For Multi-Criteria Evaluation (MCE) Analysis, we first decided on the weight of each criteria using a website that can calculate AHP (Analitycal Hierarchy Process) weights, a link provided here.

Economic output refers to the criteria related to the profit of the timber, which includes species (market values) and age (mature trees).

Economic input refers to the criteria related to the investment of the project, which includes proximity to roads (are investor willing to open new forest roads or just use existing ones) and slope (the mobility and efficiency of forestry machines).

When deciding on the criteria importance, species was said to be slightly more important than roads, age, and slope; roads are slightly more important than age and slope; age is slightly more important than slope. Here is the result obtained from the website:

From our results, we can conclude that we value economic output more than economic input based on comparing the criteria (species has the “highest” importance, which contributed to a higher percentage in economic output).

We then used the Weighted Sum tool under Spatial Analyst to overlap the four normalized raster layer, assigning the percentages shown from the result accordingly (which add up to 1):

• Species = 0.48
• Roads = 0.27
• Age = 0.16
• Slope = 0.09