Peer-Reviewed Articles
1133 Detection of Regularly Spaced
Targets in Small-Footprint LIDAR Data: Research Issues for Consideration
David L. Evans, Scott D. Roberts, John W. McCombs, and Richard L. Harrington
Abstract
Download
Full Article
The level of interest in small-footprint LIDAR use in forest assessments
is enormous, as indicated by the growing body of research published
recently on the subject. Little attention has been focused on assessment
of the detectability of tree and forest parameters under different
mission and forest settings. This paper puts forth the premise that,
for effective use in forestry, a better understanding of the underlying
sampling theory must be known to develop the most efficient solutions
to operational use of LIDAR. Examples of the issues are presented
from LIDAR data sets being used in on-going research in the Department
of Forestry at Mississippi State University.
1137 Rule-Based Classification Systems Using Classification and Regression
Tree (CART) Analysis
Rick L. Lawrence and Andrea Wright
Abstract
Download
Full Article
Incorporating ancillary data into image classification can increase
classification accuracy and precision. Rule-based classification
systems using expert systems or machine learning are a particularly
useful means of incorporating ancillary data, but have been difficult
to implement. We developed a means for creating a rule-based classification
using classification and regression tree analysis (CART), a commonly
available statistical method. The CART classification does not require
expert knowledge, automatically selects useful spectral and ancillary
data from data supplied by the analyst, and can be used with continuous
and categorical ancillary data. We demonstrated the use of the CART
classification at three increasingly detailed classification levels
for a portion of the Greater Yellowstone Ecosystem. Overall
accuracies ranged from 96 percent at level 1, to 79 percent at level 2, and
65 percent at level 3.
1143 A Robust Method for Registering Ground-Based Laser Range Images
of Urban Outdoor Objects
Huijing Zhao and Ryosuke Shibasaki
Abstract
Download
Full Article
There is a potentially strong demand for detailed 3D spatial data of
urban areas. A ground-based laser range scanner is one of the promising
devices to acquire range images of urban 3D objects. In this paper,
the authors propose an automated registration method of multiple
overlapping range images for the reconstruction of 3D urban objects.
Registration is achieved in two steps, pair-wise registration and
multiple registration assuming that one rotating axis of a laser
range scanner is almost vertical. At first, pair-wise registration
determines the approximate values of four transformation parameters,
a horizontal rotation angle and three translation parameters of a
pair of neighboring range images. Then through multiple registration,
the transformation parameters of all range images are adjusted using
the approximate values so as to minimize the total errors. An outdoor
experiment was conducted registering 42 range images to construct
a 3D model of a building on the campus of the University of Tokyo.
The accuracy of the model was examined using a 1:500-scale digital
map and GPS-measured locations of the viewpoints.
The efficiency and accuracy of the registration method is demonstrated in this
paper.
1155 Landsat TM-Based Forest Area Estimation Using Iterative Guided
Spectral Class Rejection
Jared P. Wayman, Randolph H. Wynne, John A. Scrivani, and Gregory A. Reams
Abstract
Download
Full Article
In cooperation with the USDA Forest Service Southern Research Station,
an algorithm has been developed to replace the current aerial-photography-derived
FIA Phase I estimates of forest;sh non-forest area with a Landsat
TM-based forest area estimation. Corrected area estimates were obtained
using a new hybrid classifier called Iterative Guided Spectral Class
Rejection (IGSCR) for portions of three physiographic regions of
Virginia. Corrected area estimates were also derived using the Landsat
TM-based Multi-Resolution Land Characteristic Interagency Consortium
(MRLC) cover maps. Both satellite-based corrected area estimates
were tested against the traditional photo-based estimates. Forest
area estimates were not significantly different (at the 95 percent
level) between the traditional FIA, IGSCR, and MRLC methods, although
the precision of the satellite-based estimates was lower. Map accuracies
were not significantly different (at the 95 percent level) between
the IGSCR method and the MRLC method. Overall accuracies ranged from
80 percent to 89 percent using FIA definitions of forest and non-forest
land use. Given standardization of the image rectification process
and training data properties, the IGSCR methodology is fast, objective,
and repeatable across users, regions, and time, and it outperforms
the MRLC for FIA applications.
