Analysis on Spatial Pattern of Urban Heat Island and Impervious Surface Using Linear Spectral Mixture Analysis

The increase of urban impervious surface has significant influence on the urban ecological environment, also contributes to urban waterlogging and urban heat island (UHI). Therefore, the remote sensing extraction of urban impervious surface becomes a hotspot. The widely used sub-pixel approach - Linear Spectral Mixture Analysis (LSMA) estimates the impervious surface by adding the high albedo (the bright impervious surface) and the low albedo (the dark impervious surface) from the solving percentage of different objects from mixed pixels. However for the approach, the selection of end members is difficult and some misclassifications will occur, which causes a large area error, and low accuracy of the impervious surface area. For these issue, in this study, a modified LSMA is explored combining with high-resolution image (Google earth image, 2 meters), the Principal Component Analysis (PCA) and the Normalized Difference Building Index (NDBI) to extract the impervious surface of Guangzhou city with Landsat 8 OLI, October 2015. Based on the PCA, and high resolution image are employed to accurate the selection of end members. The LSMA based on the PCA, the modified LSMA based on the PCA and NDBI are taken to extract the impervious surface of the research area. And the Google earth image is for screening the end members and testing the accuracy estimation by sample area. The result shows that after the accurate topographic correction, the modified LSMA based on the PCA and NDBI is better than the conventional LSMA based on the PCA. The area precision of impervious surface in research area by the modified LSMA is improved with the correlation coefficient is 0.947. The study indicates that taking the high resolution images, the PCA and NDBI to improve the LSMA can raise the un-mix accuracy to a certain degree.

It is well known that urban impervious surface (IS) has a warming effect on urban land surface temperature (LST). However, the influence of an IS’s structure, components, and spatial distribution on LST has rarely been quantitatively studied within strictly urban areas. Using ETM+ remote sensing images from the downtown area of Shanghai, China in 2010, this study characterized and quantified the influence of the IS spatial pattern on LST by selecting the percent cover of each IS cover feature and ten configuration metrics. The IS fraction was estimated by linear spectral mixture analysis (LSMA), and LST was retrieved using a mono-window algorithm. The results indicate that high fraction IS cover features account for the majority of the study area. The high fraction IS cover features are widely distributed and concentrated in groups, which is similar with that of high temperature zones. Both the percent composition and the configuration of IS cover features greatly affect the magnitude of LST, but the percent composition is a more important factor in determining LST than the configuration of those features. The significances and effects of the given configuration variables on LST vary greatly among IS cover features.

Urban heat island (UHI) attenuation is an essential aspect for maintaining environmental sustainability at a local, regional, and global scale. Although impervious surfaces (IS) and green spaces have been confirmed to have a dominant effect on the spatial differentiation of the urban land surface temperature (LST), comprehensive temporal and quantitative analysis of their combined effects on LST and surface urban heat island intensity (SUHII) changes is still partly lacking. This study took the plain area of Beijing, China as an example. Here, rapid urbanization and a large-scale afforestation project have caused distinct IS and vegetation cover changes within a small range of years. Based on 8 scenes of Landsat 5 TM/7ETM/8OLI images (30 m × 30 m spatial resolution), 920 scenes of EOS-Aqua-MODIS LST images (1 km × 1 km spatial resolution), and other data/information collected by different approaches, this study characterized the interrelationship of the impervious surface area (ISA) dynamic, forest cover increase, and LST and SUHII changes in Beijing’s plain area during 2009–2018. An innovative controlled regression analysis and scenario prediction method was used to identify the contribution of ISA change and afforestation to SUHII changes. The results showed that percent ISA and forest cover increased by 6.6 and 10.0, respectively, during 2009–2018. SUHIIs had significant rising tendencies during the decade, according to the time division of warm season days (summer days included) and cold season nights (winter nights included). LST changes during warm season days responded positively to a regionalized ISA increase and negatively to a regionalized forest cover increase. However, during cold season nights, LST changes responded negatively to a slight regionalized ISA increase, but positively to an extensive regionalized ISA increase, and LST variations responded negatively to a regionalized forest cover increase. The effect of vegetation cooling was weaker than ISA warming on warm season days, but the effect of vegetation cooling was similar to that of ISA during cold season nights. When it was assumed that LST variations were only caused by the combined effects of ISA changes and the planting project, it was found that 82.9% of the SUHII rise on warm season days (and 73.6% on summer days) was induced by the planting project, while 80.6% of the SUHII increase during cold season nights (and 78.9% during winter nights) was caused by ISA change. The study presents novel insights on UHI alleviation concerning IS and green space planning, e.g., the importance of the joint planning of IS and green spaces, season-oriented UHI mitigation, and considering the thresholds of regional IS expansion in relation to LST changes.

