Peer-Reviewed Articles
357 Detection of Individual Tree Crowns in Airborne Lidar Data
Barbara Koch, Ursula Heyder, and Holger Weinacker
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Laser scanning provides a good means to collect information
on forest stands. This paper presents an approach to delineate
single trees automatically in small footprint light detection and
ranging (lidar) data in deciduous and mixed temperate forests.
In rasterized laser data possible tree tops are detected with a
local maximum filter. Afterwards the crowns are delineated
with a combination of a pouring algorithm, knowledge-based
assumptions on the shape of trees, and a final detection of the
crown-edges by searching vectors starting from the trees’ tops.
The segmentation results are assessed by comparison with
terrestrial measured crown projections and with photogrammetrically delineated trees. The segmentation algorithm works
well for coniferous stands. However, the implemented method
tends to merge crowns in dense stands of deciduous trees.
365 Principals and Evaluation of Autostereoscopic Photogrammetric Measurement
Jie Shan, Chiung-Shiuan Fu, Bin Li, James Bethel, Jeffrey Kretsch, and Edward Mikhail
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Stereoscopic perception is a basic requirement for photogrammetric 3D measurement and accurate geospatial data collection. Ordinary stereoscopic techniques require operators
wearing glasses or using eyepieces for interpretation and
measurement. However, the recent emerging autostereoscopic
technology makes it possible to eliminate this requirement.
This paper studies the principles and implementation of
autostereoscopic photogrammetric measurement and evaluates its performance. We first describe the principles and
properties of the parallax barrier-based autostereoscopic
display used in this study. As an important metric property,
we quantitatively present the autostereoscopic geometry,
including viewing zones and the boundary of a viewer’s
movement for autostereoscopic measurement. A toolkit
AUTO3D is developed that has common photogrammetric
functions. The implementation principles are described by
addressing the differences compared to the ordinary stereoscopic technology. To evaluate the performance of the auto-stereoscopic measurement, images at a resolution of 25 µm
and 50 µm are measured by a group of seven (7) operators,
who are asked to digitize 18 well-defined roof points and
18 ground points. These results are evaluated by comparing
the same measurements obtained from a popular stereoscopic
photogrammetric workstation. It is shown that the precision
of autostereoscopic measurement is about 16 percent to
25 percent lower than the conventional stereo workstation.
373 Three New Implementations of the Triangular Prism Method for Computing the Fractal Dimension of Remote Sensing Images
Wanxiao Sun
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Based on Clarke’s (1986) triangular prism concept, this paper
proposes three new methods to compute the fractal dimension (D) of remote sensing images. Our first method involves
searching a pixel on each edge of a square whose digital
numbers (DN) value has the largest deviation from the central
pixel. Our second method uses a pixel on each edge of a
square whose DN deviation from the central pixel is closest to
the mean DN deviation from the central pixel to all pixels on
the same edge. In our third method, eight pixels on the four
edges of a square are used. Furthermore, common to the
three proposed methods is the use of actual DN of the central
pixel. The proposed computation methods have been tested
using both simulated fractal surfaces and real images. Results
show that the proposed methods appear to generally perform
better than Clarke’s 1986 method for synthetic images with
complex textures.
383 Object-based Analysis of Ikonos-2 Imagery for Extraction of Forest Inventory Parameters
Michael S. Chubey, Steven E. Franklin, and Michael A. Wulder
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A method is presented for deriving forest inventory information from Ikonos-2 imagery based on the analysis of image
objects rather than more conventional pixel-based image
analysis approaches. For a 77 km2 study area in southwestern Alberta, Canada, image objects representing homogeneous landscape components were delineated from Ikonos-2
data using an image segmentation routine. Decision tree
statistical analyses were used to identify correlations between
metrics derived from spectral and spatial properties of the
image objects and field-derived samples of individual forest
inventory parameters. The strongest relationships were
observed for classes of discrete land-cover types, species
composition, and crown closure.
395 Automatic Building Detection Using the Dempster-Shafer Algorithm
Yi Hui Lu, John C. Trinder, and Kurt Kubik
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An approach and strategy for automatic detection of buildings from aerial images using combined image analysis and
interpretation techniques is described in this paper. It is
undertaken in several steps. A dense DSM is obtained by
stereo image matching and then the results of multi-band
classification, the DSM, and Normalized Difference Vegetation Index (NDVI) are used to reveal preliminary building
interest areas. From these areas, a shape modeling algorithm
has been used to precisely delineate their boundaries. The
Dempster-Shafer data fusion technique is then applied to
detect buildings from the combination of three data sources
by a statistically-based classification. A number of test
areas, which include buildings of different sizes, shape, and
roof color have been investigated. The tests are encouraging
and demonstrate that all processes in this system are
important for effective building detection.
405 Predicting Sphaeropsis sapinea Damage in Pinus radiata Canopies Using Spectral Indices and Spectral Mixture Analysis
Nicholas C. Coops, Nicholas Goodwin, and Christine Stone
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Maintaining the health and condition of the forest plantation estate is critical to ensuring there are no adverse losses
in productivity. Within Australian Pinus radiata plantations
a diverse range of damaging agents are present. One significant agent is a fungal pathogen Sphaeropsis sapinea. In this
research, we detail the development of relationships between
a range of individual crown health attributes representing
symptoms of Sphaeropsis sapinea infection and high spatial
and spectral resolution remotely sensed imagery characteristics. To do this, two methods were used; the first utilized
vegetation spectral indices including simple and normalized
difference ratios, and the second, linear spectral mixture
analysis. Results indicate that spectral indices that utilize
either chlorophyll absorption wavelengths at 680 nm with
a non-chlorophyll region of the spectrum (such as 710 or
750 nm) or the slope of the upper red-edge between 710 and
740 nm were most significantly related to individual crown
damage attributes. Linear unmixing analysis consistently
extracted four fraction endmember images (sunlit canopy,
soil, shadow, and non-photosynthetic vegetation (NPV)) from
the 12 channel imagery. Multiple linear stepwise regression
models developed using mixed fractional abundances
provided similar results to those derived using spectral
indices. The NPV and shadow endmembers, in order, were
consistently identified as the most significant in these
developed models.