A Systematic Review of the Cooling Effects of Urban Forests

Air pollution and climate change amplify the urban heat island (UHI) effect, which has an adverse effect on human health. Urban forests (UFs) are important to reduce the UHI effect; however, the quantitative effect of UFs on UHI, relative to time and space, has not yet been investigated. In this study, we aimed to quantitatively measure the actual thermal environment in UFs. To this end, temperature and humidity loggers were installed in 17 UFs in Seoul for a year and analyzed according to vegetation characteristics and accessibility. The urban forests and park showed consistent temperature reduction, whereas the lawn showed higher temperature reduction effects during autumn-winter. The traffic island showed lower annual temperature reduction effect than other UFs. From spring to autumn, mixed and broadleaved forests showed better temperature reduction effect than coniferous forests. The temperature in UFs decreased by approximately 1.9°C over ~3 km from the traffic island near the city to the forest. This study revealed the difference in the cooling effect according to the type and location of UF and the vegetation structure. The functional characteristics of plants and the UF that reflects them can help reduce the negative impact of climate warming and UHI on human health.

A fieldwork study on the diurnal changes of urban microclimate in four types of ground cover and urban heat island of Nanjing, China

The Urban Heat Island (UHI) effect has been extensively studied as a global issue. The urbanization process has been proved to be the main reason for this phenomenon. Over the past 20 years, the built-up area of Zhengzhou city has grown five times larger, and the UHI effect has become increasingly pressing for the city’s inhabitants. Therefore, mitigating the UHI effect is an important research focus of the expanding capital city of the Henan province. In this study, the Landsat 8 image of July 2019 was selected from Landsat collection to obtain Land Surface Temperature (LST) by using Radiative Transfer Equation (RTE) method, and present land cover information by using spectral indices. Additionally, high-resolution Google Earth images were used to select 123 parks, grouped in five categories, to explore the impact factors on park cooling effect. Park Cooling Intensity (PCI) has been chosen as an indicator of the park cooling effect which will quantify its relation to park patch metrics. The results show that: (1) Among the five studied park types, the theme park category has the largest cooling effect while the linear park category has the lowest cooling effect; (2) The mean park LST and PCI of the samples are positively correlated with the Fractional Vegetation Cover (FVC) and with Normalized Difference Water Index (NDWI), but these are negatively correlated with the Normalized Difference Impervious Surface Index (NDISI). We can suppose that the increase of vegetation cover rate within water areas as well as the decrease of impervious surface in landscape planning and design will make future parks colder. (3) There is a correlation between the PCI and the park characteristics. The UHI effect could be mitigated by increasing of park size and reducing park fractal dimension (Frac_Dim) and perimeter-area ratio (Patario). (4) The PCI is influenced by the park itself and its surrounding area. These results will provide an important reference for future urban planning and urban park design to mitigate the urban heat island effect.

Detecting Cooling Effect of Landscape from Composition and Configuration: An Urban Heat Island Study on Hangzhou

Abstract. Global warming is widely regarded as one of the most critical environmental issue in the 21st century. It is known to exacerbate urban heat island effects, a phenomenon that could affect the livability of cities and the lives and comfort of more than half of the world population who are now living in urban areas. The issue on how to mitigate the impacts of this phenomenon and help urban dwellers adapt to it, has been, and continues to be, an important topic in the context of landscape design and urban planning. This study sought to examine the cooling effects of different land cover types from the perspectives of landscape composition and configuration. Landsat-8 OLI/TIRS data was employed to derive both landscape composition and land surface temperature (Figure 1). The objectives of this study are to: (1) detect the spatial pattern of LST in Hangzhou; (2) examine the cooling effect of land cover resources; and (3) understand the correlation between LST and land cover from the perspectives of composition and configuration. Its goal is to reflect on optimal landscape patterns from the perspective of mitigating UHI effects and building more comfortable and livable urban environments. Various spatial and statistical approaches, including urban-rural gradient, grid-based, landscape metrics, and scatter plot, were employed to analyze the derived land cover and land surface temperature (LST) maps. An urban-rural gradient analysis was performed to examine the difference in LST between the urban and rural areas. First, we created a multiple ring buffer zone from the city center of Hangzhou with a distance interval of 1 km. Second, we determined the mean LST and the density of all the land cover types (LCTs) in each ring buffer zone. Finally, the results were plotted along the urban-rural gradient for analysis (Figure 2). The results showed that forest and water were the cooling land cover types in Hangzhou during daytime. The heat core in Hangzhou was situated close to the central business district and the variance in LST in urban and rural recorded at 2.94 °C. The correlation of forest density with mean LST could be detected in both large (690 m) and small (210 m) grids, whereas the correlation of water density with mean LST was only observed in small grids. In general, forest with high aggregation and complex shape behave best in cooling LST while huge and complete water bodies had better cooling effect than scattered water patches. Besides, the minimum cooling scale of forest and water were 150 m and 120 m, respectively. The findings were considered valuable for urban planners to spatially and architecturally build and arrange the cooling land cover resources, eventually to fulfill the Chinese government’s new plan of building “livable cities”.

