Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is locat...Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones(the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami(Tuha) Basin, and Pakakuna Mountain Group) using geographical detector(Geodetector) and geographically weighted regression(GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24℃/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09℃/10a in autumn, and showed a warming trend in other seasons. Digital elevation model(DEM), latitude, wind speed, precipitation, normalized difference vegetation index(NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover(LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.展开更多
The Alborz Mountains are some of the highest in Iran,and they play an important role in controlling the climate of the country’s northern regions.The land surface temperature(LST)is an important variable that affects...The Alborz Mountains are some of the highest in Iran,and they play an important role in controlling the climate of the country’s northern regions.The land surface temperature(LST)is an important variable that affects the ecosystem of this area.This study investigated the spatiotemporal changes and trends of the nighttime LST in the western region of the Central Alborz Mountains at elevations of 1500-4000 m above sea level.MODIS data were extracted for the period of 2000-2021,and the Mann-Kendall nonparametric test was applied to evaluating the changes in the LST.The results indicated a significant increasing trend for the monthly average LST in May-August along the southern aspect.Both the northern and southern aspects showed decreasing trends for the monthly average LST in October,November,and March and an increasing trend in other months.At all elevations,the average decadal change in the monthly average LST was more severe along the southern aspect(0.60°C)than along the northern aspect(0.37°C).The LST difference between the northern and southern aspects decreased in the cold months but increased in the hot months.At the same elevation,the difference in the lapse rate between the northern and southern aspects was greater in the hot months than in the cold months.With increasing elevation,the lapse rate between the northern and southern aspects disappeared.Climate change was concluded to greatly decrease the difference in LST at different elevations for April-July.展开更多
This study assesses the changes in land use/land cover(LULC) and land surface temperature(LST) to identify their impacts from 2000 to 2020 along the coast of Kanyakumari district, India using remote sensing techniques...This study assesses the changes in land use/land cover(LULC) and land surface temperature(LST) to identify their impacts from 2000 to 2020 along the coast of Kanyakumari district, India using remote sensing techniques. Landsat images are used to estimate the LULC changes and the MODIS data for LST.The Maximum Likelihood Classification(MLC) method is used, and the LULC is classified into six categories: Agriculture Land, Barren Land, Salt Pan, Sandy Beach, Settlement, and Waterbody. Within the two decades of the present change detection study, upheave in the Settlement area of 49.89% is noticed, and the Agriculture Land is exploited by 20.09%. Salt Pan emits a high LST of 31.57°C, and the Waterbodies are noticed with a low LST of 28.9°C. However, the overall rate of LST decreased by 0.56°C during this period. This study will help policymakers make appropriate planning and management to overcome the impact of LULC and LST in the forthcoming years.展开更多
Local climate zones(LCZs)are an effective nexus linking internal urban structures to the local climate and have been widely used to study urban thermal environment.However,few studies considered how much the temperatu...Local climate zones(LCZs)are an effective nexus linking internal urban structures to the local climate and have been widely used to study urban thermal environment.However,few studies considered how much the temperature changed due to LCZs transformation and their synergy.This paper quantified the change of urban land surface temperature(LST)in LCZs transformation process by combining the land use transfer matrix with zonal statistics method during 2000–2019 in the Xi’an metropolitan.The results show that,firstly,both LCZs and LST had significant spatiotemporal variations and synchrony.The period when the most LCZs were converted was also the LST rose the fastest,and the spatial growth of the LST coincided with the spatial expansion of the built type LCZs.Secondly,the LST difference between land cover type LCZs and built type LCZs gradually widened.And LST rose more in both built type LCZs transferred in and out.Finally,the Xi’an-Xianyang profile showed that the maximum temperature difference between the peaks and valleys of the LST increased by 4.39℃,indicating that localized high temperature phenomena and fluctuations in the urban thermal environment became more pronounced from 2000 to 2019.展开更多
The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal r...The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal resolutions in extracting LST from satellite remote sensing(RS)data,the areas with complex landforms of the Eastern Qinling Mountains were selected as the research targets to establish the correlation between the normalized difference vegetation index(NDVI)and LST.Detailed information on the surface features and temporal changes in the land surface was provided by Sentinel-2 and Sentinel-3,respectively.Based on the statistically downscaling method,the spatial scale could be decreased from 1000 m to 10 m,and LST with a Sentinel-3 temporal resolution and a 10 m spatial resolution could be retrieved.Comparing the 1 km resolution Sentinel-3 LST with the downscaling results,the 10 m LST downscaling data could accurately reflect the spatial distribution of the thermal characteristics of the original LST image.Moreover,the surface temperature data with a 10 m high spatial resolution had clear texture and obvious geomorphic features that could depict the detailed information of the ground features.The results showed that the average error was 5 K on April 16,2019 and 2.6 K on July 15,2019.The smaller error values indicated the higher vegetation coverage of summer downscaling result with the highest level on July 15.展开更多
Since the reform and opening-up in 1978, the urbanization level of our country has been continuously improved and the urban development has made great progress. However, with the rapid expansion of urban construction ...Since the reform and opening-up in 1978, the urbanization level of our country has been continuously improved and the urban development has made great progress. However, with the rapid expansion of urban construction land, the population density and building density have been greatly increased, resulting in the urban heat island effect, which has negative impact on the urban thermal environment and restricts the high-quality development of urbanization. This paper focuses on how the urban surface thermal environment of Hangzhou changes in 20 years. In this paper, the characteristics of land surface temperature (LST) in Hangzhou urban area from 2000 to 2020 were studied by using Landsat images. The radiative transfer equation method is used to retrieve the land surface temperature, and the retrieval results are analyzed. The results show that: 1) the land surface temperature in Hangzhou city area has a slight upward trend in the past 20 years;2) the area of high temperature area is expanding;3) the land surface temperature in the city center area has decreased significantly in the past 20 years, while the ground temperature in other areas around the city center has increased significantly.展开更多
