| Title | Optimal subset selection of time-series MODIS images and sample data transfer with random forests for supervised classification modelling |
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| Author | Zhou, F; Zhang, A |
| Source | Sensors (Switzerland); vol. 16, no. 11, 1783, 2016., https://doi.org/10.3390/s16111783  Open Access |
| Year | 2016 |
| Alt Series | Natural Resources Canada, Contribution Series 20181058 |
| Publisher | MDPI AG |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | geophysics; remote sensing |
| Program | Canada Centre for Remote Sensing Divsion |
| Released | 2016 10 25 |
| Abstract | Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from
NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This
challenge will be greater when Sentinel 2-3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two
important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal
subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of
the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but
effective solution of sample transferring could make supervised modelling possible for applications lacking sample data. © 2016 by the authors; licensee MDPI, Basel, Switzerland. |
| GEOSCAN ID | 311412 |
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