Analyses on spatiotemporal patterns of impervious surface coverage in Changsha metropolitan area

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.

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.

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.

In this paper, we estimated impervious surface from United States census housing density data sets for the conterminous United States to examine the distribution and extent of impaired watersheds, and to estimate the risk to watersheds from development in the near future. We used regression tree methods to relate estimates of current housing density to the 2001 National Land Cover Database (NLCD) percent urban imperviousness. As of 2000, we estimate 83,749 km2 of impervious surface (IS) cover across the United States (about 9.6% lower than the NLCD). We estimate that IS cover will expand to 114,070 km2 by 2030. About 7% of eight-digit Hydrologic Unit Code (HUC) watersheds (3.6% of the conterminous United States) were stressed or degraded ( >5% IS) in 2000, and we estimated that this will increase to nearly double to 8.5% of watersheds by 2030 (6.3% of area). We explored the subtle differences of fine-grain pattern for different urban land use types by comparing our national estimates of IS to those develo...

The rapid and extensive urbanization has profound impacts on urban thermal environment. It is of great significance to comprehensively understand how urbanization affects the evolution of urban thermal environment for urban ecological safety, environmental quality, and residents' health. Based on daily land surface temperature (LST) products of MODIS Aqua satellite in the summer of 2002-2020, we investigated the evolution of urban-rural differences in surface summer thermal environment in Shanghai during 2002-2020 and its response to urban spatial renewal. We used normalized land surface temperature (NLST) and urban heat island ratio index (URI) as the surface thermal environment measurement indicators, by combining vegetation index and impervious surface cove-rage, and used M-K trend analysis and interpretation analysis. The results showed that the linear growth rate of LST in Shanghai was 0.09 ℃·a-1 (2002-2020), and that URI showed a trend of first increasing (2002-2010) and then decreasing (2010-2020). The mean summer LST was generally in the order of urban core>suburban>rural. 1.6% of the areas showed a significant cooling trend, of which 54.0% were distributed in the urban core. 39.5% of the regions showed a significant warming trend, of which 77.6% were distributed in the suburban. In general, there were concentrated significant cooling areas in the highly urbanized urban areas, while there was a significant warming trend in the suburban. The transformation from urban expansion to urban renewal was the main reason for the emergence of concentrated and significant cooling areas in the urban. Nearly 20% of the urban area showed a signi-ficant increase of vegetation coverage. Urban renewal projects such as gathering vegetation or dispersing impervious surfaces in highly urbanized areas are important ways to effectively improve the urban residential thermal environment.

Abstract. The rapid urban expansion in Abuja, Nigeria, has resulted in the replacement of land surface previously occupied by natural vegetation with various impermeable materials. This study examines the impact of the spatial distribution of impervious surfaces (IS) on land surface temperature (LST) in the study area using both graphical and quantitative approach. A Normalized Difference Impervious Surface Index (NDISI) was adopted to estimate IS and LST from Landsat ETM+ and OLI/TIRS satellite images (path: 189, row: 54) of Abuja for 4 distinct epochs of 2004, 2008, 2014 and 2018. In order to analyze the effect of IS on LST, the relationship between the normalized difference indices and LST, for each epoch, were determined using regression and correlation analyses. Results show the spatial patterns of impervious surfaces as distributed over Abuja, Nigeria and its impact on LST dynamics. It was observed that mean surface temperature increased by at least 2 °C every 4 years. Furthermore, results of the correlation analysis between NDISI and LST reveal that there exist varying positive correlations between the two variables in with correlation coefficients; R = 0.511, 0.166, 0.505, 0.785 in 2004,2008, 2014 and 2018 respectively, suggesting that impervious surfaces areas accelerate LST rise and Urban Heat Island (UHI) formation. This study gives great insight on the concept of impervious surfaces and its spatial pattern in Abuja city, Nigeria. The study recommends the widespread use of highly reflective or natural surfaces for rooftops, pavements and roads and that afforestation should be encouraged to increase green areas.

Urban ecological environment evaluation and influencing factors analysis at the residential quarter-level in Xi'an, China

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 process of the surface urban heat island (SUHI) varies with latitude, climate, topography and meteorological conditions. This study investigated the seasonal variability of SUHI in the Tehran metropolitan area, Iran, with respect to selected surface biophysical variables. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) was retrieved as nighttime LST data, while daytime LST was retrieved from Landsat 8 Thermal Infrared Sensor (TIRS) using the split-window algorithm. Both data covered the time period from September 2013 to September 2015. To assess SUHI intensity, we employed three SUHI indicators, i.e., the LST difference of urban-rural, that of urban-agriculture and that of urban-water. Physical and biophysical surface variables, including land use and land cover (LULC), elevation, impervious surface (IS), fractional vegetation cover (FVC) and albedo, were selected to estimate the relationship between LST seasonal variability and the surface properties. Results show that an inversion of the SUHI phenomenon (i.e., surface urban cool island) existed at daytime with the maximal value of urban-rural LST difference of −4 K in March; whereas the maximal value of SUHI at nighttime yielded 3.9 K in May. When using the indicators of urban-agriculture and urban-water LST differences, the maximal value of SUHI was found to be 8.2 K and 15.5 K, respectively. Both results were observed at daytime, suggesting the role of bare soils in the inversion of the SUHI phenomenon with the urban-rural indicator. Maximal correlation was observed in the relationship between night LST and elevation in spring (coefficient: −0.76), night LST and IS in spring (0.60), night LST and albedo in winter (−0.53) and day LST with fractional vegetation cover in summer (−0.41). The relationship between all surface properties with LST possessed large seasonal variations, and thus, using these relationships for SUHI modeling may not be effective. The only exception existed in the correlation between elevation and IS, which may be useful to simulate the SUHI at night. This study suggests that in semi-arid cities, such as Tehran, with the urban-rural indicator, a surface urban cool island may be observed in daytime while SUHI at nighttime; with other indicators, SUHI can be observed in both day and night. Thus, SUHI studies require the acquisition of remote sensing image data at both daytime and nighttime and careful selection of SUHI indicators.

