Integrating Satellite Imagery with Simulation Modeling to Improve Burn Severity Mapping

Abstract

Both satellite imagery and spatial fire effects models are valuable tools for generating burn severity maps that are useful to fire scientists and resource managers. The purpose of this study was to test a new mapping approach that integrates imagery and modeling to create more accurate burn severity maps. We developed and assessed a statistical model that combines the Relative differenced Normalized Burn Ratio, a satellite image-based change detection procedure commonly used to map burn severity, with output from the Fire Hazard and Risk Model, a simulation model that estimates fire effects at a landscape scale. Using 285 Composite Burn Index (CBI) plots in Washington and Montana as ground reference, we found that an integrated model explained more variability in CBI (R 2 = 0.47) and had lower mean squared error (MSE = 0.28) than image (R 2 = 0.42 and MSE = 0.30) or simulation-based models (R 2 = 0.07 and MSE = 0.49) alone. Overall map accuracy was also highest for maps created with the Integrated Model (63 %). We suspect that Simulation Model performance would greatly improve with higher quality and more accurate spatial input data. Results of this study indicate the potential benefit of combining satellite image-based methods with a fire effects simulation model to create improved burn severity maps.

Challenges of assessing fire and burn severity using field measures, remote sensing and modelling

“…Simulation models such as FOFEM (Reinhardt et al 1997), and statistical models such as CONSUME (Ottmar et al 1993) are useful for simulating the direct effects of a fire on vegetation, fuels and soils. implemented FOFEM into a spatial computer application called FIREHARM to create severity maps, which have been compared with and integrated with satellite imagery Karau et al 2014). These types of models have several advantages: (1) simulation models can provide biophysically based fire effects estimates, (2) results can be scaled to the resolution most appropriate for describing a specific effect, (3) models allow for rapid assessment because results can be simulated quickly as long as input data are available, and (4) models can be used to predict fire effects, allowing a manager to proactively prioritise resources.…”

“…The disadvantage of current empirical and simulation models is that severity predictions are only as good as the input data used to create them (Karau et al 2014), and the widely available spatial data used to develop the simulation and statistical models have high levels of uncertainty (Keane et al 2013). Current models rarely use data from ongoing severity assessments.…”

The Human and Physical Determinants of Wildfires and Burnt Areas in Israel

“…While human factors are the main driver of changes in vegetation cover in Israel (Levin 2016) and for fire ignition in Mediterranean areas, vegetation type is an important factor for understanding spatial patterns of fire frequency. Consolidation of wildfire databases using a combination of remote sensing, field work, and modeling is therefore recommended for better management and prediction of wildfire risks (Karau et al 2014). An example of such a combined approach for reconstructing fire history at the global scale was developed by Mouillot and Field (2005).…”

Different approaches make comparing studies of burn severity challenging: a review of methods used to link remotely sensed data with the Composite Burn Index

“…Many of the North American studies used overlapping CBI datasets (so the datasets are not really independente.g. Cansler and McKenzie (2012) data were used by Karau et al (2014) as well as Parks et al (Parks et al 2014(Parks et al , 2019. We also note that much of the CBI datasets analysed by the papers in this review are available in a data repository for reanalysis (Picotte et al 2019).…”