Automated modelling of digital elevation models for predictive ecosystem mapping in GIS
Bibliographic record
Abstract
This thesis is an exploratory analysis of automated mapping protocols that can be used to support Terrestrial Ecosystem Mapping and Predictive Ecosystem Mapping in British Columbia. This thesis employs neighbourhood analysis of elevation and its derivatives to discriminate the bioterrain elements defined by Terrestrial Ecosystem Mapping standards. In achieving these standards, discrimination beyond the basic topographic forms presented in current research is explored. The method developed strives to be - easily implemented by mapping projects employing standard GIS software ; flexible so that the extracted topographic forms can be tailored to varying project objectives ; compatible with the hierarchical procedure employed in Terrestrial Ecosystem Mapping ; efficient and accurate in that the process is advantageous over manual mapping methods. The effect of data quality is addressed through an assessment of DEM data interpolation techniques and classification accuracy. Random and systematic artifacts of the DEM that influence the quality of the derivatives are explored. The issue of scale-dependent shape is addressed by the constraints of objective-based mapping in which a map scale is specified and the most basic shape elements are aggregated into contiguous classes by a roving neighbourhood window. The results indicate that basic topographic elements are mapable from relief as well as first and second order elevation derivatives. These results give preliminary accuracy of 80% based on the three classes tested. The procedure requires decisions at every step, but it is felt that this complements the traditional mapping process in that it is hierarchical, and requires a synthesis of extensive knowledge of vegetation and landscape across many scales. Key Words: elevation, digital elevation model, topography, slope, aspect, curvature, Terrestrial Ecosystem Mapping, Predictive Ecosystem Mapping, scale, random, systematic error.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".