## 2 Methods
### Segment Generation  
Object-based image analysis starts with image segmentation in which pixels are grouped into polygons representing homogenous regions in space [18,41]. The segmentation process was performed using eCognition 8.7 and the eCognition server [71]. The process of segmentation with eCognition involves writing rule-sets that can incorporate multiple segmentation algorithms and hierarchical relationships and can weigh different image layers and complexity criteria to create objects  
Figure 3: Flowchart of overall change mapping methodology. Shaded boxes represent sections within the methods with the section number bolded. The \(\mathbf{x}\) represents the change threshold.  
representing the users intended goals [27]. For this analysis, the primary goal was for changed locations to result in generating separate polygons. The 13 image layers involved in the segmentation process, were the three 2006 bands, the six-layer stack from 2009, the three difference bands and a vegetation/non-vegetation mask derived from the 2009 NDVI layer. For the NDVI-based mask, positive values were mapped as vegetated and values of zero or less were mapped as non-vegetation. Because we were interested in mapping changes specifically, the segmentation was performed simultaneously across time periods. The segmentation process proceeded from large landscape features, down to progressively smaller pieces and then segments with minimal absolute value in the difference layers were coalesced leading to the final analysis set. By performing the segmentation simultaneously across the change interval, the analysis was better able to focus on delineating changed areas as separate polygons and minimized the production of sliver polygons due to random image differences [72]. The polygons were exported as a simple feature with attributes derived from eCognition (Table 1). This is an iterative procedure and particular to the specific available data sets, so a detailed explanation of the rule-set used is not included. Segments ranged in size from less than a 1/10 ha in heterogeneous areas up to around 50 ha in places like continuous forested zones and large water bodies. The final segment layer is a complete polygon map of the original image data with the intention that likely change areas are partitioned into their own polygons.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \# & **Polygon Attributes** & **Type/Units** \\ \hline
1–3 & 2006 Bands: Red, Green, Blue & Polygon Mean \\
4–6 & 2009 Bands: Red, Green, Blue & Polygon Mean \\
7 & 2009 Band 4: Infrared & Polygon Mean \\
8 & 2009 Derived: NDVI & Polygon Mean \\
9–11 & 2009–2006 Difference: Red, Green, Blue & Polygon Mean \\
12–22 & Same as 1–11 & Polygon St. Deviation \\
23 & Contrast neighbor 2006 Red & Contrast across polygon border \\
24 & Contrast neighbor 2009 Red & Contrast across polygon border \\
25–26 & 2006, 2009 Average Visible Brightness & Polygon Mean \\
27 & Brightness Difference & Polygon Mean \\
28–29 & 2006, 2009 Average Visible Saturation (gray level) & Polygon Mean \\
30 & Difference in Saturation & Polygon Mean \\
31 & Rectangular Fit & Shape Statistic \\
32 & Edge to Area Ratio & Shape Statistic \\
33 & Width of Main Branch & Meters \\
34–35 & UTM Longitude, Latitude & Meters \\
36–37 & 2006, 2009 GLCM Homogeneity Red Band & Polygon Texture \\
38 & GLCM Homogeneity Red Difference & Polygon Texture \\ \hline \hline \end{tabular}
\end{table}
Table 1: Predictor variables for the Random Forests model. Predictors 1–41 were generated directly by eCognition [71]. These variables are the means or standard deviations of the pixel values comprising the image segment. Land cover proportions were derived from extracting per segment information from the 2009 Land Cover model. Elevation was derived from sampling a 10-m digital elevation model with the segments. Texture was assessed by gray-level co-occurrence matrix, GLCM [73]. Predictors 50–59 were derived from an exported data set of linked sub-objects generated as part of the segmentation process.