用DINOv3模型从卫星图像生成全球高精度树高图，提升森林碳储量评估精度。
CHMv2: Improvements in Global Canopy Height Mapping using DINOv3
- 基于DINOv3的深度估计模型，融合多样训练数据与自动化数据处理。
- 在热带、温带等主要林区验证，误差更小，尤其改善高大森林偏差问题。
- 适合林业碳汇监测、生态修复评估及需要细粒度树冠结构的研究者使用。
精准的树冠高度信息对量化森林碳储量、监测生态恢复与退化、评估生境结构至关重要，但机载激光扫描（ALS）的高精度数据在全球分布不均。本文提出CHMv2，一个基于高分辨率光学卫星影像、利用DINOv3构建的深度估计模型生成的全球米级分辨率树冠高度图，训练数据来自多源ALS树冠高程模型。相比现有产品，CHMv2显著提升精度，减少高大森林的系统偏差，并更好保留冠层边缘、林窗等细尺度结构。性能提升得益于地理多样性训练数据的大幅扩展、自动化数据清洗与配准，以及针对树高分布特性的损失函数与采样策略。通过独立的ALS测试集和数千万条GEDI与ICESat-2观测数据验证，CHMv2在主要森林生物群落中表现一致稳定。
原文摘要 · Abstract (English)
Accurate canopy height information is essential for quantifying forest carbon, monitoring restoration and degradation, and assessing habitat structure, yet high-fidelity measurements from airborne laser scanning (ALS) remain unevenly available globally. Here we present CHMv2, a global, meter-resolution canopy height map derived from high-resolution optical satellite imagery using a depth-estimation model built on DINOv3 and trained against ALS canopy height models. Compared to existing products, CHMv2 substantially improves accuracy, reduces bias in tall forests, and better preserves fine-scale structure such as canopy edges and gaps. These gains are enabled by a large expansion of geographically diverse training data, automated data curation and registration, and a loss formulation and data sampling strategy tailored to canopy height distributions. We validate CHMv2 against independent ALS test sets and against tens of millions of GEDI and ICESat-2 observations, demonstrating consistent performance across major forest biomes.
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