Received: 8 November 2019

| Revised: 7 March 2020 | Accepted: 10 March 2020

DOI: 10.1002/ece3.6240

ORIGINAL RESEARCH

Detecting shrub encroachment in seminatural grasslands using
UAS LiDAR
Bjarke Madsen1
Signe Normand1| Urs A. Treier1

1Section for Ecoinformatics & Biodiversity,
Center for Biodiversity Dynamics in a
Changing World, Department of Biology,
Aarhus University, Aarhus C, Denmark
2

MTA Centre for Ecological Research,
Tihany, Hungary
3Discipline of Geography and Spatial
Sciences, University of Tasmania, Hobart,
Australia
Correspondence
Signe Normand, Section for Ecoinformatics& Biodiversity, Center for Biodiversity
Dynamics in a Changing World, Departmentof Biology, Aarhus University, Ny
Munkegade 116, DK-8000 Aarhus C,
Denmark.
Email:signe.normand@bios.au.dk
Funding information
Carlsbergfondet| András Zlinszky1,2| Arko Lucieer3|

Abstract
1. Shrub encroachment in seminatural grasslands threatens local biodiversity unless management is applied to reduce shrub density. Dense vegetation of Cytisus
scoparius homogenizes the landscape negatively affecting local plant diversity.
Detecting structural change (e.g., biomass) is essential for assessing negative impacts of encroachment. Hence, exploring new monitoring tools to achieve thistask is important for effectively capturing change and evaluating management
activities.
2. This study combines traditional field-based measurements with novel Light
Detection and Ranging (LiDAR) observations from an Unmanned Aircraft System(UAS). We investigate the accuracy of mapping C. scoparius in three dimensions(3D) and of structural change metrics (i.e., biomass) derived from ultrahigh-density
point cloud data (>1,000 pts/m2). Presence–absence of 12 shrub or tree genera
was recorded across a 6.7 ha seminatural grassland area in Denmark. Furthermore,
10 individuals of C. scoparius were harvested for biomass measurements. With a
UAS LiDAR system, we collected ultrahigh-density spatial data across the areain October 2017 (leaf-on) and April 2018 (leaf-off). We utilized a 3D point-basedclassification to distinguish shrub genera based on their structural appearance(i.e., density, light penetration, and surface roughness).
3. From the identified C. scoparius individuals, we related different volume metrics(mean, max, and range) to measured biomass and quantified spatial variation inbiomass change from 2017 to 2018. We obtained overall classification accuracies
above 86% from point clouds of both seasons. Maximum volume explained 77.4%of the variation in biomass.
4. The spatial patterns revealed landscape-scale variation in biomass change between autumn 2017 and spring 2018, with a notable decrease in some areas.
Further studies are needed to disentangle the causes of the observed decrease,for example, recent winter grazing and/or frost events.
5. Synthesis and applications: We present a workflow for processing ultrahigh-density
spatial data obtained from a UAS LiDAR system to detect change in C. scoparius.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,provided the original work is properly cited.
© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Ecology and Evolution. 2020;00:1–27.	﻿

www.ecolevol.org

| 1

2

|

MADSEN et al.

We demonstrate that UAS LiDAR is a promising tool to map and monitor grassland
shrub dynamics at the landscape scale with the accuracy needed for effective nature management. It is a new tool for standardized and nonbiased evaluation ofmanagement activities initiated to prevent shrub encroachment.
KEYWORDS

biomass, grassland dynamics, remote sensing, scotch broom, shrub encroachment, UAS LiDAR

1 | I NTRO D U C TI O N

flowering period (Hill, Prasad, & Leckie, 2016). However, the relatively low resolution of satellite imagery (Aplin, 2005) does not allow

Plant diversity in natural and seminatural grasslands experi-

the quantification of structural information, for example, height andences severe pressure from shrub encroachment (Timmermann,biomass, from individuals or lower concentrated areas of shrubs.

Damgaard, Strandberg, & Svenning, 2015; Wilsey, 2018).

In contrast, observations from cameras or LiDAR (Light Detection

Establishment of woody species is a successional stage gradually

And Ranging) sensors mounted on an Unmanned Aircraft Systemoccurring in grasslands and may induce encroachment if not ex-(UAS, also referred to as UAV or drone) can provide very high-reso-

posed to natural processes, such as browsing, trampling and fires,

lution information on canopy (Wallace, Lucieer, Malenovský, Turner,

or if grasslands are not managed otherwise. (D'Odorico, Okin, && Vopěnka, 2016) or vegetation structure (Forsmoo, Anderson,

Bestelmeyer, 2012). Shrub encroachment is enhanced by nutrient

Macleod, Wilkinson, & Brazier, 2018; Wang et al., 2017).

enrichment (Stevens, Dise, Mountford, & Gowing, 2004) and land-

LiDAR technology is a remote sensing method providing three-di-

use abandonment (Deák, Valkó, Török, & Tóthmérész, 2016) thatmensional (3D) point cloud information suitable for quantifying veg-

leads to a decrease in plant species richness unless management

etation structure (Lefsky, a, Cohen, W. B., Parker, G. G., & Harding,strategies are implemented to keep shrub dominance low (Kesting,

D. J., 2002). Airborne LiDAR has been used to map grassland vege-Petersen, & Isselstein, 2015). Grassland plant diversity is generally

tation (Zlinszky et al., 2014) and to explain variation in species diver-highest at intermediate levels of biomass (Ejrnæs & Bruun, 2000),sity across varying vegetation communities (Moeslund et al., 2019).

and this relationship is also found for shrub encroachment (Kesting

Especially, forestry research has successfully implemented airborne

et al., 2015). High density of Cytisus scoparius is known to homog-

LiDAR data to, for example, detect the composition of gymnosperm

enize the landscape and to decrease plant diversity, especially in

species in plantations (Donoghue, Watt, Cox, & Wilson, 2007), tograsslands (Bellingham, 1998). The presence of C. scoparius canevaluate ecosystem services (Vauhkonen, 2018) and management

favor the establishment of fast-growing species by decreasing theefforts based on forest structure (Valbuena, Eerikäinen, Packalen,soil C:N ratio and hereby increasing N availability (Haubensak && Maltamo, 2016), or to demonstrate how LiDAR can be used toParker, 2004). Cytisus scoparius vegetation is therefore consid-

measure vegetation height of sagebrush (Mitchell et al., 2011).

ered problematic to grasslands including many areas within its

Furthermore, stages of shrub encroachment and biomass estimates

European native range (Sheppard, Hodge, Paynter, & Rees, 2002).

have been mapped on a coarser resolution (30 m raster) based on

The number of C. scoparius individuals in an area is not necessarily

a LiDAR point density of 5.6 points/m2 (Sankey, Shrestha, Sankey,

representative for invasiveness, and Parker (2000) suggests that

Hardegree, & Strand, 2013). With advancing technology, it is now

structural changes in C. scoparius (e.g., biomass and density) are

possible to mount good quality scanners on UAS (Manfreda et al.,important to assess the negative impact on plant diversity. Hence,

2018). UAS LiDAR systems provide new opportunities to provide

the structural components are necessary for evaluating the risk

ultrahigh point density (>1,000 pts/m2) on demand; hence, map-of grassland plant diversity decline resulting from encroachment

ping vegetation structure with high detail and sampling frequency

and to assess the efficiency of management activities. Monitoring

becomes possible across areas of up to several square kilometers.

efforts that highlight the need for action (e.g., shrub reduction)

UAS LiDAR systems have been used to detect individual trees and toshould be strategically aligned with the actual spatial scale of man-measure metrics such as height and stem diameter (Wallace, Lucieer,

agement (Magurran, 2016). However, monitoring shrub dynamics

Watson, & Turner, 2012; Wieser et al., 2017). The ability to detectwith the detail (i.e., small grain size) and spatial extent needed forsingle trees has been found to increase with higher point densities

management is challenging (Cao, Liu, Cui, Chen, & Chen, 2018).

(Wallace, Lucieer, & Watson, 2014), and Balsi, Esposito, Fallavollita,

Remote sensing covers larger areas than classical field assess-& Nardinocchi, 2018 could contrast different shapes of horizontal

ments of structural change, which are time-consuming and pro-overlapping trees using information from the whole volume of points

vide only local information (Wachendorf, Fricke, & Möckel, 2017).

in the point cloud. Furthermore, Moeslund et al., 2019 analyzed theSatellite-based spectral information can provide valuable infor-

potential of using airborne LiDAR-derived metrics, including a bio-

mation on cover of C. scoparius at the landscape scale for areas

mass measure, to assess the diversity of different organisms (i.e.,with high shrub densities, although limited to observations in thevascular plants, fungi, lichens, and bryophytes).

| 3

MADSEN et al.

The aim of this study was to assess the value of UAS LiDAR for
monitoring structural change in a seminatural grassland threatened byencroachment of C. scoparius. We developed a semiautomatic workflow to measure structural features of different shrubs and to enable
standardized monitoring for estimating spatiotemporal changes of C
scoparius biomass. Specifically, we addressed the following questions:(a) How accurately can C. scoparius be classified in UAS LiDAR point
clouds? (b) How precise can the biomass of C. scoparius individuals be
estimated from point clouds? (c) How does the estimated C. scoparius
biomass change between two surveys at different seasons?

2 | M ATE R I A L S A N D M E TH O DS
2.1 | Study area
The study was conducted in a seminatural grassland located within

F I G U R E 1 UAS LiDAR system with background of Cytisus
scoparius shrubs. Photo credit: Urs A. Treier

Nationalpark Mols Bjerge, Denmark (56°13′40.0″N; 10°34′30.2″E).
The area is situated in a temperate climate zone with a mean annual

The planned waypoints ensured a standardized flight plan in transects

temperature of 7.5°C and mean annual precipitation of 585 mm (Fickwith approximately 15 m between flight paths. We restricted the scan

& Hijmans, 2017). The terrain elevation varies between 30 and 60 m

angle to ±55° (roll axis) with respect to the sensor pointing nadir (−90°above sea level. Graminoids and small, broad-leaved herbs character-

pitch axis) resulting in an estimated overlap of 65% and thus reducing

istic for European dry grasslands dominate the vegetation, while single

noise associated with very oblique scan angles.

standing trees and different species of shrubs are patchily distributed

Between the two flight campaigns, the position of 180 individ-

across the area. Cytisus scoparius forms dense stands in parts of theuals of 12 different shrub/tree taxa (65 of these were C. scoparius)

area and a broad-leaf forest is located toward the northeast. Besides

was determined with an RTK GNSS receiver on the ground (<2 cm

naturally occurring wildlife, the area is grazed by Galloway cattle andabsolute accuracy) for training and validation data. The additional

Exmoor ponies and thus affected by all year grazing and trampling.

genera measured, included Juniperus, Rubus, Rosa, Quercus, Betula,

The grazers were introduced in 2016 as part of a rewilding experiment

Pinus, Sambucus, Crataegus, Prunus, Malus, and Calluna, all collected

for creating a more self-regulating ecosystem (Svenning et al., 2016).

along random transects with complementary samples of less abundant species outside (Appendix 2).

2.2 | UAS LiDAR system

To validate the estimation of biomass from C. scoparius, ten individuals were harvested for dry weight measurements after 2 days of
incubation at 60°C. Before cutting the shrubs, they were measured

We integrated the Surveyor laser-scanning system (YellowScan,

with the RTK GNSS device, resulting in small manual point clouds ofMontferrier sur Lez, France) on an 8-rotary wing UAS (MK8-3500;

25 points each (Appendix 3).

Mikrokopter, HiSystems GmbH; see Figure 1). The Surveyor is a dual-return system ranging in the 903 nm wavelength with 360 degrees scanner
angle, based on the Velodyne VLP-16 “Puck” laser scanner with a maxi-

2.4 | Processing workflow

mum measurement range of 100 m and a ranging accuracy of 3 cm. The
LiDAR sensor payload weighs 1.6 kg. The Surveyor has a global naviga-

We developed a workflow for processing ultrahigh-density pointtion satellite system (GNSS) receiver and inertial measurement unit (IMU)

clouds from UAS LiDAR to detect and map structural change in

integrated that acts as a rover (Applanix APX15). We utilize a Trimble

shrubs (Figure 2). Point cloud data processing was performed

base–rover postprocessed kinematic (PPK) solution to gain subdecimeter

separately for 2017 and 2018 data with OPALS software v. 2.3.1

accuracy for the system in XYZ directions (Chaponnière & Allouis, 2016).

specifically developed for handling airborne LiDAR data (Pfeifer,
Mandlburger, Otepka, & Karel, 2014).

2.3 | Data acquisition
Fieldwork was conducted in October 2017 (leaf-on) and April 2018

2.4.1 | Preprocessing and quality control of
LiDAR data

(leaf-off). Each time the UAS LiDAR system surveyed the area in three
separate flight paths planned with the software Kopter-Tool V2.20b

The position data recorded during the flight by the LiDAR systemwith a flying height of 40 m above ground and a ground speed of 3 m/s.

were postprocessed using PosPac UAV v. 8.2 with data recorded

4|

MADSEN et al.

