{"title":"Spatio-temporal dynamics of urban heat island using Google Earth Engine: Assessment and prediction—A case study of Kathmandu Valley, Nepal","authors":"Bishal Khatri, Bipin Kharel, Pragati Dhakal, Samrat Acharya, Ujjwol Thapa","doi":"10.1016/j.cliser.2025.100560","DOIUrl":null,"url":null,"abstract":"<div><div>This study examines UHI dynamics and impacts in the rapidly urbanizing Kathmandu Valley, Nepal, using remote sensing and predictive modeling. The primary goals are to evaluate UHI trends and explore how urbanization influences temperature and climate change. To achieve these objectives, the research investigates the relationship between spectral characteristics, Land Use Land Cover (LULC), and UHI, utilizing high-resolution data from MODIS and Landsat satellites to analyze land surface temperature (LST) and land use changes over recent decades. The study employs Cellular Automata-Markov (CA-Markov) modeling to predict future UHI dynamics, taking into account climatic variability, land use changes, and population growth. Findings reveal significant increases in LST and UHI intensity due to the expansion of impermeable surfaces and loss of vegetative cover. Predictions for 2030 indicate higher LSTs, with winter temperatures ranging from 9.34 °C to 30.12 °C and summer temperatures from 19.74 °C to 42.32 °C, showing an increase compared to 2020. Additionally, the UHI effect is predicted to intensify due to expanding built-up areas, with greater seasonal variation observed in summer. The results suggest that without effective mitigation, UHI will continue to worsen, exacerbating climate-related issues. Insights into the relationship between spectral parameters, LULC, and UHI can guide strategies to mitigate UHI effects, promote sustainable urban growth, and improve urban resilience. Integrating remote sensing technologies with predictive modeling is crucial for addressing urbanization and climate change challenges.</div></div>","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"38 ","pages":"Article 100560"},"PeriodicalIF":4.0000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Climate Services","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405880725000214","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
This study examines UHI dynamics and impacts in the rapidly urbanizing Kathmandu Valley, Nepal, using remote sensing and predictive modeling. The primary goals are to evaluate UHI trends and explore how urbanization influences temperature and climate change. To achieve these objectives, the research investigates the relationship between spectral characteristics, Land Use Land Cover (LULC), and UHI, utilizing high-resolution data from MODIS and Landsat satellites to analyze land surface temperature (LST) and land use changes over recent decades. The study employs Cellular Automata-Markov (CA-Markov) modeling to predict future UHI dynamics, taking into account climatic variability, land use changes, and population growth. Findings reveal significant increases in LST and UHI intensity due to the expansion of impermeable surfaces and loss of vegetative cover. Predictions for 2030 indicate higher LSTs, with winter temperatures ranging from 9.34 °C to 30.12 °C and summer temperatures from 19.74 °C to 42.32 °C, showing an increase compared to 2020. Additionally, the UHI effect is predicted to intensify due to expanding built-up areas, with greater seasonal variation observed in summer. The results suggest that without effective mitigation, UHI will continue to worsen, exacerbating climate-related issues. Insights into the relationship between spectral parameters, LULC, and UHI can guide strategies to mitigate UHI effects, promote sustainable urban growth, and improve urban resilience. Integrating remote sensing technologies with predictive modeling is crucial for addressing urbanization and climate change challenges.
期刊介绍:
The journal Climate Services publishes research with a focus on science-based and user-specific climate information underpinning climate services, ultimately to assist society to adapt to climate change. Climate Services brings science and practice closer together. The journal addresses both researchers in the field of climate service research, and stakeholders and practitioners interested in or already applying climate services. It serves as a means of communication, dialogue and exchange between researchers and stakeholders. Climate services pioneers novel research areas that directly refer to how climate information can be applied in methodologies and tools for adaptation to climate change. It publishes best practice examples, case studies as well as theories, methods and data analysis with a clear connection to climate services. The focus of the published work is often multi-disciplinary, case-specific, tailored to specific sectors and strongly application-oriented. To offer a suitable outlet for such studies, Climate Services journal introduced a new section in the research article type. The research article contains a classical scientific part as well as a section with easily understandable practical implications for policy makers and practitioners. The journal''s focus is on the use and usability of climate information for adaptation purposes underpinning climate services.