Impacts of Building Features on the Cooling Effect of Vegetation in Community-Based MicroClimate: Recognition, Measurement and Simulation from a Case Study of Beijing

Due to the accumulation of heat, the urban environment and human health are threatened. Land surface cover has effects on the thermal environment; nevertheless, the effects of land surface features and spatial patterns remain poorly known in a community-based microclimate. This study quantified and verified the impacts of normalized difference vegetation index (NDVI) on land surface temperature (LST) (K, the slope of the trend line of a linear regression between NDVI and LST) in different building density by using building outline and Landsat 8 satellite imagery. Comparing the cooling effect and distribution of vegetation showed that the vegetative cover had a cooling effect on LST, characterized by synchronous change, and building density had a significant impact on the cooling effect of vegetation. Through identification and simulation, it was found that the key factor is the wind speed between the buildings because, in different building densities, the wind speed was different, and studies had shown that when the building density was between 0.35 and 0.50, the wind speed between buildings was higher, resulting in a better cooling effect of vegetation. This conclusion has important reference significance for urban planning and mitigating the impact of the thermal environment on human health.

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An Effect of Urban Forest on Urban Thermal Environment in Seoul, South Korea, Based on Landsat Imagery Analysis

Forests ◽

10.3390/f11060630 ◽

2020 ◽

Vol 11 (6) ◽

pp. 630

Author(s):

Peter Sang-Hoon Lee ◽

Jincheol Park

Keyword(s):

South Korea ◽

Vegetation Cover ◽

Land Surface ◽

Urban Areas ◽

Vegetation Index ◽

Urban Forest ◽

Thermal Environment ◽

Urban Forests ◽

Cooling Effect ◽

Surrounding Areas

The urban heat island effect has posed negative impacts on urban areas with increased cooling energy demand followed by an altered thermal environment. While unusually high temperature in urban areas has been often attributed to complex urban settings, the function of urban forests has been considered as an effective heat mitigation strategy. To investigate the cooling effect of urban forests and their influence range, this study examined the spatiotemporal changes in land surface temperature (LST) of urban forests and surrounding areas by using Landsat imageries. LST, the size of the urban forest, its vegetation cover, and Normalized Difference Vegetation Index (NDVI) were investigated for 34 urban forests and their surrounding areas at a series of buffer areas in Seoul, South Korea. The mean LST of urban forests was lower than that of the overall city, and the threshold distance from urban forests for cooling effect was estimated to be roughly up to 300 m. The group of large-sized urban forests showed significantly lower mean LST than that of small-sized urban forests. The group of urban forests with higher NDVI showed lower mean LST than that of urban forests with lower mean NDVI in a consistent manner. A negative linear relationship was found between the LST and size of urban forest (r = −0.36 to −0.58), size of vegetation cover (r = −0.39 to −0.61), and NDVI (r = −0.42 to −0.93). Temporal changes in NDVI were examined separately on a specific site, Seoul Forest, that has experienced urban forest dynamics. LST of the site decreased as NDVI improved by a land-use change from a barren racetrack to a city park. It was considered that NDVI could be a reliable factor for estimating the cooling effect of urban forest compared to the size of the urban forest and/or vegetation cover.

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The impact of building height on urban thermal environment in summer: A case study of Chinese megacities

PLoS ONE ◽

10.1371/journal.pone.0247786 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0247786

Author(s):

Meiya Wang ◽

Hanqiu Xu

Keyword(s):

Land Surface ◽

Urban Areas ◽

Single Channel ◽

Thermal Environment ◽

Quantitative Relationship ◽

Cooling Effect ◽

Height Range ◽

Building Height ◽

High Rise ◽

The Impact

The quantitative relationship between the spatial variation of building’s height and the associated land surface temperature (LST) change in six Chinese megacities is investigated in this paper. The six cities involved are Beijing, Shanghai, Tianjin, Chongqing, Guangzhou, and Shenzhen. Based on both remote sensing and building footprint data, we retrieved the LST using a single-channel (SC) algorithm and evaluate the heating/cooling effect caused by building-height difference via correlation analysis. The results show that the spatial distribution of high-rise buildings is mainly concentrated in the center business districts, riverside zones, and newly built-up areas of the six megacities. In the urban area, the number and the floor-area ratio of high to super high-rise buildings (>24m) account for over 5% and 4.74%, respectively. Being highly urbanized cities, most of urban areas in the six megacities are associated with high LST. Ninety-nine percent of the city areas of Shanghai, Beijing, Chongqing, Guangzhou, Shenzhen, and Tianjin are covered by the LST in the range of 30.2~67.8°C, 34.8~50.4°C, 25.3~48.3°C, 29.9~47.2°C, 27.4~43.4°C, and 33.0~48.0°C, respectively. Building’s height and LST have a negative logarithmic correlation with the correlation coefficients ranging from -0.701 to -0.853. In the building’s height within range of 0~66m, the LST will decrease significantly with the increase of building’s height. This indicates that the increase of building’s height will bring a significant cooling effect in this height range. When the building’s height exceeds 66m, its effect on LST will be greatly weakened. This is due to the influence of building shadows, local wind disturbances, and the layout of buildings.

