A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets
Reconstructing normalized difference vegetation index (NDVI) time series datasets is essential for monitoring long-term changes in terrestrial vegetation. Here, a temporal–spatial iteration (TSI) method was developed to estimate the NDVIs of contaminated pixels, based on reliable data. The NDVIs of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-07-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/7/7/8906 |
| _version_ | 1819294814045208576 |
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| author | Lili Xu Baolin Li Yecheng Yuan Xizhang Gao Tao Zhang |
| author_facet | Lili Xu Baolin Li Yecheng Yuan Xizhang Gao Tao Zhang |
| author_sort | Lili Xu |
| collection | DOAJ |
| description | Reconstructing normalized difference vegetation index (NDVI) time series datasets is essential for monitoring long-term changes in terrestrial vegetation. Here, a temporal–spatial iteration (TSI) method was developed to estimate the NDVIs of contaminated pixels, based on reliable data. The NDVIs of contaminated pixels were first computed through linear interpolation of adjacent high-quality pixels in the time series. Then, the NDVIs of remaining contaminated pixels were determined based on the NDVI of a high-quality pixel located in the same ecological zone, showing the most similar NDVI change trajectories. These two steps were repeated iteratively, using the estimated NDVIs as high-quality pixels to predict undetermined NDVIs of contaminated pixels until the NDVIs of all contaminated pixels were estimated. A case study was conducted in Inner Mongolia, China. The accuracies of estimated NDVIs using TSI were higher than the asymmetric Gaussian, Savitzky–Golay, and window-regression methods. Root mean square error (RMSE) and mean absolute percent error (MAPE) decreased by 16.7%–86.6% and 18.3%–33.0%, respectively. The TSI method performed better over a range of environmental conditions, the variation of performance by the compared methods was 1.4–5 times that of the TSI method. The TSI method will be most applicable when large numbers of contaminated pixels exist. |
| first_indexed | 2024-12-24T04:32:18Z |
| format | Article |
| id | doaj.art-d004ebf807bd4bf9a2da7376666f83d8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T04:32:18Z |
| publishDate | 2015-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d004ebf807bd4bf9a2da7376666f83d82022-12-21T17:15:23ZengMDPI AGRemote Sensing2072-42922015-07-01778906892410.3390/rs70708906rs70708906A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series DatasetsLili Xu0Baolin Li1Yecheng Yuan2Xizhang Gao3Tao Zhang4State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaState Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaState Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaState Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaState Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaReconstructing normalized difference vegetation index (NDVI) time series datasets is essential for monitoring long-term changes in terrestrial vegetation. Here, a temporal–spatial iteration (TSI) method was developed to estimate the NDVIs of contaminated pixels, based on reliable data. The NDVIs of contaminated pixels were first computed through linear interpolation of adjacent high-quality pixels in the time series. Then, the NDVIs of remaining contaminated pixels were determined based on the NDVI of a high-quality pixel located in the same ecological zone, showing the most similar NDVI change trajectories. These two steps were repeated iteratively, using the estimated NDVIs as high-quality pixels to predict undetermined NDVIs of contaminated pixels until the NDVIs of all contaminated pixels were estimated. A case study was conducted in Inner Mongolia, China. The accuracies of estimated NDVIs using TSI were higher than the asymmetric Gaussian, Savitzky–Golay, and window-regression methods. Root mean square error (RMSE) and mean absolute percent error (MAPE) decreased by 16.7%–86.6% and 18.3%–33.0%, respectively. The TSI method performed better over a range of environmental conditions, the variation of performance by the compared methods was 1.4–5 times that of the TSI method. The TSI method will be most applicable when large numbers of contaminated pixels exist.http://www.mdpi.com/2072-4292/7/7/8906reconstructiontime seriesNDVItrajectory distancetemporal-spatial correlation |
| spellingShingle | Lili Xu Baolin Li Yecheng Yuan Xizhang Gao Tao Zhang A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets Remote Sensing reconstruction time series NDVI trajectory distance temporal-spatial correlation |
| title | A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets |
| title_full | A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets |
| title_fullStr | A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets |
| title_full_unstemmed | A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets |
| title_short | A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets |
| title_sort | temporal spatial iteration method to reconstruct ndvi time series datasets |
| topic | reconstruction time series NDVI trajectory distance temporal-spatial correlation |
| url | http://www.mdpi.com/2072-4292/7/7/8906 |
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