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Atmosphere
Special Issues
Remote Sensing and GIS Applications in Urban Climate Research
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Remote Sensing and GIS Applications in Urban Climate Research

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".

Deadline for manuscript submissions: closed (30 March 2022) | Viewed by 23725

Special Issue Editors

Dr. Chuyuan Wang

E-Mail Website
Guest Editor
Department of Geography and Environmental Planning, Towson University, Towson, MD 21252, USA
Interests: remote sensing; GIS; urban climate; land use/land cover change; urban ecosystems; terrestrial ecosystems; agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Fan

E-Mail Website
Guest Editor
Department of Geography, New Mexico State University, Las Cruces, NM 88003, USA
Interests: GIScience; spatial analysis and modeling; spatial statistics; remote sensing; land change science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urbanization and urban expansion feature conversions from natural and agricultural land to built-up environment that has taken place ubiquitously worldwide at an increasing rate. Associated with the use of construction and building materials, urbanization processes modify the surface energy balance, hydrological cycle, and natural ecosystems, which have led to significant impacts on the local and regional climate. These changes have constituted serious climate challenges for urban areas that require both new analytic approaches and new sources of data and information.

Emerging geospatial technologies offer great opportunities to acquire continuous observations of the Earth’s surface, analyze the spatiotemporal patterns of changes in the landscape and climate, and make predictions for future trends. Remote sensing can also provide fundamental observations for urban areas in developing countries where other data sources are not readily available. Additionally, advances in sensors, computing technology, big data, and geospatial analytics have significantly increased the range of research questions that can be answered with traditional methods in urban climatology.

The aim of this Special Issue is to present original research articles and review work related to remote sensing and GIS applications in urban climate research. In this Special Issue, we seek original work focused on using innovative remote sensing and GIS methods to address urban climate issues. Topics include but are not limited to:

  1. Spatiotemporal analysis of urban climate;
  2. Long-term trend of urban climate change;
  3. Urbanization effects on local, regional, and global climate change;
  4. Urban landscape and climate;
  5. Urban ecosystems and climate;
  6. The urban heat island effect;
  7. Urban energy use; and
  8. Urban water resources and water use.

We are especially interested in research using remote sensing big data computing and machine learning approaches to address urban climate issues.

Dr. Chuyuan Wang
Dr. Chao Fan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Urban climate
  • Remote sensing
  • GIS
  • Spatial analysis
  • Urbanization
  • Climate change
  • Urban landscape
  • Urban ecosystems
  • Urban heat island
  • Urban water use

Published Papers (7 papers)

