## 2 Materials and Methods
### Land Change Modeling  
Land change modeling was carried out using the land change module (LCM) of TerFet Software, Version 19.0.5 [48] and specifically, the multilayer perceptron (MLP) artificial neural network model. MLP contains several interconnected nodes or (points/entities) then sums weighted inputs received from other nodes and uses an activation function mapping  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Mean** & **Minimum** & **Maximum** & **St.Dev.** \\ \hline \multirow{4}{*}{Landsat 2018 \({}^{\circ}\)} & Band 2 & 0.9432 & 0.7966 & 1.3320 & 0.0340 \\  & Band 3 & 1.0422 & 0.8643 & 1.5337 & 0.0504 \\  & Band 4 & 1.1654 & 0.8836 & 1.6703 & 0.0762 \\ \hline \multirow{4}{*}{Landsat 2019 \({}^{\circ}\)} & Band 2 & 0.9499 & 0.8105 & 1.1034 & 0.0380 \\  & Band 3 & 1.0528 & 0.8947 & 1.2704 & 0.0415 \\  & Band 4 & 1.1511 & 0.8969 & 1.4446 & 0.1081 \\ \hline \multicolumn{6}{l}{(Landsat 2018 \({}^{\circ}\) imagery: LANDSAT/LC08/C02/T1_L2/LC08_168070_20180926, Landsat 2019 \({}^{\circ}\): LANDSAT/LC08/C02/T1_L2/LC08_168070_20190929).} \\ \end{tabular}
\end{table}
Table 2: Summary statistics of Landsat imagery comparison for 2018 and 2019 at same location.  
Figure 4: Methodology flow-chat (this diagram adapted with permission from [PERSON] and [PERSON] 2017.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Land Cover Class** & **Description** \\ \hline  
(that is; a function that helps the model adapt to a given variety of input data to the linear or non-linear approach). In this case, the neural network examines potential transitions for each land-cover class based on the driver variables for the modeled landscape [48; 53]. MLP uses a backward stepwise constant forcing procedure that looks at how driver variables predict change and gives the best fit model as described further below [48]. The potential for change in land cover is based on the relationships among the transitions of land-cover classes between 2001 (t1) and 2019 (t2) as dependent drivers and six independent variables: environmental and social drivers; elevation, slope, soil texture, distance from roads, distance from rivers, and population density as described in Section 2.3 above (Table 1).  
We run separate sub-models for four transitions: Forest-mixed to Agriculture, Barren to Agriculture, Wetland to Agriculture, and Shrub-Woodland (occurred only in Ntcheu District) to Agriculture using the six input variables for each sub-model; minor land transitions are not selected due to limited influence on the study area [29; 48]. Randomly selected sets of pixels were fed through the neural network classifier using 10,000 iterations [48]. The model builds a network of neurons that starts from the input to output through a hidden layer and a signal travels from node to node and is modified by the receiving node which sums the weighted signals from previous layers and applies a non-linear function to the node before the signal is passed to the next layer [53]. The back-propagation algorithm computes a training error per iteration (training error is computed when network output is compared with desired output), this error is back-propagated through the network, and then adjusts the weights through the connections to improve model accuracy [48]. The RMS error decreases as the weights are refined [48]. The validation data are used to calculate the skill measure which is the difference between the measured transition prediction accuracy and the accuracy expected by chance [54]. The model outcomes are transition potential maps for each sub-model showing the probability for each land-cover class to experience change over a specific time interval [19].