Linking Urban Growth and Surface Temperature Change in Capital and Secondary Cities of Southeast Asia

Linking Urban Growth and Surface Temperature Change in Capital and Secondary Cities of Southeast Asia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Linking Urban Growth and Surface Temperature Change in Capital and Secondary Cities of Southeast Asia Ronghua Xu, Shankar Acharya Kamarajugedda, Raffaele Lafortezza, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6572557/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Feb, 2026 Read the published version in npj Urban Sustainability → Version 1 posted 14 You are reading this latest preprint version Abstract Rapid urbanization exacerbates heat-related environmental issues. Yet urban expansion patterns and associated heat risks in developing countries remain poorly understood. We selected sixteen capitals and secondary cities in Southeast Asia and characterized urban growth and heat hazards in their fringes using three spectral indices: PLAND (percentage of built-up areas), NDVI (Normalized Difference Vegetation Index), and LST (Land Surface Temperature). Over the past two decades, PLAND and LST in the fringes increased by 11.93% and 1.39℃, respectively. LST in the fringes of capital cities was 1.70℃ higher than in secondary cities. LST increases exceeded those in urban cores, particularly in secondary cities (+0.35℃). Highly populated and wealthier urban clusters showed marked PLAND increases and NDVI declines, but not necessarily elevated LST. We identify priority areas for land cover management and urban heat mitigation by city type and geographic location to inform sustainable regional planning. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Ecology/Climate change ecology Earth and environmental sciences/Environmental social sciences/Climate change mitigation Earth and environmental sciences/Environmental social sciences/Sustainability Urban expansion Urban form indicator Land surface temperature Thermal environment Sustainable development Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Global climate change poses urgent challenges to sustainable development, particularly in the context of rapidly developing countries 1,2 . In 2023, extreme heat records peaked, with air temperatures reaching 39.2°C in most areas 3,4 . Rapid urbanization intensifies thermal environmental risks, contributing to rising temperatures and more frequent extreme weather events, jeopardizing both ecosystems and human survival 5–7 . Since urban growth is accompanied by substantial changes in Land Use and Land Cover (LULC), it directly affects Land Surface Temperature (LST) and leads to the urban heat island (UHI) effect—whereby urban areas are generally warmer than rural areas 8,9 . Since 2000, rapid urbanization has exacerbated this impact, making it essential to explore its complex and dynamic relationship in order to mitigate UHI effects and adapt to climate change 10,11 . Recent studies have primarily examined the effect of land cover changes in temperate regions such as Europe, China, and North America, yet these insights may not be applicable to urban planning in other regions 12–15 . In particular, tropical cities display heightened sensitivity to global warming and rapid urban growth 16 . Despite Southeast Asia's diverse development models, economic stages, and policy frameworks 17,18 , UHI-related research often targets individual cities 19,20 . Hence, region-wide analysis of urban expansion and temperature dynamics is needed to inform tailored urban heat mitigation strategies. Population growth drives the expansion of built-up areas into urban fringes 21 , leading to an increase in impermeable surfaces and a reduction in blue-green spaces like vegetation and water bodies 22 . In turn, these changes affect the microclimate, elevating LST 14,23 . Urban form characterizes the spatial layout of landscape elements such as land cover 24 and building patterns 25 , and critically influences the microclimate of cities compared to their surroundings 26 . High-resolution, multi-temporal spectral imagery (Landsat, MODIS, Sentinel-2) enables monitoring of impervious and vegetation surfaces affecting urban thermal environments 27,28 . Satellite-derived LST captures spatial heterogeneity and broader land–atmosphere exchanges beyond ground station limits 23,29 . Urban expansion concentrates land cover change hotspots, yet its effects on tropical urban heat dynamics and drivers remain underexplored 16,30 . Percentage of built-up areas (PLAND), a widely used landscape pattern indicator 31 indicating impervious growth and activity intensity 22,32 . While increasing vegetation effectively mitigates the UHI effect 33 , urban growth characterized by expanding land can undermine this effort 16,23 . Therefore, integrating indicators is essential for assessing trade-offs between urban expansion and greening strategies. Urban land cover change impact on LST is scale-dependent 23,34 . Local-scale studies employing high-resolution data capture intra-urban LST variation but lack broad applicability due to limited coverage 12,22 . Regional and global-scale analyses provide valuable insights into climate modeling but miss local heterogeneity 27,35 , leading to inefficiencies in climate-related policy-making 18,36 . Investigating multi-scale perspectives is essential for understanding LST mechanisms and guiding effective heat mitigation strategies. This study examines a total of 16 capital and secondary cities in Southeast Asia—regions with an urgent need to mitigate UHI effects—to explore the relationship between urban expansion and surface temperature using remote sensing data from 2000 and 2020. To our knowledge, it is the first study examining nuanced urban forms and temperature patterns in this understudied region. Specifically, our study aims to: (1) quantify changes in urban forms and LST between 2000 and 2020 in both capital and secondary cities; (2) evaluate correlations between urban form changes and temperature increase; and (3) assess the influence of city type and socioeconomic factors on shifts in urban forms and temperature. To address these objectives, we focused on urban fringes where most of the land cover change occurs, outside the core areas already developed in 2000. By analyzing the distinct urban dynamics of recently developed areas in Southeast Asia, the findings offer context-specific insights and practical strategies that support sustainable urban development and inform effective climate adaptation. Results Differences in urban forms and temperature across Southeast Asian cities The results showed that between 2000 and 2020, there was an increase in the median PLAND by 11.93% and LST by 1.39℃ in urban clusters of Southeast Asia (Fig. 1 ). In capital and secondary cities, these increases were 12.43% and 10.87%, and 1.31℃ and 1.35℃, respectively (Fig. 1 ). While the NDVI showed a slight rise of 0.01 in capital cities, it decreased by 0.07 in secondary cities (Fig. 1 c, f). The LST in capital cities was consistently about 1.7℃ higher than that in secondary cities in both 2000 and 2020 (Fig. 1 b, e). Within urban core areas, LST was higher than that in urban fringes (Fig. 1 b, e), but the rise in LST was more substantial in urban fringes, with a difference of 0.04℃ in capitals and 0.35℃ in secondary cities (Fig. 1 b, e). This suggests a net increase in LST in Southeast Asian cities over the past 20 years, especially in secondary cities. Analysis of median urban forms and LST did not reveal clear trends between capital and secondary cities (Fig. 2 ). Higher PLAND values were primarily found in urban clusters of Thailand, Indonesia, and Malaysia (Fig. 2 a, b), while elevated LST values were mainly in Laos (Fig. 2 d, e). In 2000, the highest NDVI values were in the secondary city, and the lowest in the capital city of Cambodia (Fig. 2 g); by 2020, this trend reversed in the Philippines (Fig. 2 h). Notable changes from 2000 to 2020 included the highest median PLAND increase in Johor Bahru at 29.0%, the greatest LST rise in Manila at 5.4℃, and the largest NDVI change in Krong Pailin at -0.33, with Manila also showed the most substantial NDVI increase at 0.27 (Fig. 2 c, f, i). Trade-off of changes in urban forms and temperature from 2000 to 2020 A trade-off analysis of changes in PLAND, NDVI, and LST across urban clusters from 2000 to 2020 (Fig. 3 ) revealed that most urban clusters experienced substantial changes in PLAND, indicating that rising LST is primarily driven by changes in impermeable surface rather than by vegetation loss. In most urban clusters, there is a positive spatial correlation between PLAND and LST, and a negative spatial correlation between NDVI and LST (Table 1 ). Two distinct spatial patterns of LST and PLAND are shown in Fig. 3 b. However, some urban clusters, like Manila (MNL), Cebu (CBU) and Krong Pailin (KPL), registered a pronounced LST increase—5.4°C in MNL, 5.2°C in CBU, and 3.8°C in KPL (Fig. S5), with corresponding PLAND changes of only 1.0%, 1.5%, and 2.8%, respectively (Fig. S5a). Their NDVI trends also diverged: MNL increased by 0.27, while CBU and KPL decreased by 0.14 and 0.33, respectively. These exceptions suggest that socioeconomic factors, alongside PLAND and NDVI changes, significantly influence urban temperature dynamics. Table 1 Results of bivariate global Moran's I index analysis Urban cluster PLAND - LST NDVI - LST Abbreviation Cluster name Country 2000 2020 2000 2020 JKT* Jakarta Indonesia 0.098 0.264 -0.136 0.083 BDG Bandung Indonesia 0.452 0.042 0.053 0.112 BKK* Bangkok Thailand 0.339 0.555 0.070 0.056 CHM Chiang Mai Thailand -0.083 0.148 -0.162 -0.271 KUL* Kuala Lumpur Malaysia 0.151 0.255 -0.079 -0.097 JHB Johor Bahru Malaysia 0.322 0.342 -0.251 -0.196 PPH* Phnom Penh Cambodia -0.014 0.404 -0.117 -0.076 KPL Krong Pailin Cambodia 0.230 -0.184 -0.341 -0.078 YGN* Yangon Myanmar 0.027 -0.132 0.309 -0.050 MDL Mandalay Myanmar 0.075 0.031 -0.090 -0.025 VNT* Vientiane Laos 0.120 0.360 0.003 -0.067 PKX Pakse Laos 0.085 0.213 0.409 0.395 MNL* Manila Philippines 0.068 0.489 0.121 -0.195 CBU Cebu Philippines 0.208 0.496 -0.269 -0.122 BRN* Bandar Seri Begawah Brunei 0.237 0.361 0.017 -0.068 KBT Kuala Belait Brunei 0.447 0.569 -0.389 -0.380 Notes: An asterisk (*) denotes capitals; Nearly all Moran's I values have corresponding p-values of 0.001 (i.e., p < 0.05), except for the bolded values, which are greater than 0.05. The impact of socio-economic factors on urban forms and temperature The Spearman correlation analysis showed a significant positive relationship between population density and PLAND, indicating that urban clusters with high population density generally have higher PLAND. A similar