In this paper,the spatio-temporal variation and propagation direction of coal fire were studied in the Jharia Coalfield(JCF),India during 2006–2015 through satellite-based night-time land surface temperature(LST)imag...In this paper,the spatio-temporal variation and propagation direction of coal fire were studied in the Jharia Coalfield(JCF),India during 2006–2015 through satellite-based night-time land surface temperature(LST)imaging.The LST was retrieved from Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)night-time thermal-infrared data by a robust split-window algorithm based on scene-specific regression coefficients,band-specific hybrid emissivity,and night-time atmospheric transmittance.The LST-profile-based coal fire detection algorithm was formulated through statistical analysis of the LST values along multiple transects across diverse coal fire locations in the JCF in order to compute date-specific threshold temperatures for separating thermally-anomalous and background pixels.This algorithm efficiently separates surface fire,subsurface fire,and thermally-anomalous transitional pixels.During the observation period,it was noticed that the coal fire area increased significantly,which resulted from new coal fire at many places owing to extensive opencast-mining operations.It was observed that the fire propagation occurred primarily along the dip direction of the coal seams.At places,lateral-propagation of limited spatial extent was also observed along the strike direction possibly due to spatial continuity of the coal seams along strike.Moreover,the opencast-mining activities carried out during 2009–2015 and the structurally weak planes facilitated the fire propagation.展开更多
Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which ...Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression.展开更多
This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 mill...This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software w<span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ere</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands </span><i><span style="font-family:Verdana;">i.e</span></i></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 46.1314</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;">, and, 18.3437</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 30.9693</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> respectively. Results of Kumasi also show a higher range of temperatures from 32.6986</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 19.1077<span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span></span><span style="font-family:Verdana;">C</span><span style="font-family:Verdana;"> during the dry season. In the wet season, temperatures ranged from 26.4142</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to </span><span style="font-family:Verdana;">-</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">0</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.898728</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;">. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.</span></span></span></span>展开更多
Land surface temperature(LST)is one of the most important factors in the land-atmosphere interaction process.Raw measured LSTs may contain biases due to instrument replacement,changes in recording procedures,and other...Land surface temperature(LST)is one of the most important factors in the land-atmosphere interaction process.Raw measured LSTs may contain biases due to instrument replacement,changes in recording procedures,and other non-climatic factors.This study attempts to reduce the above biases in raw daily measurements and achieves a homogenized daily LST dataset over China using 2360 stations from 1960 through 2017.The high-quality land surface air temperature(LSAT)dataset is used to correct the LST warming biases especially evident during cold months in regions north of 40ºN due to the replacement of observation instruments around 2004.Subsequently,the Multiple Analysis of Series for Homogenization(MASH)method is adopted to detect and then adjust the daily observed LST records.In total,3.68×10^(3) effective breakpoints in 1.65×106 monthly records(about 20%)are detected.A large number of these effective breakpoints are located over large parts of the Sichuan Basin and southern China.After the MASH procedure,LSTs at more than 80%of the breakpoints are adjusted within+/-0.5℃,and of the remaining breakpoints,only 10%are adjusted over 1.5℃.Compared to the raw LST dataset over the whole domain,the homogenization significantly reduces the mean LST magnitude and its interannual variability as well as its linear trend at most stations.Finally,we perform preliminary analysis upon the homogenized LST and find that the annual mean LST averaged across China shows a significant warming trend[0.22℃(10 yr)^(-1)].The homogenized LST dataset can be further adapted for a variety of applications(e.g.,model evaluation and extreme event characterization).展开更多
This case study evaluates the seasonal variability of the Pearson's linear correlation coefficient of land surface temperature(LST)with some spectral indices like NDVI,NDWI,NDBI,and NDBaI by using a series of Land...This case study evaluates the seasonal variability of the Pearson's linear correlation coefficient of land surface temperature(LST)with some spectral indices like NDVI,NDWI,NDBI,and NDBaI by using a series of Landsat images for 1991-92,1995-96,1999-00,2004-05,2009-10,2014-15,and 2018-19.The results from the average correlation of the entire period of all-season show that the LST builds a positive correlation with NDBI(0.71)and NDBaI(0.52)while it builds a negative correlation with NDVI(-0.44).The LST-NDWI correlation is insignificant.The best correlation is noticed in the post-monsoon period,while the least correlation is observed in the winter season.This study can support the environmental planning to build a sustainable city under a similar environment.展开更多
Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-c...Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-cover and climatic conditions.This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and 2019.For this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were taken.Secondly,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were used.Thirdly,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were employed.For Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under research.The composition of red,green,and near infra-red bands was used for supervised classification.Classified images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to 2019.It has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.展开更多
