# Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest
## 3 Results  
### How Does Spatial Resolution Affect Satellite Estimates of Windthrow Tree-Mortality? Across all field subplots (0.75 ha), we sampled 341 live and 121 dead trees. This is equivalent to 616 \(\pm\) 13.3 trees ha\({}^{-1}\) (455 \(\pm\) 70.2 and 161 \(\pm\) 63.7 live and dead trees ha\({}^{-1}\), respectively) (mean \(\pm\) 95% CI). Live trees were distributed across 142 species, 86 genera, and 36 families. The mean windthrow tree-mortality measured in the field was 26.9 \(\pm\) 11.1%, with a median of 13%, and minimum and maximum values of 0% and 93%, respectively (Table S3). Variations in windthrow tree mortality were efficiently captured by three satellites evaluated in our study. Overall, the best agreement between field measurements and satellite estimates (i.e., lower Residual Deviance, AIC, S\({}_{\text{yx}}\), RMSE, Sigma, and higher R\({}^{2}\)KL) was achieved with Landsat 8, followed by Sentinel 2 and WorldView 2. This result indicates that increasing the spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2) implied a systematic loss of quality of fit of the models. Moreover, increasing the spatial resolution did not consistently modify the intercept and slope values of models. Surprisingly, all satellites showed minimum values of tree mortality ranging from \(\sim\)9-11% (intercept) and a slope close to one (Table 1). We found a strong and positive correlation between NPVnorm and field measurements of windthrow tree-mortality (\(p\) < 0.001, all, Figure 3). The mean and median of remote estimates were statistically similar for both field (F = 0.003 and \(p\) = 0.99 [mean test]; X\({}^{2}\)Kruskal-Wallis = 3.23 and \(p\)-value = 0.36 [median test]) and virtual subplots (F = 0.70 and \(p\) = 0.49 [mean test]; X\({}^{2}\)Kruskal-Wallis = 1.58 and \(p\)-value = 0.45 [median test]). Increasing the spatial resolution of images led to an overall decrease of model uncertainties (lower standard deviation, standard error, confidence interval, and interquartile interval). There was an increase in the range of estimated values in a narrower range of distribution across the disturbed landscape, which also resulted in systematic reductions of the interquartile ranges (Table 2). The sensitivity of satellites also varied across the windthrow tree-mortality gradient. Landsat 8 and Sentinel 2 had a similar trend curve that differed from that of WorldView 2 (Figure 3a). Overall, increasing the spatial resolution systematically decreased the quality of tree mortality (Figure 3b). Windthrow tree-mortality obtained from Landsat 8 and Sentinel 2 had a more uniform residual distribution than that of WorldView 2, which indicates that the sensitivity of the former satellites was less susceptible to bias at the extremes of the severity gradient (Figure 3c). Increasing spatial resolution led to higher sensitivity for capturing diffuse tree mortality, possible reflecting crown, and minor damage. However, it also led to underestimated estimates of tree mortality in severely damaged areas (Figure 4). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Residual** & \multirow{2}{*}{**AIC**} & \multirow{2}{*}{**S\({}_{\text{yx}}\)**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**Sigma**} & \multirow{2}{*}{**R\({}^{2}\)KL**} & \multicolumn{2}{c}{**Coefficients**} \\ \cline{3-4}  & **Deviance** & & & & & & **a (Intercept)** & **b (Slope)** \\ \hline Landsat 8 & 125.33 & 183.37 & 0.2096 & 0.194 & 2.116 & 0.4342 & 9.08 & 0.9837 \\ Sentinel 2 & 136.51 & 194.55 & 0.2211 & 0.209 & 2.208 & 0.3837 & 11.21 & 0.9719 \\ WorldView 2 & 150.01 & 208.05 & 0.2234 & 0.219 & 2.315 & 0.3237 & 10.61 & 0.9977 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fitting summary of GLM models used to relate field with remote sensing estimates of windthrow tree-mortality in a Central Amazon forest, Brazil.