论文标题
基于景观指标，美国全球人类定居数据的不确定性预测
Uncertainty prediction of built-up areas from global human settlement data in the United States based on landscape metrics
论文作者
论文摘要
景观异质性水平可能会影响基于遥感的土地使用 /土地覆盖分类的性能。然而，尚未明确分析构图的构图准确性与建筑区域的形态特征之间的关系，并且先前的研究通常依赖于汇总的景观指标来量化建筑区域的形态，忽略了此类计量学的细粒空间变化和规模依赖性。本文中，我们的目标是通过评估二元构建表面和焦点数据精度估算的焦点格局指标之间的关联来填补这一空白。我们通过检查景观指标的解释性能力来预测委员会的委员会和GHS构建R2018A数据产品的遗漏错误，从全球人类定居层（GHSL）（美国）的整体表面方法测试了我们的积累表面方法。我们发现，景观形状指数（LSI）表现出与焦点精度度量的最高水平。这些关系依赖于规模，并且随空间支持的水平而增加。在美国境内不同地区，我们的结果是一致的，我们发现，召回措施与不同时间时代和空间分辨率的建筑表面形态衡量的关系最强。回归分析结果（R2> 0.9）表明，在没有参考数据的情况下，可以在GHSL中估算佣金错误，并且可以在不访问数据本身的情况下对GHSL中的遗漏错误进行建模。最后，我们测试了涵盖北卡罗来纳州研究区域的不同版本的GHSL（即GHS-Built-S2）的普遍性。我们发现模型可传递性的不同水平随着景观指标和准确性估计值的空间支持而增加。
The level of landscape heterogeneity may affect the performance of remote sensing based land use / land cover classification. However, the relationship between mapping accuracy of built-up surfaces and morphological characteristics of built-up areas has not been analyzed explicitly, and previous studies typically rely on aggregated landscape metrics to quantify the morphology of built-up areas, neglecting the fine-grained spatial variation and scale dependency of such metrics. Herein, we aim to fill this gap by assessing the associations between focal landscape metrics, derived from binary built-up surfaces, and focal data accuracy estimates. We test our approach for built-up surfaces from the Global Human Settlement Layer (GHSL) for Massachusetts (USA), by examining the explanatory power of landscape metrics for predictive modeling of commission and omission errors in the GHS-BUILT R2018A data product. We find that the Landscape Shape Index (LSI) exhibits the highest levels of correlation to focal accuracy measures. These relationships are scale-dependent, and increase with the level of spatial support. Our results are consistent across different regions within the U.S., and we find that the Recall measure has the strongest relationship to measures of built-up surface morphology across different temporal epochs and spatial resolutions. Regression analysis results (R2>0.9) indicate that it is possible to estimate commission errors in the GHSL in the absence of reference data, and that omission errors in the GHSL can be modeled without accessing the data themselves. Lastly, we test the generalizability of our predictive accuracy models to a different version of the GHSL (i.e., the GHS-BUILT-S2) covering a study area in North Carolina. We find varying levels of model transferability that increases with the spatial support at which landscape metrics and accuracy estimates are calculated.
