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Thermal and Optical Remote Sensing: Evaluating Urban Green Spaces and Urban Heat Islands in a Changing Climate

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 31821

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Prof. Dr. John O. Odindi

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Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg 3209, South Africa
Interests: adoption of remotely sensed datasets in understanding urban land use; land covers; urban green spaces; urban ecosystem services; urban heat islands; climate change and urban transformation
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Dr. Elhadi Adam

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School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Private Bag 3, Wits 2050, Johannesburg, South Africa
Interests: hyperspectral and multispectral remote sensing; GIS modelling for environmental and agriculture applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Elfatih M. Abdel-Rahman

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1. Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum North, Sudan
2. Geo-Information Unit, International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya
Interests: Dr Elfatih M. Abdel-Rahman is an expert in earth observation tools, remote sensing, and geospatial modelling of land use dynamics, including crops and cropping systems, and scenario assessment of land use effects in relation to crop productivity constraints. In specific, his thematic working areas include characterizing and modelling landscape dynamics, assessing ecosystems services and fragmentation.

Dr. Yuyu Zhou

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Department of Geological & Atmospheric Sciences, Iowa State University, 3019 Agronomy Hall, Ames, IA 50011, USA
Interests: urban remote sensing; urbanization; urban heat island; building energy use; vegetation phenology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urbanization is typified by spatial and temporal transformation arising from conversion of natural to impervious and built-up surfaces. These conversions affect ecosystem functioning at local, regional and global scales and compromise their ability to provide their goods and services. Furthermore, the conversions are known to be a major driver of environmental change associated with among others, natural landscape fragmentation and related adverse effects, deterioration in environmental quality, biodiversity loss, and change in micro- and macro-climate. Hence, establishment, restoration and preservation of urban natural and green infrastructure is increasingly becoming a popular approach to dealing with adversities associated with urban processes. Understanding urban spatio-temporal ecological and natural patterns is critical for the management of urban physical, ecological and social processes. Specifically, understanding past, present and future patterns and drivers is critical for among others urban environmental management, urban spatial planning, optimal and sustainable use of urban landscapes and climate change mitigation. A recent proliferation of remotely sensed datasets offer great potential in understanding the relationship between urban process and their respective ecological and natural integrity.

This Special Issue focuses on theoretical and practical adoption of remote sensing approaches and datasets in understanding urban green and natural infrastructure and related ecosystem services. Specifically, the special issue solicits articles exploring among others: urban green spaces mapping and transformation, non-ecological urban natural assets, thermal characteristics and variability, green spaces and urban forests rehabilitation, quantification and mapping of ecosystem services and micro and macroclimate change modelling.

Prof. Dr. John O. Odindi
Prof. Dr. Elhadi Adam
Prof. Dr. Elfatih M. Abdel-Rahman
Dr. Yuyu Zhou
Guest Editors

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Keywords

Urbanization
Thermal and optical data
Green infrastructure
Urban ecosystem services
Urban green space

Published Papers (11 papers)

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18 pages, 4111 KiB

Open AccessArticle

Cooling Effects of Urban Vegetation: The Role of Golf Courses

by Thu Thi Nguyen, Harry Eslick, Paul Barber, Richard Harper and Bernard Dell

Remote Sens. 2022, 14(17), 4351; https://doi.org/10.3390/rs14174351 - 01 Sep 2022

Cited by 3 | Viewed by 2545

Abstract

Increased heat in urban environments, from the combined effects of climate change and land use/land cover change, is one of the most severe problems confronting cities and urban residents worldwide, and requires urgent resolution. While large urban green spaces such as parks and nature reserves are widely recognized for their benefits in mitigating urban heat islands (UHIs), the benefit of urban golf courses is less established. This is the first study to combine remote sensing of golf courses with Morphological Spatial Pattern Analysis (MSPA) of vegetation cover. Using ArborCam^TM multispectral, high-resolution airborne imagery (0.3 × 0.3 m), this study develops an approach that assesses the role of golf courses in reducing urban land surface temperature (LST) relative to other urban land-uses in Perth, Australia, and identifies factors that influence cooling. The study revealed that urban golf courses had the second lowest LST (around 31 °C) after conservation land (30 °C), compared to industrial, residential, and main road land uses, which ranged from 35 to 37 °C. They thus have a strong capacity for summer urban heat mitigation. Within the golf courses, distance to water bodies and vegetation structure are important factors contributing to cooling effects. Green spaces comprising tall trees (>10 m) and large vegetation patches have strong effects in reducing LST. This suggests that increasing the proportion of large trees, and increasing vegetation connectivity within golf courses and with other local green spaces, can decrease urban LST, thus providing benefits for urban residents. Moreover, as golf courses are useful for biodiversity conservation, planning for new golf course development should embrace the retention of native vegetation and linkages to conservation corridors. Full article

