scholarly journals Analysis of Seasonal Daytime Urban Thermal Environment Dynamics in a Tropical Coastal City Based on the Spatiotemporal Fusion Model

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yuan Yao ◽  
Yiling Lu ◽  
Fang Zhang ◽  
Lulu Liu ◽  
Min Liao
Keyword(s):  
Land Cover ◽  
Landscape Metrics ◽  
Seasonal Variations ◽  
Land Surface ◽  
Thermal Environment ◽  
Fusion Model ◽  
Spatiotemporal Resolution ◽  
High Spatiotemporal Resolution ◽  
Urban Thermal Environment ◽  
The Relationship

This study investigated the seasonal variations of daytime urban thermal environment (UTE) based on land surface temperature (LST) in Shenzhen of 2015. The spatial and temporal adaptive reflectance fusion model (STARFM) was used for retrieving seasonal daytime LST at high spatiotemporal resolution by combining MODIS and HJ-1B LST data. Next, the relationship between the land cover and daytime in each season was examined. Finally, daytime LST patterns were classified, and the effects of seasonal variations of high-grade daytime LSTs were analyzed with landscape metrics. The results showed that (1) the STARFM is capable of generating seasonal daytime LST data at high spatiotemporal resolution. (2) Daytime LSTs were generally higher in the western parts of Shenzhen in spring and summer. (3) Daytime LST in each land cover type showed an increasing trend form winter to summer and decreased from summer to autumn. The highest and lowest daytime LSTs in each season were observed in ISAs and water bodies. (4) Landscape metrics provided a quantitative method for describing seasonal variations in daytime LSTs, and it was found that seasons influenced the intensity and extent of daytime LSTs in Shenzhen. These findings may be helpful for urban planners developing regional urban strategies to improve daytime urban thermal comfort conditions.

scholarly journals Spatiotemporal Characteristics of Urban Surface Temperature and Its Relationship with Landscape Metrics and Vegetation Cover in Rapid Urbanization Region

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hongbo Zhao ◽  
Juntao Tan ◽  
Zhibin Ren ◽  
Zheye Wang
Keyword(s):  
Surface Temperature ◽  
Landscape Metrics ◽  
Vegetation Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Configuration ◽  
Thermal Environment ◽  
Landscape Patterns ◽  
Rapid Urbanization ◽  
Urban Thermal Environment

Under the trend of rapid urbanization, the urban heat island (UHI) effect has become a hot issue for scholars to study. In order to better alleviate UHI effect, it is important to understand the effect of landuse/landcover (LULC) and landscape patterns on the urban thermal environment from perspective of landscape ecology. This research aims to quantitatively investigate the effect of LULC landscape patterns on UHI effects more accurately based on a landscape metrics analysis. In addition, we also explore the complex relationship between land surface temperature (LST) and vegetation cover. Taking Zhengzhou City of China as a case study, an integrated method which includes the geographic information system (GIS), remote-sensing (RS) technology, and landscape metrics was employed to facilitate the analysis. Landsat data (2000–2014) were applied to investigate the spatiotemporal evolution patterns of LST and LULC. The results indicated that the mean LST value increased by 2.32°C between 2000 and 2014. The rise of LST was consistent with the trend of rapid urbanization in Zhengzhou City, which resulted in sharp increases in impervious surfaces (IS) and substantial losses of vegetation cover. Furthermore, the investigation of LST and vegetation cover demonstrated that fractional vegetation cover (FVC) had a stronger negative effect on LST than normalized differential vegetation index (NDVI). In addition, LST was obviously correlated with LULC landscape patterns, and both landscape composition and spatial configuration affected UHI effects to varying degrees. This study not only illustrates a feasible way to investigate the relationship between LULC and urban thermal environment but also suggests some important measures to improve urban planning to reduce UHI effects for sustainable development.

scholarly journals Evaluating Bi-temporal Dynamics and Trend of Urbanization-induced Land Cover Temperature in Shanghai, China

