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Mapping Burn Severity of Forest Fires in Small Sample Size Scenarios
- Source :
- Forests, Vol 9, Iss 10, p 608 (2018), Forests, Volume 9, Issue 10
- Publication Year :
- 2018
- Publisher :
- MDPI AG, 2018.
-
Abstract
- 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&ndash<br />0.57) than that of regression analysis method (RMSE = 0.53&ndash<br />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.
- Subjects :
- Future studies
010504 meteorology & atmospheric sciences
Mean squared error
small sample size
0211 other engineering and technologies
macromolecular substances
02 engineering and technology
01 natural sciences
Satellite data
Statistics
support vector regression
021101 geological & geomatics engineering
0105 earth and related environmental sciences
burn severity mapping
Alternative methods
musculoskeletal, neural, and ocular physiology
Forestry
Small sample
Regression analysis
lcsh:QK900-989
Landsat data
Support vector machine
Field plot
nervous system
lcsh:Plant ecology
Environmental science
Subjects
Details
- ISSN :
- 19994907
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- Forests
- Accession number :
- edsair.doi.dedup.....a28e17496dd7c45c5f5f6214ad6f5811