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Remote Sensing in Urban Socio-Ecological Systems Monitoring and Assessment

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

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 15264

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Prof. Dr. Tao Lin

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Institute of urban environment, Chinese academy of sciences, Xiamen 361024, China
Interests: urban environmental management and planning, urban ecology, urban metabolism
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Prof. Dr. Junxiang Li

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School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: urban ecology; landscape ecology; remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Conghe Song

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Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3220, USA
Interests: remote sensing of environment; land-cover/land-use change; ecosystem carbon and water exchange with atmosphere; human–environment interactions
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Dr. Hong Ye

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Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361024, China
Interests: urbanization and global changes; urban construction and energy usage carbon emission; intensive land use evaluation

Dr. Guoqin Zhang

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Guest Editor

Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361024, China
Interests: urban geography, urban environmental management and planning

Special Issue Information

Dear Colleagues,

Urbanization is one of the most powerful anthropogenic forces, causing dramatic environmental changes and jeopardizing sustainable development in the world. In 2018, fifty-five percent of the world’s population lived in cities, and this figure is projected to increase to 68% by 2050. The increase is driven by ongoing urban growth in both the developed and developing countries, especially in Asia and Africa. Urbanization has profoundly shaped urban socio-ecological systems by the complex interactions between population increase and urban expansion into the surrounding natural ecosystems, and exchanges of matter, energy, technologies, and information with far-away places. The social and ecological impacts of urban expansion reach far beyond the administrative boundaries of the cities. Such impacts are closely related to urban morphology and subsequent functional changes. Urban morphology evolves with urban form and landscape structures at multiple spatial and temporal scales and, consequently, urban ecosystem functions. Advances in remote sensing have facilitated the monitoring of urban morphological evolutions both at the landscape scale, such as satellite cities, suburbs, and towns, and the regional scales of major cities, megacities, and megaregions. These are the spatial scales that the changes of urban forms and their interactions with social-ecological systems happen. Remote sensing is indispensable in evaluating urban morphological evolution, including urban sprawling, urban function zoning, landscape composition and configuration. Urban morphology dictates the paths through which people, energy, matter, and information flow. How to monitor urban forms, its associated functions, and their socio-ecological consequences with the aid of remote sensing, therefore, is of great importance in providing insights for sustainable development of urban socio-ecological systems.

This open access Special Issue aims to collect a set of high-quality papers on creatively using remote sensing to evaluate the evolution of urban morphology, the subsequent changes in urban ecosystem functions, and its impacts on the dynamics of the urban social-ecological systems. We are particularly interested in papers that present innovative and multidisciplinary studies using state-of-the-art technologies and methods in remote sensing, including big data and artificial intelligence (AI). The potential topics include, but are not limited to, the following:

Urban sprawling and expansion monitoring and analysis at multiple scales;
Urban morphological (network) and functional indicators derived from remote sensing and their applications;
Evolution characteristics of regional urban form (network) and function;
Spatiotemporal impacts of urban form evolution on the surrounding natural ecosystems;
Monitoring and characterizing the interaction between urban form and function using remote sensing, AI, and big data;
Spatiotemporal monitoring and modeling the dynamics of urban social-ecological systems with different geographical and socioeconomic conditions;
Driving mechanism of urban form (network) and function evolution;
Comparisons between urban form (network) and function evolutions and their driving mechanisms in China and in other countries and regions.

Prof. Dr. Tao Lin
Prof. Dr. Junxiang Li
Prof. Dr. Conghe Song
Dr. Hong Ye
Dr. Guoqin Zhang
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

urbanization
urban form
urban function
social impact
ecological impact
driving mechanism

Published Papers (9 papers)

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Research

27 pages, 15942 KiB

Open AccessArticle

Determining the Dominant Contributions between Direct and Indirect Impacts of Long-Term Urbanization on Plant Net Primary Productivity in Beijing

