Impervious Surface Distribution Research Articles

Slowing the expansion of impervious surfaces: the key to promoting high-quality and sustainable regional development.

Sponge cities are disaster-resilient and sustainable infrastructure, and the emergence of impervious surfaces hinders the construction of sponge cities. In response to the United Nations' 2030 Sustainable Development Goals, the study of the spatio-temporal evolution and influencing factors of impervious surfaces provides an effective basis for the construction of sponge cities. In this paper, multi-source remote sensing images (Landsat 4-5 TM was used in 2000 and 2010, Landsat 8 OLI/TIRS was used in 2020) were used as data sources to construct a multi-feature impervious surface estimation model. By combining and refining the advantages of MISI, NDBI, and BUAI feature indices, we obtained the impervious surface cover of Nanchang City in 2000, 2010, and 2020. And its spatio-temporal evolution characteristics were analyzed by using the ESDA. The main factors of the impervious surface were analyzed by using the ordinary panel data model and the spatial durbin model. Results: (1) From 2000 to 2020, the impervious surface area of Nanchang City increased from 516.13 to 1075.12 km2. The overall impervious surface distribution in Nanchang City expressed a significant neighborhood distribution; (2) socio-economic factors had a positive role in promoting the impervious surface of Nanchang City. Among them, the correlation coefficient of the economic development index (0.2332), real estate investment (0.1518), and gross industrial output value (0.0453) were the most significant in the local areas; (3) the economic development index (0.2307), real estate investment (0.0251), and passenger volume (0.1679) stimulated the growth of impervious surfaces in adjacent areas, and the total population (-0.8074) had a buffering effect on adjacent areas. In order to promote the sustainable development of the region, it is necessary to propose corresponding measures and suggestions based on the impervious surface of Nanchang City.

Urbanization enhances channel and surface runoff: A quantitative analysis using both physical and empirical models over the Yangtze River basin

Urbanization has become an irreversible trend and is even more rapid in flood-prone area. However, the responses of both channel runoff and surface runoff to the urbanization and the underlying mechanisms still deserve more in-depth exploration within urban extents with limited data availability. Accordingly, we conducted a comprehensive quantitative analysis of the urbanization effects on channel and surface runoff from both statistical and model-based perspectives over three major urban agglomerations (i.e., Chengyu (CY), middle region of Yangtze River (MRYR), Yangtze River Delta (YRD)) in the Yangtze River Basin. The long-term high-resolution channel and surface runoff under different urbanization scenarios were simulated using both WRF-Hydro and SCS-CN models by integrating multiple remotely sensed, reanalysis, and in-situ datasets. Results demonstrated that the channel runoff increased by 4.4%, 5%, and 12.08% in CY, MRYR, and YRD urban agglomerations, respectively. Meanwhile, the urbanization also contributed to 30%, 21.4%, and 17.7% of the surface runoff changes in three urban agglomerations. Based on the high-resolution local climate zone (LCZ), our results highlighted that the high-rise and middle-rise buildings and compact-arranged regions were more prone to accumulating surface runoff and trigger flooding events due to the weaker infiltration capacity of land surface with the densely distribution of impervious surface. Further machine learning based investigation and case analysis also explored the potential influencing factors and mechanisms of the impact of urbanization on channel and surface runoff. Results in the current research show further evidence that policy makers of urban planning and management need to consider and ensure societal preparedness for extreme runoff and flooding events.

