Peer-Reviewed Articles
25 Automated 3D Forest Surface Model Extraction from
Balloon Stereo Photographs
Keiji Kushida, Kunihiko Yoshino, Toshihide Nagano, and
Tomoyasu Ishida
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We upgraded an automated forest digital surface model
(DSM) extraction method from balloon stereo photographs
of a tropical peat swamp forest in Narathiwat, Thailand by
evaluating the image matching accuracy and forest surface
height (FSH) estimation. We modified an image correlation
matching method based on the characteristics of the tree
crown shapes. The mismatched area was less than 3 percent
of the total area. We estimated an FSH map in a 60 m x 60
m plot by both photo estimation and field measurement,
and set the unit area for FSH averaging at 10 m x 10 m.
The root mean square of the differences between the mean
photo-estimated and mean field-measured FSH was 3.8 m,
which was revised to 1.9 m when the forest gaps were
extracted offline. These differences were within a reasonably
practical range since the range of the mean field-measured
FSH was 10.0 to 21.4 m.
37 Understory Bamboo Discrimination Using a Winter
Image
Tiejun Wang, Andrew K. Skidmore, Albertus G. Toxopeus,
and Xuehua Liu
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In this study, a new approach is presented that combines
forest phenology and Landsat vegetation indices to estimate
evergreen understory bamboo coverage in a mixed temperate
forest. It was found that vegetation indices, especially the
normalized difference vegetation index (NDVI) derived from
leaf-off (winter) images were significantly correlated with
percent understory bamboo cover for both deciduous and
mixed coniferous/deciduous forests. Winter NDVI was used
to map bamboo coverage using a binary decision tree
classifier. A high mapping accuracy for understory bamboo
presence/absence was achieved with an overall accuracy
of 89 percent (k 5 0.59). In addition, for the first time, we
successfully classified three density classes of bamboo with
an overall accuracy of 68 percent (k 5 0.48). These results
were compared to three traditional multispectral bandsbased
methods (Mahalanobis distance, maximum likelihood,
and artificial neural networks). The highest mapping
accuracy was again obtained from winter images. However,
the kappa z-test showed that there was no statistical
difference in accuracy between the methods. The results
suggest that winter is the optimal season for quantifying the
coverage of evergreen understory bamboos in a mixed forest
area, regardless of the classification methods use.
49 Accuracy Assessment of Digital Elevation Models
based on Approximation Theory
Peng Hu, Xiaohang Liu, and Hai Hu
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Empirical research in DEM accuracy assessment has observed
that DEM errors are correlated with terrain morphology,
sampling density, and interpolation method. However,
theoretical reasons for these correlations have not been
accounted for. This paper introduces approximation theory
adapted from computational science as a new framework to
assess the accuracy of DEMs interpolated from topographic
maps. By perceiving DEM generation as a piecewise polynomial
simulation of the unknown terrain, the overall accuracy
of a DEM is described by the maximum error at any DEM
point. Three linear polynomial interpolation methods are
examined, namely linear interpolation in 1D, TIN interpolation,
and bilinear interpolation in a rectangle. Their propagation
error and interpolation error, whose sum is the total
error at a DEM point, are derived. Based on the results, the
theoretical basis for the correlation between DEM error and
terrain morphology and source data density is articulated
for the first time.
57 A Conceptual Framework for the Simultaneous
Extraction of Sub-pixel Spatial Extent and Spectral
Characteristics of Crops
Ben Somers, Stephanie Delalieux, Willem W. Verstraeten, and
Pol Coppin
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The sub-pixel spectral contribution of background soils
and shadows hampers the accurate site-specific monitoring
of agricultural crop characteristics from aerial or
satellite images. To address this problem, the present
study combines measured in situ and hyperspectral data
in an alternative unmixing algorithm. The proposed
algorithm, referred to as Soil Modeling Mixture Analysis
(SMMA), incorporates a soil reflectance model in a traditional
unmixing algorithm and as such opens up the
opportunity to simultaneously extract the sub-pixel spatial
extent of crops as well as its pure spectral information.
The performance of the algorithm is evaluated using a soil
moisture reflectance model, calibrated for an in situ
measured Albic Luvisol dataset. Synthetic mixtures, i.e.,
compiled from in situ measured hyperspectral bare soil
and citrus tree canopy spectra, were decomposed and the
sub-pixel crop cover fractions (R2 > 0.94, RMSE < 0.03) and
pure vegetation signals (average extraction error 350 to
2,500 nm = 0.017, RMSE = 0.02) were adequately extracted
from the mixtures.
69 Estimation of Blufflines Using Topographic Lidar Data
and Orthoimages
Jung-Kung Liu, Rongxing Li, Sagar Deshpande, Xutong Niu,
and Tian-Yuan Shih
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Coastal zone mapping, particularly of shorelines, is critical
for safe navigation, resource management, environmental
protection, and sustainable coastal development. This paper
explains a method for extracting coastal blufflines where
airborne lidar (Light Detection and Ranging) data is integrated
with orthoimages. A historical Lake Erie bluff top line
was used as a reference line and a series of transects
created perpendicular to it. After three-dimensional elevation
profiles of these transects were obtained from a lidar
DSM (digital surface model), a new algorithm was used to
extract from these transects initial points identifying bluff
top and toe. These points were connected to form an initial
bluffline. The horizontal position of this initial bluffline was
refined with edges obtained from the orthoimages using
techniques including mean-shift segmentation, surface
reconstruction, and edge detection. Results show this
method is capable of deriving blufflines having a similar
quality to that from manual digitization.
81 Accuracy Assessment of Canadian Digital Elevation
Data using ICESat
Alexandre Beaulieu and Daniel Clavet
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The accuracy of the Canadian Digital Elevation Data (CDED)
produced over the past was based on the accuracy of the
sources used for their creation (elevation extracted form
contour lines or provincial data exchange). No means to
characterize the absolute vertical precision was available,
particularly in remote areas. A new production of CDED in the
North is currently carried out by the Centre for Topographic
Information, Natural Resources Canada with the support of
the Canadian Space Agency. Approximately 1,500 new data
sets are being produced. Altimetric data is partly acquired
with the European Remote Sensing satellite (ERS) by interferometry
(70 percent) and partly by stereo-compilation with
aerial photography (30 percent). The assessment of the
absolute altimetric accuracy of the CDEDs themselves as
opposed from the sources is required. ICESAT lidar data gives
us such an opportunity. The results obtained on the first CDED
data sets produced with ERS interferometry are very promising.
Accuracy for a group of 21 CDED is in the order of 0.34 m
± 6.22 m, i.e., 10 m at 90 percent confidence level. Accuracy
is recorded in the metadata of each data set and is freely
available on the GEOBASE portal (http://www.geobase.ca/).
87 DEM Generation Using a Digital Large Format Frame
Camera
Joachim Höhle
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Progress in automated photogrammetric DEM generation is
presented. Starting from the procedures and the performance
parameters of automated photogrammetric DEM
generation, the results of some practical tests with large
scale images are presented. The DEMs are derived from
images taken by a digital large-frame aerial camera and
checked by reference data of superior accuracy. In average,
a vertical accuracy of σ h = 13 cm or 0.20 per thousand of
the mean flying height above mean terrain has been
achieved. Some recent innovations in digital large-frame
cameras and in the processing software give hope for even
better results. In comparison with results from film-based
cameras, it can be stated that both technologies are able to
produce very dense and accurate DEMs.