(This article belongs to the Special Issue Thermal and Optical Remote Sensing: Evaluating Urban Green Spaces and Urban Heat Islands in a Changing Climate)

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15 pages, 2799 KiB

Open AccessArticle

“Cool” Roofs as a Heat-Mitigation Measure in Urban Heat Islands: A Comparative Analysis Using Sentinel 2 and Landsat Data

by Terence Mushore, John Odindi and Onisimo Mutanga

Remote Sens. 2022, 14(17), 4247; https://doi.org/10.3390/rs14174247 - 28 Aug 2022

Cited by 4 | Viewed by 2434

Abstract

Urban growth, characterized by expansion of impervious at the cost of the natural landscape, causes warming and heat-related distress. Specifically, an increase in the number of buildings within an urban landscape causes intensification of heat islands, necessitating promotion of cool roofs to mitigate Urban Heat Islands (UHI) and associated impacts. In this study, we used the freely available Sentinel 2 and Landsat 8 data to determine the study area’s Land Use Land Covers (LULCs), roof colours and Land Surface Temperature (LST) at a 10-m spatial resolution. Support Vector Machines (SVM) classification algorithm was adopted to derive the study area’s roof colours and proximal LULCs, and the Transformed Divergence Separability Index (TDSI) based on Jeffries Mathussitta distance analysis was used to determine the variability in LULCs and roof colours. To effectively relate the Landsat 8 thermal characteristics to the LULCs and roof colours, the Gram–Schmidt technique was used to pan-sharpen the 30-m Landsat 8 image data to 10 m. Results show that Sentinel 2 mapped LULCs with over 75% accuracy. Pan-sharpening the 30-m-resolution thermal data to 10 m improved the spatial resolution and quality of the Land Surface map and the correlation between LST and Normalized Difference Vegetation Index (NDVI) used as proxy for LULC. Green-colour roofs were the warmest, followed by red roofs, while blue roofs were the coolest. Generally, black roofs in the study area were cool. The study recommends the need to incorporate other roofing properties, such as shape, and further split the colours into different shades. Furthermore, the study recommends the use of very high spatial resolution data to determine roof colour and their respective properties; these include data derived from sensors mounted on aerial platforms such as drones and aircraft. The study concludes that with appropriate analytical techniques, freely available image data can be integrated to determine the implication of roof colouring on urban thermal characteristics, useful for mitigating the effects of Urban Heat Islands and climate change. Full article

(This article belongs to the Special Issue Thermal and Optical Remote Sensing: Evaluating Urban Green Spaces and Urban Heat Islands in a Changing Climate)

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21 pages, 23640 KiB

Open AccessArticle

The Surface Urban Heat Island and Key Mitigation Factors in Arid Climate Cities, Case of Marrakesh, Morocco

by Abdelali Gourfi, Aude Nuscia Taïbi, Salima Salhi, Mustapha El Hannani and Said Boujrouf

Remote Sens. 2022, 14(16), 3935; https://doi.org/10.3390/rs14163935 - 13 Aug 2022

Cited by 7 | Viewed by 2261

Abstract

The use of vegetation is one of the effective methods to combat the increasing Urban Heat Island (UHI). However, vegetation is steadily decreasing due to urban pressure and increased water stress. This study used air temperature measurements, humidity and an innovative advanced earth system analysis to investigate, at daytime, the relationship between green surfaces, built-up areas and the surface urban heat island (SUHI) in Marrakesh, Morocco, which is one of the busiest cities in Africa and serves as a major economic centre and tourist destination. While it is accepted that UHI variation is generally mitigated by the spatial distribution of green spaces and built-up areas, this study shows that bare areas also play a key role in this relationship. The results show a maximum mean land surface temperature difference of 3.98 °C across the different city neighbourhoods, and bare ground had the highest correlation with temperature (r = 0.86). The correlation between the vegetation index and SUHI is decreasing over time, mainly because of the significant changes in the region’s urban planning policy and urban growth. The study represents a relevant overview of the factors impacting SUHI, and it brings a new perspective to what is known so far in the literature, especially in arid climate areas, which have the specificity of large bare areas playing a major role in SUHI mitigation. This research highlights this complex relationship for future sustainable development, especially with the challenges of global warming becoming increasingly critical. Full article

