Spatial Pattern Of Land Surface Temperature Research Articles

Comparing the Evolution of Land Surface Temperature and Driving Factors between Three Different Urban Agglomerations in China

Increases in land surface temperature (LST) and the urban heat island effect have become major challenges in the process of urban development. However, few studies have examined variations in LST between different urban agglomerations (UAs). Based on MODIS LST data, we quantitatively analyzed the spatial and temporal evolution patterns of LST in three different UAs in China from 2000 to 2020—Beijing–Tianjin–Hebei (BTH) at the national level, the Shandong Peninsula (SP) at the regional level, and Central Shanxi (CS) at the city level—by employing urban agglomeration built-up area intensity (UABI), linear regression analyses, and geodetic detector models. The results showed the following: (1) The spatial and temporal evolution pattern of the LST in BTH was the most regularized; the spatial pattern of the LST in SP gradually evolved from “two points” to “a single branch”; and the LST of CS was easily influenced by the neighboring big cities. (2) The best-fitting coefficients for BTH, SP, and CS were R2BTH = 0.58, R2SP = 0.66, and R2CS = 0.58, respectively; every 10% increase in UABI warmed the LSTs in BTH, SP, and CS by 1.47 °C, 1.27 °C, and 1.83 °C, respectively. (3) The ranking of single-factor influence was DEM (digital elevation model) > UABI > NDVI > T2m (air temperature at 2 m) > POP (population). The UABI interacting with DEM had the strongest warming effect on LST, with the maximum value q(UABI ∩ DEM) BTH = 0.951. All factor interactions showed an enhancement of the LST in CS, but factors interacting with POP showed a weaker effect in BTH and SP, for which q(NDVI ∩ POP) BTH = 0.265 and q(T2m ∩ POP) SP = 0.261. As the development of UAs gradually matures, the interaction with POP might have a cooling effect on the environment to a certain degree.

Assessing the Impact of Natural Conditions/Socioeconomic Indicators on the Urban Thermal Environment Based on Geographic Big Data

Understanding correctly the factors influencing the urban thermal environment is a prerequisite and basis for formulating heat-island-effect mitigation policies and studying urban ecological issues. The rapid urbanization process has led to the gradual replacement of natural landscapes by products of socioeconomic activities, and although previous studies have shown that natural conditions and socioeconomic intensity can significantly influence land surface temperature (LST), few studies have explored the combined effects of both on LST, especially at a fine scale. Therefore, this study investigated the relationship between natural conditions/socioeconomic and summer daytime LST based on big data and a random forest (RF) algorithm using the city of Jinan as the study area. The results showed that the spatial pattern of LST, natural condition characteristics of the city, and socioeconomic characteristics are consistent in spatial pattern and have significant correlation. In the RF model, the fitted R2 of the regression model considering two influencing factors reaches 0.86, which is significantly higher than that of the regression model considering only one influencing factor. In the optimal regression model, topographic factors in natural conditions and socioeconomic factors in buildings and roads are very important factors influencing the urban thermal environment. Based on the results, strategies and measures for developing and managing measures related to the thermal environment are discussed in depth. The results can be used as a reference for mitigating urban heat islands in the study area or other cities with similar characteristics.

Identifying the Driving Factors of Urban Land Surface Temperature

Land surface temperature (LST) has a profound impact on urban climate and ecology, and is widely used to quantify surface urban heat islands. The spatial heterogeneity of LST is affected by natural and human factors, with seasonal differences. This study selected Dongguan, a rapidly urbanizing city in China, as an example to analyze the relationship between the spatial heterogeneity of LST in different seasons and influencing factors in six dimensions. Multi-source spatial data were combined, including Landsat images, meteorological data, digital elevation models, National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite nighttime light, and points of interest. The results show that spatial patterns of LST across different seasons were consistent, although there were local differences. Based on the GeoDetector model, the result indicated differences between separate effects and interactive effects, and identified the high temperature risk areas.

