Satellite Evidence of Decreasing Resilience in Mediterranean Plant Communities After Recurrent Wildfires

Abstract

Vegetation recovery from fire has been widely studied at the stand level in many types of terrestrial ecosystems, but factors controlling regeneration at the landscape scale are less well known. Over large areas, fire history, climate, topography, and dominant type of vegetation may affect postfire response. Increased fire frequency, as is occurring in some mediterranean-type ecosystems, may reduce ecosystem resilience, i.e., the ability to recover the pre-disturbance state. We used the Normalized Difference Vegetation Index (NDVI) from Landsat imagery to monitor vegetation recovery after successive fires in a 32 100-km 2 area of Catalonia (northeastern Spain) between 1975 and 1993. In areas burned twice, NDVI patterns indicated that regrowth after 70 mo was lower after the second fire than after the first. This trend was observed several years after burning, but not immediately following fire. Green biomass after the second fire significantly increased with longer intervals of time between fires. There was also a positive correlation between postfire NDVI and mean rainfall, whereas a negative correlation was found between NDVI and solar radiation. Forests dominated by resprouting Quercus spp. were more resilient to fire, but they showed a larger decrease in resilience after the second fire than did forests dominated by Pinus spp. that regenerate from seed. We conclude that the use of time series satellite images may help to gain further insights in postfire vegetation dynamics over large regions and long time periods.

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

“…2). Similar to Díaz-Delgado et al (2002) in their NDVI based study about post-fire resilience of Mediterranean plants, we assumed that the green vegetation (GV) fraction images (step 2) are a reasonable representation of green vegetation cover, with the following conditions: 1) though GV fraction images do not provide us a highly detailed analysis, we can observe in them general structures and trends in relative vegetation amount; 2) if the GV fraction image is saturated and does not show the high levels of green vegetation that are actually present, the error level will be higher; and 3) we will transform the original GV fraction values to maximize the information related to the ground components of interest. The VIPER Tools 1.5 software tool (www.vipertools.org) , an IDL-based ENVI (Exelis Visual Information Solutions) extension, was used to perform the MESMA procedure including the spectral library building, optimal endmember selection and the unmixing processes.…”

“…According to Díaz-Delgado et al (2002) we measured resilience with reference to aboveground biomass. Instead of a qualitative evaluation of fire damage, resilience enables us to quantitatively estimate the economic losses attributable to the loss of access to some natural resources like timber, mushroom gathering or hunting as it measures the period of time during which the resources are not available (Rodrigues et al, 2014;Román et al, 2013).…”

“…Inside these areas, the field plots were randomly lo cated. Unburned areas were chosen next to burned ones, resulting in similar topography, surficial geology, and vegetation to the burned areas as recommended by Díaz-Delgado et al (2002). Despite the oneyear time lag, we found a quantity of scorched leaves (on branches and the ground) and of char on the tree trunks adequate to evaluate the burn severity level qualitatively.…”

“…The unburned control plot procedure, which is widely used in NDVI-based studies related to the post-fire vegetation recovery (Díaz-Delgado et al, 2002;Idris et al, 2005;Lhermitte et al, 2010Lhermitte et al, , 2011Li et al, 2008;van Leeuwen et al, 2010;Veraverbeke et al, 2011), has not to our knowledge been applied to fraction images. Our study validated its use with MESMA SGV fraction images.…”

An Architecture for Adaptive Robust Modelling of Wildfire Behaviour under Deep Uncertainty

“…In Europe [6], wildfires are one of the major treats at landscape scale. Wildfires have a particular impact in the Mediterranean Europe [7][8][9] while affecting the aerosol load even in Northern part of the continent [10,11]. Whilst their immediate impact ranges from endangering human life to the destruction of economic assets, other damages exceed the spatio-temporal scale of a fire event [9].…”