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Fei Liu ◽  
Yuji Murayama
Keyword(s):  
Land Cover ◽  
Rural Area ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Thermal Environment ◽  
Impervious Surfaces ◽  
Land Utilization ◽  
Local Climate ◽  
Human System ◽  
Urban Thermal Environment

<p><strong>Abstract.</strong> In China today, massive land use/land cover change (LULCC) driven by urbanization has become a dominant phenomenon in the interactions between the human system and land surface. Drastic urbanization has changed land surface properties where a great deal of physical landscapes has been replaced by artificial buildings, and this evolution has brought about great potential risks and pressures physically and socially. In this study, taking the Shanghai metropolitan area (SMA) as the study area, we examine the spatiotemporal dynamics of temperature in urban land cover and explore the sustainable strategies for future development.</p><p>Shanghai, one of the Chinese gigantic cities, had the residential population of 24.15 million in 2015. The phenomena of urban heat island (UHI) in SMA have become prominent by the urbanization-related anthropic activities and increasing population. Until the present, a substantial number of investigations has been conducted in the Shanghai's thermal environment based on remote sensing data. However, the comprehensive assessments of urban thermal environment are not enough in Shanghai. This kind of research is expected to provide a significant paradigm for the exploration of a sustainable development of Chinese megacities, which examines the mechanism of the interactions between urban thermal environment and human system and addresses its adverse effect on the urban environment.</p><p>In this study, bi-temporal Landsat satellite data (Sep.2000 and Aug.2015) were used for the detection of LULCC and the estimation of land surface temperature (LST) in SMA. The LST was retrieved using the radiation transfer equation (RTE), and then normalized and graded in order to remove the influence of the seasonality and local climate characteristics. The spatiotemporal configuration and variation of urban thermal environment in SMA between 2000 and 2015 were synthetically monitored and evaluated through the analyses of the urban-rural gradient, gravity center change, spatial landscape pattern, and geographically weighted regression. Furthermore, the spatial determinants for the formation of UHI in SMA were identified in the analysis.</p><p>The results showed that (1) in 2000, the LST in the downtown of SMA was higher than that in the outskirts and rural area; (2) the extent of UHI in SMA was gradually expanding from 2000 to 2015 along the direction of urban growth, however, the UHI intensity in the downtown area was relatively declining due to the explosive increase of impervious surfaces in the suburban and rural area; (3) the spatiotemporal evolution of SMA's urban thermal environment was highly consistent with the track of LULCC, and greatly influenced by the spatial pattern and variation of urban landscape; (4) impervious surfaces obviously facilitated and strengthened the UHI effect, inversely, greenbelt and water space contributed to the mitigation and alleviation of the UHI effect; and (5) population convergence, industrial growth, dense land utilization, and urban development policies also affected the SMA's urban thermal environment.</p>

scholarly journals An Effective High Spatiotemporal Resolution NDVI Fusion Model Based on Histogram Clustering

2020 ◽  
Vol 12 (22) ◽  
pp. 3774
Author(s):  
Xuegang Xing ◽  
Changzhen Yan ◽  
Yanyan Jia ◽  
Haowei Jia ◽  
Junfeng Lu ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Time Series Data ◽  
Series Data ◽  
Fusion Model ◽  
Spatiotemporal Resolution ◽  
Model Based ◽  
High Spatiotemporal Resolution ◽  
Ndvi Time Series ◽  
Shape Changes

