Spatial characteristics analysis of urban thermal diurnal environment based on ECOSTRESS

doi:10.13474/j.cnki.11-2246.2024.0626

Abstract

Abstract: Land surface temperature (LST) is an important index to characterize the change of urban thermal environment, and its distribution information is of great significance for monitoring urban thermal environment. Based on ECOSTRESS data from June to August from 2018 to 2023, diurnal LST in the fourth ring road of Shenyang is obtained through the correction of LST. Mean-standard deviation method and spatial autocorrelation analysis are used to extract diurnal spatial characteristics of LST, and combined with land use data, the contribution degree of different land types to the spatial distribution of land surface temperature is analyzed. As indicated by the results, the LST in the fourth ring road of Shenyang is high in the north and low in the south, and high in the west and low in the east. There is a large difference in LST between day and night. The high temperature area is mainly concentrated in Huanggu district, Dadong district, western Shenhe district and eastern Tiexi district, while the low temperature area and the middle temperature area are mainly concentrated at the edge of the fourth ring road, the artificial LST is mostly higher than the natural LST, and the high temperature area of building land category accounted for the largest proportion, which is the main factor for the warming of LST, while the natural LST is the main factor for the cooling of LST. There are significant clustering and hot spots in the fourth ring road of Shenyang, and the diurnal variation of LST is consistent with the aggregation distribution characteristics of LST.

Key words: urban thermal environment, land surface temperature, spatial autocorrelation analysis, ECOSTRESS, Shenyang city

CLC Number:

P237

PENG Min, YAO Na, SUN Peilei, ZOU Bowen, WANG Wenshuo. Spatial characteristics analysis of urban thermal diurnal environment based on ECOSTRESS[J]. Bulletin of Surveying and Mapping, 2024, 0(6): 151-156,181.

References

[1] OOKA R.Recent development of assessment tools for urban climate and heat-island investigation especially based on experiences in Japan[J].International Journal of Climatology,2007,27(14): 1919-1930.
[2] AFLAKI A,MIRNEZHAD M,GHAFFARIANHOSEINI A,et al.Urban heat island mitigation strategies: a state-of-the-art review on Kuala Lumpur,Singapore and Hong Kong [J].Cities,2017,62: 131-145.
[3] BAHI H,MASTOURI H,RADOINE H.Review of methods for retrieving urban heat islands[J].Materials Today: Proceedings,2020,27: 3004-3009.
[4] ZHAO Lei,LEE Xuhui,SCHULTZ N M.A wedge strategy for mitigation of urban warming in future climate scenarios[J].Atmospheric Chemistry and Physics,2017,17(14): 9067-9080.
[5] 陈梦迪,张友水.福州城市扩张对地表温度影响及其空间分异分析[J].海南师范大学学报(自然科学版),2023,36(3): 301-311.
[6] 姚灵筠,王力,牛铮,等.2000—2018年长株潭城市群城市扩张及其热岛响应[J/OL].自然资源遥感,2023: 1-7.(2023-09-04).https://kns.cnki.net/kcms/detail/10.1759.P.20230904.1002.014.html.
[7] 李威洋,史海静,聂玮廷,等.基于地表参数变化的延河流域地表温度时空演变分析[J/OL].自然资源遥感,2023: 1-10.(2023-08-04).https://kns.cnki.net/kcms/detail/10.1759.p.20230803.0935.008.html.
[8] 段四波,茹晨,李召良,等.Landsat卫星热红外数据地表温度遥感反演研究进展[J].遥感学报,2021,25(8): 1591-1617.
[9] 赵欣,赵凯旭,黄晓军.基于ECOSTRESS地表温度和手机信令数据的城市人口热环境暴露风险评价: 以西安市为例[J].地理科学进展,2022,41(11): 2061-2072.
[10] 覃盟琳,宋文博,宋苑震,等.北部湾城市群热岛空间特征及演变趋势研究[J].安全与环境学报,2020,20(4): 1557-1566.
[11] ALKAFY A,RAHMAN M S,FAISAL A A,et al.Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi,Bangladesh[J].Remote Sensing Applications: Society and Environment,2020,18: 100314.
[12] BERGER C,ROSENTRETER J,VOLTERSEN M,et al.Spatio-temporal analysis of the relationship between 2D/3D urban site characteristics and land surface temperature[J].Remote Sensing of Environment,2017,193: 225-243.
[13] 李文亮,张丽娟,陈红,等.哈尔滨市城市扩展与地表热环境变化关系研究[J].地域研究与开发,2010,29(2): 49-52.
[14] 李丽光,许申来,王宏博,等.基于源汇指数的沈阳热岛效应[J].应用生态学报,2013,24(12): 3446-3452.
[15] 王植,董斌,陈炟君.基于Landsat的沈阳城市热岛效应与地表参数变化分析[J].测绘与空间地理信息,2018,41(10): 4-7.
[16] 姜诗慧,彭剑峰,宋永会,等.沈阳市2005—2012年水足迹与水资源承载力分析[J].环境工程技术学报,2017,7(1): 15-23.
[17] 王宏博,李丽光,赵梓淇,等.基于TM/ETM⁺数据的沈阳市各区城市热岛特征[J].生态学杂志,2015,34(1): 219-226.
[18] FISHER J B,LEE B,PURDY A J,et al.ECOSTRESS:NASA's next generation mission to measure evapotranspiration from the international space station[J].Water Resources Research,2020,56:e2019WR026058.
[19] CHANG Y,XIAO J F,LI X X,et al.Exploring diurnal thermal variations in urban local climate zones with ECOSTRESS land surface temperature data[J].Remote Sensing of Environment,2021,263:112544.
[20] 哈尚晨,阿里木江·卡斯木.基于土地集约利用水平的城市热岛效应影响因子分析[J].冰川冻土,2016,38(1):270-278.
[21] LEMMERZ T,HERLÉ S,BLANKENBACH J.Geostatistics on real-time geodata streams—high-frequent dynamic autocorrelation with an extended spatiotemporal Moran’s I index[J].ISPRS International Journal of Geo-information,2023,12:350.
[22] MEKONNEN A A,SIPOS T,KRIZSIK N.Identifying hazardous crash locations using empirical bayes and spatial autocorrelation[J].ISPRS International Journal of Geo-information,2023,12:85.