Weighing up the benefits and costs of waveform LiDAR

Karen Anderson, lead author of the paper, Is waveform worth it? A comparison of LiDAR approaches for vegetation and landscape characterisation, explains how waveform LiDAR can provide a more detailed view of urban vegetation structure.

What is laser scanning and how can ecologists use it?

Airborne laser scanning (ALS, sometimes called LiDAR) technology has been used successfully for many years in environmental and ecological research. Discrete return ALS systems, which measure the time taken for a laser pulse to travel to an object, are by far the most commonly used. The products generated by discrete return ALS are predominantly used to generate canopy height models for vegetated areas.

These ALS measurements are typically collected from a piloted aircraft, and when the pulse lands on the target surface it usually has a spatial ‘support’ around the size of a dinner plate (meaning that the footprint of the pulse and the area over which the measurement is collected is about 30 cm in diameter). When processed into a point cloud or interpolated product these data provide fine-grained detail of the surface being recorded.

Our research, funded by the Natural Environment Research Council BESS (Biodiversity and Ecosystem Services Sustainability) programme, aimed to describe the spatial pattern of urban green-space elements across three UK towns. In urban areas the fine-grained spatial detail from ALS data was crucial, because we wanted to capture the pattern of urban vegetation, which is notoriously patchy and highly variable spatially (see Figure 1).


We were also keen to understand the volumetric structure of the vegetation canopy, because in urban systems this is known to be important for controlling connectivity and for defining habitat quality. Vegetation volume can be inferred from canopy height models derived from discrete return ALS data (i.e. using a ‘spot height’ and assuming that the volume is filled all the way to the ground).

However, this approach is likely to provide a biased estimate of green volume because it is unlikely that the full volume beneath a tree-top, for example, would be completely full of vegetation matter. This is where waveform ALS systems can potentially help.

What is different about waveform ALS?

Waveform laser scanning provides a more sophisticated measurement than its discrete return counterpart. Rather than recording a single signal return (or sometimes up to four returns) per pulse as happens in discrete return systems, waveform ALS records the range to multiple targets within the canopy.

The signal carries information about different elements of the canopy as it illuminates them on its path to the ground (see Figure 2). This offers exciting opportunities for ecological research because waveform ALS has potential to capture data about the 3D nature of vegetation canopies. It is also exciting because many commercial laser scanners actually have the capability to capture waveform data but this remains unexploited.  Why? Well, the main reason is the computational cost of processing the waveform to allow good quality spatial and volumetric information to be extracted.

Is waveform worth it Figure 2.png

There is already a collection papers that describe the signal processing methods that should be followed to extract 3D information from waveform data. The challenge to ecologists is that most of these papers are found in technical journals and the approach is inaccessible to all but the most coding-savvy signal processing expert.

Our paper addresses a simple question: is this additional signal processing effort worth it for the ecologist interested in vegetation structure and function? We applied a relatively simple waveform processing algorithm to our own data and compared the waveform results to those captured from a discrete return ALS system. Both waveform and discrete return ALS data were captured simultaneously so we could make a direct comparison. We also used terrestrial laser scanning data collected from various field sites around the main survey zones to validate our findings and to make sense of the results (see Figure 3 for an example).

Our results demonstrate that the waveform ALS data performed more robustly over areas with vegetation than the discrete return ALS data did. We also show how the waveform can provide detailed information about the canopy understory, a critical variable in urban ecosystem science which is omitted by discrete return ALS. Finally, we show errors in discrete return ALS intensity, which were not present with waveform data.

So, what does this mean for ecologists?

Is waveform ALS data are worth the extra processing effort? I think so, and our paper demonstrates why. If you are working in systems with complex canopies our work shows how waveform ALS data are capable of providing detailed information on that crucial third dimension. We show that there are many new and open source tools which facilitate the processing of complex waveform data and these will no doubt develop over time to expedite and simplify the process of extracting 3D information from the waveform.


In my view, waveform laser data are on the cusp of an explosion: NASA will launch a new waveform laser scanner in the near future for improving understanding of the Earth’s carbon cycle and biodiversity. ‘GEDI’ (the Global Ecosystem Dynamics Investigation mission) will record waveform LiDAR measurements from the International Space Station.

This is an exciting time for spatial ecology – if you are working in areas where vegetation structure and function are central to your research, then waveform laser scanning is worth a look.

I hope our paper provides a useful and balanced view of the capabilities of this technology, and a platform from which the costs and merits of adopting this technology can be clearly understood.

I would like to acknowledge the support of the Natural Environment Research Council (NERC) and the Airborne Research and Survey Facility (ARSF) who supported the research and provided the waveform and discrete return ALS data to the project. In particular Dr Gary Llewellyn and Dr Mike Grant are acknowledged.

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