Once you have the data about the forest...©

Success or failure of a mensuration support system lies not in having the latest technology, so much as in identifying the mind processes of the users who will use the results, and in providing relevent information in a form compatible with those processes.

However, many inventory measurements and systems fail in a number of important areas. Reports are often not helpful in considering the whole scope of the problem, concentrating on specific, narrow parts of the inventory and burrying the manager under a quicksand of figures. Alternatively, the reports are too simplistic, providing only an overview with a few meaningless figures and an understanding of the forest condition is not facilitated. In the worst case, the data may be manipulated or surrounding text may be vague enough to make the data appear to be supporting a fallacious argument.

In summary, a forest mensurationist must not simply provide more information. The goal is to supply better, more relevent and useful information for use in forest management decision making The data, along with an appropriate management system must enhance the forester's view of the whole problem, spatially and temporally. It must be responsive to the quantitative and qualitative objectives and user's needs, allowing consultant-like control and explanation. Finally, it must be understandable and user-friendly.

Suggestion to improve presentation and reports

• Use Tables to summarise the data collected, but ensure that the figures are meaningful and useful.
• Use figures, graphs and charts where this improves the understanding of the relationships found in the data. However, be careful not to cause confusion or erroneous interpretations by the use of inappropriate charts - 3 dimensional charts often cause problems in this area.
• Isolate relevent assumptions and report on the sensitivity of the results to changes in these assumptions
• Review all data for errors! Never accept data just because it has been processed by the computer or by somebody else... review the data and try to find any patterns that should (or should not) exist.
• Note and explain errors (accept or rejecting their information). Do not just disreguard figures that you think are in error. Justify their inclusion or exclusion. Attempt to explain how they occurred - do not simply explain them away by saying it was operator error.

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