positive correlation exists between GDP and PLAND, particularly strong in capital cities. Furthermore, urban clusters with greater population density and GDP tend to exhibit lower NDVI, especially in secondary cities. Notably, these urban clusters also have relatively lower LST compared to others, particularly in urban growth areas (Fig. 4 ). However, between 2000 and 2020, increased population density was associated with rising LST, especially in secondary cities. Additionally, the analysis results from 2020 indicated a strong positive correlation between PLAND and building characteristics, while LST showed a significant negative correlation with building density and building volume (Fig. S6). Discussion Global urban land expansion typically combines outward suburban sprawl with vertical densification to accommodate population growth 37,38 . From 2000 to 2020, the median PLAND (percentage of built-up areas) increased by 12.43% in capital cities and 10.87% in secondary cities, indicating widespread urban growth in Southeast Asia (Fig. 1 a, d). These rates of urban growth can be linked to general urbanization drivers such as population growth and economic activity 34 (Fig. 4 ), with a slightly stronger effect with PLAND increase being higher by 1.56% in capital cities 20,39 . Meanwhile, the NDVI (Normalized Difference Vegetation Index) slightly rose in capital cities (+ 0.01) but declined notably in secondary cities (− 0.07) (Fig. 1 c, f), suggesting divergent land development strategies and resource allocations across city types. Capital cities often balance high-density development with green space planning 20 , while secondary cities, though emulating capital-led expansion, typically lack resources for similar green strategies 40,41 . This disparity contributes to greater NDVI loss in secondary cities (Fig. 1 c, f). Nevertheless, land surface temperature (LST) changes show no clear polarization, likely due to high variability across countries 18,42 . For instance, Bangkok and Manila, despite being the capitals of Thailand and the Philippines, respectively, they show strong differences in PLAND and NDVI trends (Fig. S2; Fig. S4). As secondary cities are projected to continue expanding beyond 2030 38,43 , integrating green space priorities is essential for sustainable growth. Urban areas experiencing rapid urbanization and economic growth tend to exhibit higher surface temperatures and stronger urban heat island effects 9,16 . From 2000 to 2020, capital cities in Southeast Asia were, on average, 1.7°C warmer than secondary cities, with core areas consistently hotter than fringes (Fig. 1 ; Fig. S1 ). Higher LST in urban cores is linked to dense built-up areas and limited green-blue spaces 16,22 . However, LST increases were more pronounced in urban fringes, especially in secondary cities (Fig. 1 b, e), highlighting their climate sensitivity to vegetation loss from urban sprawl. Our analysis shows that increases in PLAND, building density, and volume significantly drive LST rise (Fig. 2 ; Fig. 4 ; Fig. S6), confirming that urban form and human activities strongly influence the thermal environment 15,44,45 . Socioeconomic factors drive urbanization by increasing housing demand and energy consumption, indirectly affecting urban climate 22,34 . Our findings reveal a strong link between GDP and urban expansion in capital cities (Fig. 1 ). Notably, urban clusters with higher population density and GDP tend to show low surface temperatures (Fig. 4 ), likely due to greater investment in green infrastructure 40,46 . However, ongoing population growth can increase PLAND, reducing these benefits over time, especially in secondary cities (Fig. 1 ; Fig. 2 ), highlighting the complex, long-term climate impacts of urban development. Our study highlights Southeast Asian urban clusters with substantial land cover and surface temperature changes over the past two decades. In Johor Bahru, where PLAND rose nearly 29.0% (Fig. 2 c), controlling expansion and promoting mixed land use are priorities 18,47 . Similar measures could be promoted in Kuala Lumpur and Bandung (Fig. 2 c; Fig. S2). For clusters showing land degradation like Krong Pailin and Bandung, "greening and cooling" strategies can repurpose fragmented suburban land 48,49 . Cities like Manila, Cebu, and Yangon, despite modest PLAND changes, saw substantial temperature increases (Fig. 2 d–f), underscoring the role of local activities. Importantly, future planning should restrict energy-intensive industries to suburbs 50 , integrate greening with low-carbon transport 51,52 , and promote collaborative land use across ASEAN cities to ensure sustainable and balanced regional development 18 . Land cover change plays a critical role in affecting climate change across various spatial scales, reinforcing the need to reshape urban form to meet SDGs (Sustainable Development Goals) 11 and 13 53,54 . In short, capital cities should invest in green corridors and vertical greenery to reduce heat, mitigate climate impacts, and limit sprawl 55 . Additionally, incorporating high-density, mixed-use developments and promoting vertical greenery can help limit excessive horizontal expansion and preserve ecological services 56,57 . Secondary cities should manage rising fringe temperatures by enforcing growth boundaries and promoting sustainable land use 19 . Nature-based solutions, such as urban forest restoration and strategic land allocation, are essential for fostering resilient and inclusive urban development in the hot and humid climate of Southeast Asia. Methods Study regions and data acquisition We defined urban clusters for capital and second-largest cities in Southeast Asia using 2020 WorldPop 1 km gridded population data ( https://hub.worldpop.org/ ) and Global Administrative Areas (GADM) boundaries ( https://gadm.org/ ), selecting urban clusters that meet the following criteria 58 : (1) population density ≥ 300 people km − 2 ; (2) total population ≥ 5,000; (3) availability of high-resolution remote sensing data; and (4) and each country contributes both its capital and a qualifying secondary city. This study utilized Land Cover and Land Use (LCLU), Land Surface Temperature (LST), and the Normalized Difference Vegetation Index (NDVI) to characterize key landscape metrics, the urban temperature, and vegetation cover. A detailed description of these remote-sensing datasets is provided in Table S1 . The LCLU raster was used to compute the percentage of built-up areas 31 (PLAND). For population density, we employed the 1 km dataset rather than the 100 m product to maintain consistency with the gross domestic product (GDP) data. Assessing spatiotemporal dynamics of urban forms and land surface temperature We overlaid a 1 km grid within Southeast Asia’s administrative boundaries and applied GIS-based (ArcGIS; version10.4) density (≥ 300 ppl/km²) and population (≥ 5, 000) thresholds 58 , following the selection criteria of urban clusters in the above, to delineate 16 urban clusters in eight countries. We then analyzed multiple spatial datasets to assess the changes in land cover (represented by urban form indicators) and surface temperature of urban clusters between 2000 and 2020 10,34 . Detailed abbreviations and classification of urban clusters are provided in Table 1 . Using 2020 urban cluster boundaries, we computed PLAND, NDVI, and LST at 1 km resolution by applying 3×3 focal statistics to capture neighborhood averages for both 2000 and 2020. Pixels with PLAND > 90% in 2000 were excluded to remove urban cores and isolate the urban fringe 16 , which then served as a mask for analyzing other remote sensing indicators. We then conducted pixel-level comparisons across urban clusters to evaluate land cover changes and LST increases, while extracting core area LST values to control for interannual climate variability. Using "raster" and "ggplot2" packages in the R software (version 4.4.0), we converted processed the raster to vectors, computed median density distributions of PLAND, NDVI, and LST for 2000 and 2020 per urban cluster, and plotted standardized density curves (PLAND: 0–100%, NDVI: − 1 to 1, LST: 20–40°C) to highlight temporal changes while filtering out outliers. Comparison of capital and secondary cities and analysis of influencing factors We assessed urban form (PLAND, NDVI) and temperature (LST) changes across three perspectives: (1) comparing median density distributions for capital cities, secondary cities, and all urban clusters between 2000 and 2020; (2) calculating urban core–fringe LST differentials to isolate urban warming from regional climate influences 59 ; and (3) ranking urban clusters by median PLAND, NDVI, and LST in 2000, 2020, and over the 20-year period to identify urban expansion, vegetation loss, and temperature rise, culminating in a ternary trade-off analysis. We applied the bivariate global Moran's I index 60 to quantify spatial correlations between PLAND and LST and between NDVI and LST for 2000 and 2020. To evaluate drivers of changes in urban land cover and temperature, we used Spearman correlation to link population density and 2019 GDP with PLAND, NDVI, and LST in 2000 and 2020 34,61 , and Pearson correlations to examine relationships between 2020 building density (%), height (m), and volume (m³) from Google Earth Engine (GEE) Open Buildings dataset (Google/Search/open buildings temporal/v1) and spectral indices 29 . All analyses were performed in R. Declarations Data availability: The datasets used in this study are publicly available (Table S1 for details). Code availability: The code used for calculating and statistical results of this study is available from the authors upon reasonable request. Acknowledgments: The authors gratefully acknowledge Dr. Tingting He for kindly providing the 2020 building characteristics dataset. We also thank all members of the Resilient and Inclusive Cities Lab for their constructive feedback and stimulating discussions. Authors' Contributions: P. Hamel, S. A. Kamarajugedda, R. Lafortezza, and R. Xu conceived the ideas and designed methodology; S. A. Kamarajugedda and R. Xu collected and analyzed the data; R. Xu and P. Hamel led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication. Declaration of competing interest: The authors declare no competing financial interests. Funding: This research was funded by the National Research Foundation of Singapore, Prime Minister's Office (NRF-NRFF12-2020-0009). References Agnew, D. C., A global timekeeping problem postponed by global warming. NATURE 628 333 (2024). McManamay, R. A. et al. , Dynamic urban land extensification is projected to lead to imbalances in the global land-carbon equilibrium. COMMUN EARTH ENVIRON 5 (2024). Esper, J., Torbenson, M. & Büntgen, U., 2023 summer warmth unparalleled over the past 2,000 years. NATURE 631 94 (2024). Marks, D. & Connell, J., Unequal and unjust: The political ecology of Bangkok's increasing urban heat island. URBAN STUD (2023). Sari, R., Soytas, U., Kanoglu-Ozkan, D. G. & Sivrikaya, A., Improving the climate resilience of European cities via socially acceptable nature-based solutions. npj Urban Sustainability 3 9 (2023). Thanvisitthpon, N., Nakburee, A., Khamchiangta, D. & Saguansap, V., Climate change-induced urban heat Island trend projection and land surface temperature: A case study of Thailand?s Bangkok metropolitan. URBAN CLIM 49 (2023). Kotz, M., Levermann, A. & Wenz, L., The economic commitment of climate change. NATURE 628 551 (2024). Li, G. et al. , Global impacts of future urban expansion on terrestrial vertebrate diversity. NAT COMMUN 13 (2022). Arshad, S. et al. , Quantifying the contribution of diminishing green spaces and urban sprawl to urban heat island effect in a rapidly urbanizing metropolitan city of Pakistan. LAND USE POLICY 113 (2022). Pan, L. et al. , Understanding spatiotemporal evolution of the surface urban heat island in the Bangkok metropolitan region from 2000 to 2020 using enhanced land surface temperature. GEOMAT NAT HAZ RISK 14 (2023). He, Z. & Tang, B. H., Spatiotemporal change patterns and driving factors of land surface temperature in the Yunnan-Kweichow Plateau from 2000 to 2020. SCI TOTAL ENVIRON 896 (2023). Gao, S. et al. , Urbanization-induced warming amplifies population exposure to compound heatwaves but narrows exposure inequality between global North and South cities. NPJ CLIM ATMOS SCI 7 110 (2024). Kempf, M., Enhanced trends in spectral greening and climate anomalies across Europe. ENVIRON MONIT ASSESS 195 (2023). Feng, F. et al. , Exploring the non-linear impacts of urban features on land surface temperature using explainable artificial intelligence. URBAN CLIM 56 102045 (2024). Ren, Y. et al. , Understanding the coupling effect of multiple urban features on land surface temperature in Europe. URBAN FOR URBAN GREE 105 128723 (2025). Ramsay, E. E. et al. , Spatio-temporal development of the urban heat island in a socioeconomically diverse tropical city. ENVIRON POLLUT 316 (2023). Kamarajugedda, S. A., Johnson, J. A., McDonald, R. & Hamel, P., Carbon storage and sequestration in Southeast Asian urban clusters under future land cover change scenarios (2015–2050). FRONT ENV SCI-SWITZ 11 (2023). Ahmadzai, M. R., Zaki, P. H., Ismail, M. H., Bawon, P. & Karam, D. S., The Societal and Economic Impact of Reforestation Strategies and Policies in Southeast Asia-A Review. FORESTS 14 (2023). Rinchumphu, D., Srivanit, M., Iamchuen, N. & Aryupong, C., Exploring Summer Variations of Driving Factors Affecting Land Use Zoning Based on the Surface Urban Heat Island in Chiang Mai, Thailand. ISPRS INT J GEO-INF 13 (2024). Thaweepworadej, P. & Evans, K. L., Urbanisation of a growing tropical mega-city during the 21st century-Landscape transformation and vegetation dynamics. LANDSCAPE URBAN PLAN 238 (2023). Matthews, T. et al. , Mortality impacts of the most extreme heat events. NAT REV EARTH ENV 6 193 (2025). Ogunbode, T. O., Oyebamiji, V. O., Sanni, D. O., Akinwale, E. O. & Akinluyi, F. O., Environmental impacts of urban growth and land use changes in tropical cities. FRONT SUSTAIN CITIES 6 (2025). Zwolska, A., Polrolniczak, M. & Kolendowicz, L., Urban growth's implications on land surface temperature in a medium-sized European city based on LCZ classification. SCI REP-UK 14 (2024). Loo, B. P. Y. et al. , Cities and Urbanization: Balancing the Environmental and Socioeconomic Dimensions of Sustainability. ADV SUSTAIN SYST 8 (2024). Lu, Y., Yue, W., He, T. & Shan, Z., Urban form centrality and thermal environment: An empirical study of Chinese megacities. SUSTAIN CITIES SOC 83 103955 (2022). Liu, Z. et al. , Surface warming in global cities is substantially more rapid than in rural background areas. COMMUN EARTH ENVIRON 3 (2022). Li, D., Yan, S. & Chen, G., Effects of Urban Redevelopment on Surface Urban Heat Island. IEEE J-STARS 16 2366 (2023). Ermida, S. L., Soares, P., Mantas, V., Göttsche, F. & Trigo, I. F., Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series. REMOTE SENS-BASEL 12 1471 (2020). He, T. et al. , Quantifying the impact of urban trees on land surface temperature in global cities. ISPRS J PHOTOGRAMM 210 69 (2024). Mao, F. et al. , Land use and cover in subtropical East Asia and Southeast Asia from 1700 to 2018. GLOBAL PLANET CHANGE 226 (2023). Basu, T. & Das, A., Urbanization induced degradation of urban green space and its association to the land surface temperature in a medium-class city in India. SUSTAIN CITIES SOC 90 (2023). Ramzan, M. et al. , Remote Sensing-Based Prediction of Temporal Changes in Land Surface Temperature and Land Use-Land Cover (LULC) in Urban Environments. LAND-BASEL 11 (2022). Han, D., Zhang, T., Qin, Y., Tan, Y. & Liu, J., A comparative review on the mitigation strategies of urban heat island (UHI): a pathway for sustainable urban development. CLIM DEV 15 379 (2023). Mahtta, R. et al. , Urban land expansion: the role of population and economic growth for 300+ cities. NPJ URBAN SUSTAINABILITY 2 (2022). Hussain, S. et al. , Relation of land surface temperature with different vegetation indices using multi-temporal remote sensing data in Sahiwal region, Pakistan. GEOSCI LETT 10 (2023). Dolley, J. et al. , Analysing trade-offs and synergies between SDGs for urban development, food security and poverty alleviation in rapidly changing peri-urban areas: a tool to support inclusive urban planning. SUSTAIN SCI 15 1601 (2020). Seto, K. C., Golden, J. S., Alberti, M. & Turner, B. L., Sustainability in an urbanizing planet. Proceedings of the National Academy of Sciences 114 8935 (2017). Frolking, S., Mahtta, R., Milliman, T., Esch, T. & Seto, K. C., Global urban structural growth shows a profound shift from spreading out to building up. Nature Cities 1 555 (2024). Deton, Y. et al. , Spatiotemporal Dynamics of Urban Green Spaces and Climatic Vulnerability of Togolese Cities in the Context of Rapid Urbanisation: The Case of Lome and Kara. LAND-BASEL 14 (2025). Richards, D. R., Passy, P. & Oh, R. R. Y., Impacts of population density and wealth on the quantity and structure of urban green space in tropical Southeast Asia. LANDSCAPE URBAN PLAN 157 553 (2017). Muhamad Nor, A. N. et al. , Evolution of Green Space under Rapid Urban Expansion in Southeast Asian Cities. SUSTAINABILITY-BASEL 13 (2021). Hu, Y., Connor, D. S., Stuhlmacher, M., Peng, J. & Turner II, B. L., More urbanization, more polarization: evidence from two decades of urban expansion in China. NPJ URBAN SUSTAINABILITY 4 (2024). Gao, J. & O'Neill, B. C., Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways. NAT COMMUN 11 (2020). Yan, J. et al. , The Influence of Urban Form on Land Surface Temperature: A Comprehensive Investigation from 2D Urban Land Use and 3D Buildings. LAND-BASEL 12 (2023). Zhou, L. et al. , Relationship between urban landscape structure and land surface temperature: Spatial hierarchy and interaction effects. SUSTAIN CITIES SOC 80 (2022). Bastien-Olvera, B. A. et al. , Unequal climate impacts on global values of natural capital. NATURE 625 722 (2024). Zepp, H. & Inostroza, L., Who Pays the Bill? Assessing Ecosystem Services Losses in an Urban Planning Context. LAND-BASEL 10 (2021). Law, A. et al. , Leave no one behind: A case of ecosystem service supply equity in Singapore. AMBIO 51 2118 (2022). Das, M., Das, A. & Momin, S., Quantifying the cooling effect of urban green space: A case from urban parks in a tropical mega metropolitan area (India). SUSTAIN CITIES SOC 87 (2022). Tran, T., Tran, Q., Vu, N. T. & Vo, D. H., Renewable Energy, Urbanization, Financial Development and Environmental Degradation in the ASEAN Countries. ENVIRON URBAN ASIA 14 10 (2023). Hung, T., Uchihama, D., Ochi, S. & Yasuoka, Y., Assessment with satellite data of the urban heat island effects in Asian mega cities. INT J APPL EARTH OBS 8 34 (2006). Zhang, L., Tao, L., Yang, F., Bao, Y. & Li, C., Promoting green transportation through changing behaviors with low-carbon-travel function of digital maps. HUM SOC SCI COMMUN 11 (2024). Purio, M. A., Yoshitake, T. & Cho, M., Assessment of Intra-Urban Heat Island in a Densely Populated City Using Remote Sensing: A Case Study for Manila City. REMOTE SENS-BASEL 14 (2022). Kakooei, M. & Baleghi, Y., Spatial-Temporal analysis of urban environmental variables using building height features. URBAN CLIM 52 (2023). Wang, W., Wang, D., Chen, H., Wang, B. & Chen, X., Identifying urban ventilation corridors through quantitative analysis of ventilation potential and wind characteristics. BUILD ENVIRON 214 (2022). Tan, P. Y. & Samsudin, R., Effects of spatial scale on assessment of spatial equity of urban park provision. LANDSCAPE URBAN PLAN 158 139 (2017). Zhang, C. & Su, Y., Gains and losses in forest cover have asymmetric effects on land surface temperature. NAT CLIM CHANGE 13 776 (2023). Dijkstra, L. et al. , Applying the Degree of Urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. J URBAN ECON 125 (2021). Jing, R. et al. , Global population profile of tropical cyclone exposure from 2002 to 2019. NATURE (2023). Chen, Y., Yue, W. & La Rosa, D., Which communities have better accessibility to green space? An investigation into environmental inequality using big data. LANDSCAPE URBAN PLAN 204 (2020). Chen, J. et al. , Global 1 km x 1 km gridded revised real gross domestic product and electricity consumption during 1992-2019 based on calibrated nighttime light data. SCI DATA 9 (2022). Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformationLinkingUrbanGrowthandSurfaceTemperatureChangeinSEA.docx Cite Share Download PDF Status: Published Journal Publication published 09 Feb, 2026 Read the published version in npj Urban Sustainability → Version 1 posted Editorial decision: Revision requested 26 Aug, 2025 Reviews received at journal 21 Aug, 2025 Reviews received at journal 25 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviews received at journal 02 Jul, 2025 Reviews received at journal 06 Jun, 2025 Reviewers agreed at journal 30 May, 2025 Reviewers agreed at journal 30 May, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers invited by journal 13 May, 2025 Editor assigned by journal 04 May, 2025 Submission checks completed at journal 02 May, 2025 First submitted to journal 01 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6572557","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":456507693,"identity":"972693fa-0e61-499a-aeab-a43724b1aa84","order_by":0,"name":"Ronghua Xu","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Ronghua","middleName":"","lastName":"Xu","suffix":""},{"id":456507694,"identity":"58307241-baab-4dd1-972a-35430ea30222","order_by":1,"name":"Shankar Acharya Kamarajugedda","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Shankar","middleName":"Acharya","lastName":"Kamarajugedda","suffix":""},{"id":456507695,"identity":"e6bc1c00-32ee-4dfb-baaa-38f8637b4639","order_by":2,"name":"Raffaele Lafortezza","email":"","orcid":"","institution":"University of Bari","correspondingAuthor":false,"prefix":"","firstName":"Raffaele","middleName":"","lastName":"Lafortezza","suffix":""},{"id":456507696,"identity":"2e5be202-ad91-421c-93bb-8ffc8e3b2153","order_by":3,"name":"Perrine Hamel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYLCCBCA2ADEeVEAEJIjXknAGopqwFgaYlsQ2IrSYs/c++/Bwhw2QcTrxQeK8O3UGB5gP3uZhqLNrwKHFsue48YzEM2lARu5mg8RtzyQMDrAlW/MwHE7GpcXgRhoz0D2HGQwO5G6TSNx2GKiFx0yah+FAMk4v3H8G0vKfweD82+0/EueAtPB/A2qpw63lBhtIywEgI3cbQ2ID2BY2oBZmO1xaLHvADkvmMbjxdrNEwrHDkjMPsxlbzjE4nIBLizn7MWbGn212cgbnczd++FBzmJ/vePPDG28q6uxxOgxK8yCEmCHiiQ0EtGACnLaMglEwCkbBiAMANJxVq4N1yOoAAAAASUVORK5CYII=","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":true,"prefix":"","firstName":"Perrine","middleName":"","lastName":"Hamel","suffix":""}],"badges":[],"createdAt":"2025-05-01 14:38:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6572557/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6572557/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42949-026-00336-x","type":"published","date":"2026-02-09T15:59:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82804990,"identity":"23210604-a206-4d17-9480-1462a043face","added_by":"auto","created_at":"2025-05-15 12:11:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of urban form indicators and LST for capitals versus secondary cities in 2000 and 2020\u003c/strong\u003e. Each violin plot depicts the full distribution of grid‐cell values in the urban fringe areas, with the box spanning the 25th to 75th percentile and the horizontal black line marking the median. In panels (b), (e), and (h), red dots on the violin axis indicate the median LST of urban core area for 2000 and 2020, with their values annotated in red. The complete distribution and median LST of the urban core area are shown in Fig. S1.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6572557/v1/85301813ce6330ae6643639a.jpg"},{"id":82804994,"identity":"bfd9a988-3c1b-4656-ab1f-d57c54ce9be2","added_by":"auto","created_at":"2025-05-15 12:11:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147145,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRanking of urban clusters by urban form indicators and LST in 2000 and 2020\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003eThe abbreviations of urban clusters are listed in Table 1. The density plots—with medians marked—for each urban cluster in 2000 and 2020 are shown in Fig. S2–S4. The underlined urban clusters are emphasized.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6572557/v1/791536efcbd7c71c51419f9c.jpg"},{"id":82804991,"identity":"ec01ba14-fac3-4f69-a729-ad4a28afd0a0","added_by":"auto","created_at":"2025-05-15 12:11:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-variable changes in urban forms and LST from 2000 to 2020\u003c/strong\u003e. The ternary plot in panel (a) shows the relative median changes in PLAND, LST, and NDVI for each urban cluster. Vertices represent a dominant role of that variable, while shifts along the edge reflect a reduced role. All values are normalized, and NDVI uses absolute values. Abbreviations of urban clusters are shown in Table 1, and an asterisk (*) denotes capitals. Pairwise relationships among median changes are shown in Fig. S5. Red circles highlight the two urban clusters illustrated in panel (b): Cebu (CBU) and Kuala Lumpur (KUL), illustrating spatial patterns of changes in LST and PLAND (low LST change and high PLAND change for Kuala Lumpur; high LST change with low PLAND change for Cebu).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6572557/v1/7f75e8ea93260e8fb7ea7367.jpg"},{"id":82805191,"identity":"1abe4c38-c69e-4b69-abc5-eef8db4890c5","added_by":"auto","created_at":"2025-05-15 12:19:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67733,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman’s correlations between spectral indices and socioeconomic factors\u003c/strong\u003e. Positive correlations are shown in red and negative correlations in blue. POP and GDP denote population density (persons km⁻²) and gross domestic product (US $ km⁻²), respectively. Correlations are based on median values for each urban cluster in 2000 and 2020. Pearson correlations—including building attributes for 2020—are presented in Fig. S6. Significance levels are denoted as follows: *** \u003cem\u003ep\u003c/em\u003e ≤ 0.01, ** \u003cem\u003ep\u003c/em\u003e ≤ 0.05, * \u003cem\u003ep\u003c/em\u003e≤ 0.1.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6572557/v1/abca417b5cf2a44c5bb3f51e.jpg"},{"id":82806392,"identity":"affff551-1397-461a-bc8e-461d58254938","added_by":"auto","created_at":"2025-05-15 12:27:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":147330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch framework based on urban clusters in Southeast Asia\u003c/strong\u003e. The framework comprises four steps: Step 1 includes the spatial distribution of urban clusters across Southeast Asia (a). Step 2, illustrated here for Jakarta\u003cstrong\u003e,\u003c/strong\u003e masks out urban core areas, i.e. areas where PLAND (percentage of built-up areas) in 2000 is higher than 90% (b). Step 3 computes\u003cstrong\u003e \u003c/strong\u003erelevant spectral indices to analyze land cover and temperature characteristics, for\u003cstrong\u003e \u003c/strong\u003e2000 (c) and 2020 (d). Step 4 performs comparative and correlation analyses between capital and secondary cities. The abbreviations of urban clusters are shown in panel (a) correspond to the full names listed in Table 1.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6572557/v1/fda105bd76ee29888a391407.jpg"},{"id":102785436,"identity":"2acb6f73-30fd-4698-90c7-1e83245f5230","added_by":"auto","created_at":"2026-02-16 16:06:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1607711,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6572557/v1/f124dbd6-c38e-4ea8-95c9-b23b564ba2cd.pdf"},{"id":82805193,"identity":"642ae07d-e471-4fc1-b0b5-b913c33ad037","added_by":"auto","created_at":"2025-05-15 12:19:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":522953,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationLinkingUrbanGrowthandSurfaceTemperatureChangeinSEA.docx","url":"https://assets-eu.researchsquare.com/files/rs-6572557/v1/5ea6770a7e56c65bbc520d7f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Linking Urban Growth and Surface Temperature Change in Capital and Secondary Cities of Southeast Asia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal climate change poses urgent challenges to sustainable development, particularly in the context of rapidly developing countries\u003csup\u003e1,2\u003c/sup\u003e. In 2023, extreme heat records peaked, with air temperatures reaching 39.2\u0026deg;C in most areas\u003csup\u003e3,4\u003c/sup\u003e. Rapid urbanization intensifies thermal environmental risks, contributing to rising temperatures and more frequent extreme weather events, jeopardizing both ecosystems and human survival\u003csup\u003e5\u0026ndash;7\u003c/sup\u003e. Since urban growth is accompanied by substantial changes in Land Use and Land Cover (LULC), it directly affects Land Surface Temperature (LST) and leads to the urban heat island (UHI) effect\u0026mdash;whereby urban areas are generally warmer than rural areas\u003csup\u003e8,9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSince 2000, rapid urbanization has exacerbated this impact, making it essential to explore its complex and dynamic relationship in order to mitigate UHI effects and adapt to climate change\u003csup\u003e10,11\u003c/sup\u003e. Recent studies have primarily examined the effect of land cover changes in temperate regions such as Europe, China, and North America, yet these insights may not be applicable to urban planning in other regions\u003csup\u003e12\u0026ndash;15\u003c/sup\u003e. In particular, tropical cities display heightened sensitivity to global warming and rapid urban growth\u003csup\u003e16\u003c/sup\u003e. Despite Southeast Asia's diverse development models, economic stages, and policy frameworks\u003csup\u003e17,18\u003c/sup\u003e, UHI-related research often targets individual cities\u003csup\u003e19,20\u003c/sup\u003e. Hence, region-wide analysis of urban expansion and temperature dynamics is needed to inform tailored urban heat mitigation strategies.\u003c/p\u003e \u003cp\u003ePopulation growth drives the expansion of built-up areas into urban fringes\u003csup\u003e21\u003c/sup\u003e, leading to an increase in impermeable surfaces and a reduction in blue-green spaces like vegetation and water bodies\u003csup\u003e22\u003c/sup\u003e. In turn, these changes affect the microclimate, elevating LST\u003csup\u003e14,23\u003c/sup\u003e. Urban form characterizes the spatial layout of landscape elements such as land cover\u003csup\u003e24\u003c/sup\u003e and building patterns\u003csup\u003e25\u003c/sup\u003e, and critically influences the microclimate of cities compared to their surroundings\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHigh-resolution, multi-temporal spectral imagery (Landsat, MODIS, Sentinel-2) enables monitoring of impervious and vegetation surfaces affecting urban thermal environments\u003csup\u003e27,28\u003c/sup\u003e. Satellite-derived LST captures spatial heterogeneity and broader land\u0026ndash;atmosphere exchanges beyond ground station limits\u003csup\u003e23,29\u003c/sup\u003e. Urban expansion concentrates land cover change hotspots, yet its effects on tropical urban heat dynamics and drivers remain underexplored\u003csup\u003e16,30\u003c/sup\u003e. Percentage of built-up areas (PLAND), a widely used landscape pattern indicator\u003csup\u003e31\u003c/sup\u003e indicating impervious growth and activity intensity\u003csup\u003e22,32\u003c/sup\u003e. While increasing vegetation effectively mitigates the UHI effect\u003csup\u003e33\u003c/sup\u003e, urban growth characterized by expanding land can undermine this effort\u003csup\u003e16,23\u003c/sup\u003e. Therefore, integrating indicators is essential for assessing trade-offs between urban expansion and greening strategies.