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I am a senior landscape ecologist with The Nature Conservancy's, Central Science. I attempt to bring vigor from diverse fields such as landscape ecology, spatial statistics, remote sensing and applied mathematics to answer practical conservation questions. I hold Affiliate Associate Professor status at University of Wyoming and have over 65 publications in peer-reviewed journals.

My research is focused on spatial statistics in ecological applications, species distribution modeling, climate change, landscape genetics, Bayesian statistics, Lidar and spectral remote sensing and gradient modeling. I have ridden horses for 35 years and was a member of the US Equestrian team. I have also played guitar in several swing and bluegrass bands


1d
comment Converting LiDAR waveform fileTO ASCII or LAS file
You question is vague at best. What format is the data currently in? The newer specification of LAS are waveform compliant. If your data is truly "raw", as in straight off the sensor, you need to perform sensor corrections.
May
19
revised Unable to read raster into R: 'TIFFReadDirectory:Failed to read directory at offset'
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May
19
revised Unable to read raster into R: 'TIFFReadDirectory:Failed to read directory at offset'
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May
19
comment Unable to read raster into R: 'TIFFReadDirectory:Failed to read directory at offset'
If you set your working directory, using "setwd", it is not necessary to explicitly define the path in the raster function.
May
19
answered Unable to read raster into R: 'TIFFReadDirectory:Failed to read directory at offset'
May
18
comment Strange spatial interpolation results from ordinary kriging
Why don't you take the model variogram parameters from ArcGIS and apply them to your R model? You can then compare the actual interpolation results. You model looks overfit and I imagine that there is something in your data that is causing the autoKrige function to misspecify the model parameters. Perhaps autoKrige is fitting a pure nugget effect. Did you transform your data? Fitting Kriging models is a bit of an art and one should not rely entirely on default parameters. Take a look at the eyefit function in geoR. It will let you visualize how different parameters change your variogram.
May
14
comment Semivariogram modeling in Arc Gis Geostatistical anaalyst
It should be whatever units your projection is. If the projection is geographic I would imagine that they calculate the great circle distance and the units would be kilometers.
May
13
answered Random Forest to estimate land use in past with Landsat
May
12
answered Spatial distribution of an invasive specie across a landscape : what analysis?
May
12
reviewed Approve Am I thinking about layers all wrong?
May
12
revised Using ModelBuilder to Make Composite of NDVI Rasters in ArcMap?
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May
12
comment using variogram for texture in remote sensing
A variogram is not making much sense in this context primarily because it is not clear what you are asking. Please see forum guidance on how to write a clear question.
May
12
comment Kriging values greater than the most extreme value of the input layer?
Yes, I should have been clearer. It should not effect range and as such, global min or max. But the nugget can cause the at observation(s) value to vary. Whereas, if there is no error term it is an exact interpolator. Only transcendentally related to the OP's question.
May
12
comment weight a raster by another for a model ( R/ arcgis/maxent)
I would highly recommend reading up on how MaxEnt works before defining an interaction or weighting function. You could likely include the parameter indicating "weights" as a covariate in the model.
May
11
comment Kriging values greater than the most extreme value of the input layer?
Don't forget the nugget effect. If there is error-term specified for the model, Kriging becomes an inexact interpolator.
May
11
reviewed Approve Spatio-temporal block kriging with R package gstat?
May
11
comment Using NDVI values as covariate in survival analyses
You can certainly account for uncertainty due to NDVI variation. However, it is unlikely that the data will be "independent". Since the data represents a time-series it will undoubtedly exhibit serial autocorrelation that will need to be accounted for in your specified model.
May
11
comment Using NDVI values as covariate in survival analyses
Is your camera calibrated? How are you getting near-infra red (0.75-0.90 µm) from a camera?
May
9
comment Spatial regression returns “not enough data” in ArcGIS for Desktop
Please note that just because you are trying to use an OLS in ArcGIS does not make it a spatial regression. It would be prudent to at least take a cursory look at the literature to gain a basic understanding of regression techniques with spatial data. A spatial regression is its own statistic just like OLS and GWR. The intent of the GWR statistic is to account for nonstanioarity so, if you apply this method make sure that you have adequate local 2nd order variation to warrant it.
May
8
answered Calculating forest connectivity for raster