Quantifying Fire-Induced Changes, Using Land Surface Temperature to Indicate Changes within Vegetation.

Keywords: Remote sensing, post-fire, Landsat, land surface temperature, Normalized difference vegetation index (NDVI), burn severity, Normalized Burn Ratio, fire severity, southern California
Session Type: Virtual Guided Poster Abstract
Day: Saturday
Session Start / End Time: 2/26/2022 03:40 PM (Eastern Time (US & Canada)) - 2/26/2022 05:00 PM (Eastern Time (US & Canada))
Room: Virtual 33

Abstract

Currently, measurements of fire severity are of great interest when studying fire regimes. Methods based on remote sensing have demonstrated their ability to reliably measure burn severity across large areas, many of which are not easily accessible. A common post-burn analysis makes use of the spectral bands that have a relationship to vegetation. These analyses are based on spectral indices, with the two most used normalized difference vegetation index (NDVI) and differenced normalized burn ratio (dNBR). The addition of land surface temperature (LST) applied from the thermal band has been newly incorporated into fire severity analysis. Previous research has demonstrated that there is a clear link between soil temperature and differences in soil properties, suggesting that LST could be used as a soil burn severity indicator (Quintano et al., 2019). The satellite sensor Landsat 8 data, dNBR was used for the initial evaluation of the burn severity of the 2019 Saddle Ridge fire. Along with NDVI values and LST estimates. Due to the calibration notices issued by the United States Geological Survey (USGS) for Landsat 8 Thermal Infrared Sensor (TIRS) Band 11, only TIRS Band 10 was used for LST. Within this study, we specifically aim to answer the following questions on the recovery of the 2019 Saddle Ridge fire: How is post-fire LST influenced by pre-fire ecological variables? Will comparing NDVI and LST indirectly show the severity of the fire? Is there a trend in areas that were classified as high severity areas?