Impervious Surface Change Research Articles

Tracking the impact of the land cover change on the spatial-temporal distribution of the thermal comfort: Insights from the Qinhuai River Basin, China

China's rapid economic growth and urbanization have caused significant Land Cover Changes (LCC), worsened the Urban Heat Island (UHI) effect and reducing the Thermal Comfort (TC). Despite existing studies, there remains a gap in understanding the specific contributions of various LCC types to the TC, particularly in Qinhuai River Basin. This study addresses this gap by examining the LCC effects from 2013 to 2022 based on targeted metrics. We propose a novel TC classification model and introduce indices, including the Land Cover Contribution Index (LCI) and the Land Cover Classification Contribution Index (LCCI), to quantify the influence of different LCC types on the TC. Our findings reveal that farmland and woodland positively impact the TC, while the negative influence of impervious surfaces has intensified. The area of farmland in the most comfortable category has shown significant variability, while impermeable surfaces in uncomfortable and very uncomfortable categories have surged. Additionally, the Urban Water Body Contribution Index (U-WCI) consistently exceeded the Non-Urban Water Body Contribution Index (N-WCI), indicating an enhanced UHI effect within urban areas. This study concludes that changes in farmland and impervious surfaces are crucial for the TC and provides practical recommendations for land use planning against climate change.

Warming reality of Kozhikode Urban Area: Uncovering the heat of built-up expansion and vegetation loss

Urban expansion often comes at the expense of natural landscapes, leading to increased temperatures and environmental degradation. This study examines the effects of urbanization on land cover changes and their implications for land surface temperature (LST) by analyzing normalized difference satellite indices in the Kozhikode Urban Area (KUA), a city where substantial urbanization has significantly impacted the natural environment. By investigating changes in vegetation and impervious surfaces from 1993 to 2022, the research highlights a troubling trend: while vegetation cover has sharply declined, impermeable surfaces like roads and buildings have surged, contributing to a dramatic rise in average temperatures. The study reveals that built-up areas significantly elevate LST, whereas vegetation and water bodies offer cooling effects. The results underscore the urgent need for integrating green infrastructure into urban planning to combat rising temperatures and enhance environmental sustainability. This research emphasizes the importance of considering smaller, vegetation-rich cities like Kozhikode in broader urban heat research, as they present unique challenges and opportunities for effective climate adaptation strategies. By highlighting these issues, this study aims to inform policymakers and urban planners about the critical role of urban greening in mitigating the effects of climate change and fostering sustainable urban environments.

Investigating the effect of spatial patterns of artificial impervious surface on PM2.5 at the intra-urban scale

ABSTRACT Artificial impervious surface, as the hallmark of urbanization, contributes to urban development but simultaneously leads to urban PM2.5 pollution. However, how artificial impervious surface affected PM2.5 at the intra-urban scale was rarely investigated. By using remote-sensing techniques to derive PM2.5 concentrations and dividing urban areas into two categories with different urban development situation (areas with drastic expansion of artificial impervious surface (DEA) and areas with slight or no changes of artificial impervious surface (SNCA)), we provided a new perspective for investigating the effects of spatial patterns of artificial impervious surface on PM2.5. First, based on multi-source predictors, a two-staged XGBoost model was adopted to derive PM2.5 concentrations. Then, after delineating DEA and SNCA based on the variations of artificial impervious surface and selecting indicators, the overall and local effects of spatial patterns of artificial impervious surface were respectively explored by Original Least Square regression (OLS) and Geographically Weighted Regression (GWR). The results demonstrated that, compared with in SNCA, population and economic development contributed to higher levels of PM2.5 concentrations in DEA. Additionally, in DEA, high PM2.5 concentrations tended to occur in areas with artificial impervious surfaces exhibiting high coverage, complex shape, and uncompact distribution. In SNCA, among all landscape spatial patterns, the effect of artificial impervious surface coverage was the strongest. Spatially, artificial impervious surface coverage and economic development had much more significant impacts on PM2.5 than other indicators. Among these two indicators, the exacerbating effect of artificial impervious surface coverage on PM2.5 pollution was more pronounced in SNCA, while economic development notably brought high PM2.5 concentrations in the suburban districts of DEA. In general, the framework proposed in this study advanced the understanding of the effects of artificial impervious surface on PM2.5 and the findings are valuable for the mitigation of PM2.5 pollution at the intra-urban scale.

