By Thiago Silva, University of Stirling, and Sam Hughes, Forest Research.

​Around 13 thousand hectares of newly created woodland were reported in the UK for just 2022/23 [1], an area roughly the size of Bristol. Monitoring the development of these woodlands through field work alone would be a massive endeavour, as highlighted last year by Laura Braunholz on this very blog. So as part of our research in TreE_PlaNat, we are also testing a different approach for monitoring woodlands – by shooting lasers at them.  ​

Traditionally, satellite imagery has been used to monitor large extents of woodland and other habitats, but it has some limitations. The best freely available imagery, from the Sentinel-2 platform, gives us images with 10 metres per pixel - to go beyond that we would need to purchase expensive, commercial satellite imagery. Moreover, satellite images can be obscured by clouds, which may be a bit of a problem for monitoring land surface in the UK… ​

But even with this amazing level of detail, all we get from aerial photography is the top of the canopy. This is useful for mapping woodland extent, identifying individual tree crowns and gaps, and maybe even picking up species. But when it comes to monitoring woodlands, the vertical component of the canopy is very important. How tall are the trees? How dense is the canopy? How much understory vegetation is there? This vertical complexity is a key component for understanding habitat quality, carbon stocks, and the overall quality and health of a woodland.  ​

One useful way to look at LiDAR data is by taking a cross-section (a ‘slice’) of the point cloud, so we can look at it from the side. On the figure below, the red line shows the direction of the slice on a top and a perspective view of the point cloud, and the actual slice is shown at the bottom.  It is easy to see there how, from left to right, we transition from a more mature, taller woodland to an open gap with a single standing dead tree, and then to a younger stand with shorter and denser trees. We can also see where the beams hit the ground, revealing the curved shape of the underlying terrain.  ​

Although we can understand a lot about the structure of each woodland by just looking at the full 3D point cloud and the different slices, to be more consistent and repeatable we also try to be quantitative about it. Over the years, scientists have devised several lidar metrics, calculations based on the position and distribution of points, which enable us to measure and compare different woodlands. One of the most basic metrics is the actual canopy height. To obtain it, we can’t just measure the tops of the trees in the point cloud. A shorter tree at a higher elevation would seem taller otherwise. So one of the first steps when processing LiDAR data is to isolate ground points and interpolate them to create a Digital Terrain Model (DTM), a representation of the ground surface as if it was bare. We can also interpolate the top points of the point cloud to generate a Digital Surface Model (DSM), as if we draped a cloth on the land surface and followed its contours. We can then subtract the DTM from the DSM to get a Canopy Height Model (CHM), a representation of the woodland as if it was standing on perfectly flat ground. From the CHM, we can then calculate the height of every tree, and see how tall the woodland is on average, as well as how variable tree heights are. Tree height is strongly associated with woodland age and with wood volume and carbon stocks, so it is a very important metric.  ​

Other metrics will look at the vertical complexity of the woodland, for example by looking at how skewed the distribution of point heights is towards the top or bottom of the woodland. A point cloud where most points are clustered at the top means a dense canopy with no underbrush, while a more even distribution means there is a well-developed subcanopy. A grassland with sparse trees would be skewed towards the bottom, as most of the plant mass is close to the ground. And we have even more creative metrics, such as the ‘rumple index’ (we always chuckle saying it). It is calculated is by estimating the total surface area (again, imagine a cloth being draped over the woodland, and then measuring the area of that cloth), and then dividing it by the corresponding flat ground area. A high number will mean a very, well…rumpled surface with lots of consecutive ups and downs, meaning a woodland with trees of uneven height and lots of gaps. A smaller number means a more even, ‘tidy’ surface, for example for a planted tree monoculture. 

As we also took measurements of habitat structure on the ground, measuring tree stem density and diameter at breast height (DBH) in plots across our sites, we can compare drone metrics with field-based assessments. At TreE_PlaNat, we are using these metrics to compare how naturally colonized, planted, and mixed woodlands differ in their structure, and consequently, their habitat quality and complexity. To see some of our early results and ask any questions you may have, watch our webinar (held 17th May 2024) here. 

