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SPECIES-SPECIFIC FOREST VARIABLE ESTIMATION USING NON-PARAMETRIC
MODELING OF MULTI-SPECTRAL PHOTOGRAMMETRIC POINT CLOUD DATA
J. Bohlin*, J. Wallerman, H. Olsson, J. E. S. Fransson
Swedish University of Agricultural Sciences, Department of Forest Resource Management
SE-901 83 Umeä, Sweden, Jonas.Bohlin@slu.se
Commission VIII, WG VIII/7
KEY WORDS: Forestry, photogrammetry, estimation, inventory, spectral, stereoscopic, point cloud, mapping.
ABSTRACT
The recent development in software for automatic photogrammetric processing of multispectral aerial imagery, and the growing
nation-wide availability of Digital Elevation Model (DEM) data, are about to revolutionize data capture for forest management
planning in Scandinavia. Using only already available aerial imagery and ALS-assessed DEM data, raster estimates of the forest
variables mean tree height, basal area, total stem volume, and species-specific stem volumes were produced and evaluated. The study
was conducted at a coniferous hemi-boreal test site in southern Sweden (lat. 58? N, long. 13? E). Digital aerial images from the
Zeiss/Intergraph Digital Mapping Camera system were used to produce 3D point-cloud data with spectral information. Metrics were
calculated for 696 field plots (10 m radius) from point-cloud data and used in &-MSN to estimate forest variables. For these stands,
the tree height ranged from 1.4 to 33.0 m (18.1 m mean), stem volume from 0 to 829 m? ha'! (249 m? ha'! mean) and basal area from
0 to 62.2 m? ha'! (26.1 m? ha'! mean), with mean stand size of 2.8 ha. Estimates made using digital aerial images corresponding to
the standard acquisition of the Swedish National Land Survey (Lantmáteriet) showed RMSEs (in percent of the surveyed stand
mean) of 7.5% for tree height, 11.4% for basal area, 13.2% for total stem volume, 90.6% for pine stem volume, 26.4 for spruce stem
volume, and 72.6% for deciduous stem volume. The results imply that photogrammetric matching of digital aerial images has
significant potential for operational use in forestry.
1. Introduction
1.1 Motivation
In Nordic boreal forestry, aerial imagery has the potential to
gain increasing importance as a source of data for detailed
spatial estimates of forest variables. This is due to the recent
evolution of new and efficient algorithms for 3D data
generation using automatic matching of stereo imagery and
photogrammetric derivation of tree canopy height data.
Furthermore, the growing availability of accurate DEM data is a
key component in the use of 3D data for forest mapping
purposes. National level acquisition of Airborne Laser Scanning
(ALS) to produce accurate Digital Elevation Models (DEMs)
has been completed in several European countries. In Sweden
and Finland, among other countries, is ALS mapping ongoing.
Furthermore, Swedish National Land Survey (Lantmáteriet)
utilizes two Zeiss/Intergraph Digital Mapping Camera (DMC)
Systems to routinely map the country at an annual rate of one
third of the area. Hence, DMC data are available nation-wide at
à low cost, providing spectral data as well as 3D data of the
vegetation canopy.
1.2 Background
Forest companies commonly utilize ALS-assessed forest
information, estimated primarily using area-based methods
(Magnussen och Boudewyn, 1998; Næsset, 2002b; Næsset et
al, 2004). In boreal forest, these methods deliver stand level
estimation accuracies in terms of Root Mean Square Error
(RMSE) for tree height typically in the range of 2.5-13.6% (in
* Corresponding author.
percent of the surveyed mean), stem diameter in the range of
5.9-15.8% and stem volume 8.4-16.6% (Nasset et al., 2004;
McRoberts et al., 2010). This generally outperforms traditional
sources for forest management data, such as subjective field
estimation. Using subjective field methods RMSE of 15-25%
and about 10% RMSE for stem volume and tree height,
respectively, was achieved (Stahl, 1988; Stähl,
1992). Neasset (2002a) used scanned analog high-resolution
(0.19 m pixel size) aerial images to derive 3D data using
photogrammetric image matching. Ground elevation was
assessed using manual photo-interpretation of the images
viewed in stereo in a limited number of locations with visible
ground and interpolated to full spatial cover. At their test site in
Norway, tree height was estimated for forest stands using
regression with standard error ranging from 0.9 m to 2.1 m,
which is similar to accuracy achieved using photo-
interpretation. In Sweden, using standard aerial imagery and an
accurate DEM, Bohlin et al., (2012) estimated forest variables
from DMC imagery. For tree height, stem volume, basal area
the result shows RMSEs of 8.8%, 13.1% and 14.9%,
respectively, at stand level.
In forest management planning, tree-species information is
important. Therefore, extending the ALS methodology by
adding spectral data to achieve tree species-specific estimates
using various frameworks such as non-parametric methods like
k-MSN; Packalén and Maltamo (2007), Packalén et al. (2009)
reported plot level RMSE accuracies for pine volume, spruce
volume and deciduous volume of 33-52%, 56-63% and 84-
103%, respectively. And for stand level accuracy RMSEs, pine
volume 28%, spruce volume 32% and deciduous volume 62%
(Packalén and Maltamo, 2007).
