Abstract
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.
Abstract (translated)
森林结构复杂性指标将多种冠层特征整合为单一数值，以反映栖息地质量和生态系统功能。来自全球生态动力学调查（GEDI）的卫星激光雷达使温带和热带森林中的结构复杂性的测绘成为可能，但其稀疏采样限制了连续高分辨率地图的生成。我们提出了一种可扩展的深度学习框架，该框架融合了GEDI观测数据与多模态合成孔径雷达（SAR）数据集，以生成全球范围内、分辨率为25米的森林结构复杂性的完整墙体到墙体的地图。 我们的改进版EfficientNetV2架构，在超过1.3亿个GEDI足迹上进行了训练，实现了卓越性能（全球R²=0.82），同时参数数量少于40万个。这使得该模型成为一种易于使用的工具，研究人员可以在此框架下处理任何规模的数据集，而无需特别的计算基础设施。 此模型能够生成跨生物群落和时间跨度准确预测，并提供校准后的不确定性估计，保留了细微的空间模式。它已被用来创建2015年至2022年间全球范围内的森林结构复杂性多时间序列数据集。 通过迁移学习，这一框架可以以最小的计算成本扩展到预测其他森林结构变量。这种方法支持对全球森林结构动态进行持续、多时段监测，并为气候变化条件下的生物多样性保护和生态系统管理提供了工具。
URL
https://arxiv.org/abs/2510.06299