References
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[1] https://www.forestresearch.gov.uk/tools-and-resources/statistics/statistics-by-topic/woodland-statistics/
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[2] https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13912

[3] https://www.tandfonline.com/doi/abs/10.5589/m13-011

By Laura Braunholtz, University of Stirling

When it comes to new areas of woodland, questions remain about the extent to which the way they are created plays a role in shaping their structure, biodiversity, and functioning. Is it always true that woodlands that are created through planting will have a more homogeneous form and structure compared to naturally colonised ones? Are trees within naturally colonised woodlands more resistant to disease than those in planted areas? Do woodlands formed through different processes provide habitat for the same species of wildlife?
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In trying to answer these questions and more, myself and two field researchers, Megan Layton and Billy Dykes, found ourselves crouched in a thicket of blackthorn under the blazing mid-June sun, tape measures and clipboards in hand. It was unfortunate that the random number generator had directed us to this particular location, but as we quickly learned, in young naturally colonised woodlands you’re never too far from spiky vegetation. This was during the summer of 2023, and our team of three was carrying out the ecological fieldwork for the TreE PlaNat project. We conducted a range of surveys across our study woodlands, involving multiple trips to each site between May and September.

Preparation is key

Getting to the point of carrying out the surveys had taken considerable preparation. Risk assessments, protocols, purchasing equipment, and, of course, the all-important site selection process. Selecting the woodlands that made up our study locations involved months of reviewing the long list of potential sites, poring over maps and satellite images, consulting Woodland Grant Scheme datasets, comparing notes with colleagues at Forest Research, and the challenge of tracking down landowners and managers to gain permission to carry out our surveys. The resulting list of 28 sites was spread across central and southern England.
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We kept a number of properties consistent across our sites; they were all relatively young woodlands, ranging from 15-30 years old, adjacent to areas of established woodland and on land that had previously been arable or improved grassland. Seven of our woodlands had been planted, nine were sites of natural colonisation, and twelve came under our “mixed” category, having a combination of planting and natural colonisation processes at play. 

Into the wilderness we go

Now we were carrying out our surveys, collecting data on several metrics that we had chosen to represent the structure, ecological function and biodiversity of woodlands.
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This included setting out five 20m diameter plots at each site, within which we were identifying trees, measuring their diameter at breast height (DBH) and counting seedlings and saplings to assess tree biomass and habitat complexity. We also assessed the trees for signs of disease or pests, focusing on those of highest concern (further information: Observatree). We checked a sample of leaves from ten trees in each plot for signs of damage by insect herbivores and recorded the number of seedlings and saplings that had been browsed by mammal herbivores. Using 20 plasticine caterpillars at each site, we quantified predation pressure on insect herbivores.
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To quantify the biodiversity value, we were assessing several different taxonomic groups. In small quadrats, we looked at herbaceous vegetation, poring over keys and guidebooks to identify species of grass, ferns and other flowering plants. We set out camera traps to document sightings of mammal species and placed acoustic recorders in trees to capture the sounds of passing insects, birds and bats. On two different nights during the summer months, two moth traps were set up at each site and Billy had some very early morning starts to check the traps, identify the species within, and release all moths on site. For the final surveys in August, Dr Thiago Silva flew a drone mounted with a LiDAR sensor over all the sites to capture information about the structure of the woodlands.

Data, data, data

Reflecting on the field season now, summer seems a distant past, and November’s short, cold days see me sitting at my laptop writing code to crunch the numbers and try to answer our research questions. We collected an impressive amount of data, measuring over 5000 trees from 36 different species, as well as counting over 1000 seedlings and saplings from 24 tree species. We captured nearly 2500 moths from 398 species and identified over 130 species of herbaceous ground flora. The camera trap and AudioMoth data files are still being processed; soon, we’ll know more about the mammals and bird species using our woodland sites.

Data analysis might not sound as glamorous as frolicking among the trees, but it's the key to understanding the intricate ecological dynamics at play within these ecosystems. Spoiler alert: nature's spreadsheet is a lot messier than Excel.
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Check back soon to find out what we’ve learned!