(This article belongs to the Special Issue Thermal and Optical Remote Sensing: Evaluating Urban Green Spaces and Urban Heat Islands in a Changing Climate)

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23 pages, 7429 KiB

Open AccessArticle

Sentinel-Based Adaptation of the Local Climate Zones Framework to a South African Context

by Tshilidzi Manyanya, Janne Teerlinck, Ben Somers, Bruno Verbist and Nthaduleni Nethengwe

Remote Sens. 2022, 14(15), 3594; https://doi.org/10.3390/rs14153594 - 27 Jul 2022

Viewed by 1552

Abstract

The LCZ framework has become a widely applied approach to study urban climate. The standard LCZ typology is highly specific when applied to western urban areas but generic in some African cities. We tested the generic nature of the standard typology by taking a two-part approach. First, we applied a single-source WUDAPT-based training input across three urban areas that represent a gradient in South African urbanization (Cape Town, Thohoyandou and East London). Second, we applied a local customized training that accounts for the unique characteristics of the specific area. The LCZ classification was completed using a random forest classifier on a subset of single (SI) and multitemporal (MT) Sentinel 2 imagery. The results show an increase in overall classification accuracy between 17 and 30% for the locally calibrated over the generic standard LCZ framework. The spring season is the best classified of the single-date imagery with the accuracies 7% higher than the least classified season. The multi-date classification accuracy is 13% higher than spring but only 9% higher when a neighborhood function (NF) is applied. For acceptable performance of the LCZ classifier in an African context, the training must be local and customized to the uniqueness of that specific area. Full article

(This article belongs to the Special Issue Thermal and Optical Remote Sensing: Evaluating Urban Green Spaces and Urban Heat Islands in a Changing Climate)

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18 pages, 5825 KiB

Open AccessArticle

Step-By-Step Downscaling of Land Surface Temperature Considering Urban Spatial Morphological Parameters

by Xiangyu Li, Guixin Zhang, Shanyou Zhu and Yongming Xu

Remote Sens. 2022, 14(13), 3038; https://doi.org/10.3390/rs14133038 - 24 Jun 2022

Cited by 4 | Viewed by 2285

Abstract

Land surface temperature (LST) is one of the most important parameters in urban thermal environmental studies. Compared to natural surfaces, the surface of urban areas is more complex, and the spatial variability of LST is higher. Therefore, it is important to obtain a high-spatial-resolution LST for urban thermal environmental research. At present, downscaling studies are mostly performed from a low spatial resolution directly to another high resolution, which often results in lower accuracy with a larger scale span. First, a step-by-step random forest downscaling LST model (SSRFD) is proposed in this study. In our work, the 900-m resolution Sentinel-3 LST was sequentially downscaled to 450 m, 150 m and 30 m by SSRFD. Then, urban spatial morphological parameters were introduced into SSRFD, abbreviated as SSRFD-M, to compensate for the deficiency of remote-sensing indices as driving factors in urban downscaling LST. The results showed that the RMSE value of the SSRFD results was reduced from 2.6 °C to 1.66 °C compared to the direct random forest downscaling model (DRFD); the RMSE value of the SSRFD-M results in built-up areas, such as Gulou and Qinhuai District, was reduced by approximately 0.5 °C. We also found that the underestimation of LST caused by considering only remote-sensing indices in places such as flowerbeds and streets was improved in the SSRFD-M results. Full article

(This article belongs to the Special Issue Thermal and Optical Remote Sensing: Evaluating Urban Green Spaces and Urban Heat Islands in a Changing Climate)

