Landsat-7 ETM+ 영상과 AWS 자료를 이용한 부산의 토지피복에 따른 여름철 도시열섬포텐셜 산출

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

We live in an urbanizing world. Since 2008, more than half of humanity lives in cities, both large and small, and old and new.[...]

Investigating land use land cover changes and their effects on land surface temperature and urban heat islands in Sharqiyah Governorate, Egypt

Estimating Barcelona's metropolitan daytime hot and cold poles using Landsat-8 Land Surface Temperature

Spatial analysis of land use and cover changes: Implications of green legacy initiative on climate action in Upper Awash Basin, Ethiopia

Based on the land surface temperature (LST), the land cover classification map, vegetation coverage, and surface evapotranspiration derived from EOS-MODIS satellite data, and by the use of GIS spatial analytic technique and multivariate statistical analysis method, the urban heat island (UHI) spatial distribution of the diurnal and seasonal variabilities and its driving forces are studied in Beijing city and surrounding areas in 2001. The relationships among UHI distribution and landcover categories, topographic factor, vegetation greenness, and surface evapotranspiration are analyzed. The results indicate that: (i) The significant UHI occur in Beijing city areas in the four seasons due to high heat capacity and multi-reflection of compression building, as well as with special topographic features of its three sides surrounded by mountains, especially in the summer. The UHI spatial distribution is corresponding with the urban geometry structure profile. The LST difference is approximately 4-6℃between Beijing city and suburb areas, comparatively is 8- 10℃between Beijing city area and outer suburb area in northwestern regions. (ii) The UHI distribution and intensity in daytime are different from nighttime in Beijing city area, the nighttime UHI is obvious. However, in the daytime, the significant UHI mainly appears in the summer, the autumn takes second place, and the UHI in the winter and the spring seem not obvious. The surface evapotranspiration in suburb areas is larger than that in urban areas in the summer, and high latent heat exchange is evident, which leads to LST difference between city area and suburb area. (iii) The reflection of surface landcover categories is sensitive to the UHI, the correlation between vegetation greenness and UHI shows obviously negative. The scatterplot shows that there is the negative correlation between NDVI and LST (R2 = 0.6481). The results demonstrate that the vegetation greenness is an important factor for reducing the UHI, and large-scale construction of greenbelts can considerably reduce the UHI effect.

Land use and land cover (LULC) change is one of the most visible results of human’s modification of the terrestrial ecosystem, and it has a significant impact on climate. Urbanization has been a major force of LULC throughout human history that has had a great impact on climate change. Urban areas generally have higher absorption of solar radiation and greater thermal capacity and conductivity, leading to a relatively higher temperature in the urban areas compared with the surrounding rural areas. Studies related to the impact of land use and land cover changes in Addis Ababa are few. This study therefore examined the influence of LULC change on urban climate in Addis Ababa city from 1986 to 2011 by retrieving land surface temperature (LST) from thermal infrared (TIR) data of Landsat TM satellite data. The results show that in Addis Ababa, the area for grass land, agricultural land, forest land and bare land declined by 43.32, 16.03, 9.74, and 9.65 km2, respectively, on the other hand, built-up areas was dramatically expanded from 100.13 km2 in 1986 to 180.13 km2 in 2011 that is almost two times from the base year. Changes in LULC were accompanied by changes in LST. The average LST in 1986 was 302 K (28.88 °C) and it increased to 304 K (30.88 °C) in 2011. The change in LST is mainly associated with changes in impervious surface. The urban-rural temperature differences between the urban core and its surrounding areas show a maximum difference of 15 K. This could lead to an intensified urban heat island effect in the urban areas. The relationship between LST and Normalized Difference Vegetation Index (NDVI) clearly shows that vegetation has great impact on reducing UHI effect. Thus, development of urban green spaces is one of the most promising climate change adaptation response in order to minimize the impact of elevated temperatures on human health and comfort.