\u003c/p\u003e \u003cp\u003eUrban land cover change impact on LST is scale-dependent\u003csup\u003e23,34\u003c/sup\u003e. Local-scale studies employing high-resolution data capture intra-urban LST variation but lack broad applicability due to limited coverage\u003csup\u003e12,22\u003c/sup\u003e. Regional and global-scale analyses provide valuable insights into climate modeling but miss local heterogeneity\u003csup\u003e27,35\u003c/sup\u003e, leading to inefficiencies in climate-related policy-making\u003csup\u003e18,36\u003c/sup\u003e. Investigating multi-scale perspectives is essential for understanding LST mechanisms and guiding effective heat mitigation strategies.\u003c/p\u003e \u003cp\u003eThis study examines a total of 16 capital and secondary cities in Southeast Asia\u0026mdash;regions with an urgent need to mitigate UHI effects\u0026mdash;to explore the relationship between urban expansion and surface temperature using remote sensing data from 2000 and 2020. To our knowledge, it is the first study examining nuanced urban forms and temperature patterns in this understudied region. Specifically, our study aims to: (1) quantify changes in urban forms and LST between 2000 and 2020 in both capital and secondary cities; (2) evaluate correlations between urban form changes and temperature increase; and (3) assess the influence of city type and socioeconomic factors on shifts in urban forms and temperature.\u003c/p\u003e \u003cp\u003eTo address these objectives, we focused on urban fringes where most of the land cover change occurs, outside the core areas already developed in 2000. By analyzing the distinct urban dynamics of recently developed areas in Southeast Asia, the findings offer context-specific insights and practical strategies that support sustainable urban development and inform effective climate adaptation.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in urban forms and temperature across Southeast Asian cities\u003c/h2\u003e \u003cp\u003eThe results showed that between 2000 and 2020, there was an increase in the median PLAND by 11.93% and LST by 1.39℃ in urban clusters of Southeast Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In capital and secondary cities, these increases were 12.43% and 10.87%, and 1.31℃ and 1.35℃, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). While the NDVI showed a slight rise of 0.01 in capital cities, it decreased by 0.07 in secondary cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, f). The LST in capital cities was consistently about 1.7℃ higher than that in secondary cities in both 2000 and 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, e). Within urban core areas, LST was higher than that in urban fringes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, e), but the rise in LST was more substantial in urban fringes, with a difference of 0.04℃ in capitals and 0.35℃ in secondary cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, e). This suggests a net increase in LST in Southeast Asian cities over the past 20 years, especially in secondary cities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of median urban forms and LST did not reveal clear trends between capital and secondary cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Higher PLAND values were primarily found in urban clusters of Thailand, Indonesia, and Malaysia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b), while elevated LST values were mainly in Laos (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, e). In 2000, the highest NDVI values were in the secondary city, and the lowest in the capital city of Cambodia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg); by 2020, this trend reversed in the Philippines (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). Notable changes from 2000 to 2020 included the highest median PLAND increase in Johor Bahru at 29.0%, the greatest LST rise in Manila at 5.4℃, and the largest NDVI change in Krong Pailin at -0.33, with Manila also showed the most substantial NDVI increase at 0.27 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, f, i).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTrade-off of changes in urban forms and temperature from 2000 to 2020\u003c/h3\u003e\n\u003cp\u003eA trade-off analysis of changes in PLAND, NDVI, and LST across urban clusters from 2000 to 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed that most urban clusters experienced substantial changes in PLAND, indicating that rising LST is primarily driven by changes in impermeable surface rather than by vegetation loss. In most urban clusters, there is a positive spatial correlation between PLAND and LST, and a negative spatial correlation between NDVI and LST (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Two distinct spatial patterns of LST and PLAND are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb. However, some urban clusters, like Manila (MNL), Cebu (CBU) and Krong Pailin (KPL), registered a pronounced LST increase\u0026mdash;5.4\u0026deg;C in MNL, 5.2\u0026deg;C in CBU, and 3.8\u0026deg;C in KPL (Fig. S5), with corresponding PLAND changes of only 1.0%, 1.5%, and 2.8%, respectively (Fig. S5a). Their NDVI trends also diverged: MNL increased by 0.27, while CBU and KPL decreased by 0.14 and 0.33, respectively. These exceptions suggest that socioeconomic factors, alongside PLAND and NDVI changes, significantly influence urban temperature dynamics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of bivariate global Moran's \u003cem\u003eI\u003c/em\u003e index analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eUrban cluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePLAND - LST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNDVI - LST\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJKT*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJakarta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndonesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBandung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndonesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBKK*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBangkok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThailand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChiang Mai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThailand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKUL*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKuala Lumpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJohor Bahru\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPH*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhnom Penh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCambodia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKrong Pailin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCambodia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYGN*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYangon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMyanmar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMandalay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMyanmar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVNT*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVientiane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePKX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePakse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.085\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNL*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManila\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhilippines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCBU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCebu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhilippines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRN*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBandar Seri Begawah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrunei\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKBT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKuala Belait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrunei\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: An asterisk (*) denotes capitals; Nearly all Moran's \u003cem\u003eI\u003c/em\u003e values have corresponding p-values of 0.001 (i.e., \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except for the bolded values, which are greater than 0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eThe impact of socio-economic factors on urban forms and temperature\u003c/h3\u003e\n\u003cp\u003eThe Spearman correlation analysis showed a significant positive relationship between population density and PLAND, indicating that urban clusters with high population density generally have higher PLAND. A similar positive correlation exists between GDP and PLAND, particularly strong in capital cities. Furthermore, urban clusters with greater population density and GDP tend to exhibit lower NDVI, especially in secondary cities. Notably, these urban clusters also have relatively lower LST compared to others, particularly in urban growth areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, between 2000 and 2020, increased population density was associated with rising LST, especially in secondary cities. Additionally, the analysis results from 2020 indicated a strong positive correlation between PLAND and building characteristics, while LST showed a significant negative correlation with building density and building volume (Fig. S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGlobal urban land expansion typically combines outward suburban sprawl with vertical densification to accommodate population growth\u003csup\u003e37,38\u003c/sup\u003e. From 2000 to 2020, the median PLAND (percentage of built-up areas) increased by 12.43% in capital cities and 10.87% in secondary cities, indicating widespread urban growth in Southeast Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, d). These rates of urban growth can be linked to general urbanization drivers such as population growth and economic activity\u003csup\u003e34\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with a slightly stronger effect with PLAND increase being higher by 1.56% in capital cities\u003csup\u003e20,39\u003c/sup\u003e. Meanwhile, the NDVI (Normalized Difference Vegetation Index) slightly rose in capital cities (+\u0026thinsp;0.01) but declined notably in secondary cities (\u0026minus;\u0026thinsp;0.07) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, f), suggesting divergent land development strategies and resource allocations across city types.