Producing information from airborne LiDAR data for peatland forest management
Bibliographic record
Abstract
In Finland, peatland forests are significant for wood supply, although simultaneously, they are also important for biodiversity, carbon sequestration, water conservation, and recreation. In the 1960s and 1970s, peatland forests in Finland were extensively drained to increase tree growth and fulfil the needs of the forest sector. However, this extensive drainage has negatively impacted on the biodiversity of peatland ecosystems, and substantially increased nutrient and sediment emissions to lakes and rivers resulting in eutrophication, turbidity, and brownification of these water bodies. This dissertation presents a number of approaches to move peatland forest management in a more environmentally sound direction, which may increase the general acceptability of peatland forestry. Airborne LiDAR (Light detection and ranging, i.e., laser scanning) derived 3D point cloud provides useful data, for example, to estimate forest biomass, to identify low-productive peatland forests, to model overland water flows, and to identify wet areas. The strength of airborne LiDAR is the ability of laser pulses to pass through tree canopies and obtain accurate observations from the ground level. The information derived from airborne LiDAR can enhance the planning of peatland forest management, as much of the planning can be done remotely, and supplementary field work can be implemented in areas of strategic need. This study presented the novel idea of applying local binary patterns for the prediction of terrain trafficability, which should be considered in further studies and practice. The moisture index derived from the local neighborhood can reveal the small-scale variations in terrain moisture. This study also presented the novel idea to create spatial models to identify suitable locations for water protection structures, which may help forest managers to plan water protection of ditch network maintenance or peatland restoration operations. Overall, the utilization of airborne LiDAR-derived information for the development of peatland forestry practices shows great potential.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 0.001 |
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".