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Long time series impervious surface mapping (ISM) is important for understanding urban expansion, environmental impacts, and urban planning. There are some historical global ISM products, such as GAIA and NUACI datasets, whereas they may not meet the user’s diverse application needs in the aspects of mapping timeliness, temporal resolution, and spatial resolution. Therefore, this study proposes an automatic, rapid, and continuous impervious surface mapping and updating framework based on historical land cover datasets without using other labeled data, to improve the updating speed and spatio-temporal resolution of impervious surface maps. The main process is divided into three steps: (1) Multi-temporal samples for classification were obtained by using GAIA dataset, FROM-GLC dataset and the unsupervised continuous change detection (CCD) algorithm; (2) Quarterly long time series ISM results (ISMs) were obtained by using multi-temporal samples and quarterly features; (3) The final results were obtained by using the post-processing operations in the obtained quarterly long time series ISMs to improve the mapping accuracy. The proposed framework is applied to eight cities around the world, and the total overall accuracy (OA) and Kappa of long time series ISMs with post-processing in the eight cities are 92.64% and 0.8525, respectively, improving the OA and Kappa of those without post-processing by 1.41% and 0.0281, respectively, and those of GAIA dataset by 4.57% and 0.0914, respectively, which proved the effectiveness of the proposed method. This study also analyzed the spatial patterns of impervious surface expansion in eight cities and identified different spatial patterns of expansion that existed among the cities, while capturing the abrupt change in the spatial patterns of expansion in Rosario and Novosibirsk after the second quarter of 2021. The proposed framework achieved rapid mapping and updating of impervious surface without any labeled samples, and has the potential to map the global impervious surface continuously.

As urbanization has profound effects on global environmental changes, quick and accurate monitoring of the dynamic changes in impervious surfaces is of great significance for environmental protection. The increased spatiotemporal resolution of imagery makes it possible to construct time series to obtain long-time-period and high-accuracy information about impervious surface expansion. In this study, a three-step monitoring method based on time series trajectory segmentation was developed to extract impervious surface expansion using Landsat time series and was applied to the Xinbei District, Changzhou, China, from 2005 to 2017. Firstly, the original time series was segmented and fitted to remove the noise caused by clouds, shadows, and interannual differences, leaving only the trend information. Secondly, the time series trajectory features of impervious surface expansion were described using three phases and four types with nine parameters by analyzing the trajectory characteristics. Thirdly, a multi-level classification method was used to determine the scope of impervious surface expansion, and the expansion time was superimposed to obtain a spatiotemporal distribution map. The proposed method yielded an overall accuracy of 90.58% and a Kappa coefficient of 0.90, demonstrating that Landsat time series remote sensing images could be used effectively in this approach to monitor the spatiotemporal expansion of impervious surfaces.

Empirical evidence shows that the expansion of impervious surface threatens soil organic carbon (SOC) sequestration in urbanized areas. However, the understanding of deep soil excavation due to the vertical expansion of impervious surface remains limited. According to the average soil excavation depth, we divided impervious surface into pavement (IS20), low-rise building (IS100) and high-rise building (IS300). Based on remote-sensing images and published SOC density data, we estimated the SOC storage and its response to the impervious surface expansion in the 0–300 cm soil depth in Guangzhou city, China. The results showed that the total SOC storage of the study area was 8.31 Tg, of which the top 100 cm layer contributed 44%. The impervious surface expansion to date (539.87 km2) resulted in 4.16 Tg SOC loss, of which the IS20, IS100 and IS300 contributed 26%, 58% and 16%, respectively. The excavation-induced SOC loss (kg/m2) of IS300 was 1.8 times that of IS100. However, at the residential scale, renovating an IS100 plot into an IS300 plot can substantially reduce SOC loss compared with farmland urbanization. The gains of organic carbon accumulation in more greenspace coverage may be offset by the loss in deep soil excavation for the construction of underground parking lots, suggesting a need to control the exploitation intensity of underground space and promote residential greening.