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.

The thermal effect of urban impervious surfaces (UIS) is a complex problem. It is thus necessary to study the relationship between UIS and land surface temperatures (LST) using complexity science theory and methods. This paper investigates the long-range cross-correlation between UIS and LST with detrended cross-correlation analysis and multifractal detrended cross-correlation analysis, utilizing data from downtown Shanghai, China. UIS estimates were obtained from linear spectral mixture analysis, and LST was retrieved through application of the mono-window algorithm, using Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data for 1997–2010. These results highlight a positive long-range cross-correlation between UIS and LST across People’s Square in Shanghai. LST has a long memory for a certain spatial range of UIS values, such that a large increment in UIS is likely to be followed by a large increment in LST. While the multifractal long-range cross-correlation between UIS and LST was observed over a longer time period in the W–E direction (2002–2010) than in the N–S (2007–2010), these observed correlations show a weakening during the study period as urbanization increased.

In the present study, Landsat series remote sensing image are utilized to investigate the spatial and temporal changes of the urban heat island (UHI) in the Taiyuan city from 1990, 2004 and 2014. The main influencing factors of the UHI are analyzed in this regard. The single window algorithm is adopted to invert the land surface temperature (LST) from the thermal infrared data and analyze the spatiotemporal pattern of the LST in the studied area. Then, the urban thermal field variance index (UTFVI), normalized difference vegetation index (NDVI) and biophysical composition index (BCI) are calculated in accordance with the LST. Moreover, the degree of impervious surface of the study area is divided according to the BCI value. By analyzing the correlation between the LST, NDVI and the BCI, the influences of two major factors, green space and impervious surface, on urban heat islands are discussed. It is found that the average surface temperature of the Taiyuan city has increased by 5.17 °C during the past 24 years from 1990 to 2014, while the impervious surface area has increased by 223.53 km2, which is 1.179 times of that in 1990. There is a statistical correlation between the LST and the data of the NDVI and impervious surface, which shows that the reduction in green space and the increase in impervious surface in the city have a significant impact on the urban heat island effect. As the largest city in Shanxi province, there is an increasing demand in Taiyuan city for constructing lands during the urbanization process, which has led to a remarkable decrease in the green space and an increase in impervious surfaces. Therefore, the LST in Taiyuan city has been affected by the rapid urbanization. Furthermore, UTFVI indicates that the overall ecological environment of the Taiyuan city has deteriorated and the area of high temperature in the city has increased significantly. Results of the present study may provide a scientific basis for government departments to formulate urban planning and environmental protection policies. The present study may help the government to expand urban green spaces in the right places, control the expansion of the impervious surface of the city, alleviate the UHI effect and maintain the sustainable development of the Taiyuan city.

The conversion of vegetation land cover contributes to the retention of solar radiation in the environment, resulting in the formation of the urban heat island. This study analyses the distribution pattern of urban heat island in the traditional core of Ibadan amidst the rapid urbanization experienced. The remote sensing tool was used to analyse the trend of land surface temperature, normalized difference built-up index, and normalized difference vegetation index for the traditional Ibadan's core between the year 2000 and the year 2020. This reveals that increasing built-up areas will continue to strengthen the effects of urban heat island in the traditional core of Ibadan, while vegetated land covers will weaken the effects of urban heat island. This is because anthropogenic activities resulting from rapid urbanisation has adversely altered the natural landscape in the traditional core of Ibadan. This alteration manifests in converting vegetation land covers into physical developments and other impervious surfaces by the increasing urban population. Thus, increasing the land surface temperature. The maximum average land surface temperature of 35.34℃, 36.62℃ and 31.86℃ were record for the years 2000, 2013 and 2020 respectively. This study further recommends that urban greening and proper urban planning should be encouraged in the traditional core of Ibadan. Keywords: Land Surface Temperature, Normalized Difference Built-up Index, Normalized Difference Vegetation Index, traditional core, urban heat island

