## 2 Materials and Methods
### Spectral Mixture Analysis (SMA)  
SMA estimates the fractional abundance of \"pure\" spectral components in a pixel [45]. These pure components are called spectral endmembers, for example, non-photosynthetic vegetation (NPV; dry leaves or bare trunk and branches) and green vegetation (GV; photosynthetically active vegetation). [PERSON] et al. [23] found that the spectral reflectance of recent blowdowns (<1-2 years old) in Landsat imagery showed higher NPV and lower GV fractions compared to the surrounding and pre-blowdown forest. Therefore, the change in NPV (\(\Delta\)NPV) can be calculated to detect and quantify the intensity of a recent blowdown [26]. In this study, we applied the Linear Spectral Unmixing (LSU) algorithm available on GEE. LSU assumes that the reflectance of a pixel is a linear combination of the endmember spectra (e.g., NPV, GV, and shade). The role of LSU is to unmix their corresponding abundance fractions [46]. Assuming that \(n\) is the number of spectral bands for remote-sensing data, we used separately the first seven reflectance bands of Landsat-8 and the four reflectance bands of PlanetScope NICFI as input data to LSU. In this study, NPV, GV, and shade are the endmembers of interest. Additionally, a corresponding vector represents their contributions to the surface reflectance for each pixel, which refers to the cover fractions of endmembers: \(f=\{f_{1},f_{2},f_{3}\}\)T. Then, using an \(n\)-dimensional vector to represent the pure spectrum of each endmember, the three endmembers are represented as an \(n\times 3\) matrix \(M\). Finally, the mixed spectrum of the pixel can be expressed as an \(n\)-dimensional vector:  
\[r=Mf+\varepsilon \tag{1}\]  
where \(\varepsilon\) is noise or residual error between the model and image reflectance values. The first step in the SMA procedure is the identification of endmember spectral signature \(M\). \(M\) can be obtained from reference spectral libraries derived from field or laboratory measurements [47] but can also be derived directly from the image data [48]. The second step is the estimation of abundance fractions. This requires inversion models to solve for \(f\), and the least squares regression is commonly used to minimize the error term. The output fractions were not constrained to sum to one. In this study, to avoid differences in data variability caused by instrumental signal-to-noise, we selected the endmembers directly from the images instead of using field or lab reflectance data. This image selection approach has been used in several studies addressing spectral mixture analysis, e.g., [46; 49]. Here, we inspected typical areas of endmember occurrence in the images with the support of red-NIR scatterplots. The endmembers were selected on the GEE platform (Figure 2) to represent NPV, GV, and shade endmembers. Endmembers were selected to represent NPV, GV, and shade, and then the training data points were marked for sampling reflectance values (\(n=1009\) pixels). NPV was collected \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Landsat-8 OLI** & **PlanetScope NICFI** \\ \hline \multirow{4}{*}{Band (\(\mu\)m)} & **Blue** & 0.45–0.512 & 0.455–0.515 \\  & **Green** & 0.533–0.590 & 0.500–0.590 \\  & **Red** & 0.636–0.673 & 0.590–0.670 \\ \cline{1-1}  & **Near-Infrared** & 0.851–0.879 & 0.780–0.860 \\ \cline{1-1}  & **SWIR 1** & 1.566–1.651 & / \\ \cline{1-1}  & **SWIR 2** & 2.107–2.294 & / \\ \hline \multicolumn{2}{c}{**Spatial resolution (m)**} & 30 & 4.77 \\ \hline \multicolumn{2}{c}{**Temporal resolution (Revisit time)**} & \multicolumn{2}{c}{Daily, but monthly} \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{product mosaics} \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{available in NICFI} \\ \end{tabular}
\end{table} 
Table 1: Specifications of Landsat-8 OLI and PlanetScope NICFI data.