Not population density, but city size and per capita income influence the urban forest carbon sequestration: A case of growing cities in Arkansas, USA

The provisioning of critical ecosystem services to cities of the eastern USA depends on the health and physiological function of trees in urban areas. Although we know that the urban environment may be stressful for trees planted in highly developed areas, it is not clear that trees in urban forest patches experience the same stressful environmental impacts. In this study, we examine chlorophyll fluorescence parameters, leaf traits, foliar nutrients and stable isotope signatures of urban forest patch trees compared with trees growing at reference forest sites, in order to characterize physiological response of these native tree species to the urban environment of three major cities arranged along a latitudinal gradient (New York, NY; Philadelphia, PA; Baltimore, MD). Overall, white oaks (Quercus alba L.) show more differences in chlorophyll fluorescence parameters and leaf traits by city and site type (urban vs reference) than red maples (Acer rubrum L.). The exceptions were δ13C and δ15N, which did not vary in white oak foliage but were significantly depleted (δ13C) and enriched (δ15N) in urban red maple foliage. Across all sites, red maples had higher thermal tolerance of photosynthesis (Tcrit) than white oaks, suggesting a greater ability to withstand temperature stress from the urban heat island effect and climate change. However, the highest average values of Tcrit were found in the Baltimore urban white oaks, suggesting that species suitability and response to the urban environment varies across a latitudinal gradient. Stomatal pore index (SPI) showed inter-specific differences, with red maple SPI being higher in urban trees, whereas white oak SPI was lower in urban trees. These results demonstrate that differences in native tree physiology occur between urban and reference forest patches, but they are site- and species-specific. Data on local site characteristics and tree species performance over time remain necessary to gain insight about urban woodland ecosystem function.

This study aims to assess urban heat islands and land cover types in relation to vulnerable populations. The city of Richmond, Virginia was selected as the study area using the Census Block Group as the geographic unit of analysis. Regression analysis was carried out to examine the impacts of land cover types on ambient temperatures, while correlation analysis was used to assess the relationship between ambient temperature and vulnerable populations. Lastly, multivariate clustering analysis was performed to identify areas vulnerable to urban heat in the city. Findings suggest that: (1) impervious surfaces lead to higher ambient temperatures, while tree coverage has a cooling effect on urban heat; (2) vulnerable populations, except for older adults, tend to live in areas with higher ambient temperatures; and (3) vulnerable populations are spatially clustered in specific locations in the city. This study concludes with recommendations of mitigation measures to reduce the adverse effect of urban heat islands by applying high-albedo materials to urban surfaces and expanding tree coverage and green space.

Species difference of transpiration in three urban coniferous forests in a semiarid region of China

Urban heat island effects are well known nowadays and observed in cities throughout the World. The main reason behind the effects of urban heat island (UHI) is the transformation of land use/ land cover, and this transformation is associated with UHI through different actions: i) removal of vegetated areas, ii) land reclamation from sea/river, iii) construction of new building as well as other concrete structures, and iv) industrial and domestic activity. In rapidly developing cities, urban heat island effects increases very hastily with the transformation of vegetated/ other types of areas into urban surface because of the increasing population as well as for economical activities. In this research the effect of land use/ land cover on urban heat island was investigated in two growing cities in Asia i.e. Singapore and Johor Bahru, (Malaysia) using 10 years data (from 1997 to 2010) from Landsat TM/ETM+. Multispectral visible band along with indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Build Index (NDBI), and Normalized Difference Bareness Index (NDBaI) were used for the classification of major land use/land cover types using Maximum Likelihood Classifiers. On the other hand, land surface temperature (LST) was estimated from thermal image using Land Surface Temperature algorithm. Emissivity correction was applied to the LST map using the emissivity values from the major land use/ land cover types, and validation of the UHI map was carried out using in situ data. Results of this research indicate that there is a strong relationship between the land use/land cover changes and UHI. Over this 10 years period, significant percentage of non-urban surface was decreased but urban heat surface was increased because of the rapid urbanization. With the increase of UHI effect it is expected that local urban climate has been modified and some heat related health problem has been exposed, so appropriate measure should be taken in order to reduce UHI effects as soon as possible.

