Update to my efforts:

Starting with a subset of my original data:

Using some built-in filters with Mathematica I've been able to "isolate" the islands more or less with the following code:

gswla = GradientFilter[swla, 1];

This gives me a gradient matrix of the initial square matrix I was using.

Next, I went through the matrix and put Null on anything above a threshold value that is known for this type of data.

Table[If[gswla[[i, j]] < thresh, gswla[[i, j]], Null], {i, 1, 
   Dimensions[gswla][[1]]}, {j, 1, Dimensions[gswla][[2]]}];

This then gives me a plot of all the "Islands" isolated by "no man's land"

It is easier to see in MatrixForm:

So using this "isolated" data, how can I group these islands into their own distinguishable cluster? I figure I can use the matrix coordinates for each cluster in the gradient matrix to identify the counterpart in the original data set.

Thanks!!!

Yes.

I get the following result:

FindClusters::amtd: FindClusters is unable to automatically select an appropriate dissimilarity function for the input data

Maybe a For loop or Table would be able to find the boundaries?