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Article

Mapping Burn Severity of Forest Fires in Small Sample Size Scenarios

by 1,2,3,4, 1,3, 5 and 5,*
1
School of Geoscience and Info-Physics, Central South University, Changsha 410083, Hunan, China
2
College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, Sichuan, China
3
Spatial Information Technology and Sustainable Development Research Center, Central South University, Changsha 410083, Hunan, China
4
Chongqing Institute of Meteorological Sciences, Chongqing Meteorological Bureau, Chongqing 401147, China
5
Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
*
Author to whom correspondence should be addressed.
Forests 2018, 9(10), 608; https://doi.org/10.3390/f9100608
Received: 2 September 2018 / Revised: 26 September 2018 / Accepted: 28 September 2018 / Published: 30 September 2018
(This article belongs to the Special Issue Application of Remote Sensing on Fire Ecology)
Mapping burn severity of forest fires can contribute significantly to understanding, quantifying and monitoring of forest fire severity and its impacts on ecosystems. In recent years, several remote sensing-based methods for mapping burn severity have been reported in the literature, of which the implementations are mainly dependent on several field plots. Therefore, it is a challenge to develop alternative method of mapping burn severity using limited number of field plots. In this study, we proposed a support vector regression based method using multi-temporal satellite data to map the burn severity, evaluated its performance by calculating correlations between the predicted and the observed Composite Burn Index, and compared the performance with that of the regression analysis method (based on dNBR). The results show that the performance of support vector regression based mapping method is more accurate (RMSE = 0.46–0.57) than that of regression analysis method (RMSE = 0.53–0.68). Even with fewer training sets, it can map the detailed distribution of burn severity of forest fires and can achieve relatively better generalization, compared to regression analysis burn severity mapping methods. It could be concluded that the proposed support vector regression based mapping method is an alternative to the regression analysis method in small sample size scenarios. This method with excellent generalization performance should be recommended for future studies on burn severity of forest fires. View Full-Text
Keywords: burn severity mapping; support vector regression; small sample size; Landsat data burn severity mapping; support vector regression; small sample size; Landsat data
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MDPI and ACS Style

Zheng, Z.; Zeng, Y.; Li, S.; Huang, W. Mapping Burn Severity of Forest Fires in Small Sample Size Scenarios. Forests 2018, 9, 608. https://doi.org/10.3390/f9100608

AMA Style

Zheng Z, Zeng Y, Li S, Huang W. Mapping Burn Severity of Forest Fires in Small Sample Size Scenarios. Forests. 2018; 9(10):608. https://doi.org/10.3390/f9100608

Chicago/Turabian Style

Zheng, Zhong, Yongnian Zeng, Songnian Li, and Wei Huang. 2018. "Mapping Burn Severity of Forest Fires in Small Sample Size Scenarios" Forests 9, no. 10: 608. https://doi.org/10.3390/f9100608

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