Vegetation and Soil Carbon under Various Forest Management Types: Case of Karang Sidemen Community Forest in Lombok, Eastern Indonesia
Bibliographic record
Abstract
The condition of community-managed forest areas varies according to biophysics characteristics and management activities. This study aims to investigate the condition of vegetation and soil carbon stocks of various types of forest management by the community in Karang Sidemen Village, Lombok, Indonesia. In the study area, it was found 4 types of landuse management, namely; dense forest-like vegetation (Tp1), moderate vegetation with intensive (Tp2) and less intensive (Tp3) under-stand cultivation, and sparse vegetation resembling dryland agriculture (Tp4). Vegetation condition was analyzed based on satellite derived NDVI index and field observation. Sentinel satellite images for 2015 and 2019, with a resolution of 10x10 m was used. Field data collection was carried out in August 2019. It was made 5 sample plots of 20x20 m for each management type. Vegetation data with diameter (D) ≥ 20 cm, 10 cm ≤D< 20 cm, 2 cm ≤D< 10 cm and D< 2 cm were collected from plots of 20x20 m, subplots 10x10 m, 5x5 m and 2x2 m, respectively. Soil samples were taken diagonally on a 20x20 m plot, at a depth of 0-5 cm, 5-10 cm, 10-20 cm and 20-30 cm. The results showed that the NDVI derived vegetation index for 2015 and 2019 images showed different patterns for the four types of management. The number of species for Tp1, Tp2, Tp3 and Tp4 were 9, 15, 9 and 8 species, respectively. The dominant species are generally from groups of plants providing economic benefits such as avocado (Persea americana), candlenut (Aleurites moluccana), cocoa (Theobroma cacao), coffee (Coffea canephora), jackfruit (Artocarpus heterophyllus), mangosteen (Garcinia mangostana) and guava (Psidium guajava). Soil carbon stocks of the four types of management at a depth of 0-5 cm, 5-10 cm, 10-20 cm and 20-30 cm were18.61-21.04 tons C/ha, 16.56-20.80 tons C/ha, 29.66-34.48 tons C/ha and 27.54 - 33.66 tons C/ha, respectively. The soil carbon stock of denser vegetation is higher than that of medium and sparse vegetation. Therefore, forest management with the community needs to maintain forest-like vegetation.
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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 | 0.000 |
| 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) | 0.001 | 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".