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Sensors 2016, 16(11), 1783; doi:10.3390/s16111783

Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling

Canada Centre for Remote Sensing, Natural Resources Canada, 560 Rochester Street, 6th Floor, Ottawa, ON K1A 0E4, Canada
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Academic Editor: Petri Pellikka
Received: 9 June 2016 / Revised: 23 August 2016 / Accepted: 19 October 2016 / Published: 25 October 2016
(This article belongs to the Section Remote Sensors)
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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. View Full-Text
Keywords: time-series Moderate Resolution Imaging Spectroradiometer (MODIS); Random Forests; data mining; supervised classification modelling; land cover classification; sample transferability time-series Moderate Resolution Imaging Spectroradiometer (MODIS); Random Forests; data mining; supervised classification modelling; land cover classification; sample transferability
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhou, F.; Zhang, A. Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling. Sensors 2016, 16, 1783.

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