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Research

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12 pages, 2317 KiB  
Article
Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City
by Abdou Rachid Bah, Hamidreza Norouzi, Satya Prakash, Reginald Blake, Reza Khanbilvardi and Cynthia Rosenzweig
Atmosphere 2022, 13(2), 332; https://doi.org/10.3390/atmos13020332 - 16 Feb 2022
Cited by 5 | Viewed by 2916
Abstract
The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions [...] Read more.
The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions. Full article
(This article belongs to the Special Issue Remote Sensing and GIS Applications in Urban Climate Research)
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15 pages, 13248 KiB  
Article
Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes
by Zhengwu Cai, Chao Fan, Falin Chen and Xiaoma Li
Atmosphere 2021, 12(11), 1540; https://doi.org/10.3390/atmos12111540 - 22 Nov 2021
Cited by 1 | Viewed by 1746
Abstract
The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. This study validated the [...] Read more.
The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. This study validated the utility of the pseudo-invariant feature-based linear regression model (PIF-LRM) in normalizing multi-temporal Landsat LST to highlight the urbanization impact on temperature changes, based on five Landsat LST images during 2000–2018 in Changsha, China. Results showed that LST of PIFs between the reference and the target images was highly correlated, indicating high applicability of the PIF-LRM to relatively normalize LST. The PIF-LRM effectively removed the temporal variation of LST caused by climate factors and highlighted the impacts of urbanization caused land use and land cover changes. The PIF-LRM normalized LST showed stronger correlations with the time series of normalized difference of vegetation index (NDVI) than the observed LST and the LST normalized by the commonly used mean method (subtracting LST by the average, respectively for each image). The PIF-LRM uncovered the spatially heterogeneous responses of LST to urban expansion. For example, LST decreased in the urban center (the already developed regions) and increased in the urbanizing regions. PIF-LRM is highly recommended to normalize multi-temporal Landsat LST to understand the impact of urbanization on surface temperature changes from a temporal point of view. Full article
(This article belongs to the Special Issue Remote Sensing and GIS Applications in Urban Climate Research)
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20 pages, 11504 KiB  
Article
Quantifying the Contribution of LUCC to Surface Energy Budget: A Case Study of Four Typical Cities in the Yellow River Basin in China
by Qian Chi, Shenghui Zhou, Lijun Wang, Mengyao Zhu, Dandan Liu, Weichao Tang, Xiao Zhao, Siqi Xu, Siyu Ye, Jay Lee and Yaoping Cui
Atmosphere 2021, 12(11), 1374; https://doi.org/10.3390/atmos12111374 - 20 Oct 2021
Cited by 6 | Viewed by 1686
Abstract
With social changes and economic development, human activities inevitably lead to significant changes in land use types. Land use and land cover change (LUCC) leads to a series of changes in energy balance and surface temperature, which has an impact on the regional [...] Read more.
With social changes and economic development, human activities inevitably lead to significant changes in land use types. Land use and land cover change (LUCC) leads to a series of changes in energy balance and surface temperature, which has an impact on the regional climate. In this study, MODIS remote sensing data were used to quantify the results of the biological and geophysical effects caused by LUCC in four typical cities in the Yellow River Basin of China: Jinan, Zhengzhou, Lanzhou and Xining. The results showed the following: (1) The latent heat flux and the net radiation of the four cities were both increasing on the whole. The latent heat flux of water and forest was higher, which played a key role in energy consumption on the ground. The net radiation value of the old urban and urban expansion areas was higher, while that of the forest was lower, which indicated that human activities increased the input of surface energy. (2) The differences between latent heat flux and net radiation in areas greatly affected by human activities were much smaller than those in natural areas such as forest and grassland. This indicted that human activities increased the warming trend. In addition, most of the differences between latent heat flux and net radiation in the four cities showed a downward trend. (3) Different cities have different regulating factors for land surface temperature (LST). In Jinan and Zhengzhou, the regulation of LST by net radiation was more obvious, while in Lanzhou and Xining, the regulation of LST by latent heat flux was more pronounced. By comparing LUCC and the forced balance between energy intake and consumption in four typical cities along the Yellow River Basin, this study emphasizes the difference of energy budgets under different land use types, which has important reference value for judging the spatial difference of urban thermal environments. Full article
(This article belongs to the Special Issue Remote Sensing and GIS Applications in Urban Climate Research)
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18 pages, 9782 KiB  
Article
An Application of the LCZ Approach in Surface Urban Heat Island Mapping in Sofia, Bulgaria
by Stelian Dimitrov, Anton Popov and Martin Iliev
Atmosphere 2021, 12(11), 1370; https://doi.org/10.3390/atmos12111370 - 20 Oct 2021
Cited by 13 | Viewed by 3752
Abstract
This article presents the results of the thermal survey of the capital of Bulgaria (Sofia) carried out in August 2019, with the application of an unmanned aerial system (UAS). The study is based on the concept of local climate zones (LCZs), taking into [...] Read more.
This article presents the results of the thermal survey of the capital of Bulgaria (Sofia) carried out in August 2019, with the application of an unmanned aerial system (UAS). The study is based on the concept of local climate zones (LCZs), taking into account the influence of the features of land use/land cover and urban morphology on the urban climate. The basic spatial units used in the study are presented in the form of a regular grid consisting of 3299 cells with sides of 250 × 250 m. A total of 13 types of LCZs were identified, of which LCZs 6, 5, 8, 4, D, and A form the largest share. In the thermal imaging of the surface, a stratified sampling scheme was applied, which allowed us to select 74 cells, which are interpreted as representative of all cells belonging to the corresponding LCZ in the urban space. The performed statistical analysis of the thermal data allowed us to identify both the most thermally loaded zones (LCZs 9, 4, and 5) and the cells forming Urban Cool Islands (mainly in LCZs D and C). The average surface temperature in Sofia during the study period (in the time interval between 8:00 p.m. and 10:00 p.m.) was estimated at 20.9 °C, and between the different zones it varied in the range 17.2–25.1 °C. The highest maximum values of LST (27.9–30.6 °C) were registered in LCZ 4 and LCZ 5. The relation between the spatial structure of the urban thermal patterns and urban surface characteristics was also analyzed. Regression analysis confirmed the hypothesis that as the proportion of green areas increases, surface temperatures decrease, and, vice versa, as the proportion of built-up and impermeable areas increases, surface temperatures increase. A heat load map (via applying a z-transformation to standardize the temperature values), a map of the average surface temperature, and a map of the average intensity of the heat island on the surface were generated in the GIS environment. The results of the study adequately reflect the complex spatial model of the studied phenomenon, which gives grounds to conclude that the research approach used is applicable to similar studies in other cities. Full article
(This article belongs to the Special Issue Remote Sensing and GIS Applications in Urban Climate Research)
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26 pages, 5519 KiB  
Article
Adaptive Crop Management under Climate Uncertainty: Changing the Game for Sustainable Water Use
by Soe W. Myint, Rimjhim Aggarwal, Baojuan Zheng, Elizabeth A. Wentz, Jim Holway, Chao Fan, Nancy J. Selover, Chuyuan Wang and Heather A. Fischer
Atmosphere 2021, 12(8), 1080; https://doi.org/10.3390/atmos12081080 - 23 Aug 2021
Cited by 5 | Viewed by 3157
Abstract
Water supplies are projected to become increasingly scarce, driving farmers, energy producers, and urban dwellers towards an urgent and emerging need to improve the effectiveness and the efficiency of water use. Given that agricultural water use is the largest water consumer throughout the [...] Read more.
Water supplies are projected to become increasingly scarce, driving farmers, energy producers, and urban dwellers towards an urgent and emerging need to improve the effectiveness and the efficiency of water use. Given that agricultural water use is the largest water consumer throughout the U.S. Southwest, this study sought to answer two specific research questions: (1) How does water consumption vary by crop type on a metropolitan spatial scale? (2) What is the impact of drought on agricultural water consumption? To answer the above research questions, 92 Landsat images were acquired to generate fine-resolution daily evapotranspiration (ET) maps at 30 m spatial resolution for both dry and wet years (a total of 1095 ET maps), and major crop types were identified for the Phoenix Active Management Area. The study area has a subtropical desert climate and relies almost completely on irrigation for farming. Results suggest that there are some factors that farmers and water managers can control. During dry years, crops of all types use more water. Practices that can offset this higher water use include double or multiple cropping practice, drought tolerant crop selection, and optimizing the total farmed area. Double and multiple cropping practices result in water savings because soil moisture is retained from one planting to another. Further water savings occur when drought tolerant crop types are selected, especially in dry years. Finally, disproportionately large area coverage of high water consuming crops can be balanced and/or reduced or replaced with more water efficient crops. This study provides strong evidence that water savings can be achieved through policies that create incentives for adopting smart cropping strategies, thus providing important guidelines for sustainable agriculture management and climate adaptation to improve future food security. Full article
(This article belongs to the Special Issue Remote Sensing and GIS Applications in Urban Climate Research)
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26 pages, 37540 KiB  
Article
Spatiotemporal Impacts of Urban Land Use/Land Cover Changes on Land Surface Temperature: A Comparative Study of Damascus and Aleppo (Syria)
by Mohamed Ali Mohamed
Atmosphere 2021, 12(8), 1037; https://doi.org/10.3390/atmos12081037 - 13 Aug 2021
Cited by 6 | Viewed by 2617
Abstract
Monitoring the impact of changes in land use/land cover (LULC) and land surface temperature (LST) is of great importance in environmental and urban studies. In this context, this study aimed to analyze the dynamics of LULC and its impact on the spatiotemporal variation [...] Read more.
Monitoring the impact of changes in land use/land cover (LULC) and land surface temperature (LST) is of great importance in environmental and urban studies. In this context, this study aimed to analyze the dynamics of LULC and its impact on the spatiotemporal variation of the LST in the two largest urban cities in Syria, Damascus, and Aleppo. To achieve this, LULC changes, normalized difference vegetation index (NDVI), and LST were calculated from multi-temporal Landsat data for the period 2010 to 2018. The study revealed significant changes in LULC, which were represented by a decrease in agricultural land and green areas and an increase in bare areas in both cities. In addition, built-up areas decreased in Aleppo and increased in Damascus during the study period. The temporal and spatial variation of the LST and its distribution pattern was closely related to the effect of changes in LULC as well as to land use conditions in each city. This effect was greater in Aleppo than in Damascus, where Aleppo recorded a higher increase in the mean LST, by about 2 °C, than in Damascus, where it was associated with greater degradation and loss of vegetation cover. In general, there was an increasing trend in the minimum and maximum LST as well as an increasing trend in the mean LST in both cities. The negative linear relationship between LST and NDVI confirms that vegetation cover can help reduce LST in both cities. This study can draw the attention of relevant departments to pay more attention to mitigating the negative impact of LULC changes in order to limit the increase in LST. Full article
(This article belongs to the Special Issue Remote Sensing and GIS Applications in Urban Climate Research)
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Review

