# Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest
## 2 Materials and Methods
### Spectral Mixture Analysis and Remote Sensing Estimates of Windthrow Tree-Mortality  
We used Landsat 8 and Sentinel 2 images acquired before and after the studied windthrow event (11 September 2015 and 27 July 2016, and 25 August 2015 and 30 July 2016, respectively). For the WorldView 2, we only used an image acquired after the event (27 July 2016). Landsat 8 and Sentinel 2 were downloaded from the Google Earth Engine platform ([[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/)), accessed on 25 April 2022) [71], and WorldView 2 was purchased from Digital Globe. We selected images with the minimum percent cloud cover, the closest pass dates to each other, and the greatest proximity between the acquisition date and the occurrence of the studied windthrow (Table S2). We used Spectral Mixture Analysis (SMA) [72; 73] and quantified the fractions of endmembers [27] following a well-known routine that was established in previous studies [9; 13; 14; 16; 23]. The endmembers contain specific spectral signatures of multiple elements that make up the forest surface and can be used to compute fraction images for different targets of interest [74; 75]. We quantified the fraction of the following endmembers: green vegetation (GV, i.e., photosynthetically active vegetation), non-photosynthetic vegetation (NPV, i.e., dead vegetation), and shade (SHD) [27]. Endmembers were extracted and quantified using the software, ENVI 5.3 [76; 77; 78; 79]. We used Landsat 8 and Sentinel 2 imagery with pixel values corrected for top-of-atmosphere (TOA) reflectance. For WorldView 2, a band-by-band radiometric correction factor was used to bring pixel values to the same range as Landsat 8 and Sentinel 2 [80]. As the evaluated satellites have different numbers of bands and respective spectral ranges, we obtained the spectral signatures of the focal endmembers by using the bands common to all of them. For Landsat 8 and Sentinel 2, we used the blue, green, red, NIR, and SWIR bands. Since WorldView 2 has a smaller spectral range, we used the blue, green, red, red edge, and NIR bands. The selected bands are sensitive to the physical, chemical, and anatomical characteristics of the leaves and trunks, thereby maximizing the distinction between GV and NPV [72; 74; 75]. We used the Purity Pixel Index spectral toolkit ([81], Figure S8) that relied on a well-established and reliable method that has been extensively used [1; 14; 16; 17] to select the purest pixels from all images (e.g., areas where all trees were downed versus unaffected old-growth forest) to acquire the most accurate/representative endmembers of interest [27]. Finally, we conducted a SMA to compute fraction images of GV, NPV, and SHD [73]. Windthrows have a specific spectral signature for the NPV fraction, which originated from the deposition of large amounts of dead leaves and wood debris on the forest floor [82]. To ensure that the GV fraction represented forest patches not affected by the studied windthrow, we selected endmembers within the adjacent old-growth forest. The SHD endmember was selected from rivers in the vicinity of the impacted area. The SHD endmember quantifies the effects of shading associated with the acquisition angle of images, canopy roughness, topography, and clouds [27; 83]. Shading is highly related to the biophysical characteristics of trees and a major aspect contributing to variations in radiance and reflectance of canopies [74]. In tropical forests, shade can represent 30% of the canopy pixels and this effect can be observed in the red and NIR bands of medium- and high-resolution imagery [31]. In windthrown forests, a systematic reduction in the shadow fraction can be expected; however, the shadow fraction remains relatively high in areas that were less impacted and at the edge of gaps [23].