Calibrated Tree-Neural Fusion for Fine-Grained Vegetation Community Classification
2026-07-27 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a new computer method, Calibrated EcoTreeFuseNet-Plus, to better identify different plant communities using data from LiDAR and hyperspectral sensors. Their approach combines tree-based models and neural networks with a calibration step to improve prediction accuracy and reliability, especially when classes are very similar. They tested the model on 29 vegetation types and showed it performed well and consistently across different data splits. The calibration step notably improved the model's confidence without changing its predictions. This method helps with accurate ecological classification even when sample sizes are small and classes overlap.
Vegetation classificationLiDARHyperspectral indicesNeural networksTree ensemblesProbability calibrationMeta-learningFine-grained classificationModel stabilityExpected calibration error
Authors
Dristi Datta, Md Khalid Hasan Sakib, Manoranjan Paul
Abstract
Accurate vegetation-community classification is essential for ecological monitoring, habitat assessment, and evidence-based environmental management in heterogeneous landscapes. Existing studies often rely on standalone tree ensembles or generic neural networks, although fine-grained ecological classes frequently exhibit overlapping spectral, topographic, and structural characteristics. Many frameworks also provide limited protection against stacking leakage, insufficient probability calibration, weak minority-class evaluation, and little evidence of stability across repeated data splits. To address these limitations, this study proposes Calibrated EcoTreeFuseNet-Plus, a tree-neural probability-fusion framework that combines out-of-fold tree probabilities, EcoFuseNet-V2 outputs, validation-selected meta-learning, and post-hoc temperature scaling. Raster values from six LiDAR-derived terrain and canopy variables and two hyperspectral vegetation indices were extracted at coordinate-based reference locations. Quality control removed 26 samples with missing elevation and one sample with non-finite NDWI, producing 1,833 complete records across 29 vegetation and non-vegetation classes. On the held-out test set, the proposed model achieved an accuracy of 0.8000, a macro F1-score of 0.7768, a balanced accuracy of 0.7903, and an MCC of 0.7903. Calibration reduced the expected calibration error from 0.3866 to 0.0651 without changing class predictions. Five-seed evaluation yielded a macro F1-score of 0.7717 +/- 0.0112, indicating stable performance across repeated splits. The results demonstrate a reliable discrimination-calibration trade-off for small-sample, fine-grained ecological classification.