Grant Proposal Draft 1

The quantification of coloured dissolved organic material (CDOM) in lakes can be estimated from remote sensing data collected using handheld and airborne spectrometer sensors (Arenz et al., 1996; Hirtle & Rencz, 2003; Vertucci & Likens, 1989; Kutser et al. 2004 & 2005, Leguet et al. 2013 in print). This is an important correlation to make, as examining DOC concentrations for early identification of mercury pollution could prevent harmful damage of the water columns (Hirtle & Rencz 2003), which habitats in Canada rely on for overall ecosystem health. Kutser et al. (2005) found after analyzing Landsat, IKONOS and ALI that their imagery was adequate for concentration CDOM ranges in Nordic countries. The results showed that the 16-bit radiometric resolution of ALI, a prototype of the next generation of Landsat launched in February 2013, would be suitable for mapping CDOM in a wider range of concentrations (Kutser et al. 2005). For this reason, it is important to confirm this study, and analyze the concentration ratios of CDOM in previously studied lakes using newly acquired data from the Landsat 8. This data will be matched with field-acquired data of the CDOM concentrations in lakes across the Eastmain and Abitibi regions. These values will help confirm the relationship between the calculated and sampled values of CDOM with the Landsat 8 sensor. Additionally, with confirmation of the Landsat 8 band ratios, this study will aim to quantify the amount of carbon in lakes across other provinces of Canada using the data from Landsat 8.

References

Arenz Jr, R. F., Lewis Jr, W. M., & SAUNDERS III, J. F. (1996). Determination of chlorophyll and dissolved organic carbon from reflectance data for Colorado reservoirs. International Journal of Remote Sensing, 17(8), 1547-1565.

Brezonik, P., Menken, K. D., & Bauer, M. (2005). Landsat-based Remote Sensing of Lake Water Quality Characteristics, Including Chlorophyll and Colored Dissolved Organic Matter (CDOM). Lake and Reservoir Management, 21(4), 373-382. doi: 10.1080/07438140509354442

Canham, C. D., Pace, M. L., Papaik, M. J., Primack, A. G., Roy, K. M., Maranger, R. J., ... & Spada, D. M. (2004). A spatially explicit watershed-scale analysis of dissolved organic carbon in Adirondack lakes. Ecological Applications, 14(3), 839-854.

Dominy, S. W., Gilsenan, R., McKenney, D. W., Allen, D. J., Hatton, T., Koven, A., ... & Sidders, D. (2010). A retrospective and lessons learned from Natural Resources Canada's Forest 2020 afforestation initiative. The Forestry Chronicle, 86(3), 339-347.

Duchemin, É., Lucotte, M., Canuel, R., & Soumis, N. (2006). First assessment of methane and carbon dioxide emissions from shallow and deep zones of boreal reservoirs upon ice break‐up. Lakes & Reservoirs: Research & Management, 11(1), 9-19.

Gergel, S. E., Turner, M. G., & Kratz, T. K. (1999). Dissolved organic carbon as an indicator of the scale of watershed influence on lakes and rivers.Ecological Applications, 9(4), 1377-1390.

Hirtle, H., & Rencz, A. (2003). The relation between spectral reflectance and dissolved organic carbon in lake water: Kejimkujik National Park, Nova Scotia, Canada. International Journal of Remote Sensing, 24(5), 953-967. doi: 10.1080/01431160210154957

Kainz, M., Lucotte, M., & Parrish, C. C. (2003). Relationships between organic matter composition and methyl mercury content of offshore and carbon-rich littoral sediments in an oligotrophic lake. Canadian Journal of Fisheries and Aquatic Sciences, 60(7), 888-896.

Karim, A., Veizer, J., & Barth, J. (2008). Net ecosystem production in the great lakes basin and its implications for the North American missing carbon sink: A hydrologic and stable isotope approach. Global and Planetary Change, 61(1), 15-27.

Kortelainen, P. (1993). Content of total organic carbon in Finnish lakes and its relationship to catchment characteristics. Canadian Journal of Fisheries and Aquatic Sciences, 50(7), 1477-1483.

