Forest structural complexity metrics integrate multiple canopy attributes into a single value that reflects habitat quality and ecosystem function. Spaceborne lidar from the Global Ecosystem Dynamics Investigation (GEDI) has enabled mapping of structural complexity in temperate and tropical forests, but its sparse sampling limits continuous high-resolution mapping. We present a scalable, deep learning framework fusing GEDI observations with multimodal Synthetic Aperture Radar (SAR) datasets to produce global, high-resolution (25 m) wall-to-wall maps of forest structural complexity. Our adapted EfficientNetV2 architecture, trained on over 130 million GEDI footprints, achieves high performance (global R2 = 0.82) with fewer than 400,000 parameters, making it an accessible tool that enables researchers to process datasets at any scale without requiring specialized computing infrastructure. The model produces accurate predictions with calibrated uncertainty estimates across biomes and time periods, preserving fine-scale spatial patterns. It has been used to generate a global, multi-temporal dataset of forest structural complexity from 2015 to 2022. Through transfer learning, this framework can be extended to predict additional forest structural variables with minimal computational cost. This approach supports continuous, multi-temporal monitoring of global forest structural dynamics and provides tools for biodiversity conservation and ecosystem management efforts in a changing climate.

翻译：森林结构复杂性指标将多种冠层属性整合为单一数值，反映栖息地质量与生态系统功能。全球生态系统动力学调查（GEDI）的星载激光雷达已实现对温带与热带森林结构复杂性的制图，但其稀疏采样限制了连续高分辨率制图能力。本研究提出一种可扩展的深度学习框架，通过融合GEDI观测数据与多模态合成孔径雷达（SAR）数据集，生成全球范围、高分辨率（25米）全覆盖的森林结构复杂性地图。我们改进的EfficientNetV2架构基于超过1.3亿个GEDI足迹样本进行训练，以不足40万个参数实现了高性能预测（全局R² = 0.82），使其成为无需专用计算基础设施即可处理任意规模数据集的可推广工具。该模型能生成跨生物群落与时间段的精确预测，并提供校准的不确定性估计，同时保持精细尺度的空间格局。该模型已用于生成2015年至2022年全球多时相森林结构复杂性数据集。通过迁移学习，此框架能够以最小计算成本扩展至预测其他森林结构变量。该方法支持对全球森林结构动态进行连续多时相监测，并为气候变化背景下的生物多样性保护与生态系统管理提供工具支持。