It's All Coming Together (Finally!)

Do you remember the song, "Ain't No Mountain High Enough" by Debelah Morgan? Well, the catchy lyrics go something like this:

"Don't you know there ain't no mountain high enough

Ain't no valley low enough

Ain't no river wide enough

To keep me from getting to you."

This song perfectly describes the mission Amintas and I have been on to find data for our Reforestation model in Paragominas. However, even after searching and searching for the data over high mountains, low valleys, and wide rivers some of the data is still evasive and has been deemed non-existent. That being the case here are a few mug-shots of the data we have found... but before debuting the data...

To refresh your memory, the data is being used as variables to understand where the most suitable locations are for reforestation projects in Paragominas, Para, Brazil using Multi-Criteria Decision Analysis. Here's the roll call for the data:

• Forest Cover
• Aridity Index
• Elevation √
• Slope √
• Land Use/Land Cover
• Land Opportunity Cost
• Population Density
• Net Primary Productivity
• Soil Zoning Type √
• Potential Vegetation √
• Soil Vegetation Association √
• Fire Probability
• Bodies of Water √
• Urban Areas
• Deforestation Date √

All data without check marks are absent and remain unknown (at least for the time being).

So the model is constructed using constraints, which limit the alternative solutions under consideration, and factors, which "enhance or detract from the suitability of a specific alternative under consideration" (Eastman, 2006).

Constraints + Factors = Multi-Criteria Evaluation (solution)

*APP = 100 meters of protected area on either side of rivers/streams

*The column of the left shows the data in its raw form and on the right the data has been "normalized." A normalized data set contains data using

*SVA = Soil Vegetation Association and is created by "intersecting" or "overlaying" soil data with vegetation data.

*SRTM = radar data that shows land elevation

In the diagram above, the warmer the color, the better the sight is for reforestation activities. Of course, this map will change as more data is found and inputted into the model.

But for now the model is workable, though far from its full potential...

Sources:

Eastman, Ronald J. (2006). IDRISI Manual. Clark Labs, Clark University. Worcester, MA.

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