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Satellite-based investigation on the surface cooling effects of urban parks and their range – a case study for North Rhine-Westphalia, Germany

Erdkunde ◽

10.3112/erdkunde.2021.03.03 ◽

2021 ◽

Vol 75 (3) ◽

pp. 209-223

Author(s):

Leonie Krelaus ◽

Joy Apfel ◽

Andreas Rienow

Keyword(s):

Green Infrastructure ◽

Land Surface ◽

Methodological Approach ◽

Urban Parks ◽

Cooling Effect ◽

Landsat 8 ◽

Surface Cooling ◽

Mixed Pixels ◽

Cooling Effects ◽

The Impact

Green infrastructure (GI) has a cooling effect owing to shading and evapotranspiration and therefore has a climate regulating function within metropolitan areas. Urban parks are a type of GI that act as park cool islands (PCIs) and play a major role in mitigating the surface urban heat island. This study aims to (1) investigate the status quo of the surface cooling effect intensity of selected urban parks in North Rhine-Westphalia (NRW), including their cooling range, and to (2) propose a methodological approach for investigating the PCI intensity using remote sensing data considering the occurrence of mixed pixels. To achieve these tasks, land surface temperature values based on Landsat 8 images from three different days in 2018 and 2019 were observed. In addition, a method for the reduction of mixed pixels was developed. The results confirm a surface cooling effect of 1–5 K and thus the existence of a PCI. The impact of the surface cooling effect was found within a minimum range of 150 m. However, the process of identifying the cooling area was complicated by the high proportion of GI in cities in NRW, compared to other study areas. Further research on the influencing parameters of the surface cooling effect is needed.

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Estimation of Vegetation Proportion Cover to Improve Land Surface Emissivity

10.5194/egusphere-egu2020-16632 ◽

2020 ◽

Author(s):

Elnaz Neinavaz ◽

Andrew K. Skidmore ◽

Roshanak Darvishzadeh

Keyword(s):

Land Surface ◽

Prediction Accuracy ◽

Vegetation Index ◽

Dynamic Range ◽

Landsat 8 ◽

Surface Emissivity ◽

Threshold Method ◽

Wide Dynamic Range ◽

Land Surface Emissivity ◽

The Impact

Precise estimation of land surface emissivity (LSE) is essential to predict land surface energy budgets and land surface temperature, as LSE is an indicator of material composition. There exist several approaches to LSE estimation employing remote sensing data; however, the prediction of LSE remains a challenging task. Among the existing approaches for calculating LSE, the NDVI threshold method appears to hold well over vegetated areas. To apply the NDVI threshold method, it is necessary to know the proportion of vegetation cover (Pv). This research aims to investigate the impact of Pv's prediction accuracy on the estimation of LSE over the forest ecosystem. In this regard, a field campaign coinciding with a Landsat-8 overpass was undertaken for the mixed temperate forest of the Bavarian Forest National Park, in southeastern Germany. The Pv in situ measurements were made for 37 plots. Four vegetation indices, namely NDVI, variable atmospherically resistant index, wide dynamic range vegetation index, and three-band gradient difference vegetation index, were applied to predict Pv for further use in LSE computing. Unlike previous studies that suggested variable atmospherically resistant index can be estimated Pv with higher prediction accuracy compared to NDVI over the agricultural area, our results showed that the prediction accuracy of Pv is not different when using NDVI over the forest (R2CV = 0.42, RMSECV = 0.06). Pv was measured with the lowest accuracy using the wide dynamic range vegetation index (R2CV = 0.014, RMSECV = 0.197) and three-band gradient difference vegetation index (R2CV = 0.032, RMSECV = 0.018). &#160;The results of this study also revealed that the variation in the prediction accuracy of the Pv has an impact on the results of LSE calculation.

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How to Measure the Urban Park Cooling Island? A Perspective of Absolute and Relative Indicators Using Remote Sensing and Buffer Analysis

Remote Sensing ◽

10.3390/rs13163154 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3154

Author(s):

Wenhao Zhu ◽

Jiabin Sun ◽

Chaobin Yang ◽

Min Liu ◽

Xinliang Xu ◽

...

