The Morphology, Dynamics and Potential Hotspots of Land Surface Temperature at a Local Scale in Urban Areas

Wang, Jiong; Zhan, Qiwen; Guo, Huagui

doi:10.3390/rs8010018

Cited by 20 publications

(43 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The green space distribution can be seen in Figure 1. Within 500 m, urban green space plays an important role in cooling the surrounding environment [31]. Thus, this work selected the remote sensing image information space with a precision of 30 m × 30 m and set up 17 buffer zones within 500 m to conduct analysis and research ( Figure 2).…”

Section: Selection Of Green Space and Buffer Settingmentioning

confidence: 99%

Study of the Cooling Effects of Urban Green Space in Harbin in Terms of Reducing the Heat Island Effect

Huang

Cui

2018

Sustainability

View full text Add to dashboard Cite

The urban heat island (UHI) effect might cause extreme weather, which would seriously affect people's health, increase energy consumption and cause other negative impacts. To construct urban green spaces is a feasible strategy to effectively weaken the UHI effect. In this study, the cooling effect of green spaces on the UHI effect was carefully investigated in summer and winter in Harbin city. Specifically, the vegetation index and surface temperature information were extracted by the grid method, and based on this data, the relationship between the urban green space and the UHI effect was analyzed quantitatively. In summer, the cooling effect is more significant. The average cooling extent reached 1.65 • C, the average maximum temperature change was 7.5 • C, and the cooling range was mainly 120 m. The cooling effect can be improved by adjusting the green space area, perimeter and shape. Increasing the green area (within 37 ha) or the green circumference (within 5300 m) can most economically improve its cooling effect. The shape factor would significantly affect the cooling effect within 0.03. The simpler the green space shape, the more obvious the cooling effect. In contrast, in winter the green spaces had a certain cooling effect when there was no snow cover or little snow cover, although this was still less significant compared with the situation in summer. The average cooling extent reached 0.48 • C, the average maximum temperature change was 4.25 • C, and the cooling range was mainly 90 m. However, there is no correlation between urban green space and the UHI effect in areas mainly covered by ice and snow. This work could provide protocols for urban green space design to effectively control the UHI effect of sub-frigid cities.

show abstract

Section: Selection Of Green Space and Buffer Settingmentioning

confidence: 99%

Study of the Cooling Effects of Urban Green Space in Harbin in Terms of Reducing the Heat Island Effect

Huang

Cui

2018

Sustainability

View full text Add to dashboard Cite

show abstract

“…Previous studies have mainly focused on studying differences in ground temperatures as a function of land-use land cover and its evolution at the city level [16,16,20,[33][34][35], temporal trends in urban SUHI in urban areas [18,36,37], the refreshing impact of parks on their surroundings [15,38,39], the evolution of SUHI as a function of day and night [9,34,40,41], the impact of vegetation on LST at the urban scale [12,36,37,42], comparison of surface temperatures and air temperature [8][9][10]20,43,44], the impact of surface temperatures on health [30,41] and transversely at surface temperatures at moderate resolutions (MODIS 1 km) [16,20,[34][35][36]42,45] but, to our knowledge, there are no similar studies such as ours that analyze the thermal monitoring of site redevelopment at such a detailed spatial and temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

et al. 2019

View full text Add to dashboard Cite

Climate change is a global challenge with multiple consequences. One of its impacts is the increase in heatwave frequency and intensity. The risk is higher for populations living in urban areas, where the highest temperatures are generally identified, due to the urban heat island effect. This phenomenon has recently been taken into account by local elected officials. As a result, developers have decided to use solutions in redevelopment projects to combat high temperatures in urban areas. Consequently, the objective is to study the land-surface temperature evolution of six main urban redevelopments in Lyon, France, from 2000 to 2017. Three of them (the Confluence, Kaplan, and Museum sites) were composed of industrial areas that have undergone major transformations and are now tertiary or residential areas. Two sites have been more lightly transformed, particularly by increasing vegetation to reduce heat stress and urban flooding (Dock and Garibaldi Street). Finally, the Groupama Stadium has been built into agricultural and wooded areas. Changes in vegetation cover (NDVI), water (MNDWI), and moisture (NDMI) content, built areas (NDBI) and bare soil (NDBaI) are also monitored. The results show that the Confluence and Kaplan sites were accompanied by a decrease in surface temperature and an increase in vegetation and moisture, whereas the Groupama Stadium displayed a rise in surface temperature and a decrease in vegetation. On the other hand, the Museum, Dock, and Garibaldi sites did not exhibit clear and uniform trends, although an increase in surface temperature was shown in some statistical tests. The disparity of the results shows the necessity to include a significant amount of vegetation during redevelopment operations in order to reduce heat stress.