Mapping impervious surface change from remote sensing and GIS data: A case study in Hochiminh city, Vietnam

Impervious surface is artificial surfaces that prevent water from entering the soil. The increase in impervious surface area has led to negative impacts on the urban environment, including an increase in the risk of flooding, a decrease in vegetation cover, and the formation of urban heat islands. This paper presents the results of building a predictive model of impervious surfaces in Hochiminh city from remote sensing and GIS data. Landsat and Sentinel 2 satellite images for the period 2002–2022 are used to classify impervious surfaces and extract input layers about vegetation cover, land surface temperature, combined with GIS data (elevation, slope, aspect, distance to road, distance to hydrology, population density) for modeling and predicting impervious surface changes in future. 03 machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART) and maximum likelihood method are used to classify impervious surfaces from Landsat satellite images, then select the method with the highest accuracy. To predict the future distribution of impervious surface, this study uses Cellular Automata (CA) model and 02 artificial intelligence algorithms (Artificial Neural Network - ANN, Logistic Regression - LR). The results obtained in the study can be effectively used for urban planning, minimizing the impact of the process of increasing the impervious surface on the urban environment

Resilience of an Urban Coastal Ecosystem in the Caribbean: A Remote Sensing Approach in Western Puerto Rico

Utilization of remote sensing-derived meteorological data is a valuable alternative for tropical insular territories such as Puerto Rico (PR). The study of ecosystem resilience in insular territories is an underdeveloped area of investigation. Little research has focused on studying how an ecosystem in PR responds to and recovers from unique meteorological events (e.g., hurricanes). This work aims to investigate how an ecosystem in Western Puerto Rico responds to extreme climate events and fluctuations, with a specific focus on evaluating its innate resilience. The Antillean islands in the Caribbean and Atlantic are vulnerable to intense weather phenomena, such as hurricanes. Due to the distinct tropical conditions inherent to this region, and the ongoing urban development of coastal areas, their ecosystems are constantly affected. Key indicators, including gross primary production (GPP), normalized difference vegetation index (NDVI), actual evapotranspiration (ET), and land surface temperature (LST), are examined to comprehend the interplay between these factors within the context of the Culebrinas River Watershed (CRW) ecosystem over the past decade during the peak of hurricane season. Data processing and analyses were performed on datasets provided by Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8–9 OLI TRIS, supplemented by information sourced from Puerto Rico Water and Energy Balance (PRWEB)—a dataset derived from Geostationary Operational Environmental Satellite (GOES) data. The findings revealed a complex interrelationship among atmospheric events and anthropogenic activities within the CRW, a region prone to recurrent atmospheric disruptions. NDVI and ET values from 2015 to 2019 showed the ecosystem’s capacity to recover after a prolonged drought period (2015) and Hurricanes Irma and Maria (2017). In 2015, the NDVI average was 0.79; after Hurricanes Irma and Maria in 2017, the NDVI dropped to 0.6, while in 2019, it had already increased to 0.8. Similarly, average ET values went from 3.2339 kg/m2/day in 2017 to 2.6513 kg/m2/day in 2018. Meanwhile, by 2019, the average ET was estimated to be 3.8105 kg/m2/day. Data geoprocessing of LST, NDVI, GPP, and ET, coupled with correlation analyses, revealed positive correlations among ET, NDVI, and GPP. Our results showed that areas with little anthropogenic impact displayed a more rapid and resilient restoration of the ecosystem. The spatial distribution of vegetation and impervious surfaces further highlights that areas closer to mountains have shown higher resilience while urban coastal areas have faced greater challenges in recovering from atmospheric events, thus showing the importance of preserving native vegetation, particularly mangroves, for long-term ecosystem stability. This study contributes to a deeper understanding of the dynamic interactions within urban coastal ecosystems in insular territories, emphasizing their resilience in the context of both natural atmospheric events and human activity. The insights gained from this research offer valuable guidance for managing and safeguarding ecosystems in similar regions characterized by their susceptibility to extreme weather phenomena.

