# Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest
## 4 Discussion  
### Relating Satellite Data and Field Data  
Systematic increase of spatial resolution implied changes on the sensitivity of different satellites to capture windthrow tree-mortality at the extremes of the disturbance gradient (i.e., low and severe mortality) (Table 2). In windthrown areas there is an abrupt reduction of GV followed by a consequent increase of NPV. Nonetheless, the NPV signal can be lost in less than a year due to the fast growth of pioneer species and survivor saplings and seedlings originally suppressed at the understory of the forest [1; 14; 23]. As supported by our data, the loss of the NPV signal might be critical for medium to high spatial resolution imagery with relatively small pixel sizes, which are more likely to be influenced by the fast emergence of photosynthetic/green vegetation (GV) [27] (Figure S10). The convective storm that impacted our study region occurred about eight months (November/2015) prior to the acquisition of all satellite imagery (July/2016). Therefore, our NPV-fraction images may already contain some GV sign of the regenerating forest. This was evidenced by the relatively high reflectance values in the NIR and red edge bands (wavelength \(\sim\)0.83 \(\upmu\)m), which indicate a relatively high content of GV (i.e., photosynthesis) active vegetation) [27; 75] mixed with NPV (i.e., dead vegetation, wood debris)  
Figure 5: Windthrow tree-mortality measured in the field and estimated by remote sensing data for a Central Amazon forest, Brazil. (**a**) Windthrow tree-mortality in field subplots; (**b**) Windthrow-tree mortality in virtual subplots. In (**a,b**), we modeled remote estimates of tree mortality as a function of field measurements using GLMs. The gray-shaded areas in (**a,b**) indicate the 95% confidence interval. Field = Windthrow tree-mortality measured in the field; TM = Windthrow tree-mortality. (Figure S9). Systematic reductions of spectral mixing with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and further WorldView 2) may be related to specific admixture levels of GV. Although understory trees were not recorded in this study, our data support previous research indicating that high NPV values related to the dead vegetation tend to be rapidly diluted due to the fast natural regeneration (Figure S10). The detection of tree damage and mortality using satellite data can be affected by several factors, such as canopy heterogeneity, understory density, angle-of-view/illumination, and shadow effects [74; 78; 98]. Overall, gains in spatial resolution improve the detection accuracy of detecting treefall gaps [33; 34; 37; 42; 99] as the spectral information tend to be highly influenced by geometric features of relatively small targets [32]. However, the number of pixels needed to capture the physical properties of the surfaces associated with large-scale windthrows is highly determined by the spectral resolution of imagery [100]. Since the mean reflectance of a given area is strongly influenced by the material with the highest reflectance values [74], the lower the spatial resolution of the sensor (i.e., the larger pixel size), the greater the mixture of signals produced by surfaces with relatively high reflectance. Smaller pixels can reduce these effects [78], which is supported by the higher sensitivity of WorldView 2 to capture diffuse tree mortality in less-disturbed areas and at edges of highly disturbed forest patches. WorldView 2 also produced higher tree mortality estimates than Landsat 8 and Sentinel 2 at low and moderate windthrow severity (4% < windthrow tree-mortality \(\leq\) 40%). Given the relatively large diameter and area (mean of \(\sim\)35 m\({}^{2}\), [101; 102]) of canopy trees in our study region, this result suggests that WorldView 2 (4 m\({}^{2}\) pixel area) can potentially capture vegetation damage smaller than that measured in the field (i.e., individual tree-mortality). The intercepts for tree mortality varied from 9-11%, indicating that all satellites overestimated field values (Table 1).