The normalized difference vegetation index (NDVI) is a powerful tool for understanding past vegetation, monitoring the current state, and predicting its future. Due to technological and budget limitations, the existing global NDVI time-series data cannot simultaneously meet the needs of high spatial and temporal resolution. This study proposes a high spatiotemporal resolution NDVI fusion model based on histogram clustering (NDVI_FMHC), which uses a new spatiotemporal fusion framework to predict phenological and shape changes. Meanwhile, this model also uses four strategies to reduce error, including the construction of an overdetermined linear mixed model, multiscale prediction, residual distribution, and Gaussian filtering. Five groups of real MODIS_NDVI and Landsat_NDVI datasets were used to verify the predictive performance of the NDVI_FMHC. The results indicate that NDVI_FMHC has higher accuracy and robustness in forest areas (r = 0.9488 and ADD = 0.0229) and cultivated land areas (r = 0.9493 and ADD = 0.0605), while the prediction effect is relatively weak in areas subject to shape changes, such as flooded areas (r = 0.8450 and ADD = 0.0968), urban areas (r = 0.8855 and ADD = 0.0756), and fire areas (r = 0.8417 and ADD = 0.0749). Compared with ESTARFM, NDVI_LMGM, and FSDAF, NDVI_FMHC has the highest prediction accuracy, the best spatial detail retention, and the strongest ability to capture shape changes. Therefore, the NDVI_FMHC can obtain NDVI time-series data with high spatiotemporal resolution, which can be used to realize long-term land surface dynamic process research in a complex environment.

scholarly journals Impacts of Land Cover/Use on the Urban Thermal Environment: A Comparative Study of 10 Megacities in China

2020 ◽  
Vol 12 (2) ◽  
pp. 307 ◽  
Author(s):  
Fei Liu ◽  
Xinmin Zhang ◽  
Yuji Murayama ◽  
Takehiro Morimoto
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Rural Areas ◽  
Land Surface ◽  
Thermal Environment ◽  
Causal Mechanism ◽  
Impervious Surfaces ◽  
Rapid Urbanization ◽  
Urban Thermal Environment

Satellite-derived land surface temperature (LST) reveals the variations and impacts on the terrestrial thermal environment on a broad spatial scale. The drastic growth of urbanization-induced impervious surfaces and the urban population has generated a remarkably increasing influence on the urban thermal environment in China. This research was aimed to investigate land surface temperature (LST) intensity response to urban land cover/use by examining the thermal impact on urban settings in ten Chinese megacities (i.e., Beijing, Dongguan, Guangzhou, Hangzhou, Harbin, Nanjing, Shenyang, Suzhou, Tianjin, and Wuhan). Surface urban heat island (SUHI) footprints were scrutinized and compared by magnitude and extent. The causal mechanism among land cover composition (LCC), population, and SUHI was also identified. Spatial patterns of the thermal environments were identical to those of land cover/use. In addition, most impervious surface materials (greater than 81%) were labeled as heat sources, on the other hand, water and vegetation were functioned as heat sinks. More than 85% of heat budgets in Beijing and Guangzhou were generated from impervious surfaces. SUHI for all megacities showed spatially gradient decays between urban and surrounding rural areas; further, temperature peaks are not always dominant in the urban core, despite extremely dense impervious surfaces. The composition ratio of land cover (LCC%) negatively correlates with SUHI intensity (SUHII), whereas the population positively associates with SUHII. For all targeted megacities, land cover composition and population account for more than 63.9% of SUHI formation using geographically weighted regression. The findings can help optimize land cover/use to relieve pressure from rapid urbanization, maintain urban ecological balance, and meet the demands of sustainable urban growth.

scholarly journals Multi-Scale Relationship between Land Surface Temperature and Landscape Pattern Based on Wavelet Coherence: The Case of Metropolitan Beijing, China

2019 ◽  
Vol 11 (24) ◽  
pp. 3021 ◽  
Author(s):  
Qiong Wu ◽  
Jinxiang Tan ◽  
Fengxiang Guo ◽  
Hongqing Li ◽  
Shengbo Chen
Keyword(s):  
Landscape Pattern ◽  
Land Surface ◽  
Spatial Configuration ◽  
Thermal Environment ◽  
Pearson Correlation ◽  
Landscape Composition ◽  
Wavelet Coherence ◽  
Multi Scale ◽  
Urban Thermal Environment ◽  
The Relationship

