Land Surface Temperature Patterns Research Articles

Sustainable strategies for smart cities: Analysis of the town development effect on surface urban heat island through remote sensing methodologies

In recent years the use of satellite remote sensing techniques has proven to be a useful tool for monitoring urban surface parameters: data provided on the reflective and thermal state of the urban texture, both at local and global scale, give fundamental information on the surface urban heat island (SUHI) control of the urban planning.In this work, the retrieval of the urban albedo and land surface temperature (LST) from Landsat 7 satellite data is performed over a selected area of a town in Central Italy (Terni), exhibiting a significant urban change during the last 10 years. Comparing two satellite images on 2005 and on 2015, the spatial pattern of albedo and LST shows an average albedo decrease of 0.03 during this period and a daytime SUHI increase of 2.3°C.As highlighted by a focused local scale analysis, built-up area modifications moved towards both a reduction and an increase of the surface albedo, comparing the previous situation of the area and the reflective properties of materials chosen for the new settlements or refurbishments.The proposed analysis with remote sensing data may be considered an effective indicator able to point out if urban changes like interventions and new constructions move towards an urban sustainable development in terms of SUHI mitigation.

Diurnal and Seasonal Variation of Surface Urban Cool and Heat Islands in the Semi-Arid City of Erbil, Iraq

The influence of land surface temperature (LST) makes the near-surface layer of the troposphere a key driver of urban climate. This paper assesses the temporal formation of the daytime Surface Urban Cool Island (SUCI) and night-time Surface Urban Heat Island (SUHI) effect in Erbil, Iraq, situated in a semi-arid climate region. LST retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra and MODIS Normalized Difference Vegetation Index (NDVI) from January 2003 to December 2014 are analysed. The relationships of LST with NDVI and the Normalized Multi-band Drought Index (NMDI) are investigated in order to assess the influence of vegetation and moisture on the observed patterns of LST and the SUCI/SUHI. The results indicate that during the daytime, in summer, autumn and winter, densely built-up areas had lower LST acting as a SUCI compared to the non-urbanised area around the city. In contrast, at night-time, Erbil experienced higher LST and demonstrated a significant SUHI effect. The relationship between LST and NDVI is affected by seasonality and is strongly inverted during spring (r2 = 0.73; p < 0.01). Contrary to previous studies of semi-arid cities, a SUCI was detected, not only in the morning, but also during the afternoon.

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.

Consistent land surface temperature data generation from irregularly spaced Landsat imagery

Abstract Land surface temperature (LST), derived from satellite thermal infrared (TIR) sensors, is a key variable for characterization of urban heat island, modeling of surface energy balance, estimation of evapotranspiration and soil moisture, and retrieval of air temperature. Among the satellite TIR sensors in operation, Landsat TIR sensor provides the only feasibility for long-term reconstruction of a LST dataset for environmental applications. However, a holistic technique is not currently available to generate spatially and temporally continuous LSTs from Landsat due to its 16-day revisit frequency, impact of atmospheric conditions and the SLC (Scan Line Corrector) -off gap. Previous algorithms had been developed to overcome these limitations, it is still not possible to generate LSTs at any desired date with consistent accuracy and corrections. Therefore, this study aimed to devise an algorithm to reconstruct consistent, daily LSTs at Landsat spatial resolution based solely on Landsat imagery. By selecting Beijing, China, as the study area, a total of 512 images from 1984 to 2011 were downloaded from the USGS online portal and were consistently calibrated to surface reflectance and brightness temperature. The cloud-, cloud shadow-, and snow-contaminated pixels were excluded according to quality flags; and a further screening procedure based on temporal information of Landsat spectral bands 2, 4, and 5 was conducted. Brightness temperatures were converted to LSTs through the single channel algorithm with input of water vapor from the NCEP Reanalysis dataset. Field LSTs were collected from 11 weather stations in Beijing in the year of 2008, 2009, and 2010. The proposed algorithm included four modules: Data filtEr, temporaL segmentation, periodic and trend modeling, and GAussian process (DELTA). Accuracy assessment showed that, compared with the in situ LSTs from weather stations, satellite-derived LSTs inverted through the single channel algorithm had an average accuracy of 2.3 K. Further comparison between LSTs reconstructed from the DELTA algorithm and those collected from weather stations in the year 2008 yielded a mean error of 3.5 K. Twelve LST maps reconstructed from the DELTA in 2000 showed that LSTs of different land covers exhibited similar seasonal patterns and reached their maximal values in June/July. Using LST of every August 15th as an example, the SUHI (surface urban heat island) intensity of Beijing was computed, which ranged from 3.3 K to 5.3 K from 1984 to 2011, with an increase pattern of LST in both rural and urban areas.