by Yuan Chen, Dangui Lu, Bo Xu, Rui Ren, Zhichao Wang and Zhongke Feng

Remote Sens. 2024, 16(3), 444; https://doi.org/10.3390/rs16030444 - 23 Jan 2024

Viewed by 581

Abstract

Rapid urbanization exerts noteworthy impacts on the terrestrial ecosystem carbon budget, with pronounced effects in a metropolis such as Beijing, the capital city of China. These impacts include both Direct and Indirect Impacts. For instance, direct impacts influence regional Net Primary Productivity (NPP) by directly altering the vegetation coverage area. Concurrently, indirect impacts primarily affect regional NPP indirectly through climate change and urban vegetation management. How direct and indirect impacts contribute to the NPP is the core content of our research. Owing to that, we need to precisely assess the spatial and seasonal characteristics of the impact of urbanization in Beijing from 2000 to 2020. Firstly, a novel framework was proposed to analyze the impact of urbanization on NPP at the pixel level. Meanwhile, we employ the Proximity Expansion Index (PEI) to analyze urban expansion patterns. Results reveal that the direct impacts led to a cumulative NPP loss of 0.98 TgC, with the largest loss stemming from cropland conversion to construction land. During the last two decades, there has been a 56.87% increase in the area used for urban development in Beijing, a clear sign of swift urban expansion. Concurrently, this urban growth has had favorable indirect effects on NPP, with an average annual increase of 9.76 gC·m⁻²·year⁻¹, mainly observed in urbanized regions. Moreover, the seasonal analysis underscored that indirect impacts were primarily temperature-related, exhibiting higher values during autumn and winter within urban areas, indicating enhanced vegetation growth suitability in urban areas during these seasons. Our findings quantitatively examine the numerical relationship between direct and indirect impacts at a magnitude level. The carbon gain brought about by indirect impacts surpassed the carbon loss induced by direct impacts, with indirect impacts offsetting 29.41% of the carbon loss due to direct impacts. Ultimately, we advocate for enhanced greening initiatives in areas of Beijing with higher indirect impacts to achieve optimal carbon gain. This strategy might effectively reduce the negative impact of rapid urbanization on the carbon budget of terrestrial ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Socio-Ecological Systems Monitoring and Assessment)

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33 pages, 21842 KiB

Open AccessArticle

Evaluation of the Spatiotemporal Change of Ecological Quality under the Context of Urban Expansion—A Case Study of Typical Urban Agglomerations in China

by Yinkun Guo, Siqing Zhao, Xiang Zhao, Haoyu Wang and Wenxi Shi

Remote Sens. 2024, 16(1), 45; https://doi.org/10.3390/rs16010045 - 21 Dec 2023

Viewed by 833

Abstract

As a significant manifestation of human activities influencing natural environment, rapid urbanization has enhanced economic prosperity while simultaneously posing threats to ecological quality. Beijing–Tianjin–Hebei (BTH), the core region of the Yangtze River Delta (CYRD), and the Pearl River Delta (PRD) stand as three major economic centers characterized by the highest level of urbanization in China, encompassing areas of heightened ecological sensitivity. Nevertheless, the ecological quality at the scale of urban agglomerations remains ambiguous, with many studies failing to develop a comprehensive and effective method for comparing diverse urban agglomerations. Consequently, this study integrates multi-source remote sensing data, including information on land cover and other socio-economic parameters, to construct the Ecological Quality Index (EQI) based on the “Function–Interaction–Pressure–Stability” (FIPS) framework. Through a stratified determination of indicator weights grounded in both objective importance and empirical knowledge, we mapped the spatiotemporal changes of EQI and analyzed the impact of urbanization on ecological quality in three urban agglomerations from 2001 to 2020. We determined the following: (1) The calculated EQI can further capture the nuanced details with better performance at both underlining the discrepancy of highs and lows of EQI and describing the spatial detail of urban agglomerations’ characteristics. (2) Substantial disparities in EQI and its changes are evident across different urban agglomerations. Notably, only the average EQI improves in PRD, while ecological degradation is prominent in specific regions, such as the southeastern plains of BTH area, along the Yangtze River, and around Shanghai in CYRD and central PRD. The CYRD exhibits the largest affected area. (3) Urbanization predominantly influences ecological quality through land cover transitions. In expansion areas, ecological deterioration is significantly more pronounced, constituting approximately 90% of the total area. (4) Despite significant urbanization, city-level analysis in CYRD reveals a better coordination between urban expansion and ecological protection, with a lower intensity of ecological degradation compared to urban expansion rates. Conversely, some cities in the BTH, despite modest urban expansion, exhibit substantial declines in ecological quality, highlighting the need for targeted policy interventions. In conclusion, this study elucidates the intricate relationship between urbanization and ecological quality, offering valuable insights for the development of targeted protection strategies and sustainable urban planning. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Socio-Ecological Systems Monitoring and Assessment)

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17 pages, 4983 KiB

Open AccessArticle

Dynamics of Forest Vegetation in an Urban Agglomeration Based on Landsat Remote Sensing Data for the Period 1990–2022: A Case Study

by Elena Petrovna Yankovich, Ksenia Stanislavovna Yankovich and Nikolay Viktorovich Baranovskiy

