TERRESTRIAL APPLICATION OF THE PHYCOCYANIN CONTENT ALGORITHM

Lee Marston Bartholomew

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

December 2010

Committee:

Enrique Gomezdelcampo, Advisor

Robert Vincent

Rex Lowe ii

ABSTRACT

Enrique Gomezdelcampo, Advisor

The Phycocyanin Content algorithm (PCY) was used to quantify cyanobacteria pigments on land. The PYC was originally developed to quantify blooms of the toxic cyanobacteria, Microcystis aeruginosa, in Lake Erie. However, there were large, unexplained highlighted areas on land in the Lake Erie PYC images. The PYC algorithm, when applied on land, is negatively affected by the averaging effect on each pixel of vegetation and minerals. Filters to reduce this image noise on a terrestrial setting were developed by using a conceptual idealized dataset to establish the hypothesized relationship between chlorophyll and phycocyanin. The ratio of LANDSAT TM bands

4/3 was used as a vegetation filter and the 3/2 ratio was used to lessen the effects of iron oxides on the application on land of the PYC.

The PYC performed successfully in areas of very low vegetation as the vegetation and mineral filters worked as designed. However, further calibration is necessary for this algorithm to function as a quantification tool. The mineral filter was based on the iron oxide mineral as it is the prevalent mineral in the region. Unfortunately, the iron oxide filter also detects areas of senescent vegetation and inversely indicates areas with green vegetation, complicating the performance of PYC in these areas. iii

To my lovely wife, Kristen for her never-ending support and undying love and my kids,

Carson, Mara and Kale for their fascination with the world. iv

ACKNOWLEDGMENTS

A special thanks to Dr. Robert K. Vincent, for his ingenuity and willingness

to humor my creative whims.

To Dr. Enrique Gomezdelcampo for guiding me back to center

whenever I got out on a limb.

To Dr. Rex Lowe for the assistance with species identification.

To Dr. Benjamin Beal for the unquantifiable volumes of guidance in the laboratory.

To Gail Nader for her editing skills.

And

To all my good friends at Bowling Green State and Weber State Universities without

whom this research would have never been undertaken. v

TABLE OF CONTENTS

Page

CHAPTER I. INTRODUCTION...... 1

CHAPTER II. LITERATURE REVIEW...... 4

CHAPTER III. MATERIALS AND METHODS...... 8

Exploratory Data Analysis...... 8

Mineral Influences...... 8

Vegetative Influences ...... 9

Study Site: Algodones Wilderness Area...... 9

Spectral Influences...... 11

Image Processing...... 12

PYC Additive Filter Modifications...... 13

Target Organism...... 15

Sampling Method...... 16

CHAPTER IV. RESULTS AND DISCUSSION...... 20

Exploratory Data Analysis...... 20

Mineral Influences...... 20

Vegetative Influences...... 22

Sampling Results...... 24

Phycocyanin Quantification...... 25

CHAPTER V. CONCLUSIONS...... 28

REFERENCES...... 30 vi

LIST OF FIGURES

FIGURE Page

1 PYC applied to Lake Erie and the surrounding area...... 34

2 Conceptual idealized data representing the relationship between phycocyanin

and chlorophyll and the way they are represented by the PYC...... 35

3 Scattergram of the 03/05/10 Landsat7 ETM+ image comparing the PYC to the

NDVI...... 36

4 Location of the Algodones Wilderness Area within Imperial County, California.

…...... 37

5 Locations of the sampling sites within the Algodones Wilderness Area...... 38

6 Senescent vegetation and a carbonate platform which formed in a depression

between dunes...... 39

7 View from the top of the dunes looking southwest over the East Mesa...... 40

8 Desert woodland community at the base of the eastern fore erg of the dune.....41

9 Moderately developed cyanobacterial BSC...... 42

10 Developed Biological Soil Crust...... 43

11 PYC and vegetation ratio images...... 44

12 Matrix of LANDSAT TM Satellite overpass images versus the PYC and applied

filters...... 45

13 Matrix of scatterplots, regression lines and error bars for each of the sample

datasets...... 46

14 False color temporal composite of the PYC...... 47 vii

LIST OF TABLES

TABLE Page

1 Mineral influences on algorithms and ratios by major minerals and materials.. 48

2 Values for each pixel corresponding to the sample location when processed

using the PYC, Vegetation (4:3) ratio and the Iron Oxide (3:4) ratio...... 52

3 Reported rms error and regression for data collected ±2hr of the satellite

overpass...... 53 1 CHAPTER I. Introduction

The Phycocyanin Content algorithm (PYC) is currently used by Blue Water

Satellite, Inc. under license from Bowling Green State University, the patent owner, to determine concentrations of cyanobacteria in large freshwater bodies, particularly in Lake

Erie, where the data for the development of the algorithm was obtained (Vincent et al.,

2004). The PYC algorithm uses LANDSAT 7 Enhanced Thematic Mapper (ETM+) satellite data and process them to create pseudocolor images where every pixel located in water that appears red contains between 5 and 12 μg/L of phycocyanin and every dark blue pixel in the pseudocolor image will have a concentration below detection limits of phycocyanin (Vincent et al., 2004). In Figure 1, LANDSAT data processed with the PYC shows highlighted sites in bodies of water with phycocyanin present, but it also shows some highlighted sites on land, depending on season, and land covering vegetation.

Terrestrial application of the PYC is well beyond the scope of the intended use of the current PYC, as it is applied to a wide variety of backgrounds, including a variety of soil substrates (mineral, chemical and moisture differences), and differences in vegetation cover. This algorithm was developed to be used in fresh water, which has distinct spectral properties vastly different from minerals on land. Theoretically, because of the wide distribution of cyanobacteria on land, the PYC may be applicable in many different types of terrestrial environments, including: farmland, sand dunes, cold deserts, hot deserts, alpine tundra, and areas affected by recent wildfires, among others.

Cyanobacteria are photosynthetic bacteria for which the PYC was designed to locate in fresh water. They inhabit a wide variety of photic zones, aquatic and terrestrial environments. They also vary in their morphology and functions as much as their 2 habitats. Cyanobacteria utilize a variety of pigments for photosynthesis, some of which including chlorophyll a, are used by other organisms. The pigment targeted by the PYC is phycocyanin, a phycobiliprotien which appears blue-green in natural reflected light, but fluoresces red when viewed at a 90 degree angle from the light source (Rowan, 1989). It is this blue-green reflective and the additive red fluorescence that gives phycocyanin the unique spectral properties which make it distinguishable from other minerals or vegetation pigments in a LANDSAT processed image.

