Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis
Jump or break detection within a non-stationary time series is a crucial and challenging problem in a broad range of applications including environmental monitoring. Remotely sensed time series are not only non-stationary and unequally spaced (irregularly sampled) but also noisy due to atmospheric e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/23/4001 |
| _version_ | 1797545539529605120 |
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| author | Ebrahim Ghaderpour Tijana Vujadinovic |
| author_facet | Ebrahim Ghaderpour Tijana Vujadinovic |
| author_sort | Ebrahim Ghaderpour |
| collection | DOAJ |
| description | Jump or break detection within a non-stationary time series is a crucial and challenging problem in a broad range of applications including environmental monitoring. Remotely sensed time series are not only non-stationary and unequally spaced (irregularly sampled) but also noisy due to atmospheric effects, such as clouds, haze, and smoke. To address this challenge, a robust method of jump detection is proposed based on the Anti-Leakage Least-Squares Spectral Analysis (ALLSSA) along with an appropriate temporal segmentation. This method, namely, Jumps Upon Spectrum and Trend (JUST), can simultaneously search for trends and statistically significant spectral components of each time series segment to identify the potential jumps by considering appropriate weights associated with the time series. JUST is successfully applied to simulated vegetation time series with varying jump location and magnitude, the number of observations, seasonal component, and noises. Using a collection of simulated and real-world vegetation time series in southeastern Australia, it is shown that JUST performs better than Breaks For Additive Seasonal and Trend (BFAST) in identifying jumps within the trend component of time series with various types. Furthermore, JUST is applied to Landsat 8 composites for a forested region in California, U.S., to show its potential in characterizing spatial and temporal changes in a forested landscape. Therefore, JUST is recommended as a robust and alternative change detection method which can consider the observational uncertainties and does not require any interpolations and/or gap fillings. |
| first_indexed | 2024-03-10T14:16:54Z |
| format | Article |
| id | doaj.art-bdcd35fe6ced48718f6839ff5f65fc5e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:16:54Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bdcd35fe6ced48718f6839ff5f65fc5e2023-11-20T23:45:12ZengMDPI AGRemote Sensing2072-42922020-12-011223400110.3390/rs12234001Change Detection within Remotely Sensed Satellite Image Time Series via Spectral AnalysisEbrahim Ghaderpour0Tijana Vujadinovic1Faculty of Science, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, CanadaFaculty of Management, University of Lethbridge, 4401 University Drive W, Lethbridge, AB T1K 3M4, CanadaJump or break detection within a non-stationary time series is a crucial and challenging problem in a broad range of applications including environmental monitoring. Remotely sensed time series are not only non-stationary and unequally spaced (irregularly sampled) but also noisy due to atmospheric effects, such as clouds, haze, and smoke. To address this challenge, a robust method of jump detection is proposed based on the Anti-Leakage Least-Squares Spectral Analysis (ALLSSA) along with an appropriate temporal segmentation. This method, namely, Jumps Upon Spectrum and Trend (JUST), can simultaneously search for trends and statistically significant spectral components of each time series segment to identify the potential jumps by considering appropriate weights associated with the time series. JUST is successfully applied to simulated vegetation time series with varying jump location and magnitude, the number of observations, seasonal component, and noises. Using a collection of simulated and real-world vegetation time series in southeastern Australia, it is shown that JUST performs better than Breaks For Additive Seasonal and Trend (BFAST) in identifying jumps within the trend component of time series with various types. Furthermore, JUST is applied to Landsat 8 composites for a forested region in California, U.S., to show its potential in characterizing spatial and temporal changes in a forested landscape. Therefore, JUST is recommended as a robust and alternative change detection method which can consider the observational uncertainties and does not require any interpolations and/or gap fillings.https://www.mdpi.com/2072-4292/12/23/4001EVIjump detectionLandsat 8Least-Squares estimationNDVIspectral analysis |
| spellingShingle | Ebrahim Ghaderpour Tijana Vujadinovic Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis Remote Sensing EVI jump detection Landsat 8 Least-Squares estimation NDVI spectral analysis |
| title | Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis |
| title_full | Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis |
| title_fullStr | Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis |
| title_full_unstemmed | Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis |
| title_short | Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis |
| title_sort | change detection within remotely sensed satellite image time series via spectral analysis |
| topic | EVI jump detection Landsat 8 Least-Squares estimation NDVI spectral analysis |
| url | https://www.mdpi.com/2072-4292/12/23/4001 |
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