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Remote sensing for resources development and environmental management
Damen, M. C. J.

Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986
How few data do we need: Some radical thoughts
on renewable natural resources surveys
School of Oriental and African Studies, University of London, UK
ABSTRACT: The experience of applying remote sensing
techniques to renewable natural resources surveys and
evaluation has been to show that the necessary is too
expensive and the affordable inadequate. The promise
of remote sensing has been that it could provide
overviews sufficiently frequently to monitor and predict
agricultural change and production. In the event the
remotely sensed data sets assembled for predictive
purposes, such as that which provided a proportion of
the data used in the LACIE experiment proved to be
unwieldly even though the samples handled were only a
tiny fraction of the data sensed.
It is argued here that the time has come for remote
sensing specialists to identify economic activities which
attract returns to institutions which will benefit by advance
warning of agricultural performance at regional level,
and then to determine the minimum level of sample
coverage and frequency necessary to provide predictive
information at a price which the economic activities will
bear. The experience of users of the general purpose
remotely sensed satellite data available to date for
renewable natural resources surveys (viz Landsat data)
has led many users, and especially potential users, to
acquire extravagant expectations; they have accepted
the marvel of the comprehensive high resolution
overview enabled by the satellite perspective without
recognising the consequences of needing in addition
high radiometric and temporal resolution data to detect
information of concern. It will be concluded that any
economically viable operational system will require
economies in spatial cover through sampling, and
suggests principles according to which such economies
might be decided. The implications for sensor design
and data processing are also discussed briefly.
Keywords: sampling, remotely sensed data, land cover,
The fifteen years of handling satellite remote sensing
data for renewable resources studies have been more
remarkable for the research papers generated than for
the economic benefit of the activity. Such initial
disappointment in a new area of technology is not
unusual, however, and there are recent precedents for
example in a similar sphere, that of satellite space
communication. The 1950s and the 1960s were
decades when the optimism held out by the proponents
of satellite communication was not confirmed by effective
technological innovation. In applying remotely sensed
data to renewable natural resources studies we are
currently in a similar phase where the up-take of remote
sensing data capture and data processing systems by
professionals in relevant fields has been very partial.
Impediments to the adoption of remote sensing
procedures have been partly:
• technological,
• to do with the inertia of potential
adopting institutions
t the unreasonable expectations of the users of
general purpose satellite systems
t the lack of sampling and data reducing procedures
for the vast data sets generated in studies of the
dynamic environments of concern to renewable
natural resource surveyors.
This last aspect will be the main concern here; the other
issues will be referred to in the discussion but will not be
treated systematically.
It has been shown elsewhere (Allan 1984) that with the
launch of the Landsat satellites in the 1970s it became
possible for the first time to address the demanding
temporal features which have to be taken into account if
land-cover, agriculture and cropping are to be surveyed
comprehensively . The same the study emphasised that
there was a clear data-volume- threshold at which
current computing technology, and even future
technology, would be inadequate to handle the huge
volumes of digital information generated by sensing
systems resolving the multi-dimensional remotely sensed
data in the spatial, spectral, radiometric and temporal
domains. The study also showed that most of the useful
applications in renewable natural resources studies,
namely crop area and production monitoring, were on
the wrong side of the data volume threshold because
relatively high spatial, temporal and radiometric
resolutions were necessary to enable the reliable
detection of such features as crop extent and condition.
The only rational approach to the type of study which will
inevitably generate huge data sets is some method of
sampling or data reduction. The most obvious method is
to relax the spatial resolution reducing the data volume in
this domain by acquiring the data at a much larger pixel
size and the 1980s have seen some important
developments in the use of AVHRR data (one to four
kilometres spatial resolution depending on whether
captured at the nadir or the swath edge) as described by
Justice et al. (1985) At the one kilometre spatial
resolution the use of such data represents a reduction of
226 times in the volume of data in the spatial domain
compared with Landsat MSS; at the four kilometre level
its use represents a 3616 times reduction. Meanwhile