Quantitative analysis of spatial–temporal variation and relation of LST and relevant ecological elements

被引：0

作者：

Lin W. ^{[1
]}

机构：

[1] Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection, College of Environment and Resources, Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou

来源：

International Journal of Computers and Applications | 2020年 / 42卷 / 01期

基金：

中国国家自然科学基金;

关键词：

evapotranspiration; index-based build-up index (IBI); LST; remote sensing; urban heat island;

D O I：

10.1080/1206212X.2017.1397384

中图分类号：

学科分类号：

摘要：

Land surface temperature (LST), regional evapotranspiration (ET), build-up land information, vegetation information, and other land surface parameters are of great significance for urban scientific planning and urban ecosystem restoration. Taking Fuzhou City as an example and gaining the LST, regional ET, build-up land and vegetation information by Landsat satellite images, this paper finds out the spatial-temporal variation of the LST and ET in study area and conducts quantitative analysis for the relationship of the above land surface parameters. The results show that the distribution of ET in Fuzhou City has obvious zonation and the LST, urban build-up land and vegetation are significant factors affecting the urban heat island and regional ET. There is a clear linear positive correlation between ET and land surface vegetation cover and a significant linear negative correlation between ET and LST as well as between ET and build-up land information. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.

引用

页码：57 / 66

页数：9

共 27 条

[11]

Zhang H., Qi Z., Ye X., Et al., Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China, Appl Geogr, 44, pp. 121-133, (2013)

[12]

Jimenez-Munoz J.C., Sobrino J.A., Skokovi'c D., Et al., Land surface temperature retrieval methods from landsat-8 thermal infrared sensor data, IEEE Geosci Remote Sens Lett, 11, pp. 1840-1843, (2014)

[13]

Rozenstein O., Qin Z., Derimian Y., Et al., Derivation of land surface temperature for landsat-8 TIRS using a split window algorithm, Sensors, 14, 4, pp. 5768-5780, (2014)

[14]

Coseo P., Larsen L., How factors of land use/land cover, building configuration, and adjacent heat sources and sinks explain urban heat islands in Chicago, Landscape Urban Planning, 125, pp. 117-129, (2014)

[15]

Barsi J.A., Schott J.R., Palluconi F.D., Et al., Validation of a web-based atmospheric correction tool for single thermal band instruments. Earth Observing Systems X, Proceedings of SPIE. Vol. 5882, San Diego, (CA), (2005)

[16]

Xu H., Image based normalization technique used for landsat TM /ETM+ imagery, Geomatics Inf Sci Wuhan Univ, 32, 1, pp. 62-66, (2007)

[17]

Bastiaanssen W.G.M., Menenti M., Feddes R.A., Et al., A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation, J Hydrol, 212-213, pp. 198-212, (1998)

[18]

Bastiaanssen W.G.M., Pelgrum H., Wang J., Et al., A remote sensing surface energy balance algorithm for land (SEBAL), J Hydrol, 212-213, pp. 213-229, (1998)

[19]

Jackon R.D., Hatifield J.L., Reginato R.J., Et al., Estimation of daily evapotranspiration from one time-of-day measurements, Agric Water Manage, 7, 1-3, pp. 351-362, (1983)

[20]

Georgescu M., Moustaoui M., Mahalov A., Et al., Summer-time climate impacts of projected megapolitan expansion in Arizona, Nat Clim Change, 3, 1, pp. 37-41, (2013)

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