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16 pages, 1865 KiB  
Review
Advances of Local Climate Zone Mapping and Its Practice Using Object-Based Image Analysis
by Lei Ma, Xiaoxiang Zhu, Chunping Qiu, Thomas Blaschke and Manchun Li
Atmosphere 2021, 12(9), 1146; https://doi.org/10.3390/atmos12091146 - 05 Sep 2021
Cited by 14 | Viewed by 4500
Abstract
In the context of climate change and urban heat islands, the concept of local climate zones (LCZ) aims for consistent and comparable mapping of urban surface structure and cover across cities. This study provides a timely survey of remote sensing-based applications of LCZ [...] Read more.
In the context of climate change and urban heat islands, the concept of local climate zones (LCZ) aims for consistent and comparable mapping of urban surface structure and cover across cities. This study provides a timely survey of remote sensing-based applications of LCZ mapping considering the recent increase in publications. We analyze and evaluate several aspects that affect the performance of LCZ mapping, including mapping units/scale, transferability, sample dataset, low accuracy, and classification schemes. Since current LCZ analysis and mapping are based on per-pixel approaches, this study implements an object-based image analysis (OBIA) method and tests it for two cities in Germany using Sentinel 2 data. A comparison with a per-pixel method yields promising results. This study shall serve as a blueprint for future object-based remotely sensed LCZ mapping approaches. Full article
(This article belongs to the Special Issue Remote Sensing and GIS Applications in Urban Climate Research)
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