Extracting urban impervious surface based on optical and SAR images cross-modal multi-scale features fusion network

ABSTRACT Monitoring the spatiotemporal distribution of urban impervious surface is an essential indication for measuring the urbanization process. Optical and synthetic aperture radar (SAR) images are key data sources in urban impervious surface extraction. Because cities are highly heterogeneous scenes, using a single data source to extract urban impervious surface encounters a bottleneck in accuracy enhancement due to the limitation of a single-modal feature representation, which can be helped to overcome by fusing the two data. However, existing researches have primarily done fusion directly by layer stacking for urban impervious surface, without taking into account the modal differences between optical and SAR (optical-SAR) images, and thus cannot better realize complementarity between the two. As a result, this study proposes a cross-modal multi-scale features fusion segmentation network (CMFFNet) for optical-SAR images for urban impervious surface extraction. A cross-modal features fusion (CMFF) module is designed in the proposed CMFFNet to fully exploit the complementary information of optical-SAR images. Additionally, we propose a multi-scale features fusion (MSFF) module to fuse multi-scale features of optical-SAR images, taking into account the multi-scale characteristics of urban impervious surface. The results of the experiments demonstrate that the proposed CMFFNet outperforms current mainstream methods for extracting impervious surface.

Identification of Waterlogging-Prone Areas in Nanning from the Perspective of Urban Expansion

The objective of this study was to discern the spatial and temporal patterns of areas in Nanning that are susceptible to waterlogging, particularly during various phases of urban expansion. Furthermore, this study presents a proposal outlining strategies aimed at preventing and controlling waterlogging. These strategies are based on the integration of the concepts of sponge city and resilient city construction. This study employed remote sensing (RS) and geographic information system (GIS) techniques to provide technical support. The supervised classification method and normalized difference index method were utilized to compare and extract impervious surfaces in Nanning from 2013 to 2020. The present investigation utilized the acquired impervious surfaces to compute the fractal dimension as a weighting factor, incorporating a digital elevation model (DEM) for the purpose of conducting a hydrological analysis in ArcGIS. Based on the findings of the study, several conclusions can be derived. The following conclusions can be drawn from the study: (1) The fractal dimension of Nanning varied over the study period, with values of 1.32, 1.41, and 1.58 in 2013, 2017, and 2020, respectively. The distribution of impervious surfaces showed a decreasing trend from the city center to the periphery. Urban planning and construction activities have significantly influenced the distribution of impervious surfaces, resulting in a progressively more complex and unstable structure. (2) From 2013 to 2020, the urban expansion fractal dimension increased from 1.32 to 1.58, indicating a decrease in the stability of impervious surfaces. The areas with higher concentrations of impervious surfaces coincided with frequent waterlogging-prone areas. Furthermore, the distribution of waterlogging-prone points transformed from a concentrated pattern to a scattered one. (3) In terms of waterlogging prevention and control strategies, the old urban areas are recommended to be transformed into sponge city projects, and the new development areas are planned, designed and implemented with the concept of “resilience”.

Synergistic effects of heat and carbon on sustainable urban development: Case study of the Wuhan Urban Agglomeration

The rapid pace of urbanization has resulted in pressing climatic challenges, notably an escalation in carbon emissions and rising urban temperatures. Urban expansion frequently coincides with heightened carbon emissions, releasing substantial quantities of greenhouse gases such as CO2. This exacerbates the urban heat island (UHI) effect and poses a threat to urban living conditions. To shed light on the intricate relationship between land surface temperature (LST) and carbon emissions and provide novel insights into the synergistic regulation of “heat” and “carbon” in sustainable urban development, we conducted an in-depth investigation of the Wuhan Urban Agglomeration spanning from 2003 to 2018. Employing data on impervious surface fraction (ISF), LST, and carbon emissions, we harnessed weighted center and standard deviation ellipse analyses to explore the spatiotemporal expansion patterns of ISF, LST, and carbon emissions. We examined the relationship between LST and carbon emissions using spatial correlation analysis. The results revealed that regions with high carbon emissions and elevated LST were predominantly concentrated in the central urban areas of each city, aligned with the distribution of high-density impervious surfaces. The weighted centers of ISF, LST, and carbon emissions exhibited a progressive shift toward the central region of Wuhan, intensifying spatial clustering effects. The global Moran's I index for LST and carbon emissions increased from 0.33 in 2003 to 0.48 in 2018, signifying a strong positive correlation between LST and carbon emissions. In the clustering maps of LST and carbon emissions, the high-high clustering trend persistently expanded, signifying a shift from “dispersion” to “agglomeration” in urban heat and carbon effects. In regions marked by consistent carbon emission growth, the correlation coefficient (R) between carbon emissions and LST was 0.4, and the Pearson coefficient was 0.64, both higher than those in areas with gradual carbon emission growth. Within stable carbon emission growth areas, every 0.1-ton increase in carbon emissions has a 40% probability of a 1.33 °C temperature rise. As the rate of carbon emissions increased, the positive synergistic effect between carbon emissions and LST continued to increase. This study provides empirical support and academic groundwork to reduce urban emissions and mitigate the effects of UHIs.