The Changes of Heat Contribution Index in Urban Thermal Environment: A Case Study in Fuzhou

With the acceleration of global warming and urbanization, the problem of the thermal environment in urban areas has become increasingly prominent. In this paper, Fuzhou was selected to quantify the impact of land use cover change (LUCC) on land surface temperature (LST). The results showed that from 1993 to 2016, the land use/cover types of the study area changed greatly, especially the change of construction land, which led to an obvious change in the spatial pattern of LST. From 1993 to 2016, the spatial and temporal distribution of LST contributions in Fuzhou was uneven. The central urban area had a positive contribution to the rise of LST, while Minqing and Yongtai had a negative contribution. From the perspective of different land use/land cover types, forest or grass land, cultivated land, and water all made a negative contribution to the increase of surface temperature, while construction land made a positive contribution. Outcomes provided by the multi-distance spatial cluster analysis (Ripley’s K function) showed that there was a scale effect in the concentration and dispersion of LST; from 1993 to 2016, the concentration range of LST in the study area gradually expanded and the degree of concentration increased.

Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China

Land surface temperature (LST) is a crucial parameter in surface urban heat island (SUHI) studies. A better understanding of the driving mechanisms, influencing variations in LST dynamics, is required for the sustainable development of a city. This study used Changchun, a city in northeast China, as an example, to investigate the seasonal effects of different dominant driving factors on the spatial patterns of LST. Twelve Landsat 8 images were used to retrieve monthly LST, to characterize the urban thermal environment, and spectral mixture analysis was employed to estimate the effect of the driving factors, and correlation and linear regression analyses were used to explore their relationships. Results indicate that, (1) the spatial pattern of LST has dramatic monthly and seasonal changes. August has the highest mean LST of 38.11 °C, whereas December has the lowest (−19.12 °C). The ranking of SUHI intensity is as follows: summer (4.89 °C) > winter with snow cover (1.94 °C) > spring (1.16 °C) > autumn (0.89 °C) > winter without snow cover (−1.24 °C). (2) The effects of driving factors also have seasonal variations. The proportion of impervious surface area (ISA) in summer (49.01%) is slightly lower than those in spring (56.64%) and autumn (50.85%). Almost half of the area is covered with snow (43.48%) in winter. (3) The dominant factors are quite different for different seasons. LST possesses a positive relationship with ISA for all seasons and has the highest Pearson coefficient for summer (r = 0.89). For winter, the effect of vegetation on LST is not obvious, and snow becomes the dominant driving factor. Despite its small area proportion, water has the strongest cooling effect from spring to autumn, and has a warming effect in winter. (4) Human activities, such as agricultural burning, harvest, and different choices of crop species, could also affect the spatial patterns of LST.

Dynamics of Land Surface Temperature in Response to Land Cover Changes in Lagos Metropolis

Land Surface Temperature (LST) is one of the key environmental parameters affected by land cover change. Lagos State has been experiencing an increase in surface temperature due to growing areas of impervious surfaces caused by anthropogenic urban sprawl. While the change in LST has been established, its continuous monitoring and relationship with continuing Land Cover (LC) changes have become imperative for appropriate management and policy actions. This study investigated the effect of land cover change on LST in the rapidly urbanising Lagos metropolis. Using spatio-temporal Landsat imageries with their thermal bands and ancillary data, land cover and LST changes were assessed from 1984 - 2015. The spatial patterns of LST and LC were derived to examine the response of LST to urban growth. Findings confirmed urban sprawl in previously rural areas northward of the metropolis in LGAs such as Ikorodu, Kosofe and those fringing the state’s border with Ogun State. This also confirmed new growth areas as occurring west of the metropolis in Amuwo-Odofin LGA. The results further showed that the rapid urbanisation in Lagos metropolis has altered the surface thermal environment as indicated by increased LST. Built-up area and bare land accounted for the highest increase in LST (as high as 1.5℃ in some areas) while wetlands and other vegetated areas played a vital role in moderating the surface temperature in areas they still occupy. This provides reasonable evidence for the appropriate authorities to institute requisite policies and actions towards moderating urban sprawl while ramping up the development of urban green infrastructure to counter global warming.

