*3.5. Validation*

#### 3.5.1. WorldView-2 Based Validation

The classified WorldView-2 imagery and the AVIRIS-based MESMA cover fractions were compared using the 120 randomly selected polygons across the two image types (Table 6). The best linear correlations between WorldView-2 fractions and MESMA were observed for the GV cover, which displayed a near 1:1 relationship and had generally high r2 values regardless of the endmember selection technique used. Correlation coefficients for char were generally fairly high (between 0.59 and 0.741), however, all models showed a tendency to under-model char, as demonstrated by linear models consistently having an intercept above 0.2. Correlations were generally poor for NPV and soil, with r2 values often below 0.2. Larger endmember libraries did not always translate to higher fractional cover accuracy; for example, the highest r2 for the char fraction was found for the uSZU EMC library (0.741).

**Table 6.** The coefficient of determination (r2), intercept, and slope value of a linear fit between WorldView-2 estimated cover fractions as the dependent variable and AVIRIS derived MESMA cover fraction for different endmember and band selection techniques as the independent variable. Green vegetation is abbreviated as GV, non-photosynthetic vegetation as NPV.


Plots of the linear regression for the uSZU IES MESMA run and the WorldView-2 classifications are provided below (Figure 6). The plots are broadly representative of the trend of most MESMA versus WorldView-2 relationships. There is general support for the linear relationship of the GV identification by MESMA and the WorldView-2, with a high r2 value and little systematic error. Based on the manual interpretation of the WorldView-2 imagery, MESMA appears to be systematically modeling a lower fraction of NPV and char cover, and modeling a higher fraction of soil cover.

**Figure 6.** Scatter plots of the relationship between the 120 WorldView-2 classification points and MESMA with the IES endmember selection technique for green vegetation (GV), char, non-photosynthetic-vegetation (NPV), and soil.

#### 3.5.2. Comparison with GeoCBI Plot Data

Due to the similarity between MESMA unmixed models used in this study, we limit our comparison to only the cover fractions generated from MESMA using the IES library and the full AVIRIS bands. First, the relationship between individual cover fractions and GeoCBI was assessed (Figure 7). The relationship between the GV fraction and GeoCBI appeared to be inversely linear (a linear regression produced an intercept of 2.93, a slope of −1.89 and r2 value of 0.644). In contrast, the relationship between NPV and GeoCBI is clearly non-linear. NPV fractions are generally highest at GeoCBI values of 1.5 to 2.5 suggesting that NPV is high at moderate severities, but low at the highest and lower severity levels. The char fraction appears to be more of a binary relationship only appearing in the model at the highest GeoCBI levels (above 2.75) and is at or near zero for all other GeoCBI levels.

**Figure 7.** Scatter plots of the relationship between GeoCBI, a ground plot derived method of measuring fire severity and estimated cover fractions for green vegetation, non-photosynthetic vegetation, and char.

#### 3.5.3. Comparison with Field Tree Status Data

We also compared the MESMA cover fractions of GV, NPV, and char with the percentage of canopy level trees with mostly green, brown, and black needles (Figure 8). Similar to the GeoCBI values, the char percentage and percentage of black trees appeared to have a near binary relationship in the plots. If the percentage of field identified black trees within the plot was under 50%, then typically no char fraction was modeled for the pixel, if it was above 50%, then the char fraction was almost always modeled as close to 100%. The relationship between the brown trees and NPV appeared to generally be more linear, although the modeled NPV cover was never greater than 50% even with near 100% brown trees. The relationship between the green trees and green vegetation may be positively linear, but the amount of scattering makes interpretation difficult. It is important to remember that few AVIRIS pixels are purely tree cover, most are a mixture of tree and substrate, so a pixel with 100% brown trees may indeed be made up of 50% other materials. Finally, it is important to note that the percent of trees in the plot may not be an ideal reference for the cover fraction as it does not account for variation in tree density, and this may partially contribute to the observed biases.

**Figure 8.** Scatter plots of the relationship between percent of black, brown, and green trees, and estimated cover fractions for green vegetation, non-photosynthetic vegetation, and char.