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Thermal Environment ◽

Urban Parks ◽

Shape Index ◽

Water Area ◽

Landsat 8 ◽

Threshold Size ◽

Buffer Analysis

Urban parks have been proven to cool the surrounding environment, and can thus mitigate the urban heat island to an extent by forming a park cooling island. However, a comprehensive understanding of the mechanism of park cooling islands is still required. Therefore, we studied 32 urban parks in Jinan, China and proposed absolute and relative indicators to depict the detailed features of the park cooling island. High-spatial-resolution GF-2 images were used to obtain the land cover of parks, and Landsat 8 TIR images were used to examine the thermal environment by applying buffer analysis. Linear statistical models were developed to explore the relationships between park characteristics and the park cooling island. The results showed that the average land surface temperature (LST) of urban parks was approximately 3.6 °C lower than that of the study area, with the largest temperature difference of 7.84 °C occurring during summer daytime, while the average park cooling area was approximately 120.68 ha. The park cooling island could be classified into four categories—regular, declined, increased, and others—based on the changing features of the surrounding LSTs. Park area (PA), park perimeter (PP), water area proportion (WAP), and park shape index (PSI) were significantly negatively correlated with the park LST. We also found that WAP, PP, and greenness (characterized by the normalized difference vegetation index (NDVI)) were three important factors that determined the park cooling island. However, the relationship between PA and the park cooling island was complex, as the results indicated that only parks larger than a threshold size (20 ha in our study) would provide a larger cooling effect with the increase in park size. In this case, increasing the NDVI of the parks by planting more vegetation would be a more sustainable and effective solution to form a stronger park cooling island.

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Impact of LULC Changes on LST in Rajshahi District of Bangladesh: A Remote Sensing Approach

Journal of Geographical Studies ◽

10.21523/gcj5.19030102 ◽

2020 ◽

Vol 3 (1) ◽

pp. 11-23 ◽

Cited By ~ 3

Author(s):

Abdulla Al Kafy ◽

Abdullah Al-Faisal ◽

Mohammad Mahmudul Hasan ◽

Md. Soumik Sikdar ◽

Mohammad Hasib Hasan Khan ◽

...

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface ◽

Agricultural Land ◽

Global Climate ◽

Urban Expansion ◽

Water Bodies ◽

Landsat 8 ◽

Lulc Changes ◽

The Impact

Urbanization has been contributing more in global climate warming, with more than 50% of the population living in cities. Rapid population growth and change in land use / land cover (LULC) are closely linked. The transformation of LULC due to rapid urban expansion significantly affects the functions of biodiversity and ecosystems, as well as local and regional climates. Improper planning and uncontrolled management of LULC changes profoundly contribute to the rise of urban land surface temperature (LST). This study evaluates the impact of LULC changes on LST for 1997, 2007 and 2017 in the Rajshahi district (Bangladesh) using multi-temporal and multi-spectral Landsat 8 OLI and Landsat 5 TM satellite data sets. The analysis of LULC changes exposed a remarkable increase in the built-up areas and a significant decrease in the vegetation and agricultural land. The built-up area was increased almost double in last 20 years in the study area. The distribution of changes in LST shows that built-up areas recorded the highest temperature followed by bare land, vegetation and agricultural land and water bodies. The LULC-LST profiles also revealed the highest temperature in built-up areas and the lowest temperature in water bodies. In the last 20 years, LST was increased about 13ºC. The study demonstrates decrease in vegetation cover and increase in non-evaporating surfaces with significantly increases the surface temperature in the study area. Remote-sensing techniques were found one of the suitable techniques for rapid analysis of urban expansions and to identify the impact of urbanization on LST.

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Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

Remote Sensing ◽

10.3390/rs13020323 ◽

2021 ◽

Vol 13 (2) ◽

pp. 323

Author(s):

Liang Chen ◽

Xuelei Wang ◽

Xiaobin Cai ◽

Chao Yang ◽

Xiaorong Lu

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Maximum Temperature ◽

Maximum Temperature Difference ◽

Rapid Urbanization ◽

The Impact ◽

The City

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

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The Challenge of the Urban Compact Form: Three-Dimensional Index Construction and Urban Land Surface Temperature Impacts

Remote Sensing ◽

10.3390/rs13061067 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1067

Author(s):

