Estimating grassland curing with remotely sensed data

Wasin Chaivaranont,Jason P Evans,Yi Y Liu,Jason J Sharples

doi:10.5194/nhess-18-1535-2018

Abstract

Abstract. Wildfire can become a catastrophic natural hazard, especially during dry summer seasons in Australia. Severity is influenced by various meteorological, geographical, and fuel characteristics. Modified Mark 4 McArthur's Grassland Fire Danger Index (GFDI) is a commonly used approach to determine the fire danger level in grassland ecosystems. The degree of curing (DOC, i.e. proportion of dead material) of the grass is one key ingredient in determining the fire danger. It is difficult to collect accurate DOC information in the field, and therefore ground-observed measurements are rather limited. In this study, we explore the possibility of whether adding satellite-observed data responding to vegetation water content (vegetation optical depth, VOD) will improve DOC prediction when compared with the existing satellite-observed data responding to DOC prediction models based on vegetation greenness (normalised difference vegetation index, NDVI). First, statistically significant relationships are established between selected ground-observed DOC and satellite-observed vegetation datasets (NDVI and VOD) with an r2 up to 0.67. DOC levels estimated using satellite observations were then evaluated using field measurements with an r2 of 0.44 to 0.55. Results suggest that VOD-based DOC estimation can reasonably reproduce ground-based observations in space and time and is comparable to the existing NDVI-based DOC estimation models.

Highlights

Wildfire can be responsible for major environmental damage or changes to ecosystems (Cobb et al, 2016; Gazzard et al, 2016; Mistry et al, 2016)
The scatter plot showing the correlation between vegetation optical depth (VOD), NDVI, and combined VOD and NDVI terms is as shown in Fig. 3, while Fig. 4 shows the residual DOC unexplained by NDVI against VOD and combined VOD and NDVI terms
VOD was excluded as a predictor in the first final model, as expressed in Eq (10), because during the stepwise regression, when the NDVI and (VOD) (NDVI) terms are included as the first and second predictors, the VOD term does not contribute in improving the final model prediction (i.e. p value exceeds the acceptance threshold, preventing overfitting)

Summary

Introduction

Wildfire can be responsible for major environmental damage or changes to ecosystems (Cobb et al, 2016; Gazzard et al, 2016; Mistry et al, 2016). One of the important components in determining the severity of wildfire is fuel availability. Wildland fuels can vary considerably, both spatially and temporally (Stambaugh et al, 2011). Various interpretations and characterisations of fuel have been made in past studies as a key contribution to assessing wildfire potential (Hudec and Peterson, 2012; Jurdao et al, 2012; Sharples et al, 2009b; Stambaugh et al, 2011; Yebra et al, 2013). Fuel can be quantified by its age or the time since the last fire (Bradstock et al, 2010). Since this paper uses numbers of abbreviations that may not be familiar to the reader, please refer to the Appendices for the list of acronyms

Methods

Results

Discussion

Conclusion