AN INVESTIGATION OF REMOTELY SENSED URBAN HEAT ISLAND CLIMATOLOGY

Satellite remotely sensed temperatures are widely used for urban heat island (UHI) studies. However, the abilities of satellite surface and atmospheric data to assess the climatology of UHI face many unknowns and challenges. This research addresses the problems and potential for satellite remotely sensed UHI climatology by examining three different issues. The first issue is related to the temporal aggregation of land surface temperature (LST) and the potential biases that are induced on the surface UHI (SUHI) intensity. Composite LST data usually are preferred to avoid the missing values due to clouds for long-term UHI monitoring. The impact of temporal aggregation shows that SUHI intensities are more notably enhanced in the daytime than nighttime with an increasing trend for larger composite periods. The cause of the biases is highly related to the amount and distribution of clouds. The second issue is related to model validation and the appropriate use of LST for comparison to modeled radiometric temperatures in the urban environment. Sensor view angle, cloud distribution, and cloud contaminated pixels can confound comparisons between satellite LST and modeled surface radiometric temperature. Three practical sampling methods to minimize the confounding factors are proposed and evaluated for validating different aspects of model performance. The third issue investigated is to assess to what extent remotely sensed atmospheric profiles collected over the urban environment can be used to examine the UHI. The remotely sensed air and dew-point temperatures are compared with the ground observations, showing an ability to capture the temporal and spatial dynamics of atmospheric UHI at a fine scale. Finally, a new metric for quantifying the urban heat island is proposed. The urban heat island curve (UHIC), is developed to represent UHI intensity by integrating the urban surface heterogeneity in a curve. UHIC illustrates the relationship between the air temperature and the urban fractions, and emphasizes the temperature gradients, consequently decreasing the impact of the data biases. This research illustrates the potential for satellite data to monitor and increase our understanding of UHI climatology

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities’ increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

abstract: This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities’ increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics

Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid

In the last decades, urban climate researchers have highlighted the need for a reliable provision of meteorological data in the local urban context. Several efforts have been made in this direction using Artificial Neural Networks (ANN), demonstrating that they are an accurate alternative to numerical approaches when modelling large time series. However, existing approaches are varied, and it is unclear how much data are needed to train them. This study explores whether the need for training data can be reduced without overly compromising model accuracy, and if model reliability can be increased by selecting the UHI intensity as the main model output instead of air temperature. These two approaches were compared using a common ANN configuration and under different data availability scenarios. Results show that reducing the training dataset from 12 to 9 or even 6 months would still produce reliable results, particularly if the UHI intensity is used. The latter proved to be more effective than the temperature approach under most training scenarios, with an average RMSE improvement of 16.4% when using only 3 months of data. These findings have important implications for urban climate research as they can potentially reduce the duration and cost of field measurement campaigns

Impact of urban canopy parameters on a megacity’s modelled thermal environment

Urban canopy parameters (UCPs) are essential in order to accurately model the complex interplay between urban areas and their environment. This study compares three different approaches to define the UCPs for Moscow (Russia), using the COSMO numerical weather prediction and climate model coupled to TERRA_URB urban parameterization. In addition to the default urban description based on the global datasets and hard-coded constants (1), we present a protocol to define the required UCPs based on Local Climate Zones (LCZs) (2) and further compare it with a reference UCP dataset, assembled from OpenStreetMap data, recent global land cover data and other satellite imagery (3). The test simulations are conducted for contrasting summer and winter conditions and are evaluated against a dense network of in-situ observations. For the summer period, advanced approaches (2) and (3) show almost similar performance and provide noticeable improvements with respect to default urban description (1). Additional improvements are obtained when using spatially varying urban thermal parameters instead of the hard-coded constants. The LCZ-based approach worsens model performance for winter however, due to the underestimation of the anthropogenic heat flux (AHF). These results confirm the potential of LCZs in providing internationally consistent urban data for weather and climate modelling applications, as well as supplementing more comprehensive approaches. Yet our results also underline the continued need to improve the description of built-up and impervious areas and the AHF in urban parameterizations

Integrating Copernicus land cover data into the i-Tree Cool Air model to evaluate and map urban heat mitigation by tree cover

