Having endured much frustration trying to use Geographically Weighted Regression to determine relationships between the patch metrics I calculated using FRAGSTATS, I’ve decided to change things up a bit for my final blog post. I’ll continue working with Geographically Weighted Regression, but rather than applying it to FRAGSTATS-derived metrics (shape index, edge:area ratio etc.), I will experiment with a different LandTrendr output: forest biomass.

Dataset:

Explaining more about the dataset here will help me articulate the research question below. So, the biomass layer is a raster calculated using a Tassled Cap Transformation, which is a method of enhancing the spectral information content of Landsat imagery. It is essentially a measure of per-pixel brightness (soil), greenness (vegetation) and wetness (soil and canopy moisture). I will be using yearly time-series biomass layers and “time-stamped” clear-cut disturbance patches.

Research Question:

I’m still not sure how well I can articulate the question, but here goes: Is there a statistically significant relationship between the mean biomass value within a clear-cut patch at the timing of the clear-cut, and the mean biomass within that same patch, before and after the clear-cut?

Hypothesis:

Clear-cutting obviously results in a loss of biomass, and I expect that quantities of biomass within a clear-cut patch before, during and after a clear-cut will exhibit a significant relationship.

Approach:

While I had easy access to the biomass data, creating a dataset of disturbance patches with attributes for biomass before, during, and after the timing of each clear cut was a carpal-tunnel inducing task. I ought to have approached it programmatically, and I did try, but my Python skills were lacking. I ended up using a definition query to filter disturbance patches by year, and then ran three Zonal Statistic operations on the biomass layers (one for the year before a given set of clearcuts, one during, and one after). I then joined each biomass calculation back to the clear-cut patches. Below is an attribute table for one set of disturbance patches (note the three mean biomass values) and an example of a disturbance patch overlaid on a “before, during, and after” set of images. I did this for three sets of yearly clear-cuts, and then merged them into one dataset of roughly 700 features.

I then ran Geographically Weighted Regression, with “mean biomass after clear-cut” as the dependent variable, and “mean biomass before” and “mean biomass at timing of clear-cut” as explanatory variables.

Results:

I experimented with multiple combinations of the three biomass mean variables, and also tried adjusting the kernel type. The most significant run was that on the left, which had parameters as described above.

Significance:

While it was satisfying to finally produce some significant statistics, I recognize that the analysis is not groundbreaking. While change in biomass is certainly of interest to forest managers and ecologists, the way in which it was calculated here (as a mean of an annual snapshot within a patch) may not have significant implications.

What I learned:

If this course is nicknamed “Arc-aholics Anonymous” then you could say I had somewhat of a relapse, as most of my analyses throughout the quarter made use of tools I had used in ArcMap before. That said, I gained a much more thorough understanding of their functionality, and feel I have a better command over interpreting their sometimes perplexing results. I now have a much better idea of the types of datasets and variables that lend themselves to certain methods, and the experience of working with a large dataset of Landsat time-series-derived forest disturbance will be invaluable to my research moving forward. I learned a great deal from others in the course and am glad to have made some new contacts (especially you coding gurus). Some of the work produced in this course was truly outstanding and I feel inspired to hone my own skills further, particularly with open-source software.