Object-based classification of very high resolution panchromatic images for evaluating recent change in the structure of patterned peatlands
Bibliographic record
Abstract
AbstractAn airphoto survey carried out at the La Grande hydroelectrical complex (James Bay, Quebec) revealed numerous signs of degradation of patterned fens, with a decrease in terrestrial vegetation and increase in ponds, a process known as aqualysis. The principal goal of this study is to provide information on the present and past (the last 50 years) state of patterned peatlands, associated with the pattern of the aquatic and terrestrial compartments, and to evaluate their changing cover using remote sensing techniques. In this paper, we present a semi-automated, object-based method for QuickBird panchromatic image classification. The method emphasizes contextual information. We have also integrated texture images calculated in advance as supplementary data layers in the process of segmentation and classification. The validation of QuickBird image classification shows that the proposed method can delineate peatlands with 95% producer's accuracy and 88% user's accuracy. The overall classification accuracy in the peatlands is 81%. The same robust technique was applied to the aerial photographs taken in 1957. The classification of QuickBird images and aerial photographs was used to assess the structural development of patterned peatlands in the La Grande 3 sector over the last 50 years. The analysis shows an increase in aquatic areas for only two out of seven studied peatlands. To confirm our hypothesis of active aqualysis, additional image analysis is required from other areas of the La Grande hydroelectrical complex where the aqualysis process is more pronounced.Un inventaire aérien préliminaire effectué dans l'ensemble du complexe hydroélectrique La Grande (Baie de James, Québec) montre de nombreux signes de dégradation du compartiment terrestre (végétal) des fens structurés et sa transformation en compartiment aquatique. Ce processus de transformation des tourbières est appelé aqualyse. L'objectif principal de cette étude est de fournir information sur l'état actuel et récent (les dernières 50 ans) de tourbières structurées, concernant leurs compartiments aquatiques et terrestres, et d'évaluer le changement en utilisant la télédétection. Dans cet article, nous présentons une méthode basée objet semi-automatique de classification d'image panchromatique QuickBird. L'accent est mis sur l'information contextuelle. Nous avons aussi intégré des images texturales, calculées d'avance, comme information supplémentaire dans le processus de segmentation et de classification. La validation de la classification de l'image QuickBird montre que la méthode proposée peut détecter les milieux tourbeux avec une précision du producteur de 95 % et une précision de l'usager de 88 %. L'exactitude de classification globale dans les tourbières est de 81 %. Le même schéma robuste de classification a été appliqué à la mosaïque de photos aériennes de 1957. La classification d'image QuickBird et de photos aériennes nous a servi à évaluer l'évolution de la structure spatiale de tourbières structurées de la région La Grande-3 les 50 dernières années. L'analyse montre clairement une augmentation de la surface des classes aquatiques seulement chez deux des sept tourbières étudiées. Afin de confirmer notre hypothèse d'aqualyse active, nous devons aussi analyser les images d'autres régions du complexe hydroélectrique La Grande où l'aqualyse semble plus prononcée.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".