scholarly journals GPS and InSAR Time Series Analysis: Deformation Monitoring Application in a Hydraulic Engineering Resettlement Zone, Southwest China

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ruya Xiao ◽  
Xiufeng He
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Monitoring System ◽  
Land Surface ◽  
Southwest China ◽  
Deformation Monitoring ◽  
Hydraulic Engineering ◽  
Ground Movement ◽  
Series Analysis ◽  
Gps Monitoring

Booming development of hydropower in China has resulted in increasing concerns about the related resettlement issues. Both global positioning system (GPS) and persistent scatterer interferometric synthetic aperture radar (InSAR) time series analysis are applied to measuring the magnitude and monitoring the spatial and temporal variations of land surface displacement in Hanyuan, a hydraulic engineering resettlement zone, southwest China. The results from the GPS monitoring system established in Hanyuan match well the digital inclinometer results, suggesting that the GPS monitoring system can be employed as a complement to the traditional ground movement monitoring methods. The InSAR time series witness various patterns and magnitudes of deformation in the resettlement zone. Combining the two complementary techniques will overcome the limitations of the single method.

scholarly journals IoT Equipment Monitoring System Based on C5.0 Decision Tree and Time-series Analysis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Biaokai Zhu ◽  
Xinyi Hou ◽  
Sanman Liu ◽  
Wanli Ma ◽  
Meiya Dong ◽  
...  
Keyword(s):  
Time Series ◽  
Decision Tree ◽  
Time Series Analysis ◽  
Monitoring System ◽  
Equipment Monitoring ◽  
Series Analysis ◽  
C5.0 Decision Tree

scholarly journals Mapping air temperature using time series analysis of LST: the SINTESI approach

2013 ◽  
Vol 20 (4) ◽  
pp. 513-527 ◽  
Author(s):  
S. M. Alfieri ◽  
F. De Lorenzi ◽  
M. Menenti
Keyword(s):  
Time Series ◽  
Fourier Series ◽  
Linear Regression ◽  
Time Series Analysis ◽  
Interannual Variability ◽  
Air Temperature ◽  
Land Surface ◽  
Reference Location ◽  
Series Analysis ◽  
Annual Variability

Abstract. This paper presents a new procedure to map time series of air temperature (Ta) at fine spatial resolution using time series analysis of satellite-derived land surface temperature (LST) observations. The method assumes that air temperature is known at a single (reference) location such as in gridded climate data with grid size of the order of 35 km × 35 km. The LST spatial and temporal pattern within a grid cell has been modelled by the pixel-wise ratios r (x,y,t) of the LST at any location to the LST at a reference location. A preliminary analysis of these patterns over a decade has demonstrated that their intra-annual variability is not negligible, with significant seasonality, even if it is stable throughout the years. The intra-annual variability has been modeled using Fourier series. We have evaluated the intra-annual variability by theoretically calculating the yearly evolution of LST (t) for a range of cases as a function of terrain, land cover and hydrological conditions. These calculations are used to interpret the observed LST (x,y,t) and r (x,y,t). The inter-annual variability has been evaluated by modeling each year of observations using Fourier series and evaluating the interannual variability of Fourier coefficients. Because of the negligible interannual variability of r (x,y,t), LST (x,y,t) can be reconstructed in periods of time different from the ones when LST observations are available. Time series of Ta are generated using the ratio r (x,y,t) and a linear regression between LST and Ta. Such linear regression is applied in two ways: (a) to estimate LST at any time from observations or forecasts of Ta at the reference location; (b) to estimate Ta from LST at any location. The results presented in this paper are based on the analysis of daily MODIS LST observations over the period 2001–2010. The Ta at the reference location was gridded data at a node of a 35 km × 35 km grid. Only one node was close to our study area and was used for the work presented here. The regression of Ta on LST was determined using concurrent observations of Ta at the four available weather stations in the Valle Telesina (Italy), our study area. The accuracy of our estimates is consistent with literature and with the combined accuracy of LST and Ta. We obtained comparable error statistics when applying our method to LST data during periods different but adjacent to the periods used to model of r (x,y,t). The method has also been evaluated against Ta observations for earlier periods of time (1984–1988), although available data are rather sparse in space and time. Slightly larger deviation were obtained. In all cases five days of averages from estimated and observed Ta were compared, giving a better accuracy.

Monitoring and modelling land surface dynamics in Bermejo River Basin, Argentina: time series analysis of MODIS NDVI data

2013 ◽  
Vol 34 (15) ◽  
pp. 5429-5451 ◽  
Author(s):  
V. Barraza ◽  
F. Grings ◽  
M. Salvia ◽  
P. Perna ◽  
A.E. Carbajo ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
River Basin ◽  
Land Surface ◽  
Surface Dynamics ◽  
Modis Ndvi ◽  
Series Analysis

scholarly journals Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yu Morishita
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Urban Areas ◽  
Ground Deformation ◽  
Processing System ◽  
Economic Loss ◽  
Deformation Monitoring ◽  
Ground Subsidence ◽  
Detection Mechanism ◽  
Series Analysis

AbstractGround subsidence in urban areas is a significant problem because it increases flood risk, damages buildings and infrastructure, and results in economic loss. Continual monitoring of ground deformation is important for early detection, mechanism understanding, countermeasure implementation, and deformation prediction. The Sentinel-1 satellite constellation has globally and freely provided frequent and abundant SAR data and enabled nationwide deformation monitoring through InSAR time series analysis. LiCSAR, an automatic Sentinel-1 interferometric processing system, has produced abundant interferograms with global coverage, and the products are freely accessible and downloadable through a web portal. LiCSBAS, an open source InSAR time series analysis package integrated with LiCSAR, enables users to obtain the deformation time series easily and quickly. In this study, spatially and temporally detailed deformation time series and velocities from the LiCSAR products using LiCSBAS for 73 major urban areas in Japan during 2014–2020 were derived. All LiCSBAS processing was automatically performed using predefined parameters. Many deformation signals with various temporal and spatial features, such as linear subsidence in Hirosaki, Kujyukuri, Niigata, and Kanazawa, episodic subsidence in Sanjo, annual vertical fluctuation in Hirosaki, Yamagata, Yonezawa, Ojiya, and Nogi, and linear uplift in Chofu were detected. Unknown small nonlinear uplift signals were found in Nara and Osaka in 2018. Complex postseismic deformations from the 2016 Kumamoto earthquake were also revealed. All the deformation data obtained in this study are available on an open repository and are expected to be used for further research, investigation, or interpretation. This nationwide monitoring approach using the LiCSAR products and LiCSBAS is easy to implement and applicable to other areas worldwide.

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