1.3 Aim
This study aims to investigate the possibilities to estimate
species-specific (pine, spruce and deciduous trees) mean stem
volume (V), mean basal area (BA) and basal-area-weighted
mean tree height (7) at stand level using spectral and 3D data
from the DMC sensor in combination with ALS DEM data.
This was performed using the k-MSN estimation framework, as
described by Packalén et al. (2009).
2. Materials
2.1 Study Area and Field Data
The study area is part of the Remningstorp forest estate, which
is situated at 58?30' N, 13?40' E (Figure 1). The estate is
managed for timber production, and has relatively flat terrain.
The forest is mainly dominated by Norway Spruce (Picea
abies), Scots Pine (Pinus sylvestris) and Birch species (Betula
Spp.).
Figure 1. The Remningstorp test site (left) and orthophoto of the
area including field plot positions and stand borders (right).
Circular field plots (10 m radius) were objectively surveyed
between 2004 and 2005 using a dense grid sample design,
which was a regular quadratic grid with 40 m spacing between
adjacent plots over the 1.0 km by 2.3 km central part of the
estate. The origin of the grid was allocated randomly. Each plot
was surveyed using the methods and state-estimating models of
the Forest Management Planning Package (Jonsson et al,
1993). For plots with mean tree height less than 4 m or basal-
area-weighted mean stem diameter at breast height (i.e., 1.3 m
above ground) less than 5 cm, height and species of all saplings
and trees were recorded. For the remaining plots, callipering of
all trees at breast height including only trees greater than 5 cm
in diameter, and sub-sampling of trees to measure height and
age, were performed. Heights of remaining callipered trees on
the plots were estimated using models developed by Sóderberg
(1992) relating tree height to diameter. Plot location, was
measured using differential GPS producing sub-meter accuracy.
Correction of the forest growth between the surveys and the
date of aerial image acquisition was made by forecasting the
forest state at each plot to the year 2005, using single tree
growth models (Sóderberg, 1986). In total, 696 plots were
surveyed in 69 stands, delineated by a professional photo-
interpreter using a digital photogrammetric workstation. At
these plots the tree height range was 1.4-33.0 m (with an
average of 18.1 m), stem volume 0-829 m? ha'! (249 m ha!)
and basal area 0.0-62.2 m? ha'! (26.1 m ha”).
2.2 Remote sensing data
ALS data were captured in September 2008 by the TopEye Mk
II system with a wavelength of 1064 nm and a 25 cm footprint.
This system was operated at a flight altitude of 250 m ag,
resulting in an average density of 7 pulses per square meter.
Following the acquisition, each return was classified as a
ground or non-ground return using the progressive Triangular
Irregular Network (TIN) densification method (Axelsson, 1999,
2000) implemented in the TerraScan software (Soininen, 2004).
Then, a raster DEM with a 0.5 m by 0.5 m cell size was created
by assigning each cell the mean height value of ground returns
within the cell. Height values of raster cells without ground
returns were TIN interpolated using neighboring DEM cells.
The digital aerial images were acquired on 28 June 2005 (at
9.40 h local time) with the DMC system operated by
Lantmäteriet. It consists of four panchromatic and four spectral
camera heads. The four panchromatic images are stitched into
one, and merged with the spectral images to create one pan-
sharpened virtual image with 7680 x 13824 pixels (Hinz et al.,
2001). Eleven images were acquired, at 4800 m a.g.l. using one
flight strip with 60% along-track image overlap. As result,
Ground Sampling Distance for the image block is about 0.48 m.
Images were aerial triangulated using bundle adjustment and
radiometrically corrected by Lantmáteriet.
3. Methods
3.1 Photogrammetric matching and Classification
Then, photogrammetric image matching was performed using
the Match-T DSM software version 5.3.1 (Anon, 2011) to
produce a point cloud data set. This was done by sequential
multi-matching (Lemaire, 2008), where both least squares and
feature-based matching were combined. Following Packalén
and Maltamo (2007), the point cloud was colorized by ray
tracing each point back to the image plane coordinates, using
the exterior and interior orientations of the images. Each point
was assigned its mean spectral value from all images the point is
visible in, resulting in a NIR, Red and Green colored point
cloud. Finally, the point cloud height values were normalized by
subtracting the ALS DEM.
Based on the spectral data, the tree species class corresponding
to each point was estimated. This was performed by supervised
classification using plots with uniform species composition, i.e.
plots where more than 95% of the field surveyed volume
constituted of pine, spruce or deciduous trees (40, 351 and 18
plots, respectively) as training data. All points below 0.5 m were
regarded as ground points and therefore removed prior to the
classification. Species classification of the point cloud was
made using quadratic discriminant analysis with equal priors.
3.2 Estimation of forest variables
Ten metrics summarizing the point cloud data, such as height
distribution and spatial density characteristics, were calculated
from the tree species classified point cloud using the Fusion
software package (McGaughey, 2012) developed by US
Department of Agriculture Forest Service. These metrics Were
used as independent variables to estimate the addressed forest
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