# Post-Disaster Recovery Monitoring with Google Earth Engine
## 2 Materials and Methods
### Methods
#### 2.3.1 Image Composite Generation  
We generated the best-available-pixel image composites for the T0-T4 time steps by extracting the best observations from several Landsat 7 and 8 images for our case study (Leyte island), using pixel-based image compositing within the GEE platform, and implemented using rule-based criteria such as dates of acquisition and exclusion of clouds and shadows [45; 46; 47; 48]. To do so, we adapted a script  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Land Cover Class** & **Description** \\ \hline
**1** & Forest/Trees & All types of trees (e.g., forest, palm and banana), tree canopy coverage \textgreater{}30\% \\
**2** & Built-up & Any type of developed lands such as buildings, roads, impervious surfaces \\
**3** & Crop land & Agricultural fields with any non-tree crop type plantation \\
**4** & Water body & Bodies of water including lakes, oceans, rivers and flooded areas \\
**5** & Other & All other land cover classes (i.e., non-tree vegetation, bare land, rubble and debris) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the selected land cover classes.  
Figure 2: The proposed framework for post-disaster recovery monitoring.  
within the GEE environment provided by [PERSON] et al. [43]. The generated Landsat image composites were exported for subsequent use in the image classification. The developed script needs user-defined inputs and parameters: the coordinates and extent of Leyte island, the number of years, starting and ending date range for the selected region (i.e., Leyte), the thresholds for cloud detection and masking (10% or less) and shadows (z-score = \(-\)1), and the image collection used, (i.e., Landsat 7 ETM+ and Landsat 8 OLI). Then the areas that were masked out in the previous step were filled by the median of the selected image pixels [43; 49]. The scripts developed for this study are provided as supplementary materials in this paper.  
In addition, we calculated five well-known indices from the Landsat composite images and added to the available Landsat bands to be used in the classification step as follows:  
Normalized Difference Vegetation Index (NDVI; [50]) using Equation (1):  
\[NDVI=\frac{NIR-Red}{NIR+Red} \tag{1}\]  
The Soil-Adjusted Total Vegetation Index (SATVI; [51]) using Equation (2):  
\[SATVI=\frac{SWIR1-Red}{SWIR1+Red+0.1}*(1.1-SWIR2/2.0) \tag{2}\]  
Enhanced Vegetation Index (EVI; [52; 53]) using Equation (3):  
\[EVI=2.5*\frac{NIR-Red}{NIR+(6.0*Red)-(7.5*Blue)+1.0} \tag{3}\]  
Land Surface Water Index (LSWI; [54; 55]) using Equation (4):  
\[LSWI=\frac{NIR-SWIR1}{NIR+SWIR1} \tag{4}\]  
Normalized Difference Tillage Index (NDTI; [56]) using Equation (5):  
\[NDTI=\frac{SWIR1-SWIR2}{SWIR1+SWIR2} \tag{5}\]  
where Blue is the blue band, Red is the red band, NIR is the near-infrared band, SWIR1 is the shortwave infrared 1 band, and SWIR2 is the shortwave infrared 2 band of the Landsat image.  
NDVI was shown to be a useful tool for distinguishing green vegetation areas from other land cover types; LSWI is for detecting water bodies and when combined with NDVI improves the performance of separating crop lands and forests [57]. SATVI, EVI, and NDTI have been shown to be effective tools for identifying forest and crop types from other land cover classes [43; 58].