log-road

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A model to estimate the location of potential or existing logging roads.

log-road 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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Log Road
===========
### Overview
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)
![Alt text](https://raw.githubusercontent.com/ustroetz/log-road/master/Images/InputOSM.png) | ![Alt text](https://raw.githubusercontent.com/ustroetz/log-road/master/Images/InputStands.png) | ![Alt text](https://raw.githubusercontent.com/ustroetz/log-road/master/Images/InputCostSurface.png)
 
####Process 

 <img src="/Images/Process.gif" />

* 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) |
![Alt text](https://raw.githubusercontent.com/ustroetz/log-road/master/Images/Output.png) |

### Installation

Requires `python-gdal`, `skimage.graph`, `requests`, and `numpy`

To install, simply `python setup.py install` or work directly from the root directory.

### Cost Surface
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
```

log-road

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