\u003c/p\u003e \u003cp\u003eCapital cities often balance high-density development with green space planning\u003csup\u003e20\u003c/sup\u003e, while secondary cities, though emulating capital-led expansion, typically lack resources for similar green strategies\u003csup\u003e40,41\u003c/sup\u003e. This disparity contributes to greater NDVI loss in secondary cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, f). Nevertheless, land surface temperature (LST) changes show no clear polarization, likely due to high variability across countries\u003csup\u003e18,42\u003c/sup\u003e. For instance, Bangkok and Manila, despite being the capitals of Thailand and the Philippines, respectively, they show strong differences in PLAND and NDVI trends (Fig. S2; Fig. S4). As secondary cities are projected to continue expanding beyond 2030\u003csup\u003e38,43\u003c/sup\u003e, integrating green space priorities is essential for sustainable growth.\u003c/p\u003e \u003cp\u003eUrban areas experiencing rapid urbanization and economic growth tend to exhibit higher surface temperatures and stronger urban heat island effects\u003csup\u003e9,16\u003c/sup\u003e. From 2000 to 2020, capital cities in Southeast Asia were, on average, 1.7\u0026deg;C warmer than secondary cities, with core areas consistently hotter than fringes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Higher LST in urban cores is linked to dense built-up areas and limited green-blue spaces\u003csup\u003e16,22\u003c/sup\u003e. However, LST increases were more pronounced in urban fringes, especially in secondary cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, e), highlighting their climate sensitivity to vegetation loss from urban sprawl. Our analysis shows that increases in PLAND, building density, and volume significantly drive LST rise (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig. S6), confirming that urban form and human activities strongly influence the thermal environment\u003csup\u003e15,44,45\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSocioeconomic factors drive urbanization by increasing housing demand and energy consumption, indirectly affecting urban climate\u003csup\u003e22,34\u003c/sup\u003e. Our findings reveal a strong link between GDP and urban expansion in capital cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, urban clusters with higher population density and GDP tend to show low surface temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), likely due to greater investment in green infrastructure\u003csup\u003e40,46\u003c/sup\u003e. However, ongoing population growth can increase PLAND, reducing these benefits over time, especially in secondary cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), highlighting the complex, long-term climate impacts of urban development.\u003c/p\u003e \u003cp\u003eOur study highlights Southeast Asian urban clusters with substantial land cover and surface temperature changes over the past two decades. In Johor Bahru, where PLAND rose nearly 29.0% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), controlling expansion and promoting mixed land use are priorities\u003csup\u003e18,47\u003c/sup\u003e. Similar measures could be promoted in Kuala Lumpur and Bandung (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec; Fig. S2). For clusters showing land degradation like Krong Pailin and Bandung, \"greening and cooling\" strategies can repurpose fragmented suburban land\u003csup\u003e48,49\u003c/sup\u003e. Cities like Manila, Cebu, and Yangon, despite modest PLAND changes, saw substantial temperature increases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed\u0026ndash;f), underscoring the role of local activities. Importantly, future planning should restrict energy-intensive industries to suburbs\u003csup\u003e50\u003c/sup\u003e, integrate greening with low-carbon transport\u003csup\u003e51,52\u003c/sup\u003e, and promote collaborative land use across ASEAN cities to ensure sustainable and balanced regional development\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLand cover change plays a critical role in affecting climate change across various spatial scales, reinforcing the need to reshape urban form to meet SDGs (Sustainable Development Goals) 11 and 13\u003csup\u003e53,54\u003c/sup\u003e. In short, capital cities should invest in green corridors and vertical greenery to reduce heat, mitigate climate impacts, and limit sprawl\u003csup\u003e55\u003c/sup\u003e. Additionally, incorporating high-density, mixed-use developments and promoting vertical greenery can help limit excessive horizontal expansion and preserve ecological services\u003csup\u003e56,57\u003c/sup\u003e. Secondary cities should manage rising fringe temperatures by enforcing growth boundaries and promoting sustainable land use\u003csup\u003e19\u003c/sup\u003e. Nature-based solutions, such as urban forest restoration and strategic land allocation, are essential for fostering resilient and inclusive urban development in the hot and humid climate of Southeast Asia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy regions and data acquisition\u003c/h2\u003e \u003cp\u003eWe defined urban clusters for capital and second-largest cities in Southeast Asia using 2020 WorldPop 1 km gridded population data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hub.worldpop.org/\u003c/span\u003e\u003cspan address=\"https://hub.worldpop.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Global Administrative Areas (GADM) boundaries (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gadm.org/\u003c/span\u003e\u003cspan address=\"https://gadm.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), selecting urban clusters that meet the following criteria\u003csup\u003e58\u003c/sup\u003e: (1) population density\u0026thinsp;\u0026ge;\u0026thinsp;300 people km\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e ; (2) total population\u0026thinsp;\u0026ge;\u0026thinsp;5,000; (3) availability of high-resolution remote sensing data; and (4) and each country contributes both its capital and a qualifying secondary city.\u003c/p\u003e \u003cp\u003eThis study utilized Land Cover and Land Use (LCLU), Land Surface Temperature (LST), and the Normalized Difference Vegetation Index (NDVI) to characterize key landscape metrics, the urban temperature, and vegetation cover. A detailed description of these remote-sensing datasets is provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The LCLU raster was used to compute the percentage of built-up areas\u003csup\u003e31\u003c/sup\u003e (PLAND). For population density, we employed the 1 km dataset rather than the 100 m product to maintain consistency with the gross domestic product (GDP) data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessing spatiotemporal dynamics of urban forms and land surface temperature\u003c/h3\u003e\n\u003cp\u003eWe overlaid a 1 km grid within Southeast Asia\u0026rsquo;s administrative boundaries and applied GIS-based (ArcGIS; version10.4) density (\u0026ge;\u0026thinsp;300 ppl/km\u0026sup2;) and population (\u0026ge;\u0026thinsp;5, 000) thresholds\u003csup\u003e58\u003c/sup\u003e, following the selection criteria of urban clusters in the above, to delineate 16 urban clusters in eight countries. We then analyzed multiple spatial datasets to assess the changes in land cover (represented by urban form indicators) and surface temperature of urban clusters between 2000 and 2020\u003csup\u003e10,34\u003c/sup\u003e. Detailed abbreviations and classification of urban clusters are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eUsing 2020 urban cluster boundaries, we computed PLAND, NDVI, and LST at 1 km resolution by applying 3\u0026times;3 focal statistics to capture neighborhood averages for both 2000 and 2020. Pixels with PLAND\u0026thinsp;\u0026gt;\u0026thinsp;90% in 2000 were excluded to remove urban cores and isolate the urban fringe\u003csup\u003e16\u003c/sup\u003e, which then served as a mask for analyzing other remote sensing indicators. We then conducted pixel-level comparisons across urban clusters to evaluate land cover changes and LST increases, while extracting core area LST values to control for interannual climate variability.\u003c/p\u003e \u003cp\u003eUsing \"raster\" and \"ggplot2\" packages in the R software (version 4.4.0), we converted processed the raster to vectors, computed median density distributions of PLAND, NDVI, and LST for 2000 and 2020 per urban cluster, and plotted standardized density curves (PLAND: 0\u0026ndash;100%, NDVI: \u0026minus;\u0026thinsp;1 to 1, LST: 20\u0026ndash;40\u0026deg;C) to highlight temporal changes while filtering out outliers.\u003c/p\u003e\n\u003ch3\u003eComparison of capital and secondary cities and analysis of influencing factors\u003c/h3\u003e\n\u003cp\u003eWe assessed urban form (PLAND, NDVI) and temperature (LST) changes across three perspectives: (1) comparing median density distributions for capital cities, secondary cities, and all urban clusters between 2000 and 2020; (2) calculating urban core\u0026ndash;fringe LST differentials to isolate urban warming from regional climate influences\u003csup\u003e59\u003c/sup\u003e; and (3) ranking urban clusters by median PLAND, NDVI, and LST in 2000, 2020, and over the 20-year period to identify urban expansion, vegetation loss, and temperature rise, culminating in a ternary trade-off analysis. We applied the bivariate global Moran's \u003cem\u003eI\u003c/em\u003e index\u003csup\u003e60\u003c/sup\u003e to quantify spatial correlations between PLAND and LST and between NDVI and LST for 2000 and 2020.\u003c/p\u003e \u003cp\u003eTo evaluate drivers of changes in urban land cover and temperature, we used Spearman correlation to link population density and 2019 GDP with PLAND, NDVI, and LST in 2000 and 2020\u003csup\u003e34,61\u003c/sup\u003e, and Pearson correlations to examine relationships between 2020 building density (%), height (m), and volume (m\u0026sup3;) from Google Earth Engine (GEE) Open Buildings dataset (Google/Search/open buildings temporal/v1) and spectral indices\u003csup\u003e29\u003c/sup\u003e. All analyses were performed in R.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch3\u003eData availability:\u003c/h3\u003e\n\u003cp\u003eThe datasets used in this study are publicly available (Table S1\u0026nbsp;for details).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCode availability:\u003c/h3\u003e\n\u003cp\u003eThe code used for calculating and statistical results of this study is available from the authors upon reasonable request.\u003c/p\u003e\n\u003ch3\u003eAcknowledgments:\u003c/h3\u003e\n\u003cp\u003eThe authors gratefully acknowledge Dr. Tingting He for kindly providing the 2020 building characteristics dataset. We also thank all members of the Resilient and Inclusive Cities Lab for their constructive feedback and stimulating discussions.