Remote sensing opens opportunities to assess spatial patterns on ecological data for a wide range of ecosystems. This information can be used to more effectively design sampling strategies for fieldwork, either to capture the maximum spatial dependence related to the ecological data or to completely avoid it. The sampling design and the autocorrelation observed in the field will determine whether there is a need to use a spatial model to predict ecological data accurately. In this article, we show the effects of different sampling designs on predictions of a plant trait, as an example of an ecological variable, using a set of simulated hyperspectral data with an increasing range of spatial autocorrelation. Our findings show that when the sample is designed to estimate population parameters such as mean and variance, a random design is appropriate even where there is strong spatial autocorrelation. However, in remote sensing applications, the aim is usually to predict characteristics of unsampled locations using spectral information. In this case, regular sampling is a more appropriated design. Sampling based on close pairs of points and clustered over a regular design may improve the accuracy of the training model, but this design generalizes poorly. The use of spatially explicit models improves the prediction accuracy significantly in landscapes with strong spatial dependence. However, such models have low generalization capacities to extrapolate to other landscapes with different spatial patterns. When the combination of design and size results in sample distances similar to the range of the spatial dependence in the field, it increases predictions uncertainty.

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抽样设计在利用遥感预测空间相关生态数据时的作用

遥感为评估各种生态系统的生态数据空间模式提供了机会。该信息可用于更有效地设计实地工作的采样策略，以捕捉与生态数据相关的最大空间依赖性或完全避免它。采样设计和现场观察到的自相关将决定是否需要使用空间模型来准确预测生态数据。在本文中，我们使用一组具有越来越大的空间自相关范围的模拟高光谱数据，展示了不同抽样设计对植物性状预测的影响，作为生态变量的一个例子。我们的研究结果表明，当样本旨在估计总体参数（例如均值和方差）时，即使存在很强的空间自相关，随机设计也是合适的。然而，在遥感应用中，目标通常是使用光谱信息预测未采样位置的特征。在这种情况下，定期抽样是一种更合适的设计。基于紧密点对进行采样并在常规设计上进行聚类可能会提高训练模型的准确性，但这种设计的泛化能力很差。空间显式模型的使用显着提高了具有强烈空间依赖性的景观的预测精度。然而，这些模型外推到具有不同空间模式的其他景观的泛化能力较低。当设计和尺寸的组合导致样本距离与现场空间相关性的范围相似时，