THE CLIMATE OF THE CANARY ISLANDS BY ANNUAL CYCLE PARAMETERS

Annual cycle parameters (ACP) provide a global climatology of annual land surface temperature (LST) based on daily 1 km MODIS observations. These are based on a simple model of the annual temperature cycle and allow estimating LST patterns under largely cloud-free conditions for every day of year. Further, they deliver measures for the LST variability and the frequency of cloud occurrence. It has been demonstrated, that they reproduce important surface climate characteristics at global and urban scale but their ability to reproduce topo-climates has yet to be studied in detail. In this paper their suitability to investigate climatic variability at km scale were studied at the case of the Canary Islands (Spain). This Archipelago, has a very stable climate dominated by the Azores high and the trade wind belt, but shows a large number of micro-climates ranging from arid hot climates to cold climates. It was found that ACPs are a relevant source of climatic information at km scale in complex orography. Specifically, known features such as subsidence inversion, the resulting sea of clouds, the strong differentiation in precipitation between the flat and high islands, as well as the northern and southern slopes at the latter were clearly visible in the parameters.

THE CLIMATE OF THE CANARY ISLANDS BY ANNUAL CYCLE PARAMETERS

Abstract. Annual cycle parameters (ACP) provide a global climatology of annual land surface temperature (LST) based on daily 1 km MODIS observations. These are based on a simple model of the annual temperature cycle and allow estimating LST patterns under largely cloud-free conditions for every day of year. Further, they deliver measures for the LST variability and the frequency of cloud occurrence. It has been demonstrated, that they reproduce important surface climate characteristics at global and urban scale but their ability to reproduce topo-climates has yet to be studied in detail. In this paper their suitability to investigate climatic variability at km scale were studied at the case of the Canary Islands (Spain). This Archipelago, has a very stable climate dominated by the Azores high and the trade wind belt, but shows a large number of micro-climates ranging from arid hot climates to cold climates. It was found that ACPs are a relevant source of climatic information at km scale in complex orography. Specifically, known features such as subsidence inversion, the resulting sea of clouds, the strong differentiation in precipitation between the flat and high islands, as well as the northern and southern slopes at the latter were clearly visible in the parameters.

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

Abstract. 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.

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.

Spatial validation of large‐scale land surface models against monthly land surface temperature patterns using innovative performance metrics

AbstractLand surface models (LSMs) are a key tool to enhance process understanding and to provide predictions of the terrestrial hydrosphere and its atmospheric coupling. Distributed LSMs predict hydrological states and fluxes, such as land surface temperature (LST) or actual evapotranspiration (aET), at each grid cell. LST observations are widely available through satellite remote sensing platforms that enable comprehensive spatial validations of LSMs. In spite of the great availability of LST data, most validation studies rely on simple cell to cell comparisons and thus do not regard true spatial pattern information. The core novelty of this study is the development and application of two innovative spatial performance metrics, namely, empirical orthogonal function (EOF) and connectivity analyses, to validate predicted LST patterns by three LSMs (Mosaic, Noah, Variable Infiltration Capacity (VIC)) over the contiguous United States. The LST validation data set is derived from global High‐Resolution Infrared Radiometric Sounder retrievals for a 30 year period. The metrics are bias insensitive, which is an important feature in order to truly validate spatial patterns. The EOF analysis evaluates the spatial variability and pattern seasonality and attests better performance to VIC in the warm months and to Mosaic and Noah in the cold months. Further, more than 75% of the LST variability can be captured by a single pattern that is strongly correlated to air temperature. The connectivity analysis assesses the homogeneity and smoothness of patterns. The LSMs are most reliable at predicting cold LST patterns in the warm months and vice versa. Lastly, the coupling between aET and LST is investigated at flux tower sites and compared against LSMs to explain the identified LST shortcomings.

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.

