A Historical Perspective on the Use of GIS and Remote Sensing in Natural Resource Management, as Viewed through Papers Published in North American Forestry Journals from 1976 to 2005
Bibliographic record
Abstract
Since the introduction of geographic information systems (GIS) to natural resource management in the 1970s, there has been a logical increase in the use of GIS by natural resource management organizations. This article assesses the literature in applied North American forestry journals, which are read mainly by forest practitioners, and illustrates the trends of technological adoption by natural resource management organizations. We conclude that the diversity of GIS technology use in forestry is increasing and evolving to a high and complex level. While small-scale (local) and site-specific natural resource applications predominate the use of GIS in this literature, landscape applications have gained more attention and importance in recent years, mainly in the western and north-central United States. Although several of the journals we reviewed emphasize the practical nature and value of information, few papers were located that illustrate GIS implementation in natural resource organizations or advances in GIS technology. The professions associated with natural resource management have traditionally been adopters of technology (rather than developers), but, since GIS is so closely tied to the management and assessment of landscapes, it is possible that the issues that arise in natural resource management have had a significant impact on the development of GIS analytical techniques. We suggest that surveys be performed frequently (every five years) so that the natural resource management field can stay current with changes in technology and in employer expectations. This assessment has pointed out the trends and gaps in the forestry-related literature and suggests opportunities for future dissemination of information. Research papers lead the widespread adoption of technology by a decade or more; thus, through this work, one can envision what might become commonplace a decade from now. Those unaware of the relatively short history of the technology and how it has evolved may gain some understanding from this brief history of the use of GIS in natural resource management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.032 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".