UAS LiDAR
point cloud

Preprocessing and quality control

Applying thresholds to obtain a shrub layer point cloud

GNSS
occurrence
observations

F I G U R E 2 Flowchart of UAS LiDAR
processing chain to derive structural
information (biomass) from classified
shrub species and comparing between
two time periods

Generating point-based reference data of structural features

3D classification with machine learning

Biomass
measurements
GNSS shrub
structure points

Biomass estimation

Biomass mapping & change detection

by a Trimble base station to obtain PPK corrected trajectory information and point clouds. All flight lines were carefully consideredand selected using a QGIS (QGIS Development Team, 2019) plug-in

2.4.3 | Generating point-based reference data ofstructural features

provided by YellowScan (YellowScan, 2016) to minimize noisy datafrom UAS turns. The resulting point clouds were then evaluated with

From the 180 measured GNSS points, we gathered adjacent LiDAR

12 RTK GNSS measurements of fenceposts functioning as control

points within a size-specific area to comprise a reference dataset

points for the vertical accuracy (RMSE: 3.9 cm in 2017 and 6.1 cm

of 13 classes (11 shrub taxa, fenceposts, and shrub absence points;

in 2018) and, likewise, five ground markers in 2018 to evaluate hori-

Appendix 2). Reference data were collected as presence/absence

zontal accuracy (RMSE: 1.4 cm).

(PA) of shrubs in late 2017 and early 2018, that is, between the UAS

We performed a relative adjustment of the point cloud to im-flights. Within this period, we would only expect shrubs to disappear,

prove the alignment of objects (i.e., vegetation). The procedure wasfor example, browsing/trampling by animals, which would mean thatbased on fitting overlapping flight strips with a least squares match-such individuals will be included as a GNSS record in 2017 but would

ing technique as described in Ressl, Mandlburger, and Pfeifer (2009).

not be there in 2018. Because of the digital assignment of training/This process aligned the point clouds better relatively by, for exam-

validation data, no points would represent the shrub in 2018, andple, reducing the variation in ground point height. However, because

therefore, no points will be assigned to the given shrub class.

of the variation in flight strips, the absolute accuracy decreased to a

We developed structural features derived from the UAS LiDAR

vertical RMSE of 7.6 cm and 9.1 cm for 2017 and 2018, respectively,

point cloud to represent vegetation morphology with the aim to clas-

and a horizontal RMSE in 2018 of 6.5 cm. For further details on pre-sify shrub species (Appendix 2). We targeted the variables to repre-

processing and quality control, see Appendix 1.

sent shrub species on varying levels of scale, ranging from small-scaleleaf characteristics to larger-scale shrub growth form and shape. The

2.4.2 | Applying thresholds to obtain a shrub layer
point cloudshrub growth form is here interpreted as the general appearanceof the shrub and of how branches and leaves are arranged. Hence,
it corresponds to specific, although overlapping features, such as
structural complexity, density, and light penetration (Popescu, 2011).

We coarsely divided the point cloud into height-based vegetation

Specifically, we calculated the variance in height from a fitted nor-classes and a ground class to reduce processing time for the later shrub

mal-based plane (Pseudowaveform) to resemble roughness or struc-classification. A digital terrain model (DTM) derived from the minimum

tural complexity (Van Aardt et al., 2012). The amount of points (Point

point height within a 1x1 m moving window was used to calculate nor-count) and average distance between points (Point distance) were

malized height of points (NormZ) (Appendix 2). We then classified theused as a measure of density, while light penetration was represented

points using thresholds into ground (NormZ < 0.15 m), low vegetation

by including the count of ground points classified by the above men-

(NormZ < 0.3 m), and high vegetation (NormZ > 3.5 m) leaving a shrub

tioned threshold (Ground points count) and the average number oflayer point cloud of medium height vegetation (0.3 m < NormZ < 3.5 m).

returning echoes (No. of echoes). Additionally, we calculated a Rank

Calluna vulgaris fell into the low vegetation layer and therefore was ex-feature, where points within the search radius are ranked by lowest

cluded from the further shrub classification procedure.

to highest point and assigned a corresponding value between 0 and| 5

MADSEN et al.

100. This measure was intended to represent shrub shape and canhomogenous. A perfectly pure node refers to all observations being

particularly be useful for recognizing overhanging canopies. Also,

assigned the correct label caused by the split value. When the vari-characterizing shrub leaf and branch features, the angles between aables could not further increase the node purity, the resulting deci-point and all its neighbors within a search radius were extracted andsion tree was pruned with the complexity parameter set to 0.001 foraveraged as the negative openness for each point. The negative open-

a final simplified decision tree. We fixed the complexity parameterness refers to a conical view looking downwards, while oppositely theafter a trial process where we lowered the parameter stepwise until

positive would be pointing up. It were originally developed for pix-

the accuracy started to decrease. The aim was to run the classifica-

el-based terrain modeling by taking the mean openness angle fromtion with the lowest possible complexity parameter.

the eight neighboring pixels (or more, depending on the search radius)

The classification accuracy was assessed by randomly stratifying

in each cardinal and intercardinal direction (Yokoyama, Shirasawa, &90% of the reference data as training and 10% as testing data. The

Pike, 2002). However, all points in any possible direction within thequality and reliability of accuracy assessments are affected by thesearch radius were considered for the 3D point attributes and there-

reference data input and sampling strategy (Millard & Richardson,fore not necessarily restricted to eight directions.

2015). We therefore also assessed a 70% training to 30% validation

From the initial set of 17 structural features, we retained sevendata split (Appendix 2). Stratification was done among the defined

after testing for autocorrelation and variable importance (Table 1).

classes. We generated 100 such 90/10 and 70/30 training and val-

Variable importance was calculated manually based on the decrease

idation datasets as input for the OPALS classification algorithm.

in overall accuracy when leaving one variable out in the classifica-

For each set, classification accuracy was assessed from the result-tion process (Appendix 2). Variables with high pairwise correlation

ing confusion matrices using R to calculate overall accuracy and(ρ > .75) and low importance scores were removed from the dataset.

Kappa values, while the class-wise accuracies were evaluated with
precision, recall and the harmonic mean between the two, termed

2.4.4 | Machine learning for 3D classification ofCytisus scoparius and other shrub species“F1” (Forman, 2003). The 70/30 split resulted in <0.5% decrease in
overall accuracy and Kappa coefficient (Appendix 2). To increase
the amount of training data used in the final model for extracting
biomass metrics, we applied a model validated by the 90/10 split

For the 3D classification of the shrub point cloud, we utilized the(Appendix 2). From the predictions, we obtained the class probabili-

built-in classification procedure in the OPALS software. It uses theties allowing fuzzy classifications, that is, the membership of a classtree-based decision algorithm (De'ath & Fabricius, 2000) termedis represented by a probability value between 0 and 1 rather than arecursive partitioning via the rpart package for R (Therneau &Boolean value (true or false) as with traditional hard-boundary clas-

Atkinson, 1997). The algorithm operates by dividing the dataset forsification(Foody, 1996; Zlinszky & Kania, 2016). Finally, a classifica-

each chosen variable separately, that is, it finds the splitting value

tion was performed with 100% of the reference data and printed

for tree branches which results in the purest nodes, that is, mostinto the full point cloud for visualization purposes.

TA B L E 1

Variables selected for final classification

Variable

Overall
importance
score

Searchradius (m)Morphological features

Pseudowaveform

0.048

0.5

Point count

0.013

Ground points
count

Ecological relevance(presumed)

Computation description

Shrub growth form

Increases with structuralcomplexity (roughness)

Point height variances from anormal-fitted plane

0.5

Shrub growth form

Increases with surface
area and density

Number of points

0.006

0.1

Shrub growth form

Increases with light
penetration anddecreases with density

Number of points including groundpoints

Rank

0.002

0.1

Shrub shape and growthform

Highest values at uppersurface and lowest at
bottom canopies

Relative position in neighborhood.
Max value = 100 and min value = 0

Negative
openness

0.005

0.1

Shrub shape, leaforientation, and brancharrangement

Increases with surface
gaps

Angular distance between points
from a nadir view

Point distance

0.025

0.1

Leaf structure andbranch arrangement

Decreases with density

Average linear distance between
pointsNo. of echoes

0.004

0.1

Leaf structure andbranch arrangement

Decreases with light
penetration

Average number of strongest andlast return echoes

6|

MADSEN et al.

F I G U R E 3 Scatterplot with actual measurements of Cytisus scoparius biomass on Y-axis and LiDAR-based volume on X-axis. Dashed bluelines represents corresponding linear regression models: Biomass ~ Volume for mean, max and range values. (R2adj values for the models:mean, max and range values: .72, .77, and .61 with p-values respectively: .0012, .0005, and .0048). Formulas: Y(Mean) = 92.49 + 4,575.88X;
Y(Max) = 77.12 + 4,696.16X; Y(Range) = 92.98 + 12,327.87X

2.4.5 | Assessing variable and model transferability

model coefficients (Figure 3) from the best fit (maximum NormZ)
to calculate a biomass estimate for the rasterized variable across a

We used a Wilcoxon signed-rank test to evaluate the variable signa-

6.7 ha area and for both datasets (2017 + 2018). During the rastertures for each shrub species in the validation samples from 2017 andprojection, we incorporated class probabilities from the final fuzzy

2018 (Appendix 2). Furthermore, the classification procedure wasclassification models (see accuracy assessment in Appendix 2) toapplied to a merged point cloud, consisting of the mixed signatures

exclude points classified as C. scoparius with <60% probability forfrom 2017 and 2018.

one set of maximum NormZ values. Likewise, a second set of values

Additionally, we applied a similar classification procedure as de-was extracted by adjusting this probability threshold more strictly

scribed above, but with the variables projected into 2D raster datato 80%. At last, the change in biomass was mapped and aggregated(Appendix 2). The 2D classification was performed with R statistics

from 5 cm resolution to 2.5 m grid cells to emphasize the change.

3.5.0 (R Core Team, 2016) allowing us to test alternative classification algorithms.

3 | R E S U LT S

2.4.6 | Biomass estimation and change detection

3.1 | Detection of Cytisus scoparius in a 3D
landscape of points

To estimate the relationship between actual measured biomass andstructural information for each of the 10 harvested C. scoparius

After georeferencing and noise filtering, our UAS LiDAR system gen-shrubs, we extracted digital volume metrics based on the NormZ

erated 59 million and 53 million points during the 2017 and 2018variable equivalent to vegetation height (Appendix 3). The NormZ

flight campaign, respectively, for the 6.7 ha area. For 2017, 12.6% of

pixel values were computed using mean, max, and range (max–min)these points were classified into the shrub layer, and 6.1% in 2018.

values from the points and thereby representing volume metrics by

Likewise, 29.8% and 40.5% of the shrub layer points were classi-

2

multiplying the height with the pixel area (25 cm ). We then used thefied as C. scoparius in 2017 and 2018, respectively (see Appendix 2).

manually constructed 3D point clouds from the field GNSS measure-

The overall accuracy from the 90/10 split classification was 86.9%

ments to delimit and extract summed volume values for each of thefor 2017 and 95.2% for 2018, while the Kappa coefficient resulted

harvested shrubs (Appendix 3).

in 83.7% for 2017 and 92.9% for 2018. The 70/30 split resulted in

The extracted volume metrics were all compared for correlation<0.5% decrease in overall accuracy and Kappa coefficient (Appendix

with nonparametric Spearman's rank correlation using R statistics

2). Focusing exclusively on the C. scoparius class, the F1 measure was

3.5.0 (R Core Team, 2016). Furthermore, we developed linear mod-96.2% in 2017 and 98.4 in 2018 (Table 2). The accuracy assessment

els from the volume metrics to explain biomass from the harvested

from the specific model used for obtaining biomass differed withsamples and evaluated them by calculating adjusted R squared+0.2% in 2017 and +0.5% in 2018 from the mean of the iterativevalues (R2adj) via the lm() function in R. We used R2adj values frommodel validation with 90/10 splits (see details in Appendix 2).

the models to cope with the relatively low sample size of 10 and

The analogous merged classification showed a decrease in

thereby avoid making too optimistic conclusions. We applied theoverall accuracy (83.2%) and Kappa coefficient (78.0%), while the| 7

MADSEN et al.

