Change detection and landscape structure mapping using remote sensing
Bibliographic record
Abstract
Satellite remote sensing has long held promise as a powerful method of detecting forest canopy changes and mapping landscape structure over vast, often multi-jurisdictional forest areas. Landsat Thematic Mapper (TM) spectral response, for example, can be related accurately to changes in physiology and cover at a range of small to intermediate mapping scales. These data have been available continuously for almost 20 years; many areas have earlier satellite image archives stretching back to the 1970s. When considering spatially-explicit changes to landscapescaused by natural and human disturbancesover this time period, digital, synoptic, and repeatable satellite remotely-sensed data are emerging as the observational media of choice that forest managers must possess and use wisely. In this paper, successful use of satellite remote sensing in two of Canadas Model Forests is described. First, in the Fundy Model Forest in southeastern New Brunswick, a 15-year TM image sequence was used to detect area changes associated with different harvesting and silvicultural practices. Second, in the Foothills Model Forest in west-central Alberta, grizzly bear habitat maps have been created from multi-scene TM land cover mosaics. These map products constitute critical information on landscape change and configuration required to answer key management questions. The paper concludes with a prognosis for the future role of satellite remote sensing in sustainable forest management as data quality continues to improve (i.e., increasing spatial, spectral, temporal, and radiometric resolutions), and methods are brought into the purview of forest managers and practitioners. Key words: remote sensing, landscape change, forest fragmentation, human disturbance, management information needs, habitat
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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".