The Relationship between Urban Morphology on Impervious Surface Coverage in High-Density Areas - The HK Northern Metropolitan Area as a Case Study

Impervious surfaces are one of the key factors that impact stormwater management, especially for high density urban areas. This study explores the relationship between urban morphology and impervious surface coverage in high-density areas to address stormwater management challenges. Focusing on Hong Kong Northern Metropolitan Area as a case study, the research examines land use planning and diverse urban morphologies to understand the impact of impervious surfaces on stormwater runoff. From land use planning to analyzing different urban morphologies, the study elucidates the amount and distribution of impervious surfaces and their impact on stormwater runoff management in urban design. Findings provide valuable insights for urban design practices, enabling targeted strategies for sustainable stormwater management in high-density areas. By understanding the relationship between Hong Kong's context-specific urban form and properties of impervious surface /properties, targeted strategies can be developed to address the unique challenges, especially in the context of planning for the latest urban growth. Promoting the use of green infrastructure and nature-based solutions, the research contributes to the knowledge on urban stormwater management, emphasizing resilience in Hong Kong’s new urban environment.

Extraction and Spatiotemporal Evolution Analysis of Impervious Surface and Surface Runoff in Main Urban Region of Hefei City, China

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 Stream Corridors and Forest Patches—The Connections: A Case Study of Bloomington, IN

Streams and forests are ecosystems connected through hydrology, but few studies have looked at the connectivity between streams and forests in the context of urban development. City-made decisions affect connections between streams and forests by isolating both ecosystems. Streams are often channelized or buried to increase potential development areas. Forests often become fragmented and may be removed unless protected. Historical choices in land usage affect the sites and sizes of current urban streams, forests, and development. This affects the distribution of impervious surfaces, which separates streams from forests. Despite these barriers to stream/forest interactions, cities can experience stream/forest connectivity. Seven Bloomington watersheds are ranked on their proportions of buried streams, channelized streams, forested hydrology, forested streams, urban forest patch cover, and impervious surface cover, along with the historical presence of urbanization. Watersheds demonstrate stream/forest connectivity, with five watersheds containing 50% or greater forested stream segments. Bloomington canopy cover reduces stormwater runoff by approximately 127 kiloliters per year. These forested areas reduce flooding, reduce nutrient loading, and reduce stream conditions associated with urban stream syndrome. Understanding urban stream/forest connectivity can improve green infrastructure design and green space design, which improve urban resilience and better connect residents to the environment.

The Impacts of Redlining on Urban Heat in New York’s Capital Districts

The impact of green space, or lack thereof, on the surface temperature of cities has long been studied through the Urban Heat Island effect. However, the extent to which the historic discriminatory housing policy of redlining influences the temperatures of different neighborhoods in the same city is still being revealed. Here, the researchers investigated how Home Owners’ Loan Corporation (HOLC) maps correspond to land surface temperatures through uneven distribution of tree canopy and impervious surfaces in New York’s Capital District. Using HOLC maps for Albany, Troy, and Schenectady, the researchers calculated percent tree cover and imperviousness for each of the map’s graded blocks, and calculated the land surface temperature for each block using Landsat 8 imagery. Results showed that neighborhoods rated “hazardous” were generally hotter, more impervious, and less tree-covered than those rated “best” in Albany and Schenectady, but not in Troy. The difference between blocks rated “hazardous” and blocks rated “best” was +2.67°C in Albany, +4.18° in Schenectady, and -1.18° in Troy. These results are an example of how discriminatory housing policies are tied to a disproportional distribution of urban heat. Affected communities, like those in the Capital District, should be identified to increase resilience to the environmental, physiological, and social impacts of climate change.
 Keywords: Urban Heat Island Effect, Redlining, New York, Housing Policy, Green Space, Urban Tree Cover.

