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

2019 ◽  
Vol 229 ◽  
pp. 114-132 ◽  
Author(s):  
Chong Liu ◽  
Qi Zhang ◽  
Hui Luo ◽  
Shuhua Qi ◽  
Shiqi Tao ◽  
...  
Keyword(s):  
Time Series ◽  
Impervious Surface ◽  
Surface Dynamics ◽  
Efficient Approach

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

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).

scholarly journals Using long time series of Landsat data to monitor impervious surface dynamics: a case study in the Zhoushan Islands

2013 ◽  
Vol 7 (1) ◽  
pp. 073515 ◽  
Author(s):  
Xiaoping Zhang ◽  
Delu Pan ◽  
Jianyu Chen ◽  
Yuanzeng Zhan ◽  
Zhihua Mao
Keyword(s):  
Time Series ◽  
Impervious Surface ◽  
Landsat Data ◽  
Surface Dynamics ◽  
Long Time Series ◽  
Long Time

scholarly journals Exploring the Variation Trend of Urban Expansion, Land Surface Temperature, and Ecological Quality and Their Interrelationships in Guangzhou, China, from 1987 to 2019

2021 ◽  
Vol 13 (5) ◽  
pp. 1019
Author(s):  
Jianhui Xu ◽  
Yi Zhao ◽  
Caige Sun ◽  
Hanbin Liang ◽  
Ji Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Impervious Surface ◽  
Ecological Quality ◽  
Urban Center ◽  
Urban Centers ◽  
Rapid Urbanization

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.

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

2019 ◽  
Vol 11 (6) ◽  
pp. 640 ◽  
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.

scholarly journals Permafrost Dynamics and Degradation in Polar Arctic From Satellite Radar Observations, Yamal Peninsula

2021 ◽  
Vol 9 ◽  
Author(s):  
Kanayim Teshebaeva ◽  
Ko J. van Huissteden ◽  
Helmut Echtler ◽  
Alexander V. Puzanov ◽  
Dmitry N. Balykin ◽  
...  
Keyword(s):  
Time Series ◽  
Continuous Process ◽  
Yamal Peninsula ◽  
Observation Data ◽  
Surface Dynamics ◽  
Stream Channels ◽  
Small Stream ◽  
Surface Features ◽  
Satellite Radar ◽  
Sar Imagery

We investigate permafrost surface features revealed from satellite radar data in the Siberian arctic at the Yamal peninsula. Surface dynamics analysis based on SRTM and TanDEM-X DEMs shows up to 2 m net loss of surface relief between 2000 and 2014 indicating a highly dynamic landscape. Surface features for the past 14 years reflect an increase in small stream channels and a number of new lakes that developed, likely caused by permafrost thaw. We used Sentinel-1 SAR imagery to measure permafrost surface changes. Owing to limited observation data we analyzed only 2 years. The InSAR time-series has detected surface displacements in three distinct spatial locations during 2017 and 2018. At these three locations, 60–120 mm/yr rates of seasonal surface permafrost changes are observed. Spatial location of seasonal ground displacements aligns well with lithology. One of them is located on marine sediments and is linked to anthropogenic impact on permafrost stability. Two other areas are located within alluvial sediments and are at the top of topographic elevated zones. We discuss the influence of the geologic environment and the potential effect of local upwelling of gas. These combined analyses of InSAR time-series with analysis of geomorphic features from DEMs present an important tool for continuous process monitoring of surface dynamics as part of a global warming risk assessment.

scholarly journals An Efficient Approach towards Crimes against Women using Time Series Algorithm

2018 ◽  
Vol 179 (34) ◽  
pp. 22-26 ◽  
Author(s):  
Mayank Motwani ◽  
Pratha Purwar ◽  
Rachit Mathur ◽  
Aatif Jamshed
Keyword(s):  
Time Series ◽  
Efficient Approach ◽  
Crimes Against Women
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