  Identifying terrain within satellite image data is a key issue in
geographical information sciences, with numerous environmental and safety
implications. Many techniques exist to derive classifications from spectral
data captured by satellites. However, the ability to reliably classify
vegetation remains a challenge. In particular, no precise methods exist for
classifying forest vs. non-forest vegetation in high-level satellite images.
This paper provides an initial proposal for a static, algorithmic process to
identify forest regions in satellite image data through texture features
created from detected edges and the NDVI ratio captured by Sentinel-2 satellite
images. With strong initial results, this paper also identifies the next steps
to improve the accuracy of the classification and verification processes.