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

Lake surface water temperature (LSWT) is an important factor affecting a lake's ecological environment. In recent decades, LSWT worldwide has shown an increasing trend in the context of global climate change. This rising trend has been more evident in urban lakes. With the rapid development of urbanization, urban lakes are affected not only by climate warming but also by human activities. Among these factors, due to the increase in impervious surface coverage (ISC), the impact of thermal runoff pollution caused by precipitation events on urban lakes cannot be ignored. Therefore, this study used the Dianchi Lake watershed as a study area, and the surface water temperature of Dianchi Lake, the precipitation data, and the land use data were collected and analyzed. Based on these data, the influence of precipitation events on the surface water temperature of Dianchi Lake was analyzed. The research results show that under the background of different ISC levels and different growth rates of impervious surface area (ISA), precipitation events have different effects on the LSWT. When ISC is low and the growth rate of ISA is slow, the annual precipitation is negatively correlated with the annual average surface water temperature of Dianchi Lake (r = - 0.183). When ISC is high and the growth rate of ISA is fast, the annual precipitation is positively correlated with the average annual surface water temperature of Dianchi Lake (r = 0.65). With the increase in ISC, the correlation between seasonal precipitation and the average surface water temperature in Dianchi Lake changed from negative to positive in spring and autumn. Under the action of impervious surfaces, precipitation events have a warming effect on the surface water temperature of the lake, and this effect will be intensified with the increase in ISC.

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.

Urban ecological parameters, such as land surface emissivity, temperature, and dryness, play important role in urban ecological system. The present study aimed to explore these ecological parameters using Landsat ETM+ data. The land surface emissivity (LSE) was calculated from the normalized vegetation index (NDVI) considering different cases; land surface temperature (LST) was retrieved from the thermal band based on the method of mono-window algorithm; and land surface dryness (LSD) was derived from the NDVI via LST space, in which the two edges for the minimum and maximum LST values (LSTmin and LSTmax) were simulated using NDVI values. The results showed that there was an obvious gradient as progressed from the core city out into the countryside for the three parameters. The values for LST and LSD over water surface and vegetated surface were obvious lower than that over impervious surface, demonstrating water and vegetation had the ability to improve environmental quality. These simulation results could provide quantitative evaluations and suggestions for the environmental planning and management by government.

Rapid urbanization and changes in land use land cover (LULC) have affected the thermal character of cities across the globe. Researchers found that Remote Sensing (RS) based urban biophysical composition- vegetation coverage, built-up area coverage, impervious surface coverage, soil fractions can significantly explain land surface temperature (LST) variation both at a local and regional scale. Taking Dhaka City, Bangladesh, as an example, this study aimed at exploring the effect of biophysical composition on LST in the year 2020 and find out Urban Heat Island (UHI) clusters with different LST setting using spatial autocorrelation technique. We found that LST presented a significant positive correlation with Normalized Difference Built-up Index (NDBI), road percentage, and negative correlation with Normalized Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Bareness Index (NDBaI), porosity, water percentage, open space percentage. Further, the study conducted local Moran’s I and Hot Spots (Getis-Ord-Gi statistics) analysis to find out the location of UHI clusters. Findings showed that 34 % of neighborhoods fall within one of the four spatial patterns of UHI clusters. Study findings would help urban planners and policymakers to have cogent understating on the relationship between urban thermal behavior, and its surface biophysical composition at fine spatial scales and hopes to provide a valuable reference for community planning, resource allocation, and sustainable development. JBIP, Vol. 13, 2020 (Printed in June 2021), pp. 153-167

The worldwide urban sprawl has led to the substitution of natural vegetation- and water-dominated lands by impervious materials such as building roofs and paved roads. This has brought about significant impacts on ecosystems on a local, regional and global scale. Taking Xiamen City of Fujian province, China, as a case, this study investigated the impervious surface dynamics of the city during the last 20 years. The normalized difference impervious surface index (NDISI) was used to extract impervious surfaces of the city from Landsat TM imagery. The results show that the impervious surface area of the city has increased by about eight times at a ∼10 % average annual growth rate during the 20 study years. Further investigation of the relationships among impervious surface, land surface temperature (LST), vegetation and water has revealed a strong positive exponential relationship between impervious surface area and LST, while vegetation and water are inversely related to LST.