Remote Sens. 2023, 15(7), 1935; https://doi.org/10.3390/rs15071935 - 04 Apr 2023

Cited by 1 | Viewed by 1442

Abstract

In recent years, the vegetation cover in urban agglomerations has been changing very rapidly due to technogenic influence. Satellite images play a huge role in studying the dynamics of forest vegetation. Special programs are used to process satellite images. The purpose of the study is to analyze forest vegetation within the territory of the Tomsk agglomeration based on Landsat remote sensing data for the period from 1990 to 2022. The novelty of the study is explained by the development of a unique program code for the analysis of Landsat satellite data on the previously unexplored territory of the Tomsk agglomeration with the prospect of moving to the scale of the entire state in the future. In this study, the authors present an algorithm implemented in Python to quantify the change in the area of vegetation in an urban agglomeration using Landsat multispectral data. The tool allows you to read space images, calculate spectral indices (NDVI, UI, NDWI), and perform statistical processing of interpretation results. The created tool was applied to study the dynamics of vegetation within the Tomsk urban agglomeration during the period 1990–2022. Key findings and conclusions: (1) The non-forest areas increased from 1990 to 1999 and from 2013 to 2022. It is very likely that this is due to the deterioration of the standard of living in the country during these periods. The first time interval corresponds to the post-Soviet period and the devastation in the economy in the 1990s. The second period corresponds to the implementation and strengthening of sanctions pressure on the Russian Federation. (2) The area of territories inhabited by people has been steadily falling since 1990. This is due to the destruction of collective agriculture in the Russian Federation and the outflow of the population from the surrounding rural settlements to Tomsk and Seversk. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Socio-Ecological Systems Monitoring and Assessment)

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20 pages, 3331 KiB

Open AccessArticle

Green Space Compactness and Configuration to Reduce Carbon Emissions from Energy Use in Buildings

by Ranran Ji, Kai Wang, Mengran Zhou, Yun Zhang, Yujia Bai, Xian Wu, Han Yan, Zhuoqun Zhao and Hong Ye

Remote Sens. 2023, 15(6), 1502; https://doi.org/10.3390/rs15061502 - 08 Mar 2023

Cited by 1 | Viewed by 1610

Abstract

Building sector consists of a major part of global energy consumption and carbon emission. Reducing energy consumption in buildings can make a substantial contribution towards the strategic goal of carbon neutrality. Building energy consumption carbon emission (BECCE) is highly correlated with microclimate. Green space has long been recognized as the natural way to improve the microclimate and reduce BECCE. However, the effective distance and optimized configuration of green space for the reduction in BECCE are hardly known. To this purpose, we developed a green space compactness (GSC) index as an indicator of microclimate around the People’s Bank, located in 59 cities across China, and used statistical, deep learning, and spatial analysis methods to obtain the most effective distance with respect to the effect of GSC on BECCE. We used hot and cold spot spatial analysis methods to detect the spatial heterogeneity of BECCE and analyzed the corresponding GCS to discover the optimal way for BECCE reduction. The results clearly showed that BECCE was highly correlated with the GSC, and the influence of GSC on BECCE was the highest at the distance of 250 m from the building. The hot and cold spots analysis suggested that BECCE has a significant spatial heterogeneity, which was much higher in the north part of China. Improving the configuration of green space for certain cities could lead to considerable emission reductions. If the BEECE is reduced from 4675 tons to 486 tons, the GSC needs to be increased from 0.39 to 0.56. The study suggests that 250 m is the most effective distance to reduce BECCE, and optimal green space configuration can provide a feasible way to mitigate carbon emissions and valuable information for the development of low-carbon cities. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Socio-Ecological Systems Monitoring and Assessment)

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17 pages, 6664 KiB

Open AccessArticle

Linear and Nonlinear Land Use Regression Approach for Modelling PM_2.5 Concentration in Ulaanbaatar, Mongolia during Peak Hours