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22 pages, 7434 KiB

Open AccessArticle

Determining the Influence of Long Term Urban Growth on Surface Urban Heat Islands Using Local Climate Zones and Intensity Analysis Techniques

by Terence Darlington Mushore, Onisimo Mutanga and John Odindi

Remote Sens. 2022, 14(9), 2060; https://doi.org/10.3390/rs14092060 - 25 Apr 2022

Cited by 12 | Viewed by 2324

Abstract

Urban growth, typified by conversion from natural to built-up impervious surfaces, is known to cause warming and associated adverse impacts. Local climate zones present a standardized technique for evaluating the implications of urban land use and surface changes on temperatures of the overlying atmosphere. In this study, long term changes in local climate zones of the Bulawayo metropolitan city were used to assess the influence of the city’s growth on its thermal characteristics. The zones were mapped using the World Urban Database and Access Portal Tool (WUDAPT) procedure while Landsat data were used to determine temporal changes. Data were divided into 1990 to 2005 and 2005 to 2020 temporal splits and intensity analysis used to characterize transformation patterns at each interval. Results indicated that growth of the built local climate zones (LCZ) in Bulawayo was faster in the 1990 to 2005 interval than the 2005 to 2020. Transition level intensity analysis showed that growth of built local climate zones was more prevalent in areas with water, low plants and dense forest LCZ in both intervals. There was a westward growth of light weight low rise built LCZ category than eastern direction, which could be attributed to high land value in the latter. Low plants land cover type experienced a large expansion of light weight low rise buildings than the compact low rise, water, and open low-rise areas. The reduction of dense forest was mainly linked to active expansion of low plants in the 2005 to 2020 interval, symbolizing increased deforestation and vegetation clearance. In Bulawayo’s growth, areas where built-up LCZs invade vegetation and wetlands have increased anthropogenic warming (i.e., Surface Urban Heat Island intensities) in the city. This study demonstrates the value of LCZs in among others creating a global urban land use land cover database and assessing the influence of urban growth pattern on urban thermal characteristics. Full article

(This article belongs to the Special Issue Thermal and Optical Remote Sensing: Evaluating Urban Green Spaces and Urban Heat Islands in a Changing Climate)

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21 pages, 8607 KiB

Open AccessArticle

Quantitatively Assessing the Impact of Driving Factors on Vegetation Cover Change in China’s 32 Major Cities

by Baohui Mu, Xiang Zhao, Jiacheng Zhao, Naijing Liu, Longping Si, Qian Wang, Na Sun, Mengmeng Sun, Yinkun Guo and Siqing Zhao

Remote Sens. 2022, 14(4), 839; https://doi.org/10.3390/rs14040839 - 10 Feb 2022

Cited by 12 | Viewed by 2209

Abstract

After 2000, China’s vegetation underwent great changes associated with climate change and urbanization. Although many studies have been conducted to quantify the contributions of climate and human activities to vegetation, few studies have quantitatively examined the comprehensive contributions of climate, urbanization, and CO₂ to vegetation in China’s 32 major cities. In this study, using Global Land Surface Satellite (GLASS) fractional vegetation cover (FVC) between 2001 and 2018, we investigated the trend of FVC in China’s 32 major cities and quantified the effects of CO₂, urbanization, and climate by using generalized linear models (GLMs). We found the following: (1) From 2001 to 2018, the FVC in China generally illustrated an increasing trend, although it decreased in 23 and 21 cities in the core area and expansion area, respectively. (2) Night light data showed that the urban expansion increased to varying degrees, with an average increasing ratio of approximately 168%. The artificial surface area increased significantly, mainly from cropland, forest, grassland, and tundra. (3) Climate factors and CO₂ were the major factors that affected FVC change. The average contributions of climate factors, CO₂, and urbanization were 40.6%, 39.2%, and 10.6%, respectively. This study enriched the understanding of vegetation cover change and its influencing factors, helped to explain the complex biophysical mechanism between vegetation and environment, and guided sustainable urban development. Full article

(This article belongs to the Special Issue Thermal and Optical Remote Sensing: Evaluating Urban Green Spaces and Urban Heat Islands in a Changing Climate)

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18 pages, 7221 KiB

Open AccessArticle

Local Climate Zones and Thermal Characteristics in Riyadh City, Saudi Arabia

by Ali S. Alghamdi, Ahmed Ibrahim Alzhrani and Humud Hadi Alanazi

Remote Sens. 2021, 13(22), 4526; https://doi.org/10.3390/rs13224526 - 11 Nov 2021

