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绿地格局对城市地表热环境调节作用的多尺度分析
Multi⁃scale analysis of the effects of green space pattern on the urban surface thermal environment
城市绿化是协助城市应对气候变化、缓解城市热岛效应的有效途径，而分析影响绿地降温效应的因素及机制是合理改善城市绿化措施的理论基础。
基于Landsat?8 TIRS热红外数据反演地表温度，并通过同期Spot 6遥感影像数据解译土地利用/覆盖类型并获取绿地信息，在此基础上结合景观指标和移动窗口法，从不同尺度分析城市绿地空间格局对地表温度的影响。
在斑块水平上，斑块面积、形状以及相邻绿地面积对乔木林地斑块内部温度具有显著影响；与乔木林地不同，草地斑块的内部温度主要受斑块面积的影响，与二维形状复杂度无明显相关性。在不同地表中，水体降温效应最强，乔木林地次之，草地最弱。在类型水平上，增加乔木林地面积占比、加强边界复杂程度，以及提高林地斑块之间的聚集度，可以有效地降低区域温度；景观组分对降温强度的影响高于景观构型，结果显示每增加10 %的乔木林地覆盖面积，可以降低区域温度1.03 ℃。绿地景观格局与地表温度的相关性具有一定的尺度依赖性。
开展绿地空间格局与降温强度的关系研究，有助于实现绿地的合理配置与前瞻性布局，能够为城市绿地规划以及可持续发展规划建设提供切实可行的参考依据。
Urban greenspace can be an effective contributor to mitigation of the urban heat island (UHI) effect and adaption to urban climate change. Previous studies have confirmed that the relationship between urban green pattern and land surface temperature (LST) is sensitive to spatial resolution of applied remote sensing imagery; however, little is known about spatial extent, another scaling issue. This study examined the effects of greenspace pattern on urban cooling and the influence of spatial extent when applied to derive landscape metrics. Understanding how the spatial pattern of urban greenspace affects the cooling intensity at different spatial extents is essential for creating a more scientific urban green network to better counteract the UHI effect.
The study applied Landsat?8 TIRS imagery to derive LST data and Spot 6 imagery to retrieve the land-use and land-cover (LULC) map. The spatial pattern of woodland was measured by landscape metrics over four spatial extents/scales (90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m) using a moving-window approach based on the LULC map. The relationship between landscape metrics and LST was established using correlation analyses and regression analyses.
At patch level, the size, shape and connectivity with neighboring greenspaces all affect the cooling intensity of woodland. Meanwhile, patch area (PA) is the main factor influencing the LST of grassland. An increase in size and shape complexity can effectively reduce the LST within the greenspace. Compared with different LULC types, water performs best in urban cooling, followed by woodland and grassland, respectively. At class level, areas with a higher percentage of woodland cover experience a greater cooling effect, and a 10% increase of woodland resulted in a decrease in LST of 1.03 ℃. When given a fixed amount of woodland cover, aggregated distribution provides a stronger cooling effect than relative fragmented distribution. This study has suggested that landscape composition is more important than spatial configuration in determining the magnitude of LST. Moreover, results also demonstrated that changing spatial extent had significant impacts on the relationship between spatial pattern of urban greenspace and LST. Specifically, the significance of correlation between percentage of landscape (PLAND), mean patch size (MPS), largest patch index (LPI), and LST decreased as the spatial extent increased, and increased as the spatial extent increased between number of patches (NP), aggregation index (AI) and LST. The relationship between mean patch shape index (Shape_MN) and LST is not as sensitive as other landscape metrics to spatial extent. The findings in this study indicate that multi-scale analysis is required to fully explore the relationship between urban green pattern and LST.
Quantifying the relationship between spatial patterns of greenspaces and cooling intensity will provide a better prediction of the optimal pattern required to cool the urban environment, thus providing practical suggestions for urban greenspace planning and sustainable development.
城市绿地 / 多尺度分析 / 景观格局 / 降温效应 / 地表温度(LST)
urban greenspace / multi-scale analysis / landscape pattern / cooling effect / land surface temperature (LST)
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