Cities host more than half of the world’s population and due to global warming and land use change their vulnerability to deadly heat waves has increased. A healthy vegetated landscape can abate heat wave severity and diminish the related urban heat island through the process of evapotranspiration. This research aimed to develop a methodology for cities to use publicly available Copernicus land cover maps within the i-Tree Cool Air water and energy balance model to map air temperature and humidity. The manuscript presents proof of concept using Naples, Italy with its Mediterranean climate characterized by limited soil water for cooling via evapotranspiration. The approach achieved strong correlations between predicted and observed air temperatures across the city (r ≥ 0.89). During the warm season of 2020, forested land cover was 5°C cooler than land cover dominated by impervious cover. Simulated land cover change, limited to a 10% increase or decrease in tree cover, generated an inverse change of 0.2°C in maximum hourly air temperature, with more trees obtaining cooler air. Soil water limited the cooling, with the generally wetter spring season enabling greater cooling of air temperatures, and summer droughts without irrigation had constrained cooling. Sustainable urban design will likely require an increase in plant cover along with a reduction of impervious surfaces that absorb and reradiate heat in order to improve community resilience to heat waves

Essays on Urban Climate Model Evaluation and Application

The field of urban climatology as a subfield of atmospheric science / physical geography has developed significantly over the past 3 decades. Major advances have occurred in the theoretical understanding of the urban effect at multiple spatial and temporal scales, as well as in empirical work seeking to observe and ultimately predict urban scale phenomenon. It is this latter development, particularly in respect to urban heat and moisture, that forms the basis of this work. Less than 5 years ago, the concept of partitioning the urban area into distinct geographic units based on the potential thermal modification of the near surface climate was proposed within the field to bring greater rigor, clarity and transferability to observations made within urban areas. The Local Climate Zone (LCZ) approach has since been applied in multiple cities globally, which has demonstrated its efficacy in understanding the urban heat island (UHI) effect through observations and transferring those results across multiple cities. However as with global scale temperature anomalies, the UHI can be viewed as symptomatic of the underlying processes rather than purely as a response i.e. while we now are capable of observing enhanced air temperatures around cities, addressing the issue requires a deeper understanding of the processes that give rise to this phenomenon, particularly if solutions are to be transferred into urban planning practices and environmental policies. To that end, urban climate models are an invaluable tool for examining urban processes in more detail. However, their application in urban areas (particularly for planning problems) remains ad hoc and unsystematic. In fact, many cities in the economically developing world lack even basic data describing (i) the underlying city, its sealed surface extent, vegetation, building materials and so forth and/or (ii) knowledge of the overlying atmosphere in and around the city, required to apply such models. In this collection of papers, a formal modelling and evaluation approach is proposed, elaborated on and applied which utilises the LCZ system. While LCZs were designed strictly for observations of the air temperature at 2m, due to its generality and resulting uptake within the urban climate community, it is argued to be an effective approach for modelling, particularly in data poor settings. The LCZ approach is linked with the Surface Urban Energy and Water Balance Scheme (SUEWS) model, a mid-complex urban energy and water balance model. Hence, the approach is referred to as the LCZ-SUEWS approach. The application of the approach primarily focuses on Dublin city (Ireland). This was done as the city houses three (2 ongoing and 1 retired) eddy-covariance flux towers used to evaluate the approach, however the results are intended to be transferrable to other domains. Three primary conclusions can be drawn from this body of work. Firstly, the LCZ-SUEWS approach performs equally well in data poor, data rich settings, meaning the approach can be applied anywhere to provide an initial assessment of the urban energy balance. Secondly, the adoption of the approach yields the additional benefit of improving communication with the urban planning community in terms of illustrating the processes that give rise to the urban effect e.g. lack of photosynthecially active vegetation, standing water bodies, and high proportion of built up coverage. This allows for more geographically and physically targeted design interventions to reduce the negative impacts of urban development such as excess heat and lack of moisture. Thirdly, there is a need for an agreed framework on model evaluation which emphasises external independent evaluation and employs novel sources of observational data, for example, remote sensing. This would improve the trustworthiness of urban climate models and further encourage their uptake

