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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Assimilating optical satellite remote sensing images and field data to predict surface indicators in the Western US: Assessing error in satellite predictions based on large geographical datasets with the use of machine learning
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Assimilating optical satellite remote sensing images and field data to predict surface indicators in the Western US: Assessing error in satellite predictions based on large geographical datasets with the use of machine learning

机译：吸收光学卫星遥感图像和现场数据以预测美国西部的表面指示：基于机器学习的大地理数据集评估卫星预测的误差

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Indicators of vegetation composition, vegetation structure, bare ground cover, and gap size in drylands potentially gives information about the condition of ecosystems, in part because they are strongly related to factors such as erosion, wildlife habitat characteristics, and the suitability for some land uses. Field data collection based on points does not produce spatially continuous information about surface indicators and cannot cover vast geographic areas. Remote sensing is possibly a labor- and time-saving method to estimate important biophysical indicators of vegetation and surface condition at both temporal and spatial scales impossible with field methods. Regression models based on machine learning algorithms, such as random forest (RF), can build relationships between field and remotely sensed data, while also providing error estimates. In this study, field data including over 15,000 points from the Assessment, Inventory, and Monitoring (AIM) and Landscape Monitoring Framework (LMF) programs on Bureau of Land Management (BLM) lands throughout the Western U.S., Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) parameters, MODIS nadir BRDF-adjusted reflectance (NBAR), and Landsat 8 Operational Land Imager (OLI) surface reflectance products with ancillary data were used as predictor variables in a k-fold cross-validation approach to RF modeling. RF regression models were built to predict fourteen indicators of vegetation cover and height, as well as bare gap parameters. The RF model estimates exhibited good correlations with independent samples, with a low bias and a low RMSE. External cross-validation showed good agreement with out-of-bag (OOB) errors produced by RF and also allowed mapping prediction uncertainty. Predicted distribution maps of the surface indicators were produced by using these relationships across the arid and semiarid Western U.S. The bias and RMSE distribution maps show that the sample insufficiency and unevenly pattern of sample strongly impact the accuracy of the RF regression and prediction. The results from this study clearly show the utility of RF as a means to estimate multiple dryland surface indicators from remotely sensed data, and the reliability of the OOB errors in assessing the accuracy of the predictions.
机译：Drylands中植被组成，植被结构，裸地面覆盖和间隙大小的指标可能提供有关生态系统状况的信息，部分原因是它们与侵蚀，野生动物栖息地特征等因素密切相关，以及某些土地使用的适用性。基于点的现场数据收集不会产生关于表面指示器的空间连续信息，无法覆盖广泛的地理区域。遥感可能是一种劳动和节省时间的方法，以估算植被和表面状况的重要生物物理指标，在田间方法中不可能。基于机器学习算法的回归模型，如随机森林（RF），可以在现场和远程感测数据之间建立关系，同时也提供错误估计。在本研究中，现场数据包括评估，库存和监测（AIM）和景观监测框架（LMF）在美国西部的土地管理局（BLM）局域网上有超过15,000点，中我们的适度分辨率成像光谱辐射器（MODIS）双向反射率分配功能（BRDF）参数，MODIS Nadir BRDF调整的反射率（NAR）和Landsat 8具有辅助数据的表面反射产品（OLI）表面反射率产品，用作RF的K倍交叉验证方法中的预测因子变量造型。 RF回归模型是为预测植被覆盖和高度的十四个指标，以及裸隙参数。 RF模型估计与独立样品呈现出良好的相关性，具有低偏差和低的RMSE。外部交叉验证与RF产生的袋子外（OOB）误差显示出良好的一致性，并且还允许映射预测不确定性。通过在干旱和半干旱西方美国的这些关系中使用这些关系来产生表面指示器的预测分布图。偏差和RMSE分布图表明样品功能不全和不均匀的样品模式强烈影响RF回归和预测的准确性。本研究的结果清楚地显示了RF作为估计来自远程感测数据的多个旱地表面指示器的方法的效用，以及评估预测准确性的OOB误差的可靠性。

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