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Rapid urbanization poses a threat to various ecosystem services. Beijing has undergone extensive infrastructure development in recent years. The study aims to extract land surface temperature (LST) and land use cover (LUC) data from satellite imagery, identify urban heat island (UHI) areas in Beijing, and determine the correlation between LST, LUC,...
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... is the capital of the People's Republic of China (Figure 1) Plan (2005Plan ( -2020, it can be split into four functional zones [21,[29][30][31]. The region is characterized by a sub-humid, temperate continental monsoon climate, which brings about four distinct seasons. ...
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... Global urbanization significantly impacts climate change and challenges sustainable development, especially in urban agglomerations [1] . Rapid economic growth, especially in large cities, significantly affects urban ecosystems, changing their function and structure [2] . Uncontrolled urban growth forces urban systems to change, and the conversion of agricultural land and urban green space declines [3] . ...
... Identifying LULC changes becomes essential to determine long-term landscape impacts caused by natural processes and anthropogenic stresses [16] . Built-up land will result in Urban Heat Island (UHI) [2] . Land surface changes caused by urbanization can significantly alter the urban thermal environment and cause changes in the UHI effect [1] . ...
... Areas with LULC in the form of developed land in the south zone with NDVI values close to 0 are areas with the highest LST. Periodic macro-and medium-scale monitoring is required to assess the impact of changes from LULC to LST [2] . ...
p class="Abstract">Urban development and global climate change drive increased Land Surface Temperature (LST). Wonogiri Regency is an area that is within the development range of Surakarta City. Analysis of land surface temperature has become Important in Wonogiri Regency as a mitigation measure for urban heat islands. This study aims to measure Land Surface Temperature (LST) in Wonogiri Regency and determine the controlling factor in the form of Land Use Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), seasons, and zones by landform. Using the Google Earth Engine platform, LST calculations, LULC classifications, and NDVI calculations can compute imagery. The dataset used is USGS Landsat 8. The analytical technique used in this study is comparative descriptive, which compares other controlling factors. The analysis results show the vital role of LULC, NDVI, seasonality, and landform on LST. LULC types of built-up land, low NDVI values, dry season, and southern zone (karst) tend to have higher LST than other factors.</p
... 1) The tools associated with urban planning primarily focus on the urban form and population, addressing the city-wide scale (Su et al., 2021b;Kang et al., 2022;Amir Siddique et al., 2023). At this scale, urban form is associated with city size, compactness, boundary complexity, and contiguity (Su et al., 2021a;Kang et al., 2022). ...
Rising temperatures, a major global environmental challenge, negatively impact health, the environment, society, and the economy. Surface Urban Heat Islands (SUHI), exacerbated by urbanization and climate change, intensify vulnerabilities for urban areas and residents. Urban planning and design aime to reduce these vulnerabilities through large-scale and small-scale interventions. However, addressing the significance of the capillary effects resulting from small-scale interventions and bottom-up community engagement is important. Urban acupuncture (UA) is an emerging approach in contemporary urban planning and design that focuses on small-scale interventions to mitigate the effects of SUHIs at the community level. This study develops a framework for mitigating the impacts of SUHIs through UA implementation in urban design. The proposed framework consists of two key phases: diagnosis and prescription. During the diagnosis phase, we analyzed heat-vulnerable points to identify indicators contributing to the development and exacerbation of the SUHIs. Then, we employed the Matrix of Cross Impact Multiplications Applied to a Classification (MICMAC) technique to comprehensively assess 75 influential indicators related to urban structure across various aspects and scales, focusing on the mesoscale. Among them, 30 leading indicators were identified, of which environmental and morphological indicators emerged as significant catalysts. Moving on to the prescription phase, we developed a UA-based framework called the “5 Wh Question” which addresses five fundamental questions: why, who, what, how, and where. Our findings can provide comprehensive solutions for policymakers and urban planners to address the identified heat-vulnerable points.
... This suggests that impervious surfaces and water are the dominant types that affect LST at the landscape pattern level. Large areas of impervious materials, such as asphalt and concrete, can significantly increase LST [57], whereas water bodies play crucial roles in cooling temperature through evaporation [58,59], meaning that controlling the concentration of water and the dispersion of building surfaces is key. The landscape pattern index of vegetation and the Pearson correlation coefficient of LST show an overall upward trend. ...
Land cover changes (LCCs) due to urbanization cause urban heat islands (UHIs), significantly affecting land surface temperature (LST) through spatiotemporal changes in compositions, parameters, and patterns. Land cover and LST have been studied in various cities; however, indicative research into heterogeneous LCC’s impact on LST in less-developed cities remains incomplete. This study analyzed new Landsat images of Zhanjiang, taken from 2004 to 2022, to determine the impact of three LCC indicators (compositions, parameters, and patterns) on LSTs. The urban thermal field variance index (UTFVI) was used to describe the distribution and variation in LST. We also quantified the cooling or warming benefits of various LCCs. The results indicate that the average temperature in the land urban heat island (SUHI) area rose to 30.6 °C. The average temperature of the SUHI was 3.32 °C higher than that of the non-SUHI area, showing the characteristic of shifting to counties and multi-core development. The LST increases by 0.37–0.67 °C with an increase of 0.1 in the normalized difference building index (NDBI), which is greater than the cooling benefit of the normalized difference of vegetation index (NDVI). The impact of landscape pattern indices on impervious surfaces and water is higher than that on vegetation and cropland, with a rising influence on impervious surfaces and a decreasing impact on water. The predominant cooling patches are vegetation and water, while large areas of impervious surface and cropland aggravate UHIs for industrial and agricultural activities. These findings are intended to guide future urban layouts and planning in less-developed cities, with thermal climate mitigation as a guiding principle.
Rapid economic and social development has triggered competition for limited land space from different industries, accelerating the evolution of Beijing’s urban landscape types. The increase in impermeable surfaces and the decrease in ecological land have led to an increase in the impact on the urban thermal environment. Since previous studies have mainly focused on the impact of a single urban landscape on the urban thermal environment and lacked an exploration of the combined impact of multiple landscapes, this study applied standard deviation ellipses, Pearson correlation analysis, land surface temperature (LST) profile analysis, and hot spot analysis to comprehensively explore the influence of the evolving production–living–ecological space (PLES) pattern on LST. The results show that the average LST of various spaces continued to increase before 2009 and decreased slowly after 2009, with the highest average temperature being living space, followed by production space, and the lowest average temperature being ecological space for each year. The spatiotemporal shift path of the thermal environment is consistent with the shift trajectory of the living space center of gravity in Beijing; LST is positively correlated with living space (LS) and negatively correlated with production space (PS) and ecological space (ES). LST is positively correlated with LS and negatively correlated with PS and ES. Influenced by the change in bedding surface type, the longitudinal thermal profile curve of LST shows a general trend of “low at both ends and high in the middle”. With the change in land space type, LST fluctuates significantly, and the horizontal thermal profile curve shows a general trend of “first decreasing, followed by increasing and finally decreasing”. In addition, the hot spot analysis shows that the coverage area of very hot spots, hot spots, and warm spots increased by 0.72%, 1.13%, and 2.03%, respectively, in the past 30 years, and the main expansion direction is southeast, and very cold spots and cold spots are distributed in the northwest ecological space, and the area change first decreases and then increases.