城市热环境是城市局部气候与环境的综合表现，它与土地利用格局密切相关，但是相应的机制研究还不充分。以深圳市为例，首先基于2018年LANDSAT 8遥感影像，采用支持向量机方法和线性光谱混合模型提取土地利用与覆盖度信息，分析了城市土地覆盖对地表温度以及热量收支状况的影响。基于2003-2018的MODIS地表温度数据，进一步研究了深圳市城市热岛现象的时空变化，从地表能量的角度分析城市热岛变化背后的形成机制。结果表明，深圳市地表温度从西北到东南逐渐降低，城市不透水面温度显著高于植被覆盖区域，城市热岛效应明显。不透水面和城市植被共同影响深圳市的地表温度与热量收支状况，不透水地表与感热具有较好的相关性，城市植被与潜热具有较好的相关性。长时间序列分析表明深圳的城市热岛现象在夏季较高而冬季较低，月均热岛强度为2.14℃；对于年际变化，深圳在2003-2018表现出显著的下降趋势。归因分析显示感热通量的影响在深圳起主导作用，这一模式在全年和季节上都较为明显。结果表明深圳市经过高速扩张阶段，目前发展方向是提高建成区的利用效率，该现象强调了热传输在加强城市热岛效应过程中对近地面湍流的干扰作用。本研究可以为缓解热岛效应与景观格局优化研究提供借鉴。;Urban thermal environment is a comprehensive expression of local climate and environment. While it is closely related to land use patterns, its underlying mechanisms are largely unexplored. Urban heat island affects not only regional climate, vegetation growth and air quality, but also human health. Therefore, the urban thermal environment has been regarded as a critical variable. It is important to characterize the spatial and temporal patterns of urban thermal environments and quantify the response of the associated influencing factors. This study uses the city of Shenzhen as an example where rapid urbanization has occurred. Based on LANDSAT 8 remote sensing images collected in 2018, we first extract land cover types and coverage information using support vector machine and multiple linear spectral analysis models. The accuracies of the estimated land covers are acceptable, which provides confidence for further analyses. The impacts of urban land cover on land surface temperature and heat energy component are then investigated. Using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature dataset obtained from 2003 to 2018, this study further explores the spatiotemporal variations of urban heat island in Shenzhen, and analyzes its potential formation mechanisms from the perspective of surface energy balance. Our results show that the land surface temperature gradually decreases from northwest to Southeast of Shenzhen. This is largely attributed to the spatial distribution of impervious surfaces and the urban vegetation. Specifically, the land surface temperature of impervious surfaces is substantially higher than that of vegetation-covered area, implying that the urban heat island effect is obvious in Shenzhen. Impervious surface and urban vegetation jointly affect the surface temperature and heat budget through, for example, latent heat flux and sensible heat flux. Impervious surface is more correlated with sensible heat flux while urban vegetation is more correlated with latent heat flux. Obvious differences occur in sensible heat and latent heat among land cover types, which provides implications for the formation and elimination of urban heat island. Time series analysis regarding the period of 2003-2018 illustrates that the urban heat island effect in Shenzhen is stronger during summer season whereas relatively weaker during winter season. The urban heat island effect is remarkably exhibited using the time series dataset, with an average monthly intensity of 2.14 ℃. Regarding the internal variation, the downward trend in urban heat island intensity is shown from 2003 to 2018. This negative trend is significantly present during all seasons except spring, with a change rate of approximately 0.1 ℃/annual. Attribution analysis based on MODIS dataset and FLDAS flux dataset reveals that sensible heat flux plays a more important role in the urban heat island of Shenzhen in comparison to latent heat flux. This is consistent in the both annual and seasonal scales, and the relative contribution of latent heat flux and sensible heat flux is 42% and 58%, respectively. Results indicate that after a rapid urban expansion period in Shenzhen, the current development focus is to improve the utilization efficiency of built-up areas. This emphasizes the interference of heat transfer with the near-surface turbulence that strengthens the urban heat island effect. Our study provides reference values for mitigating the urban heat island effect and optimizing landscape patterns.