Kutser, T., Pierson, D. C., Tranvik, L., Reinart, A., Sobek, S., & Kallio, K. (2005). Using Satellite Remote Sensing to Estimate the Colored Dissolved Organic Matter Absorption Coefficient in Lakes. Ecosystems, 8(6), 709-720. doi: 10.1007/s10021-003-0148-6

Kutser, T., Pierson, D., Kallio, K., Reinart, A., & Sobek, S. (2005). Mapping lake CDOM by satellite remote sensing. Remote Sensing of Environment, 94(4), 535-540. doi: 10.1016/j.rse.2004.11.009

Leguet, J., Cardille, J. A., & Del Giorgio, P. (2013). Remote sensing of lake CDOM using non-contemporaneous field data (Unpublished master's thesis). University of Montreal.

Smith, C. K., Coyea, M. R., & Munson, A. D. (2000). Soil carbon, nitrogen, and phosphorus stocks and dynamics under disturbed black spruce forests.Ecological Applications, 10(3), 775-788.

Stedmon, C. A., Markager, S., & Kaas, H. (2000). Optical properties and signatures of chromophoric dissolved organic matter (CDOM) in Danish coastal waters. Estuarine, Coastal and Shelf Science, 51(2), 267-278.

Tranvik, L. J., Downing, J. A., Cotner, J. B., Loiselle, S. A., Striegl, R. G., Ballatore, T. J., ... & Weyhenmeyer, G. A. (2009). Lakes and reservoirs as regulators of carbon cycling and climate. Limnology and Oceanography, 54(6), 2298-2314.

Weissenberger, S., Lucotte, M., Houel, S., Soumis, N., Duchemin, É., & Canuel, R. (2010). Modeling the carbon dynamics of the La Grande hydroelectric complex in northern Quebec. Ecological Modelling, 221(4), 610-620.

Williamson, C. E., Morris, D. P., Pace, M. L., & Olson, O. G. (1999). Dissolved organic carbon and nutrients as regulators of lake ecosystems: Resurrection of a more integrated paradigm. Limnology and Oceanography, 795-803.

Williamson, C. E., Saros, J. E., Vincent, W. F., & Smol, J. P. (2009). Lakes and reservoirs as sentinels, integrators, and regulators of climate change.Limnology and Oceanography, 54(6), 2273.

Xenopoulos, M. A., Lodge, D. M., Frentress, J., Kreps, T. A., Bridgham, S. D., Grossman, E., & Jackson, C. J. (2003). Regional comparisons of watershed determinants of dissolved organic carbon in temperate lakes from the Upper Great Lakes region and selected regions globally. Limnology and Oceanography, 2321-2334.

The Ten Commandments of GIS

Ten Commandments of GIS

1. Thou shalt be humble and patient in all things, and thankful this stuff works at all; for lo, there are many paths to wisdom, and if one module faileth to work as advertised, thou shalt figure out a workaround.
2. Thou shalt manage thine geodatabases, and follow the paths to thy data files and be a good shepherd unto them, and not forsake critical files in the temp directory. 
3. Thou shalt not rename, move or copy thine GIS data files except with Arc Catalog lest they come to grief. 
4. Thou shalt set thy data frame to an appropriate coordinate system. 
5. Thou shalt specify the cellsize of thine rasters, and attend to their other Environment settings, and not blindly accept default setting lest thy project founder in confusion. 
6. Thou shalt pay attention to database field types, neither confusing integer with double nor double with integer.
7. Thou shalt not depend on corrupt Document files after thy project has crashed, lest it crash upon thee yet again; yea verily, thou shalt open a blank new map and add thine data to it, knowing where to find them.
8. Thou shalt create maps both simple and fancy, but they shall represent thy research steps forthrightly.   Thou shalt not lie with maps, neither shalt thou idolize eye-candy, even when creating TIN or 3D images to please the Philistines in charge of thy research funding.
9. Thou shalt not abuse the lab equipment, even if wroth unto blasphemy.
10. Thou shalt accept success with grace, and disappointment also, and if thy best efforts reward thee not, thou shalt retire for beer, and return to the lab another day. Thou shalt not weep over GIS.

Used from UDEL GIS in Natural Resource Management Website

Imagery Resolution Types: Spatial, Spectral, Radiometric

In trying to completely understand some readings that Prof. Cardille assigned for me, I came across this GIS website from Penn State which explained the spatial, radiometric, and spectral resolution concepts in a perfectly comprehensive manner...

