Introduction to my Master’s Thesis Research
During my internship at Ecotrust I created a cost model that estimates timber harvest costs. It tells you, how much does it cost to harvest a timber stand.
The cost model is part of Ecotrust’s Forest Planner, an online tool for forest management and scenario planning.
In my research for my master thesis I want to analyze the driving factors that influence harvest costs based on the create model. The model takes in four input variables in order to calculate the harvest cost for a stand:
- Slope in % (S)
- Skidding Distance in feet (SD)
- Trees per Acres (TPA)
- Volume per Tree in cubic feet (VPT)
It returns a Harvest Cost per ton, which can be extrapolated to the stand level.
I want to investigate if it is possible to predict relative Harvest Costs (per ton) with only explicit spatial variables, which are in the case of the model Slope and Skidding Distance.
In order to investigate the influence of each variable on the Harvest Costs, I need test input data. For that I will take timber sales data of the last ten years of the Colorado State Forest (CSF) and run the data with my cost model.
With the create test datasets I will conduct a regression analysis and want to come up with a regression model like this:
Harvest Cost ≈ β0 + β1 TPA + β2 VPA + β3 SD + β4 S
In the next step I will remove the non-spatial variables Trees per Acre and Volume per Acre.
Harvest Cost ≈ β0 + β1 SD + β2 S
My hypothesis is that Slope and Skidding Distance are sufficient to still come up with a reasonable Harvest Cost.
What is it good for?
Based on this formula I would be able to predict cost for any given location in the CSF, solely based on Skidding Distance and Slope. I could pre-generate a Harvest Cost raster covering the entire CSF showing for every location the relative Harvest Cost.