\u003c/p\u003e\n\u003ch3\u003eAuthors' Contributions:\u003c/h3\u003e\n\u003cp\u003eP. Hamel, S. A. Kamarajugedda, R. Lafortezza, and R. Xu conceived the ideas and designed methodology; S. A. Kamarajugedda and R. Xu collected and analyzed the data; R. Xu and P. Hamel led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.\u003c/p\u003e\n\u003ch3\u003eDeclaration of competing interest:\u003c/h3\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003ch3\u003eFunding:\u003c/h3\u003e\n\u003cp\u003eThis research was funded by the National Research Foundation of Singapore, Prime Minister's Office (NRF-NRFF12-2020-0009).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgnew, D. C., A global timekeeping problem postponed by global warming. \u003cem\u003eNATURE\u003c/em\u003e \u003cstrong\u003e628\u003c/strong\u003e 333 (2024).\u003c/li\u003e\n\u003cli\u003eMcManamay, R. A.\u003cem\u003e et al.\u003c/em\u003e, Dynamic urban land extensification is projected to lead to imbalances in the global land-carbon equilibrium. \u003cem\u003eCOMMUN EARTH ENVIRON\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e (2024).\u003c/li\u003e\n\u003cli\u003eEsper, J., Torbenson, M. \u0026amp; B\u0026uuml;ntgen, U., 2023 summer warmth unparalleled over the past 2,000 years. \u003cem\u003eNATURE\u003c/em\u003e \u003cstrong\u003e631\u003c/strong\u003e 94 (2024).\u003c/li\u003e\n\u003cli\u003eMarks, D. \u0026amp; Connell, J., Unequal and unjust: The political ecology of Bangkok\u0026apos;s increasing urban heat island. \u003cem\u003eURBAN STUD\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eSari, R., Soytas, U., Kanoglu-Ozkan, D. G. \u0026amp; Sivrikaya, A., Improving the climate resilience of European cities via socially acceptable nature-based solutions. \u003cem\u003enpj Urban Sustainability\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e 9 (2023).\u003c/li\u003e\n\u003cli\u003eThanvisitthpon, N., Nakburee, A., Khamchiangta, D. \u0026amp; Saguansap, V., Climate change-induced urban heat Island trend projection and land surface temperature: A case study of Thailand?s Bangkok metropolitan. \u003cem\u003eURBAN CLIM\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eKotz, M., Levermann, A. \u0026amp; Wenz, L., The economic commitment of climate change. \u003cem\u003eNATURE\u003c/em\u003e \u003cstrong\u003e628\u003c/strong\u003e 551 (2024).\u003c/li\u003e\n\u003cli\u003eLi, G.\u003cem\u003e et al.\u003c/em\u003e, Global impacts of future urban expansion on terrestrial vertebrate diversity. \u003cem\u003eNAT COMMUN\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003cli\u003eArshad, S.\u003cem\u003e et al.\u003c/em\u003e, Quantifying the contribution of diminishing green spaces and urban sprawl to urban heat island effect in a rapidly urbanizing metropolitan city of Pakistan. \u003cem\u003eLAND USE POLICY\u003c/em\u003e \u003cstrong\u003e113\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003cli\u003ePan, L.\u003cem\u003e et al.\u003c/em\u003e, Understanding spatiotemporal evolution of the surface urban heat island in the Bangkok metropolitan region from 2000 to 2020 using enhanced land surface temperature. \u003cem\u003eGEOMAT NAT HAZ RISK\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eHe, Z. \u0026amp; Tang, B. H., Spatiotemporal change patterns and driving factors of land surface temperature in the Yunnan-Kweichow Plateau from 2000 to 2020. \u003cem\u003eSCI TOTAL ENVIRON\u003c/em\u003e \u003cstrong\u003e896\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eGao, S.\u003cem\u003e et al.\u003c/em\u003e, Urbanization-induced warming amplifies population exposure to compound heatwaves but narrows exposure inequality between global North and South cities. \u003cem\u003eNPJ CLIM ATMOS SCI\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e 110 (2024).\u003c/li\u003e\n\u003cli\u003eKempf, M., Enhanced trends in spectral greening and climate anomalies across Europe. \u003cem\u003eENVIRON MONIT ASSESS\u003c/em\u003e \u003cstrong\u003e195\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eFeng, F.\u003cem\u003e et al.\u003c/em\u003e, Exploring the non-linear impacts of urban features on land surface temperature using explainable artificial intelligence. \u003cem\u003eURBAN CLIM\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e 102045 (2024).\u003c/li\u003e\n\u003cli\u003eRen, Y.\u003cem\u003e et al.\u003c/em\u003e, Understanding the coupling effect of multiple urban features on land surface temperature in Europe. \u003cem\u003eURBAN FOR URBAN GREE\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e 128723 (2025).\u003c/li\u003e\n\u003cli\u003eRamsay, E. E.\u003cem\u003e et al.\u003c/em\u003e, Spatio-temporal development of the urban heat island in a socioeconomically diverse tropical city. \u003cem\u003eENVIRON POLLUT\u003c/em\u003e \u003cstrong\u003e316\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eKamarajugedda, S. A., Johnson, J. A., McDonald, R. \u0026amp; Hamel, P., Carbon storage and sequestration in Southeast Asian urban clusters under future land cover change scenarios (2015\u0026ndash;2050). \u003cem\u003eFRONT ENV SCI-SWITZ\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eAhmadzai, M. R., Zaki, P. H., Ismail, M. H., Bawon, P. \u0026amp; Karam, D. S., The Societal and Economic Impact of Reforestation Strategies and Policies in Southeast Asia-A Review. \u003cem\u003eFORESTS\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eRinchumphu, D., Srivanit, M., Iamchuen, N. \u0026amp; Aryupong, C., Exploring Summer Variations of Driving Factors Affecting Land Use Zoning Based on the Surface Urban Heat Island in Chiang Mai, Thailand. \u003cem\u003eISPRS INT J GEO-INF\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e (2024).\u003c/li\u003e\n\u003cli\u003eThaweepworadej, P. \u0026amp; Evans, K. L., Urbanisation of a growing tropical mega-city during the 21st century-Landscape transformation and vegetation dynamics. \u003cem\u003eLANDSCAPE URBAN PLAN\u003c/em\u003e \u003cstrong\u003e238\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eMatthews, T.\u003cem\u003e et al.\u003c/em\u003e, Mortality impacts of the most extreme heat events. \u003cem\u003eNAT REV EARTH ENV\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e 193 (2025).\u003c/li\u003e\n\u003cli\u003eOgunbode, T. O., Oyebamiji, V. O., Sanni, D. O., Akinwale, E. O. \u0026amp; Akinluyi, F. O., Environmental impacts of urban growth and land use changes in tropical cities. \u003cem\u003eFRONT SUSTAIN CITIES\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e (2025).\u003c/li\u003e\n\u003cli\u003eZwolska, A., Polrolniczak, M. \u0026amp; Kolendowicz, L., Urban growth\u0026apos;s implications on land surface temperature in a medium-sized European city based on LCZ classification. \u003cem\u003eSCI REP-UK\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e (2024).\u003c/li\u003e\n\u003cli\u003eLoo, B. P. Y.\u003cem\u003e et al.\u003c/em\u003e, Cities and Urbanization: Balancing the Environmental and Socioeconomic Dimensions of Sustainability. \u003cem\u003eADV SUSTAIN SYST\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e (2024).\u003c/li\u003e\n\u003cli\u003eLu, Y., Yue, W., He, T. \u0026amp; Shan, Z., Urban form centrality and thermal environment: An empirical study of Chinese megacities. \u003cem\u003eSUSTAIN CITIES SOC\u003c/em\u003e \u003cstrong\u003e83\u003c/strong\u003e 103955 (2022).\u003c/li\u003e\n\u003cli\u003eLiu, Z.\u003cem\u003e et al.\u003c/em\u003e, Surface warming in global cities is substantially more rapid than in rural background areas. \u003cem\u003eCOMMUN EARTH ENVIRON\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003cli\u003eLi, D., Yan, S. \u0026amp; Chen, G., Effects of Urban Redevelopment on Surface Urban Heat Island. \u003cem\u003eIEEE J-STARS\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e 2366 (2023).\u003c/li\u003e\n\u003cli\u003eErmida, S. L., Soares, P., Mantas, V., G\u0026ouml;ttsche, F. \u0026amp; Trigo, I. F., Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series. \u003cem\u003eREMOTE SENS-BASEL\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e 1471 (2020).\u003c/li\u003e\n\u003cli\u003eHe, T.\u003cem\u003e et al.\u003c/em\u003e, Quantifying the impact of urban trees on land surface temperature in global cities. \u003cem\u003eISPRS J PHOTOGRAMM\u003c/em\u003e \u003cstrong\u003e210\u003c/strong\u003e 69 (2024).\u003c/li\u003e\n\u003cli\u003eMao, F.\u003cem\u003e et al.\u003c/em\u003e, Land use and cover in subtropical East Asia and Southeast Asia from 1700 to 2018. \u003cem\u003eGLOBAL PLANET CHANGE\u003c/em\u003e \u003cstrong\u003e226\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eBasu, T. \u0026amp; Das, A., Urbanization induced degradation of urban green space and its association to the land surface temperature in a medium-class city in India. \u003cem\u003eSUSTAIN CITIES SOC\u003c/em\u003e \u003cstrong\u003e90\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eRamzan, M.\u003cem\u003e et al.\u003c/em\u003e, Remote Sensing-Based Prediction of Temporal Changes in Land Surface Temperature and Land Use-Land Cover (LULC) in Urban Environments. \u003cem\u003eLAND-BASEL\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003cli\u003eHan, D., Zhang, T., Qin, Y., Tan, Y. \u0026amp; Liu, J., A comparative review on the mitigation strategies of urban heat island (UHI): a pathway for sustainable urban development. \u003cem\u003eCLIM DEV\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e 379 (2023).\u003c/li\u003e\n\u003cli\u003eMahtta, R.\u003cem\u003e et al.\u003c/em\u003e, Urban land expansion: the role of population and economic growth for 300+ cities. \u003cem\u003eNPJ URBAN SUSTAINABILITY\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003cli\u003eHussain, S.\u003cem\u003e et al.\u003c/em\u003e, Relation of land surface temperature with different vegetation indices using multi-temporal remote sensing data in Sahiwal region, Pakistan. \u003cem\u003eGEOSCI LETT\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eDolley, J.\u003cem\u003e et al.\u003c/em\u003e, Analysing trade-offs and synergies between SDGs for urban development, food security and poverty alleviation in rapidly changing peri-urban areas: a tool to support inclusive urban planning. \u003cem\u003eSUSTAIN SCI\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e 1601 (2020).\u003c/li\u003e\n\u003cli\u003eSeto, K. C., Golden, J. S., Alberti, M. \u0026amp; Turner, B. L., Sustainability in an urbanizing planet. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e114\u003c/strong\u003e 8935 (2017).\u003c/li\u003e\n\u003cli\u003eFrolking, S., Mahtta, R., Milliman, T., Esch, T. \u0026amp; Seto, K. C., Global urban structural growth shows a profound shift from spreading out to building up. \u003cem\u003eNature Cities\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e 555 (2024).