Thermal behavior and its seasonal and diurnal variability of urban green infrastructure in a mid-latitude city - Berlin

Vulova, Stenka; Kleinschmit, Birgit

doi:10.1109/jurse.2019.8809011

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…We found a positive association between daytime LST and nighttime T air . However, daytime LST indicates that forests are cooler than meadows [68], whereas the opposite is true for nighttime T air [30], [66], [67]. Nighttime LST may be a more appropriate indicator for nighttime T air [25], [30], but inconsistent data availability did not allow us to integrate it into models.…”

Section: Discussionmentioning

confidence: 99%

Summer Nights in Berlin, Germany: Modeling Air Temperature Spatially With Remote Sensing, Crowdsourced Weather Data, and Machine Learning

Vulova

Meier

Fenner

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Urban areas tend to be warmer than their rural surroundings, well-known as the "urban heat island" effect. Higher nocturnal air temperature (T air ) is associated with adverse effects on human health, higher mortality rates, and higher energy consumption. Prediction of the spatial distribution of T air is a step toward the "Smart City" concept, providing an early warning system for vulnerable populations. The study of the spatial distribution of urban T air was thus far limited by the low spatial resolution of traditional data sources. Volunteered geographic information provides alternative data with higher spatial density, with citizen weather stations monitoring T air continuously in hundreds or thousands of locations within a single city. In this article, the aim was to predict the spatial distribution of nocturnal T air in Berlin, Germany, one day in advance at a 30-m resolution using open-source remote sensing and geodata from Landsat and Urban Atlas, crowdsourced T air data, and machine learning (ML) methods. Results were tested with a "leave-one-date-out" training scheme (testing crowd ) and reference T air data (testing ref ). Three ML algorithms were compared-Random Forest (RF), Stochastic Gradient Boosting, and Model Averaged Neural Network. The optimal model based on accuracy and computational speed is RF, with an average root mean square error (RMSE) for testing crowd of 1.16°C (R 2 = 0.512) and RMSE for testing ref of 1.97°C (R 2 = 0.581). Overall, the most important geographic information system (GIS) predictors were morphometric parameters and albedo. The proposed method relies on open-source datasets and can, therefore, be adapted to many cities worldwide.

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Section: Discussionmentioning

confidence: 99%

Summer Nights in Berlin, Germany: Modeling Air Temperature Spatially With Remote Sensing, Crowdsourced Weather Data, and Machine Learning

Vulova

Meier

Fenner

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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“…Johannessen et al, 2021). Our low-cost modular autosampler design enables replication across different climates to understand what adjustments need to be made to specific green infrastructures for different climate regimes (Vulova and Kleinschmit, 2019;Mullins et al, 2020).…”

Section: Cost-benefits Efficiency Of Urban Climate Mitigation Structu...mentioning

confidence: 99%

The Autobot-WQ: A portable, low-cost autosampler to provide new insight into urban spatio-temporal water quality dynamics

Khamis

Ouellet

Croghan

et al. 2023

Front. Built Environ.

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Urbanization and the increase in urban land cover are growing concerns associated with numerous negative impacts on surface water quality. Currently, many emerging contaminants are difficult to measure with no field deployable sensors currently available. Hence, discrete grab samples are required for subsequent laboratory analysis. To capture the spatiotemporal variability in pollution pulses, autosamplers can be used, but commercial offerings are both expensive and have a large footprint. This can be problematic in urban environments where there is a high density of point source inputs and risk of vandalism or theft. Here, we present a small and robust low-cost autosampler that is ideally suited for deployment in urban environments. The design is based on “off the shelf” open-source hardware components and software and requires no prior engineering, electronics, or computer programming experience to build. The autosampler uses a small peristaltic pump to enable collection of 14 small volume samples (50 mL) and is housed in a small footprint camera case. To illustrate the technology, we present two use cases for rapid sampling of stormwater pulses of: 1) an urban river channel and 2) green roof runoff. When compared with a commercial autosampler, our device showed comparable results and enabled us to capture temporal dynamics in key water quality parameters (e.g., dissolved organic matter) following rain events in an urban stream. Water quality differences associated with differing green roof design/maintenance regimes (managed and unmanaged vegetation) were captured using the autosampler, highlighting how unmanaged vegetation has a greater potential for mitigating the rapid runoff and peaked pollutant inputs associated with impervious surfaces. These two case studies show that our portable autosampler provides capacity to improve understanding of the impact of urban design and infrastructure on water quality and can lead to the development of more effective mitigation solutions. Finally, we discuss opportunities for further technical refinement of our autosampler and applications to improve environmental monitoring. We propose a holistic monitoring approach to address some of the outstanding challenges in urban areas and enable monitoring to shift from discrete point sources towards characterization of catchment or network scale dynamics.

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“…This is largely used in vegetation mapping and is expected to be correlated with LST values [38][39][40][41]. A negative correlation between NDVI and LST is expected to vary from greener areas (high NDVI values), which potentially strengthen the cooling capacity of cities during heat waves, to those built-up areas with less capacity to buffer the impact of warm spells [26,[42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Capacity of Urban Green Infrastructure Spaces to Ameliorate Heat Wave Impacts in Mediterranean Compact Cities: Case Study of Granada (South-Eastern Spain)

Delgado-Capel

Cariñanos

Escudero-Viñolo

2023

Land

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Heat wave episodes are becoming more frequent and severe worldwide, especially in areas such as the Mediterranean region. This study is aimed at assessing the impact of heat waves in an urban environment and the ways areas of urban green infrastructure (UGI) can play key roles in moderating the impacts of these high-temperature events. We analyzed land surface temperature (LST) and normalized difference vegetation index (NDVI) data retrieved from Landsat 8/9 satellite images. These data were recorded during heat wave episodes from 2017 to 2022 in a representative Mediterranean medium-sized compact city. We carried out a correlation analysis between LST and NDVI per area type and as individual units to assess how UGI elements can contribute to the cooling of the urban matrix during heat wave episodes. Those small green spaces distributed throughout the city, defined as “Other” areas, showed stronger negative correlation. These spaces are particularly relevant for Mediterranean cities, where highly limited space in city centers hinders the possibility of having larger-surface UGI elements. The study highlights the need for further research into the composition of those small public green spaces to understand how their components enhance the city’s cooling capacity given the climate conditions and water scarcity in the Mediterranean region.

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Thermal behavior and its seasonal and diurnal variability of urban green infrastructure in a mid-latitude city - Berlin

Cited by 4 publications

References 19 publications

Summer Nights in Berlin, Germany: Modeling Air Temperature Spatially With Remote Sensing, Crowdsourced Weather Data, and Machine Learning

Summer Nights in Berlin, Germany: Modeling Air Temperature Spatially With Remote Sensing, Crowdsourced Weather Data, and Machine Learning

The Autobot-WQ: A portable, low-cost autosampler to provide new insight into urban spatio-temporal water quality dynamics

Capacity of Urban Green Infrastructure Spaces to Ameliorate Heat Wave Impacts in Mediterranean Compact Cities: Case Study of Granada (South-Eastern Spain)

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