by Odbaatar Enkhjargal, Munkhnasan Lamchin, Jonathan Chambers and Xue-Yi You

Remote Sens. 2023, 15(5), 1174; https://doi.org/10.3390/rs15051174 - 21 Feb 2023

Viewed by 1577

Abstract

In recent decades, air pollution in Ulaanbaatar has become a challenge regarding the health of the citizens of Ulaanbaatar, due to coal combustion in the ger area. Households burn fuel for cooking and to warm their houses in the morning and evening. This creates a difference between daytime and nighttime air pollution levels. The accurate mapping of air pollution and assessment of exposure to air pollution have thus become important study objects for researchers. The city center is where most air quality monitoring stations are located, but they are unable to monitor every residential region, particularly the ger area, which is where most particulate matter pollution originates. Due to this circumstance, it is difficult to construct an LUR model for the entire capital city’s residential region. This study aims to map peak PM_2.5 dispersion during the day using the Linear and Nonlinear Land Use Regression (LUR) model (Multi-Linear Regression Model (MLRM) and Generalized Additive Model (GAM)) for Ulaanbaatar, with monitoring station measurements and mobile device (DUST TRUK II) measurements. LUR models are frequently used to map small-scale spatial variations in element levels for various types of air pollution, based on measurements and geographical predictors. PM_2.5 measurement data were collected and analyzed in the R statistical software and ArcGIS. The results showed the dispersion map MLRM R² = 0.84, adjusted R² = 0.83, RMSE = 53.25 µg/m³ and GAM R² = 0.89, and adjusted R² = 0.87, RMSE = 44 µg/m³. In order to validate the models, the LOOCV technique was run on both the MLRM and GAM. Their performance was also high, with LOOCV R² = 0.83, RMSE = 55.6 µg/m³, MAE = 38.7 µg/m³, and GAM LOOCV R² = 0.77, RMSE = 65.5 µg/m³, MAE = 47.7 µg/m³. From these results, the LUR model’s performance is high, especially the GAM model, which works better than MRLM. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Socio-Ecological Systems Monitoring and Assessment)

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22 pages, 4272 KiB

Open AccessArticle

Monitoring Urban Expansion by Coupling Multi-Temporal Active Remote Sensing and Landscape Analysis: Changes in the Metropolitan Area of Cordoba (Argentina) from 2010 to 2021

by Flavio Marzialetti, Paolo Gamba, Antonietta Sorriso and Maria Laura Carranza

Remote Sens. 2023, 15(2), 336; https://doi.org/10.3390/rs15020336 - 05 Jan 2023

Cited by 1 | Viewed by 2034

Abstract

Uncontrolled and unsustainable urban sprawl are altering the Earth’s surface at unprecedented rates. This research explores the potential of active remote sensors for mapping urban areas, for monitoring urban expansion processes and for depicting landscape pattern dynamics in a metropolis of South America. Based on multi-temporal urban cover maps of Cordoba, Argentina, purposely derived from COSMO-SkyMed SAR data by urban extraction algorithms, we quantified urban surface increase and described urbanization processes that occurred during 2010–2021 in sectors with different degrees of soil sealing. We extracted urban extent in four time-steps using an Urban EXTent extraction (UEXT) algorithm and quantified urban expansion, identifying newly built areas on 2.5 ha cells. For these cells, we computed urban cover and a set of landscape pattern indices (PIs), and by projecting them in a composition vs. configuration Cartesian space we performed a trajectory analysis. SAR-based urban extraction and cover change proved to be very accurate. Overall accuracy and Cohen’s Kappa statistic evidenced very high values, always above 91.58% and 0.82, respectively, for urban extraction, and also above 90.50% and 0.72 concerning the accuracy of urban expansion. Cordoba’s urban surface significantly increased (≈900 ha in 10 years) following three main spatial processes in different city sectors (e.g., edge-expansion and outlying on peri-urban areas, and infill inside the ring road), which may have contrasting effects on the sustainability of the metropolitan area. Trajectory analysis highlighted non-linear relations between the urban cover and the PIs. Areas with very low and low urban intensity underwent a steep rise of both urban cover and PI values (e.g., urban patch dimension, complexity and number), depicting urban edge-expansion and outlying processes. In the areas with medium and high urban intensity the increase in patch dimension, along with the decrease in patch number and complexity, evidence the coalescence of urban areas that incorporate in the urban fabric the remnants of non-built up zones and fill the few residual green spaces. The proposed SAR mapping procedure coupled with landscape analysis proved to be useful to detect and depict different moments of urban expansion and, pending more tests on other cities and geographical conditions, it could be postulated among the RS indicators to monitor the achievement of the Sustainable Development Goals established by the United Nations. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Socio-Ecological Systems Monitoring and Assessment)

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21 pages, 11746 KiB

Open AccessArticle

Spatio-Temporal Heterogeneity of Ecological Quality in Hangzhou Greater Bay Area (HGBA) of China and Response to Land Use and Cover Change

by Zhenjie Yang, Chao Sun, Junwei Ye, Congying Gan, Yue Li, Lingyu Wang and Yujun Chen

