高级检索
张晓敏, 刘知微, 方寒, 等. 2023. 基于Landsat 8 TIRS地表温度数据反演的深圳城市热岛效应时空分布及土地利用的影响[J]. 气候与环境研究, 28(3): 242−250. doi: 10.3878/j.issn.1006-9585.2022.21160
引用本文: 张晓敏, 刘知微, 方寒, 等. 2023. 基于Landsat 8 TIRS地表温度数据反演的深圳城市热岛效应时空分布及土地利用的影响[J]. 气候与环境研究, 28(3): 242−250. doi: 10.3878/j.issn.1006-9585.2022.21160
ZHANG Xiaomin, LIU Zhiwei, FANG Han, et al. 2023. Spatiotemporal Distribution of an Urban Heat Island and the Influence of Land Use over Shenzhen Based on Landsat 8 TIRS Image Data in 2014–2021 [J]. Climatic and Environmental Research (in Chinese), 28 (3): 242−250. doi: 10.3878/j.issn.1006-9585.2022.21160
Citation: ZHANG Xiaomin, LIU Zhiwei, FANG Han, et al. 2023. Spatiotemporal Distribution of an Urban Heat Island and the Influence of Land Use over Shenzhen Based on Landsat 8 TIRS Image Data in 2014–2021 [J]. Climatic and Environmental Research (in Chinese), 28 (3): 242−250. doi: 10.3878/j.issn.1006-9585.2022.21160

基于Landsat 8 TIRS地表温度数据反演的深圳城市热岛效应时空分布及土地利用的影响

Spatiotemporal Distribution of an Urban Heat Island and the Influence of Land Use over Shenzhen Based on Landsat 8 TIRS Image Data in 2014–2021

  • 摘要: 基于2014~2021年Landsat 8 TIRS卫星遥感影像数据,分别采用劈窗算法(SWA)和大气校正法(ARC)反演了深圳市地表辐射温度,并利用地面站点监测数据对反演结果进行了验证,探讨了该期间深圳城市热岛效应的时空分布特征及其影响因素。研究结果表明,这两种算法反演的地表温度(TSWA TARC)与地面站点监测气温(TM)都存在显著线性相关(TSWA = 1.01×TM+2.65,TARC = 0.85×TM+5.51,p < 0.01),但劈窗算法更接近地面站点监测数据。在2014~2021年间深圳城市热岛面积(HI>0.01)未观察到显著增加的趋势(p=0.94)。深圳市城市热岛分布与城市发展格局关系密切,城市规划用地类型对城市热岛效应有显著影响。生态水域和生态绿地缓解了城市热岛效应,而交通道路和工业仓储等城市用地强化了城市热岛的形成,并且城市路网的分布和密度对城市热岛的形成有强显著性影响(p=0.003)。

     

    Abstract: Based on the Landsat 8 TIRS remote sensing image data of 2014–2021, land surface radiation temperatures over Shenzhen were retrieved using the split window algorithm (SWA) and atmospheric correction method and verified at specific sites using ground observation temperature data, and the spatial and temporal distribution and influence factors of the urban heat island effect were discussed in this study. A significant linear correlation was found between the land surface temperature from the two algorithms (TSWA) and the ground observation air temperature (TM), i.e., TSWA=1.01×TM+2.65 and TARC= 0.85×TM+5.51 (p<0.01), but the SWA result is better in reflecting the ground observation air temperature. No significant trend (p=0.94) in the urban heat island area (HI>0.01) was observed in Shenzhen in 2014–2021. The spatial distribution of the urban heat island was closely related to the urban development pattern, and the land use of urban planning significantly influenced the urban heat island. The land use types of ecological waters and ecological green spaces played an important role in mitigating the urban heat island effects, whereas the types of traffic roads and industrial storage promoted the formation of the urban heat island. The spatial distribution and density of the Shenzhen urban road network have a strongly significant effect on the formation of an urban heat island (p=0.003).

     

/

返回文章
返回