Quantifying the amount of land lost to artificial surfaces in European habitats: A comparison inside and outside Natura 2000 sites using a quasi-experimental design

The conversion of natural environments to artificial surfaces has significant irreversible impacts on biodiversity and ecosystem services. The European Union (EU) has set ambitious goals to combat land artificialisation, including achieving “no net land take” by 2050. It also plans to expand its network of protected areas, including the Natura 2000 (N2K) sites, to 30 % of its area by 2030. However, studies have shown that land artificialisation continues to occur even within N2K sites. It has not yet been examined, though, which habitats are most affected. To address this gap, we used a high-resolution spatial layer of the EU's habitats and layers showing impervious surfaces across the EU to quantify and compare the current levels of artificial surfaces within each habitat inside and outside N2K sites. We also compared the changes in impervious surfaces between 2012 and 2015 and 2015–2018. We found that the total extent of impervious surfaces, and their rates of change, were lower within the N2K sites for all habitats examined. However, patterns varied: coastal habitats, grasslands, and agricultural habitats had higher levels of artificial surfaces than others, even within N2K sites. Our findings indicate that although the planned expansion of the N2K network can help mitigate the artificialisation of threatened habitats, N2K sites do not entirely halt land conversion. Member States should further improve the management effectiveness of NK2 sites and promote financial and policy instruments addressing land artificialisation, e.g., through land recycling, to safeguard vulnerable habitats and achieve the EU's conservation objectives.

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

Historical changes in tree and impervious surface cover following urban renewal in a small postindustrial city.

Changes in tree cover and impervious surfaces have been observed across many cities in the United States over the past 70 years. Many municipalities are implementing tree planting programs in efforts to increase tree cover. A detailed understanding of historical changes in land cover can inform urban forest management. I applied a convolutional neural network image segmentation approach to historical aerial imagery to delineate changes in land cover in 1957, 1974, and 2017 in Utica, New York, a small, postindustrial city. The model predicted tree, pavement, and building land cover in each year with overall accuracies ranging from 82-87%. From 1957 to 2017, tree cover declined in many areas and impervious surface cover (buildings and pavement) increased. Tree cover gains largely occurred in uninhabited, natural areas; whereas, the greatest declines in tree coverage occurred in many residential areas following the start of the urban renewal efforts in 1957. Current tree planting efforts targeted at homeowners could drive disparities in future tree cover since several areas of Utica with low tree have a high proportion of renter occupied homes and a low median household income. Convolutional Neural Network approaches for image segmentation of aerial imagery are a helpful tool in understanding patterns in changes in tree and impervious surfaces. A better understanding of the legacies of historical policies and neighborhood-scale changes in land cover can assist in highlighting priorities for urban forest management and justice-oriented urban forestry approaches to urban tree planting.

Detecting Changes in Impervious Surfaces Using Multi-Sensor Satellite Imagery and Machine Learning Methodology in a Metropolitan Area

This study utilizes multi-sensor satellite images and machine learning methodology to analyze urban impervious surfaces, with a particular focus on Nanchang, Jiangxi Province, China. The results indicate that combining multiple optical satellite images (Landsat-8, CBERS-04) with a Synthetic Aperture Radar (SAR) image (Sentinel-1) enhances detection accuracy. The overall accuracy (OA) and kappa coefficients increased from 84.3% to 88.3% and from 89.21% to 92.55%, respectively, compared to the exclusive use of the Landsat-8 image. Notably, the Random Forest algorithm, with its unique dual-random sampling technique for fusing multi-sensor satellite data, outperforms other machine learning methods like Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Classification and Regression Trees (CARTs), Maximum Likelihood Classification (Max-Likelihood), and Minimum Distance Classification (Min-Distance) in impervious surface extraction efficiency. With additional satellite images from 2015, 2017, and 2020, the impervious surface changes are tracked in the Nanchang metropolitan region. From 2015 to 2021, they record a notable increase in impervious surfaces, signaling a quickened urban expansion. This study observes several impervious surface growth patterns, such as a tendency to concentrate near rivers, and larger areas in the east of Nanchang. While the expansion was mainly southward from 2015 to 2021, by 2021, the growth began spreading northward around the Gan River basin.

