Comparing Spectral Indices from High Resolution WorldView-2 and Sentinel-2 Satellite Imagery to Evaluate Time-Sensitive Post-Wildfire Ash Cover

Abstract:

The spatial extent and severity of soil disturbance after wildfire has critical ecological implications for hydrologic processes and ecosystem services for many months or years. The presence of ash on the ground surface indicates complete vegetation consumption and is indicative of high soil burn severity. Ash is highly transportable and is often removed from its origin within days or weeks after a fire. Thus, there is a time-sensitive element to mapping and modelling the post-fire environment that include ash cover and distribution. Remote sensing is the best method for mapping and monitoring fire effects over extended spatial and temporal scales. We collected multi-temporal ash cover and depth measurements after the 2018 Mesa Fire in Idaho to examine the spatial and temporal patterns of post-fire ash cover and load using high resolution imagery. The WorldView-2 (WV-2) satellite has a pixel size of 1.8 m and has 8 visible and NIR bands, and the Sentinel-2 satellite has 12 spectral bands with 10–20 m spatial resolution in the visible and NIR/SWIR (near- and shortwave-infrared) regions, and a return period of 5 days. Time-series analysis of the Normalized Differenced Vegetation Index (NDVI) from WV-2 data, and multiple visible-NIR-SWIR indices from Sentinel-2 were done to evaluate the relationship with ash cover over time. Results indicate significant correlations with WV-2 NDVI and ash cover (r=~ -0.6, p-value=0.02), and similar correlations between ash cover and several Sentinel-2 indices such as the Normalized Burn Ratio (NBR), Soil Adjusted Vegetation Index (SAVI), NDVI and some of their derivatives. These results are interesting because they show that a simple visible-NIR index like the NDVI may be as capable of mapping post-fire ash cover as well as indices that make use of SWIR bands. This has implications for other mapping platforms such as UAVs which often have NIR capability. The ease of identifying areas of high-severity or high-risk in an operational capacity at fine-scale would be highly beneficial to land managers.