Assessing the relationship between morphology and mapping accuracy of built-up areas derived from global human settlement data.
Assessing the relationship between morphology and mapping accuracy of built-up areas derived from global human settlement data.
复制标题
DOI：
10.1080/15481603.2022.2131192
复制
发表时间：
2022
影响因子：
6.7
通讯作者：
Leyk, Stefan
中科院分区：
文献类型：
--
作者：
Uhl, Johannes H.;Leyk, Stefan
关键词：
众所周知，景观异质性水平可能会影响基于遥感的土地利用/土地覆盖分类的性能。虽然这个问题已经对土地覆盖数据进行了深入研究，但制图精度与建筑表面形态特征之间的具体关系尚未得到详细分析，鉴于最近出现的各种全球高分辨率沉降数据集，迫切需要分析这一问题。此外，以前的研究通常依赖于聚合的、大尺度的景观指标来量化建成区的形态，忽略了这些指标的细粒度空间变化和尺度依赖性。在这里，我们的目标是通过评估从二进制构建表面和局部数据准确性估计得出的局部（焦点）景观指标之间的关联来填补这一知识空白。我们在马萨诸塞州（美国）的全球人类住区层 (GHSL) 的建筑表面上测试了我们的方法。具体来说，我们检查了景观指标对多时态 GHS-BUILT R2018A 数据产品中的佣金和遗漏错误的解释力。我们发现，平均而言，在焦点窗口中计算的景观形状指数 (LSI) 与焦点精度测量的相关性最高。这些关系与尺度相关，并且随着空间支持水平的增加而变得更强。我们发现，通过召回率测量的主题遗漏误差与不同时间时期和空间分辨率的构建表面形态的测量具有最强的关系。我们的回归分析结果（R2>0.9）根据景观指标估计准确性，证实了这些发现。最后，我们通过对回归模型进行区域分层并将其应用于不同版本的 GHSL（即 GHS-BUILT-S2）和不同的研究领域来测试我们的研究结果的普遍性。我们观察到不同水平的模型可转移性，表明准确性和景观指标之间的关系可能是特定于传感器的，并且对于大多数准确性指标来说是高度本地化的，但对于召回率指标来说是相当普遍的。这表明，建设用地的形态特征与其“地图不足”的程度之间存在着强烈且普遍的关联。
It is common knowledge that the level of landscape heterogeneity may affect the performance of remote sensing based land use / land cover classification. While this issue has been studied in depth for land cover data in general, the specific relationship between the mapping accuracy and morphological characteristics of built-up surfaces has not been analyzed in detail, an urgent need given the recent emergence of a variety of global, fine-resolution settlement datasets. Moreover, previous studies typically rely on aggregated, broad-scale 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 knowledge gap by assessing the associations between localized (focal) landscape metrics, derived from binary built-up surfaces and localized data accuracy estimates. We tested our approach for built-up surfaces from the Global Human Settlement Layer (GHSL) for Massachusetts (USA). Specifically, we examined the explanatory power of landscape metrics with respect to both commission and omission errors in the multi-temporal GHS-BUILT R2018A data product. We found that the Landscape Shape Index (LSI) calculated in focal windows exhibits, on average, the highest levels of correlation to focal accuracy measures. These relationships are scale-dependent, and become stronger with increasing level of spatial support. We found that thematic omission error, as measured by Recall, has the strongest relationship to measures of built-up surface morphology across different temporal epochs and spatial resolutions. The results of our regression analysis (R2>0.9), estimating accuracy based on landscape metrics, confirmed these findings. Lastly, we tested the generalizability of our findings by regionally stratifying our regression models and applying them to a different version of the GHSL (i.e., the GHS-BUILT-S2) and a different study area. We observed varying levels of model transferability, indicating that the relationship between accuracy and landscape metrics may be sensor-specific, and is heavily localized for most accuracy metrics, but quite generalizable for the Recall measure. This indicates that there is a strong and generalizable association between morphological properties of built-up land and the degree to which it is “undermapped”.
登录
查看更多内容
影响因子：
13.5
作者：
Khatami, Reza;Mountrakis, Giorgos;Stehman, Stephen V.
通讯作者：
Stehman, Stephen V.
影响因子：
6
作者：
Corbane, Christina;Syrris, Vasileios;Kemper, Thomas
通讯作者：
Kemper, Thomas
DOI：
10.1016/j.jag.2013.12.016
发表时间：
2014-06-01
影响因子：
7.5
作者：
Champagne, Catherine;McNairn, Heather;Shang, Jiali
通讯作者：
Shang, Jiali
影响因子：
4
作者：
Corbane, Christina;Pesaresi, Martino;Soille, Pierre
通讯作者：
Soille, Pierre
影响因子：
1.1
作者：
Freund, Y;Schapire, RE
通讯作者：
Schapire, RE