Rapid urbanization and global climate change are intensifying the Urban Heat Island (UHI) effect in cities worldwide, with consequences for human health and well-being. Urban green spaces (UGSs) mitigate extreme temperatures, but their cooling potential depends on spatial configuration, size, shape, and distribution. This study fills a geographic gap by providing one of the first detailed analyses of UGSs-Land Surface Temperature (LST) dynamics in a North African context. It combines spatial pattern analysis with temporal trend detection to comprehensively evaluate UGSs-LST relationships in Algiers from 2004 to 2022, addressing a common limitation in the literature where these approaches are often treated separately. Using freely available satellite data and open-source tools—including Landsat data from Google Earth Engine (GEE), QGIS, and RStudio—we integrate supervised classification, landscape metrics (LMs) computation via our custom PyQGIS LMs calculator, and Mann-Kendall trend analysis to quantify the static spatial configuration of green spaces and their dynamic thermal impacts over time. Findings reveal a 38% decrease in green spaces and a 10% reduction in agricultural land, accompanied by increased urbanization. Strong negative correlations between some LMs and LST were observed, with PLAND (Percentage of Landscape) explaining 61% of LST variability at an optimal 600-meter scale. This medium-sized scale differs from previous findings in other regions, highlighting the importance of context-specific analysis. LST trend analysis identified specific heat-resistant zones characterized by large, contiguous green patches. Despite greening initiatives, UGSs in Algiers continue to decline, underlining the need to preserve and strategically expand UGSs to combat rising temperatures.

中文翻译：

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用于阿尔及尔城市绿地和温度模式分析的免费卫星数据和开源工具


快速的城市化和全球气候变化正在加剧全球城市的城市热岛效应 （UHI），对人类健康和福祉产生影响。城市绿地 （UGS） 可以缓解极端温度，但其冷却潜力取决于空间配置、大小、形状和分布。本研究通过提供北非背景下 UGS 地表温度 （LST） 动态的首批详细分析之一来填补地理空白。它将空间模式分析与时间趋势检测相结合，全面评估了 2004 年至 2022 年阿尔及尔的 UGSs-LST 关系，解决了文献中这些方法通常单独处理的常见局限性。使用免费提供的卫星数据和开源工具（包括来自 Google Earth Engine （GEE）、QGIS 和 RStudio 的 Landsat 数据），我们通过自定义 PyQGIS LMs 计算器集成了监督分类、景观指标 （LMs） 计算以及 Mann-Kendall 趋势分析，以量化绿色空间的静态空间配置及其随时间变化的动态热影响。研究结果显示，绿地减少了 38%，农业用地减少了 10%，同时城市化进程加快。观察到一些 LM 和 LST 之间存在很强的负相关，PLAND（景观百分比）在最佳 600 米尺度上解释了 61% 的 LST 变异性。这种中等规模的量表与其他地区的先前发现不同，突出了特定环境分析的重要性。LST 趋势分析确定了特定的耐热区，其特征是大而连续的绿色斑块。 尽管采取了绿化措施，但阿尔及尔的 UGS 仍在下降，这凸显了保护和战略性扩大 UGS 以应对气温上升的必要性。