A COMPARATIVE ANALYSIS OF PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING RANDOM FOREST MODEL
Bibliographic record
Abstract
Abstract. Providing an accurate above-ground biomass (AGB) map is of paramount importance for carbon stock and climate change monitoring. The main objective of this study is to compare the performance of pixel-based and object-based approaches for AGB estimation of temperate forests in north-eastern of New York State. Second, the capabilities of optical, SAR, and optical + SAR data were investigated. To achieve the goals, the random forest (RF) regression algorithm was used to model and predict the AGB values. Optical (i.e. Landsat 5TM, Landsat 8 OLI, and Sentinel-2), synthetic aperture radar (SAR) (Sentinel-1 and global phased array type L-band SAR (PALSAR/PALSAR-2)), and their integration have been used to estimate the AGB. It is worth mentioning that the airborne light detection and ranging (LiDAR) AGB raster has been used as a reference data for training/testing purposes. The results demonstrate that the OBIA approach enhanced the RMSE of AGB estimation about 5.32 Mg/ha, 8.9 Mg/ha, and 5.29 Mg/ha for optical, SAR, and optical + SAR data, respectively. Moreover, optical + SAR data with the RMSE of 42.63 Mg/ha and R2 of 0.72 for pixel-based and RMSE of 37.31 Mg/ha and R2 of 0.77 for object-based approach provided the best results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".