1167 The Utility of IRS-1C LISS-III and PAN-Merged Data for Mapping
Salt-Affected Soils
R.S. Dwivedi, K.V. Ramana, S.S. Thammappa, and A.N. Singh
Abstract
Download
Full Article
Waterlogging and subsequent salinization and/or alkalization of soils
is the major land degradation process operating in the irrigated
areas of the arid and semi-arid regions of the world. Before taking
up any prevention or reclamation measures for such lands, information
on their nature, spatial extent, magnitude, distribution, and temporal
behavior is a prerequisite. In the study reported here, an attempt
has been made to evaluate the potential of high spatial resolution
(5.8 m) Panchromatic
(PAN) sensor data and the Linear Imaging Self-scanning Sensor (LISS-III) data
from the Indian Remote Sensing Satellite (IRS-1 C) for detection and delineation
of salt-affected soils in a portion of the Indo-Gangetic alluvial plains of
northern India. The approach involves the merging of LISS-III and PAN data
through an Intensity, Hue, and Saturation (IHS ) transformation, and a subsequent
supervised classification using a per-pixel Gaussian maximum-likelihood classification
algorithm. Results indicate a deterioration in the overall accuracy of salt-affected
soils derived from LISS-III data as compared to IRS-1 B LISS-II data owing
to an improvement in the spatial resolution (23.5 m for LIS-III versus 36.5
m for LISS-II), leading to enhanced intra-class spectral variability. The PAN
and LISS-III hybrid data without any transformation ranked last in terms of
overall accuracy. Overall accuracy figures for LISS-II, LISS-III, and PAN and
LISS-III hybrid data with the IHS transformation have been on the order of
89.6 percent, 85.9 percent, and 81.5 percent, respectively.
1177 Irrigated Crop Area Estimation Using Landsat TM Imagery in La
Mancha, Spain
Cecilia Martínez Beltrán and Alfonso Calera Belmonte
Abstract
Download
Full Article
A methodology for supervised classification to identify irrigated crops
with Landsat TM imagery in a semiarid zone (La Mancha, Spain) is
presented. The discrimination procedure is based on the different
crop spectral responses through time according to their phenological
evolution. Our multitemporal supervised classification includes maximum-likelihood
algorithms, decision-tree criteria, and context classifiers. We have
applied the procedure to two sets of scenes obtained for the growing
seasons of 1996 and 1997, respectively. The resulting classification
accuracy was 93.1 percent for 1996 and 90.21 percent for 1997. We
have estimated the areas occupied by each crop class by means of
intersecting the TM-derived land-use raster map and the digital rural
cadastre vector map in a geographic information system. We have assessed
the accuracy of the crop area estimation from the classified image
by comparing these areas with those calculated from the digital rural
cadastre. A median filter applied to the final classification improves
the agreement of the estimated crop areas with the cadastre data.
Additional post-classification methods to correct crop areas did
not bring any significant further improvements. Therefore, we conclude
that the context classifier is a useful and sufficient tool to improve
surface quantification.
1185 Assessing Raster Representation Accuracy Using a Scale Factor
Model
Jeong Chang Seong and E. Lynn Usery
Abstract
Download
Full Article
Raster datasets of global and continental extent are subject to error
resulting from projection transformation. This paper examines the
error problem from a theoretical perspective and develops a model
to calculate the extent of the errors. The theoretical examination
indicates that error results in two forms, areal size change of pixels
and categorical error resulting from loss or duplication of pixels.
A scale factor model, based on the horizontal and vertical scale
factors of the
projection, is developed to provide a computation of the resulting error from
specific projections. The model is experimentally tested with the cylindrical
equal area,
sinusoidal, and Mollweide projections. Results indicate that the model predicts
error within one percent of actual values and that the sinusoidal projection
is subject to smaller errors in projecting raster data than the other projections
tested.
| Top | Home |