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<p>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 Canopy Height Map version 2 (CHMv2), a global, meter-resolution canopy height map derived from high-resolution optical satellite imagery using a depth&#xa0;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.</p>

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CHMv2: Improvements in Global Canopy Height Mapping using DINOv3

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

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  • John Brandt,
  • Seungeun Yi,
  • Jamie Tolan,
  • Xinyuan Li,
  • Peter Potapov,
  • Jessica Ertel,
  • Justine Spore,
  • Huy V. Vo,
  • Michaël Ramamonjisoa,
  • Patrick Labatut,
  • Piotr Bojanowski,
  • Camille Couprie

摘要

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 Canopy Height Map version 2 (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.