show abstract

“…However, the parameters to characterize the spatio-temporal patterns of LST are limited [1,31,32], although sufficient parameters are available for UHI study such as magnitude or intensity, spatial extent, position, and orientation. Since research in geography has suggested that continuous and smooth surface supports pattern recognition more effectively [33,34], the parameterized surface models [35][36][37] and non-parametric kernel models [31,38,39] were utilized to quantify thermal characteristics and generate parameters from a smoother surface. Among them, Wang et al [31] introduced the latent pattern of LST (LLST) and a morphological parameter named the Multi-scale Shape Index (MSSI) to the urban climate community to enrich the parameter system of LST pattern mining.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Spatio-Temporal Pattern of Land Surface Temperature through Time Series Clustering: Based on the Latent Pattern and Morphology

Liu¹,

Zhan²,

Yang³

et al. 2018

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Land Surface Temperature (LST) is a critical component to understand the impact of urbanization on the urban thermal environment. Previous studies were inclined to apply only one snapshot to analyze the pattern and dynamics of LST without considering the non-stationarity in the temporal domain, or focus on the diurnal, seasonal, and annual pattern analysis of LST which has limited support for the understanding of how LST varies with the advancing of urbanization. This paper presents a workflow to extract the spatio-temporal pattern of LST through time series clustering by focusing on the LST of Wuhan, China, from 2002 to 2017 with a 3-year time interval with 8-day MODerate-resolution Imaging Spectroradiometer (MODIS) satellite image products. The Latent pattern of LST (LLST) generated by non-parametric Multi-Task Gaussian Process Modeling (MTGP) and the Multi-Scale Shape Index (MSSI) which characterizes the morphology of LLST are coupled for pattern recognition. Specifically, spatio-temporal patterns are discovered after the extraction of spatial patterns conducted by the incorporation of k-means and the Back-Propagation neural networks (BP-Net). The spatial patterns of the 6 years form a basic understanding about the corresponding temporal variances. For spatio-temporal pattern recognition, LLSTs and MSSIs of the 6 years are regarded as geo-referenced time series. Multiple algorithms including traditional k-means with Euclidean Distance (ED), shape-based k-means with the constrained Dynamic Time Warping (cDTW) distance measure, and the Dynamic Time Warping Barycenter Averaging (DBA) centroid computation method (k-cDBA) and k-shape are applied. Ten external indexes are employed to evaluate the performance of the three algorithms and reveal k-cDBA as the optimal time series clustering algorithm for our study. The study area is divided into 17 geographical time series clusters which respectively illustrate heterogeneous temporal dynamics of LST patterns. The homogeneous geographical clusters correspond to the zoning custom of urban planning and design, and thus, may efficiently bridge the urban and environmental systems in terms of research scope and scale. The proposed workflow can be utilized for other cities and potentially used for comparison among different cities.

show abstract

The Morphology, Dynamics and Potential Hotspots of Land Surface Temperature at a Local Scale in Urban Areas

Cited by 20 publications

References 49 publications

Study of the Cooling Effects of Urban Green Space in Harbin in Terms of Reducing the Heat Island Effect

Study of the Cooling Effects of Urban Green Space in Harbin in Terms of Reducing the Heat Island Effect

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

Characterizing the Spatio-Temporal Pattern of Land Surface Temperature through Time Series Clustering: Based on the Latent Pattern and Morphology

Contact Info

Product

Resources

About