Special Issue "Remote Sensing of the Urban Climate"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editor

Prof. Dr. Ramesh P. Singh
E-Mail Website
Guest Editor
School of Life and Environmental Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
Interests: remote sensing; atmospheric pollution; natural hazards, earthquakes; air quality
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Due to economic growth throughout the globe, populations living in rural areas have migrated to urban areas. The population in urban areas have increased and some of the cities in the world have become mega cities (more than 10 million people). In the last three decades, urbanization has increased throughout the globe, enhancing the demand for vehicles, energy, industry, etc. The population explosion has impacted land use and land cover affecting natural resources, hydrological cycle, environment, atmospheric pollution, air quality, etc. The land cover, forests, ponds, grasslands, agricultural lands have been replaced by buildings, roads, gardens, parks, sport fields, etc. Such changes affected the recharge of ground water and enhanced the frequency of flash floods that severely affected the ecological conditions. Urbanization is the real cause of poor air quality, which has adversely affected human health and visibility and also enhanced urban heat islands. Such changes have played an important role in climate change at the local, regional and global scale. The Special Issue on “Remote Sensing of the Urban Climate” invites papers dealing with satellite observations, data analysis and modelling associated with urban pollution, environment, ecology, dynamics of pollutants, hydrological cycle, extreme events on the changing urban climate. The Special Issue will also consider papers related to human health and mortality associated with the urban climate and pollution.

Prof. Ramesh P. Singh
Guest Editor

Manuscript Submission Information

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Keywords

  • urbanization
  • remote sensing
  • ground data
  • modelling
  • air quality
  • urban planning
  • dynamics of pollutants
  • associated natural hazards
  • extreme events
  • urban climate

Published Papers (4 papers)

