Characterizing Tree Species in Northern Boreal Forests Using Multiple-Endmember Spectral Mixture Analysis and Multi-Temporal Satellite Imagery
Bibliographic record
Abstract
Northern boreal forests are characterized by open stands whereby trees, understory background, and shadow are all significant components of the spectral response within a pixels' spatial footprint.To overcome this mixed pixel problem, accurate spectral characterization of these (endmember) components is necessary for spectral mixture analysis (SMA) to generate forest classifications at the species level.Obtaining these endmember spectra in the field, however, can be difficult or impossible.This study examined whether image endmember spectra can be identified using forest inventory information to derive dominant tree species classifications.This was tested using multiple-endmember SMA (MESMA) and single-and multi-date Landsat imagery of a forested area in the Northwest Territories, Canada.Image classifications (n ¼ 80) were generated based on 20 image-date combinations and four unmixing models.Accuracies of 80% and 82% were achieved for open and medium dense forest stands, respectively using multi-date imagery, which outperformed single-date imagery acquired at peak phenology.The overall accuracy is 72%; lower due to challenges in very open stands.The multi-date MESMA approach was robust for both compositionally pure and mixed stands.The approach merits further investigation, particularly within the context of the increasing availability of regional-scale satellite imagery enabling composite time-series and spectral-temporal image features. RÉSUMÉLes forêts bor eales nordiques sont caract eris ees par des peuplements ouverts o u les arbres, la sous-canop ee et l'ombre sont tous des composantes importantes de la r eponse spectrale dans l'empreinte spatiale d'un pixel.Pour surmonter ce probl eme de pixels mixtes, une caract erisation spectrale pr ecise de ces composantes est n ecessaire pour que l'analyze spectrale des m elanges (SMA) g en ere des classifications foresti eres au niveau des esp eces.Cependant, l'obtention des spectres de ces composantes sur le terrain peut être difficile, voire impossible.Cette etude a examin e si les spectres des diff erentes composantes de l'image peuvent être identifi es a l'aide des informations de l'inventaire forestier pour d eriver les classifications des esp eces d'arbres dominantes.Cela a et e test e a l'aide d'une SMA a multiples composantes (MESMA) et d'images Landsat d'une zone foresti ere dans les Territoires du Nord-Ouest, au Canada.Les classifications d'images (n ¼ 80) ont et e g en er ees a partir de 20 combinaisons image-date et de quatre mod eles de s eparation.Des pr ecisions de 80% et 82% ont et e obtenues pour les peuplements forestiers ouverts et moyennement denses, respectivement, en utilisant l'imagerie multitemporelle, qui a surpass e l'imagerie a date unique acquise a la ph enologie maximale.La pr ecision globale est de 72%; plus faible en raison des difficult es rencontr ees dans les peuplements tr es ouverts.L'approche MESMA multitemporelle etait robuste tant pour les peuplements purs que mixtes.Cette approche
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".