## 2 Data and Methods
### Analysis
The 25-category thematic output serves several purposes, namely: a mechanism for identifying and isolating commission error within spatially defined strata of primary land covers; a sampling template, and, a mask to spatially constrain pixel reclassification. The number of primary categories and breadth of training data dictates the quantity of subgroups and the number of classes in the thematic output. These parameters will vary by type of study and composition of landscape but the method of primary category stratification is certainly generalizable to a variety of studies, and equally, landscapes. Figure 3: Distribution of cropland clusters in T\({}_{1}\)TM\({}_{3}\) (x-axis) and T\({}_{2}\)TM\({}_{4}\) (y-axis) feature space. Dashed lines delineate the distribution of individual signatures that compose the respective cluster. TM\({}_{3}\) is utilized to illustrate soil reflectance variability and TM\({}_{4}\) to illustrate the variability in vegetation response. Surface reflectance is scaled to unsigned 8-bit. Proportional random sampling of the 25-category thematic map was used to generate validation points (Operation 3, Figure 2). A total of 477 validation points were interpretable and intersected with suitable reference land cover imagery. Since classification accuracy in Erdas IMAGINE is evaluated within a 3-pixel by 3-pixel window, square buffers (8100 m\({}^{2}\)) were constructed around the sample validation points and a land cover label assigned to each polygon through interpretation of Google Earth imagery. Classification error was evaluated through an accuracy assessment of the 25-category (subgroup) thematic map (Operation 4, Figure 2). The assessment allowed us to examine the distribution of error within strata of a primary category and identify the subgroup(s) where the greatest classification error resided. The largest detected commission errors were related to savanna misclassified as cropland and cropland as settlement. An examination of a portion of the error matrix, revealed error concentrated in the _cropland_3_ and _cropland_4_ subgroups and within the _settlement_2_ and _settlement_3_ subgroups (Table 2). \begin{table}
\begin{tabular}{c c c c c} \hline
**Subgroup** & **Forest** & **Cropland** & **Savanna** & **Settlement** & **Water** \\ \hline Cropland\_1 & 0 & 27 & 0 & 0 & 0 \\ Cropland\_2 & 0 & 32 & 0 & 0 & 0 \\ Cropland\_3 & 0 & 28 & 13* & 0 & 0 \\ Cropland\_4 & 0 & 6 & 28* & 0 & 0 \\ Cropland\_5 & 0 & 2 & 0 & 0 & 0 \\ Cropland\_6 & 0 & 1 & 0 & 0 & 0 \\ Cropland\_7 & 0 & 1 & 0 & 0 & 0 \\ Cropland\_8 & 0 & 33 & 0 & 0 & 0 \\ Cropland\_9 & 0 & 4 & 0 & 0 & 0 \\ Cropland\_10 & 0 & 2 & 0 & 0 & 0 \\ Settlement\_1 & 0 & 1 & 0 & 2 & 0 \\ Settlement\_2 & 0 & 8* & 0 & 37 & 0 \\ Settlement\_3 & 0 & 7* & 0 & 1 & 0 \\ \hline \end{tabular}
\end{table} 
Table 2: Partial classification error matrix containing cropland and settlement subgroups. Rows represent the classified subgroup map categories and columns represent reference or validation data. High commission errors are indicated by asterisks. \begin{table}
\begin{tabular}{c c c c} \hline
**Primary LC** & **Sample** & **No.** \\
**Category** & **Size** & **Subgroups** \\ \hline Forest & Tree assemblages \(\geq\) 40\% canopy closure & 88 & 5 \\ Cropland & Cultivated land for subsistence or commercial agriculture & 177 & 10 \\  & Savanna: grassland (wood canopy cover \(<\) 10\%), bushland & & \\ Savanna & savanna (10\% \(<\) bush cover \(<\) 40\%), woodland savanna & 172 & 6 \\  & (10\% \(<\) tree canopy \(<\) 40\%) [55,56]. & & \\ Settlement & Urban areas, villages, and roads & 54 & 3 \\ Water & Lakes, rivers, streams, and seasonally inundated areas & 32 & 1 \\ \hline \end{tabular}
\end{table} 
Table 1: Primary category training data for footprint at Path: 172 Row: 071: category description, sample size, and number of subgroups. Column 3 indicates the total number of training samples for each primary category. Column 4 indicates the number of subgroups generated from the hierarchical clustering of training signatures.