Urban Imperviousness Effects on Summer Surface Temperatures Nearby Residential Buildings in Different Urban Zones of Parma

Morabito, Marco; Crisci, Alfonso; Georgiadis, T.; Orlandini, Simone; Munafò, Michele; Congedo, Luca; Rota, P.; Zazzi, Michele

doi:10.3390/rs10010026

Cited by 23 publications

(25 citation statements)

References 39 publications

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“…It is noteworthy that this product is available for daytime and nighttime and, thereby, provides a significant opportunity to study diurnal variation of SUHI [82]. Past researchers also derived LST from different thermal bands of ASTER for various types of SUHI studies [83][84][85]. However, the wide application of ASTER imagery to SUHI research was largely impeded by high cost and a limited data archive compared to other sensors like Landsat and MODIS [86].…”

Section: Astermentioning

confidence: 99%

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

et al. 2018

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The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.

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Section: Astermentioning

confidence: 99%

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

et al. 2018

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“…In the existing studies for investigating SUHI effect via satellite thermal remote sensing, most of which only used the MODIS, Landsat TM/ETM+/8, and ASTER thermal band products [62,63], or additionally combined with high-resolution aerial photos/commercial satellite satellites for added interpretation [17,64,65], the results based on the unsharpened thermal band data were too coarse to examine the relationship between the observed LST and fine-scale LULC components. In contrast, our findings, though exhibiting the uncertainties of the Mean_pc_BBF between the present and ideally optimized area proportions of parcel-level LULC components, can provide more detailed information on the fine-scale intra-SUHI effect and optional choices for decision-making towards mitigating UHI effect.…”

Section: On the Data Methods And Findings Of This Studymentioning

confidence: 99%

“…One of the environmental consequences of LULC change is the urban heat island (UHI) effect. Associated with human activities and climate change [12][13][14][15][16][17][18][19], the UHI effect significantly affects emissions of air pollutants and greenhouse gases [20], meteorological disasters, and health risks [21,22].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Role of Optimized Land Use/Land Cover Components in Mitigating Summertime Intra-Surface Urban Heat Island Effect: A Study on Downtown Shanghai, China

et al. 2020

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In this study, 167 land parcels of downtown Shanghai, China, were used to investigate the relationship between parcel-level land use/land cover (LULC) components and associated summertime intra-surface urban heat island (SUHI) effect, and further analyze the potential of mitigating summertime intra-SUHI effect through the optimized LULC components, by integrating a thermal sharpening method combining the Landsat-8 thermal band 10 data and high-resolution Quickbird image, statistical analysis, and nonlinear programming with constraints. The results show the remarkable variations in intra-surface urban heat island (SUHI) effect, which was measured with the mean parcel-level blackbody sensible heat flux in kW per ha (Mean_pc_BBF). Through measuring the relative importance of each specific predictor in terms of their contributions to changing Mean_pc_BBF, the influence of parcel-level LULC components on excess surface flux of heat energy to the atmosphere was estimated using the partial least square regression (PLSR) model. Analysis of the present and optimized parcel-level LULC components and their contribution to the associated Mean_pc_BBF were comparable between land parcels with varying sizes. Furthermore, focusing on the gap between the present and ideally optimized area proportions of parcel-level LULC components towards minimizing the Mean_pc_BBF, the uncertainties arising from the datasets and methods, as well as the implications for sustainable land development and mitigating the UHI effect were discussed.

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“…Because the balance of artificial and non-artificial land is crucial to set the Habitat Quality, the final result has been synthetically evaluated using the most reliable and updated dataset on artificial land, which is the one produced by theSuperior Institute for Environmental Protection and Research (National Land Take Database) of 2019 that measures the impervious area with a semi-automatic classification by satellite images [41,42]. Therefore, the classes Industry, Urban, and Streets of the integrated LULC 2020 were statistically compared with the land take dataset (Artificial Areas) of ISPRA under the column "comparison" on the right side of Table 6 in a selected portion of the analyzed area.…”

Section: The Lulc Classificationmentioning

confidence: 99%

“…Nonetheless, the Habitat Quality of these areas remains below the average value, with relatively poor biodiversity and a high decay of the ecosystems. For these municipalities, a priority of "green-to-gray" [41,[43][44][45] measures should be delivered by local plans to recover Habitat Quality while generating a lower impact on the ecosystem. Surprisingly, even the threatened municipalities are those belonging to a hilly environment.…”

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confidence: 99%

The Utilization of Supervised Classification Sampling for Environmental Monitoring in Turin (Italy)

Salata

2021

Sustainability

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In a world threatened by climate change, the need to observe the land transformation is crucial to set environmental policies. One of the most prominent issues of environmental monitoring is the availability of updated and reliable land use data. The last land-use release in Piedmont Region (Italy) is in 2010, while the most updated Normalized Difference Vegetation Index is in 2016. To overcome this limit, in this study, a supervised classification sampling has been applied on a Sentinel-2 image produced by the Copernicus Program on 29 September 2020, using Esri ArcGIS (ver.10.8 Redlands, California, US) by accessing via ONDA-DIAS services to L2A products. After land classification, three maps were generated—the Habitat Quality, the Habitat Decay, and the Normalized Difference Vegetation Index. This study aimed at classifying the environmental status in five classes ranging from “critical” to “health” with a double perspective—(i) to make a comparative metropolitan assessment between municipalities and (ii) to evaluate the quality of urban public green areas in the city of Turin while defining a different kind of intervention. Results indicate that products derived from supervised classification sampling can be applied in a wide range of applications while reaching seasonal monitoring of the environmental status and delivering just-in-time solutions.

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Urban Imperviousness Effects on Summer Surface Temperatures Nearby Residential Buildings in Different Urban Zones of Parma

Cited by 23 publications

References 39 publications

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

Understanding the Role of Optimized Land Use/Land Cover Components in Mitigating Summertime Intra-Surface Urban Heat Island Effect: A Study on Downtown Shanghai, China

The Utilization of Supervised Classification Sampling for Environmental Monitoring in Turin (Italy)

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