A New Approach for Surface Urban Heat Island Monitoring Based on Machine Learning Algorithm and Spatiotemporal Fusion Model

Abstract

Land surface temperature (LST) is an important indicator for assessing the surface urban heat island (SUHI) effect. This paper presents a novel approach to derive LST estimates by integrating machine learning algorithm and spatiotemporal fusion model at high spatial and temporal resolution. The spatial resolutions of Landsat TM and Landsat 8 LST data were first downscaled using random forest (RF) algorithm from 120 m and 100 m, respectively, to 30 m. The resultant LST data were fused with MODerate-resolution Imaging Spectroradiometer (MODIS) LST data, by means of the Flexible Spatiotemporal Data Fusion method (FSDAF), in order to generate high spatiotemporal resolution summer daytime LST data covering the center of Chengdu city in China. The proposed new method was used to estimate the spatiotemporal variations of the summer daytime SUHI from 2009 to 2018 over Chengdu city. Results show that: (1) RF performs way better than the classical downscaling algorithm-thermal sharpening algorithm (TsHARP) for LST, and produces higher accuracy for different land covers; (2) the fused high spatiotemporal resolution summer daytime LST values were evaluated with in situ LST obtained from Chengdu Meteorological Office and the final validation results indicated that the proposed method, in generating LST dataset, can provide more details of urban thermal environment and produce higher accuracy than the traditional FSDAF; (3) significantly increasing trends of summer daytime SUHI intensity (SUHII) in the study area were observed. SUHII increased from 2.78 ℃ in 2009 to 4.04 ℃ in 2018. The highest and lowest summer daytime LST estimates were recorded over impervious surface area (ISA) and waters, respectively.

Predicting Land Surface Temperature and Land Cover Changes Based on Multisource Remote Sensing Spatio-Temporal Fusion in Hefei, Eastern China

“…16) because FSDAF integrates the thin-plate spline method and the homogeneity index to assign more residuals to subpixels with more significant errors. Specifically, FSDAF retrieves a certain number of coarse pixels containing each end element from the whole image to ensure that small objects are not overestimated as a result of small targets being ignored, and a similar finding was reported by Wang and Yao [59], [60] to produce landscape bias and misclassification.…”

Correlations of Spatial Form Characteristics on Wind–Thermal Environment in Hill-Neighboring Blocks

“…Otherwise, there are regional differences in the attributes of blocks in different cities in China and even worldwide, which shows a lack of systematic summarization. In the future, more comprehensive and interdisciplinary [50,51] research should be established based on building climate zoning combined with regional characteristics. And the results can be applied to the spatial analysis of urban design and block regeneration, which will provide more informative support for planning strategies.…”