Quantifying impervious surface changes using time series planimetric data from 1940 to 2011 in four central Iowa cities, U.S.A

This study explored the model of urban impervious surface (IS) density, land surface temperature (LST), and comprehensive ecological evaluation index (CEEI) from urban centers to suburbs. The interrelationships between these parameters in Guangzhou from 1987 to 2019 were analyzed using time-series Landsat-5 TM (Thematic Mapper), Landsat-8 OLI (Operational Land Imager), and TIRS (Thermal Infrared Sensor) images. The urban IS densities were calculated in concentric rings using time-series IS fractions, which were used to construct an inverse S-shaped urban IS density function to depict changes in urban form and the spatio-temporal dynamics of urban expansion from the urban center to the suburbs. The results indicated that Guangzhou experienced expansive urban growth, with the patterns of urban spatial structure changing from a single-center to a multi-center structure over the past 32 years. Next, the normalized LST and CEEI in each concentric ring were calculated, and their variation trends from the urban center to the suburbs were modeled using linear and nonlinear functions, respectively. The results showed that the normalized LST had a gradual decreasing trend from the urban center to the suburbs, while the CEEI showed a significant increasing trend. During the 32-year rapid urban development, the normalized LST difference between the urban center and suburbs increased gradually with time, and the CEEI significantly decreased. This indicated that rapid urbanization significantly expanded the impervious surface areas in Guangzhou, leading to an increase in the LST difference between urban centers and suburbs and a deterioration in ecological quality. Finally, the potential interrelationships among urban IS density, normalized LST, and CEEI were also explored using different models. This study revealed that rapid urbanization has produced geographical convergence between several ISs, which may increase the risk of the urban heat island effect and degradation of ecological quality.

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Multi-Level Classification Based on Trajectory Features of Time Series for Monitoring Impervious Surface Expansions

Remote Sensing ◽

10.3390/rs11060640 ◽

2019 ◽

Vol 11 (6) ◽

pp. 640 ◽

Cited By ~ 5

Author(s):

Beibei Wang ◽

Zhenjie Chen ◽

A-Xing Zhu ◽

Yuzhu Hao ◽

Changqing Xu

Keyword(s):

Time Series ◽

Environmental Changes ◽

Impervious Surface ◽

Impervious Surfaces ◽

Spatiotemporal Resolution ◽

Monitoring Method ◽

Expansion Time ◽

Trajectory Features ◽

Surface Expansion ◽

Multi Level

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.

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Permafrost seasonal surface changes revealed from Sentinel-1 InSAR time-series, Yamal peninsula

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-382-183-2020 ◽

2020 ◽

Vol 382 ◽

pp. 183-187

Author(s):

Kanayim Teshebaeva ◽

Ko J. van Huissteden ◽

Alexander V. Puzanov ◽

Dmitry N. Balykin ◽

Anton I. Sinitsky ◽

...

Keyword(s):

Time Series ◽

Active Layer ◽

Global Climate ◽

Yamal Peninsula ◽

Active Layers ◽

Eurasian Continent ◽

Time Period ◽

Sar Imagery ◽

Displacement Patterns ◽

Surface Changes

Abstract. Widespread thawing of permafrost in the northern Eurasian continent causes severe problems for infrastructure and global climate. We test the potential of Sentinel-1 SAR imagery to enhance detection of permafrost surface changes in the Siberian lowlands of the northern Eurasian continent at the Yamal peninsula site. We used InSAR time-series technique to detect seasonal surface movements related to permafrost active layer changes. The satellite InSAR time-series analysis has detected continuous movements, subsidence in three zones, which have occurred during the time period from 2017 to 2018. Observed subsidence zones show up to 180 mm yr−1 rates of seasonal active layers changes. These seasonal ground displacement patterns align well with lithology and linked to anthropogenic impact on the permafrost surface changes in the area. The results show that Sentinel-1 mission is of great importance for the longer-term monitoring of active layer thickening in permafrost regions. The combined analyses of the obtained InSAR time series with additional field observations may support regular process monitoring as part of a global warming risk assessment.

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Evaluating the Dynamic Changes of Urban Land and Its Fractional Covers in Africa from 2000–2020 Using Time Series of Remotely Sensed Images on the Big Data Platform

Remote Sensing ◽

10.3390/rs13214288 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4288

Author(s):

Zherui Yin ◽

Wenhui Kuang ◽

Yuhai Bao ◽

Yinyin Dou ◽

Wenfeng Chi ◽

...

