Using Grid Statistics and Calculations for Ecological Assessments in Saga
In this exercise you'll see how to make and interpret higher-level grids
that are based on statistics and calculations involving existing grids of a selected parameter (sea temperature in
this case). The general methods shown here can be used in countless
other situations and for other parameters of interest. "Marine physiological ecology" is
the science of habitat suitability for marine organisms, most of which
have adaptability or resistance mechanisms to deal with changes in
environmental parameters in that habitat. Temperature, oxygen, salinity and
pressure are parameters often viewed by specialists in this field, with an
interest in stresses on the biota caused by seasonal to inter-annual
changes. Products that directly visualize these parameter changes are produced
Preliminary Reading (in
OceanTeacher, unless otherwise indicated):
Author: Murray Brown
|1. Use the
methods in 2.22
Importing "Scaled" HDF Satellite SST Climatologies
into Marine GIS: Color Web to
create 12 monthly SST grids for Liberia, using a 0.05-degree target grid. This
target grid is necessary
to keep the original resolution. Because they are so close (4 km
versus 0.05 degrees) the resampling method is the basic NEAREST NEIGHBOR
algorithm. [You might only
have 4 monthly grids if you are as lazy as the instructor; that's OK.]
|2. All 12 (or 4) monthly grids must
be loaded into Saga. Proof of their identical structure (a requirement
of this method) is demonstrated by
their placement under one grid system.
|3. Select TOOLS > SPATIAL
& GEOSTATISTICS-GRIDS > STATISTICS FOR GRIDS.
This module allows you to calculate any or all of the 7 statistical measures
shown here, for example the ARITHMETIC MEAN. These measures, when they
are SET, are calculated from all 12 monthly values of each pixel, and a new
grid is created consisting of these statistical pixels.
|4. For GRID SYSTEM select the
system of the monthly SST grids.
Then click on the ellipsis (...) to the
right of NO OBJECTS.
|5. This grid object selection
|6. Use the > control to move
the data grids to the right side, as you see here. [Leave the dummy
grid behind, if you still have it loaded.]
Then click OK.
|7. Now you can select which
statistics to create. Change these settings to CREATE:
- ARITHMETIC MEAN
Then click OK.
|8. These new grids appear in
the Saga data list.
|9. Re-name the new analysis
grids appropriately, perhaps as you see here.
10. At this milestone, you might want to save any new shape(s)
or grid(s) to make sure you don't lose anything in case of problems
later on. Just name them with the above filenames (or equally
descriptive ones), and use a logical folder location (such as
PRODUCTS > SAGA > GRIDS > COLORWEB).
11. You might also save the entire project at this stage, with FILE >
PROJECT > SAVE PROJECT AS, and use the folder PRODUCTS > SAGA > PROJECTS,
perhaps with this project name:
|12. Now let's take a look at
the products. Here's the MEAN or AVERAGE distribution. This just
gives you a good idea of the general characteristics of the area, but it
does not show extreme values at all.
|13. Here's the MINIMUM
distribution. Based on your biological knowledge, you might see values
so low that they could cause death or injury to some organisms. So
based on this map, you could search for the areas where these conditions
|14. Here's the MAXIMUM
distribution. So similar to the above, you might identify lethally
high values, and then use maps to find them.
For example we know that corals start showing impacts at temperatures in
the low 30's. The range indicates that there are cells in that range,
but obviously very few of them. Where are they?
|15. One of the principles of
physiological ecology is that the annual range of environmental values (e.g.
temperature) can be just as significant as the minimum or maximum for
organism survival. If you have any target organisms for your research,
what upper limit of change can they endure, and is it exceeded in these
|16. Now we have
an idea of what we're looking for in the data maps. So based on the
above, then a "universal" SST range for Liberia would appear to be
approximately 22-30, ignoring the extremes. So let's see what the SST
maps look like, all using the same range for the palette.
|17. Here, the
VALUE RANGE has been set to the same range for all figures (22-30 degrees). Now the figures can
confidently be compared visually, and they "make sense" scientifically.
You can simply check the single, unified color palette on the top right to
estimate SST values for any figure. As a biologist, you could now use these grids to assess the highest and
lowest temperatures that usually occur here, and where they occur.
This is a very basic ecological concept that is much easier to do with these
statistical images, than by visually scanning 4 to 12 monthly images.
|18. What if you were asked to "map" the
area where annual temperature changes are 4 degrees or more, for example.
By "map" we mean to create an objectively calculated map that unambiguously shows exactly
where that area is, and the user is not required to make guesses based on
color shades. How do you do that?
|19. One way to do it is by
"classifying" the data. Select TOOLS > GRID-TOOLS > CHANGE GRID
|20. Make these selection:
- GRID SYSTEM - The SST images system
- GRID - The annual range grid
- CHANGED GRID - Set to CREATE
- REPLACE CONDITION - Set to LOW VALUE <= GRID VALUE < HIGH VALUE
Then click on the ellipsis (...) to the right of LOOKUP TABLE.
|21. The initial table has
these 3 columns, but only 2 rows. We need a total of 6 rows. You can use a right-click on any
table row to ADD RECORD or INSERT RECORD, as needed.
22. Edit the table to have these values. The REPLACE WITH values are codes for temperature ranges, not SST
- Code 102 contains all the lowest values, from 0.0 up to 2.0, so
nothing is missed
- Codes 103-106 contain SST values that step up in increments of 1.0,
from 2.0 to 6.0
- Code 107 contains values above 6.0 to the maximum possible
valid SST, so nothing is missed
Then click OK. And click OK again.
|23. You now have
a new grid named CHANGED GRID that does not contain SST values or SST
difference values at all. It contains only numerical codes. Each
pixel has been assigned a code to show where it is found in the above table.
|24. Here is the histogram for
CHANGED GRID, showing that it only has 4 different values. Apparently
there were no values in the lowest or highest bins..
|25. And here is the changed
grid, a "classified grid", showing the different code zones.
26. You might want to save this grid now, in the folder PRODUCTS >
SAGA > GRIDS with the filename sst_range_classes_11mu_day_modisa_colorweb_0.05deg
|27. If you want to visualize
only a portion of these zones, you can manipulate the NO DATA fields of the
OPTIONS panel. This is possible, because the temperature range codes
are sequential (an important thing to remember, if you do this sort of
Here the codes 102, 103 and 104 (covering temperature ranges from
0.0 to 4.0 degrees) are "masked" by declaring them to be NO DATA values.
Click SETTINGS > APPLY to see the result.
|28. This is a map of the
areas where the annual temperature range exceeds 4.0 degrees. [The
land area of Liberia is also shown, because it has a value of -99999, the
native NO DATA value in Saga.]
Maps like this are much more useful for
decision-making and presentation, if you want to emphasize a particular
area, rather than dazzle the viewer with colors. A standard graphics
editor (IrfanView) has been used to add informative labels.
|29. We hope you
saw many possibilities in the above exercise for other statistical analyses
that you can do with Saga. Once you have the library of basic grids
(which is usually 95 % of the work), then the above steps are easy and fast.