Metropolitan areas in Andean industrialized valleys with a strong geomorphological structure and a pronounced climate experience an increasing degradation of air quality, which motivates environmental policies that include the expansion of tree coverage in urban areas among the mitigation measures. Using the metropolitan area of the Aburrá Valley, Colombia, as a study case, we evaluate the removal of PM2.5 by urban trees with the Urban Forest Effects (UFORE) methodology, considering the potential effect of changing tree covers in the valley for several projected meteorological conditions under climate change and different urban management practices. The estimated removals are sensitive to the number and distribution of available ground stations, with a tendency to overestimate with fewer stations. We found that the marginal gains in removal by additional tree plantings are low in the urbanized settings. In the environmental scenarios, the main limiting factor in the removal is precipitation, compared to changes in tree cover and levels of pollution. Spatially, the increase in total removal depends on the increase in tree cover, with more benefits obtained when trees are in areas with high concentrations of PM2.5. Trees with low values of leaf area index (LAI) seem to optimize the effectiveness of the removal. Seasonally, the greatest removal occurs in rainy months when pollution levels are the highest. Based on our results, the scenarios that meet the plans and programs aimed at improving air quality would achieve removal effectiveness of around 2.5% of the total emissions of PM2.5 with urban trees. Air quality would achieve removal effectiveness of around 2.5% of the total emissions of PM2.5 with urban trees.

Large-scale retrieval of leaf area index and vertical foliage profile from the spaceborne waveform lidar (GLAS/ICESat)

District-scale cumulative heat stress mapping using very-high-resolution spatiotemporal simulation

Northern peatlands are major terrestrial carbon sinks, storing 415 ± 150 Gt of carbon. The composition of peatland vegetation affects this carbon storage capacity, and thus quantifying the vegetation helps to constrain uncertainty in peatland carbon storage estimates. Ground layer vegetation, such as Sphagnum sp. moss contributes greatly to carbon storage capacity. In forested peatlands, the tree canopy structure directly influences peatland solar insolation, soil temperature, and water table levels. Each of these factors impacts the ground layer vegetation. Currently, there is uncertainty about how the peatland tree canopy structure is influenced by elevated levels of carbon dioxide (CO2) and temperature. Providing canopy structural metrics in a nondestructive, spatially comprehensive way across different temperature and CO2 treatments is challenging for traditional methods such as destructive harvesting, Digital Hemispherical Photography (DHP), and allometric regressions. Terrestrial Laser Scanning (TLS) is well-suited to provide non-destructive detailed horizontal and vertical canopy structural information. As part of the Spruce and Peatland Responses Under Changing Environments (SPRUCE) study located in northern Minnesota, USA, we use TLS to evaluate leaf area index (LAI), leaf area density, and leaf inclination angle over time (2015 - 2022) and space of two conifer species, Picea mariana (black spruce) and Larix laricina (eastern larch). The SPRUCE site is in a treed peatland bog under elevated CO2 and temperature conditions. The research questions in this study are 1) How accurately can we predict the LAI of the spruce and larch trees using TLS data? 2) How are the spruce and larch tree canopy structures within 12 SPRUCE plots changing from 2015 - 2022? We expected 1) A volumetric pixel-based model (VCP) will predict LAI with an accuracy of 90% as validated by destructively harvested and DHP LAI estimates 2) Under elevated CO2 and temperature, LAI will increase, leaf area density will decrease in lower canopies, and leaf inclination angles will become more vertical. At the species level, we expected the spruce and larch trees to respond with opposing trends for each metric under the same treatment. Using TLS data, we developed a modified VCP model that uses measures of point contact frequency to estimate LAI, leaf inclination angles, and leaf area density. The results indicate that the model predicts LAI with a coefficient of determination of 0.89 (R2 = 0.89), an RMSE of 0.98, and a normalized RMSE = 0.17. We also found that the model maintains moderate accuracy across voxel size input parameters, suggesting it may maintain accuracy in different treatment conditions where tree structural relationships can change. Our canopy structural results supported the hypothesis that LAI increases more significantly over time under warmer conditions when compared to control plots. Lower canopy leaf area density trends did not support the hypothesis as they showed no statistically significant trends across time. Leaf inclination angle trends through time did not support the hypothesis as they tended to decrease. As temperatures increased across the temperature gradient, leaf angles became more vertical in upper canopies under elevated CO2, leading to inconclusive support for or against the hypothesis. Species data did not support the hypothesis that spruce and larch canopy structures would differ significantly under the same treatments. The larch LAI, however, did not increase as significantly through time as the spruce under elevated CO2 conditions. Additionally, we identified anomalous fluctuations in time series data and proposed potential temperature thresholds where LAI differed the most under ambient or elevated CO2 conditions. The findings from this study suggest that accurately quantifying canopy structure through time may be possible in different environmental conditions and species using TLS. We add support to previous findings that LAI increases more significantly through time under warming conditions compared to control conditions. These results demonstrate TLS’s utility for making species-level canopy structural estimates across horizontal and vertical profiles. Incorporating vertical canopy profile metrics such as leaf area density with LAI data can assist in better explaining how LAI is changing across time and temperature gradients.