Peer-Reviewed Articles
1323 A GIS-Assisted Rail Construction Econometric Model that Incorporates
LIDAR Data
David J. Cowen, John R. Jensen, Chad Hendrix, Mike Hodgson, Steven R. Schill,
and Frank Macchiaverna
Abstract
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Identifying the optimum route for new railroad lead-tracks using traditional
field methods is often time-consuming, is costly, and does not allow
for easy investigation of alternative routes. The NASA sponsored
Affiliated Research Center (ARC) at the University of South Carolina
worked with Norfolk Southern Corporation to develop a remote sensing
and GIS-assisted lead-track route selection model. The objective
was to compare the traditionally surveyed routes to those derived
using the output from the remote sensing and GIS-assisted modeling.
The critical element in the design of the model was the calculation
of a cost surface. The cost variables for the model were developed
based on expert advice from Norfolk Southern employees. The solution
employed a raster GIS econometric routing model for the exploration
of potential routes using construction cost factors such as grade
(cut and fill cost), road crossings, stream crossings, and track
cost. The use of remotely sensed data was a key element of the research.
The digital elevation model used in the grid-based econometric model
was obtained from Light Detection and Ranging (LIDAR) data with accurate
0.3- by 0.3-m (1- by 1-ft) elevation postings. The route selected
using the remote sensing and GIS-assisted modeling was similar to
the traditionally surveyed route. The GIS-based optimal path lead-track
model can be used to identify rapidly a variety of potential routes
based on the most important cost factors.
1329 Comparison of High Spatial Resolution Imagery for Efficient
Generation of GIS Vegetation Layers
Lloyd Coulter, Douglas Stow, Allen Hope, John O’Leary, Debbie Turner, Pauline
Longmire, Seth Peterson, and John Kaiser
Abstract
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Traditional vegetation mapping approaches require extensive field reconnaissance,
and normally involve delineation of vegetation boundaries through interpretation
of aerial photographs. In order to generate vegetation data layers within
a geographic information system (GIS), boundaries must be digitized and georeferenced
and vegetation attributes coded. A project — conducted through the National
Aeronautics and Space Administration Affiliated Research Center at San Diego
State University for Ogden Environmental and Energy Services, Inc. — investigated
the utility of very high spatial resolution (1-m) digital multispectral image
data for generating GIS vegetation layers. Mapping and digital encoding of
vegetation polygons was performed using USGS color-infrared (CIR) digital
orthophotographic quarter quadrangle (DOQQ) and Airborne Data Acquisition
and Registration (ADAR) 5500 imagery. The two data sources were compared
in the context of a controlled experiment which tested the utility of the
imagery under multiple mapping scenarios. The study area was a habitat reserve
within Marine Corps Air Station Miramar near San Diego, California. This
area primarily supports shrubland vegetation types typical of the Mediterranean
climate area of southern California.
CIR image data derived directly from multispectral digital cameras (e.g., ADAR System 5500) enabled more accurate classification and mapping of vegetation than did digital imagery generated from scanned CIR aerial photographs. This result is largely attributed to the higher spectral and radiometric fidelity of direct digital capture, but may also be attributed to more optimal seasonality for the data of the ADAR acquisition. While the mapping was based upon interactive, visual image interpretation and on-screen digitizing, the following image processing techniques proved to be helpful for aiding interpretation:
The overall accuracy of interpreter-derived vegetation maps was approximately 75 percent for the entire study area.
1337 Developing Forestry Products from High Resolution Digital Aerial
Imagery
Lindi J. Quackenbush, Paul F. Hopkins, and Gerald J. Kinn
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High spatial resolution digital aerial imagery provides significant advantages over many traditional image sources. However, using high resolution imagery also creates many challenges. In order to process such imagery effectively and efficiently, traditional image processing techniques need to be adapted and new techniques need to be developed. This paper presents the results of a project aimed at developing derivative products from high resolution imagery for the forestry market. The project evaluated techniques for improving computer classification results by pre-processing the imagery to mitigate canopy shading effects. Preliminary results show that equalizing intensity across the imagery by applying a chromaticity filter improved overall classification accuracy by approximately 10 percent. The project also used a correlation-based approach to automatically define individual trees. This procedure calculated the correlation between the image and templates of "typical" trees. Initial results showed that 80 percent of trees in the test scene were correctly identified but approximately 20 percent of the total tree count came from errors of commission. Future studies will evaluate the benefit of using the procedures studied as input for further processing.
