Abstract
Wildland fires play a key role in the functioning and structure of vegetation. The availability of sensors aboard satellites, such as Moderate Resolution Imaging Spectroradiometer (MODIS), makes possible the construction of a time series of vegetation indices (VI) and the monitoring of post-fire vegetation recovery. One of the techniques used to monitor post-fire vegetation is the comparison of a burned site with an adjacent unburned control site. However, to date, there is no objective method available for selecting these unburned control sites. We propose three biological criteria that the unburned sites must meet to be considered control sites, as well as statistical methods based on the analysis of the properties of the Quotient Vegetation Indices time series (QVI), to detect unburned sites that meet the proposed criteria. We also test the performance of the proposed method by checking the pre-fire difference between burned and unburned sites, assuming that the higher the number of met criteria, the greater the similarity. Therefore, we compare the differences between VI time series of burned sites and VI time series of unburned sites with the same vegetation cover that meet three, two, one, and none of the proposed criteria. In addition, we compare the quality of QVI time series that meet three, two, one, and none of the proposed criteria. Our results show that, for Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data, the difference between the time series of burned and unburned sites gradually decreases with the increase of met criteria. A gradual increase is also observed in the quality of the QVI time series with the increase of met criteria. Despite the limitations present in the proposed method, our model represents an advance from the conceptual and methodological standpoints, since this is the first proposal of a statistical method for selecting unburned control sites based on biological criteria.
Highlights
Wildland fire is a widespread disturbance in terrestrial ecosystems (Flannigan et al 2013) and plays a key role in the structure and function of vegetation (Bond and Keeley2005)
In forests, average differences between vegetation indices (VI) time series of burned and unburned sites gradually decreased with the increase of met criteria (Figure 6; MPDNDVI P < 0.0001, χ2 = 1025.4; MPDEVI P < 0.0001, χ2 = 383.4; MSENDVI P < 0.0001, χ2 = 273.4; MSEEVI P < 0.0001, χ2 = 234.6)
The Width of the Confidence Interval (WCI) values measured for the QVITSb/TSub series followed a similar reduction pattern (WCINDVI P < 0.0001, χ2 = 357.4; WCIEVI P < 0.0001, χ2 = 217.1)
Summary
Wildland fire is a widespread disturbance in terrestrial ecosystems (Flannigan et al 2013) and plays a key role in the structure and function of vegetation (Bond and Keeley2005). The availability of satellites with high temporal resolution and low spatial resolution, such as Moderate Resolution Imaging Spectroradiometer (MODIS), allows daily data collection (Huete et al 2002) This temporal resolution allows us to build time series datasets (Gitas et al 2012) and obtain metrics to characterize the post-fire vegetation functioning (Hicke et al 2003, Goetz et al 2006, Van Leeuwen 2008, Casady et al 2010, Di Mauro et al 2014). Despite the significant vegetation variability within each MODIS pixel due to its low spatial resolution, current evidence suggests that Normalized Difference Vegetation Index (NDVI) calculated with MODIS data has a good correlation with NDVI field data (Kovalskyy et al 2012)
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