中山大学地理科学与规划学院,广东 广州 510006
郑楷灿(2003年生),男;研究方向:城市气候;E-mail:zhengkc@mail2.sysu.edu.cn
廖威林(1990年生),男;研究方向:城市气候、气候变化;E-mail:liaoweilin@mail.sysu.edu.cn
收稿:2026-04-28,
修回:2026-07-07,
录用:2026-07-15,
网络首发:2026-09-20,
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郑楷灿, 廖威林. 不同网格尺度下珠三角城市群蓝绿灰空间景观格局对夏季地表温度的影响[J/OL]. 中山大学学报(自然科学版)(中英文), 2026,1-10.
Zheng Kaican, Liao Weilin. Impacts of landscape patterns of blue-green-gray spaces on summer land surface temperature across multiple grid scales in the Pearl River Delta urban agglomeration[J/OL]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2026, 1-10.
郑楷灿, 廖威林. 不同网格尺度下珠三角城市群蓝绿灰空间景观格局对夏季地表温度的影响[J/OL]. 中山大学学报(自然科学版)(中英文), 2026,1-10. DOI: 10.11714/acta.snus.ZR20260113.
Zheng Kaican, Liao Weilin. Impacts of landscape patterns of blue-green-gray spaces on summer land surface temperature across multiple grid scales in the Pearl River Delta urban agglomeration[J/OL]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2026, 1-10. DOI: 10.11714/acta.snus.ZR20260113.
蓝绿灰空间作为城市下垫面的核心构成要素,其景观格局优化对缓解城市热风险具有重要作用。然而,当前针对不同网格尺度下城市群蓝绿灰空间景观格局对地表温度的影响研究仍较为缺乏。因此,本研究选取珠三角城市群作为研究区域,基于2022年的土地覆盖数据和MODIS地表温度数据,结合XGBoost模型、SHAP方法和累积局部效应方法探究不同网格尺度下(1、2、3 km)蓝绿灰空间景观格局对夏季日间地表温度的影响。研究发现,各尺度下灰色空间景观格局均为地表温度变化的首要驱动因素,而蓝绿空间影响随尺度增大而增强。此外,各特征的累积局部效应显示,增加蓝绿空间占比、斑块密度和聚集度,减少灰色空间占比、斑块密度和聚集度,并合理调控各类空间的最大斑块规模和形状复杂度可有效降低地表温度,但均需结合具体空间尺度确定最优景观配置。本研究可为城市景观空间布局优化提供科学依据,助力城市群的可持续发展。
Blue-green-gray spaces are principal components of urban underlying surfaces, and optimizing their landscape patterns can significantly mitigate urban thermal risks. However, studies on the impacts of blue-green-gray space landscape patterns on land surface temperature (LST) across multiple grid scales in urban agglomerations remain limited. Therefore, this study selected the Pearl River Delta urban agglomeration as the study area and used land cover data and MODIS LST data for 2022 to investigate how landscape patterns of blue-green-gray spaces affect summer daytime LST across multiple grid scales (i.e., 1, 2, and 3 km) by combining the XGBoost model, SHAP method, and accumulated local effects (ALE) method. Results show that gray space landscape patterns are the primary drivers of LST variation across all scales, whereas the influence of blue and green spaces increases with grid size. In addition, the ALE results indicate that increasing the proportion, patch density, and aggregation of blue and green spaces, reducing the proportion, patch density and aggregation of gray spaces, and reasonably regulating the largest patch size and shape complexity of various spaces can effectively reduce LST. However, the optimal landscape configuration must be determined according to specific spatial scales. The findings provide a scientific basis for optimizing urban landscape patterns and support the sustainable development of urban agglomerations.
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