These charts were what helped me understand them the best:

Spatial Resolution: Spatial resolution is equated to the size of the pixel versus the amount of data collected. Depending on the speed of the imagery platform (satellite, airplane speed etc.) and the speed at which the images are collected, this can contribute to the spatial resolution. This is a good way to think about spatial resolution in terms of data collection.

Used from Penn State's Nature of Geographic Website

Spectral Resolution: Spectral resolution is measuring the wavelength that is reflected by the targeted object. For example, lower spectral resolution results in fewer large-width bands that fall at fewer increments and therefore more wavelengths are grouped into the band. Higher spectral resolution is a result of many small-width bands that fall at more increments are therefore each band contains fewer wavelengths (think.... more precision value-wise of the wavelengths captured). 

Used from Penn State's Nature of Geographic Website

Radiometric Resolution: This was the hardest resolution conceptually for me to grasp. Radiometric resolution falls upon the same style of bands like Spectral resolution, however in this case they are called "levels". Lower radiometric resolution results in fewer far-spanning levels that fall at fewer increments and therefore more radiometric values are grouped into the level. Higher radiometric resolution is a result of many small-width levels that fall at more increments are therefore each level contains fewer radiometric values (think.... more precision value-wise of the radiometric values captured). Specifically, the radiometric values measure the amplitude of the reflected light waves, and are often referred to as measuring the intensity of the light source, or the energy levels. Bits are a binary method of explaining the precision of the resolution, in terms of possible levels that the DN values can fall within. For example, 8-bit resolution is equivalent of 2^18 available values, or 256 DNs being available to fall within for the levels.

Used from Penn State's Nature of Geographic Website

4 Days, 16 Hours, 35 Minutes, 58 seconds...

Landsat 8. 

Meeting with Jeff 2/1/2013

For next week, I will read two relevant papers that Jeff will send me. He will also find the model builder stuff from his former Master's Student, in order to re-evaluate it for the Landsat image. This model was created with ArcGIS 9.3 and may have to be updated for version 10.1.

The two relevant papers are by Kutser et al. and the research took place in Sweden. The first is Kutser et al. (2004) entitled Mapping lake CDOM by satellite remote sensing. The second is Kutser et al. (2005) Using Satellite Remote Sensing to Estimate the Colored Dissolved Organic Matter Absorption Coefficient in Lakes. Both rely on radiometric resolution in order to get CDOM values from satellite, and experiment with the experimental satellite, ALI. Atmospheric correction was found to be unimportant, and they extrapolate and determine expected results for future satellites. 

We will meet next on Wednesday morning at 9 am. 

Spatial Analyst Tutorial for ArcGIS 10.1 Review

In order to increase my familiarity with ArcGIS, the Model Builder program, and various Raster analyses, Professor Cardille recommended that I complete the Spatial Analyst Tutorial for ArcGIS 10.1. This was especially beneficial as a refresher for the capabilities of ArcGIS, as I completed GEOG201: Introduction to GIS in Fall 2012. While I recently completed GEOG 308:Remote Sensing this past Fall, we primarily used ENVI 5.0 to analyze the images, and therefore it has been over a year since I have intensively worked with ArcGIS. 

Here are my top-8 impressions of the tutorial/tools that were used:

1) Double-check that you are using the compatible version of the Spatial Analyst Tutorial with the version of ArcGIS that you have. While many of the differences between ArcGIS 9.3 and 10.1 and not readily apparent, there are some functions and focuses that differ completely between the two tutorials. So, save yourself a lot of wasted time trying to translate the older version to the modern functions of 10.1.

2) “You will need approximately 90 minutes of focused time to complete the tutorial. Alternatively, you can perform the exercises in sequence one at a time, saving your results along the way when recommended.” The background information about the tutorial suggested that it would take merely 90 minutes to complete. Having spent much longer completing the tutorial, I can say that this might be overly optimistic for a GIS student. Perhaps in ideal circumstances, with high powered computers, and ample previous knowledge/experience with the Spatial Analyst tool bar, a 90 minute completion time would be true. So, unless you have those qualities, I suggest that you budget a longer period of time to complete the tutorial.