An Event-Based Resilience Index to Assess the Impacts of Land Imperviousness and Climate Changes on Flooding Risks in Urban Drainage Systems

Assessing the resilience of urban drainage systems requires the consideration of future disturbances that will disrupt the system’s performance and trigger urban flooding failures. However, most existing resilience assessments of urban drainage systems rarely consider the uncertain threats from future urban redevelopment and climate change, which leads to the underestimation of future disturbances toward system performance. This paper fills in the gap of assessing the combined and relative impacts of future impervious land cover and rainfall changes on flooding resilience in the context of a densely infilled urban catchment served by an urban drainage system in Salt Lake City, Utah, USA. An event-based resilience index is proposed to measure climate change and urbanization impacts on urban floods. Compared with the traditional resilience metric, the event-based resilience index can consider climatic and urbanized impacts on each urban flooding event; the new resilience index assist engineers in harvesting high-resolution infrastructure adaptation strategies at vulnerable spots from the system level to the junction level. Impact comparison for the case study shows that impervious urban surface changes induce greater effects on the system performance curves by magnifying the maximum failure level, lengthening the recovery duration, and aggravating the flooding severity than rainfall intensity changes. A nonlinear logarithmic resilience correlation is found; this finding shows that flooding resilience is more sensitive to the land imperviousness change due to urban redevelopment than rainfall intensity changes in the case study. This research work predicts the system response to the disturbances induced by climate change and urban redevelopment, improving the understanding of impact analysis, and contributes to the advancement of resilient urban drainage systems in changing environments.

Extraction of impervious surface with Landsat based on machine learning in Chengdu urban, China

In the process of urbanization, materials such as steel and concrete are widely used in urban construction such as road paving and house building, resulting in an increase in impervious surface area in cities. Rapid urbanization in China today has led to a huge increase in impervious surface area, which has increased the risk of urban waterlogging. Therefore, there are more reasons to conduct research on impervious surface and explore the relationship between impervious surface and urban waterlogging. This research has been done to obtain the optimal model for extracting impervious surface in Chengdu urban area and to explore the correlation between impervious surface changes and urban waterlogging in the same area. In order to obtain the optimal model, five different machine learning and deep learning algorithms such as Naive Bayes (NB), Convolutional Neural Network (CNN), Support Vector Machine (SVM), Capsules Network (CapsNet) and Random Forest (RF) have been used to extract the urban impervious surface in this study. The accuracy of the classification models is evaluated using the overall accuracy (OA), kappa coefficient (KC), and macro F1 (F1). It is found that CNN is the best option to extract the urban impervious surface of Chengdu with an OA of 0.7809. In addition, based on the optimal model to extract the impervious surface of Chengdu, a partial correlation analysis was conducted between urban waterlogging and urban impervious in the study area, it was concluded that there was a positive correlation between the two with a partial correlation coefficient of 0.979.

Impacts of urbanization and climate change on surface runoff in Thu Duc City, Vietnam

Thu Duc City (TDC), a sub-city of Ho Chi Minh City (HCMC), Vietnam, has undergone rapid transformation in land use - land cover (LULC) over the past few decades. Flooding is one of the biggest challenges to the sustainable development of the city. Quantifying excess runoff induced by urbanization and climate change is essential for developing an efficient flood risk management strategy. This research aims to evaluate the effects of the impervious surface expansion (a key indicator of urbanization) and the rainfall increase caused by climate change on the excess runoff depth in TDC. The dataset derived from LANDSAT satellite images was used to explore the spatial-temporal dynamics of impervious surface areas from 1985 to 2020. The EPA-SWMM urban rainfall-runoff model was employed to investigate the effects of increased impervious surface and climate change on excess runoff depth. The satellite data showed that TDC has 24% of the impermeable surface area currently, much lower than 75.6% in areas located in the central districts such as Nhieu Loc – Thi Nghe (NL-TN) basin, and that impermeable areas have expanded rapidly in the north-south and west-east directions since 2010. The simulated results indicated that impervious surface development increased excess runoff depth by 14.0mm between 1985 and 2020. It also showed that increasing rainfall due to climate change could raise the average excess runoff depth by 18.2%, 64.0%, and 49.4%, respectively, at the beginning, the middle, and the end of the century compared to the current stage. It suggests mainstreaming stormwater retention and detention systems into urban development plans for adapting to the future uncertainties of urbanization and climate change. It also recommends that the impervious surface areas must be maintained within design thresholds to control runoff and mitigate floods in practice. The research findings could assist policymakers and stakeholders in formulating a long-term vision to propose an efficient urban stormwater management strategy in TDC and similar growing cities.

