Geoscience Reference
In-Depth Information
change are covered and (2) satellite missions do not unnecessarily overlap (http://www.ceos.org).
The first goal can be achieved by providing timely and accurate information from satellite-derived
products. Proper use of these products, in turn, relies on our ability to ascertain their uncertainty.
The second goal is achieved through coordination among CEOS members.
As validation efforts are an integral part of “satellite missions,” part of the CEOS mission is
to reduce the likelihood of unnecessary overlap in validation efforts. The particular CEOS work
related to validation falls within the Working Group on Calibration and Validation (WGCV), which
is one of two standing working groups of CEOS (the other is the Working Group on Information
Systems and Services, WGISS). The ultimate goal of the WGCV is to ensure long-term confidence
in the accuracy and quality of Earth observation data and products through (1) sensor-specific
calibration and validation and (2) geophysical parameter and derived-product validation.
To ensure long-term confidence in the accuracy and quality of Earth observation data and
products, the WGCV provides a forum for calibration and validation information exchange, coor-
dination, and cooperative activities. The WGCV promotes the international exchange of technical
information and documentation; joint experiments; and the sharing of facilities, expertise, and
resources (http://wgcv.ceos.org). There are currently six established subgroups within WGCV: (1)
atmospheric chemistry, (2) infrared and visible optical sensors (IVOS), (3) land product validation
(LPV), (4) terrain mapping (TM), (5) synthetic aperture radar (SAR), and (6) microwave sensors
subgroup (MSSG).
Each subgroup has a specific mission. For example, the relevant subgroup for global land
product validation is LPV. The mission of LPV is to increase the quality and economy of global
satellite product validation by developing and promoting international standards and protocols
for field sampling, scaling, error budgeting, and data exchange and product evaluation and to
advocate mission-long validation programs for current and future earth-observing satellites (Jus-
tice et al., 2000). In this chapter, by considering the lessons learned from previous and current
programs, we describe a strategy to utilize LPV for current and future global land-cover (LC)
validation efforts.
3.1.2
Approaches to Land-Cover Validation
Approaches to LC validation may be divided into two primary types: statistical approaches and
confidence-building measures. Confidence-building measures include studies or comparisons made
without a firm statistical basis that provide confidence in the map. When presented with a LC map
product, users typically first carry out “reconnaissance measures” by examining the map to see
how well it conforms to regional landscape attributes, such as mountain chains, valleys, or agri-
cultural regions. Spatial structure is inspected to ensure that the map has sensible patterns of LC
that are without excessive “salt-and-pepper” noise or excessive smoothness and generalization.
Land-water boundaries are checked for continuity to reveal the quality of multidate registration.
The map is carefully examined for gross errors, such as cities in the Sahara or water on high
mountain slopes. If the map seems reasonable based on these and similar criteria, validation can
proceed to more time-consuming confidence measures. These include ancillary comparisons, in
which specific maps or datasets are compared to the map. However, such comparisons are not
always straightforward, since ancillary materials are typically prepared from input data acquired
at a different time. Also, map scales and LC units used in the ancillary materials may not be directly
comparable to the map of interest.
The Global Land Cover 2000 program has established a systematic approach for qualitative
confidence building in which a global map is divided into small cells, each of which is examined
carefully for discrepancies. This procedure is described more fully in section 2.1.
Statistical approaches may be further broken down into two types: model-based inference and
design-based inference (Stehman, 2000, 2001). Model-based inference is focused on the classifi-
cation process, not on the map
per se.
A map is viewed as one realization of a classification process
