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S.D. King and D.R. Green
sensor, will best provide the information required to solve the specific problem. In
essence, the issue to be resolved must be matched with the utility (or characteristics) of
the sensor. Initially, such a set of guidelines may be constructed using a paper-based
matrix. However, the need for rapid access to the data sets and the correct information
suggests that an electronic equivalent would be more far efficient and useful. Such a
system would allow the user to define their problem attributes by answering a set of
basic questions, and would then be guided to the imagery that would best help solve the
problem. In effect, the user would be able to simply and quickly interrogate a geospatial, multi-scale hierarchical database of satellite imagery, providing quick and
efficient access to the data and information required.
Introduction
The sociological and physical constraints of modern-day living dictate that human
beings will attempt to manage the natural environment. Most environmental
management today is operated on the basis of sustainable development, with the
objective of providing for the needs of humans without detriment to the wellbeing of the
environment. Understanding how the environment works, and understanding and
predicting future environmental processes is essential to successful management. This,
in itself, is heavily reliant upon the provision of data, information extraction from that
data, knowledge acquisition and understanding.
Most data collected about the environment is geographical in nature because it
relates to objects and features that are visible to humans on the Earth's surface. Prior to
the 1960s, much of the data collected was arguably very static in nature, being highly
descriptive and paper-based. Since the 1960s, the advent of computers, Geographical
Information Systems (GIS), and new data collection devices, such as satellites, have
given us the ability to collect, store and manipulate data in many different ways. Data
has become more interactive and 'spatialised' because we are now more readily able to
simultaneously visualise the geographical relationships between earth surface objects at
varying scales.
Although aerial photography has been used for environmental data acquisition
for most of this century, the more recent development of satellite technology, as well as
new airborne sensors has left the environmental management community with an
extremely large resource from which data can be drawn. This data is held in various
spatial, spectral and temporal resolutions, all of which have potential for use. However,
in many environmental applications, remote sensing in not fully utilised because those
who could benefit from it do not know which source of data is best suited to the issue
that they are attempting to resolve. Using coastal zone management as an example, this
paper will demonstrate how remotely sensed data may be organised into a hierarchy to
which coastal issues may be matched. Finally, a paper-based matrix that matches coastal
issues to remotely sensed data is presented, and the future development of an electronic
version is suggested.
S.D. King and D.R. Green
sensor, will best provide the information required to solve the specific problem. In
essence, the issue to be resolved must be matched with the utility (or characteristics) of
the sensor. Initially, such a set of guidelines may be constructed using a paper-based
matrix. However, the need for rapid access to the data sets and the correct information
suggests that an electronic equivalent would be more far efficient and useful. Such a
system would allow the user to define their problem attributes by answering a set of
basic questions, and would then be guided to the imagery that would best help solve the
problem. In effect, the user would be able to simply and quickly interrogate a geospatial, multi-scale hierarchical database of satellite imagery, providing quick and
efficient access to the data and information required.
Introduction
The sociological and physical constraints of modern-day living dictate that human
beings will attempt to manage the natural environment. Most environmental
management today is operated on the basis of sustainable development, with the
objective of providing for the needs of humans without detriment to the wellbeing of the
environment. Understanding how the environment works, and understanding and
predicting future environmental processes is essential to successful management. This,
in itself, is heavily reliant upon the provision of data, information extraction from that
data, knowledge acquisition and understanding.
Most data collected about the environment is geographical in nature because it
relates to objects and features that are visible to humans on the Earth's surface. Prior to
the 1960s, much of the data collected was arguably very static in nature, being highly
descriptive and paper-based. Since the 1960s, the advent of computers, Geographical
Information Systems (GIS), and new data collection devices, such as satellites, have
given us the ability to collect, store and manipulate data in many different ways. Data
has become more interactive and 'spatialised' because we are now more readily able to
simultaneously visualise the geographical relationships between earth surface objects at
varying scales.
Although aerial photography has been used for environmental data acquisition
for most of this century, the more recent development of satellite technology, as well as
new airborne sensors has left the environmental management community with an
extremely large resource from which data can be drawn. This data is held in various
spatial, spectral and temporal resolutions, all of which have potential for use. However,
in many environmental applications, remote sensing in not fully utilised because those
who could benefit from it do not know which source of data is best suited to the issue
that they are attempting to resolve. Using coastal zone management as an example, this
paper will demonstrate how remotely sensed data may be organised into a hierarchy to
which coastal issues may be matched. Finally, a paper-based matrix that matches coastal
issues to remotely sensed data is presented, and the future development of an electronic
version is suggested.