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DataSheet1_Assessing the effects of burn severity on post-fire tree structures using the fused drone and mobile laser scanning point clouds.docx

2022· dataset· en· W6964944077 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldEnvironmental Science
TopicPlant Ecology and Soil Science
Canadian institutionsnot available
Fundersnot available
KeywordsCrown (dentistry)Laser scanningTree (set theory)Point cloudRange (aeronautics)Lidar

Abstract

fetched live from OpenAlex

<p>Wildfires burn heterogeneously across the landscape and create complex forest structures. Quantifying the structural changes in post-fire forests is critical to evaluating wildfire impacts and providing insights into burn severities. To advance the understanding of burn severities at a fine scale, forest structural attributes at the individual tree level need to be examined. The advent of drone laser scanning (DLS) and mobile laser scanning (MLS) has enabled the acquisition of high-density point clouds to resolve fine structures of individual trees. Yet, few studies have used DLS and MLS data jointly to examine their combined capability to describe post-fire forest structures. To assess the impacts of the 2017 Elephant Hill wildfire in British Columbia, Canada, we scanned trees that experienced a range of burn severities 2 years post-fire using both DLS and MLS. After fusing the DLS and MLS data, we reconstructed quantitative structure models to compute 14 post-fire biometric, volumetric, and crown attributes. At the individual tree level, our data suggest that smaller pre-fire trees tend to experience higher levels of crown scorch than larger pre-fire trees. Among trees with similar pre-fire sizes, those within mature stands (age class: > 50 years) had lower levels of crown scorch than those within young stands (age class: 15—50 years). Among pre-fire small- and medium-diameter trees, those experiencing high crown scorch had smaller post-fire crowns with unevenly distributed branches compared to unburned trees. In contrast, pre-fire large-diameter trees were more resistant to crown scorch. At the plot level, low-severity fires had minor effects, moderate-severity fires mostly decreased tree height, and high-severity fires significantly reduced diameter at breast height, height, and biomass. Our exploratory factor analyses further revealed that stands dominated by trees with large crown sizes and relatively wide spacing could burn less severely than stands characterized by regenerating trees with high crown fuel density and continuity. Overall, our results demonstrate that fused DLS-MLS point clouds can be effective in quantifying post-fire tree structures, which facilitates foresters to develop site-specific management plans. The findings imply that the management of crown fuel abundance and configuration could be vital to controlling burn severities.</p>

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.176
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1760.000

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.

Opus teacher head0.014
GPT teacher head0.251
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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