#Remote Sensing

A few years ago the Bureau of Land Management office I worked for was part of a project to map vegetation in Sage-grouse habitat. To do that we got 10 cm resolution aerial imagery across the species range. The following is a summary of the project, what I learned, what I would do differently, and why the project ended without ever really contributing to any on the ground management.

I have been attempting to use random forests to classify high resolution aerial imagery. Part one of this post series was my first attempt. The aerial imagery dataset that I am working on is made up of many ortho tiles that I need to classify into vegetation categories. The first attempt was to classify vegetation on one tile. This note documents classifying vegetation across tiles.

The following note documents a proof of concept for classifying vegetation with 4 band 0.1m aerial imagery. We used sagebrush, bare ground, grass, and PJ for classes. approximately 300 training polygons were used as a training data.

The following are scripts that I used to make a cultural prediction model. It uses topographic, hydrologic and biological GIS information to predict areas where arc sites likely occur on the landscape.