9The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity 10 studies was studied for the case of the large 2007 Peloponnese wildfires in Greece. Fire 11 severity is defined as the degree of environmental change as measured immediately post-fire, 12 whereas burn severity combines the direct fire impact and ecosystems responses. Geo were retrieved using time series similarity of a pre-fire year. The analysis incorporated the 18 optimality statistic, which evaluates index performance based on displacements in the mid- 104This index takes into account the pre-fire amount of biomass, and therefore, rather than being

Brief summary. This paper evaluates the performance of three different spectral indices for 15 estimating burn severity. The indices were pre/post-fire differenced and correlated with field 16 data of severity. In addition the burned pixels' bi-temporal shifts in the corresponding bi-17 spectral feature spaces were studied. Results reveal the importance of the short-wave and mid 18 Composite Burn Index (GeoCBI) plots. In addition, indices were evaluated in terms of 28 optimality. The optimality statistic is a measure for the index's sensitivity to fire-induced 29 vegetation depletion. Results show that the GeoCBI-dNBR (differenced NBR) approach 30 yields a moderate-high R 2 = 0.65 whereas the correlation between field data and the 31 differenced NDMI (dNDMI) and the differenced NDVI (dNDVI) was clearly lower 32 (respectively R 2 = 0.50 and R 2 = 0.46). The dNBR also outperformed the dNDMI and 33 dNDVI in terms of optimality. The resulting median dNBR optimality equalled 0.51 while 34 the median dNDMI and dNDVI optimality values were respectively 0.50 and 0.40 35 (differences significant for p<0.001). However, inaccuracies observed in the spectral indices 36 approach indicate that there is room for improvement. This could imply improved 37 preprocessing, revised index design or alternative methods. 38

Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination 1804 between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R 2 maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R 2 up to 0.78). The Mid Infrared Burn Index (MIRBI)had the highest correlations for Molinia and Erica (R 2 = 0.78 and 0.42, respectively).In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R 2 = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R 2 = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps.

51Changes varied also per land cover type: changes in forests were more profound and 52 persistent than those in shrub land. The characteristic phenology of land covers (e.g. 53coniferous vs. deciduous forest) also resulted in a clearly different post-fire behavior. This 54 research provides insights in the understanding of short-term fire effects on regional climate. 55

In this study several pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment are evaluated. Therefore GeoCBI (Geo Composite Burn Index) field data of burn severity were correlated with remotely sensed measures, based on the NBR (Normalized Burn Ratio), the NDMI (Normalized Difference Moisture Index) and the NDVI (Normalized Difference Vegetation Index). In addition, the strength of the correlation was evaluated for specific fuel types and the influence of the regression model type is pointed out. The NBR was the best remotely sensed index for assessing burn severity, followed by the NDMI and the NDVI. For this case study of the 2007 Peloponnese fires, results show that the GeoCBI-dNBR (differenced NBR) approach yields a moderate-high R 2 = 0.65. Absolute indices outperformed their relative equivalents, which accounted for pre-fire vegetation state. The GeoCBI-dNBR relationship was stronger for forested ecotypes than for shrub lands. The relationship between the field data and the dNBR and dNDMI (differenced NDMI) was non-linear, while the GeoCBI-dNDVI (differenced NDVI) relationship appeared linear.