Exploring the Use of Orthophotos in Google Earth Engine for Very High-Resolution Mapping of Impervious Surfaces: A Data Fusion Approach in Wuppertal, Germany

Germany aims to reduce soil sealing to under 30 hectares per day by 2030 to address negative environmental impacts from the expansion of impervious surfaces. As cities adapt to climate change, spatially explicit very high-resolution information about the distribution of impervious surfaces is becoming increasingly important for urban planning and decision-making. This study proposes a method for mapping impervious surfaces in Google Earth Engine (GEE) using a data fusion approach of 0.9 m colour-infrared true orthophotos, digital elevation models, and vector data. We conducted a pixel-based random forest (RF) classification utilizing spectral indices, Grey-Level Co-occurrence Matrix texture features, and topographic features. Impervious surfaces were mapped with 0.9 m precision resulting in an Overall Accuracy of 92.31% and Kappa-Coefficient of 84.62%. To address challenges posed by high-resolution imagery, we superimposed the RF classification results with land use data from Germany’s Authoritative Real Estate Cadastre Information System (ALKIS). The results show that 25.26% of the city of Wuppertal is covered by impervious surfaces coinciding with a government-funded study from 2020 based on Sentinel-2 Copernicus data that defined a proportion of 25.22% as built-up area. This demonstrates the effectiveness of our method for semi-automated mapping of impervious surfaces in GEE to support urban planning on a local to regional scale.

Changes in Impervious Surfaces in Lhasa City, a Historical City on the Qinghai–Tibet Plateau

Impervious surface cover reflects the urban environment and urban expansion. Lhasa City is a historical city and one of the most populous on the Qinghai–Tibetan Plateau, and has been experiencing rapid urbanization in recent years. Analyzing the impervious surface distribution can reveal urban development characteristics and provide data for sustainable urban planning to protect the heritage. This study explored the spatial and temporal changes and expansion patterns of impervious surfaces in different zones of Lhasa City. Impervious surface maps (2014 and 2021) were extracted from Gaofen-1 images with a high spatial resolution (2 m) using an object-based image analysis method. Next, a gravity center, standard deviational ellipses and landscape indices were used to characterize impervious surface expansions in different zones. The result indicated that the impervious surface in Lhasa expanded from 51.149 km2 in 2014 to 63.299 km2 in 2021. The growth rates of impervious surfaces inside the Environmental Coordination zone were lower than in the zones outside. From 2014 to 2021, the impervious surface of Lhasa expanded in the southeast direction. Infilling and consolidation were the primary impervious surface development patterns. The expansion of the impervious surface was related to topography, population, and economic and policy factors.

Mitigating Extreme Summer Heat Waves with the Optimal Water-Cooling Island Effect Based on Remote Sensing Data from Shanghai, China.

Due to the progress in global warming, the frequency, duration and intensity of climate extremes are increasing. As one of these extremes, heat waves influence the well-being of human beings and increase societies’ energy consumption. The Water-Cooling Island (WCI) effect of urban water bodies (UWBs) is important in urban heat wave mitigation. In this paper, the impact of WCI, especially the landscape pattern of the surrounding area, was explored. The results indicate that water bodies with a larger total area and simpler shape have a longer cooling effect. In the areas surrounding UWBs, a lower percentage or discrete distribution of impervious surfaces or green land provide a longer cooling effect. The amplitude of WCI is mainly decided by the impervious surface in the surrounding areas. A lower percentage or discrete distribution of impervious surfaces or green land leads to a smaller-amplitude WCI. The gradient is impacted by the shape of the UWB and surrounding green land. A complex shape and discrete distribution of green land lead to a higher gradient of WCI. The linear regress model was significant in terms of WCI range and gradient, while the model of WCI amplitude was not significant. This indicates that WCI is directly decided by impact factors through gradient and range. The conclusions provide a methodology for WCI prediction and optimization, which is important when mitigating summer heat waves.