Tourism activities are changing the global landscape pattern. This study attempted to estimate changes in Land Use Land Cover (LULC) and Land Surface Temperature (LST) in District Buner and Shangla, Khyber Pakhtunkhwa (KPK), Pakistan, and specifically its tourist spots. Using remote sensing data from satellites (1990–2020) and future projections (2035–2050), we applied Artificial Neural Network (ANN) and Cellular Automata Markov (CA-Markov) models to examine past and future LULC and LST dynamics across two districts including four major tourist spots (Shangla Top as tourist spot one (TS1), Bar Puran (TS2), Shahida Sar (TS3), and Daggar (TS4). The LULC classification for the whole study area (1990–2020) indicates that built-up and agricultural areas increased with a net change of +0.8% and +3.2% for the Shangla and Buner districts, respectively. The highest mean LST was found in the built-up areas. The simulation results indicate an expansion of 4.5% and 5.8% of the total built-up areas, and the LST above 31 °C will cover 76% and 88% of the total areas in 2035 and 2050, respectively. This conversion is driven by tourism activities, causing urban heat island effects (UHIs), and environmental degradation. The analysis of tourist spots (1990–2020) shows the highest change in built-up areas at Shangla Top (TS1), while the highest LST (28 °C) for the Daggar (TS4). The future simulation (2035–2050) results for tourist spots show that TS4 would have the highest LULC change in built-up areas (5.67%), and TS4 would have the highest LST (31 °C) from 65.23 to 82.20%. These findings provide an essential understandings for developing long-term tourism policies meant to moderate the environmental impact of tourism in the region.

The urban heat island (UHI) effect has emerged in the literature as a major challenge to urban well-being, primarily driven by increasing urbanization. To address this challenge, this study investigates the spatiotemporal pattern of the UHI in the fast-growing city of Kisangani and within its urban–rural gradient from 2000 to 2024 using land surface temperature (LST) data from the MODIS 11A2 V6.1 product. Inferential and descriptive statistics were applied to examine the patterns of UHI and the relationships between the LST, building volume density (BVD), and vegetation density expressed by the Normalized Difference Vegetation Index (NDVI). The results showed that the spatial extent of the moderate UHI gradually increased from 16 km2 to 38 km2, while the high UHI increased from 9 km2 to 19 km2. Furthermore, although high UHI values (0.2 < UHI ≤ 0.3) are observed in urban areas and significant differences in UHI variations are detected across urban, peri-urban, and rural zones, the results indicate that the mean UHI in Kisangani’s urban areas remains below 0.2. Therefore, based on average UHI variations, Kisangani’s urban zones exhibit moderate disparities in LST compared to rural areas. Moreover, the LST variations significantly correlate with the building volume and vegetation densities. However, the influence of vegetation density as a predictor of LST gradually decreases while the influence of building volume density increases over time, suggesting the need to implement a synergistic development pathway to manage the interactions between urbanization, landscape change, and ecosystem service provision. This integrated approach may represent a crucial solution for mitigating the UHI effect in regions categorized as high-temperature zones.

Dengue, one of the main global threats to public health, has experienced a significant increase in its incidence due to the geographic expansion of the Aedes aegypti and Aedes albopictus vectors. This phenomenon is driven by factors such as climate change, urbanization and the emergence of urban heat islands (UHIs), which lead to elevated temperatures that promote the development and transmission of the virus. This study evaluates the spatial and temporal variations of land surface temperature (LST) in Córdoba, Argentina, using remote sensors such as MODIS and those on board the Landsat missions, with the objective of analyzing their relationship with UHIs and Ae. aegypti activity. Besides, we analyzed LST dynamics across data from 2014 to 2024 in a recently urbanized area in order to assess the thermal impact of land cover change. A key finding was that maximum in LST preceded peaks in Ae. aegypti oviposition by about six-seven weeks, revealing a clear lagged temperature-oviposition relationship. Spatial analysis revealed up to $5^{\circ} \mathrm{C}$ differences in LST within the city, with significantly higher values in areas with reduced vegetation. In a specific case, the conversion of a vegetated area into a public park led to an average LST increase of $4^{\circ} \mathrm{C}$ over six years, showing greater thermal intensification than a nearby stable vegetated site. Cluster analysis grouped mosquito trap locations based on LST levels, revealing consistent seasonal trends and lower normalized difference vegetation index (NDVI) values in hotter areas. Our findings confirm the influence of urban land cover changes on thermal variations and its potential to affect Ae. aegypti dynamics. As urban expansion continues, integrating thermal landscape monitoring into vector surveillance systems may help anticipate shifts in mosquito activity and dengue risk. Additionally, our results highlight the importance of using LST data at higher temporal frequency for enhanced vector risk assessment.