Blooms of some species of cyanobacteria in water have been linked with health issues resulting in sickness and death in humans and animals (Carmichael, 1997). Blooms in water also are linked to “dead zones” which are regions of benthic hypoxia caused by the sudden increase in nutrients and food energy to benthic macro and micro organisms which feast on the deluge until they consume the biologically available dissolved oxygen in respiration functions (Raven et al., 1976). Despite all this, we have the cyanobacteria to thank for the initial oxygenation of our early atmosphere and the organisms continue to provide a significant volume of biologically available oxygen globally. (Raven et. al.,

1976).

On land, the cyanobacteria have been praised for their soil stabilization, The formation of many types of Biological Soil Crusts (BSC) are dependent on the establishment of cyanobacteria as primary producers and soil stabilizers (Belnap et al.,

2001). These soil crusts in all of their stages are extremely fragile and in some cases can take many years to fully recover from a simple footprint. It has been recently suggested that exposure to some types of land based cyanobacteria through inhalation of dust can contribute to increased levels of risk of developing ALS, Alzheimer's dementia symptoms 3 or Parkinson's disease (Cox et al., 2009). Understanding the distribution of land based cyanobacteria and their temporal blooms will greatly enable scientists to better understand the relationships between cyanobacteria and the rest of the environment around them. Particularly how humans influence their growth and death spatially and how they influence positively or negatively on the health of exposed individuals of our species.

This knowledge could also be applied in order to map environmental conditions and trends temporally, including a variety of land use changes and pollution effects on

BSC. The search for bacterial life on other planets may also better progress with the refined use of multi-spectral satellite data and the use of organic pigment sensing algorithms similar to the PYC.

The purpose of this study is to determine the reason for the land-based red highlighted areas in the PYC images. The highlighted pixels on land may represent concentrations of phycocyanin pigment on land. 4 Chapter II. Literature Review

The PYC was created using a modeling concept based on a Monte Carlo-type trial and error step wise linear regression method developed by Vincent (2000) as a way to simplify long term weather forecasts based on sea surface temperature anomalies. The

PYC was created using a set of 30 data samples with a range of phycocyanin content values ranging from 0.857 to 4.866 μg/L. The algorithm was developed as a tool for mapping cyanobacteria blooms in fresh water. Many remote sensing algorithms have been developed to ultimately map the cyanobacteria in water. Mishra et al., (2009) employed airborne hyperspectral sensing, which is costly and data intensive. Their development methods attempted to deal with the spectral relationships between the different pigments that are encountered in these types of organisms and ecosystems.

While model building from this deterministic approach is certainly admirable, it is somewhat shortsighted to consider that all of the variables have been accounted for in even the most complex of models. Data fitting models, like the one used to develop the

PYC have the ability to simply tell you how your target varies in space or time without telling you or needing your input on how and why. As these models are validated they can become just as robust if not more than those created through either deterministic or empirically derived approaches.

Mishra et. al., (2009) inferred that using empirically derived algorithms ignore the influencing variables, particularly the influence of chlorophyll a on the spectra of phycocyanin and the variability in the ratio of chlorophyll to phycocyanin within the cyanobacteria as a response to environmental conditions and depending on species. This is a valid point, however, if a large dataset could be gathered over time which included 5 pixel values of many of the possible combinations of phycocyanin, chlorophyll, and other pigments found in nature then the representative data would allow for a more robust algorithm using the regression model building methods of Vincent et al., (2000).

While many algorithms have been developed to ultimately map the cyanobacteria in water (Mishra et al., 2009; Dekker 1993; Schalles and Yacobi 2000; Simis et al. 2005; etc...), very few have attempted the task on land, The variability of substrates and influences from vegetation and moisture make this a difficult prospect from the standpoint of deterministic and empirical modelers. While there may be reasons for or against the use of particular band ratios in model building, in the end it is a matter of how closely the withheld or validation data matches the model in validation after validation, whether or not we fully understand every parameter.

There are very few models or algorithms created for detecting terrestrial phycocyanin. Those that do exist focus on mapping the extent of Biological Soil Crusts,

(BSC) of which cyanobacteria can be a primary component. Cyanobacteria contain the unique photosynthetic pigment, phycocyanin (Rowan, 1989). In addition to the cyanobacteria, phycocyanin is only known to occur as an accessory pigment in two eukaryotic groups, the Rhodophyta and the Cryptophyta. In the Rhodophyta, the phycocyanin in one case has been shown to originate as a symbiosis of an endophytic cyanobacterium living inside the algae (Hoffman et al., 2005). In a hyperspectral study by Clark et al. (1990), black lichen, black soil and rock films also had the same peaks in the spectral reflection as the BSC in the study area. These black lichens, Collema spp., contain cyanobacteria and the soil and rock films may also be instances of cyanobacterial colonization. Like the Misra et. al., (2009) study, Clark et al. used airborne hyperspectral 6 sensors which are expensive and not practical for monitoring changes over time, particularly in the time interval and over such broad areas that LANDSAT data are collected.

BSCs are an important component to soil stability in virtually every environment on the surface of the Earth. They are primary colonizers in disturbed areas, including recent wildfire landscapes (Bowker et al., 2004), eolian dunes (Levin et al., 2007), over grazed regions (Muscha and Hild, 2006), pro-glacial succession (Breen and Levesque,

2006). Cyanobacteria have even been detected in cloud formations (Bowers et al., 2009)

They have been studied for their spectral properties, primarily using field and lab spectrometers and Airborne Visible IR Imaging Spectrometer (AVRIS) data (Ustin et al.,

2009; Clark et al., 1990). Developing soil crust in the middle stages of cyanobacteria colonization can be seen in Figure 9. Figure 10 illustrates a well developed BSC, removed as a plug for sampling.

