The Digital Forest: Mapping a Decade of Knowledge on Technological Applications for Forest Ecosystems
Bibliographic record
Abstract
Abstract Forest ecosystem resilience is of considerable interest worldwide, particularly given the climate crisis, biodiversity loss, and recent instances of zoonotic diseases linked to deforestation and forest loss. Novel, digital‐based technologies are also increasingly ubiquitous. We provide a more comprehensive understanding of how these new technologies are being used for forest management in different sectors and contexts, and discuss potential implications and future research needs for forestry researchers, managers, and policymakers. We carried out a literature database search and scoping review to collect peer‐reviewed articles from 2010 to 2020, and developed a forest‐technology classification to identify hardware and/or software technologies and techniques, methodology used, forest management application(s), spatial and temporal context, subsequent challenges and limitations, and opportunities. A qualitative analysis revealed a strong emphasis on remote sensing‐based innovations for forest monitoring, planning, and management, where machine‐learning techniques also play an important role in data collection, processing, and analysis. Data fusion approaches are also becoming more common, enabled by open‐source data sets and data sharing practices. More emerging technologies and applications include virtual/augmented environments for understanding human‐nature relationships and behavior patterns, automated workflows for forestry operations, and urban green infrastructure mapping and ecosystem services assessments via social media and mobile tracking applications. The continued adoption of digital‐based tools will likely bring about new research questions about forest ecosystems as dynamic social, ecological, and technological landscapes, and future work should more closely examine how forestry researchers, managers, and stakeholders can anticipate and adapt to both environmental and technological uncertainty change in a forest‐ecosystem context.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".