城市与区域生态国家重点实验室知识库

      城市人口的聚集 、 城市下垫面 的 变化 以及 人为热 的 增加 导致 城市 出现 了内部温度高于郊区 的 现象，即 城市 热岛效应。 城市 热岛 影响 城市 生态 系统 的 社会、经济、自然等 诸多 方面 并 可能 危害城市 居民的 身心健康， 是 城市 可持续发展 面临的重 大 挑战 。 如何 优化 城市绿地 景观格局 包括 绿地 覆盖比例与 空间分布，以 缓解严重 的城市热岛 是 城市生态学与景观生态学研究的热点和 前沿 。 针对该研究议题， 国内外 学者开展了大量的案例研究， 但 研究结果并不一致， 甚至 存在完全 相反的 结论， 其 原因与机理机制， 值得 深入探讨 。 是 因为 采用的 统计 分析方法不同？ 抑或是 不同 分析 尺度 所致 还是 因为绿地景观格局对温度的影响 确实 因城而 异 其 机理机制又是怎样？ 城市绿地 景观格局对温度的影响， 在 不同城市之间存在 怎样 的差异 本研究 选择多个城市 开展 对比研究，试图 回答上述问题 。 深入剖析城市绿地景观格局对城市温度的 影响 、影响过程、影响 程度 解析 其在不同 城市 之间存在的差异性 并 探讨 可能 的 原因和机理，可服务于城市绿地的科学规划和管理。
      本研究以景观生态学的理论和方法为理论指导 选择了 中国的北京、深圳以及美国的 巴尔的摩 和 萨克拉门托 这 4个具有不同的气候背景、发展程度的城市 作为主要分析对象 基于遥感数据、气象观测数据等数据源， 重点 分析了:1) 城市绿地景观格局 与 地表温度 相关性、 偏 相关性 在不同城市之间的差异性.2) 城市绿地 景观格局 对地表温度 的 影响 过程 和 调节路径 3）绿地 面积比例对地表温度的缓解程度降温效率 在 城市、国家尺度的 空间 分布规律 及 其影响因素 。 主要研究结果 如下: 
    1) 绿地景观格局 显著影响 地表温度 但 其 影响 程度和方式 在不同城市间存在差异
      不同于城市 绿地面积比例 与 地表温度 的 显著 负相关关系， 城市绿地空间配置对地表温度的影响在不同的城市之间存在差异、甚至 出现 完全相反 的现象。如增加 城市绿地平均 斑块 面积 在 萨克拉门托 能够显著 降低地表温度，但是平均斑块面积的增加 在 巴尔的摩 具有升温作用。 城市绿地面积比例、空间配置指数的共同作用 对 地表温度的 解释程度 最大 。 因此 探究 城市 绿地 面积比例、空间配置 及其 相互影响 对 地表温度 的 影响 过程 ，非常 必要和关键 。
      2）城市绿地面积 比例、空间配置及其 相互影响 对地表温度的影响 过程和调节路径 存在 差异性
      城市绿地景观格局对 地表温度 的 影响 路径主要包括：绿地面积比例的增加对 地表温度 的 直接 影响 通过 改变 绿地 空间配置 对 地表温度 的 间接 影响 。对比来看， 直接影响 的 贡献最大，而间接影响 的 贡献 相对 较小 且 在不同 城市 存在差异 。具体表现为： 间接 影响的 贡献 在 高温潮湿的城市 深圳 很 小 甚至 可以忽略 对于高温湿润的城市 巴尔的摩 间接 影响 具有 一定的贡献， 如 采用 复杂形状的 城市 绿地斑块 可对 地表温度 的下降 贡献 间接 影响的贡献 在 高温干旱的城市 更大，即 只有 通过 合理优化绿地空间配置 才能有效缓解 地表温度如增大绿地 平均 斑块面积 。但也需权衡 不同 影响路径 的相互影响 尽管 增加绿地平均斑块 面积可以有效降低地表温度， 但 同等 面积 情况下， 斑块 面积 的 增加很可能 导致 边界 密度的 减小， 从而 导致 地表温度的 增加 。
      3）降温效率在 城市 内部 存在 空间异质性 、 在 城市之间 存在 差异性 并 受气候因素的非线性影响
      本研究定义了 降温效率 即 单位绿地面积的增加，所导致 地表温度 下降的程度 来 研究在不同的城市， 绿地 覆盖面积增加 对 地表温度的 影响的差异 。 研究结果显示 逐日气象条件变化显著影响城市绿地降温效率，并呈现非线性关系 。 以 气温 为例 当 气温小于 阈值 ≈32 降温效率 将随着气温的 升高 而 上升但 当气温高于 32 时 降温效率 随着 气温 的 升高 而 下降 。 降温效率在城市内部存在 空间异质性，并且 局地绿地 覆盖 比例较小、且局地地表温度较 高 时 ，局地降温效率更 高 即 绿地的 降温能力 更 强。 针对美国 118个 城市的研究显示 在国家 尺度 上 降温效率平均值为 0.168℃℃(0.040℃℃~0.574 并呈现西南 较高 的
空间分布规律 ，但 在不同的 生物群系 之间呈 显著 差异： 高温 干旱 类型 的生物群系 的降温效率 平均值 显著高于 其他； 以阔叶植被 为主 的 生物群系的 降温 效率显著 高于其他 如 以针叶林或 灌木林 为主） 。气候因素 （如气温 、湿度以及风速均 会非线性 地 影响 降温效率 。

        Rapid urbanization, associated with land cover change such as increase of urban developed land and concentration of urban population, has resulted in many serious ecological and environmental problems, one of which is urban heat island (UHI). UHI, which is likely to be exacerbated in future years due to the synergetic effects of urbanization and global climate change, has significantly negative social, economic and ecological impacts, adversely affects human comfort and health, and finally restricts the resilient and sustainable urban development.
