Conference Proceedings

Fire-severity classification across temperate Australian forests: random forests versus spectral index thresholding

NB Tran, MA Tanase, LT Bennett, C Aponte, CMU Neale (ed.), A Maltese (ed.)

REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XXI | SPIE-INT SOC OPTICAL ENGINEERING | Published : 2019

Abstract

Machine learning and spectral index (SI) thresholding approaches have been tested for fire-severity mapping from local to regional scales in a range of forest types worldwide. While index thresholding can be easily implemented, its operational utility over large areas is limited as the optimum index may vary with forest type and fire regimes. In contrast, machine learning algorithms allow for multivariate fire classifications. This study compared the accuracy of fire-severity classifications from SI thresholding with those from Random Forests (RF). Reference data were from 3730 plots within the boundaries of eight major wildfires across the six temperate forest �functional' groups of Victo..

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Funding Acknowledgements

The authors would like to acknowledge the financial support of the Melbourne Research Scholarship program, the Vietnam International Education Cooperation Department (VIED) scholarship, and the Integrated Forest Ecosystem Research program, supported by the Victorian Department of Environment, Land, Water and Planning. We also acknowledge the support of many staff and students from the School of Ecosystem and Forest Sciences at the University of Melbourne, including valuable comments and advice to improve this manuscript.