Statistical estimation of high-resolution surface air temperature from MODIS over the Yangtze River Delta, China

High-resolution surface air temperature data are critical to regional climate modeling in terms of energy balance, urban climate change, and so on. This study demonstrates the feasibility of using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) to estimate air temperature at a high resolution over the Yangtze River Delta region, China. It is found that daytime LST is highly correlated with maximum air temperature, and the linear regression coefficients vary with the type of land surface. The air temperature at a resolution of 1 km is estimated from the MODIS LST with linear regression models. The estimated air temperature shows a clear spatial structure of urban heat islands. Spatial patterns of LST and air temperature differences are detected, indicating maximum differences over urban and forest regions during summer. Validations are performed with independent data samples, demonstrating that the mean absolute error of the estimated air temperature is approximately 2.5°C, and the uncertainty is about 3.1°C, if using all valid LST data. The error is reduced by 0.4°C (15%) if using best-quality LST with errors of less than 1 K. The estimated high-resolution air temperature data have great potential to be used in validating high-resolution climate models and other regional applications.

Satellite-based characterization of climatic conditions before large-scale general flowering events in Peninsular Malaysia

General flowering (GF) is a unique phenomenon wherein, at irregular intervals, taxonomically diverse trees in Southeast Asian dipterocarp forests synchronize their reproduction at the community level. Triggers of GF, including drought and low minimum temperatures a few months previously has been limitedly observed across large regional scales due to lack of meteorological stations. Here, we aim to identify the climatic conditions that trigger large-scale GF in Peninsular Malaysia using satellite sensors, Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS), to evaluate the climatic conditions of focal forests. We observed antecedent drought, low temperature and high photosynthetic radiation conditions before large-scale GF events, suggesting that large-scale GF events could be triggered by these factors. In contrast, we found higher-magnitude GF in forests where lower precipitation preceded large-scale GF events. GF magnitude was also negatively influenced by land surface temperature (LST) for a large-scale GF event. Therefore, we suggest that spatial extent of drought may be related to that of GF forests, and that the spatial pattern of LST may be related to that of GF occurrence. With significant new findings and other results that were consistent with previous research we clarified complicated environmental correlates with the GF phenomenon.

HISTORICAL GIS DATA AND CHANGES IN URBAN MORPHOLOGICAL PARAMETERS FOR THE ANALYSIS OF URBAN HEAT ISLANDS IN HONG KONG

Rapid urban development between the 1960 and 2010 decades have changed the urban landscape and pattern in the Kowloon Peninsula of Hong Kong. This paper aims to study the changes of urban morphological parameters between the 1985 and 2010 and explore their influences on the urban heat island (UHI) effect. This study applied a mono-window algorithm to retrieve the land surface temperature (LST) using Landsat Thematic Mapper (TM) images from 1987 to 2009. In order to estimate the effects of local urban morphological parameters to LST, the global surface temperature anomaly was analysed. Historical 3D building model was developed based on aerial photogrammetry technique using aerial photographs from 1964 to 2010, in which the urban digital surface models (DSMs) including elevations of infrastructures and buildings have been generated. Then, urban morphological parameters (i.e. frontal area index (FAI), sky view factor (SVF)), vegetation fractional cover (VFC), global solar radiation (GSR), Normalized Difference Built-Up Index (NDBI), wind speed were derived. Finally, a linear regression method in Waikato Environment for Knowledge Analysis (WEKA) was used to build prediction model for revealing LST spatial patterns. Results show that the final apparent surface temperature have uncertainties less than 1 degree Celsius. The comparison between the simulated and actual spatial pattern of LST in 2009 showed that the correlation coefficient is 0.65, mean absolute error (MAE) is 1.24 degree Celsius, and root mean square error (RMSE) is 1.51 degree Celsius of 22,429 pixels.