TA B L E 2 Accuracy assessment of the point cloud classifications of Cytisus scoparius from autumn 2017 and spring 2018. Classification
accuracies are averages from 100 model iterations of randomly selected training/validation data (90/10% split) and with standard deviations.
Overall accuracy and Kappa coefficient evaluate the classification of all classes, while the F1 score (Forman, 2003) assesses the performancein predicting the C. scoparius class. The last row presents the results from a merged classification including both the 2017 and 2018 point
cloudsOverall accuracy
Time period

Mean

Kappa coefficient
SD

Mean

F1 score—C. scoparius
SD

Mean

SD

Autumn 2017

86.9

0.006

83.7

0.007

96.2

0.005

Spring 2018

95.2

0.004

92.9

0.006

98.4

0.003

Merged

83.2

0.007

78.0

0.010

96.3

0.004

C. scoparius F1 remained equally high (96.3%) (Table 2). For the morebiomass changes in the area varied but was similar for both thresh-

traditional 2D classification approach, the random forest classifierolds. An upscaled visualization from 5 cm resolution to grid cells of

performed in general better with overall accuracies of 45.6% for

2.5 m × 2.5 m revealed a pattern of larger decreases in biomass to2017 and 42.6% for 2018 (see Appendix 2). However, the recursive

be identified in especially the northeastern part of the area, while inpartitioning classifier did not perform markedly worse (overall accu-

other parts, we observed no or a slight increase in biomass between

racy of 47.1% and 38.9% for 2017 and 2018).

the 2 years (Figure 5).

The color-coded point cloud visualizing the resulting shrub classification (Figure 4a) revealed that C. scoparius shrubs were detectedacross the area in various densities, from single individuals to large

4 | D I S CU S S I O N

thickets. When inspecting the classification probabilities in the
point cloud (Figure 4b), we recognized a greater certainty to predict

With the sole use of UAS LiDAR-derived structural information, we

C. scoparius in the leaf-off period in 2018 than in the leaf-on period.

identified shrub classes with an overall mean accuracy of more than

Furthermore, it is noticeable that C. scoparius shrubs were detected

86.9% regardless of the year and detected C. scoparius with at least

underneath the forest canopy as well (Figure 4a1).

96.2% F1 accuracy in the point cloud (Table 2). Furthermore, using asimple volume metric (NormZ) from the classified C. scoparius point

3.2 | Cytisus scoparius biomass estimation

cloud, we explained 77% of the variation in actual harvested biomass(Figure 3) and quantified a reduction of 327.8 kg during the winterperiod from autumn 2017 to spring 2018, assuming the 80% prob-

The LiDAR-derived volume metrics of NormZ (average; maximum;

ability threshold to be most accurate.

range) correlated well with the biomass measurements of the 10 harvested C. scoparius shrubs (Spearman's ρ = .87; .88; .88). However,
the inclusion of very small shrubs in the harvested samples challenged the LiDAR detection of biomass (Appendix 3). The maximum

4.1 | LiDAR-derived structural features for shrubclassification

volume resulted in the best linear fit with R2adj = .77 (Figure 3). The
mean and range metrics performed worse in a linear model with

Our study demonstrates that ecologically meaningful features canR2adj = .72 and R2adj = .60, respectively. The accuracy assessment frombe extracted from UAS LiDAR point clouds which represent thethe specific model used for obtaining biomass metrics is presented

structural appearance (i.e., growth form, shape, and leaf/branch

in Appendix 2.

orientation and arrangement) of different shrub species, especially
C. scoparius. We targeted the variables to represent shrub species on

3.3 | Cytisus scoparius biomass change

varying levels of scale, ranging from small-scale leaf characteristics
to larger-scale shrub growth form, density, and shape. For example,
C. scoparius is characterized as being light competitive (Peterson &We extracted the total biomass sum from the points with >60% andPrasad, 1998), hence, reducing the available light beneath the can-

>80% probability of being C. scoparius. This resulted in 7,500.4 andopy. However, compared with, for example, the compact canopy of

5,257.6 kg in 2017 and 2018, respectively, for >60% and, likewise,

Juniperus communis, it may allow more light to travel through, es-

5,320.9 and 4,993.0 kg for >80%, in an area of 6.7 ha. For the com-

pecially during leaf-off periods, eventually resulting in more groundparison, we included only the overlapping areas that had an aver-hits of the LiDAR beams.

age point distance below 3 cm. On the landscape scale, this resulted

The variable that explained most of the variation, Pseudowaveform,in an average biomass decline of C. scoparius from autumn 2017 towas computed with a relatively large search radius (0.5 m) and there-

spring 2018 of 33.4 and 4.9 g/m2 for the 60% and 80% probability

fore represents the variation in the structure of shrub branches andthresholds, respectively. However, on a local scale the distribution ofleaves (Table 1). By calculating on this relatively larger scale, we

8|

MADSEN et al.

| 9

MADSEN et al.

F I G U R E 4 Classification results visualized directly in the 2018 point cloud in a vegetated area (a1) and in more open land (a2). The
general classes are showing both, the main vegetation classes obtained from thresholding (Ground, Low vegetation, High vegetation) as
well as the classes obtained by the tree-based classification. The orange colors represent points classified as Cytisus scoparius. Lower figures
show 3D clips of the 2017 (b1 to b3) and 2018 (b4 to b6) C. scoparius point clouds. Points are here color-coded with the resulting classprobabilities ranging from 60% (dark blue) to 100% (light green). The generally classified ground points are colored in grey

expected the variable to be less affected by differences in leaf abun-

The performance of the LiDAR-derived structural variables todance between the two flights. From analyzing the shrub signatures,detect and distinguish shrub species in this study demonstrates a

we find Pseudowaveform to be stable between the two seasonal

promising use at the level of detail obtainable with a drone-basedstates and throughout the classes, except for Betula (Appendix 3).

platform (Table 2). In addition to the increased spatial resolution and

Structural complexity in vegetation is often assessed by the vari-structural detail, our landscape study of C. scoparius differs from Hill

ance of derived LiDAR metrics relative to a horizontal plane (Kaneet al. (2016) in being independent from observations during flower-

et al., 2010; Kukunda et al., 2019). Textural metrics in image analy-

ing periods. However, phenological events can also be considered

sis can similarly be used to distinguish structurally dissimilar species

an important aspect of understanding the development of shrub en-(Oldeland, Naftal, & Strohbach, 2017), but are limited to detect only

croachment. In particular, an increase in temporal resolution would

2D surface differences. Here, we utilize information of 3D vege-

be beneficial for remote sensing studies of vegetation to understandtation structure. The variance for example will be set relative to a

how such variables vary with seasonality. A study by Müllerová et al.

normal-based plane. Measurements of shrub growth form may be(2017) demonstrates an example of this, by recognizing two invasive

difficult to conduct and define in field-based surveys; hence, there

species from differently scaled images and throughout the season.

is a focus on retrieving metrics as, for example, diameter at breast

Hence, using spectral information in combination with LiDAR could

height (DBH; Wieser et al., 2017). Nonetheless, the high importanceextend the monitoring possibilities even further.

of Pseudowaveform for classifying shrub species suggests that shrub
growth form is highly relevant for distinguishing species. Groundpoints count and no. of echoes express the amount of light penetrating

4.2 | Workflow and classification challenges

a shrub to represent density. Density is known to be highly variable
among species (Van Leeuwen et al., 2011) while light penetration

The use of LiDAR-derived features poses many challenges and de-may also reflect habitat characteristics for the species composition

mands a novel way of developing and understanding these 3D meas-(Moeslund et al., 2019). Density was also computed more directly

ures in an ecological context. Thus, further improvements are possible

with the amount of LiDAR points within a 50 cm search radius (pointby implementing ecological or biological knowledge in the computed

count), while point distance was computed on a smaller scale mainly

variables. Depending on the focal species, the LiDAR-derived featuresemphasizing single leaf/branch structure and arrangement. Because

can be adjusted to fit specific morphological characteristics, and aof the direct influence from density, it remains challenging to esti-

more general approach could be developed to assure transferability

mate biomass in areas with high species heterogeneity (Wijesingha,

for many shrub species. For C. scoparius, the change in structural sig-

Moeckel, Hensgen, & Wachendorf, 2019), and to improve accuracy,

nature between leave-on and leave-off is expected to be low due to

it is necessary to separate the species by a classification process. The

the small leaves (Appendix 2). However, with the onset of flowering,

negative openness was used as a metric for differentiating the struc-

this might change for some of the variables. Alternatively, for othertural complexity in especially the surface of shrubs. Specifically, theshrub species it might be important to develop variables that are in-openness variable detects concaveness (Yokoyama et al., 2002) or in

dependent of seasonal states to assure transferability of the classifier.

this case holes in the vegetation surface. From the shrub signatures,

For C. scoparius, we found a significant change in the sample distri-we see that negative openness is seasonally affected by a species

butions in three of the density variables (PCount_ground, PCount,like Betula (Appendix 3) showing lower concaveness in the leaf-on

and PDist) which indicates that seasonal variability might be seen inthan leaf-off period. This can possibly be explained by leaves form-

these variables. However, it is the combined signature that is impor-

ing a relatively round and smooth canopy, whereas the branch gaps

tant for the classification process and the distinction among species.

will be more exposed during leaf-off period. Finally, to differentiate

When evaluating normalized vegetation height (NormZ) used for bio-

between C. scoparius and low tree species in the shrub point cloudmass estimation, we find no difference between the two flight dates

with overhanging canopies, for example, Malus sp. or Prunus cerasif-

in C. scoparius, which is different for Betula species. These findings

era, the classification benefitted from the Rank variable. Here, lower

highlight the need for variable adjustments and/or seasonal timing, topoints from a hanging branch without points underneath would beobtain reliable biomass estimations for a given target species.

ranked lower than C. scoparius branches, which often will have lower

We implemented a workflow to improve the assessment ofpoints beneath, from low vegetation or ground. Thus, computing

shrub biomass during encroachment. During the development, weecologically meaningful variables from 3D point clouds is possible

experienced several challenges which could affect the results andbut needs to be based on ecological knowledge and targeted on theneed careful consideration and evaluation during implementation:

focal species or vegetation class.

(a) misalignment reduce spatial accuracy, (b) thresholding of major

10|

MADSEN et al.

| 11

MADSEN et al.

F I G U R E 5 Orthophoto from the study area (Kortforsyningen Danmark, 2019) with overlay of change in maximum Cytisus scoparius
volume between 2017 and 2018 UAS LiDAR survey. A C. scoparius class probability threshold is applied and only allowing points classified
with >60% (top) and >80% (bottom) probability to be included for change detection. The change is upscaled from 5 cm to 2.5 m2 forvisualization. Blue squares represent growth, while red squares show a biomass decrease. White squares indicate no or little change

vegetation classes, for example, trees versus low vegetation, (c) sam-

we are aware that some species are possibly missing in the training

pling of training data, and (d) selection and development of targeted

data, but that does not mean that they do not exist in the point cloud.

structural features. Below we outline these challenges and provide
perspectives on future directions.

It is important to design point-based attributes to emphasize
structural features, corresponding to the species or vegetation typeBecause of observed misalignments in the combined point clouds,of interest. The described workflow is semiautomatic, given a com-

resulting from different laser beams, flight strips, and uncertainties

prehensive input dataset of UAS LiDAR point clouds, training data

in absolute GNSS positioning, we rectified the relative accuracy infor classification, and ground truth data for developing the biomass

the datasets, which negatively influence the absolute accuracy, thatmodel. Depending on the study focus and target species, it is possible

is, absolute geographic position of LiDAR points. However, the de-

to design point-based attributes that emphasize structural features,

crease in precision of XYZ-coordinates was relatively small (RMSE:

corresponding to a species or vegetation type of interest. For exam-

vertical <1.4 cm and horizontal <5.1 cm). When considering the scaleple, different search radii are required when studying differences inof which we recognize C. scoparius, we believe that a high relative ac-trees or herbs matching the level of scale. Likewise, attributes cancuracy among points is more important for describing the structural

be modified to detect specific structural features of species or indi-

features of a shrub properly. However, in monitoring programs, fixedviduals, for example, deformity caused by pathogens. The structuralfeatures (i.e., ground control points) allowing to align multiyear data

uniqueness, as defined by growth form, density, roughness, and lightwould be advisable.

penetration seen in different shrub species may be less influenced

Threshold approaches to filter data noise as well as for doing aby seasonality and physiological stress factors (i.e., water and nutri-

first general classification are essential for effective data processing.

ent availability) than spectral signatures. However, when attempting

However, finding meaningful thresholds in heterogeneous plant com-

to distinguish structural similar species including spectral informa-

munities with highly variable structures is difficult and hard to gener-tion in the classification procedure might help (Zlinszky et al., 2014).

alize across a larger area. Therefore, deciding on exact thresholding
values often is a trade-off issue, by either including an amount of noiseor excluding useful data. In our study, we emphasized to include as

4.3 | Spatial patterns of C. scoparius biomass changemuch data as possible, accepting some levels of uncertainty. Still, withbroadly defined threshold values, points representing a target species

Depending on the probability threshold for detecting a C. scoparius

may disappear in these first steps of filtering and classifying. For exam-

shrub, we estimated a decrease in biomass change of 4.9–33.4 g/m2

ple, we attempted to include training data from the small-sized shrub

between the two mapping dates. Nevertheless, we found a consist-

Calluna vulgaris; however, these points were set aside in the generalent spatial pattern of biomass change for both thresholds, indicatingclass “Low vegetation” and thus not included in the tree-based classifi-a large decline in the northeastern part of the area and no or a slightcation. Reducing the dataset is crucial for optimal processing speed andincrease in the southwestern part (Figure 5). There might be several

point cloud visualization but choosing general classes and thresholds

reasons for this observed spatial pattern:must be evaluated and depends on the target species. If targeted, theFirst, the slight increase in biomass could result from late autumn