Using more than three million Landsat satellite images, this research developed the first global impervious surface area (GISA) dataset from 1972 to 2019. Based on 120,777 independent and random reference sites from 270 cities all over the world, the omission error, commission error, and F-score of GISA are 5.16%, 0.82%, and 0.954, respectively. Compared to the existing global datasets, the merits of GISA include: (1) It provided the global ISA maps before the year of 1985, and showed the longest time span (1972–2019) and the highest accuracy (in terms of a large number of randomly selected and third-party validation sample sets); (2) it presented a new global ISA mapping method including a semi-automatic global sample collection, a locally adaptive classification strategy, and a spatio-temporal post-processing procedure; and (3) it extracted ISA from the whole global land area (not from an urban mask) and hence reduced the underestimation. Moreover, on the basis of GISA, the long time series global urban expansion pattern (GUEP) has been calculated for the first time, and the pattern of continents and representative countries were analyzed. The two new datasets (GISA and GUEP) produced in this study can contribute to further understanding on the human’s utilization and reformation to nature during the past half century, and can be freely download from http://irsip.whu.edu.cn/resources/dataweb.php .

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.

This study examines the dynamic mapping of impervious surface changes in optimising urban spatial structures and fostering sustainable development. A novel deep learning model and time-spectral-texture combination optimisation method were employed to identify pixel-based land-cover change trajectories. A piecewise linear regression model was also utilised to determine the time nodes of urban expansion. This methodology was applied to Panjin City, a resource-based city in China, to analyse temporal and spatial morphological changes related to urban expansion. The results reveal that the combination optimisation method achieved a trajectory classification accuracy of 93.10% and macro F1-score of 92.44%, with an urban expansion time identification accuracy of 84.24%. Panjin City’s built-up area increased from 312.75 to 489.49 km² between 1990 and 2020, reflecting a growth rate of 56.51% and an average expansion speed of 5.89 km²/year. Furthermore, the spatial compactness of impervious surfaces declined, with urban expansion patterns shifting from leapfrog and edge expansion to infilling after 2016. These findings emphasise the need for strategic urban planning to enhance land-use efficiency and promote sustainable development, offering valuable insights for urban expansion mapping in other cities.

Soil organic carbon (SOC) is a major terrestrial carbon reservoir, crucial for the global carbon cycle and climate change. However, the impact of urbanization-induced cropland encroachment on SOC remains underexplored. This study quantified SOC loss in the top 20 cm (SOC20) and 100 cm (SOC100) soil layers in the Jiangsu-Zhejiang-Shanghai (JZH) region from 1985 to 2019 using high-resolution land cover dataset and multi-temporal SOC maps. Our results show that the cumulative cropland encroachment area in the study area reached 18 925.65 km2, approximately three times the area of Shanghai. The encroached areas of cropland in Jiangsu, Zhejiang, and Shanghai accounted for 59.72%, 31.49%, and 8.79% of the total, respectively. The cumulative SOC100 loss in the JZH region was approximately 65.31 ± 32.45 Tg C, with the SOC20 loss contributing about 32.97%, emphasizing the importance of deep SOC pool. The cumulative SOC20 (SOC100) losses in Jiangsu, Zhejiang, and Shanghai contributed approximately 55.36% (57.74%), 35.76% (31.96%), and 8.87% (10.3%) to the total losses in the JZH region, respectively. Moreover, the annual average SOC100 loss accounted for about 8.6% to 25.59% of the terrestrial carbon sink flux (11.24 Tg C yr−1) in the JZH region, emphasizing that SOC loss due to cropland encroachment cannot be overlooked when evaluating the regional carbon sink capacity. Additionally, the positive correlation between SOC loss and regional gross domestic product highlights the trade-off between economic development model of urban expansion through cropland encroachment and the resulting substantial SOC loss. This study emphasizes the importance of assessing the impacts of urbanization on regional SOC stocks, especially with regard to deep soil, and provides scientific insights for future urban planning and land management in this region.