This paper explores the potential to improve the impervious surface estimation accuracy using a multi-stage approach on the basis of vegetation-impervious surface-soil (V-I-S) model. In the first stage of Spectral Mixture Analysis (SMA) process, pixel purity index, a quantitative index for defining endmember quality, and a 3-dimensional endmember selection method were applied to refining endmembers. In the second stage, instead of obtaining impervious surface fraction by adding high and low albedo fractions directly, a linear regression model was built between impervious surface and high/low albedo using a random sampling method. The urban impervious surface distribution in the urban central area of Shanghai was predicted by the linear regression model. Estimation accuracy of spectral mixture analysis and impervious surface fraction were assessed using root mean square (RMS) and color aerial photography respectively. In comparison with three different research methods, this improved estimation method has a higher overall accuracy than traditional Linear Spectral Mixture Analysis (LSMA) method and the normalized SMA model both in root mean square error (RMSE) and standard error (SE). However, the model has a tendency to overestimate the impervious surface distribution.

Direct applications of remote sensing thermal infrared (TIR) data in landscape ecological research are rare due to limitations in the sensors, calibration, and difficulty in interpretation. Currently there is a general lack of methodology for examining the relationship between land surface temperatures (LST) derived from TIR data and landscape patterns extracted from optical sensors. A separation of landscapes into values directly related to their scale and signature is a key step. In this study, a Landsat ETM+ image of Indianapolis, Unites States, acquired on June 22, 2000, was spectrally unmixed (using spectral mixture analysis, SMA) into fraction endmembers of green vegetation, soil, high albedo, and low albedo. Impervious surface was then computed from the high and low albedo images. A hybrid classification procedure was developed to classify the fraction images into seven land use and land cover (LULC) classes. Using the fractional images, the landscape composition and pattern were examined. Next, pixel-based LST measurements were correlated with the landscape fractional components to investigate LULC based relationships between LST and impervious surface and green vegetation fractions. An examination of the relationship between the LULC and LST maps with landscape metrics was finally conducted to deepen understanding of their interactions. Results indicate that SMA-derived fraction images were effective for quantifying the urban morphology and for providing reliable measurements of biophysical variables. LST was found to be positively correlated with impervious surface fraction but negatively correlated with green vegetation fraction. Each temperature zone was associated with a dominant LULC category. Further research should be directed to the theoretical and applied implications of describing such relationships between LULC patterns and urban thermal conditions.

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

The biophysical composition index (BCI)-based linear spectral mixture model (LSMM) is used in this study to extract the impervious surface (IS), vegetation, and soil coverage of the main urban region (MUR) of Hefei City over the 2001–2021 period. In addition, the Soil Conservation Service-Curve Number (SCS-CN) model is first applied to simulate the surface runoff (SR) in the MUR of Hefei City over the past 21 years, then assessed for simulation accuracy using typical waterlogging points in the study area. On this basis, the spatiotemporal evolution of IS and SR and their relationships in the MUR of Hefei City are investigated and discussed in this study. The obtained results showed that (1) the root-mean-square error (RMSE), mean absolute error (MAE), and systematic error (SE) values of the BCI index-based LSMM are smaller than those of the LSMM, demonstrating a higher extraction accuracy of urban IS extraction of the BCI index-based LSMM. (2) The IS area of the MUR of Hefei City exhibits an increasing trend from 107.555 km2 in 2001 to 387.660 km2 in 2021. In addition, the change rate and change intensity values indicate an increasing–decreasing–increasing trend. The highest change rate and change intensity values are 24.839 km2/year and 23.094%, respectively, and were observed in the 2001–2005 period. (3) The simulated SR (165–195 mm) in the MUR of Hefei City demonstrates an increasing trend in the 2001–2021 period at a rainfall intensity value of 200 mm/d. In addition, the simulated SR amount in the central area exhibits slight changes, while that in the surrounding areas shows substantial variations. (4) The distribution of IS and SR in the MUR of Hefei City reveals strong directional variations, which are all affected by geographical conditions. The IS coverage and SR show high positive correlation coefficients in different years. (5) The present study provides primary data for effective urban planning, water resources management and regulation, and disaster prevention and mitigation in Hefei City, as well as a scientific reference for future studies on urban IS, SR, and their quantitative relationships in other regions.

