Analysis of thermal environment effect and its influencing factors in the main urban area of Kunming
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摘要:
以昆明市主城区为研究对象，根据2000年、2010年和2020年3期Landsat TM/OLI遥感影像，反演3个时期的地表温度，采用标准差椭圆分析热环境效应的时空演变，并使用地理探测器探讨热环境效应的影响因素。结果表明：近20 年来，昆明市主城区热岛区集中分布在城镇建设密集区域，热环境方向主轴由东北—西南走向转为西北—东南走向；前期热环境重心向西北方向偏转了57.5°，偏移2.47 km，后期向西北方向偏转24.25°，偏移0.86 km；有极低温区向低温区、较低温区转变，较高温区与高温区向极高温区转变的态势，其中，官渡区的热环境效应增强趋势最显著；建筑用地和植被覆盖度对地表温度产生的影响力最大，建筑用地与高程交互作用时会增加对地表温度的影响力，水体与建筑用地或不透水面的交互作用减弱了水体对热环境效应的缓解作用。
Abstract:Taking the main urban area of Kunming as the research object, based on LandsatTM/OLI remote sensing images of three periods in 2000, 2010 and 2020, the surface temperatures in three periods were inversed. The temporal and spatial evolution of the thermal environment effect was analyzed with the standard deviation ellipse, and the influencing factors of thermal environment effect were discussed by the geographic detector. The results showed that in the recent 20 years, the heat island areas in the main urban area of Kunming had been concentrated in those with intensive urban construction, and the main axis of the thermal environment direction had changed from northeast-southwest to northwest-southeast. In the early stage, the center of the thermal environment deflected 57.5° to the northwest and shifted by 2.47 km, and in the later stage, deflected 24.25° to the northwest and shifted by 0.86 km. There was a trend of transition from extremely low-temperature zone to low-temperature zone and relatively low-temperature zone, and from relatively high-temperature zone and high-temperature zone to extremely high-temperature zone. Among them, the enhancement trend of the thermal environment effect in Guandu District was the most significant. The building lot and fractional vegetation cover (FVC) had the greatest influence on the surface temperature. The influence on the surface temperature would be increased when the interaction between building lot and elevation. And the interaction between water and building lot or impermeable surface would weaken the mitigation effect of water on the thermal environment effect.
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Key words:
- thermal environment effect /
- surface temperature /
- geodetector /
- impact factor /
- Kunming City
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图 2 昆明市主城区地表温度等级
Figure 2. Surface temperature grade of the main urban area of Kunming
图 3 2000—2020年昆明主城区热环境方向变化与重心迁移
Figure 3. Change of thermal environment direction and migration of center in the main urban area of Kunming from 2000 to 2020
图 4 昆明市主城区各行政区热岛区面积占比
Figure 4. Proportion of heat island area of each administrative region in the main urban area of Kunming
图 5 昆明市主城区热环境影响因素空间分布
Figure 5. Spatial distribution of influencing factors of thermal environment in the main urban area of Kunming
表 1 数据来源
Table 1. Data sources
数据名称 时间 来源 用途 Landsat TM/OLI 2000年2月12日
2010年2月7日
2020年1月18日地理空间数据云( http://www.gscloud )反演地表温度与获取地表信息 DEM数据 2020年 地理空间数据云( http://www.gscloud )获取高程与坡度信息 土地利用类型图 2020年 全球地理信息公共产品( http://globeland30.org/ )计算景观格局指数 POI指数 2020年 中国科学院资源环境科学与数据中心
（https://www.resdc.cn/ ）计算社会经济活动指数 
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表 2 昆明市主城区地表温度等级划分
Table 2. Grade classification of surface temperature in the main urban area of Kunming
温度等级 T 极低温区 T<u−2.5std 低温区 u−2.5std≤T<u−1.5std 较低温区 u−1.5std≤T<u−0.5std 中温区 u−0.5std≤T<u+0.5std 较高温区 u+0.5std≤T<u+1.5std 高温区 u+1.5std≤T<u+2.5std 极高温区 T≥u+2.5std 注：T为研究区域的地表温度；u为地表温度的平均值；std为标准差。
