Log Road models logging roads based on existing OSM street data, forest stands, and a cost surface.
####Input
| OSM street data | Forest Stands | Cost Surface |
|---|---|---|
| OSM | Shapefile (EPSG3857) | GeoTIFF (EPSG32610) |
 |
 |
 |
####Process
- Buffer from the stands are created based on a specified skidding distance.
- Fishnet grid covering the buffer extent is created.
- Dictionary is created. For each grid cell the intersecting buffers are recorderd:
{GridCellID:[BufferID1,BufferID2,...]} - OSM data are downloaded and get burned into the Cost Surface
- Cost Surface gets updated: Areas outside of the stands are more expensive, areas at the boundary of the stands are less expensive
- Buffers intersecting with OSM streets are removed from the dicitionary.
- Loop over Dictionary:
- Cell from dictionary with most intersecting buffers is selected.
- New road from cell to closest road is created.
- Buffers intersecting with new road are removed from the dicitionary.
- New roads are written to output shapefile.
####Output
| New Roads |
|---|
| Shapefile (EPSG3857) |
 |
Requires python-gdal, skimage.graph, requests, and numpy
To install, simply python setup.py install or work directly from the root directory.
Ideally the CostSurface consists for actuall $ US values in order to estimate the actual costs of construction to roads. But for the porpuse to identify the length and location of the new roads, a relativ coast surface is sufficient.
test_costsurface.py with CostSurface.py can be used to create a cost surface based on variables slope and river (buffered rivers).
In CostSurface.py the relation between the two can be specified. We received the best test results with the following parameters:
costSurfaceArray[costSurfaceArray > 50] **=2
costSurfaceArray[riverArray == 1] **=5