A COMPARISON OF RANDOM FOREST AND LIGHT GRADIENT BOOSTING MACHINE FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF LANDSAT, ALOS PALSAR, AND AIRBORNE LIDAR DATA
Bibliographic record
Abstract
Abstract. Sustainable forest management is a critical topic which contributes to ecological, economical, and socio-cultural aspect of the environment. Providing accurate AGB maps is of paramount importance for sustainable forest management, carbon accounting, and climate change monitoring. The main goal of this study was to leverage the potential of two machine learning algorithms for predicting AGB using optical and synthetic aperture radar (SAR) datasets. To achieve this goal random forest (RF) and light gradient boosting machine (LightGBM) models were deployed to predict AGB values in Huntington Wild Forest (HWF) in Essex County, NY using continuous forest inventory (CFI) plots. Both models were trained and evaluated based on airborne light detection and ranging (LiDAR) data, Landsat imagery, advanced land observing satellite (ALOS) phased array type L-band Synthetic Aperture Radar (PALSAR), and their combination. The integration of airborne LiDAR, optic, and SAR datasets provided the best results in terms of root mean square error (RMSE) and mean bias error (MBE). The RF model outperformed the LightGBM in all scenarios (LiDAR, Landsat 5, ALOS PALSAR, and their combination). The RF model was able to predict AGB values with the RMSE of 51.90 Mg/ha and MBE of −0.189 Mg/ha for the combination of LiDAR, optic, and SAR data, while LightGBM estimated the AGB values with the RMSE of 52.78 Mg/ha and MBE of −0.253 Mg/ha. LightGBM is more sensitive to noise and there are lots of hyperparameters that need to be tuned which highly affect its performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".