Télédétection et ressources en eau/Remote sensing and water resources
179
Land Qualities [LQ] on the supply side. LQ’s are complex attributes of land such as ‘ability to
supply water’, which is a composite of constituent Land Characteristics [LC], which are
measurable properties of the land such as soil depth. The other attractive aspect of the
Framework is the implicit recognition of socio-economic factors in the planning process; the
definition of a LUT could be, for example, ‘low input maize, rainfed, good market access’. A
LUT can be current or proposed. More recent ‘versions’ of the Framework idea (FAO, 1993)
deal more explicitly with the social context of land use and planning.
If the Framework is just that, a framework, the Guidelines series offer some practical advice
on content as regards a particular LUT. The most relevant Guidelines for WH suitability
assessment are for rainfed agriculture (FAO, 1983), and irrigated agriculture (FAO, 1985). WH
is nebulous in that it has attributes of both, and in varying proportions as a function of the type of
WH system being evaluated. Thus there is a need for guidelines specifically for WH for those
involved in natural resource management in semi-arid lands, and in a format which accomodates
the range of WH options available, applications possible, and actors involved. An EC review of
SWC/WH (Catizonne, 1995) recommends the development of a GIS and/or expert system that
can aid resource professionals considering WH. Quantitative land evaluation is currently tending
towards a linkage of GIS, expert systems and crop simulation models (Wopereis et al., 1994). An
example of such an approach specifically for WH, using the FAO Framework as a conceptual
structure but operationalized within a GIS and Expert System, is outlined as Figure 1, in the form
of an iterative decision tree developed as this research.
Land evaluation, expert systems and GIS for WH assessment
A tool within which to operationalize an assessment methodology for WH would be the
Automated Land Evaluation System [ALES] (Rossiter et al., 1995). ALES is a useful practical
planning tool because it is based on the FAO Framework, which has been widely used/emulated
by natural resource professionals, but is structured as an expert system [ES], facilitating the
involvement of ‘local experts’ - i.e., land users - in the information generation process.
Furthermore, it can be outputted in a map format that is widely understood by interfacing with a
geographic information system [GIS].
GIS is a powerful tool which can be used in the field, to ensure relevance through client
participation; results can consequently be achieved quickly and at low cost by using GIS for
participatory mapping (Holme and Tagg, 1996; Hutchinson and Toledano, 1995). Further-more,
certain GIS’s such as Idrisi (Eastman, 1995) can accomodate uncertainty in decision making,
using fuzzy sets and Baysian probabilities to represent and propagate uncertain facts and beliefs,
which can be taken advantage of by ‘tagging’ data generated by local experts with a degree of
confidence provided by the expert. One should note, however, Burrough and Frank’s (1995)
caveat that GIS’s implicitly embody a cultural concept of space; as such GIS can ‘impose’ a way
of viewing the landscape onto the local expert. Nevertheless, by using an ES in WH evaluation
the gap between outside and local experts, probably the primary cause of the failure of WH to
live up to its potential, can hopefully be diminished.
Expert Systems purport to emulate the human reasoning process (Chidely et al., 1993). By
using an ES one hopes to elicit the heuristics [rules of thumb] local experts use to arrive at
decisions, in this case regarding land use. This information is of great value for understanding the
society in which the expert operates, both for potential application in situ and for ascertaining the
validity of exporting expert knowledge to other semi-arid areas.
179
Land Qualities [LQ] on the supply side. LQ’s are complex attributes of land such as ‘ability to
supply water’, which is a composite of constituent Land Characteristics [LC], which are
measurable properties of the land such as soil depth. The other attractive aspect of the
Framework is the implicit recognition of socio-economic factors in the planning process; the
definition of a LUT could be, for example, ‘low input maize, rainfed, good market access’. A
LUT can be current or proposed. More recent ‘versions’ of the Framework idea (FAO, 1993)
deal more explicitly with the social context of land use and planning.
If the Framework is just that, a framework, the Guidelines series offer some practical advice
on content as regards a particular LUT. The most relevant Guidelines for WH suitability
assessment are for rainfed agriculture (FAO, 1983), and irrigated agriculture (FAO, 1985). WH
is nebulous in that it has attributes of both, and in varying proportions as a function of the type of
WH system being evaluated. Thus there is a need for guidelines specifically for WH for those
involved in natural resource management in semi-arid lands, and in a format which accomodates
the range of WH options available, applications possible, and actors involved. An EC review of
SWC/WH (Catizonne, 1995) recommends the development of a GIS and/or expert system that
can aid resource professionals considering WH. Quantitative land evaluation is currently tending
towards a linkage of GIS, expert systems and crop simulation models (Wopereis et al., 1994). An
example of such an approach specifically for WH, using the FAO Framework as a conceptual
structure but operationalized within a GIS and Expert System, is outlined as Figure 1, in the form
of an iterative decision tree developed as this research.
Land evaluation, expert systems and GIS for WH assessment
A tool within which to operationalize an assessment methodology for WH would be the
Automated Land Evaluation System [ALES] (Rossiter et al., 1995). ALES is a useful practical
planning tool because it is based on the FAO Framework, which has been widely used/emulated
by natural resource professionals, but is structured as an expert system [ES], facilitating the
involvement of ‘local experts’ - i.e., land users - in the information generation process.
Furthermore, it can be outputted in a map format that is widely understood by interfacing with a
geographic information system [GIS].
GIS is a powerful tool which can be used in the field, to ensure relevance through client
participation; results can consequently be achieved quickly and at low cost by using GIS for
participatory mapping (Holme and Tagg, 1996; Hutchinson and Toledano, 1995). Further-more,
certain GIS’s such as Idrisi (Eastman, 1995) can accomodate uncertainty in decision making,
using fuzzy sets and Baysian probabilities to represent and propagate uncertain facts and beliefs,
which can be taken advantage of by ‘tagging’ data generated by local experts with a degree of
confidence provided by the expert. One should note, however, Burrough and Frank’s (1995)
caveat that GIS’s implicitly embody a cultural concept of space; as such GIS can ‘impose’ a way
of viewing the landscape onto the local expert. Nevertheless, by using an ES in WH evaluation
the gap between outside and local experts, probably the primary cause of the failure of WH to
live up to its potential, can hopefully be diminished.
Expert Systems purport to emulate the human reasoning process (Chidely et al., 1993). By
using an ES one hopes to elicit the heuristics [rules of thumb] local experts use to arrive at
decisions, in this case regarding land use. This information is of great value for understanding the
society in which the expert operates, both for potential application in situ and for ascertaining the
validity of exporting expert knowledge to other semi-arid areas.