C. vulgaris shrubs could most likely be identified by running the classi-

or early spring growth. The shrubs are not expected to grow during afication procedure on the low vegetation points and could perhaps be

Danish winter season; however, the timing of flight campaigns may still

implemented in a future study including herbaceous vegetation.

include some late autumn or early spring growth. In addition, C. scopar-

Generally, one should aim for balanced training/validation data

ius branches contain chlorophyll, which might be able to induce growth(Millard & Richardson, 2015). However, this can be challenging when

in warmer periods and favor early growth in spring. The reason why

working in natural areas with a patchy and heterogeneous distribu-

biomass increase was mainly observed toward the southwest of thetion of vegetation. First, a random sampling strategy to collect data

study area could be low inter- and intraspecific competition or vari-objectively is desired, a strategy we pursued with the transect ap-

ation in microclimatic conditions. A freestanding individual without

proach. However, this method may only cover the more dominantneighbors is more likely to grow and expand due to lack of competition

species and is not enough to catch the full variation of shrub species

when the conditions are suitable. In contrast, competing neighbors

in an area. Second, it might be impossible to equally sample rare spe-

may supress growth of C. scoparius individuals in densely populated

cies occurring only in few places, even if all individuals are sampled.

surroundings. The possible intraspecific interaction has previously

We therefore included datapoints outside our transects from lessbeen raised in the literature (Paynter, Fowler, Memmott, & Sheppard,

abundant shrub species, as it would not present the full picture of the1998), and in accordance with this, the observed spatial pattern in bio-area if ignoring those. When covering extents of several hectares,

mass changes suggests that C. scoparius in the open land (midwestern

12|

MADSEN et al.

part) tended to slightly increase, while the denser stands toward thefrom a manned aircraft were applied to map coarse-scaled shrub

northeast of the study area showed a decline in biomass (Figure 5).

encroachment characteristics from a single species (Sankey et al.,Second, the reasons for a biomass decline could be either leaf fall2013), we are now able to separate species or genera directly in the(Peterson & Prasad, 1998), which due to the summed 2.5 m grids, is ex-

point cloud and to detect fine-scale biomass dynamics from a target

pected to be larger in denser stands, or external factors, such as graz-

species by utilizing a drone platform. With the use of the establisheding or frost. Galloway cattle and Exmoor ponies graze the area as partworkflow based on a UAS LiDAR system, we provide a new approachof a rewilding initiative, and no additional food sources are supplied

for monitoring shrub species dynamics. In this study, C. scoparius isto the animals. Hence, at wintertime when the green vegetation be-covering the spatial extent corresponding to management opera-

comes sparse, the animals might feed on hardy shrubs like C. scoparius,

tions, but also capturing the local-scale information needed for de-or cause damage to shrubs by trampling. Therefore, one alternative hy-

tecting change and its spatial variation. Again, the benefits of using

pothesis explaining the spatial pattern of C. scoparius biomass change

LiDAR are the vegetation penetration ability (Lefsky et al., 2002) andis that the animals are favoring the northeastern part of the study area.

our findings suggest that LiDAR-derived point clouds are of such aThis area is substantially more forested, and the grazers might bene-

quality, that detecting species of interest even beneath a covering

fit from shelter provided by the trees during harsh winter conditions.

canopy is achievable, as, for example, forest understory species. This

Alternatively, abiotic conditions linked to topographic features, such

will make it possible to monitor and guide management programs foras light availability and freezing temperatures during winter might limit

noxious invaders such as Rhododendron ponticum (Sanders, 2017) or

plant growth and harsh winters would potentially cause C. scoparius torare species such as Allium tricoccum beneficial for indicating favoreddie (Peterson & Prasad, 1998). Extracting such topographic variables

nature or ecological conditions (Leduc & Knudby, 2018). In a study

describing light availability and protection against freezing could pro-by Chance et al. (2016), LiDAR is utilized to map the distribution ofvide predictors for the observed change in biomass. The availability of

two invasive shrub species in an urban environment with reported

terrain topography data from the UAS LiDAR point cloud would facili-

lower accuracies for shrubs beneath closed canopies. The impor-tate such a follow-up study at very high resolution.

tance of mapping understory vegetation is further highlighted in a

While the observed pattern in biomass change is independent

review by Hernandez-Santin, Rudge, Bartolo, & Erskine, 2019, andof the probability thresholds for C. scoparius detection, the absolutethe findings from our study should be relevant and encouraging tobiomass change might be overestimated. When comparing C. scopar-

further studies in this direction.

ius class probabilities in three areas with varying population densities

Shrub encroachment is influenced by several factors that makes(Figure 4b), there are more C. scoparius points in 2017 than in 2018,it a complex issue for management and conservation at landscapes

especially in areas with dense stands. In the leaf-on period (2017), aand regional scales (Cao et al., 2018). The presented workflow pro-larger number of forbs, tall grasses, and shrubs have probably been

vides a potential improvement on several aspects of nature monitor-

included in the training data for C. scoparius, as they grow within anding. By the semiautomatic processing and the use of a UAS-basedaround shrubs. This probably also explains why in 2017 more C. sco-

platform, the temporal resolution is no longer limited to snapshot

parius points have been classified with lower probabilities than in 2018

observations, for example, once a year or less. Hence, field opera-(Figure 4b and Appendix 2). More LiDAR points included from entan-

tions can be carried out on multiple times through a season, with thegled vegetation can lead to a larger volume estimate per individual andinclusion of beneficial seasonal states of vegetation. However, we

probably cause slight overestimation of C. scoparius biomass in 2017.

also demonstrate that UAS LiDAR data allow for comparable measures of shrub encroachment even in different seasonal states and

4.4 | Relevance for nature management

hereby displaying the potential multipurpose use from this type of
data. Our comparison with a similar 2D remote sensing classification
approach (Appendix 2) highlights the benefits from using three-di-

The spatial variation in C. scoparius biomass change indicates thatmensional information. Hence, we increase the spatial dimensional-

shrub dynamics differ in the subareas of the mapped area, possi-

ity from traditional 2D mapping studies by recognizing shrub taxa inbly due to varying importance of ecological drivers, also yet to be

three-dimensional space contributing to reliable biomass estimation

studied (Figure 5). Our findings highlight that detection of change inwhich is much needed for deeper understanding of shrub encroach-shrub density and biomass with high resolution is important whenment dynamics. With the fine-scale information about biomass, it

assessing shrub encroachment in monitoring programs for nature

will be easier to detect and manage dense stands of C. scoparius ormanagement. Traditionally, remote sensing studies have classified

other shrub species during encroachment with potential negative

vegetation with a two-dimensional approach from spectral informa-

effects on plant diversity (Kesting et al., 2015).

tion or rasterized LiDAR information. These raster-based methods
are efficient in identifying cover or presence of certain grassland
species and vegetation types (Hellesen & Matikainen, 2013; Zlinszky

5 | CO N C LU S I O N

et al., 2014). However, to quantify biomass changes and to fully understand the effect of shrub encroachment on plant diversity more

Our study presents a novel method for assessing shrub dynamics

comprehensive knowledge is needed. While LiDAR observations

in 3D, based on the arrangement and orientation of points in space.

| 13

MADSEN et al.

We demonstrate an efficient way of determining specific structural

AU T H O R C O N T R I B U T I O N S

features for classifying shrub species. Seven different point-based

Bjarke Madsen: Conceptualization (lead); data curation (lead); for-structural variables were developed based on ecological knowledge

mal analysis (lead); investigation (lead); methodology (lead); projectto distinguish C. scoparius from other vegetation. Using a tree-based

administration (lead); resources (lead); visualization (lead); writ-classification procedure, we identified 11 different shrub species

ing – original draft (lead); writing – review and editing (lead). Urs

with an overall accuracy of 86.9% and 95.2% in two independent

A. Treier: Conceptualization (lead); data curation (equal); formal

point clouds acquired in 2017 and 2018, respectively. Derived fromanalysis (equal); investigation (equal); methodology (equal); project

the point cloud derived height-based maximum volume of C. sco-

administration (equal); resources (equal); supervision (equal); valida-parius shrubs, we established a linear model to explain 77.4% of thetion (supporting); visualization (supporting); writing – original draft

variation seen in actual measurements biomass from 10 harvested(supporting); writing – review and editing (supporting). András

and weighed individuals. Projected biomass in C. scoparius with an

Zlinszky: Formal analysis (supporting); investigation (supporting);

80% class probability declined by 4.8 g/m2 between 2017 and 2018.

methodology (supporting); software (supporting); validation (sup-However, we found substantial spatial variation in biomass change

porting); writing – original draft (supporting); writing – review andwith a patchy distribution of C. scoparius decrease and or growth.

editing (supporting). Arko Lucieer: Investigation (supporting); su-

These findings suggest that the combined information from small- to

pervision (supporting); validation (supporting); writing – originallarge-scale observations is necessary to fully understand shrub en-

draft (supporting); writing – review and editing (supporting). Signe

croachment and highlights the potential application of this method

Normand: Conceptualization (equal); formal analysis (equal); funding

for fine-scale management of C. scoparius populations on a land-acquisition (lead); investigation (equal); methodology (equal); project

scape scale. Moreover, our study encourages more studies inves-administration (equal); supervision (lead); validation (equal); writing

tigating the causes of C. scoparius biomass change variation and its– original draft (supporting); writing-review and editing (supporting).

effect on plant diversity.
Our study contributes with new and advancing methods within

DATA AVA I L A B I L I T Y S TAT E M E N T

the field of remote sensing, ecology and nature management by

The data used in this study are available through the Dryad Datademonstrating an application for ecological monitoring. Natural

Repository: https://doi.org/10.5061/dryad.547d7​wm55.

systems in Denmark, including grasslands, are monitored by theDanish national monitoring program (Svendsen, Bijl, Boutrup, &ORCID

Norup, 2005). The promising results of detecting change in bio-

Bjarke Madsen

mass related to encroachment of C. scoparius may inspire improv-

Urs A. Treiering some of the traditional field-based measurements toward a

András Zlinszky

more objective evaluation on the state of natural systems. The

Arko Lucieer

presented work potentially improves monitoring programs in na-

Signe Normand

https://orcid.org/0000-0002-4490-8710
https://orcid.org/0000-0003-4027-739X
https://orcid.org/0000-0002-9717-0043
https://orcid.org/0000-0002-9468-4516
https://orcid.org/0000-0002-8782-4154

ture management as it allows to quantify biomass change timely
and at a fine spatial scale informing managers to implement man-

REFERENCES

agement strategies that help sustaining biodiversity during shrub

Aplin, P. (2005). Remote sensing: Ecology. Progress in Physical Geography,
29(1), 104–113. https://doi.org/10.1191/03091​3305p​p 437pr
Balsi, M., Esposito, S., Fallavollita, P., & Nardinocchi, C. (2018). Singletree detection in high-density LiDAR data from UAV-based survey.
European Journal of Remote Sensing, 51(1), 679–692. https://doi.
org/10.1080/22797​254.2018.1474722
Bellingham, P. J. (1998). Shrub succession and invasibility in a New
Zealand montane grassland. Australian Journal of Ecology, 23, 562–
573. https://doi.org/10.1111/j.1442-9993.1998.tb007​66.x
Cao, X., Liu, Y., Cui, X., Chen, J., & Chen, X. (2018). Mechanisms, monitoring and modeling of shrub encroachment into grassland : A
review. International Journal of Digital Earth, 1–17, https://doi.
org/10.1080/17538​947.2018.1478004
Chance, C. M., Coops, N. C., Plowright, A. A., Tooke, T. R., Christen,A., & Aven, N. (2016). Invasive shrub mapping in an urban environment from hyperspectral and LiDAR-derived attributes. Frontiersin Plant Science, 7(OCTOBER2016), 1–19. https://doi.org/10.3389/
fpls.2016.01528
Chaponnière, P., & Allouis, T. (2016). The YellowScan Surveyor: 5cm
Accuracy Demonstrated Study Site and Dataset. http://www.micro​geo.
it/publi​c /userf​i les/Droni​/ Yello​w Scan​S urve​yor_white​P aper_accur​
acy.pdf
D'Odorico, P., Okin, G. S., & Bestelmeyer, B. T. (2012). A synthetic
review of feedbacks and drivers of shrub encroachment in arid

encroachment.
AC K N OW L E D G M E N T S
BM and the remote sensing equipment were funded by a Carlsberg
Foundation Distinguished Associate Professor Fellowships (to SN).
We are thankful to the efforts from people in AU Physics workshop
in relation to implementing the LiDAR sensor to the UAS. Pierre
Chaponnière from the YellowScan Company introduced us to the
LiDAR system and assisted during the first data collection. We kindly
appreciate the services from the OPALS support team regarding
data processing. In addition, thanks are granted to Molslaboratoriet(Natural history museum Aarhus, Denmark) for accessibility to thestudy area and making rooms available for work during field campaigns. Finally, BM is grateful for being hosted in the TerraLuma research group during his research stay at the University of Tasmania,
Australia.
C O N FL I C T S O F I N T E R E S T
The authors declare no conflict of interest.

