| 摘要: |
| 近年来，受全球气候变暖与快速城镇
化的叠加影响，城市极端高温频发，热环境问
题凸显，严重威胁到城市人居环境质量。城市
形态是影响热环境时空分布的关键因素，如何
通过优化形态要素缓解城市高温问题，降低
城市热浪风险已成为当前学界关注的热点问
题。本研究以西安市主城区410个街区单元为
研究对象，重点关注地表热环境，利用遥感影
像数据、建筑矢量数据、谷歌地图数据，通过
ENVI地表温度反演、ArcGIS空间统计分析、
Fragstats景观格局分析等方法量化了研究区
内地表热环境时空变化特征，并从“二维地表
覆盖—三维空间组合—景观格局特征”三个
维度对街区形态要素进行系统描述，在此基
础上，引入主成分分析方法科学评估了各形态
要素对地表热环境的贡献。研究结果表明：第一，2000—2019年西安市热岛由中心向周边连片式蔓延，一般高温区为商业密集区（如钟楼、小
寨等）、集中工业物流区（如北部工业区、西部物流仓储区、西南部工业区等），低温区为城市公
园、绿地水体周边，总体而言，西安市热环境延展方向与城市扩张方向基本吻合；第二，2000—
2019年研究区内平均地表温度上升7.29 ℃，平均热岛强度上升3.15 ℃，约99.7%为热岛区，总升
温量达3 491.84 ℃/km2
，其中四级、五级热岛强度区逐年递增，一级、二级热岛强度区逐年减
小，整体升温效果显著，热岛效应不断增强；第三，二维地表覆盖、类型水平景观格局是影响地
表热环境最为主要的两类主控因素，指标权重占比为36.64%、35.50%，其中建设用地占比、建筑
密度正向影响突出，贡献度为0.658、0.319，绿地占比负向影响最大，贡献度为-0.718。建筑高度
变异度、孔隙率、天空开阔度等三维空间指标的贡献则相对较小，该类指标权重仅占9.39%。本
研究结果可为优化城市形态改善局地热环境，缓解其负面效应提供重要科学依据。 |
| 关键词: 地表热环境 形态要素 主成分分析 贡献度评估 时空变化 |
| DOI：10.13791/j.cnki.hsfwest.20230605 |
| 分类号: |
| 基金项目:国家自然科学基金项目（52278087）；陕西省自然科
学基础研究计划资助项目（2023-JC-QN-0468）；陕
西省社会科学基金项目（2023J012）；陕西省哲学社
会科学重大理论与现实问题研究（2022HZ1202）；
陕西省教育厅一般专项科研计划项目（22JK0118） |
|
| Assessing the Contribution and Impact of the Block Morphological Factors on the Surface Thermal Environment Using Multi-Source Data |
|
GAO Yuejing,ZHAO Jingyuan
|
| Abstract: |
| In recent years, due to the combined impact of global climate change and rapid
urbanization, overheating issues have occurred frequently in numerous cities. As one of the well known heat-related phenomena, urban heat island (UHI) has been recognized to have a high
occurrence probability, wide impact, and high risk in the future. Generally, the UHI refers to land
surface temperature (LST) and air temperature in urban areas that are significantly higher than those
in the surrounding rural areas. Recent studies have shown that the UHI effect can increase energy
consumption, accelerate air pollution, and affect urban prosperity and livability. This inevitably
poses a negative impact on human health, especially for elderly people aged above 65. Thus, it
is significant to pay more attention to the rising temperatures in urban areas. How to effectively
alleviate such serious weather-related problems and reduce urban heat-wave risk has become a
critical issue in the current process of high-quality urban development.
Urban morphology is a key factor affecting the spatiotemporal distribution of the thermal
environment. The different morphology patterns directly affect the surface heat storage and
evaporation, further influencing the distribution of horizontal and vertical wind distribution.
Specifically, the transformation of urban morphology in two-dimensional and three-dimensional
directions significantly affects the regional heat accumulation. In the process of urbanization, a
large number of buildings and roads with high heat storage capacity continuously dissipate heat,
accompanied by traffic heat emissions and anthropogenic heat emissions, causing changes in heat
exchange and airflow between the surface and atmospheric environment. This further affects the
local thermal environment within the city, resulting in a microclimate phenomenon where the
temperature in the urban built-up areas is significantly higher than that in the surrounding suburbs.
Thus, rational optimization of urban morphology can effectively alleviate the UHI effect. Numerous
studies have explored the spatiotemporal characteristics and the impact of morphological factors on
the thermal environments to address the problem of the UHI effect. However, these studies mainly focus on the two-dimensional land surface cover by using traditional method such as correlation analysis and linear regression, lacking a systematic analysis
with multidimensional indicators. Furthermore, the contribution of different morphological indicators to the local thermal environment has not yet been fully
understood.
To address the aforementioned knowledge gap, 410 block units in the main urban area of Xi’an were the study object, with a focus on the surface
thermal environment. ENVI land surface temperature (LST) inversion, ArcGIS spatial statistical analysis, and Fragstats landscape pattern analysis
were adopted to quantify the spatiotemporal variation of the surface thermal environment by using remote sensing image data, building vector data, and
Google Map data. Meanwhile, block morphological features were systematically described in terms of two-dimensional land cover, three-dimensional
space combination, and landscape pattern characteristics. Furthermore, the principal component analysis method was used to scientifically evaluate the
contribution of various morphological elements to the surface thermal environment. This study found that from 2000 to 2019, the heat island in Xi’an spread
from the center to the periphery. Generally, high-temperature areas are concentrated commercial areas (Bell Tower), industrial areas in the north, storage
areas in the west, industrial areas in the southwest, etc., while low-temperature areas are urban parks and water bodies. The average LST increased by 7.29 ℃,
the average UHII increased by 3.15 ℃, and the total amount of LST increased by 3491.84 ℃/km2. Furthermore, two-dimensional land cover and landscape type patterns have a significant influence on the thermal environment at the block level, with index weight ratios of 36.64% and 35.50%. The proportion of
construction land and building density have prominent positive influence, with the contribution degree of 0.658 and 0.319. The proportion of green space has
the largest negative influence, with a contribution degree of -0.718. By contrast, three-dimensional spatial indicators such as BH, P, and SVF have a relatively
minor impact, with an index weight ratio of 9.39%. The results of the study can provide an important scientific basis for optimizing the urban morphology to
improve the local thermal environment and mitigate its negative effects. |
| Key words: Surface Thermal Environment Morphological Factors Principal Component Analysis Contribution Assessment Spatial-Temporal Evolution |