Impact of MODIS Quality Control on Temporally Aggregated Urban Surface Temperature and Long-Term Surface Urban Heat Island Intensity

Surface Urban Heat Island (SUHI) is a phenomenon of high spatial and temporal variability. However, studies that investigate urban land surface temperature (LST) observed in different seasons frequently utilize a single satellite measurement and do not incorporate temporal composites. Temporally aggregated data increase clear sky coverage, which is important in many aspects of urban climatology. However, it is critical to account for possible errors and quality of the data that are utilized. The objective of this paper is to analyze the impact of MODIS Quality Control (QC) and the view angle on temporally aggregated urban surface temperature and long-term SUHI intensity. To achieve this, a weighted arithmetic mean was utilized whose weights were based on the view angle of satellite observation and the MODIS QC flags; namely, LST retrieval errors and emissivity errors. In order to investigate the impact of the MODIS QC on long-term LST composites, five exponential powers were applied to weights during the temporal aggregation process, resulting in five thresholds of best quality pixel promotion. It was found that there are significant differences between temporal composites that take into account the MODIS QC and the view angle and those that do not (obtained by means of a simple arithmetic mean with no weights applied), in terms of spatial distribution and density distribution of urban and rural LST. The differences were more distinctive in spring or daytime cases than in autumn or nighttime cases. The impact of the MODIS QC and the view angle on temporal composites was highest in the city center. Ten SUHI indicators were utilized. It was found that the impact on long-term SUHI intensity is weaker than on the spatial pattern of LST and that SUHI indicators are inconsistent.

Remote Sensing Based Vegetation Indices Analysis to Improve Water Resources Management in Urban Environment

Normalized Difference Vegetation Index (NDVI) is estimated from Landsat 8 sensor acquired in December 2014 to drive the different water-related indices like as NDVI and its derivatives. Different vegetation indices (VIs) have been extracted exclusively. The temperature-vegetation index (TVX) space was constructed to investigate the influence of land changes over LST. In this paper, the effect of urban heat island is analyzed using the Landsat TM data in 2009 as a case study in Gulbarga City. The standard algorithms were applied to retrieve the land surface temperature (LST). The spatial pattern of LST in the study area is retrieved to characterize their local effects on urban heat island. In addition, the correlation between LST and the normalized difference vegetation index (NDVI), the normalized difference build-up index (NDBI) is analyzed to explore the impacts of the green land and the build-up land on the urban heat island. The changes of Land surface temperature were related to many factors, including changes in land use, land surface parameters, seasonal variation, climatic condition and economic development, etc. The result showed that the land use change was an important driver for LST increase in the TVX space migrated from the dense-vegetation low temperature condition to the sparse vegetation-high temperature condition. The results showed that the interconnections between different VIs vary. Findings from the current work conducted are anticipated to contribute decisively toward an inclusive VIs assessment of its overall verification. It can be utilized for a multitude of water management applications since it is a valuable indicator of the surface moisture and evapotranspiration: the assessment of agricultural and urban water consumption; the negotiation and monitoring of water and alternative water management practices.

Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data

Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity. However, the characterization of climatic variables, especially surface temperature, still remains a critical bottleneck for a comprehensive understanding of habitats in Kilimanjaro. This study applies satellite-based estimates of land surface temperature (LST), from 2001 to 2011, to delineate a thorough characterization of the spatiotemporal patterns of surface temperature in Mount Kilimanjaro. The ample spatial coverage and continuous observations provided by the satellite measurements allowed the detailed description of characteristics so far poorly understood or not yet described in the literature. We demonstrate that the spatial patterns of LST in this region are rather complex, in the sense that it is characterized by non-linear behaviors and strong interactions with land cover and topography. Daytime observations (measured at 10:30 am) were shown to be strongly influenced by land cover characteristics, which is responsible for defining not only the spatial patterns (e.g., lapse rate) but also the seasonal signature of LST. At nighttime measurements (10:30 pm), the influence of land cover virtually disappears and the spatial patterns are mostly driven by altitude. Moreover, this study provides a brief assessment of LST trends observed within the analyzed period.

