Canada’s urban areas have experienced extensive growth over the past quarter century; however, there has been no consistent, spatially explicit approach for quantifying the loss and gain of greenness in cities nationally. Herein, we developed a novel urban greenness score metric using greenness fractions from a multi-decadal time series (1984–2016) of spectrally unmixed annual Landsat satellite image composites to characterize final year (2016) greenness and its overall change for 18 major Canadian urban areas, summarized by census dissemination area (DA). The applied validation procedure confirmed correlation coefficients (ρ) ranging from 0.67 – 0.85 between reference and estimated greenness fractions, indicating that spectral unmixing is an appropriate method for extracting urban greenness from a time series of medium spatial resolution satellite imagery. Most DAs across Canada sustained a moderate (∼20 % – 40 %) or low (≲ 20 %) level of greenness between 1984 and 2016, but overall there was a decreasing trend in greenness. Eastern urban areas maintained the most greenness over time, while urban areas in the Prairies had the greatest increase in greenness. Densely populated urban areas experienced the greatest loss in greenness (16 % of DAs); whereas, urban areas with a moderately-low density experienced the greatest increase (14 % of DAs). In agreement with previous studies, we found that greenness was negatively associated with urban infilling, with lower greenness levels typically found in urban cores, and greenness loss most often found in the urban periphery in conjunction with urban expansion. Methods presented in this analysis take advantage of the open and longstanding Landsat archive, as well as multiple spatial scales, including sub-pixel unmixing techniques, pixel level greenness faction data summarized for management units, and analysis conducted nationally. The developed urban greenness score provides a comprehensive framework to understand current urban greenness and relate it to its recent past, which supports long-term strategic planning, and can be transferred to other regions across spatial and temporal scales.

在过去的25年中，加拿大的城市地区经历了广泛的增长。然而，在全国范围内，还没有一致的，空间明确的方法来量化绿色损失和获得。在此，我们使用光谱未混合的年度Landsat卫星图像合成物的多个十年时间序列（1984-2016）中的绿色度分数，开发了一种新颖的城市绿色度分数指标，以表征加拿大18个主要城市的去年（2016）绿色度及其总体变化区域，按人口普查传播区域（DA）进行汇总。应用的验证程序确认了相关系数（ρ）介于参考值和估计的绿色度之间，介于0.67 – 0.85之间，表明光谱分解是从中等空间分辨率卫星图像的时间序列中提取城市绿色度的合适方法。在1984年至2016年之间，加拿大大部分DA都保持了中度（〜20％– 40％）或低度（≲20％）的绿色水平，但总体而言，绿色度呈下降趋势。随着时间的推移，东部城市地区的绿化程度最高，而大草原地区的城市地区的绿度增加幅度最大。人口稠密的城市地区遭受的绿色损失最大（占DA的16％）；而密度适中的城市地区增幅最大（占DA的14％）。与先前的研究一致，我们发现绿色与城市填埋负相关，通常在城市核心区发现较低的绿色度，而在城市外围地区，随着城市的扩张，绿色度降低最为常见。该分析中介绍的方法利用了长期开放的Landsat档案以及多个空间尺度，包括亚像素分解技术，为管理单位汇总的像素级绿色派系数据以及在全国范围内进行的分析。发达的城市绿色度得分提供了一个全面的框架，可以了解当前的城市绿色度并将其与最近的过去联系起来，这支持长期的战略规划，并且可以跨时空范围转移到其他地区。该分析中介绍的方法利用了长期开放的Landsat档案以及多个空间尺度，包括亚像素分解技术，为管理单位汇总的像素级绿色派系数据以及在全国范围内进行的分析。发达的城市绿色度得分提供了一个全面的框架，可以了解当前的城市绿色度并将其与最近的过去联系起来，这支持长期的战略规划，并且可以跨时空范围转移到其他地区。该分析中介绍的方法利用了长期开放的Landsat档案以及多个空间尺度，包括亚像素分解技术，为管理单位汇总的像素级绿色派系数据以及在全国范围内进行的分析。发达的城市绿色度得分提供了一个全面的框架，可以了解当前的城市绿色度并将其与最近的过去联系起来，这支持长期的战略规划，并且可以跨时空范围转移到其他地区。