Shrub characterization using terrestrial laser scanning and implications for airborne LiDAR assessment
Bibliographic record
Abstract
AbstractSagebrush-steppe ecosystems across the United States Intermountain West are experiencing major structural and functional changes. Scientists and managers need effective technologies to understand such dynamic changes across broad spatial scales. We tested the capacity of terrestrial laser scanning (TLS) to automatically determine structural information of individual shrubs (principally Artemisia tridentata) and shrub canopies in eastern Washington, USA. Because current airborne LiDAR systems have technological constraints that may limit their utility in shrub-dominated ecosystems, we used the TLS data both in their basic form and to simulate high resolution discrete-return airborne LiDAR data with sample densities of 4 and 16 points m−2. Through spatial wavelet analysis we automatically detected the locations of up to 78% of all individual shrubs identified in the field and up to 88% of shrubs with a crown diameter > 1.5 m. Shrub height and canopy cover derived from TLS data were significantly correlated with field measurements (respectively, r2 = 0.94 and 0.51, p < 0.001, α = 0.05). Automated detection of individual shrub crown area using the high resolution simulated airborne LiDAR dataset was also significantly correlated with field measurements (r2 = 0.47, p ≤ 0.01). Our results indicate that TLS data are useful for automatic quantification of shrub structure and provide a glimpse to the utility of the next generation of small-footprint airborne LiDAR instruments in quantifying shrub biophysical parameters across broad areas.Les écosystèmes de steppe à armoise sur l'ensemble de la région Intramontagneuse ouest des États-Unis sont soumis à des changements structurels et fonctionnels importants. Les scientifiques et les gestionnaires ont ainsi besoin de technologies efficaces pour comprendre de tels changements dynamiques à travers des échelles spatiales variées. On a testé le potentiel des données SLT (scanner laser terrestre) pour déterminer automatiquement l'information structurelle des arbustes individuels (principalement Artemisia tridentata) et des couverts d'arbustes dans l'est de l'état de Washington, aux États-Unis. Étant donné que les systèmes LiDAR aéroporté actuels présentent des contraintes technologiques qui peuvent limiter leur utilité dans les écosystèmes dominés par des arbustes, on a utilisé des données SLT dans leur forme initiale de même que pour simuler des données LiDAR aéroporté haute résolution à retour discret avec des densités d'échantillonnage de 4 et de 16 points m−2. Par le biais d'une analyse spatiale en ondelettes, on a détecté automatiquement la localisation de plus de 78% de tous les arbustes individuels identifiés sur le terrain et de plus de 88% des arbustes avec un diamètre de couronne > 1,5 m. La hauteur des arbustes et le couvert dérivés des données SLT étaient corrélés significativement avec les mesures au sol (r2 = 0,94 et 0,51, p < 0,001, α = 0,05 respectivement). La détection automatisée de la surface de la couronne des arbustes individuels utilisant l'ensemble des données simulées LiDAR aéroporté haute résolution était également significativement corrélée avec les mesures au sol (r2 = 0,47, p ≤ 0,01). Nos résultats montrent que les données SLT sont utiles pour la quantification automatique de la structure des arbustes et que celles-ci donnent un aperçu du potentiel de la prochaine génération d'instruments LiDAR aéroporté à faible empreinte pour la quantification des paramètres biophysiques des arbustes sur de grands espaces.[Traduit par la Rédaction] AcknowledgementsThe total station was generously loaned by A. Abdel-Rahim. We thank J. Cline, P. Shilpakar, A. Biholar, and N. Turinski for field and laboratory assistance. S. Bunting, A. Hudak, S. Martinuzzi, R. Robberecht, A. Smith, J. Hicke, K. Vierling, M. Wulder, H. Zhou, and two anonymous reviewers provided helpful discussions and comments that strengthened earlier versions of the manuscript. Partial funding for this project was provided by the University of Idaho Harold Heady Professorship in Rangeland Ecology, and Bureau of Land Management grant L08AC14585.
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 | 0.000 | 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".