instruments, campaigns or wavelengths and advance the utilization of bi- and multi-spectral information added to 3D scans by novel spectral lidars.

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.

On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements

The Dual-Wavelength Echidna Lidar (DWEL), a full waveform terrestrial laser scanner (TLS), has been used to scan a variety of forested and agricultural environments. From these scanning campaigns, we summarize the benefits and challenges given by DWELs novel coaxial dual-wavelength scanning technology, particularly for the three-dimensional (3D) classification of vegetation elements. Simultaneous scanning at both 1064 nm and 1548 nm by DWEL instruments provides a new spectral dimension to TLS data that joins the 3D spatial dimension of lidar as an information source. Our point cloud classification algorithm explores the utilization of both spectral and spatial attributes of individual points from DWEL scans and highlights the strengths and weaknesses of each attribute domain. The spectral and spatial attributes for vegetation element classification each perform better in different parts of vegetation (canopy interior, fine branches, coarse trunks, etc.) and under different vegetation conditions (dead or live, leaf-on or leaf-off, water content, etc.). These environmental characteristics of vegetation, convolved with the lidar instrument specifications and lidar data quality, result in the actual capabilities of spectral and spatial attributes to classify vegetation elements in 3D space. The spectral and spatial information domains thus complement each other in the classification process. The joint use of both not only enhances the classification accuracy but also reduces its variance across the multiple vegetation types we have examined, highlighting the value of the DWEL as a new source of 3D spectral information. Wider deployment of the DWEL instruments is in practice currently held back by challenges in instrument development and the demands of data processing required by coaxial dual- or multi-wavelength scanning. But the simultaneous 3D acquisition of both spectral and spatial features, offered by new multispectral scanning instruments such as the DWEL, opens doors to study biophysical and biochemical properties of forested and agricultural ecosystems at more detailed scales.

Classifying ecosystems with metaproperties from terrestrial laser scanner data

Abstract In this study, we introduce metaproperty analysis of terrestrial laser scanner (TLS) data, and demonstrate its application through several ecological classification problems. Metaproperty analysis considers pulse level and spatial metrics derived from the hundreds of thousands to millions of lidar pulses present in a single scan from a typical contemporary instrument. In such large aggregations, properties of the populations of lidar data reflect attributes of the underlying ecological conditions of the ecosystems. In this study, we provide the Metaproperty Classification Model to employ TLS metaproperty analysis for classification problems in ecology. We applied this to a proof‐of‐concept study, which classified 88 scans from rooms and forests with 100% accuracy, to serve as a template. We then applied the Metaproperty Classification Model in earnest, to separate scans from temperate and tropical forests with 97.09% accuracy (N = 224), and to classify scans from inland and coastal tropical rainforests with 84.07% accuracy (N = 270). The results demonstrate the potential for metaproperty analysis to identify subtle and important ecosystem conditions, including diseases and anthropogenic disturbances. Metaproperty analysis serves as an augmentation to contemporary object reconstruction applications of TLS in ecology, and can characterize regional heterogeneity.

Improving waveform lidar processing toward robust deconvolution of signals for improved structural assessments

In a typical waveform light detection and ranging (lidar) system, the received pulse can be represented by the convolution of the system impulse response, the outgoing pulse, and the underlying signal representing actual target interactions. Deconvolution is the process of removing the contribution of the system impulse response and outgoing pulse from the received signal, so that the true interactions may be seen. In many examples, deconvolution has been shown to expose fine structure within the waveform, which may be used to improve accuracy when estimating the vertical location of certain features. For instance, the exact location of the ground may be more accurately determined by separating the response of the ground from that of understory vegetation or vegetative ground cover. However, in order for the deconvolution to be successful, the impulse response and outgoing pulse must be known, and many deconvolution methods are sensitive to small errors in the estimation of these inputs. In this study, we propose a deconvolution method that uses a flat target response in place of the impulse response and outgoing pulse.

Studying canopy structure through 3-D reconstruction of point clouds from full-waveform terrestrial lidar

This study presents a three-dimensional (3-D) forest reconstruction methodology using the new and emerging science of terrestrial full-waveform lidar scanning, which can provide rapid and efficient measurements of canopy structure. A 3-D forest reconstruction provides a new pathway to estimate forest structural parameters such as tree diameter at breast height, tree height, crown diameter, and stem count density (trees per hectare). It enables the study of the detailed structure study with respect to the canopy (foliage or branch/trunk), as well as the generation of a digital elevation model (DEM) and a canopy height model (CHM) at the stand level. Leaf area index (LAI) and Foliage area volume density profile directly estimated from voxelized 3-D reconstruction agree with measurements from field and airborne instrument. A 3-D forest reconstruction allows virtual direct representation of forest structure, and provides consistent and reliable validation data sources for airborne or spaceborne data.