14|

grasslands. Ecohydrology, 5(5), 520–530. https://doi.org/10.1002/
eco.259
Deák, B., Valkó, O., Török, P., & Tóthmérész, B. (2016). Factors threatening grassland specialist plants – A multi-proxy study on the vegetation of isolated grasslands. Biological Conservation, 204, 255–262.
https://doi.org/10.1016/j.biocon.2016.10.023
De'ath, G., & Fabricius, K. E. (2000). Classification and regression trees:
A powerful yet simple technique for ecological data analysis. Ecology,
81(11), 3178–3192.
Donoghue, D. N. M., Watt, P. J., Cox, N. J., & Wilson, J. (2007). Remotesensing of species mixtures in conifer plantations using LiDAR heightand intensity data. Remote Sensing of Environment, 110(4), 509–522.
https://doi.org/10.1016/j.rse.2007.02.032
Ejrnæs, R., Bruun, H. H. (2000). Gradient analysis of dry grassland vegetation in Denmark. Journal of Vegetation Science, 11(4), 573–584.
https://doi.org/10.2307/3246587
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of
Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
Foody, G. M. (1996). Fuzzy modelling of vegetation from remotelysensed imagery. Ecological Modelling, 85(1), 3–12. https://doi.
org/10.1016/0304-3800(95)00012​-7
Forman, G. (2003). An extensive empirical study of feature selection.
Journal of Machine Learning Research, 3, 1289–1305.
Forsmoo, J., Anderson, K., Macleod, C. J. A., Wilkinson, M. E., & Brazier,R. (2018). Drone-based Structure-from-Motion photogrammetry
captures grassland sward height variability. Journal of Applied Ecology,
55.6, 2587–2599. https://doi.org/10.1111/1365-2664.13148
Haubensak, K. A., & Parker, I. M. (2004). Soil changes accompanying invasion of the exotic shrub Cytisus scoparius in glacial outwash prairies
of western Washington [USA]. Plant Ecology, 175(1), 71–79. Retrieved
from https://www.jstor.org/stabl​e/20146672
Hellesen, T., & Matikainen, L. (2013). An object-based approach formapping shrub and tree cover on grassland habitats by use of LiDAR
and CIR orthoimages. Remote Sensing, 5(2), 558–583. https://doi.
org/10.3390/rs502​0558
Hernandez-Santin, L., Rudge, M. L., Bartolo, R. E., & Erskine, P. D. (2019).
Identifying species and monitoring understorey from UAS-derived
data: A literature review and future directions. Drones, 3(1), 9. https://doi.org/10.3390/drone​s3010009
Hill, D. A., Prasad, R., & Leckie, D. G. (2016). Mapping of Scotch broom(Cytisus scoparius) with landsat imagery. Weed Technology, 30(02),539–558. https://doi.org/10.1614/WT-D-15-00038.1
Kane, V. R., McGaughey, R. J., Bakker, J. D., Gersonde, R. F., Lutz, J. A., &Franklin, J. F. (2010). Comparisons between field- and LiDAR-based
measures of stand structural complexity. Canadian Journal of Forest
Research, 40(4), 761–773. https://doi.org/10.1139/x10-024
Kesting, S., Petersen, U., & Isselstein, J. (2015). Humped-back shaped response of plant species richness to increasing shrub encroachment
in calcareous grasslands. Community Ecology, 16(2), 189–195. https://doi.org/10.1556/168.2015.16.2.6
Kukunda, C. B., Beckschäfer, P., Magdon, P., Schall, P., Wirth, C., & Kleinn,
C. (2019). Scale-guided mapping of forest stand structural heterogeneity from airborne LiDAR. Ecological Indicators, 102, 410–425.
https://doi.org/10.1016/j.ecoli​nd.2019.02.056
Leduc, M., & Knudby, A. J. (2018). Mapping wild leek through theForest Canopy using a UAV. Remote Sensing, 10.1(70), https://doi.
org/10.3390/rs100​10070
Lefsky, M. A., Cohen, W. B., Parker, G. G., & Harding, D. J. (2002). Lidar remote sensing for ecosystem studies. BioScience, 52(1), 19–30. https://doi.org/10.1641/0006-3568(2002)052[0019:LRSFE​S]2.0.CO;2
Magurran, A. E. (2016). How ecosystems change. Science, 351, 448–449.
https://doi.org/10.1126/scien​ce.aad6758
Manfreda, S., McCabe, M., Miller, P., Lucas, R., Pajuelo Madrigal, V.,
Mallinis, G., … Toth, B. (2018). On the use of unmanned aerial systems

MADSEN et al.

for environmental monitoring. Remote Sensing, 10(4), 1–28. https://doi.org/10.3390/rs100​4 0641
Millard, K., & Richardson, M. (2015). On the importance of training data
sample selection in Random Forest image classification: A case study
in peatland ecosystem mapping. Remote Sensing, 7(7), 8489–8515.
https://doi.org/10.3390/rs707​0 8489
Mitchell, J. J., Glenn, N. F., Sankey, T. T., Derryberry, D. W. R., Anderson,
M. O., & Hruska, R. C. (2011). Small-footprint lidar estimations ofsagebrush canopy characteristics. Photogrammetric Engineeringand Remote Sensing, 77(5), 521–530. https://doi.org/10.14358​/
PERS.77.5.521
Moeslund, J. E., Zlinszky, A., Ejrnæs, R., Brunbjerg, A. K., Bøcher, P. K.,
Svenning, J.-C., & Normand, S. (2019). LIDAR explains diversity ofplants, fungi, lichens and bryophytes across multiple habitats andlarge geographic extent. BioRxiv, https://doi.org/10.1101/509794
Müllerová, J., Brůna, J., Bartaloš, T., Dvořák, P., Vítková, M., & Pyšek, P.
(2017). Timing is important: Unmanned aircraft vs. satellite imagery
in plant invasion monitoring. Frontiers Plant Science, 8(May), 1–13.
https://doi.org/10.3389/fpls.2017.00887
Oldeland, J., Naftal, L., & Strohbach, B. J. (2017). The potential of UAV
derived image features for discriminating savannah tree species.
In R. Díaz-Delgado, R. Lucas, & C. Hurford (Eds.), The roles of remotesensing in nature conservation (pp. 183–201). Springer, Cham: Springer
International Publishing. https://doi.org/10.1007/978-3-319-64332​-8
Parker, I. M. (2000). Invasion dynamics of Cytisus scoparius: A matrix
model approach. Ecological Applications, 10(3), 726–743. https://doi.
org/10.2307/2641041
Paynter, Q., Fowler, S. V., Memmott, J., & Sheppard, A. W. (1998). Factors
affecting the establishment of Cytisus scoparius in southern France:
Implications for managing both native and exotic populations. Journalof Applied Ecology, 35(4), 582–595.
Peterson, D. J., & Prasad, R. (1998). The biology of Canadian weeds. 109.
Cytisus scoparius (L.) Link. Canadian Journal of Plant Science, 78(1961),
497–504. https://doi.org/10.4141/P97-079
Pfeifer, N., Mandlburger, G., Otepka, J., & Karel, W. (2014). OPALS – A
framework for Airborne Laser Scanning data analysis. Computers,
Environment and Urban Systems, 45, 125–136. https://doi.
org/10.1016/j.compe​nvurb​sys.2013.11.002
Popescu, S. (2011). Lidar remote sensing. In Q. Weng (Ed.), Advances inenvironmental remote sensing: Sensors, algorithms, and applications (pp.
57–80). Boca Raton, FL: Taylor & Francis.
QGIS Development Team. (2019). QGIS geographic information system.
Retrieved from http://www.qgis.org/
R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Retrieved from https://www.r-proje​c t.org
Ressl, C., Mandlburger, G., & Pfeifer, N. (2009). Investigating adjustmentof airborne laser scanning strips without usage of GNSS/IMU trajectory data. Laser Scanning 2009, IAPRS, XXXVIII(1), 195–200.
Sanders, A. (2017). Mapping the distribution of understorey rhododendron ponticum using low-tech multispectra UAV derived imagery. In
R. Díaz-Delgado, R. Lucas, & C. Hurford (Eds.). The roles of remotesensing in nature conservation (pp. 167–181). Chem, Switzerland:
Springer.
Sankey, T., Shrestha, R., Sankey, J. B., Hardegree, S., & Strand, E. (2013).
Lidar-derived estimate and uncertainty of carbon sink in successional
phases of woody encroachment. Journal of Geophysical Research:
Biogeosciences, 118(3), 1144–1155. https://doi.org/10.1002/
jgrg.20088
Sheppard, A. W., Hodge, P., Paynter, Q., & Rees, M. (2002). Factors
affecting invasion and persistence of broom Cytisus scoparius inAustralia. Journal of Applied Ecology, 39(5), 721–734. https://doi.
org/10.1046/j.1365-2664.2002.00750.x
Stevens, C. J., Dise, N. B., Mountford, J. O., & Gowing, D. J. (2004). Impactof nitrogen deposition on the species richness of grasslands. Science

| 15

MADSEN et al.

(New York, N.Y.), 303(5665), 1876–1879. https://doi.org/10.1126/
scien​ce.1094678
Svendsen, L., van der Bijl, L., Boutrup, S., & Norup, B. (2005). National
monitoring and assessment programme for the aquatic and terrestrial.
Aarhus, Denmark: National Environmental Research Institute.
Svenning, J.-C., Pedersen, P. B. M., Donlan, C. J., Ejrnæs, R., Faurby,
S., Galetti, M., … Vera, F. W. M. (2016). Science for a wilder
Anthropocene: Synthesis and future directions for trophic rewilding research. Proceedings of the National Academy of Sciences of theUnited States of America, 113(4), 898–906. https://doi.org/10.1073/
pnas.15025​56112
Therneau, T. M., & Atkinson, E. J. (1997). An introduction to recursive partitioning using the RPART routines. Rochester, MN: Mayo Foundation.
Timmermann, A., Damgaard, C., Strandberg, M. T., & Svenning, J. C.
(2015). Pervasive early 21st-century vegetation changes acrossDanish semi-natural ecosystems: More losers than winners and ashift towards competitive, tall-growing species. Journal of Applied
Ecology, 52(1), 21–30. https://doi.org/10.1111/1365-2664.12374
Valbuena, R., Eerikäinen, K., Packalen, P., & Maltamo, M. (2016). Gini
coefficient predictions from airborne lidar remote sensing display the effect of management intensity on forest structure.
Ecological Indicators, 60, 574–585. https://doi.org/10.1016/j.ecoli​
nd.2015.08.001
Van Aardt, J. A., Wu, J., Mcglinchy, J., Wessels, K. J., Mathieu, R. S.,
Kennedy Bowdoin, T., … Asner, G. P. (2012). On using discrete return
Lidar distributions as a proxy for waveform Lidar signals when modeling vegetation. IEEE International Geoscience and Remote Sensing
Symposium (IGARSS), Munich, Germany.
Van Leeuwen, M., Hilker, T., Coops, N. C., Frazer, G., Wulder, M. A.,
Newnham, G. J., & Culvenor, D. S. (2011). Assessment of standing
wood and fiber quality using ground and airborne laser scanning: A
review. Forest Ecology and Management, 261(9), 1467–1478. https://doi.org/10.1016/j.foreco.2011.01.032
Vauhkonen, J. (2018). Predicting the provisioning potential of forest
ecosystem services using airborne laser scanning data and forest resource maps. Forest Ecosystems, 5(1), 1–19. https://doi.org/10.1186/
s4066​3-018-0143-1
Wachendorf, M., Fricke, T., & Möckel, T. (2017). Remote sensing as a tool
to assess botanical composition, structure, quantity and quality oftemperate grasslands. Grass and Forage Science, 73(1), 1–14. https://doi.org/10.1111/gfs.12312
Wallace, L., Lucieer, A., Malenovský, Z., Turner, D., & Vopěnka, P.
(2016). Assessment of forest structure using two UAV techniques:
A comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests, 7(3), 62. https://doi.org/10.3390/
f7030062
Wallace, L., Lucieer, A., & Watson, C. S. (2014). Evaluating tree detection
and segmentation routines on very high resolution UAV LiDAR ata.