           Urban vegetation bas considered to be the significantly effective avenues and solutions to mitigate the extreme urban heat because of UHI. Thus, exploring the influence of urban vegetation landscape pattern on urban temperature has become a major research focus in urban heat mitigating and urban ecology. Currently, numerous studies have shown that the landscape pattern of urban vegetation (e.g. especially urban tree, which was mainly considered in this study), including composition (e.g. percent of urban trees, Ptree) and configuration (e.g. mean patch size, MPS), could significantly affect urban temperature (e.g. land surface temperature, LST). However, the inconsistent or contradictory results usually exist in previous studies, for example edge density of urban trees has negative effect on urban temperature in some studies, but positive in others. And this inconsistency and contradictoriness prevent the application of results to urban greening planning and management. Detecting in-depth the variations of the relationship between urban vegetation landscape pattern and urban temperature among different cities, and exploring the reasons for such spatial variations could provide guidance for urban greening planning and management policies for cities with different background.
          In this study, based on the theory and methodology of landscape ecology, we mostly focused on four study cities, which have different climate background and development, including Beijing and Shenzhen in China, and Baltimore and Sacramento in the United States. First, the spatial heterogeneity of relationship between spatial pattern of urban trees and LST is revealed and characterized. Second, we clarified the cooling paths of Ptree and spatial configuration on LST. And finally, we defined cooling efficiency (CE) as the LST reduction if 1% of urban trees increased, and revealed the spatial heterogeneity of CE at the city scale and continental scale. And further we explored the influencing factors (e.g. climatic factors) of such spatial heterogeneity. The main results and conclusions are as follows:
         1) All configuration metrics of urban trees were significantly, negatively correlated with LST, across all analytical scales, at four cities. After controlling for the effects of percent cover of trees, however, the correlations (i.e., partial correlations) between configuration metrics and LST changed greatly, in terms of magnitude, significance, and even direction. Notably, mean patch size (MPS) had significantly positive effects on LST in Baltimore, but negative effects in Sacramento. This is due to the different effects of spatial configuration of urban trees on evapotranspiration and shading in different cities, resulting in different net effects on LST. Although both the increase of Ptree and optimization of spatial configuration of urban vegetation can affect LST, their interaction could explain most of urban heat mitigating.
        2) The increase of Ptree can affect LST directly, and it is the most important way to alleviate LST, at the same time, the increase of Ptree can also affect LST via changing spatial configuration of urban trees, but with limited contribution, especially in hot and rainy cities. For hot and humid cities, the indirect paths can explain some of LST, for example using complex urban tree patches while increasing urban trees could explain 13.61% of urban cooling in Baltimore. However, in hot and dry cities, LST can be effectively alleviated only by optimizing the spatial configuration of urban trees, such as increasing the mean patch size (MPS) (68.13%). However, it should be noted the interaction of MPS and edge density (ED), for example, although the increasing of MPS can effectively reduce LST, it is also necessary to weigh the decrease of ED caused by the increase of MPS and which leads to the incraese of LST.3) Ordinary least squares (OLS) model was used to quantify the relationship between Ptree and LST. And we defined the absolute value of this coefficient as cooling efficiency (CE) which could quantify the magnitude of LST reduction by one unit of urban vegetation (e.g., 1% of Ptree) increase. CE varied greatly within a city and at the continental scale:
         3.1) At the city scale, daily meteorological conditions nonlinearly affected CE. Taking air temperature as an example, the increase of air temperature can increase CE, and restrict it when the air temperature exceeded about 32℃℃—The extreme higher temperature will rapidly inhibit CE and weaken the mitigating effects. Air temperature could explain more variations of LST than humidity and wind speed. Additionally, the local CE (i.e. the absolute values of local regression coefficients after using GWR) also showed significant spatial heterogeneity, and affected by the local conditions non-linearly, for example local CE decreased sharply with the increase of local Ptree, but was stable after the certain threshold of local Ptree (e.g. 20-30%), suggesting that urban greening should give priority to places where short of urban trees for the higher local CE.
          3.2) At the continental scale, CE, with the average value of 0.168 and ranging from 0.040 to 0.574, was higher in southwestern cities, especially in hot and dry biomes but not significantly different among other remaining biomes, within which CE was typically greater in biomes dominated by broadleaf trees compared to those dominated by coniferous or sparse trees. Additionally, climate context affected CE non-linearly with threshold, for example increasing temperature and wind speed first improve CE, but after a certain threshold, can reduce it which was the same at the city scale. Similarly, CE could be inhibited by increasing humidity firstly, and be stable after a certain point.