Thermal remote sensing of ice-debris landforms using ASTER: an example from the Chilean Andes

Abstract. Remote sensors face challenges in characterizing mountain permafrost and ground thermal conditions or mapping rock glaciers and debris-covered glaciers. We explore the potential of thermal imaging and in particular thermal inertia mapping in mountain cryospheric research, focusing on the relationships between ground surface temperatures and the presence of ice-debris landforms on one side and land surface temperature (LST) and apparent thermal inertia (ATI) on the other. In our case study we utilize ASTER daytime and nighttime imagery and in-situ measurements of near-surface ground temperature (NSGT) in the Mediterranean Andes during a snow-free and dry observation period in late summer. Spatial patterns of LST and NSGT were mostly consistent with each other both at daytime and at nighttime. Daytime LST over ice-debris landforms was decreased and ATI consequently increased compared to other debris surfaces under otherwise equal conditions, but NSGT showed contradictory results, which underlines the complexity and possible scale dependence of ATI in heterogeneous substrates with the presence of a thermal mismatch and a heat sink at depth. While our results demonstrate the utility of thermal imaging and ATI mapping in a mountain cryospheric context, further research is needed for a better interpretation of ATI patterns in complex thermophysical conditions.

Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong

In this paper, the effect of urban heat island is analyzed using the Landsat TM data and ASTER data in 2005 as a case study in Hong Kong. Two algorithms were applied to retrieve the land surface temperature (LST) distribution from the Landsat TM and ASTER data. The spatial pattern of LST in the study area is retrieved to characterize their local effects on urban heat island. In addition, the correlation between LST and the normalized difference vegetation index (NDVI), the normalized difference build-up index (NDBI) is analyzed to explore the impacts of the green land and the build-up land on the urban heat island by calculating the ecological evaluation index of sub-urban areas. The results indicate that the effect of urban heat island in Hong Kong is mainly located in three sub-urban areas, namely, Kowloon Island, the northern Hong Kong Island and Hong Kong International Airport. The correlation between LST and NDVI, NDBI also indicates that the negative correlation of LST and NDVI suggests that the green land can weaken the effect on urban heat island, while the positive correlation between LST and NDBI means that the built-up land can strengthen the effect of urban heat island in our case study. Although satellite data (e.g., Landsat TM and ASTER thermal bands data) can be applied to examine the distribution of urban heat islands in places such as Hong Kong, the method still needs to be refined with in situ measurements of LST in future studies.

Dynamics of Land Surface Temperature in Response to Land-Use/Cover Change

In this study, we employed Geographical Information Systems and remote sensing techniques to investigate the impact of land-use/cover change on land surface temperature (LST) in a rapidly urbanisation city, Kunming in south-west China. Spatial patterns of LST and land use for 1992 and 2006 were derived from Landsat images to examine how LST responded to urban growth. Remote sensing indices were used to quantify land-use types and employed as explanatory variables in LST modelling. The geographically weighted regression (GWR), a location dependent model, was performed to explore the influences of the spatially varied land-use conditions on the LST patterns. Results revealed that rapid urbanisation in Kunming altered the local thermal environment, particularly in increasing the LST in the zone surrounding the urban core. Remote sensing indices demonstrated that water and vegetation played an important role in mitigating the urban heat island effect, while built-up and barren land accounted for the increase in LST. The GWR improved the goodness-of-fit for LST modelling and provided insights into the spatially varied relationship between LST and land-use conditions.