scholarly journals Detecting Change Dates from Dense Satellite Time Series Using a Sub-Annual Change Detection Algorithm

2015 ◽  
Vol 7 (7) ◽  
pp. 8705-8727 ◽  
Author(s):  
Shanshan Cai ◽  
Desheng Liu
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Detection Algorithm ◽  
Annual Change

scholarly journals Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

2011 ◽  
Vol 8 (6) ◽  
pp. 10333-10367
Author(s):  
J. Van doninck ◽  
J. Peters ◽  
H. Lievens ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Change Detection ◽  
Incidence Angle ◽  
Arable Land ◽  
Correlation Coefficients ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Second Phase ◽  
Wide Swath

Abstract. A change detection algorithm is applied on a three year time series of ASAR Wide Swath images in VV polarization over Calabria, Italy, in order to derive information on temporal soil moisture dynamics. The algorithm, adapted from an algorithm originally developed for ERS Scatterometer, was validated using a simple hydrological model incorporating meteorological and pedological data. Strong positive correlations between modelled soil moisture and ASAR soil moisture were observed over arable land, while the correlation became much weaker over more vegetated areas. In a second phase, an attempt was made to incorporate seasonality in the different model parameters. It was observed that seasonally changing vegetation and soil moisture mainly affected the multitemporal incidence angle normalization. When applying a seasonal angular normalization, correlation coefficients between modelled soil moisture and retrieved soil moisture increased overall. Attempts to account for seasonality in the other model parameters did not result in an improved performance.

scholarly journals Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

2012 ◽  
Vol 16 (3) ◽  
pp. 773-786 ◽  
Author(s):  
J. Van doninck ◽  
J. Peters ◽  
H. Lievens ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Change Detection ◽  
Incidence Angle ◽  
Arable Land ◽  
Correlation Coefficients ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Second Phase ◽  
Wide Swath

Abstract. A change detection algorithm is applied on a three year time series of ASAR Wide Swath images in VV polarization over Calabria, Italy, in order to derive information on temporal soil moisture dynamics. The algorithm, adapted from an algorithm originally developed for ERS scatterometer, was validated using a simple hydrological model incorporating meteorological and pedological data. Strong positive correlations between modelled soil moisture and ASAR soil moisture were observed over arable land, while the correlation became much weaker over more vegetated areas. In a second phase, an attempt was made to incorporate seasonality in the different model parameters. It was observed that seasonally changing surface properties mainly affected the multitemporal incidence angle normalization. When applying a seasonal angular normalization, correlation coefficients between modelled soil moisture and retrieved soil moisture increased overall. Attempts to account for seasonality in the other model parameters did not result in an improved performance.

scholarly journals BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis

2021 ◽  
Vol 13 (16) ◽  
pp. 3308 ◽  
Author(s):  
Dainius Masiliūnas ◽  
Nandin-Erdene Tsendbazar ◽  
Martin Herold ◽  
Jan Verbesselt
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Missing Values ◽  
Confusion Matrix ◽  
Detection Algorithm ◽  
Information Criteria ◽  
Modis Imagery ◽  
Similar Accuracy

BFAST Lite is a newly proposed unsupervised time series change detection algorithm that is derived from the original BFAST (Breaks for Additive Season and Trend) algorithm, focusing on improvements to speed and flexibility. The goal of the BFAST Lite algorithm is to aid the upscaling of BFAST for global land cover change detection. In this paper, we introduce and describe the algorithm and then compare its accuracy, speed and features with other algorithms in the BFAST family: BFAST and BFAST Monitor. We tested the three algorithms on an eleven-year-long time series of MODIS imagery, using a global reference dataset with over 30,000 point locations of land cover change to validate the results. We set the parameters of all algorithms to comparable values and analysed the algorithm accuracy over a range of time series ordered by the certainty of that the input time series has at least one abrupt break. To compare the algorithm accuracy, we analysed the time difference between the detected breaks and the reference data to obtain a confusion matrix and derived statistics from it. Lastly, we compared the processing speed of the algorithms using both the original R code as well as an optimised C++ implementation for each algorithm. The results showed that BFAST Lite has similar accuracy to BFAST but is significantly faster, more flexible and can handle missing values. Its ability to use alternative information criteria to select the number of breaks resulted in the best balance between the user’s and producer’s accuracy of detected changes of all the tested algorithms. Therefore, BFAST Lite is a useful addition to the BFAST family of unsupervised time series break detection algorithms, which can be used as an aid in narrowing down areas with changes for updating land cover maps, detecting disturbances or estimating magnitudes and rates of change over large areas.

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

Inland wetland time-series digital change detection based on SAVI and NDWI indecies: Wadi El-Rayan lakes, Egypt

2020 ◽  
Vol 19 ◽  
pp. 100347 ◽  
Author(s):  
Asmaa Nasser Mohamed Eid ◽  
C.O. Olatubara ◽  
T.A. Ewemoje ◽  
Mohamed Talaat El-Hennawy ◽  
Haitham Farouk
Keyword(s):  
Time Series ◽  
Change Detection
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