Land surface temperature(LST) causes the phase change of water, links to the partitioning of surface water and energy budget, and becomes an important parameter to hydrology, meteorology, ecohydrology, and other resea...Land surface temperature(LST) causes the phase change of water, links to the partitioning of surface water and energy budget, and becomes an important parameter to hydrology, meteorology, ecohydrology, and other researches in the high mountain cold regions. Unlike air temperature, which has common altitudinal lapse rates in the mountainous regions, the influence of terrain leads to complicated estimation for soil LST. This study presents two methods that use air temperature and solar position,to estimate bare LST with high temporal resolution over horizontal sites and mountainous terrain with a random slope azimuth. The data from three horizontal meteorological stations and fourteen LST observation fields with different aspects and slopes were used to test the proposed LST methods. The calculated and measured LST were compared in a range of statistical analysis, and the analysis showed that the average RMSE(root mean square error),MAD(mean absolute deviation), and R^2(correlation coefficient) for three horizontal sites were 5.09℃,3.66℃, 0.92, and 5.03℃, 3.52℃, 0.85 for the fourteen complex terrain sites. The proposed methods showed acceptable accuracy, provide a simple way to estimate LST, and will be helpful for simulating the water and energy cycles in alpine mountainous terrain.展开更多
Global warming has attracted much concern about the worldwide organization, civil society groups, researchers, and so forth because the worldwide surface temperature has been expanding. This investigation intends to a...Global warming has attracted much concern about the worldwide organization, civil society groups, researchers, and so forth because the worldwide surface temperature has been expanding. This investigation intends to assess and compare the ability of a combination of land cover indices to predict the future distribution of land surface temperatures in Freetown using the Polynomial model analysis. Landsat satellite images of 1988, 1998, 2000, 2010, and 2018 of the Freetown Metropolitan zone were utilized for analysis. The investigation had adopted two land covers indices, Modification of normalized difference water index and Urban Index (UI) (e.g., MNDWI and UI) and applied a multi regression equation for forecasting the future LST. The stimulation results propose that the development will be accompanied by surface temperature increases, especially in Freetown’s western urban area. The temperature prevailing in the west of the metropolitan area may increase in the city somewhere in the range </span></span><span><span><span>from</span></span></span><span><span><span> 1988 to 2018. Additionally, the results of the LST prediction show that the model is perfect. Our discoveries can be represented as a helpful device for policymakers and community awareness by giving a scientific basis for sustainable urban planning and management.展开更多
Land Surface Temperature (LST) plays an important role in characterizing surface energy conversion and climate.Currently,there is a contradiction between temporal resolution and spatial resolution of commonly used LST...Land Surface Temperature (LST) plays an important role in characterizing surface energy conversion and climate.Currently,there is a contradiction between temporal resolution and spatial resolution of commonly used LST data sources.With Xi’an City as the research object,Multilayer Perceptron (MLP) model was used to downscale 1,000 m × 1,000 m LST product to 250 m × 250 m based on MODIS data.The fitting effect was compared with that of traditional multiple linear regression model,and the LST retrieved from Landsat 8 OLI_TIRS was used as the reference to evaluate the accuracy.The results showed that the R~2 of LST data fitted by MLP model in the daytime and at night was above 0.85,and the predicted residuals followed the normal distribution.The model had good fitting effect,and the fitting effect of LST in the daytime was better than that at night,while the output LST was lower than the original LST.Compared with multiple linear regression model,the R~2 of MLP model was larger and the RMSE was smaller both in the daytime and at night.The MLP model had not only stronger explanatory ability,but also more accurate prediction results.After downscaling by MLP model,the spatial resolution of LST was improved,which could reflect the spatial distribution pattern of LST and landscape features of underlying surface more accurately.The test results of LST retrieved from Landsat 8 OLI_TIRS showed that the covariance of the two was positive and the correlation coefficient was 0.951 3.The MLP model achieves the expected downscaling effect well,and has important application significance in acquiring high-resolution LST.展开更多
Urbanization, as a result of anthropogenic activities, reduces the vegetated and green spaces and thereby increases the impervious surfaces in cities. This in turn increases the surface temperature of cities when comp...Urbanization, as a result of anthropogenic activities, reduces the vegetated and green spaces and thereby increases the impervious surfaces in cities. This in turn increases the surface temperature of cities when compared to the rural regions resulting in the formation of urban heat island. Especially, in under-developed countries, it is very crucial to obtain timely and accurate information on the urban trend and its development due to the higher increasing rate of population growth and lack of infrastructural facilities and regulations to mitigate the adverse consequences of urbanization. The current study analyzes the urban development of Greater Accra, Ghana using Landsat 7 dataset acquired in 2002, 2013, and 2020. Further, the influence of urban growth on the land surface temperature (LST) and land cover variability (LCV) indices including NDVI (Normalized Difference Vegetation Index), NDBI (Normalized Difference Built-Up Index), and NDWI (Normalized Difference Water Index) is identified during the study periods. Results suggest that the area of built-up quadrupled between 2002 and 2020 in the study region. Shannon’s entropy-based analysis suggests that the urban development in the region is heterogeneous or dispersive in nature. Further, the regression analysis highlights a strong positive relation between LST and NDBI (0.755) and a negative relation is found associated between LST and NDVI (0.4417) and LST and NDWI (0.76). Results of the study could be useful to design sustainable urban socio-economic and environmental planning policies in the study region.展开更多
