# Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest
## 4 Discussion
### Relating Satellite Data and Field Data
The variation of intercepts (Figure 3a)may also be related to scaling mismatches between field and imagery data and fine-scale variations of environmental attributes beyond the scope of this study. Furthermore, our models indicate that uncertainties associated with remote estimates of windthrow tree-mortality are larger at the extremes of the disturbance gradient (Figure 3b). R\({}^{2}\) values indicate that our models captured between 43% and 56% of the variation in field-measured mortality. We believe these values are robust given the variation of disturbance intensity typical of windthrow forests [1; 2; 4] and the time gap between the studied event and the acquisition date of employed images. Estimated tree-mortality was accurate (i.e., similar to the ground truth) across an extent range of windthrow severity (25-42%, moderate to high severity). As previously reported, these severities accounted for decadal changes in floristic composition [1; 62; 103], biomass stock and balance [2; 4], organic soil carbon stocks [104], and insect diversity [105; 106] in other Central and Northwestern Amazon forests affected by windthrows. Future investigations are needed to improve the remote detection of smaller canopy disturbances across local-to-regional variations in topography, climate, soils, and forest attributes [42; 43; 44; 107]. High-resolution remote sensing models (HR-models) assume that elements are larger than image pixels; low-resolution models (LR-models) assume the opposite [108]. This means that the spatial arrangement of varying elements within an image can be detected directly in HR-models because individual elements can be individualized. This is usually not achieved with LR-models [21; 108]. As predicted from our best model (Landsat 8), areas of forest that experienced high severity windthrow lost \(\geq\) 50% of live trees. While a similar estimate of tree mortality can be attributed to two independent areas, they can have contrasting disturbance gradients given by the spatial distribution of damaged/dead trees (i.e., contiguous or diffuse) across impacted areas (Figure 3a). In windthrows, the process of detecting and estimating tree mortality will transit between HR- and LR-models, depending on the level of disturbance severity and the size of pixels. For example, in a highly impacted area (e.g., assuming 50% dead trees and 50% live trees) inside one Landsat 30 m \(\times\) 30 m pixel, the spatial autocorrelation among the trees is not detected because tree crowns and trunks are, in general, smaller than the pixel size. In this case, an unique reflectance value (i.e., single pixel) shall represent the different targets (live and dead trees). The resulting measurement is highly influenced by the objects with high reflectance [75; 100]. However, in HR-models, the same area is represented by several pixels (e.g., 2 m \(\times\) 2 m pixel of WorldView 2). In this case, the spatial arrangement of the vegetation becomes relevant because trees are larger than the pixels, which tend to resemble their neighbors because they are likely to capture the signature of the same homogeneous target [27]. In HR-models, the varying information captured by several pixels will represent the entire set of live and dead trees. [21]. Depending on the size and severity of the windthrow in relation to the pixel size, HR- and LR-models will have different relationships between the number of dead trees in the field and the spectral/spatial information extracted at pixel level. Our results support that the more pixels within the subplots, the more detail on the spectral variability across the disturbed forest. However, tree damage (e.g., defoliation, broken branches, and partial loss of crown) should also be quantified in future forest inventories to maximize the potential of existing sensors with higher spatial-resolution than those we employed here (e.g., Planet Scope [109], Ikonos [110] and QuickBird [111]). A precise match between field and remote sensing data also improves landscape estimates of tree mortality.