TEMPORAL AND SPATIAL ANALYSIS OF CLASSIFICATION TREE FOR IMPERVIOUS SURFACE MAPPING FROM SENTINEL-2 MSI DATA

Abstract. For studies of urban development, it is an important method for obtaining the distribution of impervious surface (IS) areas and their dynamic change from remote sensing data. The dilemma of the same spectrum for different features and different spectrums for the same features, posed by the complexity of the IS objects, is the fundamental obstacle encountered in the extraction of urban IS areas. In this study, an automatic extraction method for urban IS areas is proposed and analyzed, based on classification and regression tree (CART) and ensemble learning strategies. The Sentinel-2 MSI data of 30 cities in China from 2018 to 2020 were selected for IS extraction experiments. We perform temporal and spatial modeling of the splitting threshold offset in the classification model to explore the effect of time and space on IS extraction. The obtained offset models show that the temporal variation is not significant, while the spatial offsets have more obvious linear relationships.

Quantitative analysis and prediction of urban heat island intensity on urban-rural gradient: A case study of Shanghai

Urbanization causes enormous land use/land cover (LULC) changes, which have become significant drivers of land surface temperature (LST) change in rapidly urbanizing city, and the changes in LULC subsequently increase the LST to form urban heat island (UHI). This paper first identified the spatial distribution pattern of the LULC changes and surface urban heat island intensity (SUHII) in the study area in recent 20 years based on Landsat TM/OLI data. And later, the relationship between the distribution of impervious surface (IS) and vegetable coverage (VC) and SUHII was analyzed quantitatively. Then, the land cover and land surface temperature (LST) in Shanghai in 2027 under three development modes were simulated and predicted based on FLUS model and geospatial analysis. The results showed that (1) Regional land cover and LST had obvious differences in gradient distribution from urban to rural areas, and the outer ring road (Ring3) was an obvious dividing line; (2) the proportion of IS and VC were significantly positively (|R| > 0.695) and negatively (|R| > 0.328) correlated with LST; (3) under the three different scenario development models, the ecological space protection model effectively regulated the SUHII, which was 15.91% less than the SUHII in 2017 (34% inside Ring3 and 14% outside Ring3). The results could provide a reference for the rational allocation of urban land and landscape optimization in reducing SUHII` in typical urbanized areas.

Response relationship between urban thermal environment and impervious surface distribution density at multi-spatial scale： A case study of Beijing

Response relationship between urban thermal environment and impervious surface distribution density at multi-spatial scale： A case study of Beijing

Driving Factors of Land Surface Temperature in Urban Agglomerations: A Case Study in the Pearl River Delta, China

Land surface temperature (LST) in urban agglomerations plays an important role for policymakers in urban planning. The Pearl River Delta (PRD) is one of the regions with the highest urban densities in the world. This study aims to explore the spatial patterns and the dominant drivers of LST in the PRD. MODIS LST (MYD11A2) data from 2005 and 2015 were used in this study. First, spatial analysis methods were applied in order to determine the spatial patterns of LST and to identity the hotspot areas (HSAs). Second, the hotspot ratio index (HRI), as a metric of thermal heterogeneity, was developed in order to identify the features of thermal environment across the nine cities in the PRD. Finally, the geo-detector (GD) metric was employed to explore the dominant drivers of LST, which included elevation, land use/land cover (LUCC), the normalized difference vegetation index (NDVI), impervious surface distribution density (ISDD), gross domestic product (GDP), population density (POP), and nighttime light index (NLI). The GD metric has the advantages of detecting the dominant drivers without assuming linear relationships and measuring the combined effects of the drivers. The results of Moran’s Index showed that the daytime and nighttime LST were close to the cluster pattern. Therefore, this process led to the identification of HSAs. The HSAs were concentrated in the central PRD and were distributed around the Pearl River estuary. The results of the HRI indicated that the spatial distribution of the HSAs was highly heterogeneous among the cities for both daytime and nighttime. The highest HRI values were recorded in the cities of Dongguan and Shenzhen during the daytime. The HRI values in the cities of Zhaoqing, Jiangmen, and Huizhou were relatively lower in both daytime and nighttime. The dominant drivers of LST varied from city to city. The influence of land cover and socio-economic factors on daytime LST was higher in the highly urbanized cities than in the cities with low urbanization rates. For the cities of Zhaoqing, Huizhou, and Jiangmen, elevation was the dominant driver of daytime LST during the study period, and for the other cities in the PRD, the main driver changed from land cover in 2005 to NLI in 2015. This study is expected to provide useful guidance for planning of the thermal environment in urban agglomerations.