In remote sensing studies of land surface temperatures (LST), thematic land-use and land-cover (LULC) data are frequently employed for simple correlation analyses between LULC types and their thermal signatures. Development of quantitative surface descriptors could improve our capabilities for modeling urban thermal landscapes and advance urban climate research. This study developed an analytical procedure based upon a spectral unmixing model for characterizing and quantifying the urban landscape in Indianapolis, Indiana. A Landsat Enhanced Thematic Mapper Plus image of the study area, acquired on 22 June 2002, was spectrally unmixed into four fraction endmembers, namely, green vegetation, soil, high and low albedo. Impervious surface was then computed from the high and low albedo images. A hybrid classification procedure was developed to classify the fraction images into seven land-use and land-cover classes. Next, pixel-based LST measurements were related to urban surface biophysical descriptors derived from spectral mixture analysis (SMA). Correlation analyses were conducted to investigate land-cover based relationships between LST and impervious surface and green vegetation fractions for an analysis of the causes of LST variations. Results indicate that fraction images derived from SMA were effective for quantifying the urban morphology and for providing reliable measurements of biophysical variables such as vegetation abundance, soil, and impervious surface. An examination of LST variations within census block groups and their relationships with the compositions of LULC types, biophysical descriptors, and other relevant spatial data shows that LST possessed a weaker relation with the LULC compositions than with other variables (including urban biophysical descriptors, remote sensing biophysical variables, GIS-based impervious surface variables, and population density). Further research should be directed to refine spectral mixture modeling. The use of multi-temporal remote sensing data for urban time-space modeling and comparison of urban morphology in different geographical settings are also feasible.

Endmember selection is the key to success in pixel unmixing which plays an important role in urban impervious surface abundance extraction. During the extraction, however, there has been a problem for the discrimination of impervious surfaces and soils because of their similarity in spectral. This increase the difficulty in distinguishes impervious surface and soil in endmember selection. To address this issue, in the current study, the biophysical composition index (BCI) and soil adjusted vegetation index (SAVI) were introduced to enhance the information of impervious surface and bare soil in the study area. Then, by selecting high albedo, low albedo, soil and vegetation endmembers with the utilization of the histogram of the indices and minimum noise fraction (MNF) scatter plot, we applied spectral mixture analysis (SMA) to extract impervious surface abundance. The scene of multispectral Landsat TM image was acquired allowing for the interpretation and analysis of impervious surfaces distribution. Experiments and comparisons indicate that this method performs well in estimating subpixel impervious surface distribution with relatively high precision and small bias.

Accurate estimations of impervious surface areas are essential for urban planning development. Linear spectral mixture analysis (LSMA) is commonly adopted to extract the impervious surface (IS) fraction in a mixed pixel at the subpixel scale. However, owing to errors in the spectra of pure pixels selected from remote sensing images, incorrect fractions of different land cover types often emerge after unmixing. In this study, two Landsat 8 Operational Land Imager (OLI) images-acquired on 20 September 2019 (Path/Row: 121/44) and 14 November 2019 (Path/Row: 122/44)-of Guangzhou and Shenzhen were unmixed by LSMA using spectral indices in endmember selection. A post-processing model using the Dry Bare-soil Index (DBSI) and Normalized Difference Vegetation Index (NDVI) as thresholds was established to improve the IS fraction of the LSMA result. Comparative analysis reveals that LSMA with the post-processing model achieves better performance for IS fraction extraction (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.910 and 0.926 and root mean square error [RMSE] = 10.08% and 10.83% for Guangzhou and Shenzhen, respectively), and the distribution of IS is basically consistent with the IS of the actual areas. The post-processing model solves the problem of overestimation of pervious surface and underestimation of impervious surface.

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