Peer Reviewed Articles
1013 The IGBP-DIS Global 1-Km Land-Cover Data Set DISCover: A
Project Overview
Alan S. Belward, John E. Estes, and Karen D. Kline
Abstract
Since 1992 the International Geosphere Biosphere Programmer's
(IGBP) Data and Information System (DIS) has been
working towards the completion of a validated global land cover
data set, DISCover. This 1-km resolution data set consists
of 17 cover classes identified on the basis of the science
requirements of the IGBP's core projects. DISCover has been
created from over 4.4 Terabytes of data from the Advanced
Very High Resolution Radiometer collected from 23 receiving
stations. These data were processed and assembled into a
coherent set of monthly Normalized Difference Vegetation
Index composites (April 1992 to April 1993) and classified
using unsupervised techniques with past-classification
refinement. The first global land-cover classification was
completed in July 1997. The IGBP-DIS Land Cover Working
Group, in turn, convened a Validation Working Group to
provide and implement a validation method to provide
statistical statements concerning the accuracy of the global
land-cover product and to allow the estimation of the error
variance in areal totals of classes globally and within regions.
The validation workshop was completed in September 1998
and the analysis by March 1999. This paper describes the
history of the DISCover version 1.0 implementation.
1021 An Analysis of the IGBP Global Land-Cover Characterization
Process
Thomas P. Loveland, Zhiliang Zhu, Donald 0. Ohlen, Jessyln F
Brown, Bradley C. Reed, and Limin Yang
Abstract
The International Geosphere Biosphere Programme (IGBP) has
called for the development of improved global land-cover data
for use in increasingly sophisticated global environmental
models. To meet this need, the staff of the U.S. Geological
Survey and the University of Nebraska-Lincoln developed and
applied a global Land-cover characterization methodology
using 1992-1993 1-km resolution Advanced Very High
Resolution Radiometer (AVHRR) and other spatial data. The
methodology, based on unsupervised classification with
extensive postclassification refinement, yielded a multi-layer
database consisting of eight land-cover data sets, descriptive
attributes, and source data. An independent IGBP accuracy
assessment reports a global accuracy of 73.5 percent, and
continental results vary from 63 percent to 83 percent.
Although data quality, methodology, interpreter performance,
and logistics affected the results, significant problems were
associated with the relationship between AVHRR data and finescale,
spectrally similar land-cover patterns in complex
natural or disturbed landscapes.
1033 Landsat Thematic Mapper Registration Accuracy and its
Effects on the IGBP Validation
Gregory J. Husak, Brian C. Hadley, and Kenneth C. McGwire
Abstract
Research associated with the International Geosphere/Biosphere Programme Data and Information System Cover
(DISCover) validation exercise revealed possible registration
errors in the high-resolution data employed. Systematically
corrected National Landsat Archive Production Systems
(NLAPS) Thematic Mapper (TM) data used in the validation of
the DISCover dataset was compared with precision registered
Multi-Resolution Land Characteristics (MRLC) TM data available
only for the conterminous United States. A consistent
offset in the systematically corrected data was discovered to
be approximately 2 km to the east and south. The impacts of
this bias on the validation of the IGBP DISCover dataset are
examined and show that possibly 20 percent of the IGBP pixels
for the conterminous United States could be adversely affected
by this registration error. Fractal dimension is shown to have
a close relationship with the effect of the offset on a class
and may be used as a predictive tool for areas outside the
conterminous United States.
1041 Image Interpretation Keys for Validation of Global Land-Cover
Data Sets
Melissa Kelly, John E. Estes, and Kevin A. Knight
Abstract
Photointerpretation keys originated from the need to efficiently
train aerial photographic interpreters. Keys provide interpreters
with a baseline of signature information that can be
widely applied to images illustrating differing land covers,
features. facilities, processes, and activities. Keys were produced
far the International Geosphere Biosphere Programme
Data Information Systems (IGBP-DIS) DISCover Validation effort.
This effort had two objectives: to provide a prototype set of
image interpretation keys that could be produced efficiently
and cost effectively and to create a foundation of enduring
reference materials that could be refined and enlarged to
facilitate the validation of future global lond-cover products.