3) It is important to check and understand the errors that can slow down the completion of the tutorial. The tutorial does a great job about mentioning trouble areas and giving suggestions or hints, however I found that many places online have forums and blogs about how to fix errors that also arise. For example, if you try to save a raster file with more than 12 letters or with spaces from ArcGIS, the function will automatically not work until you shorten the name or remove the space.

4) It is important to read the steps carefully while completing the tutorial. For example, I accidentally chose Euclidean Direction instead of Euclidean Distance in my model, and this resulted in a skewed output that was not similar to the one pictured in the tutorial. After rereading the tutorial, and the components of my Model, I was able to identify my error. 

5) It is helpful to rename data so that you can adequately view it in the weighted overlay tables. While the tutorial has the ‘hint’ that you can put your curser over the area to read the name, this did not occur on my computer. 

6) One aspect of Model Builder that I found to be slightly difficult to get used to was having to check many functions at the same time. When I chose incorrect/flawed inputs in Model Builder, I found that it was more difficult to find the problem in the Model Builder suite versus if I had completed each function on its own and could easily see the error as it was calculated. Perhaps this is something that I would just become more comfortable with after working with Model Builder more often.

7) It is important, like with any computer work, that you save your files often, and ArcGIS is not an exception to this. I most likely spent half of the time completing the Spatial Analyst Tutorial with ‘Not responding’, which would prolong the completion of the tutorial. Additionally, if I chose incorrect inputs, sometimes I would be faced with random quits that could have wiped out my data if I hadn't saved the project.

8) Just after working with Model Builder for a few hours, it is clear that it definitely reduces time spent on processing large amounts of data, assuming all of your inputs are correct. I am excited to explore this further. 

Of course, it is rewarding to finish the tutorial and have a suggested location for the new school. This tutorial makes it apparent that working with ArcGIS or similar suites gets easier with time and familiarity.  I found that I was already more comfortable using the program by the time it took to complete the tutorial itself. 

Notes: Hirtle & Rencz (2003)

This paper is the earliest (circa 2003) of my collection that will be used for my paper that will be handed in for April exploring the implementation of satellite imagery and GIS programs in research for analyzing Dissolved Organic Carbon (DOC).  For this reason, I will post the important aspects that I believe will be helpful for my paper. I have also included the abstract, and the proper APA citation for your viewing pleasure. All of the citations can be found in the reference section of Hirtle & Renez 2003.

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Hirtle, H., & Rencz, A. (2003). The relation between spectral reflectance and dissolved organic carbon in lake water: Kejimkujik National Park, Nova Scotia, Canada. International Journal of Remote Sensing, 24(5), 953-967. doi: 10.1080/01431160210154957

Abstract. The ability to predict dissolved organic carbon (DOC) concentrations based on spectral reflectance of lake water was examined in Kejimkujik National Park. Spectral reflectance from both ground and satellite remote sensing platforms were used to create regression models for the prediction of DOC with r² values of 0.94 and 0.72 respectively. The location of the peak wavelength of the ground spectral measurements and a cluster analysis of the satellite measurements both separated the lakes into two distinct groups with different DOC concentrations. An analysis of the potential sources of DOC identified three variables important for the prediction of DOC concentrations within the lake, flushing rate and the area of both deciduous forest and open area within the watershed (r² =0.41). As DOC concentrations are related to mercury concentrations (r²=0.86) these models could be used to assist in the identification of lakes that are sensitive to mercury pollution (Hirtle & Renez 2003). 
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This paper examines the high concentration of mercury found within the lakes in the Kejimkujik Park in Nova Scotia. Specifically, the paper tries to determine the possibility of predicting DOC in a lake using remote sensing procedures. Firstly, the project aimed to predict DOC using water spectra from the ground and correlates them with the spectra given from the satellite remote sensing images.  Secondly, the project aimed to correlate potential sources of DOC to predict the concentration within the lake.

A relationship between mercury concentrations and DOC has been found, as the existence of DOC in a lake results in increased mercury in the water system over time.  The relationship between mercury and dissolved organic matter has been examined both within park lakes, as well as in other places around the world (examine study done by Clair et al. 1998). This is an important correlation to make, as examining DOC concentrations for early identification of mercury pollution could prevent harmful damage of the water columns.