The unique spectral properties of cyanobacteria have led to the development of many algorithms to analyze remote sensing satellite data in order to quantify their occurrence, condition, and trend in different ecosystems. Visually, in the field on land, cyanobacteria colonies appear blackish. They can be located easily in their colonial and symbiotic forms and can be quantified in the early development of BSCs, before mosses and lichen become established using relatively simple spectral indices (Belnap et al.,

2001). One of the simple algorithms used in the past for mapping changes in soil crust is the Brightness Index (BI) algorithm, which is a combination of the red, green and near infra-red bands. Also, the the Normalized Difference Vegetation Index (NDVI) algorithm has been shown to be useful in identifying the mature BSC's (Zaady et al., 2007). It uses 7 only the near infrared (LANDSAT band 4) and the Red (LANDSAT band 3). The ratio of

LANDSAT TM bands 4/3 is used commonly for vegetation analysis and produces results analogous to those presented by the NDVI. The NDVI contains a total range of -1 to 1, but is normalized to use the threshold of 0 as the lower limit and 1 as the upper limit of the vegetation influences. Because the 4/3 is not normalized to a range, it simply gives values between 0 and 2 including all of the possible influences of vegetation, even in small quantities. In the Zaady et al. (2007) study, plots were disturbed then the spectral reflection was taken in temporally spaced images and the recolonization was observed.

The BI algorithm worked well in the early part of the growth, but the NDVI worked better for the latter. While the BI and the NDVI algorithms have been used with

LANDSAT data (Zaady et al., 2007), they are not able to discriminate between other causes of brightness like minerals and other types of vegetation.

To this point much has been done, but not on a practical level, where low cost monitoring and proven results time and time again can be achieved. If the PYC or modifications of it can be used to map one facet of the activity and distribution of cyanobacteria on land (phycocyanin reflectance) then it becomes a tool which can be useful to researchers worldwide to locate areas for research in everything from epidemiological studies to understanding the temporal and spatial changes in populations. 8 Chapter III. Materials and Methods

Exploratory Data Analysis

Exploratory data analysis was performed in order to determine the relationships between the PYC and other potential algorithm influences. Most algorithms, including the PYC, are threshold-based. Thus, there is always other information present other than the information you would like to display. It is important to understand these influencing factors and attempt to quantify their interactions in an effort to either create thresholds to remove them or additions to the algorithm to filter them out. The hypothesis that the PYC can be utilized to determine concentrations of phycocyanin on land makes the assumption that all influencing factors are taken out in the existing threshold, or can be removed by an additive filter.

First, a visual assessment of was made of images over many frames in many locations, including mountains and deserts in Utah, Arizona, New Mexico and California, where BSC and other land based cyanobacteria colonies are known to form without vegetation cover (Belnap et al., 2001). These areas provided the opportunity to observe the gross influences of vegetation and surface mineralogy on the PYC.

Mineral Influences

Second, in order to quantify the mineral and vegetative influences which had been observed, a list of common minerals, vegetation and their spectra was compiled from the

USGS spectral reflectance data library (Clark et al., 2007), averaging the spectra over the bandwidths observed by the Landsat TM satellites, similar to the methods used by Perry and Vincent, (2009). In Table 1, the PYC, NDVI, the Iron Oxide ratio (IOR) (LANDSAT bands 3/2) and the Vegetation Ratio (VR)(LANDSAT bands 4/3) were mathematically 9 applied to this data to mimic what values would return given reflectance of a pixel containing 100% of the mineral or vegetation.

Vegetative Influences

Finally, due to the extreme influences of vegetation observed in the first two steps of the exploratory data analysis, a vegetation analysis was performed on each image using the NDVI, PYC and VR. The NDVI and VR were used synonymously as they both return essentially the same information, only the normalization is different. Locations were observed using both separate pseudocolor, like the images in Figure 11, and merged false color images, with the PYC displayed as 0–255 values of red and the NDVI or VR displayed as 0-255 values of green. The vegetation analysis consisted of a series of scattergrams like the one in Figure 3, of all the pixels in the PYC and NDVI. These scattergrams were created for 15 different image frames to better understand the relationship and rational curve was fit to the data and the correlations calculated.

It was determined from the exploratory data analysis that a study site needed to be selected provide a laboratory where ground-truthing could occur. Ideally an area with a variety of minerals and vegetation cover in large enough areas to be observed by 30- meter pixels without interference from objects on the pixel edge. Public access to the area must be permitted, and sampling of surface materials restricted but permitted. Human influences must be at a minimum for the duration of the sampling period. Mineral influences should be relatively constant spatially or have data available as to the spatial variability and composition of surface minerals.

Study Site

A study site was chosen based on the known influences of the algorithm, and a 10 need for a study area which is likely to be cloud free and in an overlap area between two paths that the L5 and L7 satellites travel.

The Algodones Wilderness Area (AWA) in the Imperial Sand Dunes is a system of

Eolian dunes in Imperial County, California. The location of the study area is shown in

Figure 4. It is located in the satellite overlap region shared by both WRS-2 Path 39 and path 38 in row 37. The area is relatively cloud free, and has large areas of native vegetation, dunes, and a variety of bedrock and agriculture. The AWA is managed by the

Bureau of Land Management (BLM), with local offices in El Centro, California.

Permission was received from the BLM for minimal sampling with restrictions from the field office in El Centro.

The AWA comprises an area of 25,895 acres. It is situated directly north of the busiest section for off-highway vehicle OHV use in the Imperial Dunes Recreation area.

The Ben Hulse Highway divides the two regions (Wright, 1999). There are stark vegetation differences between the two regions, and it is evident that disturbance by humans is rare in the wilderness area (Luckenbach and Bury, 1983). Locations of the sampling sites can be viewed in Figure 5.

Vegetation communities include psammophytic (sand loving) plants throughout the dunes, as well as shrub communities on either side of the dunes within the wilderness area. Psammophytic communities tend to cluster in lower areas and grow upwards as the sand inundates the area as shown in Figure 6. Plant cover within the shrub communities can range from 0–80% as shown in Figure 7. There are also desert woodland communities, shown in Figure 8 which form on the east side of the dunes as water from the dunes drains into the colluvium. Vegetation cover in these communities ranges from 11 30–100% (Luckenbach and Bury, 1983).

The AWA is located just southeast of the modern Salton Sea,an inland brine sea, created due to a breached dike in the Imperial Valley in 1905. It covers a small portion of the intermittent extent of the Pleistocene and Holocene Lake Cahuilla. Lake Cahuilla formed when the Colorado River diverted from flowing south into the Gulf of California to flowing north into the modern Imperial Valley due to a tectonic settling or tilting that occurred in the region (Waters 1983). Colorado River flooding events have also resulted in water being diverted from reaching the Gulf of California. The most recent of these flooding events occurred in 1905–1906, and there is record of it happening in the 1800s as well (Waters, 1983). The composition of the dunes suggests that they were formed from sand deposited by the Colorado River into Lake Cahuilla. Longshore drift transported the modern dune sand from the mouth of the Colorado to the southeastern shores of Lake Cahuilla, where it washed up on shore and was blown by the prevailing winds to form the modern dunes (Winspear and Pye, 1995).