Land surface temperature (LST) is a basic determinant of the global thermal behavior of the Earth surface. LST is a vital consideration for the appraisal of gradual thermal change for urban areas to examine the streng...Land surface temperature (LST) is a basic determinant of the global thermal behavior of the Earth surface. LST is a vital consideration for the appraisal of gradual thermal change for urban areas to examine the strength of the thermal intensity of the surface of urban heat island (SUHI) and to see how hot the surface of the Earth would be in a particular location. In this respect, the most developed urban city like Dhaka Metropolitan Area (DMA), Bangladesh is considered for estimation of LST, and Normalized Difference Vegetation Index (NDVI) changes trend in more developed and growing developing areas. The focus of this study is to find out the critical hotspot zones for further instantaneous analysis between these two types of areas. The trends of long-term spatial and temporal LST and NDVI are estimated applying Landsat images-Landsat 5-TM and Landsat OLI_TIRS-8 for the period of 1988 to 2018 for DMA and for developed and growing developing areas during the summer season like for the month of March. The supervised classification was used to estimate the land cover categories and to generate the LST trends maps of the different percentiles of LSTs over time using the emissivity and effective at sensor brightness temperature. The study found the change in land cover patterns by different LST groups based on 50th, 75th, and 90th percentile where the maximum LST for the whole DMA went up by 2.48<span style="white-space:nowrap;">°</span>C, 1.01<span style="white-space:nowrap;">°</span>C, and 3.76<span style="white-space:nowrap;">°</span>C for the months of March, April, and May, respectively for the period of 1988 to 2018. The highest difference in LST was found for the most recently developed area. The moderate change of LST increased in the built-up areas where LST was found more sensitive to climate change than the growing developed areas. The vegetation coverage area decreased by 6.74% in the growing, developing areas compared to the developed areas from 1988 to 2018. The findings of the study might be helpful for urban planners and researchers to take up appropriate measures to mitigate the thermal effect on urban environment.展开更多
Wetlands play a key role in regulating local climate as well as reducing impacts caused by climate change. Rapid observations of the land surface temperature(LST) are, therefore, valuable for studying the dynamics of ...Wetlands play a key role in regulating local climate as well as reducing impacts caused by climate change. Rapid observations of the land surface temperature(LST) are, therefore, valuable for studying the dynamics of wetland systems. With the development of thermal remote sensing technology, LST retrieval with satellite images is a practicable way to detect a wetland and its neighboring area’s thermal environment from a non-point visual angle rather than the traditional detection from a point visual angle. The mono-windows(MW) method of retrieving LST was validated. On the basis of estimated LST, we used Geographical Information System(GIS) technology to study the impact of wetland reclamation on local temperatures at a regional scale. Following that, correlations between LST and the wetland were analyzed. The results show that: 1) It is feasible to retrieve the LST from Landsat 8 OLI satellite images with MW model. The model was validated with the land surface temperature observed in four meteorological stations when the satellite scanned the study region. The satellite retrieval error was approximately 1.01°C. 2) The relationship between the spatial distribution of land surface temperatures and the Zhalong wetland was analyzed based on GIS technology. The results show that wetland has an obvious influence on LST, and that this influence decreases with increasing distance from the wetland. When the distance from the wetland was less than 500 m, its influence on LST was significant. Results also illustrated that the effect of the wetland’s different land use/land cover’s LST distribution varied with different seasons.展开更多
The urban heat island(UHI) effect has significant effects on the quality of life and public health. Numerous studies have addressed the relationship between UHI and the increase in urban impervious surface area(ISA), ...The urban heat island(UHI) effect has significant effects on the quality of life and public health. Numerous studies have addressed the relationship between UHI and the increase in urban impervious surface area(ISA), but few of them have considered the impact of the spatial configuration of ISA on UHI. Land surface temperature(LST) may be affected not only by urban land cover, but also by neighboring land cover. The aim of this research was to investigate the effects of the abundance and spatial association of ISAs on LST. Taking Harbin City, China as an example, the impact of ISA spatial association on LST measurements was examined. The abundance of ISAs and the LST measurements were derived from Landsat Thematic Mapper(TM) imagery of 2000 and 2010, and the spatial association patterns of ISAs were calculated using the local Moran’s I index. The impacts of ISA abundance and spatial association on LST were examined using correlation analysis. The results suggested that LST has significant positive associations with both ISA abundance and the Moran’s I index of ISAs, indicating that both the abundance and spatial clustering of ISAs contribute to elevated values of LST. It was also found that LST is positively associated with clustering of high-ISA-percentage areas(i.e.,>50%) and negatively associated with clustering of low-ISA-percentage areas(i.e.,<25%). The results suggest that, in addition to the abundance of ISAs,their spatial association has a significant effect on UHIs.展开更多
A large-scale afforestation project has been carried out since 1999 in the Loess Plateau of China. However, vegetation-induced changes in land surface temperature (LST) through the changing land surface energy balance...A large-scale afforestation project has been carried out since 1999 in the Loess Plateau of China. However, vegetation-induced changes in land surface temperature (LST) through the changing land surface energy balance have not been well documented. Using satellite measurements, this study quantified the contribution of vegetation restoration to the changes in summer LST and analyzed the effects of different vegetation restoration patterns on LST during both daytime and nighttime. The results show that the average daytime LST decreased by 4.3°C in the vegetation restoration area while the average nighttime LST increased by 1.4°C. The contributions of the vegetation restoration project to the changes in daytime LST and nighttime LST are 58% and 60%, respectively, which are far greater than the impact of climate change. The vegetation restoration pattern of cropland (CR) converting into artificial forest (AF) has a cooling effect during daytime and a warming effect at nighttime, while the conversion of CR to grassland has an opposite effect compared with the conversion of CR to AF. Our results indicate that increasing evapotranspiration caused by the vegetation restoration on the Loess Plateau is the controlling factor of daytime LST change, while the nighttime LST change is affected by soil humidity and air humidity.展开更多
基金supported by the Third Xinjiang Scientific Expedition Program(2021xjkk0801).
