Airborne LiDAR and SAR estimation of forest top height
E. D. Wallington1* and J.C. Suárez2
1
Forestry Commission, Silvan House, 231 Corstorphine Road, Edinburgh, EH12 7AT,
UK. *e-mail: edward.wallington@forestry.gsi.gov.uk
2
Forest Research, Northern Research Station, Roslin, Midlothian, EH25 9SY, UK.
_______________________________________________________________________
Abstract
Top height is an important parameter in production forestry and ecological
studies. This paper demonstrates the use of two commercially available remote
sensing systems for estimating forest top height, and discusses the operational
use of such systems with regard to monitoring sustainable forestry indicators
through enhancement of traditional inventory methods.
Airborne LiDAR and X-band interferometric Synthetic Aperture Radar (InSAR)
systems were used to retrieve forest stand top height with comparison to ground
measurements to three test sites in the UK. Top height estimation was
achieved with errors of around 2-6% from LiDAR and 18-24% from SAR.
Potential reasons for errors are discussed, and the use of remote sensing is
discussed in relation to operational sustainable forest management at a range of
scales.
Keywords: LiDAR, InSAR, Forest parameter retrieval, Top Height.
_______________________________________________________________________
Introduction
Monitoring and information reporting to promote adaptive and sustainable forest
management has emerged as an important component in the field of forest management
(Hickey et al., 2005). Sustainable forest management is a complex process, requiring input
and collaboration from a number of forest sectors (e.g. management for timber, biodiversity,
conservation, recreation, health etc) and related organisations using a wide range of data
sources and inventory techniques. A set of key indicators and guidelines have been
developed in the UK, as in other countries, to help guide sustainable forestry management
practices (FC, 2002; FC, 2004). Earth observation (EO), through the use of remote sensing
systems, has the capability to provide valuable information for sustainable forestry
management (Rosenqvist et al., 2003; Hall, 2000). This is achieved through input into
management decisions and continued monitoring towards meeting, assessing and
maintaining the indicators of sustainable forestry. Top height is an important parameter in
traditional production forestry (West, 2004) that can also be used as an indicator in studies
looking at, for example, forest stand structure, growth dynamics, bio-fuel estimations or
carbon sequestration - all of which need to be assessed and monitored as contributions
towards sustainable forest management.
This paper demonstrates the use of commercially available airborne remote sensing systems
in the UK to provide cost-effective estimates of forest top height (defined in this study as the
average height of the 100 tallest trees/ha, H100; Philip, 1994). The work presented is a
comparative study between commercial LiDAR (Light Detection and Ranging; Aronoff and
Petrie, 2005) and Interferometric Synthetic Aperture Radar (InSAR; Henderson and Lewis,
1998) data validated with ground reference data, as a supplement to traditional field
inventory. Previous studies have estimated forest height to within 7-30% for LiDAR (e.g.
Nilsson, 1996; Zimble et al., 2003) and 4-90% for radar (e.g. Yong et al. 2003; Walker et al.
2004) dependent on system and wavelength used. The perceived advantages of these two
airborne systems are the high resolution of the data and their versatility to capture data over
a large geographical area at will. Additional advantages are the increasingly competitive
costs and the possibility to process the data into a product compatible with many
Geographical Information Systems (GIS).
Study areas and data sets
Study sites
Three study sites were considered in the UK: Coed Y Brenin Forest District in North Wales
(N52°49’12” W3°53’27” lat/long), Kielder Forest District in North England (N55°11’44”
W2°32,11” lat/long) and Aberfoyle in SW of Scotland (N56°10’00” W4°22’00” lat/long).
Forest stands consisted of Sitka spruce (Picea sitchensis) plantation. The SAR data were
analysed over the three study sites: Kielder, Coed Y Brenin and Aberfoyle, whilst the LiDAR
were analysed over selected stands in Kielder and Aberfoyle.
Data sets
InSAR Digital Surface Model
The InSAR (Henderson and Lewis, 1998) derived Digital Surface Model (DSM) was supplied
by Intermap Technologies (Intermap, 2005), and was geo-referenced to the UK national grid
(OSGB36) prior to delivery. The DSM represents the first surface the signal came into
contact with, whether ground or vegetation canopy. The DSM is presented with a pixel size
of 5x5 m and a vertical RMSE of between 0.5 – 2.0m, dependent on flying height (Intermap,
2003).