The methodology of key development and production for this
effort originated as an ideal, but has by necessity been shaped
by pragmatic considerations. Factors including cost, time, and
availability of imagery and ancillary data limited broad
applicability of the keys developed. Research included determination
of the most effective band combination, signature
development and application, consistent implementation of a
minimum mapping unit, use of signature elements in the
evaluation of key use, and development of a product useful
for cross applications. We continue to expand the present set
of keys and develop a digital infrastructure for a global
reference library of key materials. These materials will
expedite future validation efforts and lead to greater confidence
in the accuracy of future global land-cover products.
1051 Thematic Validation of High-Resolution Global Land-Cover
Data Sets
Joseph Scepan
Abstract
This paper describes a procedure to validate the thematic
accuracy of the International Geosphere-Biosphere Programme.
Data and Information System (IGBP-DIS) DISCover
(Version 1.O) l kilometer Global Land-Cover Data Set. Issues
of data set sampling design, image geometry and registration,
and core sample interpretation procedures are addressed.
Landsat Thematic Mapper and SPOT satellite image data were
used to verify, 379 primary core samples selected from DISCover
1.O using a stratified random sampling procedure. The goal
was to verify a minimum of 25 samples per DlSCover class;
this was accomplished for 13 of the 15 verified classes. Three
regional Expert Image Interpreters independently verified
each sample, and a majority decision rule was used to determine
sample occuracy. For the 15 DISCover classes validated,
the average class accuracy was 59.4 percent with accuracies
for the 15 verified DISCover classes ranging between 40.0 percent
and 100 percent. The overall area-weighted accuracy of the
data set was determined to be 66.9 percent. When only samples
which had a majority interpretation for errors as well as for
correct classification were considered, the average class accuracy
of the data set was calculated to be 73.5 percent.
1061 The IGBP DiSCover Confidence Sites and the System for
Terrestrial Ecosystem Parameteriza-tion: Tools for Validating Global
Land-Cover Data
Douglas Muchoney, Alan Strahler, John Hodges, and Janet LoCastro
Abstract
The IGBP Validation Confidence Site database provides a set
of 379 land-cover maps, each containing an IGBP Core
Validation sample. Each map is 448 km² in area and is
delineated and labeled by photointerpretation of Landsat or
SPOT satellite imagery at a scale of 1:125,000. Within each
map, land-cover types and polygons are assigned descriptive
labels and parameter codes for vegetation attributes, including
life form, cover, height. and phenology for canopy and ground
layers. These attributes are a subset of parameters defined by
the System for Terrestrial Ecosystem Parameterization (STEP),
a site model and database that characterizes land surface and
vegetation for use in global algorithm training, testing, and
validation of land-cover data. Because the maps are linked to
the core samples, they provide a large, consistent dataset that
is stratified to represent equally all of the world's major
vegetation form classes and land-cover types. The confidence
site database has three primary applications: (1) as a set of
validation benchmarks for alternate regional or global landcover
classifications emphasizing vegetation attributes, (2) as
a secondary information source fur studying core sample
accuracy issues, and (3) as a source of training and test sites
for regional and global supervised classification of coarse-resolution satellite imagery.
1069 The Global Land-Cover Characteristics Data-base: The
Users’ Perspective
Jesslyn F Brown, Thomas R. Loveland, Donald 0.Ohlen, and Zhi-liang
Zhu
Abstract
A unique global land-cover characteristics database developed
by the U.S. Geological Survey has been available to users since
mid-1997. Access to the date is through the Internet under the
EROS (Earth Resources Observation Systems) Data Center's
home page (http://edtwww.cr.uags.gov/landdaac/glcc/glcc.html).
Since the release of the database, the data have been
incorporated into various environmental research and
modeling applications, including mapping global biodiversity,
mesoscale climate simulations, carbon cycle modeling, and
estimating habitat destruction. Since the early stages of the
project, user feedback has provided a means to understand data
utility in applications, garner suggestions for data improvements,
and gain insights into the technical challenges faced by
users. Synthesis of user feedback provided a means to generate
a user profile and derive a list of applications-critical criteria
for landcover data. User suggestions have lead to revisions in
the database, including label changes, alternative classification
schemas and additional projections for the data.