文摘Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones(the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami(Tuha) Basin, and Pakakuna Mountain Group) using geographical detector(Geodetector) and geographically weighted regression(GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24℃/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09℃/10a in autumn, and showed a warming trend in other seasons. Digital elevation model(DEM), latitude, wind speed, precipitation, normalized difference vegetation index(NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover(LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.
文摘The Alborz Mountains are some of the highest in Iran,and they play an important role in controlling the climate of the country’s northern regions.The land surface temperature(LST)is an important variable that affects the ecosystem of this area.This study investigated the spatiotemporal changes and trends of the nighttime LST in the western region of the Central Alborz Mountains at elevations of 1500-4000 m above sea level.MODIS data were extracted for the period of 2000-2021,and the Mann-Kendall nonparametric test was applied to evaluating the changes in the LST.The results indicated a significant increasing trend for the monthly average LST in May-August along the southern aspect.Both the northern and southern aspects showed decreasing trends for the monthly average LST in October,November,and March and an increasing trend in other months.At all elevations,the average decadal change in the monthly average LST was more severe along the southern aspect(0.60°C)than along the northern aspect(0.37°C).The LST difference between the northern and southern aspects decreased in the cold months but increased in the hot months.At the same elevation,the difference in the lapse rate between the northern and southern aspects was greater in the hot months than in the cold months.With increasing elevation,the lapse rate between the northern and southern aspects disappeared.Climate change was concluded to greatly decrease the difference in LST at different elevations for April-July.
文摘This study assesses the changes in land use/land cover(LULC) and land surface temperature(LST) to identify their impacts from 2000 to 2020 along the coast of Kanyakumari district, India using remote sensing techniques. Landsat images are used to estimate the LULC changes and the MODIS data for LST.The Maximum Likelihood Classification(MLC) method is used, and the LULC is classified into six categories: Agriculture Land, Barren Land, Salt Pan, Sandy Beach, Settlement, and Waterbody. Within the two decades of the present change detection study, upheave in the Settlement area of 49.89% is noticed, and the Agriculture Land is exploited by 20.09%. Salt Pan emits a high LST of 31.57°C, and the Waterbodies are noticed with a low LST of 28.9°C. However, the overall rate of LST decreased by 0.56°C during this period. This study will help policymakers make appropriate planning and management to overcome the impact of LULC and LST in the forthcoming years.
基金Under the auspices of National Natural Science Foundation of China(No.42271214,41961027)Key Program of Natural Science Foundation of Gansu Province(No.21JR7RA278,21JR7RA281)+1 种基金the CAS‘Light of West China’Program(No.2020XBZGXBQNXZ-A)Basic Research Top Talent Plan of Lanzhou Jiaotong University(No.2022JC01)。
文摘Local climate zones(LCZs)are an effective nexus linking internal urban structures to the local climate and have been widely used to study urban thermal environment.However,few studies considered how much the temperature changed due to LCZs transformation and their synergy.This paper quantified the change of urban land surface temperature(LST)in LCZs transformation process by combining the land use transfer matrix with zonal statistics method during 2000–2019 in the Xi’an metropolitan.The results show that,firstly,both LCZs and LST had significant spatiotemporal variations and synchrony.The period when the most LCZs were converted was also the LST rose the fastest,and the spatial growth of the LST coincided with the spatial expansion of the built type LCZs.Secondly,the LST difference between land cover type LCZs and built type LCZs gradually widened.And LST rose more in both built type LCZs transferred in and out.Finally,the Xi’an-Xianyang profile showed that the maximum temperature difference between the peaks and valleys of the LST increased by 4.39℃,indicating that localized high temperature phenomena and fluctuations in the urban thermal environment became more pronounced from 2000 to 2019.
基金Supported by the National Key R&D Plan(2018YFC1506500)Open Research Fund Project of Key Laboratory of Ecological Environment Meteorology of Qinling Mountains and Loess Plateau of Shaanxi Provincial Meteorological Bureau(2020Y-13)+1 种基金Open Research Fund of Shangluo Key Laboratory of Climate Adaptable City(SLSYS2022007)Shangluo Demonstration Project of Qinling Ecological Monitoring Service System(2020-611002-74-01-006200)。
文摘The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal resolutions in extracting LST from satellite remote sensing(RS)data,the areas with complex landforms of the Eastern Qinling Mountains were selected as the research targets to establish the correlation between the normalized difference vegetation index(NDVI)and LST.Detailed information on the surface features and temporal changes in the land surface was provided by Sentinel-2 and Sentinel-3,respectively.Based on the statistically downscaling method,the spatial scale could be decreased from 1000 m to 10 m,and LST with a Sentinel-3 temporal resolution and a 10 m spatial resolution could be retrieved.Comparing the 1 km resolution Sentinel-3 LST with the downscaling results,the 10 m LST downscaling data could accurately reflect the spatial distribution of the thermal characteristics of the original LST image.Moreover,the surface temperature data with a 10 m high spatial resolution had clear texture and obvious geomorphic features that could depict the detailed information of the ground features.The results showed that the average error was 5 K on April 16,2019 and 2.6 K on July 15,2019.The smaller error values indicated the higher vegetation coverage of summer downscaling result with the highest level on July 15.