Ordnance Survey DEM
The Ordnance Survey (OS) Profile 10m Digital Elevation Model (OSDEM) was used as a
ground reference surface. The OSDEM has a pixel size of 10x10 m, with a stated accuracy
of ± 5m (OS, 2001), but this is expected to be significantly improved over small areas. As this
is the only high resolution wide area coverage ground surface for the UK, it is assumed to be
a representation of the true ground surface for the purposes of this study.
LiDAR
Airborne LiDAR was obtained by the Environment Agency using an Optech ALTM2033
scanner (Optech, 2005). The first survey was undertaken in Aberfoyle in September 2002 at
a flying altitude of 1,000m a.s.l. with a sampling intensity was 3-4 returns per m2, a beam
divergence of 10 cm and scanning angle was 20 degrees. A second area in Kielder forest
was surveyed in April 2003 with a sampling intensity varied from 6 to 23 returns per m2, a
beam divergence of up to 1 m and a scanning angle of 10 degrees. Data were distributed in
ASCII XYZ format, with first and last returns, and their corresponding intensities, located to
the OS national grid. RMSE were ± 40 cm in X and Y and ± 9-15 cm in Z.
Ground reference data
Standard forest inventory techniques (Philip, 1994; Husch et al., 2003; Hamilton, 1975) were
used to establish the top height of the sample stands. This height was then used as the true
top height of the stand. Individual tree locations were mapped using differential GPS and a
total station, with x/y positional accuracy of <1m; these locations were subsequently used for
locating individual trees in the LiDAR imagery for analysis.
Top height retrieval from remote sensing
INSAR
In all the stands, height values from the DSM and OSDEM were retrieved from 50 x 50m
plots (¼ ha). Care was taken to extract values away from potential error sources, for
example edge effects which have a significant impact on height retrieval (Woodhouse et al.,
In press). Subtraction of the OSDEM from the DSM was performed to recover the height per
pixel within the plot (Wallington et al., 2004). Retrieved stand top height (Hr100) was
estimated by averaging the highest 25 pixel heights. This techniques follows standard forest
practice of taking the tallest 100 trees/ha (Philip, 1994). Retrieved top height was then
compared to the measured top height per stand (Figure 1).
40
Wales
Kielder
Aberfoyle
Retrieved top height
30
20
10
Aberfoyle
y = 0.82x
R2 = 0.90
Wales
y = 0.79x
R2 = 0.84
Kielder
y = 0.76x
R2 = 0.94
0
0
10
20
30
40
Measured top height
Figure 1. Retrieved top height from SAR in Wales (Coed Y Brenin), Kielder and Aberfoyle.
1:1 line is shown.
Top height retrieval for the three test sites gave consistent results (Figure 1) with
underestimations of 18% (Aberfoyle), 21% (Wales) and 24% (Kielder). The average
underestimation for the three data sets combined was 23% (Table 1). The retrieved height
was an underestimation as expected. This was attributed to signal interaction with scatterers
within the canopy of a similar size to the signal wavelength, and the resultant effect on
penetration and attenuation through the canopy. At shorter wavelengths (X-band),
penetration is limited to the upper canopy, and as such the resultant height of the scattering
phase centre is predominately dominated by scattering from the smaller scatterers in the
canopy. The results show that X-band does not easily penetrate the dense canopy of Sitka
spruce stands and this is attributed to the compactness of their canopies. In contrast, other
species, such as Scots pine, present less dense canopies and will allow more penetration of
the signal through them (Wallington et al., 2005).
Further to the above mentioned penetration, a number of other factors may also be affecting
the height retrieval, thus producing underestimations. The aforementioned edge effects
have been shown to significantly reduce height retrieval (Woodhouse et al., In press) due to
the relative contributions of canopy and ground scattering to the resulting scattering phase
centre, with increased ground contribution resulting in a lower height. Other sources of
potential error include the density of the stand, the height of the trees, canopy shape and the
slope of the underlying ground surface in relation to the sensor position (Izzawati et al.,
2004). The OSDEM has an accuracy of ± 5m, and it is reasonable to assume that an error in
the ground surface may be contributing to the height underestimation.