Han Yan ◽

Kai Wang ◽

Tao Lin ◽

Guoqin Zhang ◽

Caige Sun ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Urban Form ◽

Three Dimensional ◽

Compact Form ◽

Landsat 8 ◽

Driving Mechanism ◽

Building Density ◽

Building Height

Cities are growing higher and denser, and understanding and constructing the compact city form is of great importance to optimize sustainable urbanization. The two-dimensional (2D) urban compact form has been widely studied by previous researchers, while the driving mechanism of three-dimensional (3D) compact morphology, which reflects the reality of the urban environment has seldom been developed. In this study, land surface temperature (LST) was retrieved by using the mono-window algorithm method based on Landsat 8 images of Xiamen in South China, which were acquired respectively on 14 April, 15 August, 2 October, and 21 December in 2017, and 11 March in 2018. We then aimed to explore the driving mechanism of the 3D compact form on the urban heat environment (UHE) based on our developed 3D Compactness Index (VCI) and remote sensing, as well as Geo-Detector techniques. The results show that the 3D compact form can positively effect UHE better than individual urban form construction elements, as can the combination of the 2D compact form with building height. Individually, building density had a greater effect on UHE than that of building height. At the same time, an integration of building density and height showed an enhanced inter-effect on UHE. Moreover, we explore the temporal and spatial UHE heterogeneity with regards to 3D compact form across different seasons. We also investigate the UHE impacts discrepancy caused by different 3D compactness categories. This shows that increasing the 3D compactness of an urban community from 0.016 to 0.323 would increase the heat accumulation, which was, in terms of satellite derived LST, by 1.35 °C, suggesting that higher compact forms strengthen UHE. This study highlights the challenge of the urban 3D compact form in respect of its UHE impact. The related evaluation in this study would help shed light on urban form optimization.

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Detecting Urban Landscape Factors Controlling Seasonal Land Surface Temperatures: From the Perspective of Urban Function Zones

10.21203/rs.3.rs-172267/v1 ◽

2021 ◽

Author(s):

Tong Li ◽

Ying Xu ◽

Lei Yao

Keyword(s):

Landscape Structure ◽

Land Surface ◽

Urban Landscape ◽

Landscape Planning ◽

Thermal Environment ◽

Metropolitan Region ◽

Biophysical Parameters ◽

Landscape Factors ◽

Urban Function ◽

The Impact

Abstract Understanding of the impact on the thermal effect by urbanization is of great significance for urban thermal regulation, it is essential to determine the relationship between the urban heat island (UHI) effect and the complexities of urban function and landscape structure. For this purpose, we conducted a case research in the metropolitan region of Beijing, China, and >5000 urban blocks assigned with different urban function zones (UFZs) were identified as the basic spatial analysis units. Seasonal land surface temperature (LST) retrieved from remote sensing data were used to represent the UHI characteristics of the study area, and surface biophysical parameters, building forms, and landscape pattern metrics were selected as the urban landscape factors. Then, the effects of urban function and landscape structure on the UHI effect were examined by spatial regression models. The results indicated that: (1) Significant spatio-temporal heterogeneity of LST were found in the study area, and there was obvious temperature gradient with “working-living-resting” UFZs; (2) All the types of urban landscape factors showed significant contribution to seasonal LST, and sorted by surface biophysical factors > building forms > landscape factors. However, their contributions varied in different seasons; (3) The major contribute factors showed a certain difference due to the variation of urban function and landscape complexity. This study expands understanding on the complex relationship among urban landscape, function, and thermal environment, which could benefit urban landscape planning for UHI alleviation.

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Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province

Indonesian Journal of Geography ◽

10.22146/ijg.36113 ◽

2018 ◽

Vol 50 (2) ◽

pp. 154

Author(s):

Ardiansyah Ardiansyah ◽

Revi Hernina ◽

Weling Suseno ◽

Faris Zulkarnain ◽

Ramadhani Yanidar ◽

...

Keyword(s):

High Resolution ◽

Urban Environment ◽

Vegetation Index ◽

Training Sample ◽

Predictor Variables ◽

Landsat 8 ◽

Building Density ◽

Multi Index ◽

Water Index ◽

Index Approach

This study developed a model to identify the percent of building density (PBD) of DKI Jakarta Province in each pixel of Landsat 8 imageries through a multi-index approach. DKI Jakarta province was selected as the location of the study because of its urban environment characteristics. The model was constructed using several predictor variables i.e. Normalized Difference Built-up Index (NDBI), Soil-adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and surface temperature from thermal infrared sensor (TIRS). The calculation of training sample data was generated from high-resolution imagery and was correlated to the predictor variables using multiple linear regression (MLR) analysis. The R values of predictor variables are significantly correlated. The result of MLR analysis shows that the predictor variables simultaneously have correlation and similar pattern to the PBD based on high-resolution imageries. The Adjusted R Square value is 0,734, indicates that all four variables influences predicting the PBD by 73%.

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