发表机构
University of Cambridge（剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用地理空间基础模型嵌入和Sentinel特征在罗马尼亚南喀尔巴阡山脉检测原始森林，发现缓冲空间验证削弱GFM优势，TESSERA在无缓冲下最优，但缓冲后优势消失，强调空间验证对模型迁移的重要性。
AI 中文摘要
原始森林在最少人为干扰下经过数百年演替，形成结构复杂且生物多样性的林分。在欧洲，保护原始森林需要既能对单个林分地块进行准确测绘，又能推广至整个大陆范围应用的制图方法。地理空间基础模型（GFM）嵌入支持标签稀缺的土地分类，但其在原始森林检测中的价值尚不明确。在此，我们对罗马尼亚南喀尔巴阡山脉211,893公顷区域（典型的山毛榉-云杉景观，属于阿尔卑斯生物地理区）进行原始森林制图。我们构建了高置信度、专家参考的原始森林和非原始森林地块标签。我们在常见的地形和人类可达性预测因子基线之上，添加AlphaEarth、TESSERA v2和Sentinel-1/2特征，并在有和无10公里训练-测试缓冲区的空间分块验证下进行比较，以限制残差自相关。加入缓冲区后，GFM和Sentinel-1/2预测因子相对于基线将精确率-召回率AUC提高了0.21-0.25 [95%置信区间：0.15-0.34]，表明光谱数据包含空间稳健的原始森林信号。在无缓冲空间验证下，TESSERA的PR-AUC为0.84 [0.79-0.88]，优于Sentinel-1/2（+0.08 [+0.05至+0.11]）和AlphaEarth（+0.08 [+0.04至+0.12]）。然而，在10公里缓冲区下，这一优势缩小至+0.04 [-0.01至+0.11]和+0.03 [-0.04至+0.10]，置信区间与无差异一致。在10米分辨率下，卷积神经网络相对于基于像素的XGBoost没有带来额外收益。与四个国家和大陆尺度产品的比较显示非原始森林标签的重要性，并揭示我们的预测与实地校准地图之间81%的一致性。我们得出结论，在将原始森林检测模型迁移到未见景观时，缓冲空间验证至关重要，并为未来工作提供我们的标签和预测。
英文摘要
Old-growth forests develop over centuries under minimal anthropogenic disturbance, producing structurally complex and biodiverse stands. In Europe, protecting them requires mapping that is accurate for individual forest parcels yet deployable continent-wide. Geospatial foundation model (GFM) embeddings enable label-scarce land classification, but their value for old-growth detection remains unknown. Here, we map old-growth forests across 211,893 ha of Romania's Southern Carpathians, a beech-spruce landscape typical of the Alpine Biogeographic Region. We construct high-confidence, expert-informed reference labels for old-growth and non-old-growth parcels. We add AlphaEarth, TESSERA v2 and Sentinel-1/2 features to a common baseline of topographic and human-access predictors, then compare them under spatially blocked validation with and without 10 km train-test buffers to limit residual autocorrelation. With buffering, GFM and Sentinel-1/2 predictors increase precision-recall AUC by 0.21-0.25 [95% CIs: 0.15-0.34] relative to baseline, indicating spectral data contain a spatially robust old-growth signal. With a PR-AUC of 0.84 [0.79-0.88], TESSERA outperforms Sentinel-1/2 (+0.08 [+0.05 to +0.11]) and AlphaEarth (+0.08 [+0.04 to +0.12]) under unbuffered spatial validation. At a 10 km buffer, however, this advantage narrows to +0.04 [-0.01 to +0.11] and +0.03 [-0.04 to +0.10], intervals consistent with no difference. At 10 m resolution, convolutional neural networks add no benefit over pixel-based XGBoost. Comparisons with four national- and continental-scale products show the importance of non-old-growth labels, and reveal 81% agreement between our predictions and a field-calibrated map. We conclude that buffered spatial validation is vital when transferring old-growth detection models to unseen landscapes, and provide our labels and predictions for future work.
Comments34 pages, including supplementary material (19-page main article with 7 figures and 3 tables; 15-page supplement with 9 figures and 21 tables). Submitted for publication. Data: https://doi.org/10.5281/zenodo.22693148 (embargoed until publication); code: https://github.com/ratsakatika/detecting-old-growth-forests
