用哨兵-2影像生成亚米级林冠高模型，实现大范围森林连续监测。
VibrantSR: Sub-Meter Canopy Height Models from Sentinel-2 Using Generative Flow Matching
- 基于生成式流匹配，从10米分辨率哨兵-2影像重建0.5米精度林冠高模型。
- 在22个生态区验证中，林冠高≥2米时平均误差仅4.39米，优于多个卫星基准。
- 无需昂贵航空数据，适合大陆尺度森林碳汇与动态监测应用。
我们提出VibrantSR（Vibrant超分辨率），一种生成式超分辨率框架，用于从10米分辨率的哨兵-2影像估算0.5米精度的林冠高模型（CHMs）。与受限于获取频次不规律的航空影像方法不同，VibrantSR利用全球可得的哨兵-2季节合成数据，实现季至年尺度的一致性监测。在西美22个美国环保署三级生态区进行空间分离验证，针对林冠高≥2米的情况，平均绝对误差为4.39米，优于Meta（4.83米）、LANDFIRE（5.96米）和ETH（7.05米）等卫星基准。尽管基于航空影像的VibrantVS（2.71米MAE）仍具更高精度，但VibrantSR实现了无需依赖高成本且时间间隔大的航空采集的大规模森林监测与碳核算能力。
原文摘要 · Abstract (English)
We present VibrantSR (Vibrant Super-Resolution), a generative super-resolution framework for estimating 0.5 meter canopy height models (CHMs) from 10 meter Sentinel-2 imagery. Unlike approaches based on aerial imagery that are constrained by infrequent and irregular acquisition schedules, VibrantSR leverages globally available Sentinel-2 seasonal composites, enabling consistent monitoring at a seasonal-to-annual cadence. Evaluated across 22 EPA Level 3 eco-regions in the western United States using spatially disjoint validation splits, VibrantSR achieves a Mean Absolute Error of 4.39 meters for canopy heights >= 2 m, outperforming Meta (4.83 m), LANDFIRE (5.96 m), and ETH (7.05 m) satellite-based benchmarks. While aerial-based VibrantVS (2.71 m MAE) retains an accuracy advantage, VibrantSR enables operational forest monitoring and carbon accounting at continental scales without reliance on costly and temporally infrequent aerial acquisitions.
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