Nature-Based Designs to Mitigate Urban Heat: The Efficacy of Green Infrastructure Treatments in Portland, Oregon

Makido, Yasuyo; Hellman, Dana; Shandas, Vivek

doi:10.3390/atmos10050282

Cited by 50 publications

(38 citation statements)

References 68 publications

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“…This study has shown the interest of the complementary use of the latter two types of data, in particular LiDAR for a precise view of vegetation densities (high, medium, or low) but also remote sensing for surface temperature and water, humidity, and vegetation indices to a lesser extent. While we expected very satisfactory results with random forest modelling, confirming the results of previous studies [63,97,116], we were surprised to note also the very high performance of the classical multiple linear regression and PLS regression, with very low RMSE and often below 0.5 • C.…”

Section: Implication Of Important Predictors In Urban Air Temperaturesupporting

confidence: 89%

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A New Approach for Understanding Urban Microclimate by Integrating Complementary Predictors at Different Scales in Regression and Machine Learning Models

Alonso¹,

Renard²

2020

Remote Sensing

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Climate change is a major contemporary phenomenon with multiple consequences. In urban areas, it exacerbates the urban heat island phenomenon. It impacts the health of the inhabitants and the sensation of thermal discomfort felt in urban areas. Thus, it is necessary to estimate as well as possible the air temperature at any point of a territory, in particular in view of the ongoing rationalization of the network of fixed meteorological stations of Météo-France. Understanding the air temperature is increasingly in demand to input quantitative models related to a wide range of fields, such as hydrology, ecology, or climate change studies. This study thus proposes to model air temperature, measured during four mobile campaigns carried out during the summer months, between 2016 and 2019, in Lyon (France), in clear sky weather, using regression models based on 33 explanatory variables from traditionally used data, data from remote sensing by LiDAR (Light Detection and Ranging), or Landsat 8 satellite acquisition. Three types of statistical regression were experimented: partial least square regression, multiple linear regression, and a machine learning method, the random forest regression. For example, for the day of August 30th, 2016, multiple linear regression explained 89% of the variance for the study days, with a root mean square error (RMSE) of only 0.23 °C. Variables such as surface temperature, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) have a strong impact on the estimation model. This study contributes to the emergence of urban cooling systems. The solutions available vary. For example, they may include increasing the proportion of vegetation on the ground, facades, or roofs, increasing the number of basins and water bodies to promote urban cooling, choosing water-retaining materials, humidifying the pavement, increasing the number of public fountains and foggers, or creating shade with stretched canvas.

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Section: Implication Of Important Predictors In Urban Air Temperaturesupporting

confidence: 89%

“…This highlights the real need to use green and blue spaces solutions in order to limit the UHI and improve the thermal comfort. [97,116,139]…”

Section: Discussionmentioning

confidence: 99%

A New Approach for Understanding Urban Microclimate by Integrating Complementary Predictors at Different Scales in Regression and Machine Learning Models

Alonso¹,

Renard²

2020

Remote Sensing

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“…This can be done, for example, by increasing green vegetation in urban areas or by planting trees along roadsides in dry areas (e.g., [101]). However, as noted by [102], there is no one mitigation solution that fits all cases, and any mitigation must consider the specific characteristics of the specific geographic area.…”

mentioning

confidence: 99%

Remotely Sensed Derived Land Surface Temperature (LST) as a Proxy for Air Temperature and Thermal Comfort at a Small Geographical Scale

