用于细粒度植被群落分类的校准树神经融合

中文摘要 AI 辅助

准确的植被群落分类对于生态监测、栖息地评估和基于证据的环境管理至关重要。现有研究常依赖独立的树集成或通用神经网络，存在诸多局限。本研究提出校准生态树融合网络升级版（Calibrated EcoTreeFuseNet-Plus），结合多种方法。提取相关变量值并进行质量控制后，在测试集上该模型取得了一定准确率等指标。校准降低了预期校准误差，五种子评估表明性能稳定，结果展示了小样本、细粒度生态分类中可靠的判别校准权衡。

英文摘要

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.

发表机构

• School of Information Technology and Engineering, Sydney Metropolitan Institute of Technology (SydneyMet)（悉尼都会理工学院信息技术与工程学院）
• Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology（拉杰沙希工程技术大学电气与电子工程系）
• School of Computing, Mathematics and Engineering, Charles Sturt University（查尔斯·斯特尔特大学计算、数学与工程学院）

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