The step from environmental survey and monitoring to environmental management re
quires linking ecology with socio-economic information. Economic growth and population
growth often are driving variables for undesirable environmental change. Consequently,
socio-economic surveys and strategies for intervention are essential in environmental man
agement education.
3. Environmental Education: learning new paradigms
We as education specialists face an exceptional challenge. Today, new spatiotemporal
scales, new parameters and new problems in man-environment relations imply new
paradigms.
The following spatial scales are used in the Netherlands Institute for Environmental
Management (RIVM 1985):
• Global (e.g. global warming)
• Continental (e.g. acid rain)
• Regional (e.g. smog)
• Drainage basin (e.g. eutrophication; flooding)
• Local (e.g. heavy metal pollution)
These represent scales of environmental impacts. Economic activities as source of environ
mental effects however might act on a different scale and therefore, demand remedial
actions at different scales. "Think globally, act locally" summarizes this nicely.
Thinking in hierarchical spatiotemporal levels of organization is standard for biologist
(either in space: from molecule, cell, tissue, organ, organism, population, community,
biome, biosphere; or in time: diurnal, seasonal =► evolution) and is also natural for geogra
phers. However environmental managers with monodisciplinary backgrounds such as econ
omy, law, or engineering might require considerable re-orientation to acquire spa
tiotemporal concepts and perception.
The major educational challenge today is to think NEW. Conceptual models (as in text
books, expertise, expert systems) are mostly based on both conventional measurements
and on traditional problems often at different spatiotemporal scales. Let me illustrate this
with the well-known rain gauge. The common rain gauge taught us to think, and recently
to model, in terms of mm of rain per point location. However for many purposes "mm of
rain" is the correlate (= land attribute) and not the operational variable (= land quality).
The operational variable for crop growth is moisture availability. Remote Sensing can give
us a better correlate or land attribute (in this case: satellite measured vegetation index) of
the operational variable moisture availability; moreover remote sensing measures areas
instead of points.
What we often see in the scientific community is the attempt to transform remote sensing
measurements (e.g. Vegetation Index or NDVI) and parameters (e.g. Leaf Area Index,
LAI) into traditional parameters (e.g. mm of rain) and from there into the required biophy
sical quantity (e.g. crop production). Instead we should attempt to build vegetation index
driven models. Such models combined with the current remote sensing technology will
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