文摘Since the reform and opening-up in 1978, the urbanization level of our country has been continuously improved and the urban development has made great progress. However, with the rapid expansion of urban construction land, the population density and building density have been greatly increased, resulting in the urban heat island effect, which has negative impact on the urban thermal environment and restricts the high-quality development of urbanization. This paper focuses on how the urban surface thermal environment of Hangzhou changes in 20 years. In this paper, the characteristics of land surface temperature (LST) in Hangzhou urban area from 2000 to 2020 were studied by using Landsat images. The radiative transfer equation method is used to retrieve the land surface temperature, and the retrieval results are analyzed. The results show that: 1) the land surface temperature in Hangzhou city area has a slight upward trend in the past 20 years;2) the area of high temperature area is expanding;3) the land surface temperature in the city center area has decreased significantly in the past 20 years, while the ground temperature in other areas around the city center has increased significantly.
文摘In this paper,the spatio-temporal variation and propagation direction of coal fire were studied in the Jharia Coalfield(JCF),India during 2006–2015 through satellite-based night-time land surface temperature(LST)imaging.The LST was retrieved from Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)night-time thermal-infrared data by a robust split-window algorithm based on scene-specific regression coefficients,band-specific hybrid emissivity,and night-time atmospheric transmittance.The LST-profile-based coal fire detection algorithm was formulated through statistical analysis of the LST values along multiple transects across diverse coal fire locations in the JCF in order to compute date-specific threshold temperatures for separating thermally-anomalous and background pixels.This algorithm efficiently separates surface fire,subsurface fire,and thermally-anomalous transitional pixels.During the observation period,it was noticed that the coal fire area increased significantly,which resulted from new coal fire at many places owing to extensive opencast-mining operations.It was observed that the fire propagation occurred primarily along the dip direction of the coal seams.At places,lateral-propagation of limited spatial extent was also observed along the strike direction possibly due to spatial continuity of the coal seams along strike.Moreover,the opencast-mining activities carried out during 2009–2015 and the structurally weak planes facilitated the fire propagation.
文摘Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression.
文摘This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software w<span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ere</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands </span><i><span style="font-family:Verdana;">i.e</span></i></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 46.1314</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;">, and, 18.3437</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 30.9693</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> respectively. Results of Kumasi also show a higher range of temperatures from 32.6986</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 19.1077<span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span></span><span style="font-family:Verdana;">C</span><span style="font-family:Verdana;"> during the dry season. In the wet season, temperatures ranged from 26.4142</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to </span><span style="font-family:Verdana;">-</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">0</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.898728</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;">. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.</span></span></span></span>
基金This work was supported by the National Science Fund for Distinguished Young Scholars(Grant No.41925021)the National Natural Science Foundation of China(Grant No.41875106).
文摘Land surface temperature(LST)is one of the most important factors in the land-atmosphere interaction process.Raw measured LSTs may contain biases due to instrument replacement,changes in recording procedures,and other non-climatic factors.This study attempts to reduce the above biases in raw daily measurements and achieves a homogenized daily LST dataset over China using 2360 stations from 1960 through 2017.The high-quality land surface air temperature(LSAT)dataset is used to correct the LST warming biases especially evident during cold months in regions north of 40ºN due to the replacement of observation instruments around 2004.Subsequently,the Multiple Analysis of Series for Homogenization(MASH)method is adopted to detect and then adjust the daily observed LST records.In total,3.68×10^(3) effective breakpoints in 1.65×106 monthly records(about 20%)are detected.A large number of these effective breakpoints are located over large parts of the Sichuan Basin and southern China.After the MASH procedure,LSTs at more than 80%of the breakpoints are adjusted within+/-0.5℃,and of the remaining breakpoints,only 10%are adjusted over 1.5℃.Compared to the raw LST dataset over the whole domain,the homogenization significantly reduces the mean LST magnitude and its interannual variability as well as its linear trend at most stations.Finally,we perform preliminary analysis upon the homogenized LST and find that the annual mean LST averaged across China shows a significant warming trend[0.22℃(10 yr)^(-1)].The homogenized LST dataset can be further adapted for a variety of applications(e.g.,model evaluation and extreme event characterization).
文摘This case study evaluates the seasonal variability of the Pearson's linear correlation coefficient of land surface temperature(LST)with some spectral indices like NDVI,NDWI,NDBI,and NDBaI by using a series of Landsat images for 1991-92,1995-96,1999-00,2004-05,2009-10,2014-15,and 2018-19.The results from the average correlation of the entire period of all-season show that the LST builds a positive correlation with NDBI(0.71)and NDBaI(0.52)while it builds a negative correlation with NDVI(-0.44).The LST-NDWI correlation is insignificant.The best correlation is noticed in the post-monsoon period,while the least correlation is observed in the winter season.This study can support the environmental planning to build a sustainable city under a similar environment.
文摘Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-cover and climatic conditions.This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and 2019.For this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were taken.Secondly,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were used.Thirdly,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were employed.For Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under research.The composition of red,green,and near infra-red bands was used for supervised classification.Classified images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to 2019.It has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.
