城市公园景观秋冬季动态热效应

Assessment of constructing canopy urban heat island temperatures from thermal images: An integrated multi-scale approach

The estimation of spatially resolved near-surface air temperature (NSAT) has been extensively performed in previous studies using satellite-derived land surface temperature (LST) from MODIS. However, there remains a need for estimating daily NSAT based on LST data from other satellites, which has important implications for integrating multi-source LST in estimating NSAT and ensuring the continuity of satellite-derived estimates of NSAT over long-term periods. In this study, we conducted a comprehensive comparison of LST derived from Metop with MODIS LST in the modeling and mapping of daily NSAT. The results show that Metop LST achieves consistent predictive performance with MODIS LST in estimating daily NSAT, and models based on Metop LST or MODIS LST have overall predictive performance of about 1.2–1.4 K, 1.5–2.0 K, and 1.8–1.9 K in RMSE for estimating Tavg, Tmax, and Tmin, respectively. Compared to models based on nighttime LST, daytime LST can improve the predictive performance of Tmax by about 0.26–0.28 K, while performance for estimating Tavg or Tmin using different schemes of LST is comparable. Models based on Metop LST also exhibit high consistency with models utilizing MODIS LST in terms of the variability in predictive performance across months, with RMSE of 1.03–1.82 K, 1.3–2.49 K, and 1.26–2.66 K for Tavg, Tmin, and Tmax, respectively. This temporal variability in performance is not due to sampling imbalance across months, which is confirmed by comparing models trained using bootstrapped samples in balance, and our results imply that sampling representativeness, complicated by retrieval gaps in LST, is an important issue when analyzing the variability in predictive performance for estimating NSAT. To fully assess the predictive capability of Metop LST in estimating daily NSAT, more studies need to be performed using different methods across areas with a range of scales and geographical environments.

Assessing the role of Mediterranean evergreen oaks canopy cover in land surface albedo and temperature using a remote sensing-based approach

ABSTRACTModerate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data of MODIS 11A2 and Normalized Difference Vegetation Index (NDVI) products of MODIS 13Q1 were used to analyse the spatial and temporal changes of LST and its relationship with vegetation from 2001 to 2015 in the subtropical region of Fujian Province, China. The LST and NDVI products were reconstructed by Savitzky–Golay (S-G) filtering for abnormal values or missing data due to the influence of cloud contamination. The LST of Fujian decreases from its southeast coast to its northwest mountain area, and the annual average highest LST is greater than 30°C while the lowest LST is about 15°C. In the past 15 years, the average LST in Fujian has declined gradually due to an increase in fractional vegetation coverage (FVC), but the highest LST increased from 30.95°C to 33.05°C across south-east coastal zone cities due to rapid urbanization. From 2001 to 2015, the LST of Fujian Province trended slightly downward; the LST area, however, significantly decreased by 18.85% of the total area of the province and was twice that of the (significant) increase in LST. The changing trend distributions of LST are opposite to those of FVC in general, but LST decreases to greater extent as FVC increases when FVC is above 0.2 – every 10% increase in FVC is accompanied by about a 1.1°C decrease in LST.

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.

With the acceleration of urbanization, the heat island effect, as a prominent feature of urban climate, has attracted more attention. Differences in urban landscape patterns have an essential impact on the urban thermal environment. The objective of the study is to examine the impact of urban landscape types and patterns on surface temperature. Taking Zhengzhou City, China, as an example, using Google Earth remote sensing images, an urban landscape type map was created, and landscape indices were calculated. The land surface temperature (LST) of the study area was retrieved by the Landsat-8 thermal infrared band. Correlation analysis indicated that the relationships between urban landscape patterns and the thermal environment were as follows: (i) The scale indices (percentage of landscape (PLAND), largest patch index (LPI), edge density (ED), patch density (PD)) of urban landscape types with cooling effect (water body, riverfront area, park, high-rise building) were significantly negative correlated with mean LST of each partition. (ii) Conversely, there were significant positive correlations between the PLAND and LPI of landscape types with warming effect (block, development land, railway land) and the LST of the partition. (iii) The DIVISION index of the four kinds of landscapes with cooling effect was highly positively correlated with LST, and the DIVISION and SPLIT indices of the three kinds of landscapes with warming effect displayed a remarkable negative relationship with LST. Therefore, under the condition of scale control, integrated distribution of landscape with cooling effect, scattered distribution of landscape with warming effect, and reduced connectivity of landscape with warming effect will contribute to effectively alleviating the formation of urban heat islands.

Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization.

This study analyzed the spatial distribution of heat zones in the urban area of Manaus between 2012 and 2022, evaluating the variability of land surface temperature (LST) and its relationship with building morphology (density, height, albedo), vegetation cover (height, area, NDVI, albedo), and air temperature (AT) at the intraurban scale (local climate zones, neighborhoods, and blocks). The multitemporal analysis (2012–2022) of LST in the urban area of Manaus revealed a fragmented pattern of “heat archipelagos,” characteristic of a multinuclear urban growth process, highlighting zones and populations potentially impacted by heat extremes. The spatial distribution of LST was strongly associated with urban morphology and vegetation presence, exhibiting high variability at the intra-urban scale. Typologies with dense tree cover presented the lowest LST values, whereas those with high building density and verticalization exhibited the highest LST values. A direct relationship was observed between the three-dimensional morphology (building height and density) and material properties (roof albedo) with LST across the analyzed scales. The observed heating patterns corresponded to LCZ classes, validating the methodology for the city of Manaus. The use of high-spatial-resolution satellite data enabled the assessment of surface and atmospheric warming at local and micro scales, as well as the quantification (attribution) of the contribution of urban typologies. In densely built-up areas, characterized by extensive pavement and low ventilation, there was strong LST–AT coupling, resulting in intense sensible heat exchange and joint temperature increases. In sectors with a greater presence of continuous and dense vegetation, thermal decoupling occurred, with lower LST and more stable AT, due to the combined effects of shading, evapotranspiration, and solar radiation interception.

Anthropogenic land alterations in Maun village have transformed natural vegetation into urban infrastructure, including pavements, and residential and commercial areas, leading to elevated Land Surface Temperature (LST). This urban expansion resulted from economic growth driven by population increase and tourism-related development. This study aims to evaluate the relationship between Land Use and Land Cover Changes (LULCCs) and LST. Utilising Landsat 5-TM and Landsat-8 data from 1990, 2000, and 2020, we employed a random forest algorithm for supervised classification, generating Land Use and Land Cover (LULC) maps. The mono-window algorithm was used to extract LST data from Landsat 5 and 8 images, alongside Normalised Difference Vegetation Index (NDVI) maps. s regression analysis assessed the LST-NDVI correlation. Results indicate that urban LULCCs significantly contribute to rising LST. Minimum and maximum LST values for 1990, 2000, and 2020 were 18.6°C, 22.8°C, 22.6°C, and 26.7°C, 34.5°C, and 42.1°C, respectively. NDVI values ranged from −0.2 to 0.56 in 1990, −0.17 to 0.58 in 2000, and 0.07 to 0.46 in 2020. Roads, pavements, barren land, and built-up areas displayed the highest LST (44.6°C), while water bodies and healthy vegetation exhibited the lowest (16.1°C). Additionally, NDVI exhibited a negative correlation with LST. Our findings emphasise the role of human activities in exacerbating LST. They highlight the need for regulated urban growth patterns to ensure sustainable development. Moreover, quantifying spatiotemporal variations in LULC, LST, and NDVI holds importance for conserving land resources and enhancing land use planning policies. Policymakers and city planners can utilise this research to mitigate heat stress effects and promote sustainable urban environments by evaluating distribution maps of LULC, NDVI, and LST.

