Peer-Reviewed Articles
783 Patterns in Forest Clearing Along the Appalachian Trail Corridor
David Potere, Curtis E. Woodcock, Annemarie Schneider, Mutlu Ozdogan, and Alessandro Baccini
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Forest clearing in the vicinity of the Appalachian Trail
National Park undermines the Trail’s value as a wilderness
retreat for millions of annual hikers. We estimate that
75,000 hectares of forest were lost to clearing during the
decade of the 1990s inside a 16 km-wide corridor centered
on the Trail. This loss represents 2.45 percent of forests
within 8 km of the 3,500 km-long trail. Managed forest
harvests in northern New England accounted for 76.8
percent of forest clearing. The factor most closely related
to forest clearing is land ownership: only 0.29 percent of
protected forests were cleared, while unprotected and
managed forests were cleared at rates of 2.05 percent and
4.03 percent, respectively. A combination of boosted decision tree classifiers, multitemporal Kauth-Thomas transforms and the GeoCover Landsat dataset enabled a single,
un-funded analyst to rapidly map land-cover change at 28.5-
meter resolution within a 3.8 million hectare study area that
spanned 16 Landsat scenes.
793 Impact of Lidar Nominal Post-spacing on DEM Accuracy and Flood Zone Delineation
George T. Raber, John R. Jensen, Michael E. Hodgson, Jason A. Tullis, Bruce A. Davis, and Judith Berglund
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Lidar data have become a major source of digital terrain
information for use in many applications including hydraulic
modeling and flood plane mapping. Based on established
relationships between sampling intensity and error, nominal
post-spacing likely contributes significantly to the error
budget. Post-spacing is also a major cost factor during lidar
data collection. This research presents methods for establishing a relationship between nominal post-spacing and its
effects on hydraulic modeling for flood zone delineation.
Lidar data collected at a low post-spacing (approximately
1 to 2 m) over a piedmont study area in North Carolina was
systematically decimated to simulate datasets with sequentially higher post-spacing values. Using extensive first-order
ground survey information, the accuracy of each DEM
derived from these lidar datasets was assessed and reported.
Hydraulic analyses were performed utilizing standard
engineering practices and modeling software (HEC-RAS). All
input variables were held constant in each model run except
for the topographic information from the decimated lidar
datasets. The results were compared to a hydraulic analysis
performed on the un-decimated reference dataset. The
sensitivity of the primary model outputs to the variation in
nominal post-spacing is reported. The results indicate that
base flood elevation does not statistically change over
the post-spacing values tested. Conversely, flood zone
boundary mapping was found to be sensitive to variations
in post-spacing.
805 Building Boundary Tracing and Regularization from Airborne Lidar Point Clouds
Aparajithan Sampath and Jie Shan
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Building boundary is necessary for the real estate industry,
flood management, and homeland security applications.
The extraction of building boundary is also a crucial and
difficult step towards generating city models. This study
presents an approach to the tracing and regularization of
building boundary from raw lidar point clouds. The process
consists of a sequence of four steps: separate building and
non-building lidar points; segment lidar points that belong
to the same building; trace building boundary points; and
regularize the boundary. For separation, a slope based 1D
bi-directional filter is used. The segmentation step is a
region-growing approach. By modifying a convex hull
formation algorithm, the building boundary points are
traced and connected to form an approximate boundary.
In the final step, all boundary points are included in a
hierarchical least squares solution with perpendicularity
constraints to determine a regularized rectilinear boundary.
Our tests conclude that the uncertainty of regularized
building boundary tends to be linearly proportional to the
lidar point spacing. It is shown that the regularization
precision is at 18 percent to 21 percent of the lidar point
spacing, and the maximum offset of the determined building boundary from the original lidar points is about the
same as the lidar point spacing. Limitation of lidar data
resolution and errors in previous filtering processes may
cause artefacts in the final regularized building boundary.
This paper presents the mathematical and algorithmic
formulations along with stepwise illustrations. Results from
Baltimore city, Toronto city, and Purdue University campus
are evaluated.
813 Comparison of Segment and Pixel-based
Non-parametric Land Cover Classification
in the Brazilian Amazon Using Multitemporal
Landsat TM/ETM+ Imagery
Katherine A. Budreski, Randolph H. Wynne, John O. Browder, and James B. Campbell
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This study evaluated segment-based classification paired
with non-parametric methods (CART® and kNN) and inter-annual, multi-temporal data in the classification of an
11-year chronosequence of Landsat TM/ETM+ imagery in the
Brazilian Amazon. The kNN and CART® classification methods, with the integration of multi-temporal data, performed
equally well in the separation of cleared, re-vegetated, and
primary forest classes with overall accuracies ranging from
77 percent to 91 percent, with pixel-based CART® classifications resulting in significantly lower variance than all other
methods (3.2 percent versus an average of 13.2 percent).
Segmentation did not improve classification success over
pixel-based methods with the used datasets. Through
appropriate band selection methods, multi-temporal bands
were chosen in 38 of 44 total classifications, strongly
suggesting the utility of inter-annual, multi-temporal data for
the given classes and region. The land-cover maps from this
study allow for an accurate annualized analysis of land-cover and landscape change in the region.
829 Estimating Species Abundance in a
Northern Temperate Forest Using
Spectral Mixture Analysis
Lucie C. Plourde, Scott V. Ollinger, Marie-Louise Smith, and Mary E. Martin
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Effective, reliable methods for characterizing the spatial
distribution of tree species through remote sensing would
represent an important step toward better understanding
changes in biodiversity, habitat quality, climate, and nutrient cycling. Towards this end, we explore the feasibility of
using spectral mixture analysis to discriminate the distribution and abundance of two important forest species at the
Bartlett Experimental Forest, New Hampshire. Using hyper-spectral image data and simulated broadband sensor data,
we used spectral unmixing to quantify the abundance of
sugar maple and American beech, as opposed to the more
conventional approach of detecting presence or absence of
discrete species classes. Stronger linear relationships were
demonstrated between predicted and measured abundance
for hyperspectral than broadband sensor data: R2 = 0.49
(RMSE = 0.09) versus R2 = 0.16 (RMSE = 0.19) for sugar
maple; R2 = 0.36 (RMSE = 0.18) versus R2 = 0.24 (RMSE =
0.33) for beech. These results suggest that spectrally unmixing hyperspectral data to estimate species abundances holds
promise for a variety of ecological studies.
841 Exploring the Geostatistical Method
for Estimating the Signal-to-Noise
Ratio of Images
P.M. Atkinson, I.M. Sargent, G.M. Foody, and J. Williams
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The signal-to-noise ratio (SNR) has been estimated for
remotely sensed imagery using several image-based methods
such as the homogeneous area (HA) and geostatistical (GS)
methods. For certain procedures such as regression, an
alternative SNR (SNRvar), the ratio of the variance in the
signal to the variance in the noise, is potentially more
informative and useful. In this paper, the GS method was
modified to estimate the SNRvar, referred to as the SNRvar(GS).
Specifically, the sill variance c of the fitted variogram model
was used to estimate the variance of the signal component
and the nugget variance c0 of the fitted model was used to
estimate the variance of the noise. The assumptions required
in this estimation are presented. The SNRvar(GS) was estimated
using the modified GS method for six different land-covers
and a range of wavelengths to explore its properties. The
SNR*var(GS) was found to vary as a function of both wave-
length and land-cover. The SNR*var(GS) represents a useful
statistic that should be estimated and presented for different
land-cover types and even per-pixel using a local moving
window kernel.