Tree counting of tropical tree plantations using the maximum probability spectral features of high-resolution satellite images and drones
Bibliographic record
Abstract
Information on tree plantation structures, such as tree type, density, and tree height, is essential for developing smart agriculture and plantation management strategies to support production estimation and investment, including biomass for carbon sequestration estimation. In this study, multisource remote sensing data from radar (Sentinel-1 C band), optical (Pléiades), and drones (multispectral drone) were used to support effective and cost-efficient sustainable tree plantation management in Siak Regency, Riau Province, Indonesia. Tree plantation maps were created using the difference backscatter VH and VV from Sentinel-1. Tree counting was then performed using Pléiades red, green, and blue visible bands and multispectral drone bands using a maximum a posteriori pixel-based classifier integrated with a filter function and statistical estimation. The validation of the tree map using manual measurements yielded accuracies ranging from approximately 79% to 97%. Tree heights were calculated from the difference between the Digital Surface Model (DSM) derived from drone data and the Digital Terrain Model (DTM) obtained from DEM Nasional (DEMNAS) data. Further improvements in the current map accuracy can be achieved using a combination of remote sensing and field measurements of tree structure inventories. • Tree plantations map, tree structure and tree height inventory produced using integrated SAR, optical imaging and UAV • Tree counting developed using The Maximum A Posteriori (MAP) classifier of high-resolution satellite image and drone • Tree height developed using drone DSM and DTM data
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".