The dunes are currently migrating in an easterly direction with the grain size and composition variations gradational between locations. Sand is not sourced from the surrounding partially vegetated bench to the west or the colluvium to the east, on which the dunes lie. The exception of this is on the ramp on the western side, where grains from the East Mesa are moving up the ramp by saltation (Winspear and Pye, 1995).

Spectral Influences

Mineral and vegetation spectral influences were calculated from the theoretical model shown in Figure 2 and the mineral values found in Table 1. Filtering corrections for the influences applied to the PYC. There are many psammophytic (sand loving) plants 12 throughout the AWA (Luckenbach and Bury, 1983). These plants may have an averaging effect on the spectra recorded in each pixel as they may occupy most of the area in a pixel.

Minerals can have a profound influence on algorithms. The Mineralogy of the

AWA was reported by Winspear and Pye (1995). They determined the mean non-opaque mineral composition of the dunes at Algodones to be quartz (74.8%), K-feldspar

(2.44%), plagioclase (15.79%) and volcanics (2.02%) with 2.15% lost on ignition. It was assumed that the remainder were the omitted opaques, assumed to be iron oxides, likely goethite.

In addition to the mineral and spectral influences, the effects of shade and slope on the Digital Number (DN) of each pixel can also be significant. By using spectral ratios, the effects of both shade and slope (and sun glint on specular surfaces) can be negated (Vincent, 1997). The effects of atmospheric haze on the algorithm are diminished by the implementation of dark object subtraction for band i (DOi). The DOi is one less than the minimum digital number value for each band, which is identified as the darkest pixel for each band within the image. If atmospheric haze was a constant, then correction could be built into the PYC as a value or multiplicative factor on ratios within the algorithm. To correct for differences in atmospheric haze from image to image, the DOi for each image is subtracted from each pixel before the algorithm can be implemented.

Subtracting the DOi also corrects for additive electronic offset and multiplicative electronic gain created by satellite sensor processing (Vincent, 1997).

Image Processing

Remote sensing images from LANDSAT 7 Enhanced Thematic Mapper+ SLC-off 13 (L7) and LANDSAT 5 Thematic Mapper (L5) satellite data were used in this study. The remote sensing imagery was processed using ERMapper v. 7.0 software (ERDAS, 2007).

The algorithm is designed to model phycocyanin content in μg/L with the dark background of water (Vincent et al., 2004) and is shown in equation (1).

PC = 47.7 – 9.21(R3:1)+ 29.7(R4:1) – 118(R4:3) - 6.81(R5:3) + 41.9(R7:3) – 14.7(R7:4) (1)

Where:

PC= Phycocyanin Content in micrograms/liter (μg/l) R3:1=Ratio of dark object subtracted LANDSAT TM band 3 to band 1. R4:1=Ratio of dark object subtracted LANDSAT TM band 4 to band 1. R4:3=Ratio of dark object subtracted LANDSAT TM band 4 to band 3. R5:3=Ratio of dark object subtracted LANDSAT TM band 5 to band 3. R7:3=Ratio of dark object subtracted LANDSAT TM band 7 to band 3. R7:4=Ratio of dark object subtracted LANDSAT TM band 7 to band 4. L5 images contain 7 spectral bands while L7 satellite images contain 8 bands, but only bands 1, 3 ,4 ,5 and 7 from both satellites are used in the PYC. Band 6 was not utilized because it is in the thermal region and thus deals with emissions rather than reflectance of light.

PYC Additive Filter Modifications

An additive algorithm was created in order to decrease the negative averaging influence of vegetation on the PYC algorithm. This was accomplished by combining the

PYC and the dark object corrected VR. This filter was created by using a conceptual idealized database of values to represent the qualitative observations made while performing the exploratory data analysis. Values were normalized in Figure 2 for display purposes. The model included a suspected looping back effect in the PYC that has been observed at values higher than 12 by other users of the algorithm (Vincent, personal 14 communication). The additive filtering model is represented mathematically by:

Pmin PC=2PV −Vmin∗  Vmax−Vmin (2)

Where: PC = Phycocyanin content in μg/m2

P = Pixel values from the PYC algorithm

V = Dark object corrected vegetation values from either the VR (4:3 Ratio) or the

NDVI.

Pmin = Minimum Value of the PYC over the entire LANDSAT TM image.

Vmin = Minimum value of the VR over the entire LANDSAT TM image.

Vmax= Maximum value of VR over the entire LANDSAT TM image

This model, called the Vegetation filtered PYC (VPYC), assumes that within the image there is at least one pixel of very dense vegetation, and at least one pixel of very low or no vegetation.

A threshold-based mask sourced from either the NDVI or the VR was created in order to create a limit to the vegetative influences, since at some point vegetative cover would approach100% and effectively block the satellite from imaging the ground surface.

It could be argued that the mask could be omitted by inferring that underneath the vegetation cover is an ideal location for some types of cyanobacteria at high quantities.

The mineral influences on the PYC were addressed by creating a filter which uses the Iron Oxide (IO) ratio of LANDSAT bands 3/2. Influences from opaque iron oxide minerals in the sand as well as iron oxide coatings on the grains of other minerals may influence the algorithm. The IOPYC filter is given as Equation 3:

PC=100∗ P−100∗S −1.6 (3)

Where: PC = Phycocyanin content in μg/m2 15 P = Pixel values from the PYC algorithm

V =Dark object corrected vegetation values from the VR (4/3).

S = Dark object corrected values from the IO (Iron Oxide) 3/2 Ratio.

The two filters were then combined into a single normalized algorithm. This algorithm was termed the Vegetation and Iron oxide filtered PYC (VIPYC). The iron oxide ratio was normalized and divided by the normalized vegetation term and multiplied by the PYC then simplified to Equation 4 where the Pmin value divided out in simplification.

V −VminSmax−Smin PC=100∗P (4) S −SminVmax−Vmin

Where: PC = Phycocyanin content in μg/m2

P = Pixel values from the PYC algorithm

V =Dark object corrected vegetation values from the VR.

S = Dark object corrected values from the IO (Iron Oxide) 3:2 Ratio.

Vmin = Minimum value of the VR over the entire LANDSAT TM image.

