Quantifying the seasonal cooling capacity of ‘green infrastructure types’ (GITs): An approach to assess and mitigate surface urban heat island in Sydney, Australia

Bartesaghi-Koc, Carlos; Osmond, Paul; Peters, Alan

doi:10.1016/j.landurbplan.2020.103893

Cited by 53 publications

(34 citation statements)

References 64 publications

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“…The NDVI represents the greenness of the park, and a high NDVI indicates more vegetation [55,56]. Vegetation can reduce the LST through shadows and evapotranspiration, which has been proven by many studies [30,[57][58][59]. Consequently, if the PA was smaller than the threshold, increasing the NDVI of parks by adding more vegetation would provide a stronger cooling effect [54].…”

Section: The Effects Of Landscape Metrics On the Park Lst And Park Cooling Islandmentioning

confidence: 99%

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

et al. 2021

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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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Section: The Effects Of Landscape Metrics On the Park Lst And Park Cooling Islandmentioning

confidence: 99%

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

et al. 2021

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“…Many of the neighbourhoods that will exist in the second half of the 21 st century are already built. Green infrastructure interventions have been found to reduce heat from between 1 to 12°C depending on their type, configuration and location (Bartesaghi-Koc et al, 2020;Saaroni et al, 2018). Interventions include areas along public roads, parks, gardens, roofs, and walls of private buildings.…”

Section: Combining Green Infrastructure and Accessibilitymentioning

confidence: 99%

Planning for Age-Friendly Cities

Scott

2021

Planning Theory & Practice

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“…As an important part of urban ecosystems (Hu et al, 2021), GI provides green spaces for cities, and benefit people's physical and mental health (Venkataramanan et al, 2019;Zhang et al, 2021). In addition, GI can alleviate urban flooding and urban heat island effect (Venkataramanan et al, 2019;Dai et al, 2021;Ouyang et al, 2021;Bartesaghi-Koc et al, 2020), and accelerate sustainable development (Hu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, GI is usually scattered over urban areas and has different forms with a fine spatial scale. The mapping of GI based on remote sensing images with insufficient spatial resolution or fewer data features has a large uncertainty (Bartesaghi-Koc et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

Jia

Cui

Liu

2022

Front. Environ. Sci.

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Green infrastructure (GI), such as green roofs, is now widely used in sustainable urban development. An accurate mapping of GI is important to provide surface parameterization for model development. However, the accuracy and precision of mapping GI is still a challenge in identifying GI at the small catchment scale. We proposed a framework for blue-green-gray infrastructure classification using machine learning algorithms and unmanned aerial vehicle (UAV) images that contained digital surface model (DSM) information. We used the campus of the Southern University of Science and Technology in Shenzhen, China, as a study case for our classification method. The UAV was a DJI Phantom 4 Multispectral, which measures the blue, green, red, red-edge, and near-infrared bands and DSM information. Six machine learning algorithms, i.e., fuzzy classifier, k-nearest neighbor classifier, Bayes classifier, classification and regression tree, support vector machine (SVM), and random forest (RF), were used to classify blue (including water), green (including green roofs, grass, trees (shrubs), bare land), and gray (including buildings, roads) infrastructure. The highest kappa coefficient was observed for RF and the lowest was observed for SVM, with coefficients of 0.807 and 0.381, respectively. We optimized the sampling method based on a chessboard grid and got the optimal sampling interval of 11.6 m to increase the classification efficiency. We also analyzed the effects of weather conditions, seasons, and different image layers, and found that images in overcast days or winter days could improve the classification accuracy. In particular, the DSM layer was crucial for distinguishing green roofs and grass, and buildings and roads. Our study demonstrates the feasibility of using UAV images in urban blue-green-gray infrastructure classification, and our infrastructure classification framework based on machine learning algorithms is effective. Our results could provide the basis for the future urban stormwater management model development and aid sustainable urban planning.

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Quantifying the seasonal cooling capacity of ‘green infrastructure types’ (GITs): An approach to assess and mitigate surface urban heat island in Sydney, Australia

Cited by 53 publications

References 64 publications

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

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

Planning for Age-Friendly Cities

Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

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