Background and Objectives: The outlines a study that investigates the variations in land surface temperature (LST) and land use in Phrae Province, Thailand, during two distinct climate events: The El Niño event in 2020 and the La Niña event in 2024. The study highlights the impact of climate change, including the increasing frequency of these events, on the environment and agriculture. It emphasizes the importance of geospatial technology in monitoring these changes and utilizing satellite imagery to understand land temperature trends for effective disaster prevention planning. The study begins by acknowledging the role of climate change, both natural and human-induced, in contributing to global warming and exacerbating the frequency of El Niño and La Niña events. These climate phenomena lead to severe weather changes that directly impact sectors such as agriculture, often resulting in natural disasters. The objective of this research is twofold: 1) Compare Land Surface Temperature (LST) Variations: The study aims to analyze and compare the variations in LST during two distinct periods, the El Niño event of 2020 and the La Niña event of 2024, within Phrae Province.2) Examine the Relationship between LST and Land Use Types: It seeks to explore how different types of land use—such as forest land, agricultural land, and urban areas—affect LST during these events. Methodology: The methodology involves the use of Landsat 8 satellite imagery from both 2020 and 2024 to gather the data needed for the analysis. The images were processed in the following steps: 1) Composite Band Images: Landsat imagery was used to create composite band images, a process that allows the extraction of relevant data for analysis. 2) Supervised Classification: The images were classified using the Maximum Likelihood Classification algorithm. Training areas were designated to guide the classification, which aimed to categorize land use into three primary types: forest land, agricultural land, and urban/built-up land. 3) Land Surface Temperature (LST) Analysis: The next step was converting the satellite image’s Digital Number (DN) values into radiance, which were then translated into brightness temperature. To adjust for the surface emissivity, the brightness temperature was further refined to provide an accurate measure of LST. 4) Categorizing LST by Land Use Type: After deriving the LST values, the study categorized the temperature ranges according to the different land use types, helping to identify temperature patterns related to land use changes. Main Results: The study presents several key findings related to both land use and LST: 1) land use classification (2020 vs. 2024): In 2020, forest land made up 66.3% of Phrae Province’s total land area, followed by agricultural land (25.7%) and urban/built-up areas (8%). By 2024, the land use pattern had changed slightly. Forest land decreased to 63.5%, while agricultural land increased to 26.9%, and urban/built-up land grew to 9.6%. 2) Land Surface Temperature (LST) Data (2020 and 2024) showed that in 2020, the highest recorded temperature was 44.2°C in March, and the lowest was 23.8°C in August. While, in 2024, the highest recorded temperature was 43.7°C in April, and the lowest was 24.2°C in August. The Monthly average temperature comparison between two events expressed that in 2020, the highest and lowest average temperature found in March (34°C) and August (10°C), respectively. While, in 2024, it was found in April (34°C) and August (10.8°C), respectively. The LST varied among land use types. In 2020, the average LST for forest areas was lowest (21.5°C) and followed by urban/built-up land (32.3°C) and agricultural land (36.2°C), respectively. While, in 2024, it still be found as the same trend as in 2022 but the forest areas shown increased LST (24.4°C), while agricultural land and urban/built-up areas experienced a slight decrease, 34.5°C and 32.3°C, respectively. Conclusion: This study concludes that land surface temperature (LST) variations are significantly influenced by the El Niño and La Niña climate phenomena, with the effects modulated by land use type. Key findings reveal that: (1) During the El Niño event in 2020, agricultural and urban/built-up areas recorded elevated LSTs. This is likely attributable to the intensified heat and drier conditions commonly associated with El Niño episodes. (2) In contrast, during the La Niña event in 2024, forested areas exhibited comparatively higher LSTs than agricultural and urban zones. Although La Niña typically brings cooler and wetter weather, it can also produce localized temperature anomalies, particularly in dense forest regions with limited capacity for heat dissipation. The study underscores the crucial role of geospatial technologies in tracking and analyzing climate-induced variations in land use and surface temperatures. Such insights are essential for local governments and policymakers to design informed, spatially targeted strategies for disaster risk reduction and climate adaptation. This is especially pertinent in agriculturally reliant regions such as Phrae Province, where climate variability poses direct risks to livelihoods and food security. Moreover, the findings contribute to the expanding literature on climate-land interactions, emphasizing the complexity of climate impacts across different ecosystems. The application of satellite data in this context proves to be a powerful tool for environmental monitoring, enabling more accurate and timely assessments of climatic effects. Ultimately, this research advocates for enhanced integration of geospatial analysis in climate resilience planning, ensuring that both human and natural systems are better equipped to navigate future environmental challenges.