Intraurban Analysis of Surface Urban Heat Island From Disagregated Thermal Radiance Images

Surface Urban Heat Islands (SUHI) are areas with higher surface temperatures than their surroundings. Several studies have used thermal images from satellites to research the influence of urbanization on surface temperature patterns, however the low spatial resolution of thermal sensors limits the analysis of LST intraurban variations. Attempting to overcome this limitation, we used the Enhanced Physical Model (EPM) for disaggregation of land surface temperature (DLST) to generate fine scale LST for Sao Paulo city in Brazil. This method uses a linear regression and Planck’s law to combine NDVI, NDWI and UI to estimate LST at finer spatial detail. First, we calibrate the method by upscaling an ASTER thermal band to 1000 m and using EPM to estimate the original 100 m thermal band. The original and estimated ASTER thermal bands achieved and R² of 0.66. Following, we apply the EPM model to estimate the LST at 15 m and compare it with data from meteorological stations. The 15 m LST image facilitated the identification of potential SUHIs. The EPM model provides an enhanced product with higher level of spatial detail, which allows researchers to identify changes of surface temperature that would not be evident from an ASTER LST (90 m spatial resolution) product. In summary, the model allowed us to quantify and map the influence of different urbanization patterns on the LST distribution.Ilhas de calor de superfície (ICS)são áreas com temperature de superfície maior do que as áreas ao redor. Vários estudos tem usado imagens termais de satélite para investigar a influência da urbanização nos padrões de temperatura de superfície; entretanto a baixa resolução espacial dos atuais sensores termais limita a análise dos padrões de variação intraurbana de temperatura de superfície. Com o objetivo de surpassar essa limitação, nós utilizamos o the Enhanced Physical Model (EPM) para gerar dados de temperatura de superfície com maior nível de detalhamento para a cidade de São Paulo- Brasil. Esse método utiliza um modelo de regressão linear e a lei de Planck para combinar NDVI, NDWI e UI para estimar a temperatura de superfície com maior nível de detalhes espaciais.  Primeiro, para calibrar o modelo, nós reamostramos uma banda termal ASTER para 1000 m e utilizamos o método EPM para estimar a banda original de 100 m. A banda termal estimatada de 100 m atingiu um R2= 0.66 em relação a banda termal original. A seguir,  nós aplicamos o método EPM para estimar a temperatura de superfície à 15 m. A imagem de temperatura de superfície de 15 m facilitou a identificação de potenciais ilhas de calor de superfície. O modelo EPM fornece um produto com alto grau de detalhamento espacial, o que permite que pesquisadores identifiquem as mudanças de temperatura de superfície que não seriam evidentes na imagem  termal ASTER original (90 m de resolução espacial). Em suma, o modelo nos permitiu quantificar e mapear a influência de diferentes padrões de urbanização na distribuição dos padrões de temperatura de superfície

A Review on Different Modeling Techniques

In this study, the importance of air temperature from different aspects (e.g., human and plant health, ecological and environmental processes, urban planning, and modelling) is presented in detail, and the major factors affecting air temperature in urban areas are introduced. Given the importance of air temperature, and the necessity of developing high-resolution spatio- temporal air-temperature maps, this paper categorizes the existing approaches for air temperature estimation into three categories (interpolation, regression and simulation approaches) and reviews them. This paper focuses on high-resolution air temperature mapping in urban areas, which is difficult due to strong spatio-temporal variations. Different air temperature mapping approaches have been applied to an urban area (Berlin, Germany) and the results are presented and discussed. This review paper presents the advantages, limitations and shortcomings of each approach in its original form. In addition, the feasibility of utilizing each approach for air temperature modelling in urban areas was investigated. Studies into the elimination of the limitations and shortcomings of each approach are presented, and the potential of developed techniques to address each limitation is discussed. Based upon previous studies and developments, the interpolation, regression and coupled simulation techniques show potential for spatio-temporal modelling of air temperature in urban areas. However, some of the shortcomings and limitations for development of high-resolution spatio- temporal maps in urban areas have not been properly addressed yet. Hence, some further studies into the elimination of remaining limitations, and improvement of current approaches to high-resolution spatio-temporal mapping of air temperature, are introduced as future research opportunities