Urbanization is closely associated with land use land cover (LULC) changes that correspond to land surface temperature (LST) variation and urban heat island (UHI) intensity. Major districts of Bangladesh have a large population base and commonly lack the resources to manage fast urbanization effects, so any rise in urban temperature influences the population both directly and indirectly. However, little is known about the impact of rapid urbanization on UHI intensity variations during the winter dry period in the major districts of Bangladesh. To this end, we aim to quantify spatiotemporal associations of UHI intensity during the winter period between 2000 and 2019 using remote-sensing and geo-spatial tools. Landsat-8 and Landsat-5 imageries of these major districts during the dry winter period from 2000 to 2020 were used for this purpose, with overall precision varying from 81% to 93%. The results of LULC classification and LST estimation showed the existence of multiple UHIs in all major districts, which showed upward trends, except for the Rajshahi and Rangpur districts. A substantial increase in urban expansion was observed in Barisal > 32%, Mymensingh > 18%, Dhaka > 17%, Chattogram > 14%, and Rangpur > 13%, while a significant decrease in built-up areas was noticed in Sylhet < −1.45% and Rajshahi < −3.72%. We found that large districts have greater UHIs than small districts. High UHI intensities were observed in Mymensingh > 10 °C, Chattogram > 9 °C, and Barisal > 8 °C compared to other districts due to dense population and unplanned urbanization. We identified higher LST (hotspots) zones in all districts to be increased with the urban expansion and bare land. The suburbanized strategy should prioritize the restraint of the high intensity of UHIs. A heterogeneous increase in UHI intensity over all seven districts was found, which might have potential implications for regional climate change. Our study findings will enable policymakers to reduce UHI and the climate change effect in the concerned districts.

Detection of land use and land cover change and land surface temperature in English Bazar urban centre

The role of local land-use on the urban heat island effect of Tel Aviv as assessed from satellite remote sensing

<p>Climate change is becoming a global threat to human well-being and the sustainability of the planet Earth. The central cores of urban centers are significantly observed warmer than their surrounding outskirts or rural areas, which is identified as the urban heat island (UHI) effect fueled by massive Land Use Land Cover (LULC) change. The main research aim was to examine the Spatio-temporal variation of UHI dynamics based on the land surface temperature (LST), Normalized Difference Vegetation Index (NDVI) and built-up density in the urban centers (Jimma, Bedelle, Bonga, and Sokorru) landscape using techniques of remote sensing. In this study, Landsat thematic mapper (TM) for 1987 and Landsat Operational Imagery (OLI) for 2018 in the extraction of LST were used for examining UHI. Also, LULC, NDVI and built-up density of the urban centers were analyzed. The results of the study showed that the urban core had greater LST and UHI values, due to an increase of built-up density and a decline of green space. The result of LST mean value range rose from 20.1 ℃ (Bonga) to 23.3 ℃ (Sokorru) in 1987 and 22.67 ℃ (Bedelle) to 24.74 ℃ (Bonga), and 24.72 ℃ (Sokorru) in 2018, while the maximum observed LST value range from 28.97 ℃ (Jimma) in 1987 to 32.61 ℃ (Bonga) in 2018. The maximum range of UHI mean value was from 11.23 ℃ (1987) to 14.04 ℃ (2018), while the maximum observed UHI value ranged from 19.63 ℃ in 1987 to 23.32 ℃ in 2018 over Jimma city. The computed correlation at 5% significance results showed LULC change has a significant association with surface air temperature (<em>r</em> = 0.621, Sig. (2-tailed) = 0.031) accompanied by UHI impacts. We recommended urban authorities, policymakers, and urban planners should consider the effects of LST and UHI in urban planning to realize climate-smart urban centers of tomorrow in urban centers of southwest Ethiopia.</p>

Climate is changing no doubt, with anthropogenic activities considered as the main driver of the change. Flooding, drought and urban heat island (UHI) are some impacts of climate change (CC). Additionally, land use land cover change (LULCC) adds more pressure to the growing cities. Population is increasing with urban cities expected to accommodate the majority of the people while UHI is expected to increase with CC and LULCC. Adaption is the best option to minimize impacts of UHI; however, researchers are required to identify the cause, impacts and ways to cope with the impacts. Therefore, this study investigated how the Land Surface Temperature (LST) of Babati, a fast-growing town in Tanzania, is changing with CC and LULCC. Remote sensing was used with LULC classification, which was performed using a maximum likelihood algorithm, and LST retrieval involved computational formulas using bands R, NIR, and TIR. Results showed a rise in built-up areas, suggesting urbanization at the expense of farmlands and bare land. The LULCC together with global warming from 2002 to 2022 contributed to the increasing average LST by 0.7˚C signifying that more impacts are expected in the future under a business-as-usual scenario. The findings also indicate that higher vegetation density is associated with lower LST and vice versa. This relationship highlights the critical role of vegetation in regulating temperature and suggests that enhancing vegetation cover may be an effective strategy for mitigating urban heat. Improving agricultural practices as population increases and promoting sustainable urbanization in Babati and similar towns are necessary to mitigate UHI effects.