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

Douglas, Ewan S.; Martel, Jason; Ли, Жан; Howe, Glenn A.; Hewawasam, Kuravi; Marshall, R. A.; Schaaf, Crystal B.; Cook, Timothy A.; Newnham, Glenn; Strahler, Alan H.; Chakrabarti, Supriya

doi:10.1109/lgrs.2014.2361812

Cited by 70 publications

(62 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fig. 3(c) shows the short (σ = 0.23 m) but well sampled DWEL pulse (Douglas et al, 2015). This is near-Gaussian, but the detector filters out high-frequency returns, leading to an oscillation at the trailing edge.…”

Section: Lidar Systemsmentioning

confidence: 98%

“…This paper will explore the different methods to measure lidar return energy and which are more accurate over different surfaces. Several new lidar systems optimised for vegetation are in development covering terrestrial, airborne and satellite based systems (Danson et al, 2014;Douglas et al, 2015;Murooka et al, 2013;Wallace, Nichol, & Woodhouse, 2012) and so this type of data will become ever more common.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy

Hancock

Armston

et al. 2015

Remote Sensing of Environment

Self Cite

View full text Add to dashboard Cite

a b s t r a c tFull waveform lidar has a unique capability to characterise vegetation in more detail than any other practical method. The reflectance, calculated from the energy of lidar returns, is a key parameter for a wide range of applications and so it is vital to extract it accurately. Fifteen separate methods have been proposed to extract return energy (the amount of light backscattered from a target), ranging from simple to mathematically complex, but the relative accuracies have not yet been assessed. This paper uses a simulator to compare all methods over a wide range of targets and lidar system parameters. For hard targets the simplest methods (windowed sum, peak and quadratic) gave the most consistent estimates. They did not have high accuracies, but low standard deviations show that they could be calibrated to give accurate energy. This may be why some commercial lidar developers use them, where the primary interest is in surveying solid objects. However, simulations showed that these methods are not appropriate over vegetation. The widely used Gaussian fitting performed well over hard targets (0.24% root mean square error, RMSE), as did the sum and spline methods (0.30% RMSE). Over vegetation, for large footprint (15 m) systems, Gaussian fitting performed the best (12.2% RMSE) followed closely by the sum and spline (both 12.7% RMSE). For smaller footprints (33 cm and 1 cm) over vegetation, the relative accuracies were reversed (0.56% RMSE for the sum and spline and 1.37% for Gaussian fitting). Gaussian fitting required heavy smoothing (convolution with an 8 m Gaussian) whereas none was needed for the sum and spline. These simpler methods were also more robust to noise and far less computationally expensive than Gaussian fitting. Therefore it was concluded that the sum and spline were the most accurate for extracting return energy from waveform lidar over vegetation, except for large footprint (15 m), where Gaussian fitting was slightly more accurate. These results suggest that small footprint (≪15 m) lidar systems that use Gaussian fitting or proprietary algorithms may report inaccurate energies, and thus reflectances, over vegetation. In addition the effect of system pulse length, sampling interval and noise on accuracy for different targets was assessed, which has implications for sensor design.

show abstract

Section: Lidar Systemsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy

Hancock

Armston

et al. 2015

Remote Sensing of Environment

Self Cite

View full text Add to dashboard Cite

show abstract

“…Four vegetation indexes were used in the classification process and achieved an overall accuracy of up to 91%. Other studies used the TLS system for measuring the three-dimensional structure of forest canopies by two lasers with wavelengths of 1063 nm and 1545 nm [29] and discriminating leaves from woody material in forestry areas by using data collected at dual-wavelengths (i.e., 1064 and 1548 nm) [30]. A few attempts have been conducted to combine different flight missions from airborne LiDAR systems of the same study area [31,32].…”

Section: Multispectral Lidar Systemsmentioning

confidence: 99%

Using Multispectral Airborne LiDAR Data for Land/Water Discrimination: A Case Study at Lake Ontario, Canada

2018

View full text Add to dashboard Cite

Coastal areas are environmentally sensitive and are affected by nature events and human activities. Land/water interaction in coastal areas changes over time and, therefore, requires accurate detection and frequent monitoring. Multispectral Light Detection and Ranging (LiDAR) systems, which operate at different wavelengths, have become available. This new technology can provide an effective and accurate solution for the determination of the land/water interface. In this context, we aim to investigate a set of point features based on elevation, intensity, and geometry for this application, followed by a presentation of an unsupervised land/water discrimination method based on seeded region growing algorithm. The multispectral airborne LiDAR sensor, the Optech Titan, was used to acquire LiDAR data at three wavelengths (1550, 1064, and 532 nm) of a study area covering part of Lake Ontario in Scarborough, Canada for testing the discrimination methods. The elevationand geometry-based features achieved an average overall accuracy of 75.1% and 74.2%, respectively, while the intensity-based features achieved 63.9% accuracy. The region growing method succeeded in discriminating water from land with more than 99% overall accuracy, and the land/water boundary was delineated with an average root mean square error of 0.51 m. The automation of this method is restricted by having double returns from water bodies at the 532 nm wavelength.

show abstract

“…Additionally high separability between trunk and tree crown is given. Douglas et al (2015) apply the so called dual wavelength Echidna LiDAR (DWEL), which is a TLS scanner operating with 1,064 nm and 1,548 nm wavelengths. They use the system for an outdoor experiment separating canopy (leafs) from tree trunks calculating a normalized difference of channel intensity.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Potential of Multispectral Airborne Lidar for Topographic Mapping and Land Cover Classification

Wichmann¹,

Bremer

Lindenberger³

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Recently multispectral LiDAR became a promising research field for enhanced LiDAR classification workflows and e.g. the assessment of vegetation health. Current analyses on multispectral LiDAR are mainly based on experimental setups, which are often limited transferable to operational tasks. In late 2014 Optech Inc. announced the first commercially available multispectral LiDAR system for airborne topographic mapping. The combined system makes synchronic multispectral LiDAR measurements possible, solving time shift problems of experimental acquisitions. This paper presents an explorative analysis of the first airborne collected data with focus on class specific spectral signatures. Spectral patterns are used for a classification approach, which is evaluated in comparison to a manual reference classification. Typical spectral patterns comparable to optical imagery could be observed for homogeneous and planar surfaces. For rough and volumetric objects such as trees, the spectral signature becomes biased by signal modification due to multi return effects. However, we show that this first flight data set is suitable for conventional geometrical classification and mapping procedures. Additional classes such as sealed and unsealed ground can be separated with high classification accuracies. For vegetation classification the distinction of species and health classes is possible.

show abstract

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

Cited by 70 publications

References 10 publications

Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy

Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy

Using Multispectral Airborne LiDAR Data for Land/Water Discrimination: A Case Study at Lake Ontario, Canada

Evaluating the Potential of Multispectral Airborne Lidar for Topographic Mapping and Land Cover Classification

Contact Info

Product

Resources

About