IEEE Transactions on Geoscience and Remote Sensing, 52(12), 7619–
7628. https://doi.org/10.1109/TGRS.2014.2315649
Wallace, L., Lucieer, A., Watson, C., & Turner, D. (2012). Developmentof a UAV-LiDAR system with application to forest inventory.
Remote Sensing, 4(6), 1519–1543. https://doi.org/10.3390/rs406​
1519
Wang, D., Xin, X., Shao, Q., Brolly, M., Zhu, Z., & Chen, J. (2017). Modelingaboveground biomass in Hulunber grassland ecosystem by using unmanned aerial vehicle discrete LiDAR. Sensors, 17(12), 1–19. https://doi.org/10.3390/s1701​0180
Wieser, M., Mandlburger, G., Hollaus, M., Otepka, J., Glira, P., & Pfeifer,
N. (2017). A case study of UAS borne laser scanning for measurement of tree stem diameter. Remote Sensing, 9(11), 1154. https://doi.
org/10.3390/rs911​1154
Wijesingha, J., Moeckel, T., Hensgen, F., & Wachendorf, M. (2019).
Evaluation of 3D point cloud-based models for the prediction of grassland biomass. International Journal of Applied Earth Observation andGeoinformation, 78(May 2018), 352–359. https://doi.org/10.1016/j.
jag.2018.10.006
Wilsey, B. J. (2018). Conservation and restoration of grasslands (Chapter8). In B. J. Wilsey (Ed.), The biology of grasslands (pp. 143–163). Oxford,
UK: Oxford University Press. https://doi.org/10.1093/oso/97801​
98744​511.001.0001
YellowScan. (2016). YellowScan Surveyor – User Manual. https://www.
yello​wscan​-lidar.com/produ​c ts/surve​yor/
Yokoyama, R., Shirasawa, M., & Pike, R. J. (2002). Visualizing topography
by openness: A new application of image processing to digital elevation models. Photogrammetric Engineering & Remote Sensing, 68(3),257–265. https://doi.org/10.1016/j.jtbi.2012.08.013
Zlinszky, A., & Kania, A. (2016). Will it blend? Visualization and accuracy
evaluation of high-resolution fuzzy vegetation maps. International
Archives of the Photogrammetry, Remote Sensing and Spatial
Information Sciences – ISPRS Archives, 41(July), 335–342. https://doi.
org/10.5194/isprs​archi​ves-XLI-B2-335-2016
Zlinszky, A., Schroiff, A., Kania, A., Deák, B., Mücke, W., Vári, Á., …
Pfeifer, N. (2014). Categorizing grassland vegetation with full-waveform airborne laser scanning: A feasibility study for detecting natura
2000 habitat types. Remote Sensing, 6(9), 8056–8087. https://doi.
org/10.3390/rs609​8 056

APPENDIX 1

2017, Applanix, Richmond Hill, Ontario, Canada). From three flights in

How to cite this article: Madsen B, Treier UA, Zlinszky A,
Lucieer A, Normand S. Detecting shrub encroachment inseminatural grasslands using UAS LiDAR. Ecol Evol.
2020;00:1–27. https://doi.org/10.1002/ece3.6240

each year, 32 strips in 2017 and 43 in 2018 were selected for further
L I DA R P O I N T C LO U D P R O C E S S I N G A N D Q UA L I T Y

analysis using the Yellowscan QGIS plugin (YellowScan, 2016). This

CONTROL

tool basically determines the GPS-time used as delimiter for the flight

This document describes the procedure UAS LiDAR point cloud

strip data to be further processed. These strips were afterwards re-processing, quality control, and relative position adjustment. Point

adjusted, to make sure that data gathered while the UAS was turning

clouds have been collected from UAS LiDAR flights in Mols Bjerge,

were excluded. At last, data were delimited to a scan angle of ±55

Denmark, 6 October 2017 and 23 April 2018.

degrees minimizing the amount of noisy points in the dataset.

Pre-processing

Quality control

Prior to these analyses, the point clouds had been post-processed

Quality was assessed by creating point density maps to evaluate theand geo-referenced using the software POSPac UAV (v8.2, Dec

coverage throughout the area of interest (Figure A1.1a,b). Likewise,

16|

MADSEN et al.

the dislocation differences between the overlapping flight strips(v2.10, GPL software, 2019, retrieved from http://www.cloud​compa​

were calculated and visualized on raster maps (Figure A1.1c–f) as

re.org/).

well as in cross-sections of the point cloud (Figure A1.2). Thicknessof the ground points layer was evaluated by measuring a cross sec-

Relative adjustment of point cloudtion in a place with a minimum of vegetation (i.e., ground thickness

It is possible to reduce the differences between overlapping flightshould be low) using the point measurement tool in CloudCompare

strips (i.e., relative accuracy) caused by system inaccuracies (e.g.,
GNSS and IMU precision) by applying a least squares matching

(a)

(b)

(c)

(d)

(e)

(f)

F I G U R E A 1 . 1 Top figures show maps of point density from Mols 2017 flight (a) and Mols 2018 flight (b). The middle figures presents
the strip-differences calculated from Mols 2017 (c) and 2018 (d) before the relative georeferencing procedure and bottom after the relative
georeferencing

| 17

MADSEN et al.

(a)

(b)

(c)

(d)

F I G U R E A 1 . 2 Cross-sections from Mols 17 flight (a and c) and Mols 18 flight (b and d) before (top) and after relative georeferencing(bottom). Points are colored by the Point ID from each flight strip(LSM) technique to each overlapping strip pair (Ressl et al., 2009).

Outliers

These errors are visible as rectangular artifacts in Figure A1.1c–f

Outliers in the point cloud, that is, erroneous records of LiDAR returns,

and as differently colored layers in Figure A1.2, each caused by dis-

will affect further processing and the quality of point classification.

junct flight strips. The adjusting procedure is performed with theHence, such outliers have been deleted by attributing each point with

GeoRefApprox module in OPALS (v2.3.1, 2018, TU Vienna, Austria,

the number of points within a search radius of 1 m, that is, a sphere

http://geo.tuwien.ac.at/opals​/html/Modul​eGeor​efApp​rox.html) by

with a diameter of 2 m around the given point and then applying akeeping one strip as a reference and adjusting the others relative totwo-step procedure. First, we deleted all points that had no neighbors

this reference strip. The reference strip was chosen as the closest towithin the 1 m search radius, which corresponds to single noise points.

the median position of all strips, which is the standard setting. After

Second, as noise can appear in clumps we also deleted all points withthe relative geo-referencing, the quality control was performed once

average number of neighbors <3 from points within a search radiusmore and visualized for comparison (Figures A1.1 and A1.2).

of 3 m, that is, a sphere with a diameter of 6 m around a given point.

F I G U R E A 1 . 3 Boxplot showing Z coordinate deviation
measurements from 2017 and 2019 surveys before and after
relative point cloud adjustment (gref). Outliers are presented as
circles, while median and mean values are marked with a line andcross, respectively

F I G U R E A 1 . 4 Boxplot showing XY coordinate deviation
measurements from 2018 surveys before and after relative point
cloud adjustment (gref). Median and mean values are marked with aline and cross respectively

18|

MADSEN et al.

Vertical deviation

Horizontal deviation

2017

2018

2017_adj

2018_adj

20182018_adj

RMSE

0.039484

0.060631

0.076016

0.091212

0.014

0.064713

MAD

0.018583

0.025667

0.03175

0.030532

0.00696

0.03888

TA B L E A 1 . 1 Calculated root meansquare error (RMSE) and mean absolutedifference (MAD) from vertical deviation
measurements in CloudCompare before
and after relative adjustment (*_adj)

F I G U R E A 1 . 5 Cross-section
illustrating the point cloud accuracy
between flights from Mols 2017 (Red) and2018 (Blue). Bars in the lower right show
the scale bar in white (values in meters),
while the point cloud orientation ispresented as blue (Z-axis) and red (X-axis)

2017 point cloud2018 point cloud
Absolute precision

Concluding remarks

We measured 12 fencepost-tops with a differential GNSS system

Our findings indicate that the UAS LiDAR system used for this study(Trimble Real-time Kinematic) with centimeter-level precision. The

performed with an absolute position error below 10 cm in both, ver-measured GNSS points were compared to the closest point rec-tical and horizontal directions. The absolute accuracy is lower thanognized as a fencepost in the point clouds. The deviation in thereported in the technical specification for the LiDAR system; how-

Z coordinate could then be measured for each fencepost, which

ever, the assessment is based on the position of fencepost which has

could be distinguished in the point clouds. We found an average

been measured with a GNSS RTK system providing ≤2 cm precision.

deviation of 3.2 cm in the 2017 data and 4.8 cm in 2018, which is

Furthermore, we applied a flight strip adjustment procedure thatwithin the accuracy of 5 cm given by the technical specifications

increases the relative accuracy of the point clouds on the expenseof the system (YellowScan Surveyor Data Sheet, https://www.

of slightly shifting the point clouds in absolute geographic space.

yello​wscan​-lidar.com/produ​c ts/surve​yor/). However, the accu-

Precise alignment of the point clouds was, however, consideredracy of the absolute position of these control points decreased

most important for the overall purpose of classifying shrubs.

with deviations of 6.5 cm and 8.3 cm, respectively, after the relative geo-referencing of flight strips, that is, relative adjustment of
the point clouds (Figure A1.3).
In addition, we measured the systems XY precision in the 2018
survey by using five ground control markers. Here, we found the deviation of 1.1 cm to increase to 4.7 cm after the relative adjustment

REFERENCES
Ressl, C., Mandlburger, G., & Pfeifer, N. (2009). Investigating adjustmentof airborne laser scanning strips without usage of GNSS/IMU trajectory data. Laser Scanning 2009, IAPRS, XXXVIII(1), 195–200.

(Figure A1.4).
APPENDIX 2
Using these measurements, we calculated the root mean squareerror (RMSE) and mean absolute difference (MAD), which are

C L A S S I F I C AT I O N P R O C E D U R E

presented in Table A1.1.

This document provides details on the entire classification pro-

At last, we overlaid the point clouds from each year to visualize

cess from preparing and selecting attributes to applying validation

the consistency of the two surveys (Figure A1.5). It is noticeable

data for a tree-based classification model. The data were extracted

that the red points (2017) seem a bit higher located for groundfrom point clouds collected from UAS LiDAR flights in Mols Bjerge,

points most probably a vegetation effect in the leaf-on period.

Denmark, 6 October 2017 and 23 April 2018.

Overall, the red and blue points are still in seemingly good aligned/intermixed.

A training dataset and feature variables to characterize the targeted classes are essential inputs in supervised machine learning

| 19

MADSEN et al.

classification approaches (Waldhauser et al., 2014). The classifica-

50–100 m. Along each transect every 100 m, the nearest shrub oftion workflow targets the recognition of Cytisus scoparius shrubs ineach species in the area was sampled, where each shrub was meas-a highly heterogenous and vegetated area of semi-natural grassland.

ured as close to the central stem as possible. Additionally, extra

The workflow in this appendix is described in a three-step proceduresamples outside the transect paths were made to complement theof (a) preparing and training the model, (b) computing and evaluating

training data for less abundant species. The training dataset was

input attributes, and (c) final classification results.

used in two different divisions to evaluate possible influence of an
unbalanced training dataset. The full model “ALL” contains all meas-

Preparing and training the model

ured points in their original separate classes, and the number of indi-

Prior to the machine learning process, the full point cloud was di-viduals within each class is presented in Figure A2.1. Whereas Model

vided into ground, low vegetation, high vegetation, and a shrub layer“CYTISUS” shows the balance of training data when only the focus

of points. The thresholding procedure was based on the normalizedclass C. scoparius was kept by itself, while all other classes were me.

point height (NormZ), which is calculated by subtracting a 1 × 1 m
digital terrain model (DTM) derived from the lowest points from theGenerating training and validation points

height of each point.

Because Opals modules for attribute assignments are not always

All points with NormZ < 0.15 m were assigned as ground temporarily. Before concluding on the final ground classification, we transferredworking with shapefiles the reference data needed to be pre-processed for implementation in an Opals data manager file (ODM).

points in low densities (<10 pts in spheres with 0.25 m radius) back to

Buffers of 25, 35, and 50 cm were created around the refer-

non-ground, to adjust for wrongly classified points in less penetrable

ence datapoints in QGIS and selected appropriately to cover1 × 1 m cells. Second, we calculated a mean of NormZ within cylinders

a single object of each class. The 50 cm buffer was used for all

corresponding to vegetation sizes (low vegetation dimensions: 0.1 m

shrub classes except Calluna vulgaris, which would be decreased

radius and 0.3 m height; high vegetation dimensions: 0.25 m radius

to 35 cm buffer radius along with the Not Shrub class. The fen-

and 5 m height). We then thresholded the points into low vegetation

ceposts themselves are varying in radius between 10 and 20 cm,(NormZ < 0.3 m) and high vegetation (NormZ > 3.5 m) leaving a shrub

and as they would not always be completely straight, the 25 cm

layer point cloud of medium height vegetation (0.3 m < NormZ < 3.5 m).

buffer was used here.
In QGIS, the buffered reference points were rasterized to 5 × 5 m

Training a tree-based model

GeoTIFF's for each class displaying the reference cells with 1 and theTwo separate sets of reference data were collected in between therest of the flight area with 0.

two flight surveys. They consist of single differential GNSS measure-

Having the reference information in raster files allows it toments of different objects in the area. In the period between refer-be stored as attributes in the working ODM with OpalsAddinfo.

ence data and LiDAR flights no major change to, for example, shrubs

Afterwards, all points overlapping in 2D with the raster cells equal toare expected and following the described method below for genera-

1 could be assigned as validation points in the given class. To ensuretion of validation points, it should be safe to use the two datasetsno classes overlapped in the 2D buffer area, the ground, low vegeta-

individually and together.

tion, and high vegetation classes were assigned separately.