Vmax= Maximum value of the VR over the entire LANDSAT TM image.

Smin = Minimum value of the IO over the entire LANDSAT TM image.

Smax =Maximum value of IO over the entire LANDSAT TM image.

The Pmin value divided out and the equation was simplified.

Target Organism

The target organism in the dunes is described as Microcoleus vaginatus (Vaucher)

Gomont ex Gomont (Seigesmund et al., 2008). It is not only the dominant soil

cyanobacterium in the region, it dominates both the biological soil crusts and the eolian

cyanobacteria within the dune systems. It is ubiquitous not only in the region but nearly 16 world-wide. It belongs to the family Oscillatoriaceae, due to the cell division and thylakoid arrangement (Hoffmann et al 2005). Microcoleus vaginatus is a very large (1–

5mm in length) filamentous organism which reproduces in sheathes, and contains no nitrogen-fixing heterocysts. Non-respiratory or photosynthetic byproducts of the organisms are insoluble polysaccharides from the expended sheathes (Boyer et al, 2002).

While it is common to think of the organisms as forming in the depressions or moisture traps, it is a more accurate description that the organisms are ubiquitous. They do vary greatly, however in quantity and level of photosynthetic and respiratory activity based on moisture, temperature and sunlight conditions (Belnap et al, 2001).

Sampling Methods

Sample transects were chosen to cover three different areas within the AWA. One transect covered an area which consisted of low vegetation cover, one of intermediate vegetation cover, and one with a mixture of high to low vegetation.

In the first (low vegetation) area, four sample locations with four replicates per location were collected for the 03/04/10 L5 image. These samples were collected in the eastern side of the dunes, where PYC values are highest within the dunes. Vegetation is low and the sand grain size is somewhat finer.

The second (intermediate vegetation cover) area samples corresponded with the

03/05/10 L7 image. There were 6 samples with 4 replicates for 5 of the samples and 12 replicates for 1 sample. These samples were taken in a linear transect up the center of the dunes. Vegetation was more sporadic but stronger bunched in groups of shrubs. The occurrence of BSC communities on inter dune fine particle carbonate sediment platform areas is also scattered. Samples were gathered from a variety of intermediately vegetated 17 and non-vegetated areas.

The final sample date 03/06/10 had 7 samples with 4 replicates taken over a wide range of environments including bare dune, partially vegetated dune ramp and bench scrub communities with dense, well developed biological soil crusts. These samples are divided between images which they were collected closest to in order to best represent the phycocyanin levels as they were at the time of satellite overpass.

Spatial autocorrelation of PYC imagery was performed and sampling interval of

>250 meters was employed, unless obvious changes in vegetation, mineral or BSC density was noticed. Sample sites were selected in areas where the cover of vegetation and noticeable cyanobacterial colonization were consistent for a 30 m radius to ensure that sampling was representative. Samples were collected from estimated centroid locations within dune communities where applicable. Samples taken are assumed to be representative based on field observations.

Three sets of samples were collected to coincide with LANDSAT overpasses. The first set consisted of four sample locations and four replicates per sample collected within 2 hours prior to and 3 hours after the L5 Path 39 Row 37 overpass on March 4,

2010 . The second set consisted of seven samples with four replicates per sample collected within 2 hours of the L7 Path 38 Row37 satellite overpass on March 5, 2010 .

The third set consisted of 4 sample locations and 4 replicates per location collected on

March 6, 2010, which was the closest date to the March 12, 2010 L7 Path 39 Row 37 overpass. Additional samples were collected, but were excluded from this study because sample locations fell within the L7 SLC-Off voids in the images. These voids appear as stripes of null data in the image and can be observed in Figure 1 and Figure 14. 18 To collect the samples a surface scrape was made of the top surface of the soil and an area10mm x 85mm of the substrate was removed using a flat piece of polycarbonate plastic sharpened at one end and an inverted open petri dish. The method could best be compared to collecting samples using a petri dish as a cookie cutter, then sliding the polycarbonate blade through the sediment at the base of the petri dish in order to lift the sample from the ground surface. The samples were placed in a sealed plastic bag and frozen in the field using dry ice. They were hand carried frozen to the lab, and kept frozen and dark until testing was completed. Ice baths were employed where thawed conditions were necessary during sample preparation. In order to properly address issues such as growth and/or desiccation of the organisms, no samples were dried.

Phycocyanin Extraction and Quantification

The samples were weighed, and four 2 g replicate samples removed from each sample and placed into centrifuge tubes. 10 ml of 0.5 molar Tris buffer pH7 was added to the samples in the tubes in order to break up the cells and extract the phycocyanin. Tubes were inverted for 30 seconds and re-frozen for three days. The tubes were thawed using a water bath and kept in a covered ice bath. Samples were sonicated for 1 minute on the highest setting “10” then centrifuged for 10 minutes at setting “4”.

To quantify the amount of phycocyanin present in the samples, their fluorescence was measured using a Turner Designs TD700 Fluorometer with a cool white mercury vapor lamp and phycocyanin filter sets ( lEX = 630 nm and lEM = 660 nm) and phycocyanin optical Kit (P/N: 10-305). The phycocyanin is water soluble, and when sonicated free from the cells, it can be detected. The polysaccharide sheathes and broken cells however are not soluble in the Tris solution. They are also less dense than water and stratify at the 19 top in the tube after centrifuging. Consistent pipetting methods were employed to draw the sample from various levels in the tube , therefore reducing the influence of the readings in the fluorometer if only phycocyanin drawn from the top of the centrifuged tube were used. The pipetting method consisted of extracting the top 6 ml of solution , then placing this amount into another tube and invert it for 30 seconds. The fluorometer cuvette was filled with 3 ml drawn from the 6 ml present in the tube. Multioptional raw fluorescence mode was used in order to later convert fluorescence units to μg/L phycocyanin. A solid red standard was used to provide a standard point that can be later quantified, graphed and points extrapolated using the standard linear equation (French et al., 1956; Glazer et al., 1973; Kao et al., 1975; Downes & Hall, 1998; Furuki et al., 2003;

Vincent et al., 2004; Katarzyna et al., 2005; Patel et al., 2005; Simis et al., 2005).

Values obtained from the fluorometer after automatic blank subtraction (baseline) were normalized to the red solid standard. The solid red standard has both a low and a high standard. Given that the red solid standard has corresponding values that equal

Low= 0.05 μg/L and the High= 300.18 μg/L the slope of the line is y=0.3585x+24.181.

