The goal of the analysis was to identify areas of low topographic exposure. The red areas illustrate low exposure. |

Exposure is calculated based on the height and distance of the surrounding horizon. These two measures combine to form the angle of inflection. It is this angle that we use to quantify topographic exposure. When a large topographic feature, like a mountain, is far in the distance the angle of inflection is low.

The angle of inflection is 45 degrees from the horizon |

When the same mountain is closer the angle of inflection is higher. Therefore higher angle of inflection is equal to lower exposure or higher protection.

Based on advice from the windthrow expert Dr Stephen J Mitchell, it was determined to calculate the angle of inflection at 100 meter intervals from 100 to 2000 meters. The maximum angle was then extracted to define the true value of exposure. This of course needs to be calculated for each cell within the raster dataset. Within GRASS this is accomplished with the r.mapcalc command.

First a little about neighbor calculations in GRASS. When we are looking at different spots in the horizon, we are essentially performing a neighbor function. The proper language for this is:

dem[3,1]This translates into the raster cell three up and one to the right of the current cell. So if we wanted to subtract every cell in layer

*dem*from the one that is three up and one to the right it would look like this.

r.mapcalc newlayer = dem[3,1] - demNot sure why you'd want to do that.... But there you go.

Now let's look at our

*topex*equation. To calculate the angle of inflection we need to get the

*atan*of height difference divided by the distance. This was determined based on simple trigonometry. The grass equation looks like this:

r.mapcalc incl4north = "atan(((dem - dem[4,0])) / ((25 * 4)))"This creates a layer

*incl4north*which holds the angle of inclination for 4 cells to the North. Note we now need brackets around the equation. The

*dem*and all subsequent raster layers have 25 meter cells. This is used to calculate the distance by multiplying by the number of cells. This one calculation creates a raster layer containing the exposure for 100 meters in just one direction.

The goal is to take the maximum inclination for all distances from 100m to 2000m. Luckily there is a max() function for the

*r.mapcalc*command. The following command calculates the maximum inclination for all intervals to the North:

r.mapcalc topex_n = "max(atan(((dem - dem[4,0])) / ((25 * 4))), \

atan(((dem[8,0] - dem)) / ((25 * 8))), \

atan(((dem[12,0] - dem)) / ((25 * 12))), \

atan(((dem[16,0] - dem)) / ((25 * 16))), \

atan(((dem[20,0] - dem)) / ((25 * 20))), \

atan(((dem[24,0] - dem)) / ((25 * 24))), \

atan(((dem[28,0] - dem)) / ((25 * 28))), \

atan(((dem[32,0] - dem)) / ((25 * 32))), \

atan(((dem[36,0] - dem)) / ((25 * 36))), \

atan(((dem[40,0] - dem)) / ((25 * 40))), \

atan(((dem[44,0] - dem)) / ((25 * 44))), \

atan(((dem[48,0] - dem)) / ((25 * 48))), \

atan(((dem[52,0] - dem)) / ((25 * 52))), \

atan(((dem[56,0] - dem)) / ((25 * 56))), \

atan(((dem[60,0] - dem)) / ((25 * 60))), \

atan(((dem[64,0] - dem)) / ((25 * 64))), \

atan(((dem[68,0] - dem)) / ((25 * 68))), \

atan(((dem[72,0] - dem)) / ((25 * 72))), \

atan(((dem[76,0] - dem)) / ((25 * 76))), \

atan(((dem[80,0] - dem) / ((25 * 80))))"

Now we need to do the same for the remaining 7 directions; NE, E, SE, S, SW, W, & NW. East, South and West are simple. Just rearrange the existing values. The remaining directions require a little more trigonometry. The hypotenuse is still the constant of

*25 * (the number of cells)*. However the remaining side of the triangle requires the following equation:cos(45) * (25 * # cells)

I chose to calculate this in perl with a function like this:

# Calculate one side of a right Isosceles triangle.

sub is () {

$value = floor(cos(deg2rad(45)) * $_[0]);

return $value;

}

Then plug the function into the mapcalc command like this:

atan(((dem - dem[" . is(4) . "," . is(4) . "])) / ((25 * 4)))This is just the way I decided to do it. You could use that same trigonometry formula however you choose.

At this point we should have eight exposure raster layers. All that is required now is merge them together adding up the values. The command

*r.series*does this beautifully:

r.series input=topex_n,topex_ne,topex_e,topex_se,topex_s,topex_sw, \Viola!... We now have a complete TOPEX layer... Called

topex_w,topex_nw output=topex method=sum

*topex*.

TOPEX highlighting areas of low exposure. Draped over Google's Terrain basemap. |

To make use of the layer outside GRASS I like to convert it to a geotiff like this:

r.out.gdal -c input=topex createopt="COMPRESS=LZW" \If you have the GDAL library installed you can also generate a pyramid on it like this:

output=topex.tif nodata=0

gdaladdo topex.tif 2 4 8 16 32 64

## 2 comments:

Tansi, we're out of BC as well. What a great an informative post! Thanks for sharing.

Would you be able to help shed some light on some of the practical or specific uses of a TOPEX layer? I work with lots of different technicians and I'd like to share with them on how they can use this in their work for decision making purposes.

We receive notification from governments and industry on proposed projects and are needed to provide comments and concerns (wildlife, resource extraction, environment and cultural monitoring, archaeology, etc...)

For example, is this something that can be used as a constraint on some type of wildlife habitat and corridor prediction analysis? Or is this something that a wind turbine/farm company could use in their site selection?

Thanks for your comment SaultDon.

Yes... The TOPEX layer is great for modelling plant species. In turn animal probability, suitability etc. could also be determined. I used this dataset for Yellow Cedar decline.

Jamie

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