Cited by 6 | Viewed by 2903

Abstract

Using the local climate zone (LCZ) framework and multiple Earth observation input features, an LCZ classification was developed and established for Riyadh City in 2017. Four land-cover-type and four urban-type LCZs were identified in the city with an overall accuracy of 87%. The bare soil/sand (LCZ-F) class was found to be the largest LCZ class, which was within the nature of arid climate cities. Other land-cover LCZs had a lower coverage percentage (each class with <7%). The compact low-rise (LCZ-3) class was the largest urban type, as urban development in arid climate cities tends to extend horizontally. The daytime surface thermal characteristics of the developed LCZs were analyzed at seasonal timescales using land surface temperature (LST) estimated from multiple Landsat 8 satellite images (June 2017–May 2018). The highest daytime mean LST was found over large low-rise (LCZ-8) class areas throughout the year. This class was the only urban-type LCZ class that demonstrated a positive LST departure from the overall mean LST across seasons. Other urban-type LCZ classes showed lower LSTs and negative deviations from the overall mean LSTs. The overall thermal results suggested the presence of the surface urban heat island sink phenomenon as urban areas experienced lower LSTs than their surroundings. Thermal results demonstrated that the magnitudes of LST differences among LCZs were considerably dependent on the way the region of interest/analysis was defined. This was related to the types of LCZ classes presented in the study area and the spatial distribution and abundance of these LCZ classes. The developed LCZ classification and thermal results have several potential applications in different areas including planning and urban design strategies and urban health-related studies. Full article

(This article belongs to the Special Issue Thermal and Optical Remote Sensing: Evaluating Urban Green Spaces and Urban Heat Islands in a Changing Climate)

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18 pages, 5844 KiB

Open AccessArticle

Comparative Analysis of Variations and Patterns between Surface Urban Heat Island Intensity and Frequency across 305 Chinese Cities

by Kangning Li, Yunhao Chen and Shengjun Gao

Remote Sens. 2021, 13(17), 3505; https://doi.org/10.3390/rs13173505 - 03 Sep 2021

Cited by 8 | Viewed by 2496

Abstract

Urban heat island (UHI), referring to higher temperatures in urban extents than its surrounding rural regions, is widely reported in terms of negative effects to both the ecological environment and human health. To propose effective mitigation measurements, spatiotemporal variations and control machines of surface UHI (SUHI) have been widely investigated, in particular based on the indicator of SUHI intensity (SUHII). However, studies on SUHI frequency (SUHIF), an important temporal indicator, are challenged by a large number of missing data in daily land surface temperature (LST). Whether there is any city with strong SUHII and low SUHIF remains unclear. Thanks to the publication of daily seamless all-weather LST, this paper is proposed to investigate spatiotemporal variations of SUHIF, to compare SUHII and SUHIF, to conduct a pattern classification, and to further explore their driving factors across 305 Chinese cities. Four main findings are summarized below: (1) SUHIF is found to be higher in the south during the day, while it is higher in the north at night. Cities within the latitude from 20° N and 40° N indicate strong intensity and high frequency at day. Climate zone-based variations of SUHII and SUHIF are different, in particular at nighttime. (2) SUHIF are observed in great diurnal and seasonal variations. Summer daytime with 3.01 K of SUHII and 80 of SUHIF, possibly coupling with heat waves, increases the risk of heat-related diseases. (3) K-means clustering is employed to conduct pattern classification of the selected cities. SUHIF is found possibly to be consistent to its SUHII in the same city, while they provide quantitative and temporal characters respectively. (4) Controls for SUHIF and SUHII are found in significant variations among temporal scales and different patterns. This paper first conducts a comparison between SUHII and SUHIF, and provides pattern classification for further research and practice on mitigation measurements. Full article

(This article belongs to the Special Issue Thermal and Optical Remote Sensing: Evaluating Urban Green Spaces and Urban Heat Islands in a Changing Climate)

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16 pages, 70705 KiB

Open AccessArticle

How to Measure the Urban Park Cooling Island? A Perspective of Absolute and Relative Indicators Using Remote Sensing and Buffer Analysis

by Wenhao Zhu, Jiabin Sun, Chaobin Yang, Min Liu, Xinliang Xu and Caoxiang Ji

Remote Sens. 2021, 13(16), 3154; https://doi.org/10.3390/rs13163154 - 09 Aug 2021

