Growth of a star (the transformation process that some students go through to become a mature researcher)–which stage are you in?

Knowing everything stage
Student: “I have designed a supercomputer even before graduate school.”
Faculty: speechless
Totally beaten up stage
Student: speechless
Faculty: smiling at the student’s progress so communication is possible now.
Confidence buildup stage
Student: “I am not stupid after all.” (student thinks)
Faculty: “Uh oh, she is ready to argue.” (faculty think)
Calling the shot stage
Faculty: “I am going to design an n-processor supercomputer.”
Student: “You are crazy, because …”

I’m gearing up for a second trip to Tutuila of American Samoa in January 2011, to conduct the accuracy assessment on the updated landcover/vegetation map.

Very excited!

While in the mean time, I’m gonna start putting down some of my thoughts, albeit randomly, about the future works right here. It would be nice to get some discussions going the earlier the better. The more involvement we have from people on the ground, the better, for sure.

So, here it goes.

Great efforts by GIS specialist Neil Gurr and Dr. Mark Schmaedick (American Samoa Community College)!!    This is the first time such a mapping project is conducted with very close cooperation with our island partners  every single step of the way.  They actually performed a majority of the task.  Again, great work by Neil!

About the mapping method, it’s basically the same as we have been mapping the pacific island for the past 3 years but with a few tweaks.  This time, however, we relied much more heavily on actual field visit.  This was perhaps the biggest benefit having our island partner actively involved in the process.  It not only should boost the accuracy of the map, but also helps our partner to better understand such data as well as to master the technology.

We will soon finalize the data package including metadata and make it available.  We will also conduct a thorough review of the process, from which to direct our future island mapping projects in a better direction.

In the SWARS Resources section, you will find links to many good stuff.  If you are working on SWARS, you will like them!

Cheers!

So here, I present you a “slightly fine-tuned” map I made from the priority data we just produced in our Palau SWARS example.

This map shows you not only the overall distribution of the three priority land classes (high, medium, low) across the entire island.  It also illustrates the distribution of the three classes within each watershed.  Better yet, the size of each pie is in propotion to the land area of that particular watershed.  I wish I had another adminstrative polygon layer, something like a village boundary.  Since I don’t, I used the watershed layer to run the stats.

Now, how would you tell this story?

The Story about RCV

The concept of Raster Class Value(RCV) might be a bit confusing, understandably so.  It’s therefore necessary to paint some more ink on the subject.  I surely do hope this extra effort would be helpful.

Let’s use an example of car buying.  Say you are looking for a new car and have eyes fixed on 3 models B, F, and T.  All 3 of them look rather attractive overall, but each has its distinctive pros and cons.  B is a gorgeous car with great reputation but the price tag is kind of intimidating.  F has its quite unique history and some cultural fame attached, also lots of horsepower.  But F is notorious for its reliability.  T looks rather dull, even boring, but you pretty much get everything from it — reliable, great gas mileage, plenty of accessories, etc.

Obviously, if there is that one criterion you emphasize so much and would not give in a single inch, things would be easier.  For example, if you are absolutely fixed on your budget, then B is likely to be the first one out.  On the other hand, if you definitively do not want to deal with broken car and all that issues for the years to come, you might as well just kick out F first.  If you, however, are determined not to have a vehicle that looks just like the other 90% cars sitting in the shopping mall’s parking lot, I guess a T wouldn’t be your first choice. So you keep looking and comparing and just can’t decided which one is the clear winner.

You look it up on some “expert” websites, and you notice they all use this “comparing chart” thingy.  Basically, they all drum up this numeric “scale” say 1 to 5,  then they break a vehicle down into individual components or features and measure each feature using that numeric scale.  So, you would see model B has a measurement of horsepower-4, gas millage-3, styling-5, etc.  Then by adding up those individual numbers, you get a overall “score” for each model.  Whichever comes up the best would therefore be the number one recommendation for your purchase.  Simple enough.

That numeric scale, is our RCV scheme!

Just like the vehicle evaluation scale, it really doesn’t matter what are the numbers you use.  Using 1 through 5 (step by 1) doesn’t make that much of a difference from using 100 through 500 (step by 100).  The point here is to assess the relative performance/value of each vehicle in that particular feature.  They all have engine horsepower, but 500hp is obviously stronger than 200hp thus deserve 5; getting 45 miles per gallon is definitely more desirable than 15 miles per gallon thus also should be given the top ranking of 5.

For our project, the “vehicles” we are comparing are different locations in our project area.  What we are assessing is their relative importance/significance to the issue we identified to study.

Apparently we don’t seemingly have such distinctive individual “cars” to look at and compare.  What we deal with here, are the individual land cells/grids, say a 10 meter by 10 meter cell!  The features we are measuring are things like distance to river, existence of invasive plants, proximity to fire risk, and being able to contribute to public drinking water system, etc.

The Raster Class Value Scheme is, therefore, just like the numeric scale used to evaluate vehicle features, a common measurement system we create to compare the relative importance of land at different locations in a particular aspect in terms of natural resources or threats.

Having such a relatively objective value system would enable use to conduct an assessment that involves many features/aspects, apply simple but scientific mathematical calculations, and produce clear, understandable, and explainable results.

What is the exact numeric scheme to use?  Really, it doesn’t matter that much.  You should be able to produce the same result using either 1 through 5 or 100 through 500, as I have demonstrated during the Hawaii Workshop.  My only recommendation here is to use a scheme that contains more numeric values than you think you would absolutely need.  If you need at least 5 values, instead of using 1 through 5 (step by 1: 1, 2, 3, 4, 5), you should start by using 1 through 10 (step by 2: 2, 4, 6, 8, 10).
Another important point, you should always make sure you use the full range of the scheme, by at least assigning the highest value in the scheme to the most important class within each layer.  Because if you don’t, you are implicitly applying a layer weighting!!  Leave that to the layer weights!!!