- View SourceDear List:

I got a question about the uncertainty associated with the Conditional

Stochastic Simulated results, the question is:

I have soil C data for a sample area, within that area, there are woodland

and grassland. The variance of C data in woodland is higher than that in the

grassland. Now I want to use Conditional Stochastic Simulation to simulate a

bunch of images of soil C on the area. I will use one single variogram model

to do the simulation. The questions is: Do the estimated C data more

variable under woodland than those under grassland (simulated soil C data in

woodland have a larger variance than those in grassland)? or a single

variogram model means uniform or random variance of simulated data?

Thank you very much

Feng Liu

Graduate Student

Texas A&M University - View SourceHi Feng,

You are in the ideal situation where it would make sense

to separate the two populations and do the variography

and simulation separately for each stratum, since clearly

the pattern of variability of soil carbon is expected to be

different under these two landcovers (I am sure that besides the

variance, the range and nugget effect must be pretty different)

and also the average C concentration should vary a lot .

In most situations it is difficult to consider different populations since:

1. we might not have enough data within each stratum to compute a

reliable variogram,

2. the boundaries of the different strata might be fuzzy,

3. the multi strata approach creates discontinuities in the map

that are not physically realistic.

I don't know your data but I am pretty sure that none of these limitations

apply.

Now to answer your question, the use of a single semivariogram implies

an assumption of stationarity, hence the fact that the

variance/covariance does not depend on the location within the study area,

which is clearly not your case. Even wen using a single variogram,

the variability in your simulated map will be influenced to some

extent by your data (conditioning observations). Hence, I suspect

that more variability will appear on woodland anyways.

By analogy, the same happens when you use an isotropic semivariogram

in simulation/estimation, while the data display strong anisotropy.

The simulated/estimated map will display some anisotropy, which is

always reassuring.

Cheers,

Pierre

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Dr. Pierre Goovaerts

President of PGeostat, LLC

Chief Scientist with Biomedware Inc.

710 Ridgemont Lane

Ann Arbor, Michigan, 48103-1535, U.S.A.

E-mail: goovaert@...

Phone: (734) 668-9900

Fax: (734) 668-7788

http://alumni.engin.umich.edu/~goovaert/

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On Sun, 10 Oct 2004, Feng Liu wrote:

> Dear List:

>

> I got a question about the uncertainty associated with the Conditional

> Stochastic Simulated results, the question is:

>

> I have soil C data for a sample area, within that area, there are woodland

> and grassland. The variance of C data in woodland is higher than that in the

> grassland. Now I want to use Conditional Stochastic Simulation to simulate a

> bunch of images of soil C on the area. I will use one single variogram model

> to do the simulation. The questions is: Do the estimated C data more

> variable under woodland than those under grassland (simulated soil C data in

> woodland have a larger variance than those in grassland)? or a single

> variogram model means uniform or random variance of simulated data?

>

> Thank you very much

>

> Feng Liu

> Graduate Student

> Texas A&M University

>

>

>

>