Goldblatt

Addas

Crull

et al. 2021

Land

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Urban Heat Islands (UHIs) and Urban Cool Islands (UCIs) can be measured by means of in situ measurements and interpolation methods, which often require densely distributed networks of sensors and can be time-consuming, expensive and in many cases infeasible. The use of satellite data to estimate Land Surface Temperature (LST) and spectral indices such as the Normalized Difference Vegetation Index (NDVI) has emerged in the last decade as a promising technique to map Surface Urban Heat Islands (SUHIs), primarily at large geographical scales. Furthermore, thermal comfort, the subjective perception and experience of humans of micro-climates, is also an important component of UHIs. It remains unanswered whether LST can be used to predict thermal comfort. The objective of this study is to evaluate the accuracy of remotely sensed data, including a derived LST, at a small geographical scale, in the case study of King Abdulaziz University (KAU) campus (Jeddah, Saudi Arabia) and four surrounding neighborhoods. We evaluate the potential use of LST estimates as proxy for air temperature (Tair) and thermal comfort. We estimate LST based on Landsat-8 measurements, Tair and other climatological parameters by means of in situ measurements and subjective thermal comfort by means of a Physiological Equivalent Temperature (PET) model. We find a significant correlation (r = 0.45, p < 0.001) between LST and mean Tair and the compatibility of LST and Tair as equivalent measures using Bland-Altman analysis. We evaluate several models with LST, NDVI, and Normalized Difference Built-up Index (NDBI) as data inputs to proxy Tair and find that they achieve error rates across metrics that are two orders of magnitude below that of a comparison with LST and Tair alone. We also find that, using only remotely sensed data, including LST, NDVI, and NDBI, random forest classifiers can detect sites with “very hot” classification of thermal comfort nearly as effectively as estimates using in situ data, with one such model attaining an F1 score of 0.65. This study demonstrates the potential use of remotely sensed measurements to infer the Physiological Equivalent Temperature (PET) and subjective thermal comfort at small geographical scales as well as the impacts of land cover and land use characteristics on UHI and UCI. Such insights are fundamental for sustainable urban planning and would contribute enormously to urban planning that considers people’s well-being and comfort.

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“…Therefore, the spatial identification of different levels of urban heat environment risk zone and the spatial-temporal change analysis of topological relationship among the risk zones will help to improve the scientific basis of green infrastructures spatial planning [45,46]. Efficient green infrastructure as nature-based solutions will be of great significance to mitigate the risk of urban heat environment and maintain the sustainable development of the city [47].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

De-chao

Sun

et al. 2019

Sustainability

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For urban climatic environments, the urban heat island (UHI) effect resulting from land use and land cover change (LUCC) caused by human activities is rapidly becoming one of the most notable characteristics of urban climate change due to urban expansion. UHI effects have become a significant barrier to the process of urbanization and sustainable development of the urban ecological environment. Predicting the spatial and temporal patterns of the urban heat environment from the spatial relationship between land use and land surface temperature (LST) is key to predicting urban heat environment risk. This study established an Urban Heat Environment Risk Model (UHERM) as follows. First, the urban LST was normalized and classified during three different periods. Second, a Markov model was constructed based on spatio-temporal change in the urban heat environment between the initial year (2005) and middle year (2010), and then a cellular automata (CA) model was used to reveal spatial relationships between the urban heat environments of the two periods and land use in the initial year. The spatio-temporal pattern in a future year (2015) was predicted and the accuracy of the simulation was verified. Finally, the spatio-temporal pattern of urban heat environment risk was quantitatively forecasted based on the decision rule for the urban heat environment risk considering both the present and future status of the spatial characteristics of the urban heat environment. The MODIS LST product and LUCC dataset retrieved from remote sensing images were used to verify the accuracy of UHERM and to forecast the spatio-temporal pattern of urban heat environment risk during the period of 2015–2020. The results showed that the risk of urban heat environment is increasing in the Chongqing metropolitan area. This method for quantitatively evaluating the spatio-temporal pattern of urban heat environment risk could guide sustainable growth and provide effective theoretical and technical support for the regulation of urban spatial structure to minimize urban heat environment risk.

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Nature-Based Designs to Mitigate Urban Heat: The Efficacy of Green Infrastructure Treatments in Portland, Oregon

Cited by 50 publications

References 68 publications

A New Approach for Understanding Urban Microclimate by Integrating Complementary Predictors at Different Scales in Regression and Machine Learning Models

A New Approach for Understanding Urban Microclimate by Integrating Complementary Predictors at Different Scales in Regression and Machine Learning Models

Remotely Sensed Derived Land Surface Temperature (LST) as a Proxy for Air Temperature and Thermal Comfort at a Small Geographical Scale

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

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