1075 The DISCover Validation Image Interpretation Process
Joseph Scepan, Gunter Menz, and Matthew C. Hansen
Abstract
Thematic validation of the International Geosphere Biosphere
Data and Information System (IGBP-QIS) Global 1-Kilometer
Land-Cover Data Set (DISCover) was performed utilizing a "state-of-the-practice" technique by a team of Expert images
Interpreters (EII) examining subscenes extracted from 379
digital Landsat (Thematic Mapper) and SPOT images.
The 15 validated IGBP Land-cover classes (Snow/Ice and
Water were not assessed) were not equally interpretable on
the TM and SPOT imagery. Interpreter confidence was highest
for Evergreen Broadleaf Forests and Urban/BuiIt-up DISCover
classes while Grasslands and Permanent Wetlands were interpreted
with relatively less confidence. Analysis of image interpretation
in each of the 13 validation regions indicates that
confidence in interpretations for North America/Canada
(Region 1) and Central Asia/Japan (Region 11) are lower than
average. Confidence in interpretations is significantly higher
than average for North America/US (Region 2). Northern and
Southern South America (Regions 4 and 5), and Southest Asia
and China (Region 12). In this study, variations in interpretation
confidence are also noted between regions or based upon
the geographic location of samples.
This exercise demonstrates that Landsat TM and SPOT imagery can be efficiently used to validate high-resolution global land-cover products. The results suggest that the utility of and confidence that may be placed in this technique depends upon the land-cover classification scheme used and the quality of digital and ancillary data available to aid interpreters. Another important factor is the relative confidence of the interpreters to verify the land cover within their respective areas of the globe.
1083 Implications of Land-Cover Misclassification for Parameter
Estimates in Global Land-Surface Models: An Example from the Simple Biosphere
Model (SiB2)
R.S. DeFries and S.O. Los
Abstract
One of the primary applications of the global 1-km land-cover
DISCover product is to derive biophysical and ecological
parameters for a range of land-surface models, including
biosphere-atmosphere, biogeochemical, and ecological
models. The validation effort reported in this special issue
enables a realistic assessment of the implications of misclassification
errors for parameter estimates within the models.
In most land-surface models, cover types are aggregated to
coarser groupings than the 17 IGBP classes for estimating
parameters, with aggregation schemes varying with individual
models and individual parameters within each model. Misclassification
errors are consequential only when they occur
between cover types that are not aggregated by the model. We
use examples of two biophysical parameters-leaf area index
and surface roughness-as estimated for use in the Simple
Biosphere Model (SiB2) and other modeling applications to
quantify the effects of misclassification on parameter estimates,
SiB2 relies on satellite data as well as land-cover
information for estimating the biophysical parameters. Consequences
of misclassification are likely to be greater for those
models that do not use satellite data. Mean class accuracy
based on those sites for which a majority of interpreters agreed
(percentage of validation pixels classified correctly out of total
number of validation pixels, averaged over all classes), adjusted
by area of each cover type in the IGBP Discover product,
is 78.6 when all misclassification errors are included. By
excluding misclassification errors when they are inconsequential
for leaf area index and surface roughness length
estimates, mean class accuracies are 90.2 and 87.8, respectively
The results illustrate that misclassification errors are
most meaningfully viewed in the context of the application of
the land-cover information.
1089 The Way Forward
John Estes, Alan Bel ward, Thomas Loveland, Joseph Scepan, Alan
Strahler, John Townshend, and Chris Justice
Abstract
This paper focuses on the lessons learned in the conduct of
the International Geosphere Biosphere Program's Data and
Information System (IGBP-DIS), global 1-km Land-Cover Mapping
Project [DISCover).There is still considerable fundamental
research to be conducted dealing with the development and
validation of thematic geospatial products derived from a
combination of remotely sensed and ancillary data. Issues
include database and data product development, classification
legend definitions, processing and analysis techniques, and
sampling strategies. A significant infrastructure is required to
support an effort such as DISCover. The infrastructure put in
place under the auspices of the IGBP-DIS serves us a model,
and must he put in place to enable replication and development
of projects such as DISCover.
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