\u003c/li\u003e\n\u003cli\u003eDeton, Y.\u003cem\u003e et al.\u003c/em\u003e, Spatiotemporal Dynamics of Urban Green Spaces and Climatic Vulnerability of Togolese Cities in the Context of Rapid Urbanisation: The Case of Lome and Kara. \u003cem\u003eLAND-BASEL\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e (2025).\u003c/li\u003e\n\u003cli\u003eRichards, D. R., Passy, P. \u0026amp; Oh, R. R. Y., Impacts of population density and wealth on the quantity and structure of urban green space in tropical Southeast Asia. \u003cem\u003eLANDSCAPE URBAN PLAN\u003c/em\u003e \u003cstrong\u003e157\u003c/strong\u003e 553 (2017).\u003c/li\u003e\n\u003cli\u003eMuhamad Nor, A. N.\u003cem\u003e et al.\u003c/em\u003e, Evolution of Green Space under Rapid Urban Expansion in Southeast Asian Cities. \u003cem\u003eSUSTAINABILITY-BASEL\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e (2021).\u003c/li\u003e\n\u003cli\u003eHu, Y., Connor, D. S., Stuhlmacher, M., Peng, J. \u0026amp; Turner II, B. L., More urbanization, more polarization: evidence from two decades of urban expansion in China. \u003cem\u003eNPJ URBAN SUSTAINABILITY\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e (2024).\u003c/li\u003e\n\u003cli\u003eGao, J. \u0026amp; O\u0026apos;Neill, B. C., Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways. \u003cem\u003eNAT COMMUN\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e (2020).\u003c/li\u003e\n\u003cli\u003eYan, J.\u003cem\u003e et al.\u003c/em\u003e, The Influence of Urban Form on Land Surface Temperature: A Comprehensive Investigation from 2D Urban Land Use and 3D Buildings. \u003cem\u003eLAND-BASEL\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eZhou, L.\u003cem\u003e et al.\u003c/em\u003e, Relationship between urban landscape structure and land surface temperature: Spatial hierarchy and interaction effects. \u003cem\u003eSUSTAIN CITIES SOC\u003c/em\u003e \u003cstrong\u003e80\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003cli\u003eBastien-Olvera, B. A.\u003cem\u003e et al.\u003c/em\u003e, Unequal climate impacts on global values of natural capital. \u003cem\u003eNATURE\u003c/em\u003e \u003cstrong\u003e625\u003c/strong\u003e 722 (2024).\u003c/li\u003e\n\u003cli\u003eZepp, H. \u0026amp; Inostroza, L., Who Pays the Bill? Assessing Ecosystem Services Losses in an Urban Planning Context. \u003cem\u003eLAND-BASEL\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e (2021).\u003c/li\u003e\n\u003cli\u003eLaw, A.\u003cem\u003e et al.\u003c/em\u003e, Leave no one behind: A case of ecosystem service supply equity in Singapore. \u003cem\u003eAMBIO\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e 2118 (2022).\u003c/li\u003e\n\u003cli\u003eDas, M., Das, A. \u0026amp; Momin, S., Quantifying the cooling effect of urban green space: A case from urban parks in a tropical mega metropolitan area (India). \u003cem\u003eSUSTAIN CITIES SOC\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003cli\u003eTran, T., Tran, Q., Vu, N. T. \u0026amp; Vo, D. H., Renewable Energy, Urbanization, Financial Development and Environmental Degradation in the ASEAN Countries. \u003cem\u003eENVIRON URBAN ASIA\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e 10 (2023).\u003c/li\u003e\n\u003cli\u003eHung, T., Uchihama, D., Ochi, S. \u0026amp; Yasuoka, Y., Assessment with satellite data of the urban heat island effects in Asian mega cities. \u003cem\u003eINT J APPL EARTH OBS\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e 34 (2006).\u003c/li\u003e\n\u003cli\u003eZhang, L., Tao, L., Yang, F., Bao, Y. \u0026amp; Li, C., Promoting green transportation through changing behaviors with low-carbon-travel function of digital maps. \u003cem\u003eHUM SOC SCI COMMUN\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e (2024).\u003c/li\u003e\n\u003cli\u003ePurio, M. A., Yoshitake, T. \u0026amp; Cho, M., Assessment of Intra-Urban Heat Island in a Densely Populated City Using Remote Sensing: A Case Study for Manila City. \u003cem\u003eREMOTE SENS-BASEL\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003cli\u003eKakooei, M. \u0026amp; Baleghi, Y., Spatial-Temporal analysis of urban environmental variables using building height features. \u003cem\u003eURBAN CLIM\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eWang, W., Wang, D., Chen, H., Wang, B. \u0026amp; Chen, X., Identifying urban ventilation corridors through quantitative analysis of ventilation potential and wind characteristics. \u003cem\u003eBUILD ENVIRON\u003c/em\u003e \u003cstrong\u003e214\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003cli\u003eTan, P. Y. \u0026amp; Samsudin, R., Effects of spatial scale on assessment of spatial equity of urban park provision. \u003cem\u003eLANDSCAPE URBAN PLAN\u003c/em\u003e \u003cstrong\u003e158\u003c/strong\u003e 139 (2017).\u003c/li\u003e\n\u003cli\u003eZhang, C. \u0026amp; Su, Y., Gains and losses in forest cover have asymmetric effects on land surface temperature. \u003cem\u003eNAT CLIM CHANGE\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e 776 (2023).\u003c/li\u003e\n\u003cli\u003eDijkstra, L.\u003cem\u003e et al.\u003c/em\u003e, Applying the Degree of Urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. \u003cem\u003eJ URBAN ECON\u003c/em\u003e \u003cstrong\u003e125\u003c/strong\u003e (2021).\u003c/li\u003e\n\u003cli\u003eJing, R.\u003cem\u003e et al.\u003c/em\u003e, Global population profile of tropical cyclone exposure from 2002 to 2019. \u003cem\u003eNATURE\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eChen, Y., Yue, W. \u0026amp; La Rosa, D., Which communities have better accessibility to green space? An investigation into environmental inequality using big data. \u003cem\u003eLANDSCAPE URBAN PLAN\u003c/em\u003e \u003cstrong\u003e204\u003c/strong\u003e (2020).\u003c/li\u003e\n\u003cli\u003eChen, J.\u003cem\u003e et al.\u003c/em\u003e, Global 1 km x 1 km gridded revised real gross domestic product and electricity consumption during 1992-2019 based on calibrated nighttime light data. \u003cem\u003eSCI DATA\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-urban-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjurbansustain","sideBox":"Learn more about [npj Urban Sustainability](https://www.nature.com/npjurbansustain/)","snPcode":"42949","submissionUrl":"https://submission.springernature.com/new-submission/42949/3","title":"npj Urban Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Urban expansion, Urban form indicator, Land surface temperature, Thermal environment, Sustainable development","lastPublishedDoi":"10.21203/rs.3.rs-6572557/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6572557/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRapid urbanization exacerbates heat-related environmental issues. Yet urban expansion patterns and associated heat risks in developing countries remain poorly understood. We selected sixteen capitals and secondary cities in Southeast Asia and characterized urban growth and heat hazards in their fringes using three spectral indices: PLAND (percentage of built-up areas), NDVI (Normalized Difference Vegetation Index), and LST (Land Surface Temperature). Over the past two decades, PLAND and LST in the fringes increased by 11.93% and 1.39℃, respectively. LST in the fringes of capital cities was 1.70℃ higher than in secondary cities. LST increases exceeded those in urban cores, particularly in secondary cities (+0.35℃). Highly populated and wealthier urban clusters showed marked PLAND increases and NDVI declines, but not necessarily elevated LST. We identify priority areas for land cover management and urban heat mitigation by city type and geographic location to inform sustainable regional planning.\u003c/p\u003e","manuscriptTitle":"Linking Urban Growth and Surface Temperature Change in Capital and Secondary Cities of Southeast Asia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 12:10:55","doi":"10.21203/rs.3.rs-6572557/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-26T14:24:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-21T06:58:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T19:56:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36073576638616643024604119105452017580","date":"2025-07-21T07:05:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-02T06:37:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-06T09:03:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325437684728126410793877296121567857148","date":"2025-05-30T18:45:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65570800863060618723551519565869074363","date":"2025-05-30T11:44:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210831887667748474454146532106530354311","date":"2025-05-28T15:08:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4489104299227759860535109921028728175","date":"2025-05-14T01:39:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-13T14:18:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-04T06:56:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-02T17:46:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Urban Sustainability","date":"2025-05-01T14:22:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-urban-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjurbansustain","sideBox":"Learn more about [npj Urban Sustainability](https://www.nature.com/npjurbansustain/)","snPcode":"42949","submissionUrl":"https://submission.springernature.com/new-submission/42949/3","title":"npj Urban Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5acd56f8-e9bb-492a-907d-5d1668b6a83a","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48521964,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":48521965,"name":"Earth and environmental sciences/Ecology/Climate change ecology"},{"id":48521966,"name":"Earth and environmental sciences/Environmental social sciences/Climate change mitigation"},{"id":48521967,"name":"Earth and environmental sciences/Environmental social sciences/Sustainability"}],"tags":[],"updatedAt":"2026-02-16T16:04:08+00:00","versionOfRecord":{"articleIdentity":"rs-6572557","link":"https://doi.org/10.1038/s42949-026-00336-x","journal":{"identity":"npj-urban-sustainability","isVorOnly":false,"title":"npj Urban Sustainability"},"publishedOn":"2026-02-09 15:59:25","publishedOnDateReadable":"February 9th, 2026"},"versionCreatedAt":"2025-05-15 12:10:55","video":"","vorDoi":"10.1038/s42949-026-00336-x","vorDoiUrl":"https://doi.org/10.1038/s42949-026-00336-x","workflowStages":[]},"version":"v1","identity":"rs-6572557","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6572557","identity":"rs-6572557","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}