Monitoring Spatial Patterns of Land Surface Temperature and Urban Heat Island for Sustainable Megacity

Land use/cover change has brought major changes on land surface, including urban heat budget, temperature regimes, urban hydrology and others, leading to unsustainable environments. Remote sensing has played an important role in monitoring these changes across the globe, especially where instrumental data is not available for such observations. The study uses Landsat 5 TM remote sensing data (26 October 2010) to monitor spatial patterns of land surface temperature and urban heat island in the city of Mumbai, India. The thermal data was corrected and converted to top of the atmosphere radiance values and further to temperature in degrees Celsius from Kelvin scale using Erdas Imagine 9.2. The study reveals that urban areas have the highest temperature, ranging from 28°C for low built-up density and 34°C for high built-up and high-rise urban areas. Relatively, lower temperature is associated with forests and water bodies. The temperature varies with the vegetation density and depth of water. High vegetation density is related with low temperature (23°C) and low density with high temperature (26°C). Higher temperature (25°C) is recorded for shallow water and lower temperature (21°C) for deep water. The rocky bare surface record temperature, ranges between 26°C and 28°C. The east–west and north–south profile of surface temperature confirm the heat island phenomena in Mumbai. The central and southern parts of the city have the highest temperature (34°C) whereas the city outskirts have lower temperatures (23°C). For megacities to be sustainable, the spatial spread and composition of urban growth needs to be regulated and constantly monitored.

Revealing the precipitation dependency of regional in time and in space thermal anomaly peaks in SW USA

The selective variance reduction technique that applies linear regression models to the principal components of multi-temporal night monthly averaged land surface temperature (LST) imagery splits the variance associated to elevation, latitude, longitude in SW USA for the year 2007. The reconstructed multi-temporal imagery indicate the positive or negative deviation (thermal anomaly) from the elevation, latitude, longitude predicted LST. The spatial and temporal patterns of thermal anomalies are revealed by K-means clustering. The mean precipitation computed per month per cluster interprets the observed fluctuations in the temporal pattern of thermal anomaly. Thus, it is possible to isolate the regional thermal anomaly component from the residual component induced from local weather phenomena (precipitation and possibly snow melting, surface water flow and water table depth variations). The regional thermal anomaly component is more likely to relate to geology (lithology-thermal inertia of rocks) and geophysics (geothermal fields) in the study area. The influence of precipitation to the temporal pattern of LST could provide a sort of evaluation of the precipitation data sets available.

Impact of Industrial Activities on Land Surface Temperature Using Remote Sensing and GIS Techniques - A Case Study in Jubail, Saudi Arabia

Land Surface temperature (LST) is one of the most important variables for determining the state within the climate system. Thermal infrared (TIR) remote sensing used to monitor air temperature and affecting microclimate in urban areas. TIR remote sensing techniques have been applied for analyzing LST patterns and its relationship with surface characteristics, assessing urban heat island (UHI), and relating LST with surface energy fluxes to characterize landscape properties and processes. In the present study, a remote sensing was combined with a geographic information system (GIS) environment for determine the impact of industrial areas on Surface Temperature using of TIR Remote Sensing through Landsat ETM+ data (band 6.1) and comparing the relationships between urban surface temperature and land cover types in Jubail City in Saudi Arabia. The study showed the increase of urban surface temperature near the industrial area in comparison with suburban areas. The center of the heat island was concentrated above the industrial area and its adjacent urban areas. Iron and steel factories raise the temperature to 80°C which affects air temperature of nearby areas. This effect may extend to the buffer zone area ranging from 500-2000 m

Surface Heat Island in Shanghai and Its Relationship with Urban Development from 1989 to 2013

The continuous expansion of impervious artificial surfaces in cities has significantly influenced the urban thermal environment. This paper examines the spatiotemporal variation of the diurnal surface urban heat island (SUHI) in Shanghai from 1989 to 2013, a period during which the city has experienced drastic development changes. A remote sensing approach was taken to derive the spatial patterns of Shanghai’s land surface temperature (LST) from Landsat Thematic Mapper (TM) images and Operational Land Imager (OLI) data. The LST pattern was further classified into five LST classes to look at the relative SUHI intensity level across the whole city. Spatial analyses, namely, spatial association and centroid movement analysis, were conducted to reveal the trends of LST changes at both local and holistic scales. To understand the potential drivers for the present spatiotemporal variation of SUHI, different indicators including land use change, population density, night light data, and vegetation were analyzed and compared with LST changes. Based on the quantitative analysis and the socioeconomic context of Shanghai, “heating up” regions were identified, possible reasons for such SUHI variation were summarized, and districts that are most vulnerable to extreme heat conditions were projected. In terms of implication for urban development, planning and design recommendations were suggested to improve the urban thermal environment in Shanghai.