基金supported primarily by the National Basic Research Program of China(2013CBA01806)the National Natural Sciences Foundation of China(41401041)the Open Research Fund of State Key Laboratory of Cryosphere Sciences(SKLCS-OP-2013-06)
文摘Land surface temperature(LST) causes the phase change of water, links to the partitioning of surface water and energy budget, and becomes an important parameter to hydrology, meteorology, ecohydrology, and other researches in the high mountain cold regions. Unlike air temperature, which has common altitudinal lapse rates in the mountainous regions, the influence of terrain leads to complicated estimation for soil LST. This study presents two methods that use air temperature and solar position,to estimate bare LST with high temporal resolution over horizontal sites and mountainous terrain with a random slope azimuth. The data from three horizontal meteorological stations and fourteen LST observation fields with different aspects and slopes were used to test the proposed LST methods. The calculated and measured LST were compared in a range of statistical analysis, and the analysis showed that the average RMSE(root mean square error),MAD(mean absolute deviation), and R^2(correlation coefficient) for three horizontal sites were 5.09℃,3.66℃, 0.92, and 5.03℃, 3.52℃, 0.85 for the fourteen complex terrain sites. The proposed methods showed acceptable accuracy, provide a simple way to estimate LST, and will be helpful for simulating the water and energy cycles in alpine mountainous terrain.
文摘Global warming has attracted much concern about the worldwide organization, civil society groups, researchers, and so forth because the worldwide surface temperature has been expanding. This investigation intends to assess and compare the ability of a combination of land cover indices to predict the future distribution of land surface temperatures in Freetown using the Polynomial model analysis. Landsat satellite images of 1988, 1998, 2000, 2010, and 2018 of the Freetown Metropolitan zone were utilized for analysis. The investigation had adopted two land covers indices, Modification of normalized difference water index and Urban Index (UI) (e.g., MNDWI and UI) and applied a multi regression equation for forecasting the future LST. The stimulation results propose that the development will be accompanied by surface temperature increases, especially in Freetown’s western urban area. The temperature prevailing in the west of the metropolitan area may increase in the city somewhere in the range </span></span><span><span><span>from</span></span></span><span><span><span> 1988 to 2018. Additionally, the results of the LST prediction show that the model is perfect. Our discoveries can be represented as a helpful device for policymakers and community awareness by giving a scientific basis for sustainable urban planning and management.
文摘Land Surface Temperature (LST) plays an important role in characterizing surface energy conversion and climate.Currently,there is a contradiction between temporal resolution and spatial resolution of commonly used LST data sources.With Xi’an City as the research object,Multilayer Perceptron (MLP) model was used to downscale 1,000 m × 1,000 m LST product to 250 m × 250 m based on MODIS data.The fitting effect was compared with that of traditional multiple linear regression model,and the LST retrieved from Landsat 8 OLI_TIRS was used as the reference to evaluate the accuracy.The results showed that the R~2 of LST data fitted by MLP model in the daytime and at night was above 0.85,and the predicted residuals followed the normal distribution.The model had good fitting effect,and the fitting effect of LST in the daytime was better than that at night,while the output LST was lower than the original LST.Compared with multiple linear regression model,the R~2 of MLP model was larger and the RMSE was smaller both in the daytime and at night.The MLP model had not only stronger explanatory ability,but also more accurate prediction results.After downscaling by MLP model,the spatial resolution of LST was improved,which could reflect the spatial distribution pattern of LST and landscape features of underlying surface more accurately.The test results of LST retrieved from Landsat 8 OLI_TIRS showed that the covariance of the two was positive and the correlation coefficient was 0.951 3.The MLP model achieves the expected downscaling effect well,and has important application significance in acquiring high-resolution LST.
文摘Urbanization, as a result of anthropogenic activities, reduces the vegetated and green spaces and thereby increases the impervious surfaces in cities. This in turn increases the surface temperature of cities when compared to the rural regions resulting in the formation of urban heat island. Especially, in under-developed countries, it is very crucial to obtain timely and accurate information on the urban trend and its development due to the higher increasing rate of population growth and lack of infrastructural facilities and regulations to mitigate the adverse consequences of urbanization. The current study analyzes the urban development of Greater Accra, Ghana using Landsat 7 dataset acquired in 2002, 2013, and 2020. Further, the influence of urban growth on the land surface temperature (LST) and land cover variability (LCV) indices including NDVI (Normalized Difference Vegetation Index), NDBI (Normalized Difference Built-Up Index), and NDWI (Normalized Difference Water Index) is identified during the study periods. Results suggest that the area of built-up quadrupled between 2002 and 2020 in the study region. Shannon’s entropy-based analysis suggests that the urban development in the region is heterogeneous or dispersive in nature. Further, the regression analysis highlights a strong positive relation between LST and NDBI (0.755) and a negative relation is found associated between LST and NDVI (0.4417) and LST and NDWI (0.76). Results of the study could be useful to design sustainable urban socio-economic and environmental planning policies in the study region.
