Abstract
Fine-scale forest monitoring is essential for understanding canopy structure and its dynamics, which are key indicators of carbon stocks, biodiversity, and forest health. Deep learning is particularly effective for this task, as it integrates spectral, temporal, and spatial signals that jointly reflect the canopy structure. To address this need, we introduce THREASURE-Net, a novel end-to-end framework for Tree Height Regression And Super-Resolution. The model is trained on Sentinel-2 time series using reference height metrics derived from LiDAR HD data at multiple spatial resolutions over Metropolitan France to produce annual height maps. We evaluate three model variants, producing tree-height predictions at 2.5 m, 5 m, and 10 m resolution. THREASURE-Net does not rely on any pretrained model nor on reference very high resolution optical imagery to train its super-resolution module; instead, it learns solely from LiDAR-derived height information. Our approach outperforms existing state-of-the-art methods based on Sentinel data and is competitive with methods based on very high resolution imagery. It can be deployed to generate high-precision annual canopy-height maps, achieving mean absolute errors of 2.62 m, 2.72 m, and 2.88 m at 2.5 m, 5 m, and 10 m resolution, respectively. These results highlight the potential of THREASURE-Net for scalable and cost-effective structural monitoring of temperate forests using only freely available satellite data. The source code for THREASURE-Net is available at: this https URL.
Abstract (translated)
精细尺度的森林监测对于理解冠层结构及其动态变化至关重要，这些是衡量碳储量、生物多样性和森林健康状况的关键指标。深度学习在此任务中特别有效，因为它能够整合光谱、时间序列和空间信号，这些信号共同反映了冠层结构的情况。为了满足这一需求，我们引入了THREASURE-Net，这是一种新颖的端到端框架，用于树木高度回归及超分辨率生成。该模型利用Sentinel-2卫星的时间序列数据进行训练，并结合来自法国大都会地区多尺度空间分辨率LiDAR高精度数据所推导出的高度指标来生成年度高度图谱。我们评估了三种模型变体，在2.5米、5米和10米的分辨率下分别产生了树木高度预测。THREASURE-Net不依赖任何预训练模型，也不需要非常高的分辨率光学图像来训练其超分辨率模块；相反，它仅从LiDAR衍生的高度信息中学习。 我们的方法在基于Sentinel数据的方法中超越了现有最先进的技术，并且与基于非常高分辨率影像的方法具有竞争力。它可以部署以生成高精度的年度冠层高度图谱，在2.5米、5米和10米的分辨率下分别实现了平均绝对误差为2.62米、2.72米和2.88米的结果。这些结果突显了THREASURE-Net利用仅限于免费获取的卫星数据就具备对温带森林进行可扩展且成本效益高的结构监测的巨大潜力。 THREASURE-Net的源代码可以在以下链接找到：this https URL。
URL
https://arxiv.org/abs/2512.11524
