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Experiences with Applied DSM: Protocol, Availability, Quality and Capacity Building

10.9 Conclusions

A similar staged process to that described above is recommended to any DSM practitioner needing to build support for, and comfort with, adoption of new DSM technologies. Potential DSM practitioners are encouraged to apply and evaluate all viable mapping alternatives, to select the most appropriate method and to then

10 Experiences with Applied DSM 133 demonstrate its capability to support full scale operational mapping in their locality at a scale that is most relevant to their needs.

Based on the author’s experiences as described above, the single strongest im-pediment to widespread application of new DSM methods may well be our own hesitancy to believe in ourselves and to just “do it”. You will not know what is possible until you try to produce maps for your own areas using data that are avail-able to you. You may well be surprised at what can be achieved using existing data sources and existing methodologies. If we wait until perfection is possible, data are easy to acquire and models are easy to build and apply, we could end up waiting a long time. For the moment, a lot is possible with just a little effort and a little optimism.

It is hoped that this chapter will encourage individuals with an interest in ap-plying new predictive mapping techniques to embrace change and to try to create useful, operational maps for large areas in their own regions of interest.

Acknowledgments The comments and analysis contained in this chapter are based largely on experience developing and applying predictive ecological mapping techniques in the province of BC, Canada. This predictive ecological mapping work was initiated and conceptualized by Dr. David Moon, managed by Nona Philips and financially supported by Tolko Industries Ltd., West Fraser Mills Ltd, and several other companies that are now assimilated into these two main forest industry clients. Ray Coup´e provided all of the local ecological expertise that was used in developing methods and rules.

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Chapter 11

Towards a Data Quality Management

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