A review of PlanetScope CubeSats for forest monitoring
Bibliographic record
Abstract
Satellite remote sensing has been a cornerstone of forest monitoring, enabling the observation of extensive areas at regular intervals. In 2014, Planet Labs introduced PlanetScope, a constellation of Earth observation CubeSats capable of delivering near-daily optical data at a 3 m resolution across the globe. The unique combination of high temporal and spatial resolution, along with comprehensive coverage, positions PlanetScope as a valuable tool for a wide range of forestry applications. This systematic literature review explores the diverse applications of PlanetScope in forestry research, detailing the ecosystems studied, the spatial and temporal characteristics of the datasets, analytical methods employed, and integration with other remote sensing technologies. We comment on potential strengths and weaknesses of the available datasets, compare models developed using PlanetScope with those derived from other remote sensing data sources, identify key areas for future research, and finally provide recommendations and considerations for prospective users of PlanetScope data. • Comprehensive review of how PlanetScope data is used in forest monitoring. • The spatiotemporal characteristics of PlanetScope datasets are assessed. • Methods for preprocessing and analyzing PlanetScope data are evaluated. • Future research directions and suggestions for using PlanetScope data are provided.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".