A scale-sensitive framework for the spatially explicit accuracy assessment of binary built-up surface layers
A scale-sensitive framework for the spatially explicit accuracy assessment of binary built-up surface layers
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DOI：
10.1016/j.rse.2022.113117
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发表时间：
2022-03
影响因子：
13.5
通讯作者：
Johannes H. Uhl;S. Leyk
中科院分区：
文献类型：
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作者：
Johannes H. Uhl;S. Leyk
为了更好地了解人类住区的动态，对地理空间建成表面数据集的不确定性的全面了解至关重要。虽然分类网格化数据的本地化准确性评估框架已被提出来考虑分类准确性的空间非平稳性，但这种方法尚未应用于（二进制）建成的土地数据。这类数据不同于土地覆盖数据等其他数据，因为城乡连续体的建筑物表面密度差异很大，导致类别转换不平衡，造成基于小的基本样本量的人口稀少的混淆矩阵。在本文中，我们的目标是填补这一空白，测试共同协议措施，其适用性和可扩展性，以衡量本地化的准确性建成的表面数据。我们研究了本地化的准确性的评估支持的敏感性，以及分析的单位，并分析了当地的准确性和建成区的密度/结构相关的属性之间的关系，跨城乡轨迹和随着时间的推移。我们的实验是基于多时相的全球人类住区层（GHSL）和参考数据库的马萨诸塞州（美国）。我们发现常用的协议措施之间的适应性强的变化，和不同程度的敏感性的评估支持。然后，我们应用我们的框架来评估1975年至2014年期间本地化GHSL数据的准确性。除了增加精度沿着农村-城市梯度，我们发现，精度一般会随着时间的推移，主要是在我们的研究区域的城市周边的致密化过程。此外，我们发现，来自GHSL的本地化致密化措施往往高估城市周边的致密化过程发生在1975年和2014年之间，由于在GHSL时代1975年的遗漏错误的水平较高。
To better understand the dynamics of human settlements, thorough knowledge of the uncertainty in geospatial built-up surface datasets is critical. While frameworks for localized accuracy assessments of categorical gridded data have been proposed to account for the spatial non-stationarity of classification accuracy, such approaches have not been applied to (binary) built-up land data. Such data differs from other data such as land cover data, due to considerable variations of built-up surface density across the rural-urban continuum resulting in switches of class imbalance, causing sparsely populated confusion matrices based on small underlying sample sizes. In this paper, we aim to fill this gap by testing common agreement measures for their suitability and plausibility to measure the localized accuracy of built-up surface data. We examine the sensitivity of localized accuracy to the assessment support, as well as to the unit of analysis, and analyze the relationships between local accuracy and density / structure-related properties of built-up areas, across rural-urban trajectories and over time. Our experiments are based on the multi-temporal Global Human Settlement Layer (GHSL) and a reference database for the state of Massachusetts (USA). We find strong variation of suitability among commonly used agreement measures, and varying levels of sensitivity to the assessment support. We then apply our framework to assess localized GHSL data accuracy over time from 1975 to 2014. Besides increasing accuracy along the rural-urban gradient, we find that accuracy generally increases over time, mainly driven by peri-urban densification processes in our study area. Moreover, we find that localized densification measures derived from the GHSL tend to overestimate peri-urban densification processes that occurred between 1975 and 2014, due to higher levels of omission errors in the GHSL epoch 1975.