The Morphology, Dynamics and Potential Hotspots of Land Surface Temperature at a Local Scale in Urban Areas

Current characterization of the Urban Heat Island (UHI) remains insufficient to support the effective mitigation and adaptation of increasing temperatures in urban areas. Planning and design strategies are restricted to the investigation of temperature anomalies at a city scale. By focusing on Land Surface Temperature of Wuhan, China, this research examines the temperature variations locally where mitigation and adaptation would be more feasible. It shows how local temperature anomalies can be identified morphologically. Technically, the MODerate-resolution Imaging Spectroradiometer satellite image products are used. They are first considered as noisy observations of the latent temperature patterns. The continuous latent patterns of the temperature are then recovered from these discrete observations by using the non-parametric Multi-Task Gaussian Process Modeling. The Multi-Scale Shape Index is then applied in the area of focus to extract the local morphological features. A triplet of shape, curvedness and temperature is formed as the criteria to extract local heat islands. The behavior of the local heat islands can thus be quantified morphologically. The places with critical deformations are identified as hotpots. The hotspots with certain yearly behavior are further associated with land surface composition to determine effective mitigation and adaptation strategies. This research can assist in the temperature and planning field on two levels: (1) the local land surface temperature patterns are characterized by decomposing the variations into fundamental deformation modes to allow a process-based understanding of the dynamics; and (2) the characterization at local scale conforms to planning and design conventions where mitigation and adaptation strategies are supposed to be more practical. The weaknesses and limitations of the study are addressed in the closing section.

Assessing the impact of urban expansion to the state of thermal environment of peri-urban areas using indices

In this paper we assess the impact of urban expansion to the thermal environment in peri-urban areas in the urban agglomeration of Athens for the period 1994–2010. Firstly, indices based on the use of earth observation data are used to distinguish urbanized from non-urbanized areas, and define built up features and area evolution at high spatial resolution. The urban expansion of Athens is more pronounced to the north and the west of the city, as well as to the south along the coastline. Secondly, the thermal environment is studied through the estimation of land surface temperature (LST) using Landsat satellite data. Areas, where land cover change has occurred due to urban expansion, show significantly smaller LST differences over the years as compared to a reference area in the urban center; a continuum of similar LST values extends from the city center to the peri-urban areas. This finding is of importance regarding urban climate as a temporal modification of the LST pattern in the peri-urban areas of Athens over the years, may potentially impact energy exchange, local circulation patterns, thermal comfort as well as energy consumption for cooling.

An Unmanned Airship Thermal Infrared Remote Sensing System for Low-Altitude and High Spatial Resolution Monitoring of Urban Thermal Environments: Integration and an Experiment

Satellite remote sensing data that lacks spatial resolution and timeliness is of limited ability to access urban thermal environment on a micro scale. This paper presents an unmanned airship low-altitude thermal infrared remote sensing system (UALTIRSS), which is composed of an unmanned airship, an onboard control and navigation subsystem, a task subsystem, a communication subsystem, and a ground-base station. Furthermore, an experimental method and an airborne-field experiment for collecting land surface temperature (LST) were designed and conducted. The LST pattern within 0.8-m spatial resolution and with root mean square error (RMSE) value of 2.63 °C was achieved and analyzed in the study region. Finally, the effects of surface types on the surrounding thermal environment were analyzed by LST profiles. Results show that the high thermal resolution imagery obtained from UALTIRSS can provide more detailed thermal information, which are conducive to classify fine urban material and assess surface urban heat island (SUHI). There is a significant positive correlation between the average LST of profiles and the percent impervious surface area (ISA%) with R2 around 0.917. Overall, UALTIRSS and the retrieval method were proved to be low-cost and feasible for studying micro urban thermal environments.

