The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: The case of the large 2007 Peloponnese wildfires in Greece

The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies was studied for the case of the large 2007 Peloponnese wildfires in Greece. Fire severity is defined as the degree of environmental change as measured immediately post-fire, whereas burn severity combines the direct fire impact and ecosystems responses. Geo Composite Burn Index (GeoCBI), two pre-/post-fire differenced Thematic Mapper (TM) dNBR assessments and a Moderate Resolution Imaging Spectroradiometer (MODIS) dNBR time series were used to analyze the temporal dimension. MODIS dNBR time series were calculated based on the difference between the NBR of the burned and control pixels, which were retrieved using time series similarity of a pre-fire year. The analysis incorporated the optimality statistic, which evaluates index performance based on displacements in the mid-infrared-near infrared bi-spectral space. Results showed a higher correlation between field and TM data early post-fire (R² = 0.72) than one-year post-fire (R² = 0.56). Additionally, mean dNBR (0.56 vs. 0.29), the dNBR standard deviation (0.29 vs. 0.19) and mean optimality (0.65 vs. 0.47) were clearly higher for the initial assessment than for the extended assessment. This is due to regenerative processes that obscured first-order fire effects impacting the suitability of the dNBR to assess burn severity in this case study. This demonstrates the importance of the lag timing, i.e. time since fire, of an assessment, especially in a quickly recovering Mediterranean ecosystem. The MODIS time series was used to study intra-annual changes in index performance. The seasonal timing of an assessment highly impacts what is actually measured. This seasonality affected both the greenness of herbaceous resprouters and the productivity of the control pixels, which is land cover specific. Appropriate seasonal timing of an assessment is therefore of paramount importance to anticipate false trends (e.g. caused by senescence). Although these findings are case study specific, it can be expected that similar temporal constraints affect assessments in other ecoregions. Therefore, within the limitations of available Landsat imagery, caution is recommended for the temporal dimension when assessing post-fire effects. This is crucial, especially for studies that aim to evaluate trends in fire/burn severity across space and time. Also, clarification in associated terminology is suggested.     

Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)

Multi-temporal change detection is commonly used in the detection of changes to ecosystems. Differencing single band indices derived from multispectral pre- and post-fire images is one of the most frequently used change detection algorithms. In this paper we examine a commonly used index used in mapping fire effects due to wildland fire. Subtracting a post-fire from a pre-fire image derived index produces a measure of absolute change which then can be used to estimate total carbon release, biomass loss, smoke production, etc. Measuring absolute change however, may be inappropriate when assessing ecological impacts. In a pixel with a sparse tree canopy for example, differencing a vegetation index will measure a small change due stand-replacing fire. Similarly, differencing will produce a large change value in a pixel experiencing stand-replacing fire that had a dense pre-fire tree canopy. If all stand-replacing fire is defined as severe fire, then thresholding an absolute change image derived through image differencing to produce a categorical classification of burn severity can result in misclassification of low vegetated pixels. Misclassification of low vegetated pixels also happens when classifying severity in different vegetation types within the same fire perimeter with one set of thresholds. Comparisons of classifications derived from thresholds of dNBR and relative dNBR data for individual fires may result in similar classification accuracies. However, classifications of relative dNBR data can produce higher accuracies on average for the high burn severity category than dNBR classifications derived from a universal set of thresholds applied across multiple fires. This is important when mapping historic fires where precise field based severity data may not be available to aid in classification. Implementation of a relative index will also allow a more direct comparison of severity between fires across space and time which is important for landscape level analysis. In this paper we present a relative version of dNBR based upon field data from 14 fires in the Sierra Nevada mountain range of California, USA. The methods presented may have application to other types of disturbance events.     

Assessment of fire severity and species diversity in the southern Appalachians using Landsat TM and ETM+ imagery

Relatively little is known about the disturbance ecology of large wildfires in the southern Appalachians. The occurrence of a 4000-ha wildfire in the Linville Gorge Wilderness area in western North Carolina has provided a rare opportunity to study a large fire with a range of severities. The objectives of this study were to 1) assess the potential for using multi-temporal Landsat imagery to map fire severity in the southern Appalachians, 2) examine the influences of topography and forest community type on the spatial pattern of fire severity; and 3) examine the relationship between predicted fire severity and changes in species richness. A non-linear regression equation predicted a field-based composite burn index (CBI) as a function of change in the Normalized Burn Ratio (dNBR) with an R² of 0.71. Fire severity was highest on drier landforms located on upper hillslopes, ridges, and on southwest aspects, and was higher in pine communities than in other forest types. Predicted CBI was positively correlated with changes in species richness and with the post-fire cover of pine seedlings (Pinus virginiana, P. rigida, and P. pungens), suggesting that burn severity maps can be used to predict community-level fire effects across large landscapes. Despite the relatively large size of this fire for the southern Appalachians, severity was strongly linked to topographic variability and pre-fire vegetation, and spatial variation in fire severity was correlated with changes in species richness. Thus, the Linville Gorge fire appears to have generally reinforced the ecological constraints imposed by underlying environmental gradients.