## 2 Materials and Methods
### Land Cover Classification
#### Object-Oriented Classification with RandomForest  
Random Forest (RF) is a widely used algorithm for remote sensing, being a powerful option for integrating different imagery sources and ancillary data sources into image classification workflows [64; 65]. RF classification has been demonstrated to work well in land cover classification tasks like this one because it is effective with relatively small samples of training data and not particularly sensitive to noise [27]. The method is also ideal because it can achieve good accuracy without detailed hyperparameter optimization, and provides a robust estimate of the trained model's uncertainty [23; 24; 27]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \\ \hline \hline \multicolumn{1}{c}{**Bands**} & **B1—Blue** & **B2—Green** & **B3—Red** & **B4—NIR** & **S—Syn** \\ \hline \hline \multicolumn{1}{c}{} & B2—Blue & X & & & & \\ \multicolumn{1}{c}{} & B3—Green & & X & & & \\ \multicolumn{1}{c}{} & B4—Red & & & X & & \\ \multicolumn{1}{c}{} & B5—Red Edge & & & & & X \\ \multicolumn{1}{c}{} & B6—Red Edge & & & & & X \\ \multicolumn{1}{c}{} & B7—Red Edge & & & & & X \\ \multicolumn{1}{c}{} & B8—NIR & & & & X & \\ \multicolumn{1}{c}{} & B8A—Narrow NIR & & & & X & \\ \multicolumn{1}{c}{} & B11—SWIR & & & X & & \\ \multicolumn{1}{c}{} & B12—SWIR & & & X & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Band combinations for fusing PlanetScope with Sentinel-2 images. Segmentation  
We created the objects with a multiresolution algorithm where PlanetScope bands and fused Sentinel-2 bands (Red, NIR) were given equal weight. The images were divided into segments of relatively homogenous pixel composition, where the mean of band values of pixels within each segment is used to represent \"objects.\" We set the segmentation scale parameter at 40 pixels, where each pixel is about 9 m\({}^{2}\). During the segmentation process, the similarity of pixels was determined by two values ranging from 0 to 1. The first value is a shape criterion, where higher values mean that the color of pixels is given less influence over what is included or excluded from each segment. The second value is a compactness criterion, where higher values emphasize spatial proximity, causing resulting objects to be more compact. We used a trial-and-error approach to determine the values (shape: 0.7; compactness: 0.3) where objects such as built environment, living fences, and pastures were selected most completely while excluding adjacent objects of different categories. On each island, at least 20 object samples were selected for each category using GPS control points and drone images acquired during fieldwork as reference. These object samples represent areas where the cover category covers the total extension of the sample. From this data sample, we selected 50 stratified random pixel sample subsets for training the classification model. Random Forest Classification  
RF classification was performed in R Studio open-source statistical software, where we used the _randomForest_, _Caret_, and _raster_ packages to produce all classifications. For each island classification, _randomForest_ sampled 1000 classification trees and tried four variables at each split to calculate the out-of-bag samples (rfOOB). The rfOOB are used to create a cross-validated prediction error for the model and to formulate a measure of feature importance [66]. We used two sets of metrics to gauge the importance of 20 different variables from three different sources (orthophoto, PlaneScope, Fused S2 + PS;) in the _randomForest_ model. The first metric we used is the Variable Importance (_Caret_ Package), which is the mean of the scaled (from 0-100) class-specific decreases in accuracy. The second set of metrics we used included the Mean Decrease Impurity Importance (MDI) or Mean Decrease in Gini, as well as the Mean Decrease Accuracy (MDA, _randomForest_ package). MDI measures how important a variable is for estimating the value of a target class across all of the decision trees that make up the forest.