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首页> 外文期刊>International journal of remote sensing >Spatial Random Forest (S-RF): A random forest approach for spatially interpolating missing land-cover data with multiple classes
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Spatial Random Forest (S-RF): A random forest approach for spatially interpolating missing land-cover data with multiple classes

机译：空间随机森林（S-RF）：具有多个类的空间内插土数据的随机森林方法

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摘要

Land-cover maps are important tools for monitoring large-scale environmental change and can be regularly updated using free satellite imagery data. A key challenge with constructing these maps is missing data in the satellite images on which they are based. To address this challenge, we created a Spatial Random Forest (S-RF) model that can accurately interpolate missing data in satellite images based on a modest training set of observed data in the image of interest. We demonstrate that this approach can be effective with only a minimal number of spatial covariates, namely latitude and longitude. The motivation for only using latitude and longitude in our model is that these covariates are available for all images whether the data are observed or missing due to cloud cover. The S-RF model can flexibly partition these covariates to provide accurate estimates, with easy incorporation of additional covariates to improve estimation if available. The effectiveness of our approach has been previously demonstrated for prediction of two land-cover classes in an Australian case study. In this paper, we extend the method to more than two classes. We demonstrate the performance of the S-RF method at interpolating multiple land-cover classes, using a case study drawn from South America. The results show that the method is best at predicting three land-cover classes, compared with 5 or 10 classes, and that other information is needed to improve performance as the number of classes grows, particularly if the classes are unbalanced. We explore two issues through a sensitivity analysis: the influence of the amount of missing data in the image and the influence of the amount of training data for model development and performance. The results show that the amount of missing data due to cloud cover is influential on model performance for multiple classes. We also found that increasing the amount of training data beyond 100,000 observations had minimal impact on model accuracy. Hence, a relatively small amount of observed data is required for training the model, which is beneficial for computation time.
机译：陆地覆盖地图是监控大规模环境变化的重要工具，可以使用免费卫星图像数据定期更新。构造这些地图的关键挑战是缺少它们所基于卫星图像中的数据。为了解决这一挑战，我们创建了一个空间随机森林（S-RF）模型，可以基于在感兴趣的图像中的训练数据的适度训练数据中准确地插入卫星图像中的缺失数据。我们证明这种方法只有只有最小数量的空间协变量，即纬度和经度都是有效的。仅在我们的模型中使用纬度和经度的动机是这些协变量可用于所有图像，无论是否因云覆盖而丢失数据。 S-RF模型可以灵活地分配这些协变量，以提供准确的估计，简单地融合额外的协变量，以改善估计值。我们之前已经证明了我们方法的有效性用于预测澳大利亚案例研究中的两个陆地覆盖类别。在本文中，我们将该方法扩展到两个以上的类。我们使用南美洲绘制的案例研究证明了S-RF方法在内插多地覆盖类别时的性能。结果表明，该方法最适合预测三个陆地覆盖类，与5或10个类相比，随着类别的数量增长，特别是如果类别不平衡，则需要其他信息来提高性能。我们通过敏感性分析探讨了两个问题：图像中缺失数据量的影响以及模型开发和性能的培训数据的影响。结果表明，由于云覆盖引起的缺失数据量对多个类的模型性能有影响力。我们还发现，增加了100,000个观察的培训数据量对模型准确性的影响最小。因此，训练模型需要相对少量的观察数据，这是有益的计算时间。

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  • 来源
    《International journal of remote sensing》 |2021年第10期|3756-3776|共21页
  • 作者

    Holloway-Brown Jacinta; Helmstedt Kate J.; Mengersen Kerrie L.;

  • 作者单位

    Queensland Univ Technol Sch Math Sci ACEMS Brisbane Qld Australia;

    Queensland Univ Technol Sch Math Sci ACEMS Brisbane Qld Australia|Queensland Univ Technol Sch Math Sci Brisbane Qld Australia;

    Queensland Univ Technol Sch Math Sci ACEMS Brisbane Qld Australia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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