We measured in total 180 shrub individuals from 12 different

This method of generating validation points in a point cloud en-

shrub/tree taxa with an RTK GNSS device, to create the dataset forsures that no false points are introduced in the training model as thetraining and validation. The reference data gathering was performed

actual points are measured with the LiDAR system itself, and thein random transects throughout the area and spaced approximately

reference data used as a guide for assigning the points.

(a) MODEL: ALL
CytisusNot Shrub

Fenceposts Rubus

Rosa

CallunaBetulaQuercus

PrunusWood

Sambucus

Malus

Pinus

Crataegus

65

31

29

No of Individuals

F I G U R E A 2 . 1 Training and validation
data for classification with separate
classes of all shrub species (a) and where
only the class of interest is kept and allother species was pooled into one class(b). The number of individuals measured
within each class is printed on the figure2429(b) MODEL CYTISUS
Cytisus

No of Individuals

65

Not Cytisus

179

Juniperus

14 14 8 8 8 43322

20|

MADSEN et al.

Computing and evaluating input attributes

shrub genera, and in particularly C. scoparius. Two types of models
were processed, one containing all shrub classes (All) and one

Input attributeswith C. scoparius alone from each flight, resulting in a total of 4

The tree-based classification procedure available in the OPALS soft-models. The importance scores were derived from a “leave-one-ware package is applied on a point-based level, meaning that eachout” procedure, meaning that each classification was processed

point is classified independently from other points. However, before

firstly with all variables and afterwards one time without each of

the classification process is initiated, each input attribute was aver-the variables. In that way, it was possible to calculate the average

aged within a cylinder with radius 0.25 m and height ±2.5 m fromdifference in overall accuracy (OA), completeness and correct-

every point (see Figure A2.2). In this way, the “salt and pepper ef-

ness when a variable was not included (Figure A2.3). The OA is

fect,” known also from image classification (Blaschke, Lang, Lorup,

measured as the number of correctly classified points, that is, theStrobl, & Zeil, 2000), is avoided and the scale of the input attributes

sum of true positives (TP) and true negatives (TN) divided by theis more likely to correspond to the size of the target shrubs.

total number of validation points. Additionally, “completeness” isDuring the averaging process, the point-cloud was filtered forthe ratio between TP and TP + false negatives (FN) and “correct-

ground (Normalized Z < 0.15 m), low vegetation (0.15 m > Normalizedness” is the ratio between TP and TP + false positives (FP; Heipke,

Z < 0.30 m), and high vegetation (Normalized Z > 3.5 m) to exclude

Mayer, Wiedemann, & Jamet, 1997). Completeness and correct-

points without interest to the shrub classification. Yet, the filteredness are equivalent to the terms recall and precision, respectively,

points would have contributed as neighboring points in the attributealso referred to in this document (Forman, 2003). For evaluation,

computation before the averaging step and therefore should be con-the accuracy measures were summed without weight and ranked

sidered when interpreting the results.

from best to worst (Table A2.1).

Variable selection

to avoid autocorrelation. Variable values were extracted from theThis section describes the procedure on which variables have been

point cloud to a cell-based raster and analyzed with R statistics. For

selected to classify shrubs from two different flights (Autumn 2017

extracting the cell values, we chose a search radius of 0.25 m equaland Spring 2018).

to the averaging cylinder used for each variable before the classifica-

Furthermore, the variables were evaluated in a correlation matrix

tion. Furthermore, we only extracted values from the classification
1. Initially, several variables were computed with the OPALS

input points, that is, shrub points. Correlations were made sepa-

software and each on three different scales (search radius:

rately for each dataset (Mols17 and Mols18), and the variables were0.1, 0.25, and 0.5 m). From these, a set of 17 variables were

evaluated for both cases. We allowed only variables with maximum

selected manually to sort out variables identical on the threepairwise correlation of ρ = .75 keeping the most important variable

scales.

in case of autocorrelation. Variables with importance ranks above
10 or summed importance values below 0.005 were also rejected2.

The 17 “Base” variables were all used for the first clas-

sification process to identify their importance in distinguishingfor further proceeding.

Evaluating shrub signatures
We used the shrub locations to extract raster values for the NormZ
variable, and each of the seven variables used in the classification
around a 25 cm buffer. The samples were compared for each yearof data acquisition with a Wilcoxon signed-rank test to evaluate thedifferences among signatures in the variables for each shrub species(Table A2.2). Calluna and Pinus samples were excluded because oflimited sample sizes. From the reference data, we see that Betula

5m

shrubs differ more within the selected variables, while Crataegus
and Juniperus show no signature differences between the years. For
the Cytisus shrubs, we find a temporal variation in three variables (pval < .05), namely PCount_ground, PCount, and PDist. However, the

0.5 m
F I G U R E A 2 . 2 Sketch of averaging cylinder used forhomogenizing point cloud attributes to characterize a whole shrubor larger parts of it. Central point indicated by blue circle is assigned
the average attribute value based on all the points contained in thecylinderNormZ variable used for estimating biomass remains similar for all
shrubs except Betula. This indicates that our biomass estimations aretransferable in time. Furthermore, we would expect that the density
variables from Cytisus shrubs are most affected by temporal change,
as even small leaves will contribute to an increase if there are many.
In that sense, we would also have expected more temporal difference in Crataegus and Quercus shrubs, which have bigger leaves thanCytisus. However, it could be explained by temporal variability among

| 21

MADSEN et al.

0.06
0.05
0.04
0.03
0.02
0.01
0
–0.01

Average of Importance_overall
Average of Importance_Complete
Average of Importance_Correct

F I G U R E A 2 . 3 Average values of variable importance from four different models, distinguishing the overall importance (blue),completeness (orange), and correctness (grey)
TA B L E A 2 . 1 Variable importance based on overall accuracy, completeness and correctness scores. The three measures were summed
without additional weights (sum) and ranked from best to worst (1–17) according to each measure before averaging (rank). Color indicatesthe variables that are kept in the models for the next step (green), and those left out due to lack of importance with a rank threshold ≤ 10(Red) or autocorrelation with pairwise cor >.75 (yellow)
Variable

Overallaccuracy

CompletenessCorrectness

Rank

Sum

PseudowaveL

0.047808195

0.00902837

0.047616142

1

0.104453

PDist

0.025160961

0.00811807

0.000653427

5.333333

0.033932

PCountL

0.013440036

0.002558371

0.01761165

3.666667

0.03361

Pcount_ground0.006272326

0.002858146

0.008466972

50.017597

NoOfEchos

0.003692739

0.004184393

0.006445916

60.014323

NegOpen0.004667219

2.81416E−05

0.009419098

70.014114

Range

0.004524673−0.005458463

0.014149932

90.013216

NormZ

0.006381884

0.003413731−9.15274E−05

7.333333

0.009704

Planarity

0.001868897−0.000228497

0.005266631

100.006907

PCountM

0.003716534−0.000250142

0.003000489

10

0.006467

Rank

0.001781419

0.002352647

0.001707153

100.005841

PseudowaveS

0.00252669

0.000889567

0.001788535

9.333333

0.005205

Linearity

0.000436458

0.000820137

0.000593598

12.33333

0.00185

PDensL0.001645286

0.000281219−0.000591189

12.66667

0.001335

PCountS

0.001047605−0.002076538−0.001163251

14.66667−0.00219

StdDev−0.001450466−0.005435544

0.004469112

13.33333−0.00242

PosOpen−0.003849571−0.003340636−0.004994585

16.33333−0.01218

species, where the leaves could have been present or not during bothclassified as shrubs for the 2017 and 2018 data, respectively.

flights. Alternatively, the old senescent leaves and additional plant

The rest were generally classified as ground (2017:25.4%;

litter from other species growing together with the shrubs may affect

2018:35.8%), low vegetation (42.8%; 52.0%), and high veg-

the shrub signatures to be interpreted as more equal.

etation (19.3%; 5.9%). From the shrub point cloud, 13 classes
could be distinguished from the classification model. The class

Our findings regarding shrub signatures reveal many new possibilities in terms of understanding vegetation dynamics but would
require a much more focused effort for data collection which was

distribution results are provided as number of points and as
percentages (Figure A2.4). For this inventory, each point was
assigned the class containing the highest probability value.

beyond the scope of the present study.

Classification accuracy assessment

Final classification results

We assessed the accuracy of the final classification input by per-

We classified a total of 59,127,474 points from the 2017 survey

forming a randomly stratified selection of input training/valida-and 53,886,091 from 2018. Of these, 12.6% and 6.2% weretion subsets and running 100 permutations for each classification

22|

MADSEN et al.

TA B L E A 2 . 2 Differences in shrub signatures between 2018 and 2017 flights for each variable and corresponding Wilcoxon p-values
comparing the samples. p-Value significance codes are illustrated by circles: p-val ≤.05 (black); p-val ≤ .1 (grey); p-val >.1 (white)

ClassName
NegOpenWilcoxon P-val
PCount_ground
Wilcoxon P-val
NoOfEchoes
Wilcoxon P-val
PCountWilcoxon P-val
PDist
Wilcoxon P-val
PseudoW
Wilcoxon P-val
Rank
Wilcoxon P-val
NormZ
Wilcoxon P-val

Bet

Cra
Cyt
Jun
Mal
Pru
Que
Ros
Rub
Sam
0.203
0.051
0.037 –0.052 0.110
0.209 –0.155
0.093
0.062
0.177
0.000
0.700
0.053
0.660
0.800
0.990
0.170
0.054
1.000
0.100
–349.380 –231.487 –86.837 –129.871 –26.028 –205.587 –192.760 –187.280 –117.462 –267.771
0.002
0.400
0.009
0.490
0.200
0.003
0.083
0.001
0.140
0.100
–0.023
0.014
0.009
0.022
0.005
0.001
0.041
0.001 –0.008 –0.038
0.019
0.400
0.780
0.850
0.800
0.340
0.860
0.340
0.410
0.100
1.704 –6.202 –3.343 –4.870 –1.050 –2.722 –4.018 –5.119
2.139 –5.437
0.680
0.700
0.003
0.490
0.800
0.470
0.320
0.054
0.680
0.200
0.003
0.005
0.003
0.001
0.006
0.007 –0.003
0.006 –0.001
0.008
0.110
0.400
0.004
0.320
0.200
0.610
0.630
0.018
0.740
0.100
–0.107 –0.052 –0.003
0.028 –0.162
0.027
0.172 –0.014 –0.010 –0.240
0.004
0.700
0.770
0.510
1.000
0.920
0.460
0.440
0.390
0.400
–8.071 –1.494 1.719
0.634 13.389 –6.642 –5.127
1.860 –4.650 –17.613
0.860
0.010
0.100
0.007
0.700
0.200
0.460
0.800
0.150
0.460
–0.454 –0.089 0.045
0.103 –0.430 –0.210
0.419
0.045 –0.164 –0.601
0.003
0.700
0.390
0.480
0.800
0.920
0.410
0.680
0.079
0.400

Number of points

2,500,000

SHRUB CLASSIFICATION

2,000,000
1,500,000
1,000,000
500,000
0

Cytisus

Rubus

BetulaRosa

PrunusJuniperus Sambucus

Quercus

Fencepost Not Shrub Crataegus

Malus

Pinus

Leaf-on 2017

2149253

1212659929891945480554034382772282725265339228855227072115358

82151

57169

Leaf-off 20181367183443762

15741724647477488

429764

145360120262107609

40483

43191

84554

85379

Malus
1.1%

Pinus
0.8%

2.5%

2.5%

Percentage of shrub points

50.0%

SHRUB CLASSIFICATION

40.0%
30.0%
20.0%
10.0%
0.0%
Leaf-on 2017

Cytisus
28.9%

Rubus
16.3%

Betula
12.5%

Rosa
12.7%

Prunus7.5%

Juniperus Sambucus
5.1%
3.8%

Quercus3.6%

Leaf-off 2018

40.8%

13.3%

4.7%

7.4%

2.3%

12.8%

3.6%

4.3%

Fencepost Not Shrub Crataegus
3.1%
3.1%
1.6%
3.2%

1.2%

1.3%

F I G U R E A 2 . 4 Distribution of classified shrub points from the 2017 survey (green) and 2018 survey (blue) in numbers (top) andpercentage of total shrub points (bottom)

model (2017 and 2018). Furthermore, each model was evalu-

positives (FP), and false negatives (FN), whereof the accuracy met-

ated with two different splits of training/validation data, that is,rics was derived.

90/10 and 70/30. For each permutation, a confusion matrix was

To assess the general classification performance, we calculated

computed showing true positives (TP), true negatives (TN), false

the overall accuracy (OA) and Kappa coefficient (Kap) (Fielding &| 23

MADSEN et al.

