Mapping Burn Severity of Forest Fires in Small Sample Size Scenarios.

Songnian Li,Wei Huang,Yongnian Zeng,Zhong Zheng

doi:10.32920/14638737.v1

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 = 0.46–0.57) 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.

Highlights

Forest fires, as a major ecological disturbance agent in forest ecosystems, annually destroy millions of hectares of forest land around the world [1–3]
Burn severity of forest fires has been estimated through field investigations, e.g., Composite Burn Index (CBI) and a modified version of the Composite Burn Index (GeoCBI), which demands a great deal of manpower, money and time [7,8]
The spatial distributions of of burn severity are more detailed in Support vector regression (SVR)-based maps than that of regression analysis (RA)-based maps

Summary

Introduction

As a major ecological disturbance agent in forest ecosystems, annually destroy millions of hectares of forest land around the world [1–3]. Mapping the severity of such fires for their impact assessment has become widely accepted by forest managers and scientific researchers [4]. The quantitative mapping of burn severity is very useful to managers who want to improve the management responses effectively and timely, and helps researchers to understand the relation between fires and forest ecosystems accurately to obtain more detailed insights [5,6]. Forests 2018, 9, 608 sensing technique has become the major choice of burn severity mapping over large areas, because of its characteristics of large-scale monitoring, short revisiting cycle and low cost [9]. The Landsat TM/ETM+ has been recognized as the most appropriate one to map burn severity of forest fires, due to the fact that its spatial (~30 m) and temporal resolutions (8−16 days) are sufficient and it is available for free [11]

Methods

Results

Conclusion