Classification of Landscape Types Based on Land Cover, Successional Stages and Plant Functional Groups in a Species-Rich Forest in Hainan Island, China
Bibliographic record
Abstract
Large-scale identification of landscape types in species-rich forest ecosystems is a challenge to landscape designers and forest ecologists. With a systematic grid-sample-plot investigation and landscape-attributes extraction of SPOT-5 imagery in a tropical forest region in Hainan Island, China, we developed a landscape classification system of land cover, successional stages, and dominant plant functional groups in species-rich forest ecosystems. We classified the study landscape into eight land cover types, four successional stages, and six functional patch types, with accuracies at ≥ 78%. The patches dominated by the pioneer functional groups were mainly distributed in areas of early recovery stages on sunny slopes at elevations < 850 m, while the climax functional groups had more occupancies in the late recovery stages on shaded slopes at elevations > 850 m. The slope gradient had no significant influence on the patch distribution patterns in the study region. Our results show that species-rich forest landscapes can be classified into patch types of different dominant functional groups and successional stages through remote sensing in conjunction with ground survey and GIS.
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 | 0.000 | 0.000 |
| 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".