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.

Response of ecosystem services to impervious surface changes and their scaling effects in Loess Plateau ecological Screen, China

The complexity and fragility of the Yellow River Basin ecosystem limits its economic growth and sustainable social development as a strategic planning area for development in western China. The Chinese government has established the Loess Plateau Ecological Screen (LPES) in the region to eliminate or mitigate the negative ecological impacts of human activities represented by the expansion of impervious surfaces (IS) through active ecological conservation and restoration. However, there are few studies that quantify the effects of impervious surfaces on ecosystem services (ES). To fill this gap, this study takes the LPES in China as an example and explores the response of ES to IS changes and its scale effect from 2000 to 2020. Based on remote sensing, meteorological, soil, hydrological, social, and economic data using GIS spatial analysis techniques. The results show that: From 2000 to 2020, the urbanization of the LPES developed rapidly, and the IS increased rapidly. The increase in IS affected the supply of ES, which decreased with the increase in IS growth rate, and this phenomenon had a scale effect. Overall, except for soil conservation service (SCS) - IS, carbon storage service (C) - IS at the administrative scale, the negative correlation increased with increasing scale, while the opposite was true at the grid scale. There were thresholds for the response of ES to IS, and the thresholds were also influenced by the scale of study. The smaller the scale was, the lower the threshold was. However, there were differences in the ranking of each ES reaching the threshold with increasing IS at the grid-scale and administrative division scale. The ecosystem services composite index (ESCI) was found to be the best indicator for exploring the relationship between IS and ES compared to other single ecosystem services indices, with the largest negative correlation with IS and the least influenced by scale effects. Given the obvious scale effect of IS on ES, this study suggests that the development of ecological management programs at the national level should be macroscopically regulated at the provincial level, with specific measures at smaller grid scales (5 Km × 5 Km) to constrain IS expansion..

Assessing the synergistic modulation of evapotranspiration by global impervious surface and vegetation changes

The different characteristics of impervious urban surfaces and natural underlying surfaces affect water and heat balances, with consequent impacts on evapotranspiration (ET). The global impervious surface area in 1982 was only 34% of that in 2015, motivating an assessment of the net effect of impervious surface changes on ET under rapid impervious surface expansion. In this study, we incorporated the van Dijk model's interception evaporation (Ei) algorithm into the PT-JPL (Priestley-Taylor Jet Propulsion Laboratory) model, and added an impervious surface evaporation (Eu) module, to enable urban ecosystem simulations. This improved model (named the PT-JPLim model) was validated against annual mean ET calculated by P-Q in 8,205 catchments, and against ET measured at 203 flux towers in natural ecosystems and 11 flux towers in urban ecosystems. Calibration showed that the PT-JPLim model performed well (catchments: R = 0.36; natural ecosystems: R = 0.78, RMSE = 13.17 mm; urban ecosystems: R = 0.79, RMSE = 11.74 mm). Additionally, we examined the reliability of modelled ET and its partitioning, by comparison with observed soil evaporation (Eb), Ei, and satellite-based products. By controlling for the impervious surface fraction in the model, it was concluded that the net increase in Eu due to increased impervious surface changes was 0.23 mm/yr, with larger increases in eastern China, India, Europe and southeastern North America than in other regions. The effects of impervious surface and vegetation changes on ET were then analyzed for global impervious surface, with a ratio of 1/4.1 for the effects of impervious surfaces to vegetated surfaces on ET. The contributions of transpiration (Et), Eu, and Eb changes to ET changes on impervious surfaces were 65%, 27% and 8% respectively. These results indicate that synergistic modulation of ET by impervious surface changes and vegetation changes should not be ignored when studying urban eco-hydrological processes.