Remote Sens. 2022, 14(21), 5613; https://doi.org/10.3390/rs14215613 - 07 Nov 2022

Cited by 6 | Viewed by 2603

Abstract

Human activities have been stressing the ecological environment since we stepped into the Anthropocene Age. It is urgent to formulate a sustainable plan for balancing socioeconomic development and ecological conservation based on a thorough understanding of ecological environment changes. The ecological environment can be evaluated when multiple remote sensing indices are integrated, such as the use of the recently prevalent Remote Sensing-based Ecological Index (RSEI). Currently, most of the RSEI-related studies have focused on the ecological quality evolution in small areas. Less attention was paid to the spatio-temporal heterogeneity of ecological quality in large-scale urban agglomerations and the potential links with Land Use and Cover Change (LUCC). In this study, we monitored the dynamics of the ecological quality in the Hangzhou Greater Bay Area (HGBA) during 1995–2020, using the RSEI as an indicator. During the construction of the RSEI, a percentile de-noising normalization method was proposed to overcome the problem of widespread noises from large-scale regions and make the RSEI-based ecological quality assessment for multiple periods comparable. Combined with the land use data, the quantitative relationship between the ecological quality and the LUCC was revealed. The results demonstrated that: (1) The ecological quality of the HGBA degraded after first improving but was still good (averaged RSEI of 0.638). It was divergent for the prefecture-level cities of the HGBA, presenting degraded, improved, and fluctuant trends for the cities from north to south. (2) For ecological quality, the improved regions have larger area (57.5% vs. 42.5%) but less increment (0.141 vs. −0.195) than the degraded regions. Mountains, downtowns, and coastal wetlands were the hot spots for the improvement and urbanization, and reclamation processes were responsible for the degradation. (3) The ecological quality was improved for forests and urban areas (△RSEI > 0.07) but degraded for farmland (∆RSEI = −0.03). As a result, the ecological cost was reduced among human-dominant environments (e.g., farmland, urban areas) while enlarged for the conversion from nature-(e.g., forests) to human-dominant environments. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Socio-Ecological Systems Monitoring and Assessment)

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21 pages, 30915 KiB

Open AccessArticle

Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle

by Tiantian Bao, Ruifan Wang, Linghan Song, Xiaojie Liu, Shuangwen Zhong, Jian Liu, Kunyong Yu and Fan Wang

Remote Sens. 2022, 14(21), 5540; https://doi.org/10.3390/rs14215540 - 03 Nov 2022

Cited by 5 | Viewed by 1843

Abstract

Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Socio-Ecological Systems Monitoring and Assessment)

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21 pages, 13999 KiB

Open AccessArticle

Long-Term Spatiotemporal Characteristics and Impact Factors of Land Surface Temperature of Inhabited Islands with Different Urbanization Levels

by Junmao Zhang, Tao Lin, Caige Sun, Meixia Lin, Yulin Zhan, Yuan Chen, Hong Ye, Xia Yao, Yiyi Huang, Guoqin Zhang and Yuqin Liu

Remote Sens. 2022, 14(19), 4997; https://doi.org/10.3390/rs14194997 - 08 Oct 2022

Cited by 3 | Viewed by 1522

Abstract

Surface thermal environment (STE) is closely related to the comfort and health of residents, affecting regional livability, and its spatial and temporal changes are deeply affected by the urbanization process. Considering there is a lack of effective comparative analysis on STE in different urbanized inhabited islands, the special geographical unit and vital human settlement environment, long-term spatiotemporal characteristics and impact factor quantitative analyses were performed in two inhabited islands via the RS and GIS methods. The results suggest that the surface heat amplitude of the highly urbanized Xiamen Island decreases, with the surface heat intensity continuing to increase from 2000 to 2020, while that of the lowly urbanized Kinmen Island is reversed. Although the land surface temperature (LST) of the two inhabited islands shows similar spatial distribution characteristics with evident cold/hot spots, the geographical distribution characteristics of high LST zones are significantly different, and the thermal landscape of Xiamen Island is more fragmented, discrete, and simple in shape, as revealed by the landscape metrics. We demonstrate that the area proportion between cooling land (water body and greenland) and warming land (bare land and impervious surface) is the most influential factor of LST in the two islands while the marine environment is a unique contributor to STE of inhabited islands compared with inland cities, where the seawater around the island can reduce LST over a range of distances, and the influence of elevation on LST is mostly indirect. These results provide a scientific basis and case support for understanding the STE situation of inhabited islands with different urbanization levels. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Socio-Ecological Systems Monitoring and Assessment)

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