用遥感数据和随机森林模型，首次绘制出阿拉斯加北坡所有水体的深度图。
Mapping bathymetry of inland water bodies on the North Slope of Alaska with Landsat using Random Forest
- 用已有模拟深度数据生成合成训练集，弥补实地数据不足
- 模型在208景卫星影像上验证，整体相关系数达0.76
- 成果公开可查，为生态研究提供首个全区域像素级深度地图
阿拉斯加北坡以众多小型水体为主，对当地居民与野生动物至关重要，但其深度信息因实地测量成本高、难度大而严重缺乏。本文利用多光谱Landsat数据，训练随机森林回归模型预测该区域水体深度。为克服实地数据稀缺问题，采用先前研究的模拟深度结果作为合成训练数据，扩充了训练样本多样性。最终模型比仅依赖实地数据的模型更具鲁棒性。将其应用于2016至2018年的208景Landsat 8影像，生成的深度图在验证中整体$ r^{2} $值达到0.76。该成果已通过橡树岭国家实验室分布式档案中心（ORNL-DAAC）发布，是首个针对阿拉斯加北坡全区域的像素级水体深度制图。
原文摘要 · Abstract (English)
The North Slope of Alaska is dominated by small waterbodies that provide critical ecosystem services for local population and wildlife. Detailed information on the depth of the waterbodies is scarce due to the challenges with collecting such information. In this work we have trained a machine learning (Random Forest Regressor) model to predict depth from multispectral Landsat data in waterbodies across the North Slope of Alaska. The greatest challenge is the scarcity of in situ data, which is expensive and difficult to obtain, to train the model. We overcame this challenge by using modeled depth predictions from a prior study as synthetic training data to provide a more diverse training data pool for the Random Forest. The final Random Forest model was more robust than models trained directly on the in situ data and when applied to 208 Landsat 8 scenes from 2016 to 2018 yielded a map with an overall $r^{2}$ value of 0.76 on validation. The final map has been made available through the Oak Ridge National Laboratory Distribute Active Archive Center (ORNL-DAAC). This map represents a first of its kind regional assessment of waterbody depth with per pixel estimates of depth for the entire North Slope of Alaska.
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