## 3 Results
### Evaluation of Model 2: Settlement_2 Subgroup  
Four predictors were selected for the _settlement_2_subgroup model to differentiate cropland from settlement: Fraction\({}_{\text{veg}}\), Fraction\({}_{\text{Litter}}\), Fractions\({}_{\text{hade}}\), and the seasonal difference in visible green reflectance (Difference\({}_{\text{B2}}\)). The likelihood ratio test was statistically significant (\(G^{2}54.31\), df = 4, \(p<0.0001\)). The Hosmer-Lemeshow test (\(X^{2}\) 7.736, df = 8, sig. = 0.460) and pseudo R\({}^{2}\) values indicated a good model fit, ([PERSON] and [PERSON] R\({}^{2}\) = 0.798 and [PERSON] R\({}^{2}\) = 0.697). A comparison of nested and non-nested models (4 predictor _vs._ 10 predictor model) strongly favored the nested over the full model (difference in BIC = 15.729). All predictors were statistically significant at the 0.01 level of significance. A one unit change in Fraction\({}_{\text{Litter}}\) resulted in the greatest increase in log odds of a pixel being cropland, followed by the vegetation and shade fractions. Seasonal difference in visible green lowered the log odds of a pixel being cropland. (Table A2). Of the 52 known cropland plots, 48 were classified correctly, and 18 of the known 26 non-cropland plots classified correctly for an overall accuracy of 84.6% (Table A3). Omission error of _cropland_ decreased and commission error of _settlement_ was no longer detected. PPV and NPV both exceeded 82%, however, confidence intervals were much broader than those observed in the previous model. Recoding reduced the size of the _settlement_2_ thematic category by 33.3%, removing all error detected by the validation dataset. A comparative assessment between the initial classification and post-correction classification revealed an absolute reduction in cropland commission error of 17.24% to an error of 7.04%, and decrease in omission error of 3.4% to 13.16% (Table 4). The greatest reduction in error was found in the \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**(a)**} \\ \hline
**Predictor** & \(\boldsymbol{\beta}\) & **SE \(\boldsymbol{\beta}\)** & \(\boldsymbol{e^{\theta}}\) & **Wald’s \(\boldsymbol{\chi^{2}}\) (\(\boldsymbol{df}\) = 1)** \\ \hline Fraction\({}_{\text{veg}}\) & \(-0.2089***\) & 0.0423 & 0.8115 & 24.36*** \\ Fraction\({}_{\text{soil}}\) & \(-0.4225***\) & 0.0985 & 0.6554 & 18.38*** \\ Fraction\({}_{\text{Litter}}\) & \(-0.1428***\) & 0.0502 & 0.869 & 8.10*** \\ Difference\({}_{\text{B4}}\) & 0.0604*** & 0.0130 & 1.0622 & 21.57*** \\ Constant & 12.2058*** & 2.2976 & NA & NA \\ \hline \hline  & \multicolumn{3}{c}{**(b)**} \\ \hline  & \multicolumn{3}{c}{**Predicted**} \\ \hline
**Observed** & \multicolumn{3}{c}{**Land Cover**} & **Percentage** \\ \cline{2-4}  & \multicolumn{3}{c}{**Cropland**} & **Savanna** & **Correct** \\ \hline Land cover Cropland & 89 & 7 & 92.7 \\ Savanna & 6 & 74 & 92.5 \\ Overall Percentage & & & 92.6 \\ \hline  & PPV: 91.36\%; 95\% CI: 82.99\% \(-\) 96.44\% & \\  & NPV: 93.68\%; 95\% CI: 86.75\% \(-\) 97.63\% & \\ \hline \hline \end{tabular} _***p \(<0.001\)._  
\end{table} 
Table 3: **(a) Summary of predictor effects for _cropland_3_ subgroup model. (b) Classification table for _cropland_3_ subgroup model. Classification cutoff value is 0.500. PPV = Positive predicted value and NPV = Negative predicted value. **savanna class where omission error was reduced by 28.78% although this resulted in a slight uptick in commission error of 3.08%. Overall, no measure of error in any category exceeded 15%. The method described in this paper was subsequently applied to the adjacent datasets, categorizing land cover within the TM footprints at Path: 172 Row: 072 and Path: 173 Row: 071 (Figure 5). In total, 4 primary category subgroups were reclassified within the former footprint and 6 subgroups within the latter. Overall classification accuracy in the Path: 172 Row: 072 footprint was 85.4% and 86.5% in Path: 173 Row: 071. Cropland omission error was 12.8% and 7.3% respectively, commission error 9.4% and 14.2% (Table A4).