Evaluating Multitemporal Sentinel-2 data for Forest Mapping using Random Forest
Abstract: The mapping of land cover using remotely sensed data is most effective when a robust classification method is employed. Random forest is a modern machine learning algorithm that has recently gained interest in the field of remote sensing due to its non-parametric nature, which may be better suited to handle complex, high-dimensional data than conventional techniques. In this study, the random forest method is applied to remote sensing data from the European Space Agency’s new Sentinel-2 satellite program, which was launched in 2015 yet remains relatively untested in scientific literature using non-simulated data. In a study site of boreo-nemoral forest in Ekerö mulicipality, Sweden, a classification is performed for six forest classes based on CadasterENV Sweden, a multi-purpose land covermapping and change monitoring program. The performance of Sentinel-2’s Multi-SpectralImager is investigated in the context of time series to capture phenological conditions, optimal band combinations, as well as the influence of sample size and ancillary inputs.Using two images from spring and summer of 2016, an overall map accuracy of 86.0% was achieved. The red edge, short wave infrared, and visible red bands were confirmed to be of high value. Important factors contributing to the result include the timing of image acquisition, use of a feature reduction approach to decrease the correlation between spectral channels, and the addition of ancillary data that combines topographic and edaphic information. The results suggest that random forest is an effective classification technique that is particularly well suited to high-dimensional remote sensing data.
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