Remote Sensing Technologies for Enhancing Forest Inventories: A Review
Bibliographic record
Abstract
Forest inventory and management requirements are changing rapidly in the context of an increasingly complex set of economic, environmental, and social policy objectives.Advanced remote sensing technologies provide data to assist in addressing these escalating information needs and to support the subsequent development and parameterization of models for an even broader range of information needs.This special issue contains papers that use a variety of remote sensing technologies to derive forest inventory or inventory-related information.Herein, we review the potential of 4 advanced remote sensing technologies, which we posit as having the greatest potential to influence forest inventories designed to characterize forest resource information for strategic, tactical, and operational planning: airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), and high spatial resolution (HSR)/very high spatial resolution (VHSR) satellite optical imagery.ALS, in particular, has proven to be a transformative technology, offering forest inventories the required spatial detail and accuracy across large areas and a diverse range of forest types.The coupling of DAP with ALS technologies will likely have the greatest impact on forest inventory practices in the next decade, providing capacity for a broader suite of attributes, as well as for monitoring growth over time.Résumé.Les exigences en matière d'inventaire forestier et de gestion évoluent rapidement dans le contexte d'un ensemble d'objectifs de politique économique, environnementale et sociale de plus en plus complexe.Les technologies de télédétection avancées fournissent des données pour aider à répondre à ces besoins croissants d'information et pour soutenir les futurs développements et le paramétrage de modèles pour une gamme encore plus large de besoins en information.Ce numéro spécial contient des articles qui utilisent une variété de technologies de télédétection pour obtenir des informations sur l'inventaire forestier ou liées à l'inventaire forestier.Ici, nous passons en revue le potentiel de 4 technologies de télédétection avancées que nous estimons comme ayant le plus grand potentiel d'influencer les inventaires forestiers conc ¸us pour caractériser l'information des ressources forestières pour la planification stratégique, tactique et opérationnelle: le balayage laser aéroporté «airborne laser scanning (ALS)», le balayage laser terrestre (TLS), la photogrammétrie aérienne numérique (DAP), et l'imagerie optique satellite à haute résolution spatiale (HSR) ou à très haute résolution spatiale (VHSR).Le ALS, en particulier, s'est révélé être une technologie transformatrice, offrant aux inventaires forestiers des détails spatiaux ainsi que la précision nécessaires sur de grandes surfaces et un large éventail de types de forêts.Le couplage de la DAP avec les technologies du ALS aura probablement le plus grand impact sur les pratiques d'inventaire forestier dans la prochaine décennie en fournissant la capacité d'obtenir un ensemble plus large d'attributs, ainsi que la surveillance de la croissance au fil du temps.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".