Monthly Archives: March 2014

D-Lib Article Submission for Figshare Article


The NSF-funded Data Observation Network for Earth (DataONE)​ was introduced to D-Lib readers in the January/February issue of 2011.

DataONE has nearly concluded its five-year grant period and has produced a body of work examining practices and perceptions surrounding data sharing. Over the course of assessing communities of researchers, librarians, and others involved in the data life cycle, the DataONE assessments working group identified a new population of users: early adopters of data sharing infrastructure.

Using the Web 3.0 service “Figshare” as an exemplar, The University of Tennessee at Knoxville coordinated with Figshare’s founder to design an online survey exploring opinions and motivations of early adopters. Compared to earlier survey instruments drawing responses in the range of thousands, this survey drew a small sample size.

Nonetheless, the data provides insights, and a poster presented at UT’s 2014 College of Communication and Information Research Symposium offering a first glimpse of the data has captured interest online:

Given D-lib’s audience and early interest in DataONE, I have drafted a manuscript discussing the data at length and in context with the hope of publishing in D-Lib. The manuscript is 5,149 words, excluding the 200 word abstract, but including image captions (140 words) and headings (138 words).

Please find the manuscript for your consideration attached (Jessel-Birch-5150.docx). I have attempted to follow the conventions outlined by D-Lib, but realize I may have missed some points. I am happy to modify the manuscript as needed to make the article suitable for publication in D-Lib.

Possible Spatial Data Inputs for MaxEnt Species Distribution Models in Great Smoky Mountains N.P.

Environmental layers are available to the public via IRMA.

Source metadata are not available at

I have attempted to map or cross-walk the layers listed by the Simmerman et. al paper to the names of datasets available for download from IRMA.

Table. Mapping from UTK names to IRMA names.

1 Soil Organic Type Soil Classification
2 Topographic Convergence Index Topographic Wetness Index
3 Solar Radiation Data 30 -m Potential Solar Radiation
4 Terrain Shape Index 30-m Topographic Shape Index
5 Terrain Shape Index 30-m Topographic Ruggedness Index Model
6 Digital Elevation Model 30-m Lidar Digital Elevation Model
7 Slope in Degrees 30-m Lidar Slope Model
8 Understory Density Classes Understory Vegetation at GRSM
9 Leaf On Canopy Cover Overstory Vegetation at GRSM
10 Vegetation Classes Vegetation Classification Great Smoky Mountains NP Vegetation Classification

Note: I am grateful to which made it possible to easily create this table from plain text.  I expect to add this to my “toolkit” of useful items and it saved me a lot of time.

ATBI Mapping Program: Species Distribution Models for Great Smoky Mountains National Park

Spatial Data Diversity Supporting Herpetofaunal Research in Great Smoky Mountains National Park

2014 North Carolina PARC Poster.

Reduced file size image of poster presented at North Carolina Partners in Amphibian and Reptile Conservation (NCPARC) meeting, March 2014.

Poster presented at 2014 North Carolina Partners in Amphibian and Reptile Conservation Meeting.

              Jessel, Tanner; Super, Paul E.; Colson, Thomas (2014): Spatial Data Diversity Supporting Herpetological Research in Great Smoky Mountains National Park. figshare.

SDM re-projected for Google Earth, OSM with gdal2tile

I think I have stumbled upon the solution for tiling the png image.
To re-project the PNG image, we can geo-reference the images with GDAL, then warp the geo-referenced image to the correct projection, also with GDAL.
The process is described here:
First, enter “gdalinfo Abies_fraseri.png” into terminal to get the bounds of the PNG image.
This yields the following output:
Corner Coordinates:
Upper Left  (    0.0,    0.0)
Lower Left  (    0.0, 1302.0)
Upper Right ( 2899.0,    0.0)
Lower Right ( 2899.0, 1302.0)
Center      ( 1449.5,  651.0)
A template and implementation for our PNG files is demonstrated here:
gdal_translate -of VRT -a_srs EPSG:4326 -gcp 0 0 ULlong ULlat -gcp UPPERRIGHTPx 0 URlong URlat -gcp LOWERRIGHTPx LOWERRIGHTPy LRlong LRlat Abies_fraseri.png Abies_fraseri.vrt
gdal_translate -of VRT -a_srs EPSG:4326 -gcp 0 0 -84.000683874 35.7889383688 -gcp 2899.0 0 -83.0424855 35.7889383688 -gcp 2899.0 1302.0 -83.0424855 35.426963641 Abies_fraseri.png Abies_fraseri.vrt

For “ULlong” (Upper Left Long) and so forth I used a bounding box tool ( to determine the following bounds of the PNG – however, if there is a more “official” known boundary, it would be wise to use that instead.
                                35.426963641,-84.000683874 – bottom left / lower left
                                35.426963641,-83.0424855 bottom right / lower right
                                35.7889383688,-83.0424855 – top right / upper right
                                35.7889383688,-84.000683874 – top left / upper left
Next, take the .vrt file and warp it:
    gdalwarp -of VRT -t_srs EPSG:4326 Abies_fraseri.vrt Abies_fraseri_2.vrt
This creates a folder with tiles for a KML network link.
see “doc.kml” in the attached zip folder which you may open in Google Earth.
There is also an Open Map Layers HTML page in the attached zip folder.
To add trails in Google Earth
My opinion is it would be more interesting and informative to have the species occurrences added as a separate layer.  Therefore, I would prefer to create placemarkers / waypoints for species locations rather than black squares on the images.  Is it possible to generate PNG images with the likely distribution but not the black squares?  If so, would that take a long time to re-do to make these tiled overlays?
Also, although I “hacked” the PNG bounds, I’m still worried it the overlay’s georeferencing be “off” a bit and would love to use a more trustworthy set of coordinates than those derived from my best judgement and
From here, I think it would be smart to document a workflow for doing a lot of PNG to VRT to KML at a time.  I haven’t tried processing a whole directory of images at once yet – just playing with the Abies_fraseri.png for now.
The doc.kml and virtual raster dataset work with Google Map API and Open Layers map.  I think they both run on Javascript.

Fraser Fir Portable Network Graphic with “Excised” Background

This is the PNG at

I used GIMP (GNU image manipulator) to remove the solid (non suitable) habitat.

I’m uploading the resultant image to this blog post:



Fraser fir image with “brown” background removed using select by color, invert selection, and mask.