文摘Land surface temperature (LST) is a basic determinant of the global thermal behavior of the Earth surface. LST is a vital consideration for the appraisal of gradual thermal change for urban areas to examine the strength of the thermal intensity of the surface of urban heat island (SUHI) and to see how hot the surface of the Earth would be in a particular location. In this respect, the most developed urban city like Dhaka Metropolitan Area (DMA), Bangladesh is considered for estimation of LST, and Normalized Difference Vegetation Index (NDVI) changes trend in more developed and growing developing areas. The focus of this study is to find out the critical hotspot zones for further instantaneous analysis between these two types of areas. The trends of long-term spatial and temporal LST and NDVI are estimated applying Landsat images-Landsat 5-TM and Landsat OLI_TIRS-8 for the period of 1988 to 2018 for DMA and for developed and growing developing areas during the summer season like for the month of March. The supervised classification was used to estimate the land cover categories and to generate the LST trends maps of the different percentiles of LSTs over time using the emissivity and effective at sensor brightness temperature. The study found the change in land cover patterns by different LST groups based on 50th, 75th, and 90th percentile where the maximum LST for the whole DMA went up by 2.48<span style="white-space:nowrap;">°</span>C, 1.01<span style="white-space:nowrap;">°</span>C, and 3.76<span style="white-space:nowrap;">°</span>C for the months of March, April, and May, respectively for the period of 1988 to 2018. The highest difference in LST was found for the most recently developed area. The moderate change of LST increased in the built-up areas where LST was found more sensitive to climate change than the growing developed areas. The vegetation coverage area decreased by 6.74% in the growing, developing areas compared to the developed areas from 1988 to 2018. The findings of the study might be helpful for urban planners and researchers to take up appropriate measures to mitigate the thermal effect on urban environment.
基金Under the auspices of National Key Research and Development Program of China(No.2016YFA0602301-1)Strategic Planning Project of Northeast Institute of Geography and Agroecology(IGA)Chinese Academy of Sciences(No.Y6H2091001)
文摘Wetlands play a key role in regulating local climate as well as reducing impacts caused by climate change. Rapid observations of the land surface temperature(LST) are, therefore, valuable for studying the dynamics of wetland systems. With the development of thermal remote sensing technology, LST retrieval with satellite images is a practicable way to detect a wetland and its neighboring area’s thermal environment from a non-point visual angle rather than the traditional detection from a point visual angle. The mono-windows(MW) method of retrieving LST was validated. On the basis of estimated LST, we used Geographical Information System(GIS) technology to study the impact of wetland reclamation on local temperatures at a regional scale. Following that, correlations between LST and the wetland were analyzed. The results show that: 1) It is feasible to retrieve the LST from Landsat 8 OLI satellite images with MW model. The model was validated with the land surface temperature observed in four meteorological stations when the satellite scanned the study region. The satellite retrieval error was approximately 1.01°C. 2) The relationship between the spatial distribution of land surface temperatures and the Zhalong wetland was analyzed based on GIS technology. The results show that wetland has an obvious influence on LST, and that this influence decreases with increasing distance from the wetland. When the distance from the wetland was less than 500 m, its influence on LST was significant. Results also illustrated that the effect of the wetland’s different land use/land cover’s LST distribution varied with different seasons.
基金Under the auspices of the National Social Science Foundation of China(No.16BJY039)
文摘The urban heat island(UHI) effect has significant effects on the quality of life and public health. Numerous studies have addressed the relationship between UHI and the increase in urban impervious surface area(ISA), but few of them have considered the impact of the spatial configuration of ISA on UHI. Land surface temperature(LST) may be affected not only by urban land cover, but also by neighboring land cover. The aim of this research was to investigate the effects of the abundance and spatial association of ISAs on LST. Taking Harbin City, China as an example, the impact of ISA spatial association on LST measurements was examined. The abundance of ISAs and the LST measurements were derived from Landsat Thematic Mapper(TM) imagery of 2000 and 2010, and the spatial association patterns of ISAs were calculated using the local Moran’s I index. The impacts of ISA abundance and spatial association on LST were examined using correlation analysis. The results suggested that LST has significant positive associations with both ISA abundance and the Moran’s I index of ISAs, indicating that both the abundance and spatial clustering of ISAs contribute to elevated values of LST. It was also found that LST is positively associated with clustering of high-ISA-percentage areas(i.e.,>50%) and negatively associated with clustering of low-ISA-percentage areas(i.e.,<25%). The results suggest that, in addition to the abundance of ISAs,their spatial association has a significant effect on UHIs.
基金funded by the National Key Research and Development Program of China (2016YFC0401306)the National Science Fund for Distinguished Young Scholars (51625904)the International Science & Technology Cooperation Program of China (2016YFE0102400)
文摘A large-scale afforestation project has been carried out since 1999 in the Loess Plateau of China. However, vegetation-induced changes in land surface temperature (LST) through the changing land surface energy balance have not been well documented. Using satellite measurements, this study quantified the contribution of vegetation restoration to the changes in summer LST and analyzed the effects of different vegetation restoration patterns on LST during both daytime and nighttime. The results show that the average daytime LST decreased by 4.3°C in the vegetation restoration area while the average nighttime LST increased by 1.4°C. The contributions of the vegetation restoration project to the changes in daytime LST and nighttime LST are 58% and 60%, respectively, which are far greater than the impact of climate change. The vegetation restoration pattern of cropland (CR) converting into artificial forest (AF) has a cooling effect during daytime and a warming effect at nighttime, while the conversion of CR to grassland has an opposite effect compared with the conversion of CR to AF. Our results indicate that increasing evapotranspiration caused by the vegetation restoration on the Loess Plateau is the controlling factor of daytime LST change, while the nighttime LST change is affected by soil humidity and air humidity.
