Abstract Understanding rapid global change requires land cover maps with broad spatial extent, but also fine spatial and temporal resolution. Developing such maps presents a unique challenge, as variability in relationships between spectral characteristics (i.e., predictors) and a response variable is likely to increase with the size of the region across which a model is built and applied. Although most mapping approaches apply the same predictor-response relationships globally across the entire modeling region, learned relationships from one local area may be invalid for another when predicting across broad extents. Here, we adapted a spatial ensemble approach borrowed from species distribution modeling to land cover mapping, and evaluated whether the approach could faithfully represent spatial variation in relationships between land cover and spectral data. The spatiotemporal exploratory model (STEM) uses an ensemble of regression trees defined within spatially overlapping support sets, producing a broad-extent map that reflects variability at the spatial scale of each constituent support set. As test cases for reference maps, we used 30-m-resolution forest canopy and impervious surface cover layers from the 2001 U.S. National Land Cover Database (NLCD) for the states of Washington, Oregon, and California. When testing strategies for support set size and sampling intensity, we found that predictor-response relationships were strongest when individual components of the spatial ensemble were small and when sampling intensity was high. Compared to aspatial bagged decision tree and random forest models, we found that the STEM approach successfully captured variation in our source maps, both globally and at scales smaller than the modeling region. Leveraging the spatial structure of a STEM, we also mapped per-pixel spatial variation in prediction confidence and the importance of different predictor variables. After testing appropriate spatial ensemble and sampling strategies, we extended the predictor-response relationships gleaned from the 2001 source maps into a yearly time series based on temporally-smoothed spectral data from the LandTrendr algorithm. The end products were yearly forest canopy and impervious surface cover time series representing 1990–2012. Formal evaluation showed that our temporally extended maps also closely resembled NLCD maps from 2011. The aim of this research was to cultivate the implicit relationships between spectral data and a given map, not improve them, but as the need for time series maps produced at both broad extents and fine resolutions increases, our results demonstrate that an ensemble of locally defined estimators is potentially more appropriate than conventional ensemble models for land cover mapping across broad extents.

中文翻译：

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一种用于大面积地表特性测绘的空间集成方法

摘要 理解快速的全球变化需要具有广泛空间范围的土地覆盖图，但也需要良好的空间和时间分辨率。开发此类地图提出了独特的挑战，因为光谱特征（即预测变量）和响应变量之间关系的可变性可能会随着构建和应用模型的区域的大小而增加。尽管大多数映射方法在整个建模区域中全局应用相同的预测变量-响应关系，但在广泛范围内进行预测时，从一个局部区域学习到的关系可能对另一个局部区域无效。在这里，我们采用了从物种分布建模中借用的空间集成方法来绘制土地覆盖图，并评估该方法是否可以忠实地表示土地覆盖和光谱数据之间关系的空间变化。时空探索模型 (STEM) 使用在空间重叠支持集内定义的回归树集合，生成反映每个组成支持集空间尺度可变性的广泛地图。作为参考地图的测试案例，我们使用了 2001 年美国国家土地覆盖数据库 (NLCD) 中华盛顿州、俄勒冈州和加利福尼亚州的 30 米分辨率森林冠层和不透水地表覆盖层。在测试支持集大小和采样强度的策略时，我们发现当空间集合的单个组件较小且采样强度较高时，预测变量-响应关系最强。与非空间袋装决策树和随机森林模型相比，我们发现 STEM 方法成功地捕获了我们源图中的变化，无论是在全局范围内还是在小于建模区域的尺度上。利用 STEM 的空间结构，我们还绘制了预测置信度的每像素空间变化以及不同预测变量的重要性。在测试了适当的空间集合和采样策略之后，我们根据来自 LandTrendr 算法的时间平滑光谱数据，将从 2001 年源地图收集的预测器-响应关系扩展到年度时间序列。最终产品是代表 1990-2012 年的年度森林冠层和不透水地表覆盖时间序列。正式评估表明，我们的时间扩展地图也与 2011 年的 NLCD 地图非常相似。