Spatial variation of the daytime Surface Urban Cool Island during the dry season in Erbil, Iraqi Kurdistan, from Landsat 8

Differences between the energy balance of cities and their non-urban surroundings exist due to modification of surface properties. In temperate and sub-tropical climates, these differences are manifest as the Urban Heat Island (UHI) effect. However in more arid environments man-made modifications of the environment can cause urban cooling relative to the surrounding landscape particularly during the dry season. This research examines the spatial formation of the daytime Surface Urban Cool Island (SUCI) effect of Erbil city in Iraq, as a case study of cities in semi-arid climates. Six satellite images acquired by Landsat 8 during the period from 1st July to 19th September 2013 are used to retrieve Land Surface Temperature (LST), identify Land Use/Land Cover (LULC) classes and investigate the spatial variation of LST and the SUCI intensity. In order to find out the key drivers of the observed patterns of LST, the relationship with wetness, brightness, bareness, built-up and vegetation index maps are examined. The results indicate that densely built-up areas, such as central districts of the city, green areas and water bodies, had lower LST acting as cool islands, compared to the non-urbanized area around the city. In contrast, the airport, open spaces and new low-density housing developments on the outskirts of the city, experienced higher LST and showed an SUHI effect. A very strong inverse relationship is evident between surface temperature and wetness index (r=−0.9; p<0.01). A strong positive correlation (r=0.75; p<0.00001) is apparent with the brightness index. In contrast, between surface temperature and the greenness index a moderate negative correlation was found (r=−0.39; p<0.01) for a typical dry season day. The results show that during the daytime residential areas in the city centre recorded an LST of 46.2±1.74°C. Urban Cool Island Intensity (UCII) of the city ranged from 3.5 to 4.6°C compared to a 10km buffer zone around the city. This study shows that during the dry season in some cities, such as Erbil, the surface wetness is the main determinant of the UCI effect, and not vegetation cover.

Characterizing fractional vegetation cover and land surface temperature based on sub-pixel fractional impervious surfaces from Landsat TM/ETM+

Estimating the distribution of impervious surfaces and vegetation is important for analysing urban landscapes and their thermal environment. The application of a crisp classification of land-cover types to analyse urban landscape patterns and land surface temperature (LST) in detail presents a challenge, mainly due to the complex characteristics of urban landscapes. In this article, sub-pixel percentage impervious surface areas (ISAs) and fractional vegetation cover (FVC) were extracted from bitemporal Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data by linear spectral mixture analysis (LSMA). Their accuracy was assessed with proportional area estimates of the impervious surface and vegetation extracted from high-resolution data. A range approach was used to classify percentage ISA into different categories by setting thresholds of fractional values and these were compared for their LST patterns. For each ISA category, FVC, LST, and percentage ISA were used to quantify the urban thermal characteristics of different developed areas in the city of Fuzhou, China. Urban LST scenarios in different seasons and ISA categories were simulated to analyse the seasonal variations and the impact of urban landscape pattern changes on the thermal environment. The results show that FVC and LST based on percentage ISA can be used to quantitatively analyse the process of urban expansion and its impacts on the spatial–temporal distribution patterns of the urban thermal environment. This analysis can support urban planning by providing knowledge on the climate adaptation potential of specific urban spatial patterns.

Spectral index utility for summer urban heating analysis

The land surface temperature (LST) retrieval in an urban environment by thermal remote sensing is a widespread scientific topic and several studies have been made to point out the correspondence between spectral indices and LST pattern. This work evaluates the potential of the spectral indices introduced in the literature, more than 30, by assessing their correlation with the summer LST in the heterogeneous urban area of Rome, Italy, considering two different triplets of images acquired during 2009 and 2011 by the Landsat Thematic Mapper (TM). The spectral indices have been divided into vegetation and built up-soil indices employing the reflective TM bands, and then ranked on the basis of their linear and monotonic relationship with the LST. Vegetation indices have a strong negative correlation with LST: vegetation area (VA), nonlinear index, modified soil adjusted vegetation index exhibit a greater Pearson and Spearman correlation coefficient with LST. The more useful spectral indices for built up and soil analysis, exhibiting a greater positive correlation with LST, are the impervious surface area (ISA), the bare soil index, the index-based built-up index, and the normalized difference built-up index. Interesting indications of the impact on the spectral index performance of specific land-cover classes embedded in an urban environment, such as the bare soil and the water classes were pointed out; for example, the reduction of the ISA and VA capability to display the full dynamic range of the LST pattern.