Cited by 25 | Viewed by 3661

Abstract

Urban parks have been proven to cool the surrounding environment, and can thus mitigate the urban heat island to an extent by forming a park cooling island. However, a comprehensive understanding of the mechanism of park cooling islands is still required. Therefore, we studied 32 urban parks in Jinan, China and proposed absolute and relative indicators to depict the detailed features of the park cooling island. High-spatial-resolution GF-2 images were used to obtain the land cover of parks, and Landsat 8 TIR images were used to examine the thermal environment by applying buffer analysis. Linear statistical models were developed to explore the relationships between park characteristics and the park cooling island. The results showed that the average land surface temperature (LST) of urban parks was approximately 3.6 °C lower than that of the study area, with the largest temperature difference of 7.84 °C occurring during summer daytime, while the average park cooling area was approximately 120.68 ha. The park cooling island could be classified into four categories—regular, declined, increased, and others—based on the changing features of the surrounding LSTs. Park area (PA), park perimeter (PP), water area proportion (WAP), and park shape index (PSI) were significantly negatively correlated with the park LST. We also found that WAP, PP, and greenness (characterized by the normalized difference vegetation index (NDVI)) were three important factors that determined the park cooling island. However, the relationship between PA and the park cooling island was complex, as the results indicated that only parks larger than a threshold size (20 ha in our study) would provide a larger cooling effect with the increase in park size. In this case, increasing the NDVI of the parks by planting more vegetation would be a more sustainable and effective solution to form a stronger park cooling island. Full article

(This article belongs to the Special Issue Thermal and Optical Remote Sensing: Evaluating Urban Green Spaces and Urban Heat Islands in a Changing Climate)

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18 pages, 5694 KiB

Open AccessArticle

Urban Sprawl and Changes in Land-Use Efficiency in the Beijing–Tianjin–Hebei Region, China from 2000 to 2020: A Spatiotemporal Analysis Using Earth Observation Data

by Meiling Zhou, Linlin Lu, Huadong Guo, Qihao Weng, Shisong Cao, Shuangcheng Zhang and Qingting Li

Remote Sens. 2021, 13(15), 2850; https://doi.org/10.3390/rs13152850 - 21 Jul 2021

Cited by 22 | Viewed by 4398

Abstract

Sustainable development in urban areas is at the core of the implementation of the UN 2030 Agenda and the Sustainable Development Goals (SDG). Analysis of SDG indicator 11.3.1—Land-use efficiency based on functional urban boundaries—provides a globally harmonized avenue for tracking changes in urban settlements in different areas. In this study, a methodology was developed to map built-up areas using time-series of Landsat imagery on the Google Earth Engine cloud platform. By fusing the mapping results with four available land-cover products—GlobeLand30, GHS-Built, GAIA and GLC_FCS-2020—a new built-up area product (BTH_BU) was generated for the Beijing–Tianjin–Hebei (BTH) region, China for the time period 2000–2020. Using the BTH_BU product, functional urban boundaries were created, and changes in the size of the urban areas and their form were analyzed for the 13 cities in the BTH region from 2000 to 2020. Finally, the spatiotemporal dynamics of SDG 11.3.1 indicators were analyzed for these cities. The results showed that the urban built-up area could be extracted effectively using the BTH_BU method, giving an overall accuracy and kappa coefficient of 0.93 and 0.85, respectively. The overall ratio of the land consumption rate to population growth rate (LCRPGR) in the BTH region fluctuated from 1.142 in 2000–2005 to 0.946 in 2005–2010, 2.232 in 2010–2015 and 1.538 in 2015–2020. Diverged changing trends of LCRPGR values in cities with different population sizes in the study area. Apart from the megacities of Beijing and Tianjin, after 2010, the LCRPGR values were greater than 2 in all the cities in the region. The cities classed as either small or very small had the highest LCRPGR values; however, some of these cities, such as Chengde and Hengshui, experienced population loss in 2005–2010. To mitigate the negative impacts of low-density sprawl on environment and resources, local decision makers should optimize the utilization of land resources and improve land-use efficiency in cities, especially in the small cities in the BTH region. Full article

(This article belongs to the Special Issue Thermal and Optical Remote Sensing: Evaluating Urban Green Spaces and Urban Heat Islands in a Changing Climate)

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