Keyword(s):

Time Series ◽

Surface Area ◽

Urban Area ◽

Impervious Surface ◽

Remotely Sensed ◽

Urban Land ◽

Living Environment ◽

Impervious Surface Area ◽

Continental Scale ◽

Remotely Sensed Images

Dramatic urban land expansion and its internal sub-fraction change during 2000–2020 have taken place in Africa; however, the investigation of their spatial heterogeneity and dynamic change monitoring at the continental scale are rarely reported. Taking the whole of Africa as a study area, the synergic approach of normalized settlement density index and random forest was applied to assess urban land and its sub-land fractions (i.e., impervious surface area and vegetation space) in Africa, through time series of remotely sensed images on a cloud computing platform. The generated 30-m resolution urban land/sub-land products displayed good accuracy, with comprehensive accuracy of over 90%. During 2000–2020, the evaluated urban land throughout Africa increased from 1.93 × 104 km2 to 4.18 × 104 km2, with a total expansion rate of 116.49%, and the expanded urban area of the top six countries accounted for more than half of the total increments, meaning that the urban expansion was concentrated in several major countries. A turning green Africa was observed, with a continuously increasing ratio of vegetation space to built-up area and a faster increment of vegetation space than impervious surface area (i.e., 134.43% vs., 108.88%) within urban regions. A better living environment was also found in different urbanized regions, as the newly expanded urban area was characterized by lower impervious surface area fraction and higher vegetation fraction compared with the original urban area. Similarly, the humid/semi-humid regions also displayed a better living environment than arid/semi-arid regions. The relationship between socioeconomic development factors (i.e., gross domestic product and urban population) and impervious surface area was investigated and both passed the significance test (p < 0.05), with a higher fit value in the former than the latter. Overall, urban land and its fractional land cover change in Africa during 2000–2020 promoted the well-being of human settlements, indicating the positive effect on environments.

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GISD30: global 30-m impervious surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform

10.5194/essd-2021-285 ◽

2021 ◽

Author(s):

Xiao Zhang ◽

Liangyun Liu ◽

Tingting Zhao ◽

Yuan Gao ◽

Xidong Chen ◽

...

Keyword(s):

Time Series ◽

Landsat Imagery ◽

Impervious Surface ◽

Google Earth ◽

Reflectance Spectra ◽

Impervious Surfaces ◽

Surface Dynamics ◽

Google Earth Engine ◽

Surface Dynamic ◽

Spatiotemporal Consistency

Abstract. Accurately mapping impervious surface dynamics has great scientific significance and application value for urban sustainable development research, anthropogenic carbon emission assessment and global ecological environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral generalization method and time-series Landsat imagery, on the Google Earth Engine cloud-computing platform. Firstly, the global training samples and corresponding reflectance spectra were automatically derived from prior global 30 m land-cover products after employing the multitemporal compositing method and relative radiometric normalization. Then, spatiotemporal adaptive classification models, trained with the migrated reflectance spectra of impervious surfaces from 2020 and pervious surface samples in the same epoch for each 5° × 5° geographical tile, were applied to map the impervious surface in each period. Furthermore, a spatiotemporal consistency correction method was presented to minimize the effects of independent classification errors and improve the spatiotemporal consistency of impervious surface dynamics. Our global 30 m impervious surface dynamic model achieved an overall accuracy of 91.5 % and a kappa coefficient of 0.866 using 18,540 global time-series validation samples. Cross-comparisons with four existing global 30 m impervious surface products further indicated that our GISD30 dynamic product achieved the best performance in capturing the spatial distributions and spatiotemporal dynamics of impervious surfaces in various impervious landscapes. The statistical results indicated that the global impervious surface has doubled in the past 35 years, from 5.116 × 105 km2 in 1985 to 10.871 × 105 km2 in 2020, and Asia saw the largest increase in impervious surface area compared to other continents, with a total increase of 2.946 × 105 km2. Therefore, it was concluded that our global 30 m impervious surface dynamic dataset is an accurate and promising product, and could provide vital support in monitoring regional or global urbanization as well as in related applications. The global 30 m impervious surface dynamic dataset from 1985 to 2020 generated in this paper is free to access at http://doi.org/10.5281/zenodo.5220816 (Liu et al., 2021b).

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