ASYMMETRIC FUZZY CLASSIFICATION NETWORKS FOR CONSTRUCTION LAND DETECTION IN HIGH RESOLUTION REMOTE SENSING IMAGES

Abstract. Urbanization is an essential phase of a nation's economic development. A very effective way to examine urban growth is to look at how impervious surface changes over time, however impervious surface can only show the current situation in terms of urban development. Compared to the existing methods, the use of construction bare land to monitor urban growth has the following benefits over currently used techniques. First, it is possible to track the progress of structures being constructed as part of urban expansion. The second is to assess the city's development intensity and identify the inward expansion. Therefore, the detection of construction bare land is of great significance for the development of a more sophisticated urban dynamic perception technology. This paper proposes an asymmetric fuzzy classification network (AFCNet) for detection of construction bare land scenes. The generation of fuzzy sample sets, the backbone network, and the proposed fuzzy classification module make up the method's three primary components. The deep features of the scene are then extracted using the fuzzy classification network and converted into ambiguity. Finally, the ambiguity is converted into predicted probability using the fuzzy weight vector. The fuzzy sample set is generated to introduce more prior information into the network. High-level features are extracted using the backbone network. Fuzzy classification methods based on spectral features are used to improve the performance of scene classification. The results demonstrate that the OA of our method is higher than all other comparison methods.

Multitemporal impervious surface estimation via an optimized stable/change pixel detection approach

ABSTRACT Remote sensing techniques have proved its efficacy for the impervious surface mapping, which is a significant indicator of urbanization process and environmental status. However, systematic and random errors in the existing methods still impact the reliability of subpixel impervious surface estimation, generating compounded errors when conducting multitemporal monitoring. The compounded errors of the conventional methods often significantly impact the temporal consistency of the results. In this study, a novel method based on a straightforward pixel change detection approach was put forward to improve the estimation of multitemporal impervious surface area. Two experimental areas located in Rome in Italy and Shenzhen in China were chosen to testify the generality of the proposed method to estimate different types of impervious surfaces worldwide. By reducing the compounded errors, the proposed method demonstrated its efficiency in achieving higher accuracy in both study areas without involving extensive data sources and intensive manual tasks. Compared with the conventional classification and regression tree algorithm, the overall mean average error and root mean square error of this study declined by more than 15.55% and 8.63%, respectively, and R2 increased from approximately 0.93 to 0.96. The proposed method also drastically reduced the standard deviation of the multitemporal percent ISA of the stable pixels. The accurate change estimation of percent ISA has been a fundamental but challenging issue associated with monitoring and understanding the urban environment. Therefore, our proposed method, with its improved ability to estimate impervious surface change both spatially and temporally, can provide accurate information required for urban environment research.

Quantifying the impacts of land cover change on catchment-scale urban flooding by classifying aerial images

Stormwater urban drainage systems are historically designed to mitigate certain recurrence interval flooding events. However, increases in impervious land cover due to urban redevelopment enhance the surface imperviousness and then magnifies urban floods over the design criteria by elevating flooding peaks and volumes. In this paper, the object-based image classification was developed to evaluate the impervious land cover impacts on system peak flooding rate and total flooding volume. The object-based aerial image classification quantified the spatiotemporal changes in impervious surfaces with an average accuracy of 92%. A drainage model, based on a stormwater drainage system in Salt Lake City, Utah, USA, was calibrated and validated under four storm events with an average of Nash-Sutcliffe efficiency of 0.69 and 0.46 for calibration and validation outcomes. Results show that impervious areas increased up to 32% under impacts induced by urban redevelopment from 1950 to 2018. The land cover imperviousness resulted in a maximum of 575% and 753% growth in flooding peak and volume, respectively, under storm events from 10-year to 100-year return periods. Implications of this research seek to inform homeowners and engineers of the flooding risks in human-altered landscapes based on remote sensing image classification.