LIDAR
A normalised forest canopy model is obtained by subtracting bare ground values from the
canopy layer. The canopy layer is retrieved from the first laser return that measures the
intensity of the signal as it first encounters an object in the ground. The last return will
provide information about the location and height of the mid-point of the last strong waveform
that is normally associated with the terrain.
In order to approximate a model of the ground surface, the last returns were filtered to
eliminate those hits being intercepted by the forest canopy and, therefore, not reaching the
underlying terrain. The method involved an initial selection of points within a kernel of 10x10
m according to the local minima (Suárez et al., 2005a). Then, a basic DTM model was
constructed using an interpolation method based on Kriging with no anisotropy. After, this a
second selection of ground hits was performed by comparing LiDAR hits to this model within
an empirically defined threshold of +/- 30 cm. This operation densified the initial selection of
points allowing the construction of a higher resolution DTM using the same interpolation
method.
A normalised canopy height model was calculated for every laser hit as the difference
between each first return and the resulted terrain model. Individual tree heights were
accurately predicted in 73% of the cases within ±1m and 96% within ±2m. Generally,
individual tree heights were 7-8% shorter than observed due to the low number of laser hits
intercepted by the apices. In Kielder, the higher density of returns per m2 (6 to 23) reduced
underestimations to 2% (Figure 2, Table 1).
35
Aberfoyle
Kielder
Estimated top height
30
25
20
Kielder
y = 0.98x
R2 = 0.99
Aberfoyle
y = 0.93x
R2 = 0.91
15
15
20
25
30
35
Measured top height
Figure 2. Retrieved top height from LIDAR in Aberfoyle and Kielder. 1:1 line is shown.
Table 1. Summary of errors in estimations for retrieved top height (H100).
Plot No.
1
2
3
4
5
6
7
8
9
10
11
12
Measured
H100
(m)
21.7
20.8
18.9
28.8
25.0
32.8
28.2
20.3
21.4
17.9
20.8
21.2
Kielder
Retrieved H100
LiDAR
InSAR
(%)
(%)
24.92
18.76
24.40
21.04
28.81
0.96
13.88
0.01
33.04
4.19
6.80
0.56
1.73
1.08
-
Average
Combined average
2.19
23.55
4.54
22.82
Measured
H100
(m)
27.2
22.6
22.6
25.7
21.7
22.0
25.5
28.6
26.1
26.1
23.8
24.3
Aberfoyle
Retrieved H100
LiDAR
InSAR
(%)
(%)
9.97
16.11
9.47
15.79
4.74
4.89
18.27
4.54
25.16
6.39
5.32
7.40
11.16
4.86
35.14
6.88
22.09
Summary and Conclusion
This paper has demonstrated the use of commercially available airborne LiDAR and InSAR
remote sensing systems to estimate stand top height, the following results were found:
•
•
LiDAR gave an overall underestimation of between 2-6%, resulting in an average error of
4.54%.
Likewise, SAR gave an overall underestimation of between 18-24%, with an average
error of 22.82%.
These results indicate that both airborne LiDAR and short wavelength SAR interferometry
have the capability to provide accurate estimates of height. The ability of remote sensing
products to cover large areas rapidly and to provide a sampling intensity that approaches full
coverage as opposed to traditional sampling strategies (Suárez, et al., 2005b) will allow
more detailed assessment and monitoring for sustainable forest management and related
industries. Both of the airborne data sets discussed in this paper are available commercially,
and as such, the techniques described are nearing operational status, with the cost of the
data and the availability of data providers being the driving factors that will determine their
business use. Further potential is foreseen with the ever increasing availability and use of
satellite systems for data capture, allowing even greater areas to be rapidly mapped.
Acknowledgments
The authors would like to thank Intermap Technologies and the Environment Agency for
kindly supplying the data for the test sites. Thanks are also due to Stuart Snape (UK Forest
Enterprise), Daniel Donoghue and Pete Watt (both of Durham University) for providing
supplementary ground data, and to Iain Woodhouse and Izzawati of Edinburgh University for
assistance with the interpretation of radar modelling results. This study was funded by the
NERC and the UK Forestry Commission.
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