Impervious surfaces (IS) are one of the most important components of the earth’s surface, and understanding how IS have expanded is vital. However, few studies on IS or urbanization have focused on the cradle of the Chinese nation—the Yellow River Basin (YRB). In this study, the Random Forest and Temporal Consistency Check methods were employed to generate long-term maps of IS in the YRB based on Landsat imagery. To explore the dynamics and differences in IS, we developed a spatiotemporal analysis and put forward regional comparisons between different research units of the YRB. We documented the remote sensing-based ecological index (RSEI) in multiple circular zones to discuss the ecological effects of the expansion of IS. The IS extraction strategy achieved excellent performance, with an average overall accuracy of 90.93% and kappa coefficient of 0.79. The statistical results demonstrated that the spatial extent of IS areas in the YRB increased to 18,287.36 km2 in 2020 which was seven times more than that in 1986, at rates of 166 km2/a during 1986–2001, 365 km2/a during 2001–2010, and 1044 km2/a during 2011–2020. Our results indicated that the expansion and densification of IS was slow in core urban areas with high initial IS fraction (ISF), significant in the suburban or rural areas with low initial ISF, and obvious but not significant in the exurb rural or depopulated areas with an initial ISF close to 0. The multiyear RSEI indicated that environmental quality of the YRB had improved with fluctuations. The ecological effects of the impervious expansion slightly differed in urban core areas versus outside these areas. When controlling the urban boundary, more attention should be paid to the rational distribution of ecologically important land. These results provide comprehensive information about IS expansion and can provide references for delineating urban growth boundaries.

Rapid urbanization has produced serious heat effects worldwide. However, the literature lacks a detailed study on heat effects based on the directions and types of urban expansion. In this work, a typical city with an extremely hot summer climate, Hangzhou, was selected as a case study to determine the relationships between the urban heat-effect dynamics and spatiotemporal patterns of impervious surface expansion. Based on long-term Landsat imagery, this study characterized the spatiotemporal patterns of urban expansion and normalized surface temperatures in Hangzhou City from 2000 to 2020 using object-based backdating classification and a generalized single-channel algorithm with the help of a land-use transfer matrix, expansion index, and spatial centroids. Relevant policies, industries, and traffic networks were discussed to help explain urban expansion and thermal environment changes. The results demonstrated that in 2020, the area of impervious surfaces covered 1139.29 km2. The majority of the gains were in farmland, water, and forests, and the annual growth rate was 32.12 km2/year beginning in 2000. During the expansion of impervious surfaces, the city warmed at a slower rate, and more thermal contributions came from sub-urban areas. The southeast-oriented expansion of impervious surfaces was the key reason for the spatiotemporal dynamics of the urban heat effects. The dominant urban edge expansion intensified the local heat effects. This research provides a Landsat-based methodology for better understanding the heat effects of urban expansion.

As an important ecological environmental factor, the lake water surface temperature (LSWT) has an important impact on the ecological diversity of lakes and watersheds. With the acceleration of urbanization in China, the impact of urban expansion on LSWT can not be ignored. In this study, we introduced the spatial influence(G) equation, selected MOD11A2, impervious surface (IS), digital elevation model (DEM) and Landsat series remote sensing images as data sources, and took six lakes with rapid urban expansion in China as the empirical research object to explore the variation characteristics of urban expansion and LSWT in six lake watersheds and the spatial influence of urban expansion on LSWT. Finally, the following conclusions can be drawn: The results show that 1) The IS in the six watersheds all experienced significant expansion, with an increase of 1.80–3.91 times. 2) From the annual average LSWT from 2001 to 2018, only Poyang Lake’s LSWT-night shows a cooling trend, while other lakes, whether LSWT-day or LSWT-night, show a warming trend. 3) G is used to comprehensively consider the area change of IS in the watershed, the influence of distance and the change of lake area, which can quantify the impact of IS on LSWT, so as to further explain and describe the spatial influence process and characteristics of IS expansion on LSWT.