Abstract Metrics representing exposure to the natural environment are widely used in environmental health-related studies. They are calculated using a variety of different data sources representing greenspace and a range of buffer sizes representing human interaction with the environment. Previous studies have identified issues relating to buffer distance and scaling effects on greenspace exposure assessments when using satellite image-derived metrics. We evaluate the spatial scale sensitivity of three common greenspace metrics (i.e., Normalised Difference Vegetation Index- NDVI, Leaf Area Index- LAI, and Land Use and Land Cover-LULC), using lacunarity analysis, as a scale-dependent measure of heterogeneity based on the principles of fractals. By producing a ‘lacunarity curve’ across multiple spatial scales, we defined the scale-variances for specific greenspace metrics, including the upper scale limit at which the metrics become invariant, approximately 640 m for Sentinel-2 and 480 m for Landsat-8. Each of the greenspace metrics we considered exhibited scale sensitivities, meaning that each is expected to have a different influence on the strength and significance of the statistical associations found between greenspace exposure and health depending on the spatial scale of analysis (e.g., buffer distance). Using lacunarity curves, we produced a novel composite, multi-scale greenspace ‘exposure index’ in which each input scale is weighted according to its relative scale sensitivity. We also created a multi-scale, multi-metric map combining the different vegetation measures while accounting for scale. We found that cumulative exposure gradients across a large urban conurbation are even more marked when using our multi-scale ‘exposure index’ maps compared to traditional approaches. Our multi-scale, composite greenspace ‘exposure index’ mapping techniques are not as vulnerable to scale effects as traditional approaches and can be readily transferred to the analysis of other environmental exposure variables such as air pollution.

中文翻译：

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遥感绿色空间指标的规模效应以及如何减轻它们以进行环境健康暴露评估

摘要 表示暴露于自然环境的指标被广泛用于与环境健康相关的研究。它们是使用代表绿色空间的各种不同数据源和代表人类与环境交互的一系列缓冲区大小来计算的。先前的研究已经确定了在使用卫星图像衍生指标时与缓冲区距离和缩放对绿色空间暴露评估的影响有关的问题。我们评估了三个常见绿色空间指标（即归一化差异植被指数 - NDVI、叶面积指数 - LAI 和土地利用和土地覆盖 - LULC）的空间尺度敏感性，使用空隙度分析，作为基于异质性的尺度相关度量关于分形原理。通过产生跨多个空间尺度的“空缺曲线”，我们定义了特定绿色空间指标的尺度方差，包括指标不变的尺度上限，Sentinel-2 约为 640 m，Landsat-8 约为 480 m。我们考虑的每一个绿色空间指标都表现出尺度敏感性，这意味着根据分析的空间尺度（例如，缓冲距离），预计每个指标都会对绿色空间暴露与健康之间的统计关联的强度和显着性产生不同的影响。使用空隙曲线，我们产生了一种新颖的复合多尺度绿色空间“暴露指数”，其中每个输入尺度根据其相对尺度敏感性进行加权。我们还创建了一个多尺度、多度量的地图，结合了不同的植被措施，同时考虑了比例。我们发现，与传统方法相比，使用我们的多尺度“曝光指数”地图时，大型城市群的累积曝光梯度更加明显。我们的多尺度、复合绿色空间“暴露指数”制图技术不像传统方法那样容易受到尺度效应的影响，并且可以很容易地转移到其他环境暴露变量（如空气污染）的分析中。