The relationship between urban landscape pattern and land surface temperature (LST) is one of the core issues in urban thermal environment research. Although previous studies have shown a significant correlation between LST and landscape pattern, most were conducted at a single scale and rarely involve multi-scale effects of the landscape pattern. Wavelet coherence can relate the correlation between LST and landscape pattern to spatial scale and location, which is an effective multi-scale correlation method. In this paper, we applied wavelet coherence and Pearson correlation coefficient to analyze the multi-scale correlations between landscape pattern and LST, and analyzed the spatial pattern of the urban thermal environment during the urbanization of Beijing from 2004 to 2017 by distribution index of high-temperature center (HTC). The results indicated that the HTC of Beijing gradually expands from the main urban zone and urban function extended zone to the new urban development zone and far suburb zone, and develops from monocentric to polycentric spatial pattern. Land cover types, such as impervious surfaces and bare land, have a positive contribution to LST, while water and vegetation play a role in mitigating LST. The wavelet coherence and Pearson correlation coefficients showed that landscape composition and spatial configuration have significant effects on LST, but landscape composition has a greater effect on LST in Beijing metropolitan area. Landscape composition indexes (NDBI and NDVI) showed significant multi-scale characteristics with LST, especially at larger scales, which has a strong correlation on the whole transect. There was no significant correlation between the spatial configuration indexes (CONTAG, DIVISION, and LSI) and LST at smaller scales, only at larger scales near the urban area has a significant correlation. With the increase of the scale, Pearson correlation coefficient calculated by spatial rectangle sampling and wavelet coherence coefficient have the same trend, although it had some fluctuations in several locations. However, the wavelet coherence coefficient diagram was smoother and less affected by position and rectangle size, which more conducive to describe the correlation between landscape pattern index and LST at different scales and locations. In general, wavelet coherence provides a multi-scale method to analyze the relationship between landscape pattern and LST, helping to understand urban planning and land management to mitigate the factors affecting urban thermal environment.

Remote Sensing-Based Approach to Identify the Influence of Land Use/Land Cover Change on the Urban Thermal Environment

2021 ◽  
pp. 217-240
Author(s):  
Abdulla-Al Kafy ◽  
Muhaiminul Islam ◽  
Soumik Sikdar ◽  
Tahera Jahan Ashrafi ◽  
Abdullah Al-Faisal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Thermal Environment ◽  
Land Use Land Cover ◽  
Urban Thermal Environment

scholarly journals Quantifying the Effects of Urban Form on Land Surface Temperature in Subtropical High-Density Urban Areas Using Machine Learning

2019 ◽  
Vol 11 (8) ◽  
pp. 959 ◽  
Author(s):  
Yanwei Sun ◽  
Chao Gao ◽  
Jialin Li ◽  
Run Wang ◽  
Jian Liu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Form ◽  
Urban Areas ◽  
Thermal Environment ◽  
High Density ◽  
Cooling Effects ◽  
Urban Thermal Environment ◽  
The Impact

It is widely acknowledged that urban form significantly affects urban thermal environment, which is a key element to adapt and mitigate extreme high temperature weather in high-density urban areas. However, few studies have discussed the impact of physical urban form features on the land surface temperature (LST) from a perspective of comprehensive urban spatial structures. This study used the ordinary least-squares regression (OLS) and random forest regression (RF) to distinguish the relative contributions of urban form metrics on LST at three observation scales. Results of this study indicate that more than 90% of the LST variations were explained by selected urban form metrics using RF. Effects of the magnitude and direction of urban form metrics on LST varied with the changes of seasons and observation scales. Overall, building morphology and urban ecological infrastructure had dominant effects on LST variations in high-density urban centers. Urban green space and water bodies demonstrated stronger cooling effects, especially in summer. Building density (BD) exhibited significant positive effects on LST, whereas the floor area ratio (FAR) showed a negative influence on LST. The results can be applied to investigate and implement urban thermal environment mitigation planning for city managers and planners.

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