Basics of Geostatistics
Geostatistics is a branch of statistics that is used to characterize
spatial distributions and to produce estimates of variables at unsampled
locations. The idea behind geostatistics is quite simple: samples
taken closer together are more similar on average than samples taken
farther apart. For example, if you have a grid of points over an
area, and measure some variable at each point:
You might find that on average, points that are right next to each
other have values that are more similar than points that are 2 or
3 units apart. A lot of environmental factors like soils, climate,
tree growth, species distribution, topography, etc. show this characteristic.
Using this principle, we can make a mathematical model
(variogram) of how dissimilarity changes with distance:
As the separation distance increases, the dissimilarity, on the
vertical axis, increases and then levels off. This mathematical
model can then used in a procedure known as kriging (named after
a statistician named Krige) to help estimate values for variables
at unknown locations; kriging is basically a weighted averaging
approach, with weights coming from the variogram.
Kriging and its variants (e.g., cokriging, kriging
with local means, indicator kriging, and various types of simulation)
have been used for years in the mining industry, and only recently
in the forestry community.
One useful aspect of the kriging-based simulation procedures is
that the error estimate for a given point is not only based on the
density of surrounding samples, but also on how similar those surrounding
samples are. The resulting "error maps", which are associated
with the estimates, are useful when evaluating the final map product.
NE-FIA has used variography and various forms of kriging
to create maps of FIA attributes such as basal area, species importance,
volume, and presence/absence probability. We are pursuing ways to
improve the fine-scale accuracy of geostatistical maps by incorporating
ancillary information, like satellite imagery, into the analyses.
One example of our geostatistically-derived maps can be found at