Characterizing the spatiotemporal response of runoff to impervious surface dynamics across three highly urbanized cities in southern China from 2000 to 2017

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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An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense Landsat time series stacks

Remote Sensing of Environment ◽

10.1016/j.rse.2019.04.025 ◽

2019 ◽

Vol 229 ◽

pp. 114-132 ◽

Cited By ~ 16

Author(s):

Chong Liu ◽

Qi Zhang ◽

Hui Luo ◽

Shuhua Qi ◽

Shiqi Tao ◽

...

Keyword(s):

Time Series ◽

Impervious Surface ◽

Surface Dynamics ◽

Efficient Approach

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Remote sensing of impervious surface dynamics of Xiamen City, southeastern China

2011 19th International Conference on Geoinformatics ◽

10.1109/geoinformatics.2011.5980729 ◽

2011 ◽

Author(s):

Hanqiu Xu ◽

Dongfeng Lin ◽

Fei Tang ◽

Chengxi Wei

Keyword(s):

Remote Sensing ◽

Impervious Surface ◽

Surface Dynamics ◽

Southeastern China ◽

Xiamen City

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A Scheme for the Long-Term Monitoring of Impervious−Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine

Remote Sensing ◽

10.3390/rs11161891 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1891 ◽

Cited By ~ 3

Author(s):

Hanzeyu Xu ◽

Yuchun Wei ◽

Chong Liu ◽

Xiao Li ◽

Hong Fang

Keyword(s):

High Frequency ◽

Impervious Surface ◽

Google Earth ◽

Impervious Surfaces ◽

Surface Dynamics ◽

Google Earth Engine ◽

Long Term Monitoring ◽

Disturbance And Recovery ◽

Term Monitoring

Impervious surfaces are commonly acknowledged as major components of human settlements. The expansion of impervious surfaces could lead to a series of human−dominated environmental and ecological issues. Tracing impervious surface dynamics at a finer temporal−spatial scale is a critical way to better understand the increasingly human-dominated system of Earth. In this study, we put forward a new scheme to conduct long-term monitoring of impervious−relevant land disturbances using high frequency Landsat archives and the Google Earth Engine (GEE). First, the developed region was identified using a classification-based approach. Then, the GEE-version LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) was used to detect land disturbances, characterizing the conversion from vegetation to impervious surfaces. Finally, the actual disturbance areas within the developed regions were derived and quantitatively evaluated. A case study was conducted to detect impervious surface dynamics in Nanjing, China, from 1988 to 2018. Results show that our scheme can efficiently monitor impervious surface dynamics at yearly intervals with good accuracy. The overall accuracy (OA) of the classification results for 1988 and 2018 are 95.86% and 94.14%. Based on temporal−spatial accuracy assessments of the final detection result, the temporal accuracy is 90.75%, and the average detection time deviation is −1.28 a. The OA, precision, and recall of the sampling inspection, respectively, are 84.34%, 85.43%, and 96.37%. This scheme provides new insights into capturing the expansion of impervious−relevant land disturbances with high frequency Landsat archives in an efficient way.

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Automatically Monitoring Impervious Surfaces Using Spectral Generalization and Time Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta

Journal of Remote Sensing ◽

10.34133/2021/9873816 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Xiao Zhang ◽

Liangyun Liu ◽

Xidong Chen ◽

Yuan Gao ◽

Mihang Jiang

Keyword(s):

Yangtze River ◽

Landsat Imagery ◽

Impervious Surface ◽

Yangtze River Delta ◽

Surface Reflectance ◽

Impervious Surfaces ◽

The Yangtze River ◽

Surface Dynamics ◽

Training Samples ◽

The Yangtze River Delta

Accurately monitoring the spatiotemporal dynamics of impervious surfaces is very important for understanding the process of urbanization. However, the complicated makeup and spectral heterogeneity of impervious surfaces create difficulties for impervious surface monitoring. In this study, we propose an automatic method to capture the spatiotemporal expansion of impervious surfaces using spectral generalization and time series Landsat imagery. First, the multitemporal compositing and relative radiometric normalization methods were used to extract phenological information and ensure spectral consistency between reference imagery and monitored imagery. Second, we automatically derived training samples from the prior MSMT_IS30-2020 impervious surface products and migrated the surface reflectance of impervious surfaces in the reference period of 2020 to other periods (1985–2015). Third, the random forest classification method, trained using the migrated surface reflectance of impervious surfaces and pervious surface training samples at each period, was employed to extract temporally independent impervious surfaces. Further, a temporal consistency check method was applied to ensure the consistency and reliability of the monitoring results. According to qualitative and quantitative validation results, the method achieved an overall accuracy of 90.9% and kappa coefficient of 0.859 in identifying the spatiotemporal expansion of impervious surfaces and performed better in capturing the impervious surface dynamics when compared with other impervious surface datasets. Lastly, our results indicate that a rapid increase of impervious surfaces was observed in the Yangtze River Delta, and the area of impervious surfaces in 2000 and 2020 was 1.86 times and 4.76 times that of 1985, respectively. Therefore, it could be concluded that the proposed method offered a novel perspective for providing timely and accurate impervious surface dynamics.

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