High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning
High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning
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DOI：
10.3389/frwa.2023.1128758
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Kehan Yang;Aji John;D. Shean;J. Lundquist;Ziheng Sun;Fangfang Yao;Stefan Todoran;N. Cristea
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文献类型：
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作者：
Kehan Yang;Aji John;D. Shean;J. Lundquist;Ziheng Sun;Fangfang Yao;Stefan Todoran;N. Cristea
山区积雪为森林和草地生态系统提供了关键的水资源，这些生态系统正因全球变暖而经历快速变化。在这些生态系统中的积雪异质性的准确表征需要在高空间分辨率的积雪观测，但大多数现有的积雪数据集有一个粗糙的分辨率。为了提高我们对草地和森林积雪的观测能力，我们开发了一个机器学习模型，以大约3米的空间分辨率从PlanetScope图像生成积雪覆盖区（SCA）地图。该模型在美国西部和瑞士的四个不同地点的103张无云图像中获得了0.75的中值F1分数。如果将森林地区排除在评价之外，则更为准确（F1分数= 0.82）。我们在内华达州和加州的两个研究地点进一步测试了7,741个山地草甸的模型性能。它实现了中位数F1得分为0.83，更大，更简单的几何形状的草地比更小，形状更复杂的草地具有更高的准确性。虽然在靠近森林冠层或在森林冠层下的区域绘制SCA仍然具有挑战性，但该模型可以准确地识别相对较大的森林间隙（即，15米10米）到森林边缘。我们的研究强调了高分辨率卫星图像在绘制森林地区和草地山区积雪覆盖图方面的潜力，并对在预计雪会发生重大变化的世界中推进生态水文研究产生了影响。
Mountain snowpack provides critical water resources for forest and meadow ecosystems that are experiencing rapid change due to global warming. An accurate characterization of snowpack heterogeneity in these ecosystems requires snow cover observations at high spatial resolutions, yet most existing snow cover datasets have a coarse resolution. To advance our observation capabilities of snow cover in meadows and forests, we developed a machine learning model to generate snow-covered area (SCA) maps from PlanetScope imagery at about 3-m spatial resolution. The model achieves a median F1 score of 0.75 for 103 cloud-free images across four different sites in the Western United States and Switzerland. It is more accurate (F1 score = 0.82) when forest areas are excluded from the evaluation. We further tested the model performance across 7,741 mountain meadows at the two study sites in the Sierra Nevada, California. It achieved a median F1 score of 0.83, with higher accuracy for larger and simpler geometry meadows than for smaller and more complexly shaped meadows. While mapping SCA in regions close to or under forest canopy is still challenging, the model can accurately identify SCA for relatively large forest gaps (i.e., 15m 10m) to forest edges. Our study highlights the potential of high-resolution satellite imagery for mapping mountain snow cover in forested areas and meadows, with implications for advancing ecohydrological research in a world expecting significant changes in snow.