Quantifying land use effects on forested riparian buffer vegetation structure using LiDAR data
Bibliographic record
Abstract
Quantifying variability of forested riparian buffer (FRB) vegetation structure with variation in adjacent land use supports an understanding of how anthropogenic disturbance influences the ability of riparian systems to perform ecosystem services. However, quantifying FRB structure over large regions is a challenge and requires efficient data collection and processing methods that integrate conventional in situ vegetation sampling with remote sensing data. This study uses automated algorithms to process airborne light detection and ranging (LiDAR) data for mapping of riparian vegetation height, canopy cover and corridor width along 5,900 transects using methods validated in 80 mensuration plots in central Pennsylvania, USA. The key objective of this study was to use airborne LiDAR data to quantify differences in edge vs interior vegetation structure as influenced by buffer width and adjacent land use type, continuously throughout a watershed. Riparian vegetation height, canopy cover and buffer width were estimated along FRB transects adjacent to developed (residential/commercial and agricultural) and undeveloped (grassland) land use types and compared to reference transects within larger forested areas and thus without an edge. On average, buffers adjacent to developed land use types were narrower than those adjacent to natural, undeveloped land use types. Approximately 50% of streams in the watershed had FRB corridors ≤30 m wide. Only 23% of streams had a corridor width ≥200 m, the width recommended to support key ecosystem services. Undeveloped land use types contained taller riparian vegetation and wider corridors, whereas developed land use types contained shorter riparian vegetation and narrow FRB corridors. Edge effects also affected vegetation structure. Vegetation height was 5–8 m shorter at the interface between the FRB and the adjacent land use (the matrix) than in the naturally occurring stream edge or in the corridor interior. Canopy cover was not influenced by adjacent land use type or width. This study demonstrates that airborne LiDAR data can be used to accurately map riparian buffer vegetation width, height and canopy cover to support ecological based management of riparian corridors over wide areas.
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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 | 0.000 | 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 | 0.000 | 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 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".