Seasonal dynamics of land surface temperature and urban thermal comfort with land use land cover pattern in semi-arid Indian cities: Insights for sustainable Urban Management

Urbanisation is accelerating urban land use dynamics and this has a significant impact on land surface temperature (LST). Impervious surfaces and increase in air pollution has led to the increase in land surface temperature. This study reports on the use of geospatial technologies to monitor and quantify changes in LST using remotely sensed data in the City of Tshwane. Land surface temperature was retrieved using the winter and summer Landsat datasets for 1997 and 2015 and the MODIS data from 2000 to 2015. Land surface temperature was extracted using emissivity and satellite temperature as input parameters. The spatial and temporal variations in the LST were retrieved to show the effects of land cover change on LST. Change in LST was also analysed on different land cover types using transects across the study area. The study revealed an increase in land surface temperature between the years. It also showed that impervious surfaces had a higher LST compared to the non-impervious surfaces. The results revealed variations in LST between non-cropped and cropped agricultural areas, where the former had higher LST than the latter. Temporal trends revealed a notable increase in LST in the urban areas and there were some seasonal variations in LST with high LST values in summer and low values in winter. Cross-section analysis along transects revealed spatio-temporal thermal variations across different land cover types.

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA’s Air Resources Laboratory (ARL)’s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

In a world experiencing rapid urbanization, the phenomenon of land surface temperature (LST) variation has invited substantial attention due to its profound impact on the environment and human well-being. Changes in land use and land cover (LULC) within urban areas significantly influence the dynamics of LST and are a major driver of urban eco-environmental change. The complex connections between LULC dynamics, LST, and climate change are investigated in this systematic review, with a focus on the combined effects of these variables and the use of Machine Learning (ML) techniques. The data in this study, based on peer-reviewed publications from the past 25 years, were obtained from Science Direct and Web of Science databases. Based on our findings, Landsat is the most widely used dataset for analyzing the impacts of LULC on LST. Additionally, built-up areas, vegetation, and population density had the biggest effects on LST values based on focused studies. This systematic review reveals that Artificial Neural Networks (ANNs), Cellular Automata-Markov (CA-Markov), and Random Forest (RF) are the most used ML techniques in predicting LULC and LST. The study finds that NDBI and NDVI are recognized as the key LULC indices that have strong correlations with LST. We also highlight key LULC classes that have the most impact on LST variation. To validate the results, these studies employ Pearson correlation, the NDVI and NDBI index, and other linear regression methods. This review concludes by highlighting future research directions and the current need for interdisciplinary efforts to address the intricate dynamics of LULC and the Earth’s surface temperature.

Land surface temperature (LST) is an important driving factor in the land-atmosphere energy cycle. To examine the spatiotemporal patterns of LST changes and the internal mechanisms driven by multiple factors, we used a trend analysis method on TRIMS LST data from 2000 to 2020 in the Qingling-Daba Mountains. The optimal parameter geographic detector (OPGD) model was used to detect the influence of twelve factors, including elevation, precipitation, albedo, relative humidity (RH) and normalized difference vegetation index (NDVI), on the spatial distribution of LST, as well as to explore the dominant factors affecting LST differentiation in the study area. The results showed that: (1) From 2000 to 2020, the average annual LST of the Qinling-Daba Mountains was 18.17 °C. The warming trend was obvious (0.034 °C/a), and the warming effect at nighttime (0.066 °C/a) was stronger than that during daytime (0.0004 °C/a). The difference between day and night temperature (DIF) was decreasing. (2) The seasonal changes in LST and DIF in the Qinling-Daba Mountains were significant, and the spatial distribution of their average values in the summer was slightly larger and fluctuated more than in the other seasons. (3) Elevation was the main driving factor affecting the spatial distribution of LST, with the contribution scores of 62.9% in the daytime and 92.7% in the nighttime. The controlling effects of these factors were generally stronger in the nighttime than in the daytime. (4) Nighttime elevation had the strongest interaction with precipitation (contribution score of 95%), while daytime elevation had the strongest interaction with albedo (contribution rate of 83%). We revealed the temporal and spatial variation in LST in the Qinling-Daba Mountains since 2000 and explored the main driving factors involved, thereby improving our understanding of LST changes in the Qinling-Daba Mountains. This study can provide a scientific basis for distinguishing dominant drivers of LST dynamics in the Qinling-Daba Mountains.