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  • Knight Final Paper and Poster

Site Suitability Analysis for Algae Biofuel-Producing Farms Using Raster Data

 

Description

 

This project is concerned with a demonstration of best siting for algae farms for use in biofuel production in the state of Florida . Although to date the regions utilized are mostly chosen because of their high levels of insolation (a requirement for growing algae for biofuel), considering other factors could possibly lead to better sitings of farms. Many recent developments in algae farming technology use pre-existing infrastructure to increase fuel output. Such infrastructure includes wastewater and sewage treatment plants and carbon dioxide emitters. Both these types of installations emit nutrients that can be used in algae production.

Additionally, rather than produce the fuel in areas located far away from consumers, as currently occurs when it is grown in rural, desert environments, taking the locations of highways and electrical transmission lines into consideration could also lead to different ideas of best-practice siting for algae farms. Furthermore, in many larger states, much of the land is federally owned. Thus it is important to exclude these areas, in addition to regions of high population density (as algae farms require large, flat tracts in order to be built) when searching for best siting. Also, although growing it requires much less water than other ethanol-producing crops like corn or palm, algae also must have water in order to be grown. Therefore it is helpful to include proximity to water as a factor.

While the best placing of future algae farms in Florida is important, this project is largely significant for my purposes in terms of demonstrating how conversion to raster images, reclassification, and raster calculation can be applied for any kind of site suitability analysis.

 

Data Sources

Sewage treatment plants, point – (Florida Geographic Data Library, www.FGDL.org, http://www.fgdl.org/metadataexplorer/full_metadata.jsp?docId={A8FB28F2-3CAF-441B-A3D3-87643AB14A8C}&loggedIn=false , 1:24,000 scale, November 1997)

 

Wastewater treatment plants, point – (Florida Geographic Data Library, www.FGDL.org, http://www.fgdl.org/metadataexplorer/full_metadata.jsp?docId={0B884614-DFDF-44D0-A907-A9BCE7606361}&loggedIn=false , 1:24,000 scale, 1995)

 

Transmission lines, arc – (Florida Geographic Data Library, www.FGDL.org, http://www.fgdl.org/metadataexplorer/full_metadata.jsp?docId={27563959-B2F6-4CFF-9D3B-22E667418984}&loggedIn=false , 1:24,000 scale, May 1999)

 

Highways, arc – (Florida Geographic Data Library, www.FGDL.org, http://www.fgdl.org/metadataexplorer/full_metadata.jsp?docId={CC920989-95BD-4EF6-BC83-D4021A6CB4E0}&loggedIn=false , 100,000 scale, 2007)

 

Florida counties, polygon – (Florida Geographic Data Library, www.FGDL.org, http://www.fgdl.org/metadataexplorer/full_metadata.jsp?docId={6C674F7A-D27C-4A22-A922-363C68E55725}&loggedIn=false , 1:125,000 scale, July 1997)

 

Public land, polygon – (Florida Geographic Data Library, www.FGDL.org, http://www.fgdl.org/metadataexplorer/full_metadata.jsp?docId={7118FCEF-D11F-4F34-BA6B-6E90420E527F}&loggedIn=false , 1:24,000 scale, December 2008)

 

Land cover, raster – (Florida Geographic Data Library, www.FGDL.org, http://www.fgdl.org/metadataexplorer/full_metadata.jsp?docId={C97444C1-F2CC-4607-9FAE-F1D72C2355B7}&loggedIn=false , 1:24,000, May 2005)

 

Florida hydrography, polygon – (Florida Geographic Data Library, www.FGDL.org, http://www.fgdl.org/metadataexplorer/full_metadata.jsp?docId={511EE207-8132-4B4F-AF4D-74DEC331B1BE}&loggedIn=false , 1:100,000 scale, 1990)

 

Insolation, polygon – (National renewables Energy Laboratory, http://www.nrel.gov/gis/data_analysis.html , 10 km, 1990)

Carbon dioxide emitters, database – (Project Vulcan, http://www.purdue.edu/eas/carbon/vulcan/research.php , 2007)

 

Methods

 

Step 1: Add polygon, arc, and point data layers to ArcMap, ensuring they are all in the same coordinate system (GCS_North_American_1983_HARN).

Step 2: Extract data for Florida from the Project Vulcan carbon dioxide emitters database, saving it as a DBF file. Use the “Join” function to combine carbon dioxide data with a copy of the county boundaries layer, using the “FIPS” field as the based-on layer.

Step 3: Add Land cover raster layer. Regroup “classes” field based on best land type for siting algae farms. Most land types, such as hardwood forest, open prairie, or bare soil, go into “Open space”. Water- and swamp-based layers go into “unbuildable”. Low-impact and high-impact urban are each separated into their own groups.

Step 4: After enabling Spatial Analyst in the extensions, set Spatial Analyst options. Set Cell size and Analyst extent both to “same as Land cover layer”, making a 90 m cell size the same at least the same area as the Land cover layer. Also set the county boundaries layer to be the Analysis mask, to make sure all created rasters are the same size.

Step 5: Convert the highway, transmission line, sewage treatment plants, and wastewater treatment plants to rasters using the “Distance > Straight line” tool in the Spatial analyst toolbar.

