Monitoring the state of a large boreal forest region in eastern Canada through the use of multitemporal classified satellite imagery
Bibliographic record
Abstract
AbstractMultitemporal classification of Landsat imagery was used to measure and monitor the state of the forest over a large area (11.6 million ha) of boreal forest in eastern Canada using four criteria for a 20 year period (1985–2005). The Enhancement-Classification Method was used in this study. Forty-eight thematic classes based on Canada's National Forest Inventory were identified, then grouped into 13 indicators, and reorganized within four main criteria: (i) forest versus nonforest land cover, (ii) forest development stage, (iii) forest cover type, and (iv) forest cover density. Validation based on 2973 high-resolution geo-referenced digital aerial colour photos of the 2005 classified images showed an overall accuracy of the four criteria of 83%, 68%, 58%, and 62%, respectively. The change in each indicator between 1985 and 2005 could be summarized as: (i) a decrease in productive forest area of 0.4% (approx. 43 000 ha); (ii) a 4.6% decrease in mature stand area, with a concomitant increase in areas classified as vegetated (1.3%) and regenerated (3.4%); (iii) concentration of harvesting pressure on coniferous and mixed stands with respective reductions of 8.2% and 0.8%, due to their conversion to deciduous stands; and (iv) an increase in low-density stands (3.1%) and a decrease in high-density stands (8.3%). These results demonstrate that medium-resolution (30 m) remote sensing tools can be used both to monitor the state of the boreal forest and to produce key indicators, which were extracted from the multidate Landsat satellite imagery.Une classification multi temporelle d'images Landsat a été utilisée afin d'évaluer l'état de la forêt sur une période de 20 ans (1985–2005) dans une grande région (11,6 millions ha) de la forêt boréale de l'est du Canada à l'aide de quatre critères. La méthode de classification améliorée ECM a été utilisée pour cette étude. Quarante-huit classes thématiques basées sur l'inventaire forestier national ont été identifiées et regroupées en treize indicateurs selon quatre critères: (i) forêt non-forêt; (ii) stade de développement forestier; (iii) type de couvert forestier; (iv) densité du couvert forestier. La précision globale à l'aide de 2973 photos couleur à haute résolution pour les images classifiées en 2005 selon les quatre critères a été de 83%, 68%, 58% et 62% respectivement. Les changements observés pour chaque indicateur entre 1985 et 2005 peuvent être décrits comme suit: (i) une diminution de la superficie de forêt productive de 0,4% (~43000 ha); (ii) une diminution de 4,6% de la superficie des peuplements matures et une augmentation de la superficie des zones revégétées (1.3%) et régénérées (3,4%); (iii) une diminution de la superficie des peuplements résineux et mixtes respectivement de 8,2% et 0,8% et une augmentation du couvert feuillu due principalement à la succession végétale suite à la récolte forestière et (iv) une augmentation de la superficie des peuplements ouverts (3,1%) et une diminution des peuplements denses (8,3%). Ces résultats démontrent que les outils de la télédétection multi-spectrale à moyenne résolution (30 m) peuvent être utilisés à la fois pour faire un suivi opérationnel de l'état de la forêt et pour produire des indicateurs clés extraits à partir des images satellites classifiées. AcknowledgementsThis research was enabled through funding from the Economic Development Agency of Canada for the Regions of Quebec and the NSERC/UQAT/UQAM Industrial Chair in Sustainable Forest Management. The work was also supported by the Canadian Space Agency through the EOSD project led by the Canadian Forest Service. The authors are grateful to Philippe Villemaire, Stephen Côté, Luc Guindon, and Guy Simard from Laurentian Forestry Centre (LFC) for their scientific and technical contributions. The digital aerial photos were collected by the Géo-3D company. We also would like to thank Dr. P.Y. Bernier from the LFC and Dr. W.F.J. Parsons from the Centre for Forest Research for their valuable comments and for the English-language review of the manuscript.Notes 1Source: http://www.climat-quebec.qc.ca/
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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".