URBAN HEAT ISLAND FOOTPRINT EFFECTS ON BIO-PRODUCTIVE RURAL LAND COVERS SURROUNDING A LOW DENSITY URBAN CENTER

Abstract. The urban heat island (UHI) is a common effect caused by urbanization and has been studied to evaluate the thermal condition in cities worldwide. However, most previous UHI analyses are performed in major metropolitan cities. This study conducts a spatiotemporal analysis of UHI in a rapidly expanding low-density suburban centre and determines how bio-productive land covers react and the extent of the disturbance to each land cover based on time series land surface temperatures extracted from Landsat 7 ETM+ images. Two methods applied and compared are the single exponential decay method, which measures UHI footprint (UHIFP) on vegetation phenology, and the two dimensional Gaussian surface, which quantifies the influence based on distance from the local urban perimeter. Three spectral indices (Normalized Difference Vegetation Index (NDVI), Moisture Index (NDMI), and the Enhanced Vegetation Index (EVI)) were extracted and the residuals from the Gaussian model were compared based on these indices in order to better understand the thermal variations of each land cover within a UHI. The results show that the UHIFP of the studied low-density suburban centre is 1.4 times larger than the size of the urban centre, marginally smaller than previous analyses performed within high-density metropolises. All vegetated land covers experienced their maximum cooling effects before reaching the UHIFP perimeter while urban surfaces begin to diverge from the Gaussian model outside of the UHIFP. The residuals of sparse vegetation maintained strong correlations with each index throughout the growing season while NDMI retained the strongest relationships with every land cover. This study has helped us better understand the UHI effects of small communities with varied vegetation phonology based on the distribution of built-up pervious and impervious surfaces within the neighbourhood structure. The similar results from both methods indicate a strong urban cover influence overpowering the dominant distribution of agricultural surfaces throughout the growing season.

Impervious surface extraction based on different methods from multiple spatial resolution images: a comprehensive comparison

ABSTRACT Many efforts have been devoted to extracting impervious surfaces based on different methods from multiple spatial resolution images. Differences in extraction methods and spatial resolutions are significant and have led to discrepant performances in terms of the impervious surface extraction accuracy. However, which extraction method is more suitable for which kind of spatial resolution image in practice is poorly understood. This study systematically compared the performances of 12 methods of impervious surface extraction for four spatial resolution images (i.e. Landsat 8 [30 m], Sentinel-2A [20 m], Sentinel-2A [10 m], and Gaofen-2 [4 m]) in three testing areas. The results indicated that for the medium-spatial resolutions of 30 and 20 m, the support vector machine (SVM) method was considered as the optimal classification method with the highest accuracy of impervious surface extraction. For the high-spatial resolutions of 10 and 4 m, the object based image analysis (OBIA) method obtained the highest accuracy of the impervious surface distribution. Furthermore, the perpendicular impervious surface index (PISI) outperformed the other indices in obtaining the impervious surface distribution, with the highest accuracy for four spatial resolution images. These comprehensive assessments can provide a valuable guidance for future impervious surface extraction from different spatial resolutions.