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Research

Open AccessArticle
Urban Heat Island Formation in Greater Cairo: Spatio-Temporal Analysis of Daytime and Nighttime Land Surface Temperatures along the Urban–Rural Gradient
Remote Sens. 2021, 13(7), 1396; https://doi.org/10.3390/rs13071396 - 05 Apr 2021
Viewed by 727
Abstract
An urban heat island (UHI) is a significant anthropogenic modification of urban land surfaces, and its geospatial pattern can increase the intensity of the heatwave effects. The complex mechanisms and interactivity of the land surface temperature in urban areas are still being examined. [...] Read more.
An urban heat island (UHI) is a significant anthropogenic modification of urban land surfaces, and its geospatial pattern can increase the intensity of the heatwave effects. The complex mechanisms and interactivity of the land surface temperature in urban areas are still being examined. The urban–rural gradient analysis serves as a unique natural opportunity to identify and mitigate ecological worsening. Using Landsat Thematic Mapper (TM), Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS), Land Surface Temperature (LST) data in 2000, 2010, and 2019, we examined the spatial difference in daytime and nighttime LST trends along the urban–rural gradient in Greater Cairo, Egypt. Google Earth Engine (GEE) and machine learning techniques were employed to conduct the spatio-temporal analysis. The analysis results revealed that impervious surfaces (ISs) increased significantly from 564.14 km2 in 2000 to 869.35 km2 in 2019 in Greater Cairo. The size, aggregation, and complexity of patches of ISs, green space (GS), and bare land (BL) showed a strong correlation with the mean LST. The average urban–rural difference in mean LST was −3.59 °C in the daytime and 2.33 °C in the nighttime. In the daytime, Greater Cairo displayed the cool island effect, but in the nighttime, it showed the urban heat island effect. We estimated that dynamic human activities based on the urban structure are causing the spatial difference in the LST distribution between the day and night. The urban–rural gradient analysis indicated that this phenomenon became stronger from 2000 to 2019. Considering the drastic changes in the spatial patterns and the density of IS, GS, and BL, urban planners are urged to take immediate steps to mitigate increasing surface UHI; otherwise, urban dwellers might suffer from the severe effects of heatwaves. Full article
(This article belongs to the Special Issue Remote Sensing of the Urban Climate)
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Open AccessArticle
Experimental Study on the Inverse Estimation of Horizontal Air Temperature Distribution in Built Spaces Using a Ground-Based Thermal Infrared Spectroradiometer
Remote Sens. 2021, 13(4), 562; https://doi.org/10.3390/rs13040562 - 05 Feb 2021
Viewed by 751
Abstract
Air temperature is an important physical indicator for urban and architectural environments; however, it is difficult to obtain its distributive characteristics by field measurements owing to the limitations of current measuring instruments. In this context, this study was conducted to demonstrate whether a [...] Read more.
Air temperature is an important physical indicator for urban and architectural environments; however, it is difficult to obtain its distributive characteristics by field measurements owing to the limitations of current measuring instruments. In this context, this study was conducted to demonstrate whether a small and portable ground-based thermal infrared spectroradiometer can be used to estimate the horizontal air temperature distribution in built spaces. For this estimation, we first calculated a forward model using radiative transfer simulations, and the air temperature distribution was inversely estimated from the observed radiance using the model. To regularize the estimated air temperature, we used the maximum a posteriori method, which uses prior information. To verify this estimation method, we conducted measurement experiments in two types of built spaces that had different air temperature distributions within spaces that were approximately 20 m long. Moreover, we conducted a parametric case study on the prior information. As a result, we were able to estimate the air temperature distribution with an average root mean square error (RMSE) of 1.3 °C for all cases when the average RMSE of the prior information for all cases was 2.1 °C. This improvement in the RMSE indicates that this method is able to remotely estimate the horizontal air temperature distribution in built spaces. Full article
(This article belongs to the Special Issue Remote Sensing of the Urban Climate)
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Open AccessArticle
Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka
Remote Sens. 2020, 12(7), 1191; https://doi.org/10.3390/rs12071191 - 08 Apr 2020
Cited by 7 | Viewed by 1856
Abstract
Spatial urban growth and its impact on land surface temperature (LST) is a high priority environmental issue for urban policy. Although the impact of horizontal spatial growth of cities on LST is well studied, the impact of the vertical spatial distribution of buildings [...] Read more.
Spatial urban growth and its impact on land surface temperature (LST) is a high priority environmental issue for urban policy. Although the impact of horizontal spatial growth of cities on LST is well studied, the impact of the vertical spatial distribution of buildings on LST is under-investigated. This is particularly true for cities in sub-tropical developing countries. In this study, TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-XDEM), Advanced Spaceborne Thermal Emission and Reflection (ASTER)-Global Digital Elevation Model (GDEM), and ALOS World 3D-30m (AW3D30) based Digital Surface Model (DSM) data were used to investigate the vertical growth of the Dhaka Metropolitan Area (DMA) in Bangladesh. Thermal Infrared (TIR) data (10.6-11.2µm) of Landsat-8 were used to investigate the seasonal variations in LST. Thereafter, the impact of horizontal and vertical spatial growth on LST was studied. The result showed that: (a) TanDEM-X DSM derived building height had a higher accuracy as compared to other existing DSM that reveals mean building height of the Dhaka city is approximately 10 m, (b) built-up areas were estimated to cover approximately 94%, 88%, and 44% in Dhaka South City Corporation (DSCC), Dhaka North City Corporation (DNCC), and Fringe areas, respectively, of DMA using a Support Vector Machine (SVM) classification method, (c) the built-up showed a strong relationship with LST (Kendall tau coefficient of 0.625 in summer and 0.483 in winter) in comparison to vertical growth (Kendall tau coefficient of 0.156 in the summer and 0.059 in the winter), and (d) the ‘low height-high density’ areas showed high LST in both seasons. This study suggests that vertical development is better than horizontal development for providing enough open spaces, green spaces, and preserving natural features. This study provides city planners with a better understating of sustainable urban planning and can promote the formulation of action plans for appropriate urban development policies. Full article
(This article belongs to the Special Issue Remote Sensing of the Urban Climate)
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Open AccessArticle
Urban Mapping Accuracy Enhancement in High-Rise Built-Up Areas Deployed by 3D-Orthorectification Correction from WorldView-3 and LiDAR Imageries
Remote Sens. 2019, 11(6), 692; https://doi.org/10.3390/rs11060692 - 22 Mar 2019
Cited by 7 | Viewed by 1632
Abstract
Orthorectification is an important step in generating accurate land use/land cover (LULC) from satellite imagery, particularly in urban areas with high-rise buildings. Such buildings generally appear as oblique shapes on very-high-resolution (VHR) satellite images, which reflect a bigger area of coverage than the [...] Read more.
Orthorectification is an important step in generating accurate land use/land cover (LULC) from satellite imagery, particularly in urban areas with high-rise buildings. Such buildings generally appear as oblique shapes on very-high-resolution (VHR) satellite images, which reflect a bigger area of coverage than the real built-up area on LULC mapping. This drawback can cause not only uncertainties in urban mapping and LULC classification, but can also result in inaccurate urban change detection. Overestimating volume or area of high-rise buildings has a negative impact on computing the exact amount of environmental heat and emission. Hence, in this study, we propose a method of orthorectfiying VHR WorldView-3 images by integrating light detection and ranging (LiDAR) data to overcome the aforementioned problems. A 3D rational polynomial coefficient (RPC) model was proposed with respect to high-accuracy ground control points collected from the LiDAR data derived from the digital surface model. Multiple probabilities for generating an orthrorectified image from WV-3 were assessed using 3D RCP model to achieve the optimal combination technique, with low vertical and horizontal errors. Ground control point (GCPs) collection is sensitive to variation in number and data collection pattern. These steps are important in orthorectification because they can cause the morbidity of a standard equation, thereby interrupting the stability of 3D RCP model by reducing the accuracy of the orthorectified image. Hence, we assessed the maximum possible scenarios of resampling and ground control point collection techniques to bridge the gap. Results show that the 3D RCP model accurately orthorectifies the VHR satellite image if 20 to 100 GCPs were collected by convenience pattern. In addition, cubic conventional resampling algorithm improved the precision and smoothness of the orthorectified image. According to the root mean square error, the proposed combination technique enhanced the vertical and horizontal accuracies of the geo-positioning process to up to 0.8 and 1.8 m, respectively. Such accuracy is considered very high in orthorectification. The proposed technique is easy to use and can be replicated for other VHR satellite and aerial photos. Full article
(This article belongs to the Special Issue Remote Sensing of the Urban Climate)
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