## 3 Results
### Accuracy Assessment  
A standard accuracy assessment was conducted on the Landsat imagery classified based on Random Forest supervised classification algorithm (described in Section 2.4) and used to generate confusion matrices (supplementary materials: Tables S1-S6) and kappa statistics (including coefficient) for 2001, 2009, and 2019 [57; 58]. Overall accuracy for both districts was very high and ranged from 93% to 99%. (Tables 6 and 7). The producer's accuracy, which shows the percentage of pixels that were correctly classified in each class, was also very high ranging from 92% to 99% across the classes. The user's accuracy was also calculated. This shows the percentage likelihood that a pixel correctly classified to a particular class represents that class on the ground, and values ranged from 91% to 99%. Overall, the accuracy and the Kappa coefficients support the LULC products and their use in diverse post-classification methods [57; 59].  
Similarity in spectral signatures for land-cover classes particularly in savanna and woody savanna landscapes has been found to reduce accuracy in forest-mixed and cropland spectral classification [60]. In this study, shrub-woodland areas as found mainly in the more fragmented landscapes of the Ntcheu study area, and rural built-up areas (such as grass-hatched housing) have similar spectral signatures and are difficult to distinguish  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dedza** & \multicolumn{2}{c}{**2001**} & \multicolumn{2}{c}{**2009**} & \multicolumn{2}{c}{**2019**} \\ \hline  & **Producer’s** & **User’s** & **Producer’s** & **User’s** & **Producer’s** & **User’s** \\  & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** \\ \hline Agriculture & 100 * & 100 * & 100 * & 100 * & 99.5 & 99.5 \\ \hline Built-up & 100 * & 100 * & 98.4 & 98.5 & 96.8 & 96.8 \\ \hline Forest-mixed & 100 * & 100 * & 99 & 99.1 & 100 * & 100 * \\ \hline Barren & 99.4 & 99.4 & 100 * & 100 * & 97.7 & 97.8 \\ \hline Wetland & 98.1 & 98.1 & 97.1 & 97.1 & 99 & 100 * \\ \hline Water & 100 * & 100 * & 97.2 & 97.7 & 100 * & 100 * \\ \hline Overall accuracy & 0.99 & & 0.99 & & 0.986 & \\ \hline Kappa Coefficient & 0.99 & & 0.99 & & 0.986 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy assessment summary for classified land-cover maps for Dedza district. (* accuracy value of 100 has been rounded).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ntcheu** & \multicolumn{2}{c}{**2001**} & \multicolumn{2}{c}{**2009**} & \multicolumn{2}{c}{**2019**} \\ \hline  & **Producer’s** & **User’s** & **Producer’s** & **User’s** & **Producer’s** & **User’s** \\  & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** \\ \hline Agriculture & 96.5 & 96.5 & 94.3 & 94.3 & 98.3 & 98.4 \\ \hline Built-up & 98.2 & 98.3 & 93.8 & 93.9 & 99.3 & 99.4 \\ \hline Barren & 96.1 & 96.1 & 93.5 & 93.6 & 100 * & 100 * \\ \hline Other & 94.5 & 95.1 & 94.5 & 94.6 & 98.8 & 98.8 \\ \hline Wetland & 94.6 & 94.6 & 92.2 & 92.3 & 97.8 & 97.8 \\ \hline Forest-mixed & 96.7 & 96.7 & 92.7 & 92.7 & 99.4 & 99.4 \\ \hline Shrub-Woodland & 92.4 & 92.4 & 91.6 & 91.6 & 98.3 & 100 \\ \hline Overall accuracy & 0.95 & & 0.93 & & 0.99 & \\ \hline Kappa Coefficient & 0.94 & & 0.92 & & 0.97 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy assessment summary for classified land-cover maps for Ntcheu district. (* 100 value accuracy has been rounded to one decimal point).  
at the 30 m spatial resolution of the Landsat imagery used. Classification was also aided by field observations and Google Earth Explorer as references for ground truthing and features identification [46].