F I G U R E A 2 . 5 Density plots showing
the resulting accuracy distribution from100 permutations of the classification
models, including both the 2017 and 2018flights. The accuracy metrics used for thegeneral classification are overall accuracy
and Kappa coefficient, each shown withthe subsets selected by a 90/10 or 70/30
split of the training/validation data.
Below the density plots, corresponding
tables highlight the calculated meanand standard deviation (SD) for each
distribution

Bell, 1997). The Kappa coefficient compares the observed accuracy

model to be overwritten each time, it was not possible to pro-with the expected accuracy by random chance. For class-wise ac-duce a mean or median model output to be applied for biomass

curacy, we used precision (P) and recall (R) values to calculate theextraction. Since the model used for biomass projections differedharmonic mean termed “F1” (Forman, 2003). The F1 is calculated byonly minimal to the average of the 100 permutated models andthe ratio of (2 * recall * precision) and the sum of recall and precision.

thus shows comparable accuracy, we are confident that no bias has

For the definition of OA, recall (i.e., completeness) and precision (i.e.,been introduced by applying this model.

correctness) see above. The results from the accuracy assessment
are presented in Figure A2.5 for OA and Kappa, while the class-wiseMerged classification

accuracies are shown in Figure A2.6.
Density plots from the 100 permutations of the OA and Kap

To assess model transferability, we applied the classifica-

show the highest accuracy for the 2018 dataset (OA mean = 0.95

tion process to a merged point cloud containing both flights

and Kap mean = 0.93), while the different ways of splitting train-(2017 + 2018). Using the mixed signatures from two different

ing/validation subsets are performing equal. The equal performanceseasonal states showed an expected decrease in overall accu-

of the 90/10 and 70/30 splits was also found for the class-wise as-racy (Table A2.4). The Cytisus class remained high in accuracy

sessment; hence, only results from the 90/10 splits are presented incompared to the other classes, whereof some would be more

Figure A2.6.

affected by seasonal state (e.g., Betula) or small sample sizes

Applied classification model(Pinus = 2 individuals).
Additional 2D classification

The final classification models from each year were computed

We performed a traditional 2D classification based on the ras-

after the 100 validation iterations. For each year, the classifica-terized variables for comparison with the 3D process. The entire

tion was used to derive biomass metrics in the project area. The

classification was performed with the “caret” package in R sta-

resulting accuracy metrics are presented in Table A2.3 and thetistics using the same GNSS observations as the 3D point cloud

extraction of biomass metrics in Appendix 3. The final model wasclassification and the rasterized variables as input data. For thisselected after the accuracy assessment of the 100 models butcase, we split the reference data in 70% for training and 30% forwith a single similar 90/10 split validation. In a comparison withvalidation. Besides the recursive partitioning algorithm (Rpart),

the mean overall accuracy from the 100 models, the model output

we tried using a random forest (RF), gradient boosting machines

used for the biomass projections differed with +0.2% in 2017 and(GBM), and deep neural network (DNN). Each trained model+0.5% in 2018. As the iterative validation process required each

was built using the average resampled estimates from a 10-fold

24|

MADSEN et al.

| 25

MADSEN et al.

F I G U R E A 2 . 6 Density plots showing the resulting class-wise accuracy distribution from 100 permutations of the classification models
for the 2017 (Mols17) and 2018 flights (Mols18). The class-wise accuracies are evaluated on the 90/10 split with the F1 value, based on the
precision (P) and recall (R) values (Forman, 2003). Below the density plots, corresponding tables highlight the calculated mean and standard
deviation (SD) for each distribution

TA B L E A 2 . 3 Accuracy measures from the final models used for calculating biomass from the 2017 and 2018 flights. Accuracies derived
from the resulting confusion matrix are presented as overall accuracy and Kappa coefficient. The class-wise accuracies are presented as
precision, recall, and F1 measures
Accuracy assessment—Final model
2017

2018

Overall Accuracy

0.8706

0.956609

Kappa

0.83940.935633

Class-wise accuracies
Precision
2017

Recall
2018

2017

F1
2018

2017

2018

Cytisus

0.894

0.967

0.969

0.985

0.930

0.976

Juniperus

0.895

0.934

0.802

0.955

0.846

0.945

Rubus

0.812

0.941

0.892

0.981

0.850

0.961

Rosa

0.860

0.961

0.697

0.956

0.770

0.958

Quercus

0.823

0.879

0.706

0.884

0.760

0.881

Pinus

0.810

0.955

0.734

0.750

0.770

0.840

Not Shrub

0.884

0.986

0.880

0.839

0.882

0.907

Fencepost

0.923

0.957

0.701

0.941

0.797

0.949

Sambucus

0.937

0.914

0.873

0.914

0.904

0.914

Crataegus

0.915

0.992

0.628

0.967

0.744

0.979

Prunus

0.840

1.000

0.817

0.804

0.828

0.892

Malus

0.888

0.960

0.925

0.829

0.906

0.890

Betula

0.865

0.933

0.833

0.933

0.849

0.933

cross-validation procedure repeated three times. The classification predictions on the validation set were assessed with overall
accuracy and Kappa values (Table A2.4). The resulting overall accuracies did not exceed 50%, and Kappa values were all lower
than 0.25.

The classification results presented in Table A2.5 suggested a
relatively similar performance for the different classifiers. The DNN
showed the highest overall accuracy for the 2018 (48.2%) data andlower for 2017 (39.7%), while it failed to deliver Kappa values above
0, possibly because of a lack of training data and/or insufficient

The 2D classification did not achieve similar good results as the

preprocessing of the variables. When evaluating both seasons (2017point-based approach (Table A2.5) and regardless of the classifierand 2018), the RF algorithm showed higher overall accuracies (45.6%

used, and the overall accuracy does not exceed 50%. The variables

for 2017 and 42.6% for 2018), and for 2017, it achieved the highest

used are somehow modified for a 3D classification but should still

Kappa value (24.7%). The Rpart classifier, similar to the one used for

be indicative for the different species. The major issue, causing the

3D classification, was not markedly worse than the DNN with over-low performance in the 2D approach, is most probably the reduced

all accuracies in 2017 and 2018 of 47.1% and 38.9%, respectively. It

information gained from the training data with one less dimension.

can be further noticed that all classifiers show higher accuracies in

Therefore, rethinking the creation, assignment, and increasing thethe leaf-on period, which is opposite of the 3D classification, thatamount of training data would be necessary to solve this question

performs better with the leaf-off data. This again indicates a needfrom a 2D perspective.

for seasonal timing for especially 2D based classifications.

26|

MADSEN et al.

TA B L E A 2 . 4 Accuracy measures from the merged classification including the 2017 and 2018 point clouds. Accuracies derived from theresulting confusion matrix are presented as overall accuracy and Kappa coefficient. The class-wise accuracies are presented as precision,
recall, and F1 measures
Accuracy assessment—Final model
70 split

90 split

Overall Accuracy

0.829

0.832

Kappa

0.775

0.780

Class-wise accuracies
Precision
70 split

Recall90 split

70 split

F1
90 split

70 split

90 split

Cytisus

0.839

0.843

0.963

0.963

0.963

0.963

Juniperus

0.869

0.873

0.784

0.786

0.784

0.786

Rubus

0.767

0.770

0.838

0.843

0.838

0.843

Rosa

0.768

0.758

0.612

0.625

0.612

0.625

Quercus

0.807

0.817

0.617

0.623

0.617

0.623

Pinus

0.702

0.701

0.204

0.206

0.204

0.206

Not Shrub

0.940

0.932

0.672

0.680

0.672

0.680

Fencepost

0.962

0.963

0.766

0.787

0.766

0.787

Sambucus

0.874

0.877

0.774

0.774

0.774

0.774

Crataegus

0.895

0.910

0.667

0.666

0.667

0.666

Prunus

0.760

0.759

0.642

0.652

0.642

0.652

Malus

0.887

0.911

0.841

0.843

0.841

0.843

Betula

0.813

0.819

0.746

0.755

0.746

0.755

TA B L E A 2 . 5 Overall accuracy and Kappa values resulting froma 2D raster classification of shrub species. Both datasets from 2017and 2018 were classified with four different classifiers (Model
name)
Model name

Overall accuracy

Kappa

Rpart170.47060.1915

Rpart18

0.38890.1449RF170.45590.2465RF18

0.42590.1838GBM170.39710.1761

GBM18

0.38890.1518DNN170.39710DNN18

0.48150Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models.
Environmental Conservation, 24(1), 38–49. https://doi.org/10.1017/
S0376​89299​7000088
Forman, G. (2003). An extensive empirical study of feature selection.
Journal of Machine Learning Research, 3, 1289–1305.
Heipke, C., Mayer, H., Wiedemann, C., & Jamet, O. (1997). Evaluation of
automatic road extraction. International Archives of Photogrammetry
and Remote Sensing, 32, 151–160.
Waldhauser, C., Hochreiter, R., Otepka, J., Pfeifer, N., Ghuffar, S.,
Korzeniowska, K., & Wagner, G. (2014). Automated classificationof airborne laser scanning point clouds. Springer Proceedings inMathematics and Statistics, 97, 269–292. https://doi.org/10.1007/9783-319-08985​- 0_12

APPENDIX 3
E X T R AC T I N G DATA FO R B I O M A S S E S T I M AT I O NThis document provides information on the digital collection of biomass data recognized through the UAS LiDAR point clouds from

REFERENCES

Mols with drone flights on 6 October 2017 and 23 April 2018. The

Blaschke, T., Lang, S., Lorup, E., Strobl, J., & Zeil, P. (2000). Objectoriented image processing in an integrated GIS/remote sensing
environment and perspectives for environmental applications.
In A. Cremers, & K. Greve (Eds.), Umweltinformation für Planung,
Politik und Öffentlichkeit/Environmental information for planning,
politics and the public. (Vol. 2, pp. 555–570). Marburg, Germany:
Metropolis Verlag.

aim was to relate the structural volume variables calculated fromeach individual Cytisus scoparius shrub in the point clouds to 10 harvested individuals in various sizes, which have been dried and measured for actual biomass. The individuals were harvested on 2 May2018 and incubated at 60°C for two days before measuring. We thencompared the measures with the 2018 point cloud.

| 27

MADSEN et al.

TA B L E A 3 . 1 Values from the 10 harvested individuals bymeasured dry weight biomass in gram (Weight) and extracted
volume in m3 from exported raster data (Mean, Max and Range)

F I G U R E A 3 . 1 A Cytisus scoparius individual represented by
LiDAR derived point clouds from 2017 (Red) and 2018 (Blue) and by
the manually constructed point cloud Cyt3D (Pink). As a reference,
the terrain points are visualized as grey points
Data collection
Before cutting the shrubs, the structure and shape were measured withan RTK GNSS device, resulting in small manually created point cloudsof each 25 points (Cyt3D in Figure A3.1). The Cyt3D point clouds wereconstructed by always measuring top and bottom points first, defined

Individual

Weight

Mean

Max

Range

1

132

0.011960.007860.000332

430.8

0.07370

0.093420.038223

626.5

0.132620.11979

0.028304

33.7

0.00550

0.00555

0.000105

104.6

0.019230.01183

0.002566

287.6

0.03568

0.048440.02597748.8

0.001310.001310.00000

8

153.1

0.018020.02552

0.012239554.6

0.038440.04625

0.0169210

107.1

0.003130.003650.00102C. scoparius points), wherefrom volume metrics based on normalizedheight could be derived. The normalized height values were each exported as raster formats in 5 cm pixels to represent volume (FigureA3.2). From the 25 manually constructed points, we derived a convex
hull area with 25 cm buffer around to delimit the area representing
an individual shrub.

as the most upper splitting of branches and the lowest part measurable
on the main stem. The additional 23 points were measured in various

Values from each pixel within the defined areas were then ex-heights and distances from the main stem, but always on a branch split

tracted and summed for the 10 measured shrubs and used for theor directly on the main stem. This was expected to minimize the inclu-

biomass models. Three of the harvested shrubs were not revealed

sion of the smaller, and in wind moving branches, which is possibly notin the classified C. scoparius LiDAR points, but two were instead

detectable by the UAS LiDAR system. In Figure A3.1, the alignment offound among the generally classified low vegetation class andthe manual constructed point cloud to the LiDAR point cloud is visual-

one was classified as Fencepost. These were still included in theized to highlight the precision of the different data sources.

biomass models, to evaluate how well we estimate biomass from
LiDAR derived point clouds but were not used in the total biomass

Generating and collecting digital biomass data

estimation as it also relies on the ability to classify C. scoparius. The

As described in the classification document (Appendix 2), we clas-

measured and extracted values for the 10 individual shrubs are

sified C. scoparius into separate point clouds (i.e., only containing

presented in Table A3.1.

F I G U R E A 3 . 2 Raster image of
normalized height values (blue shades)from C. scoparius point cloud in 5 cm
pixels. Pink dots represents 25 manuallyconstructed points (Cyt3D), which wereused to delimit the convex hull area of anindividual shrub with 25 cm buffer (pinkarea)