提出连续树高变化回归任务，用不确定性标注提升森林碳汇监测精度
Uncertainty-aware tree height change regression
- 构建3米分辨率的连续树高变化数据集，含空间化不确定性标注
- 在10598平方公里区域验证，支持对植被动态的精细量化分析
- 面向遥感与生态建模研究者，助力森林碳汇长期监测
监测树冠高度变化对理解碳汇和森林动态至关重要。遥感技术可实现大范围、一致性的观测，日益与地理空间基础模型（GFMs）结合。然而现有方法与数据集将问题简化为二值变化检测，忽略了植被变化的连续性及标签固有的不确定性。本文提出树冠高度变化（CHC）数据集，覆盖西班牙北部与西部10598平方公里区域，提供3米分辨率的连续树高差及对应的时空不确定性信息，并配套同期的PlanetScope卫星影像序列。基于此数据集，我们定义了不确定性感知的变化回归任务，提出相应评估指标与GFMs微调策略。进一步评估了前沿GFMs表现，揭示了连续树高变化估计的潜力方向与现存挑战。
原文摘要 · Abstract (English)
Monitoring canopy height change is essential for understanding carbon sinks and forest dynamics. Remote sensing enables consistent, large-scale observations of such changes, increasingly integrated with deep learning architectures such as Geospatial Foundation Models (GFMs). However, existing methods and datasets frame the problem as binary change detection, which overlooks both the continuous nature of change, especially for vegetation, and the inherent uncertainty in labels. We present the Canopy Height Change (CHC) dataset, providing 3 $\mathrm{m}$ resolution continuous canopy height differences and associated spatially resolved uncertainties across 10598 $\mathrm{km}^2$ of northern and western Spain. The dataset is paired with a co-located time series of PlanetScope satellite imagery. Based on the dataset, we introduce the task of uncertainty-aware change regression, associated metrics and strategies for fine-tuning GFMs. Furthermore, we evaluate state-of-the-art GFMs and highlight promising directions and remaining challenges for advancing continuous canopy height change estimation.
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