For background information on DOC and what causes the dark colour in lakes, we can look towards the Hirtle & Renez paper:

"DOC is the fraction of total organic carbon in water that passes through a 0.45mm pore-diameter filter (Wetzel 1983). DOC concentrations in lakes generally range from 0.5–30ppm (Driscoll et al. 1995, Watras et al. 1995, Cai et al. 1999) but can reach 30–50ppm in wetlands and marshes (Choi et al. 1998). It is a mixture of simple substances such as sugars, fatty acids and alkanes, and of complex polymeric molecules. These have a wide range of molecular weights and are often referred to as humic acids. DOC also contains fulvic acids, which are water-soluble, natural organic substances of low molecular weight. Fulvic acids are chelating agents that can bind and hold metal ions in solution (Driscoll et al. 1995, Gergel et al. 1999). Tannins are a type of fulvic acid, which consist of any group of phenolic compounds that are produced by metabolism in trees and plants and are part of the degradation- resistant materials formed during the decomposition of vegetation. Tannins are found in any water where large quantities of vegetation have decayed and can impart a faintly yellowish to brown colour to the water.

DOC is produced both autochthonously (within the water column) and alloch- thonously (from terrestrial sources) (Hessen and Tranvik 1998). Autochthonous DOC is often colourless, composed of humic acids, and breaks down to carbon dioxide within 48 hours (Gergel et al. 1999). Allochthonous DOC has humic and fulvic acid components, which are products of the degradation of vegetative lignin and cellulose. Fulvic acids are more stable than humic acids and can remain in circulation for months to years. These sources are responsible for imparting a stained colour to the lake water (Wetzel 1983)." (Hirtle & Renez 2003).

Obviously, the differing colors due to the DOC deposits in the lake impact the optical properties of the water in comparison with clear, unstained lake water (Bukata and Jerome 1997).  In optical terms, the color impacts the scattering and absorption of light, which would in turn impact the spectral signature that is given off by the lake water. Specifically, DOC absorbs light across the UV (280–400nm) and visible (400–700nm) portions of the electromagnetic spectrum, which reduces the potential for reflectance (Witte et al. 1982). Light absorption decreases with increasing wavelength no matter the concentration of DOC (Schindler et al. 1992). The high relative reflectance in the red and yellow wavelengths leaves the water appearing stained (Wetzel 1983).

So, in the end, this paper finds that the two ground spectral measures provide two useful methods of analysis are useful for the identification of DOC concentrations in lakes. The first method (predict DOC using water spectra from the ground and correlates them with the spectra given from the satellite remote sensing images) required little analysis, and separated the lakes examined into two DOC classes including ‘low’ (with DOC concentrations of 2.8 to 4.4 ppm) and ‘high’ (with DOC from 4.6 to 9.2 ppm). In turn, separating the lakes into two classes could actually more easily identify certain lakes with a greater vulnerability for Hg accumulation, and in turn reduce the amount of field sampling. Additionally, the second method (correlate potential sources of DOC to predict the concentration within the lake using spectral data), created a regression equation with a strong r² value, which demonstrates how it is a suitable predictor for DOC concentrations in the  Kejimkujik Park area with DOC concentrations ranging from 2.9 to 13.1 ppm. The authors also propose that this data, and/or models could be useful in other regions that share similar ranges of DOC concentrations.

The Cluster Analysis groups the two lakes into classes with the similar peak wavelength locations in the spectral signature. Results were found to be the same for both raw and corrected data, which indicates that using satellite data is as simple and useful as using ground measurements. I found this especially interesting about the results from this project was that predictive ability was decreased with atmospheric corrected data. This would be interesting to explore (or read other studies that explore this), as corrected data are standard for temporal analysis in the remote sensing analysis realm. However, atmospheric correction algorithms can be costly and easily influenced by human error when using the wrong inputs. In this case, two regression equations were used, but the one that used the raw image data equation had the highest r² value (0.72) in this scenario and would therefore give the best prediction of DOC concentrations. In this case, the predictive ability of the regression line is less strong, however doesn’t rely on field work which makes it easier to work with.

The final, and least strong relationship used land cover classifications to predict DOC concentrations in the lakes in Kejimkujik Park.  In this case, the data’s regression equation had an r² equal to 0.41. The authors note that although showing the least strong relationship, this method helped to identify the terrestrial sourced DOC. In this case, three variables were found to impact the DOC concentrations the most, being the flushing rate, the area of deciduous forest, and the open area. 

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