Step 6: Convert the insolation (using the “GHIANN” field – power in watt-hours per meter squared per year), public land (using any field), hydrography (using any field), and carbon dioxide emitters (using the “total CO2” field) to rasters using the “Convert > Features to Raster” tool.

Step 7: Determine ranks for each of rasters with which they will be reclassified.

Rank

0

1

2

3

4

5

Layer

 

 

 

 

 

 

Sewage Treatment (m)

-------------

>401

301-400

201-300

101-200

<100

Wastewater Treatment (m)

-------------

>401

301-400

201-300

101-200

<100

Water (m)

-------------

>401

301-400

201-300

101-200

<100

Transmission Lines (m)

-------------

>401

301-400

201-300

101-200

<100

Highways (m)

-------------

>401

301-400

201-300

101-200

<100

CO2 output (tons/yr)

-------------

<0.102645

0.102645 - 0.299156

0.299156 - 0.569495

0.569495 - 2.047417

>2.047417

Insolation

-------------

<4605

4606-4671

4672-4782

4783-4856

>4857

Public Land  

On

-------------

-------------

-------------

-------------

-------------

Land cover

Unbuildable (swamps, water), High Impact Urban

-------------

Low Impact Urban

Open Space

-------------

-------------

For the rasters that were created through the “Distance > Straight line” method, it was established that the ranks increase as proximity to the original arc or point decreases. For the insolation and carbon dioxide emitters, breaks were based on five-class quantiles of the created raster. Finally, we do not want to build on water, so we have classed both these regions as 0, or no build.

Step 8: Use the Reclassify tool to create new rasters from each of the just-created rasters (including the Land cover layer), setting the number of classes from 1 to 5, depending on what the above ranks require.

Step 9: Use the Raster Calculator tool to multiply all the layers together. Zones most desirable for algae farm construction will have the highest values, while zones less desirable will have lower values, with zones not suitable for building at all (such as Public or Unbuildable land), being reduced to zero.

Step 10: Redefine categories of calculated layer into five quantiles, excluding areas with values of zero (so they will not appear on the map.

 

Difficulties

 

One of the initial difficulties was that I added the insolation layer first and it did not have a defined coordinate system. When I added the FGDL layers later, they appeared far away and at a different scale, and it took me some time to figure out that I should place the layers that had a defined coordinate system first, and the insolation layer second, selecting only the area around Florida and exporting it, creating a new layer that was the right size and in the same coordinate system.

A second difficulty was how I dealt with the hydrography and public land rasters. During reclassification, I had originally forgotten to set No Data to reclass as 0. This resulted in two rasters in which the areas that were public land or water existed, with all other regions empty. When I used the raster calculator, the entire resulting map was thus equal to 0. Although I reclassified the Public land layer, I realized that the “no build” hydrography layer was already being taken care of by the “unbuildable” class in the land cover layer. I then got rid of this layer and used the hydrography dataset only for distance rankings.

Another difficulty was that I first generated the final map with High Impact Urban having a rank of one. This resulted in many metropolitan areas being very suitable for algae farms, as they are often very close to roads and transmission lines. As algae farms need a lot of room to be built, I decided to exclude these areas and reclassed my reclassified land cover layer with High Impact Urban set to 0.

 

Conclusions

 

To conclude, I would be very interested to see what the results of this project would be if the vector suitability analysis approach were used. Although I would imagine using the select by attribute and the select by location processes would take a large amount of time, many of the original layers are in vector form, and time spent selecting might actually be faster than the time that is spent converting these layers to rasters. Despite the length of time necessary for converting features to rasters and reclassifying layers, I felt this approach worked very well. One suggestion I would have for anyone that is doing site suitability analysis with a large number of criteria is to perform each step to all the layers at the same time, rather than adding, converting, ranking, and reclassifying layers one at a time. Each time one of these steps is performed, a new layer is created, and keeping track of what layer to use can easily become confusing. Creating Group Layers went a long way to helping me to remember which layers to use in each consecutive step.

Additionally, looking at my final map, I think that my suitability may weight highways too heavily. Many of the highest-ranked areas are small corridors along highway routes and while proximity to transportation is important, other factors, such as proximity to nutrient sites (sewage plants, wastewater treatment plants, and high CO 2 emitters), is much more significant.

Also, I would like to see this project redone on a smaller scale, perhaps using the final map from this project as an indication of the best regions to begin studying. I would like to take a more in-depth look at the literature, and find the exact constraints for suiting algae farms, possibly even taking into account different species of algae.

 

References

 

Kram, J. Permitted Ponds—Siting Hurdles for Algae Development . (February 2009). Biodisel Magazine. Retrieved on May 1, 2009 from http://www.biodieselmagazine.com/article.jsp?article_id=3160.

 

Bee, W. Is Algae BioFuel Ready to hit the US Market? . (April 2008). RideLust. Retrieved on May 1, 2009 from http://www.ridelust.com/is-algae-biofuel-ready-to-hit-the-us-market/.

 

McDermott, M. Algae For Biofuels Grown on Waste Water in Austin, Texas . (October 2008). Treehugger. Retrieved on May 1, 2009 from http://www.treehugger.com/files/2008/10/algae-for-biofuels-grown-with-waste-water-austin-texas-video.php.