This equation is used to extrapolate the PC values in μg/L from the fluorometer units.

Every time the fluorometer is used, or any parameter changed in the instrument, the red standard needs to be retested on the fluorometer and a new slope equation created to fit the data.

As this study is land based and the PYC algorithm was developed for water the phycocyanin concentration values obtained from the fluorometer in μg/L value were converted to μg/m2 to obtain the actual earth surface density of phycocyanin by assuming that the average optical depth of the soil using the average of the bands used in the PYC 20 is ~ 1mm. The conversion for the PYC is calculated by multiplying the phycocyanin concentration by a factor of 100. 21

Chapter IV. Results and Discussion

Exploratory Data Analysis

It was determined that the highlighted areas on land in the PYC images were all areas where cyanobacteria are expected to be found and visible on satellite imagery in relatively large quantities, such as fallow fields, desert areas (biological soil crusts), sand dunes, and wildfire-burned regions. Based on the NDVI and the VR no areas which had significant vegetative cover to mask the ground were highlighted in the PYC. Land areas not highlighted by the PYC include sites that have little cyanobacteria visible to the satellite, typically because of dense vegetation, land use, or substrates not favorable to phycocyanin producing cyanobacterial colonization. They also include areas with a mineral or vegetation composition combination that masks the signal of the phycocyanin by lowering the pixel value enough to remove it from the 0–5 contrast stretch window. It was observed, for example, that salt flats and evaporite basins are not highlighted by the

PYC, and yet they are known locations for ubiquitous cyanobacteria colonization.

Pixels containing high density of vegetation had more negative PYC values.

These pixel values could reach < -5,000 in some extreme cases in agricultural fields. The highest values observed in PYC images are between 12 and 350. The pixels containing these values appear red in the 0–5 transform contrast stretch. The observed influences of vegetation and minerals were divided and filtered separately.

Mineral Influences

The USGS data (Clark et al., 2007) was averaged over the bandwidth of each of the six reflective 30 meter LANDSAT TM satellite bands. The dark object equivalent 22 ratios of these bands were mathematically derived and applied to each of the minerals in

Table 1. These included the ratios of LANDSAT TM bands 3:1, 4:1, 4:3, 5:3, 7:3, and

7:4, which are the ratios used in the PYC. In addition the 3:2 or iron oxide ratio was added. The VR is 4:3 ratio and the NDVI was calculated by using the individual bands.

Riebeckite (270), a sodium silicate end member of the amphibole series, had the highest mineral reflectance value in the PYC. The next higher mineral reflectance was realgar

(158), an arsenic sulfide, followed by other influences which increase the pixel value other than phycocyanin content, including the jarosite series, and many of the iron oxides, including hematite and goethite. Biotite, malachite and tourmaline also had a reflectance value in the PYC. The higher the value of the mineral in Table 1, the higher the averaging power that it possesses to increase the final pixel value . Conversely, the lower the pixel value, the more averaging power it has to lower the PYC. It was determined that most non-iron bearing minerals influences on the PYC typically range in between -80 and 0 as can be seen in Table 1.

By creating weighted averages based on the mineralogical composition of the dunes and the East Mesa area to the west of the dunes as reported by Winspear and Pye

(1995) and mineral values in Table 1, a value of -26.57 was calculated as average influence of the mineral composition on the PYC pixels in the dunes.

It was determined, based on the mineralogical influences within the sand grains, that a value of 26.57 should be added to the PYC in order to compensate for the influence of the sand on the values. It was also determined that the variability of the iron oxides in the study area was also affecting the pixel values in the PYC.

The challenges with the iron oxide ratio was that it also highlighted areas of 23 senescent vegetation. This can be observed in Table 1 under the (grass, senescent) row.

The value of 1.34 is just less than the values of the iron oxides in the table which are between 1.48 and 1.96. Desert plants often go through periods of senescence, this creates false moderately high iron oxide readings in the image when using the VIPYC. Some desert plants may appear almost senescent at times as a way to cope with the hot, dry conditions until wet conditions return. Also, realgar and iron oxides are high in the IO, just like in the PYC, and while that is the intent to diminish the strength of the effect of these minerals on the PYC, it may also diminish the ability of the PYC to discriminate phycocyanin. Once again, the best solution would be to simply gather a large dataset and create a new algorithm.

Vegetative Influences

Table 1, which was used for determining the influences of materials on the ratios and algorithms could not be used for vegetation in the same manner, since vegetation densities change more drastically from pixel to pixel. This is difficult to apply without actual vegetative cover data to remove the effects of vegetation on the PYC pixels. There is a gradation of vegetation cover in the study area from 0–100% vegetative cover. Pixels reading highly negative values are always influenced heavily by vegetation. The most negative values in Table 1 are those representing conifers (-750) . Conifer leaves are rounded and angular and create shadows with different darker values from other vegetation (Vincent, 1997). Closest in numerical value in Table 1 to the conifers is grass

(-605).

In order to understand the mathematical nature of the relationship between negative pixel value observed in vegetated areas with the PYC and vegetation cover, a 24 total image scattergram was created using the PYC and the NDVI on a LANDSAT TM image. There was a strong negative correlation (-0.998), as shown in Figure 3. This correlation value ranged from -0.999 to 0.38 from image to image both spatially and temporally. Fitting a curve to the entire domain of this scattergram yielded a rational equation NDVI = 150/(x-1600), where x= pixel value from the PYC and the NDVI is a function of x with a correlation value of -0.937.

A strong negative correlation of -0.53 to -0.99 was observed in 15 scattergrams comparing the PYC and a dark object corrected version of the NDVI (NDVIDO). When scattergrams were created in 100% water images with known phycocyanin presence the correlation was 0.12 to -0.40. The stronger negative correlation in land images is expected as areas completely covered with vegetation would mask any cyanobacteria on the soil surface. However, in the study site, areas of sparse vegetation had a correlation value range of -0.68 to -0.93 for NDVI and PYC. This strong, but not perfect correlation between land vegetation and the PYC may allow a mathematical additive filter such as

NDVI or VR to remove the averaging effects of vegetation ..

