Forests are a global environmental priority that need to be monitored frequently and at large scales. Satellite images are a proven useful, free data source for regular global forest monitoring but these images often have missing data in tropical regions due to climate driven persistent cloud cover. Remote sensing and statistical approaches to filling these missing data gaps exist and these can be highly accurate, but any interpolation method results are uncertain and these methods do not provide measures of this uncertainty. We present a new two-step spatial stochastic random forest (SS-RF) method that uses random forest algorithms to construct Beta distributions for interpolating missing data. This method has comparable performance with the traditional remote sensing compositing method, and additionally provides a probability for each interpolated data point. Our results show that the SS-RF method can accurately interpolate missing data and quantify uncertainty and its applicability to the challenge of monitoring forest using free and incomplete satellite imagery data. We propose that there is scope for our SS-RF method to be applied to other big data problems where a measurement of uncertainty is needed in addition to estimates.

中文翻译：

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随机空间随机森林（SS-RF）用于插值缺失土地覆盖数据的概率

森林是全球环境优先事项，需要经常进行大规模监测。卫星图像是经过证明的有用的免费数据源，可用于定期全球森林监测，但是由于气候驱动的持续云层覆盖，这些图像在热带地区经常缺少数据。存在填补这些缺失的数据缺口的遥感和统计方法，这些方法可以非常准确，但是任何插值方法的结果都是不确定的，并且这些方法不能提供这种不确定性的度量。我们提出了一种新的两步空间随机随机森林（SS-RF）方法，该方法使用随机森林算法来构造Beta分布以内插丢失的数据。该方法的性能可与传统的遥感合成方法相媲美，并为每个插值数据点提供了一个概率。我们的结果表明，SS-RF方法可以准确地插补丢失的数据并量化不确定性及其适用于使用免费和不完整卫星图像数据进行森林监测的挑战。我们建议将我们的SS-RF方法应用于其他大数据问题，这些问题除了估计值外还需要测量不确定性。