If your ultimate objective is to classify different mangrove communities, drones can be used to accomplish this. Drones are equipped with sensors, so they can create maps from above. The question at hand, however, is what kind of sensor technology is most appropriate for this use. Because the drone platform allows you to choose what can be mounted as a sensor. Using an RGB camera is among the most popular choices. Mounting a Light Detection and Ranging (LiDAR) sensor system is one of the alternative options. But you might think as to which sensor would be most useful to me.

A recent study found that, when compared to LiDAR, the RGB camera is relatively better at identifying mangrove communities. However, the same study recommends a combination approach using both RGB camera and LiDAR if one wishes to achieve even higher performances. It is merging data from both active and passive remote sensing.

Geographically tagged data is highly valuable. It possesses the potential to provide significant insights when considered in entirety. A plan view of any such data pertaining to the area of interest serves as a valuable tool for informed decision-making.

There is a catch, though. There is a cost associated with the data. There is always a medium path that must be taken. You will eventually have to give up on the pursuit of accuracy. That would be the most you would be willing to spend on the project.

Achieving precision is largely possible for a project at the local scale. For example, mapping trees using light detection and ranging (LiDAR) data and high spatial resolution satellite imagery. However, when the same level of accuracy is needed for a continental-scale tree mapping, the same technology becomes unaffordable. There, our only option would be to use medium-resolution satellite imagery. The technology available for local projects does not always allow for scaling up. Therefore, choosing the mapping scale is crucial.