The LANDSAT frame showing the study area contains one location visible in the northwest corner of Figure10 which is of some interest. It is strongly highlighted in the

NDVI, VR, IO and the PYC. This area, visible in the Northeast corner of Figure 11 is within a bombing range and thus was not accessible for sampling. Highlighted is the location of a small isolated mountain peak surrounded by colluvium from the Chocolate

Mountains to the east. While not directly related to the results of the study, it is worthy of pointing out that there are other unknown mineral and vegetation influences on all of the algorithms and ratios. This area in particular is of note, because it contains both the 25 darkest and brightest pixels within the frame for all algorithms and ratios used.

Sampling Results

The largest sources of error within this study came from the assumptions made before and during sample collection. The area was selected for the possibility of 4 satellite overpass images within 9 days. The field freezing method came as a result of a bloom that occurred in one of my previous unfrozen samples that was assumed dry enough not to have significant change in the sample. The assumption that no significant change in the samples would occur over a short period of time was incorrect. Freezing them seemed like a good solution, however, it is suspected that because there are at least two different dominant cyanobacterium that are contributing phycocyanin readings in different samples, that the stronger readings from the sites containing the unknown organism may have higher readings because that organism may bias the results by producing phycocyanin more rapidly after thawing than the Microcoleus vaginatus.

There is some uncertainty as to the validity of the data for one other major reason.

The sample size, which can be closely correlated to changes in pixel value in the PYC, should be those closest temporally to the time of overpass. In Figure 14 all three available cloud free images were overlaid as a blue, green, red image with The L5 March 4th as red,

L7 March 5th as green and L7 March 12th as blue. Areas where there were not a change in value between the three dates was displayed as shades of grey to white. Changes in hue toward red indicate that the March 4th image is higher in relative value than the other dates. This is a simple technique to display the changes in phycocyanin concentrations over 3 frames. This image indicates the uncertainty that occurs and increases with time as land cyanobacteria blooms occur in different pixels based on local conditions. Samples 26 should be collected as close as possible to the time of satellite overpass, and those samples should remain in the same state until phycocyanin quantification can occur.

Ideally, raw fluorescence values could be determined in the field quickly and accurately, thus removing any error associated with the transport and laboratory issues.

Phycocyanin Quantification

All samples contained Microcoleus vaginatus, the target organism. However, two samples from the East Mesa (creosote shrub community vegetated area west of the dunes) contained a BSC forming heterocyst incorporating cyanobacteria (Nostocales).

This unidentified cyanobacterium had a strong association with a fungal hyphae rich soil crust. The two samples containing the highest Phycocyanin fluorescence readings were from the two samples containing this BSC. In addition, small globular colonies with cells of the same shape and size, which may be early growth forms of the Nostoc species, were found in these samples. These two samples create apparent outliers in the data. These samples are also associated with shrub communities. The reflectance (absorbance) of the vegetation is contributing to the lack of fit of these samples to the data. It is also possible that these organisms have a slightly different suite of pigments which change the values indicated by the PYC.

Other single celled non-filamentous or trichome forming cyanobacteria besides the target organism (Microcoleus vaginatus) were not ubiquitous. They were present in small quantities in some samples, and contributed to the overall fluorescence of the sample.

Pseudocolor satellite imagery representing the data and applied filters can be seen in Figure 12. The PYC and VPYS appear very similar in these images, however, there are 27 distinct differences on a pixel to pixel basis, which is represented in the statistics.

Estimated phycocyanin content (PC) values from the PYC, VPYC and VIPYC are graphed in Figure 13. Closest correlations between the algorithms and the datum occur in the 03/04/10 L5 image. The PYC performed with a root means squared error of 202.61 ug/m2 (Table3). When put in terms of the range of data, this yields the best rms error/range of 23.68% and a R2 value of 0.77. Vegetation filters in the VPYC gave slight improvements to the R2 value compared to the PYC up to 0.78, however the rms error/range was 26.23%. The highest R2 values were achieved by the VIPYC with an R2 value of 1.0. The rms error however is also the highest of all the samples in the VIPYC for 03/04/10 at 606.47 ug/m2.

The 03/05/10 data do not correlate well with the estimated values from the PYC,

VPYC or the VIPYC. R2 values ranged from 0.20 to 0.30, with the VPYC getting the highest R2 value of 0.30 but also the highest overall rms error/range of 87.86%.

The final dataset consists of samples from 03/06/10 which were compared to the

03/12/10 L7 image. The highest PC value for all the samples which can be seen as sample 166 in Table 2 was found in this dataset, but the L7 SLC-Off stripes eliminated the associated imagery data. The PYC on the remainder of the 03/06/10 samples received the highest correlations (albeit from a negative slope) as observed in Figure 13 and Table

3. This latter set of samples contained the highest vegetation, and also the highest actual phycocyanin values, and so the negative correlation may be due to the increase in vegetation (thus the negative correlation). The VPYC filter shifted the slope positive, as it is designed to do, but R2 values decreased, partially due to the temporal disparity of the sample times and the L7 overpass. 28 The slope of the regression lines in Figure 13 in each of the scattergrams was found to vary from positive to negative. While it is easy to simply judge performance of algorithms based on the statical fitness, the slope of the line was found to be an important indicator of what is actually happening between the PYC, VR and IO. Samples plotted in column 1 have very little vegetation influence, column 2, moderate and column 3, variable from low to high influences. The negative slope in row 1 columns 2 and 3 is in stark contrast with the positive one in column 1. The conceptual model in Figure 2 showed how the PYC theoretically reacts to increases in both chlorophyll and phycocyanin. The scale on the x axis of the scattergrams in Figure 13 is also an important factor in determining the effects on the PYC. In row 1 column 3, the effects of vegetation can be visualized by applying the conceptual model to this scattergram. A curve was manually drawn from 0,0 then moving positive toward the group of three

(which have low vegetation). This line then curved back towards the x axis between the rest of the samples (with higher vegetation). Observe how well the VPYC moved those high vegetation data points to the right, creating a natural regression. The same concept was applied to interpret the samples in the second column, where the same trend is observed. In the first column, the vegetation filter actually decreases the fit of the data, because in the low vegetation areas it overestimated vegetation.

29 CHAPTER V. Conclusions

The PYC algorithm estimated moderately well the content of phycocyanin on land when vegetation or mineral influences on the PYC were at a minimum. The largest detrimental effect on the algorithm was the averaging influence of vegetation on pixel values. Three additive filter algorithms were created to reduce the strong influences of vegetation and iron oxides the image pixels. These resulted in algorithms that fit the observed data better, in that the slope of the regression line was positive and represented samples were placed correctly based on the hypothesis that vegetation has a negative effect on the pixel values in the PYC. The RMS error and the R2 did not improve in all cases due to the use of these additive filters.