Spatio-Temporal Analysis of Impervious Surface Expansion in the Qinhuai River Basin, China, 1988–2017

Impervious surfaces are key indicators for urbanization monitoring and watershed degradation assessment over space and time. However, most empirical studies only extracted impervious surface from spatial, temporal or spectral perspectives, paying less attention to integrating multiple dimensions in acquiring continuous changes in impervious surfaces. In this study, we proposed a neighborhood-based spatio-temporal filter (NSTF) to obtain the continuous change information of impervious surfaces from multi-temporal Landsat images in the Qinhuai River Basin (QRB), Jiangsu, China from 1988–2017, based on the results from semi-automatic decision tree classification. Moreover, we used the expansion intensity index (EII) and the landscape extension index (LEI) to further characterize the spatio-temporal characteristics of impervious surfaces on different spatial scales. The preliminary results showed that the overall accuracies of the final classification were about 95%, with the kappa coefficients ranging between 0.9 and 0.96. The QRB underwent rapid urbanization with the percentage of the impervious surfaces increasing from 2.72% in 1988 to 25.6% in 2017. Since 2006, the center of urbanization expansion was shaped from the urban built-up areas of Nanjing and Jiangning to non-urban built-up areas of the Jiangning, Lishui, and Jurong districts. The edge expansion occupied 73% on average among the different landscape expansion types, greatly beyond outlying (12%) and infilling (15%). The window size in the NSTF has a direct impact on the subsequent analysis. Our research could provide decision-making references for future urban planning and development in the similar basins.

Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features

As the capital city of China, Beijing has experienced unprecedented economic and population growth and dramatic impervious surface changes during the last few decades. An application of the classification method combining the spectral and textural features based on Random Forest was conducted to monitor the spatial and temporal changes of Beijing’s impervious surfaces. This classification strategy achieved excellent performance in the impervious surface extraction in complex urban areas, as the Kappa coefficient reached 0.850. Based on this strategy, the impervious surfaces inside Beijing’s sixth ring road in 1997, 2002, 2007, 2013, and 2017 were extracted. As the development of Beijing has a special regional feature, the changes of impervious surfaces within the sixth ring road were assessed. The findings are as follows: (1) the textural features can significantly improve the classification accuracy of land cover in urban areas, especially for the impervious surface with high albedo. (2) Impervious surfaces within the sixth ring road expanded dramatically from 1997 to 2017, had three expanding periods: 1997–2002, 2002–2007, and 2013–2017, and only shrank in 2007–2013. There are different possible major driving factors for each period. (3) The region between the fifth and sixth ring roads in Beijing underwent the most significant changes in the two decades. (4) The inner three regions are relatively highly urbanized areas compared to the outer two regions. Urbanization processes in the interior regions tend to be completed compared to the exterior regions.

Assessment spacial impervious surface changes in Can Tho city from 2015 to 2018 using L andsat images

The rapid urbanization increases the impermeable surface that leads to reduce the water permeability into the soil, resulting in reducing the infiltration and increasing the surface runoff. This study aims to extract the impermeable surface using Landsat satellite images and to monitor changes in the impervious surface in the period 2015 –2018 in Can Tho city. This study applied the Objectbased Image Analysis (OBIA) method with the Rule-based Image Analysis algorithm on Landsat 8 fusion data with multispectral images and spatial resolution of 15 m in the period 2015 – 2018 using impermeable surface extraction indicators. Impermeable surface map in 2015 and 2018 with high accuracy assessment (T = 95.5% and K = 0.91, 2015; T = 94% and K = 0.88, in 2018) was established and the fluctuation of the impermeable surface increased from 13,746.6 ha (9.55%) to 19,966.67 ha (13.87%) from 2015 to 2018. The research result indicated the infrastructure development situation and urban construction in Can Tho city during three years increasing of 4.32% of the total natural area in Can Tho city.

Combined Effects of Impervious Surface Change and Large-Scale Afforestation on the Surface Urban Heat Island Intensity of Beijing, China Based on Remote Sensing Analysis

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.