The rapid population growth, intensive use of natural resources, and inadequate planning for sustainable cities have diminished people’s access to the natural environment. This has resulted in insufficient greenspace provision and increased exposure inequity in neighborhoods, particularly in arid regions. This study aimed to map urban greenspaces (UGS) in Greater Zarqa Municipality (GZM) and innovatively combine multiple greenspace exposure indicators to identify neighborhoods that are deprived of greenspace. A random forest classification algorithm, utilizing sentinel-2 images and vegetation indices, were used to classify the GZM land into built-up, greenspace, and undeveloped lands. Per capita greenspace, greenspace density, mitigation, restoration, and instoration exposure indicators were calculated and mapped for GZM neighborhoods. The suitability modeler technique was then applied to create an integrated UGS exposure indicator, identifying urban neighborhoods deprived of greenspace. The classification achieved an overall accuracy of 96 % and revealed a very poor urban greenspace density of 2.1 % that is unequally distributed among neighborhoods. The Zarqa Qasabah and Russeifa were poorly vegetated compared to the Hashemyiah district. UGS exposure indicators showed that over 80 % (97 out of 121) of neighborhoods did not meet the minimum per capita greenspace, 10 % greenspace density, or acceptable potential for mitigation, restoration and instoration of greenspace exposure. The combined UGS exposure indicator ranked 4 % of GZM neighborhoods (covering 5 % of GZM land) as high UGS exposure areas, containing only 0.2 % of the built-up area and housing 0.5 % of population. In contrast, the majority of neighborhoods (56 %) were classified as having poor UGS exposure, encompassing 56 % of the built-up area and accommodating 67 % of the GZM population. This integrated indicator approach effectively assessed UGS exposure and provided an unbiased estimation for identifying greenspace-deprived neighborhoods. The findings can guide decision-makers and urban planners in promoting greenspace exposure equity and developing new UGSs in underserved areas.

中文翻译：

---

使用综合绿地暴露指标评估干旱城市社区绿地可用性的空间差异


人口的快速增长、自然资源的密集使用以及对可持续城市的规划不足，减少了人们接触自然环境的机会。这导致绿地供应不足，并加剧了社区的曝光不平等，尤其是在干旱地区。本研究旨在绘制大扎尔卡市 （GZM） 的城市绿地 （UGS） 地图，并创新性地结合多个绿地暴露指标来识别被剥夺绿地的社区。利用 sentinel-2 图像和植被指数的随机森林分类算法将 GZM 土地分类为建筑区、绿地和未开发土地。计算并绘制了 GZM 社区的人均绿地、绿地密度、缓解、恢复和灾变暴露指标。然后，应用适宜性建模器技术来创建集成的 UGS 暴露指标，以识别没有绿地的城市社区。该分类实现了 96% 的总体准确率，并揭示了非常差的城市绿地密度，为 2.1%，在社区之间分布不均。与 Hashemyiah 区相比，Zarqa Qasabah 和 Russeifa 的植被很差。UGS 暴露指标显示，超过 80%（121 个中的 97 个）的社区没有达到最低人均绿地面积、10% 的绿地密度或可接受的缓解、恢复和减少绿地暴露的潜力。综合 UGS 暴露指标将 4% 的 GZM 社区（覆盖 5% 的 GZM 土地）列为 UGS 高暴露区域，仅包含 0.2% 的建筑面积和 0.5% 的人口。 相比之下，大多数社区 （56%） 被归类为 UGS 暴露率低，占建筑面积的 56%，容纳了 67% 的 GZM 人口。这种综合指标方法有效地评估了 UGS 暴露，并为识别绿地被剥夺的社区提供了公正的估计。这些发现可以指导决策者和城市规划者促进绿地暴露公平性，并在服务不足的地区开发新的 UGS。