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表 3 2个影响因子对因变量交互作用的类型
Table 3. Types of the interaction of two influencing factors on dependent variables
判断依据 交互作用 q(x1∩x2)<min［q(x1)，q(x2)］ 非线性减弱 min(q(x1),q(x2))<q(x1∩x2)<max［q(x1)，q(x2)］ 单因子非线性减弱 q(x1∩x2)> max［q(x1)，q(x2)］ 双因子增强 q(x1∩x2)= q(x1)+ q(x2) 独立 q(x1∩x2)> q(x1)+ q(x2) 非线性增强
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表 4 2000—2020年昆明市主城区地表温度等级面积及占比
Table 4. Area and proportion of surface temperature grades in the main urban area of Kunming from 2000 to 2020
温度等级 2000年 2010年 2020年 面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/% 极低温区 30.06 1.13 0.00 0.00 6.89 0.26 低温区 251.88 9.51 264.73 9.92 273.23 10.24 较低温区 413.00 15.59 567.73 21.27 537.60 20.14 中温区 1 061.86 40.78 964.14 36.12 945.14 35.41 较高温区 810.96 30.61 704.32 26.38 768.32 28.78 高温区 97.23 3.67 161.96 6.07 129.20 4.84 极高温区 4.51 0.17 6.62 0.25 9.11 0.34
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表 5 昆明市主城区各行政区地表温度等级面积统计
Table 5. Statistics table of heat island area of each administrative region in the main urban area of Kunming
km2 行政区 2000年 2010年 2020年 较高温区 高温区 极高温区 较高温区 高温区 极高温区 较高温区 高温区 极高温区 呈贡区 232.65 35.60 0.72 179.36 41.48 0.62 191.74 28.71 1.20 官渡区 204.13 5.85 0.08 198.58 43.77 1.50 226.35 32.77 2.02 盘龙区 80.12 6.59 0.11 70.17 16.09 0.65 68.31 10.24 0.54 五华区 114.31 22.00 1.78 88.32 25.18 1.70 103.71 24.33 2.76 西山区 179.20 26.01 1.68 167.51 34.03 1.96 177.58 31.53 2.34
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表 6 昆明市主城区地表温度与影响因子的相关性
Table 6. Correlation between surface temperature and influencing factors in the main urban area of Kunming
项目 FVC MNDWI NDBI NDISI COHESION DIVISION SHDI SHEI 高程 坡度 POI 相关系数 −0.033 7 −0.360 2 0.596 7 −0.197 7 0.085 9 0.200 3 0.217 8 0.200 3 −0.037 6 0.000 6 0.066 9 显著水平 0.000 0 0.014 9 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.613 2 0.000 0 0.000 0
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表 7 影响因子对昆明市主城区地表温度的影响力
Table 7. Influence of influencing factors on the surface temperature in the main urban area of Kunming
项目 FVC MNDWI NDBI NDISI COHESION DIVISION SHDI SHEI 高程 坡度 POI 解释力 0.349 2 0.264 5 0.372 5 0.210 8 0.110 8 0.110 8 0.110 2 0.110 8 0.142 5 0.063 3 0.010 9 显著水平 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.005 0 1.000 0
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表 8 影响因子的交互作用结果
Table 8. Results of the interaction of influencing factors
交互类型 交互影响力 交互作用 交互类型 交互影响力 交互作用 FVC∩MNDWI 0.42 双因子增强 NDBI∩坡度 0.44 非线性增强 FVC∩NDBI 0.42 双因子增强 NDISI∩COHESION 0.28 双因子增强 FVC∩NDISI 0.37 双因子增强 NDISI∩DIVISION 0.28 双因子增强 FVC∩COHESION 0.45 双因子增强 NDISI∩SHDI 0.28 双因子增强 FVC∩DIVISION 0.45 双因子增强 NDISI∩SHEI 0.28 双因子增强 FVC∩SHDI 0.44 双因子增强 NDISI∩高程 0.37 非线性增强 FVC∩SHEI 0.45 双因子增强 NDISI∩坡度 0.30 非线性增强 FVC∩高程 0.49 双因子增强 COHESION∩DIVISION 0.11 双因子增强 FVC∩坡度 0.42 非线性增强 COHESION∩SHDI 0.11 双因子增强 MNDWI∩NDBI 0.28 单因子非线性减弱 COHESION∩SHEI 0.11 双因子增强 MNDWI∩NDISI 0.23 单因子非线性减弱 COHESION∩高程 0.30 非线性增强 MNDWI∩COHESION 0.34 双因子增强 COHESION∩坡度 0.18 非线性增强 MNDWI∩DIVISION 0.34 双因子增强 DIVISION∩SHDI 0.11 双因子增强 MNDWI∩SHDI 0.34 双因子增强 DIVISION∩SHEI 0.11 双因子增强 MNDWI∩SHEI 0.34 双因子增强 DIVISION∩高程 0.30 非线性增强 MNDWI∩高程 0.47 非线性增强 DIVISION∩坡度 0.18 非线性增强 MNDWI∩坡度 0.38 非线性增强 SHDI∩SHEI 0.11 双因子增强 NDBI∩NDISI 0.38 双因子增强 SHDI∩高程 0.30 非线性增强 NDBI∩COHESION 0.45 双因子增强 SHDI∩坡度 0.17 双因子增强 NDBI∩DIVISION 0.45 双因子增强 SHEI∩高程 0.30 非线性增强 NDBI∩SHDI 0.45 双因子增强 SHEI∩坡度 0.18 非线性增强 NDBI∩SHEI 0.45 双因子增强 SHEI∩坡度 0.17 双因子增强 NDBI∩高程 0.52 非线性增强
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