Attenuating the surface Urban Heat Island within the Local Thermal Zones through land surface modification

Wang, Jiong; Ouyang, Wanlu

doi:10.1016/j.jenvman.2016.11.059

Cited by 53 publications

(34 citation statements)

References 61 publications

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“…Furthermore, this study complements the zoning scheme in urban planning in the sense of sustainability. Although sustainability has been defined as the simultaneous sustainability of society, economy, and environment [64], conventional zoning still places the emphasis on the functions of society and the economy and divides the land use types accordingly into residential, industrial, or commercial zones [53]. Some achievements in the science community have contributed to the compensation of such bias.…”

Section: Discussionmentioning

confidence: 99%

“…The k-means and Back-Propagation neural networks (BP-Net) are incorporated to recognize the spatial patterns of LST and form a basic understanding of the temporal variances. The two approaches are jointly applied as a combination of an unsupervised clustering algorithm and a supervised classification algorithm is beneficial for pattern recognition of static data [52,53]. The k-means algorithm is selected as it is identified to be more efficient than hierarchical, spectral, or k-medoids methods [23].…”

Section: The Spatial Patternmentioning

confidence: 99%

“…In this paper, we incorporate LLST with MSSI to represent the numerical and morphological feature of LST. The optimal k is chosen where 99% of the data variance can be interpreted while more clusters lead to less significant improvement [53].…”

Section: The Spatial Patternmentioning

confidence: 99%

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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

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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.

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Section: Discussionmentioning

confidence: 99%

Section: The Spatial Patternmentioning

confidence: 99%

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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

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“…In support of mitigation plans, satellite imagery in the thermal infrared (TIR) can be used as a tool to identify thermal "hot spots" and "cold spots" and correlate the state of the thermal environment to land use composition [17][18][19][20]. "Hot spots" and "cold spots" are defined as areas which consistently demonstrate higher or lower land surface temperature (LST), respectively, as compared to their surrounding areas.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Thermal Hot and Cold Spots in Urban Areas in Support of Mitigation Plans to Counteract Overheating: Application for Athens

Mavrakou

Polydoros

Cartalis

et al. 2018

Climate

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Mitigation plans to counteract overheating in urban areas need to be based on a thorough knowledge of the state of the thermal environment, most importantly on the presence of areas which consistently demonstrate higher or lower urban land surface temperatures (hereinafter referred to as "hot spots" or "cold spots", respectively). The main objective of this research study is to develop a methodological approach for the recognition of thermal "hot spots" and "cold spots" in urban areas during summer; this is accomplished with (a) the combined use of high and medium spatial resolution satellite data (Landsat 8 and Terra-MODIS, respectively); (b) the downscaling of the Terra-MODIS satellite data so as to acquire spatial resolution similar to the Landsat one and at the same time take advantage of the high revisit time as compared to the respective one of Landsat (16 days); and (c) the application of a statistical clustering technique to recognize "hot spots" and "cold spots". The methodological approach was applied as a case study for the urban area of Athens, Greece for a summer period. Results demonstrated the capacity of the methodological approach to recognize "hot spots" and "cold spots", revealed a strong relationship between land use and "hot spots" and "cold spots", and showed that the average land surface temperature (LST) difference between the "hot spots" and "cold spots" can reach 9.1 • K.

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“…The LCZ classification distinguishes built environments more finely than other similar classification schemes such as local thermal zone (LTZ) classification [24]. In further studies based on the LCZ scheme, different methods of classification and various data sources are considered in order to achieve better classification results [25,26].…”

Section: Introductionmentioning

confidence: 99%

Impact of Urban Climate Landscape Patterns on Land Surface Temperature in Wuhan, China

2017

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Facing urban warming, mitigation and adaptation strategies are not efficient enough to tackle excessive urban heat, especially at the local scale. The local climate zone (LCZ) classification scheme is employed to examine the diversity and complexity of the climate response within a city. This study suggests that zonal practice could be an efficient way to bridge the knowledge gap between climate research and urban planning. Urban surfaces classified by LCZ are designated as urban climate landscapes, which extends the LCZ concept to urban planning applications. Selecting Wuhan as a case study, we attempt to explore the climatic effect of landscape patterns. Thermal effects are compared across the urban climate landscapes, and the relationships between patch metrics and land surface temperature (LST) are quantified. Results indicate that climate landscape layout is a considerable factor impacting local urban climate. For Wuhan, 500 m is an optimal scale for exploring landscape pattern-temperature relationships. Temperature contrast between surrounding landscape patches has a major influence on LST. Generally, fragmental landscape patches contribute to heat release. For most climate landscape types, patch metrics also have a significant effect on thermal response. When three metrics are included as predictive variables, 53.3% of the heating intensity variation can be explained for the Large Lowrise landscape, while 57.4% of the cooling intensity variation can be explained for the Water landscape. Therefore, this article claims that land-based layout optimization strategy at local scale, which conforms to planning manner, should be taken into account in terms of heat management.

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Attenuating the surface Urban Heat Island within the Local Thermal Zones through land surface modification

Cited by 53 publications

References 61 publications

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

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

Recognition of Thermal Hot and Cold Spots in Urban Areas in Support of Mitigation Plans to Counteract Overheating: Application for Athens

Impact of Urban Climate Landscape Patterns on Land Surface Temperature in Wuhan, China

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