1349 Use of Remotely Sensed Data for Assessing Crop Hail
Damage
Albert J. Peters, Steven C. Griffin, Andrés Viña,
and Lei Ji
Abstract
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Crop hail damage is a major problem in the Great Plains, causing substantial
losses to farmers. Traditional crop hail loss adjustment is very labor-intensive.
The loss adjuster must physically survey the damaged field, which is time
consuming and subject to considerable error due to the difficulty in determining
the relative size and location of the damage. We describe a tool based on
remote sensing technology that can be used to help standardize the sampling
procedure, estimate areas of similar damage, and speed the selection and
location of field sampling. While it is not likely that loss adjustment can
be done without ground reference information, it is possible that considerable
improvements and cost savings could be made with the assistance of this technology.
The NASA Affiliated Research Center (ARC) at the University of Nebraska-Lincoln
working with IGF Insurance Company used airborne multispectral imagery and
close-range hyperspectral data to evaluate the impact of artificially induced
hail damage on corn and soybean fields. Additionally, a Landsat Thematic
Mapper scene, covering a severe hailstorm in western Iowa on 02 July 1999,
was evaluated for its potential contribution in evaluation of hail damage
on crops. The results showed that broadband multispectral imagery is adequate
for detection of the ground area and relative level of hail damage in corn
and soybean crops. We believe that damage assessment based on remote sensing
techniques would be faster and more accurate than currently used field-oriented
procedures.
1357 Spaceborne Imaging Radar in Support of Forest Resource Management
Jonathan W. Chipman, Thomas M. Lillesand, James D. Gage, and Samuel
Radcliffe
Abstract
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Imaging radar systems have a unique potential for monitoring certain biophysical
characteristics of forest ecosystems, but the extent and limitations of this
technology for forest management applications have not been assessed. An
ongoing series of studies at the University of Wisconsin-Madison's Affiliated
Research Center (ARC) is addressing these applications, using dual-wavelength,
multipolarization Shuttle Imaging Radar-C (SIR-C) imagery for a variety of
sites in the western Great Lakes region. The initial research focused on
the combination of optical and radar data for land-cover mapping; this developed
into an investigation of biometric applications of radar imagery, deriving
various stand parameters of interest to forestland managers. A third question
is now being addressed - how the complex mosaic of forest types in the region
affects the accuracy of interferometric radar-based estimates of elevation.
Further research is needed on practical applications of multiwavelength radar
systems for monitoring changing conditions in natural and managed forests.
1367 Predicting Forest Stand Characteristics with Airborne Scanning
Lidar
Joseph E. Means, Steven A. Acker, Brandon J. Fitt, Michael Renslow, Lisa
Emerson, and Chad Hendrix
Abstract
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Currently, commercial forestry applications of airborne scanning lidar are
limited to geo-technical applications such as creation of digital terrain
models for layout of roads or logging systems. We investigated the feasibility
of predicting characteristics of forest stands with lidar data in a university-industry
partnership. Lidar lends itself well to such applications because it allows
direct measurement of important structural characteristics of height and
canopy closure. We found that lidar data can be used to predict the stand
characteristics of height, basal area, and volume quite well. The potential
for commercial applications appears bright. Lidar data can be used to estimate
stand characteristics over large areas or entire forests. After the process
is streamlined, it should be possible to provide maps of height, basal area,
and volume in such areas within a few weeks of the lidar collection flight.
1373 The Application of TM Imagery and GIS Data in the Assessment of Arid Lands
Water and Land Resources in West Texas
Paul Neville, Robert I. Coward, Richard P. Watson, Michael Inglis, and Stan
Morain
Abstract
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The objective of this research was to develop an image-based geographic information
system (GIS) that could help evaluate ground water quality and land development
potential in the Salt Basin of Trans-Pecos Texas. The stimulus for this effort
was to evaluate whether the groundwater geochemistry and soil surface chemistry
could be determined using commercial, off-the-shelf, geo-spatial technology.
If so, these tools could be used to determine the commercial value of land
and water for agricultural, industrial, and municipal purposes. The results
are promising. Landsat Thematic Mapper imagery, used in conjunction with
the geospatial databases, allowed areas to be identified that showed a potential
for good-quality groundwater with arable land suitable for multiple uses.
Image maps were produced to characterize the surface and subsurface attributes,
and were used by the commercial partner to evaluate land development potential.
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