Monitoring the status of the vegetation is required for nature conservation. This monitoring task is time consuming as kilometers of area have to be investigated and classified. To make this task more manageable, remote sensing is used. The acquisition of airplane remote sensing data is dependent on weather conditions and permission to fly in the busy airspace above the Netherlands. These conditions make it difficult to get a new, dedicated acquisition every year. Therefore, alternatives for this dependency on dedicated airplane surveys are needed. One alternative is the use of optical satellite imagery, as this type of data has improved rapidly in the last decade both in terms of resolution and revisit time. For this study, 0.5 m resolution satellite imagery from the Superview satellite is combined with geometric height data from the Dutch national airborne LiDAR elevation data set AHN. Goal is to classify vegetation into three different classes: sand, grass and trees, apply this classification to multiple epochs, and analyze class transition patterns. Three different classification methods were compared: nearest centroid, random forest and neural network. We show that outcomes of all three methods can be interpreted as class probabilities, but also that these probabilities have different properties for each method. The classification is implemented for 11 different epochs on the Meijendel en Berkheide dunal area on the Dutch coast. We show that mixed probabilities (i.e. between two classes) agree well with class transition processes, and conclude that a shallow neural network combined with pure training samples applied on four different bands (RGB + relative DSM height) produces satisfactory results for the analysis of vegetation transitions with accuracies close to 100%.