Analysis of the Relationship between Land Surface Temperature and Wildfire Severity in a Series of Landsat Images

Abstract

Abstract:The paper assesses spatio-temporal patterns of land surface temperature (LST) and fire severity in the Las Hurdes wildfire of Pinus pinaster forest, which occurred in July 2009, in Extremadura (Spain), from a time series of fifteen Landsat 5 TM images corresponding to 27 post-fire months. The differenced Normalized Burn Ratio (dNBR) was used to evaluate burn severity. The mono-window algorithm was applied to estimate LST from the Landsat thermal band. The burned zones underwent a significant increase in LST after fire. Statistically significant differences have been detected between the LST within regions of burn severity categories. More substantial changes in LST are observed in zones of greater fire severity, which can be explained by the lower emissivity of combustion products found in the burned area and changes in the energy balance related to vegetation removal. As time progresses over the 27 months after fire, LST differences decrease due to vegetation regeneration. The differences in LST and Normalized Difference Vegetation Index (NDVI) values between burn severity categories in each image are highly correlated (r = 0.84). Spatial patterns of severity and post-fire LST obtained from Landsat time series enable an evaluation of the relationship between these variables to predict the natural dynamics of burned areas.

Temporal and spatial variability of daytime land surface temperature in Houston: Comparing DISCOVER‐AQ aircraft observations with the WRF model and satellites

“…LST presents strong spatial heterogeneity and temporal variability, which depend on solar radiation, land surface properties (e.g., land cover types, vegetation and soil conditions, and surface albedo), and the atmospheric conditions such as water vapor and clouds [Vinnikov et al, 2008;Duan et al, 2014;Jin and Dickinson, 2010]. Being complemented by the surface air temperature measured at~2 m above the ground, LST as well as its temporal evolution (e.g., interannual, seasonal, and diurnal variability) is important for studying climate variability, detecting changes in land cover and urban environments, monitoring drought and crop conditions, and indicating wildfire severity [e.g., Gutman, 1998, 1999;Jin and Dickinson, 2002;Wan et al, 2004;Braganza et al, 2004;Sun et al, 2006aSun et al, , 2006bWeng et al, 2004;Weng, 2009;Hansen et al, 2010;Vlassova et al, 2014]. gov/gmd/grad/surfrad/) provide long-term frequent observations on a scale of several meters or smaller.…”

A MODIS-based method for detecting large-scale vegetation disturbance due to natural hazards: a case study of Wenchuan earthquake stricken regions in China

“…Terrestrial surface vegetation damage is normally caused by various natural hazards, such as wildfire (Di Mauro et al 2014;Vlassova et al 2014), insect outbreaks (Neigh et al 2014), hurricanes (Lam et al 2011), floods (Powell et al 2014), droughts (Horion et al 2014;Vicente-Serrano et al 2013), ice and snow storms (Chen and Sun 2010;Sun et al 2012), and earthquakes (Wang et al 2014;Xu et al 2014). Vegetation damage can result in losing terrestrial biomass, increasing the carbon dioxide emission, changing land surface albedo, and further influence global warming (Running 2008;Sivakumar and Christakos 2011).…”

Variabilidad espacio-temporal de la temperatura de superficie en ecosistemas de dehesa estimada mediante imágenes Landsat TM: el papel del arbolado

“…(Daughtry et al, 2000;Hwang et al, 2011;Turner et al, 1999). Las imágenes Landsat también poseen una banda en el infrarrojo térmico que posibilita la espacialización de la Ts (Vlassova et al, 2014;Weng et al, 2004).…”