A different approach to the problem of mapping phycocyanin on land would be to collect a much larger, but temporally closer dataset and build a new model, particularly one which does not use the 4/3 vegetation ratio. This ratio within the PYC has the greatest leveraging power and is very important in the function of the algorithm. By using additive filters, which in essence changes the way this ratio is leveraged may change the way the PYC functions to the point that it no longer has the ability to quantify phycocyanin on land, even though vegetation has been nominalized.

The fact that this algorithm was designed for use on water, and yet still works with land as a background is more than remarkable. It is a testament to the power of linear regression model building methods, and their ability to discriminate variables in nature without having a perfect understanding of all the parameters involved. This does not mean that we can blindly apply algorithms which have been created without trying to understand the influencing factors, however. Research such as this can take algorithms 30 derived using the linear regression methods and use other applications to understand influencing factors.

The application of the PYC and VPYC algorithms on land increase our understanding of the response of cyanobacteria to changes in environmental conditions.

As we continue to build datasets of these changes over time, a more clear picture of the condition and trend of the cyanobacterium in both the aqueous and terrestrial environments will be observed. Mapping these changes and combining them with other datasets of environmental and anthropogenic influences will provide a detailed story of how the human/cyanobacterium coexistence ensues.

Applications of the PYC on land include mapping the effects of desertification or restoration efforts. Because inoculations or pellets of cyanobacterial material are being broadcasted and tested as a remediation method, the effectiveness of large scale monitoring of the effects of these efforts would greatly be enhanced by the use of the

PYC on temporal LANDSAT data. And finally, the PYC could easily be tested on

Imagery from intra-solar planetary exploration. If organic pigments like phycocyanin can be viewed from satellite imagery with soil as a background, then the application to Mars and even the Moon and Venus is feasible. Future work regarding the remote sensing of

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Figure 1. PYC applied to Lake Erie and surrounding area. Left: natural color image of a subset of the 5/10/10 Path 20 Row 31 LANDSAT7 overpass. The western basin of Lake Erie and the surrounding city of Toledo and agriculture of Lucas and Wood Counties, Ohio. Right: 0-5 contrast stretched PYC image showing a bloom of cyanobacteria in Lake Erie, and highlighted areas, representing some of the fallow agricultural fields on land. Blue represents <1 ug/l and red represents >5 ug/l. 35

Conceptual Model 60 Phycocyanin 40 PYC Chlorophyll

s 20 t i n U

0 e c n

a -20 t c e l f -40 e R -60

-80 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Observation number sorted by chlorophyll Figure 2. Conceptual idealized data representing the relationship between phycocyanin and chlorophyll and the way they are represented by the PYC. This visualization tool was used to develop the relationship filter between the PYC and the VR. 36

Figure 3. Scattergram of the 03/05/10 Landsat7 ETM+ image comparing the PYC (x- axis) to the NDVI (y axis). High values in the NDVI indicate high vegetation, while high values in the PYC indicate high phycocyanin. The rational curve created by this combination is unique and displays the unique relationship between the two pigments in nature. Higher NDVI values may also be either masking or averaging down the Phycocyanin values, thus the need to remove some of the more minor influences from the algorithm. 37

Figure 4. Location of the Algodones Wilderness Area within Imperial County, California. 38

Figure 5. Locations of the sampling sites within the Algodones Wilderness Area. 39

Figure 6. Senescent vegetation and a carbonate platform which formed in a depression between dunes. Psammophytic (sand loving) communities include many species endemic to the AWA. Cyanobacteria form crusts which bind the fine particles together forming these layered platforms. This image representative of the area where samples were collected 3/04/10. 40

Figure 7. View from the top of the dunes looking southwest over the East Mesa. The stark difference between the dune psammophytic and shrub communities is easily seen. 41

Figure 8. Desert Woodland community at the base of the eastern fore erg of the dune. Vegetation is high with water seeping from the dunes as well as collecting from the colluvium to the east. 42

Figure 9. Moderately developed cyanobacterial BSC. The blackish areas represent the areas of cyanobacteria colonization. This image was procured within one hour of a precipitation event and is in stark contrast to the way it looked before. 43

Figure 10. Developed cyanobacterial BSC. Sampled Plug indicates the ability of Mcrocoleaus vaginatus to hold soil particles together. 44

Figure 11. PYC and vegetation ratio images. L5 03/04/10 overpass image with PCA (Left) and Vegetation Ratio 4/3 (Right) the increasing Phycocyanin trend from west to east in the dunes is evident in ground- truthing as the area is ubiquitous with moist interdune areas where cyanobacteria levels are high. Red areas in the vegetation window to the east of the dunes are desert woodland communities with dense vegetation approaching 100% cover. The areas in red on the right of the dunes are desert scrub communities, also with moderately high vegetation approaching 80% cover. The area in the northwest of the image is undetermined as access is forbidden due to its current land use of navy bombing range. Mineralogy of the colluvium is distinctly different from that of both the dunes and the bench to the west. 45

Figure 12. Matrix of LANDSAT TM Satellite overpass images versus the PYC and applied filters. Note the similarities in the PYC and the VPYC. There are differences which are significant, but not visible at this scale. 46

Figure 13. Matrix of scatterplots, egression lines and error bars for each of the sample datasets. Note the shape of the conceptual curve fitted to the 3/12/10 PYC scattergram. 47

Figure 14. False color temporal composite of the PYC, showing the changes over time. The L5 overpass on 03/04/10 is displayed in red, 03/05/10 in green, and 03/12/10 in blue. Black grey and white colors indicate no relative change between the images. Red hues indicate stronger PYC values on 03/04/10, Blue indicates stronger values on the 03/12/10 image. Black indicates lack of values, or in this case, dense vegetation. The fuschia, red and yellow stripes correspond with the SLC off stripes from the LANDSAT 7 satellite. 48 TABLES

Table 1: Mineral influences on algorithms and ratios by major minerals and materials. Sorted by reflectivity values of the PYC. Also listed are the NDVI, which shares a close ordering to the 4/3 vegetation ratio and the Iron Oxide 3/2 ratio. Notice the close inverse ordering between the NDVI and the PYC and the grouping of Iron Oxide Minerals in the positive values of the PYC. Reflectivity data courtesy USGS. MATERIAL PYC NDVI VR IO RIEBECKITE IN-7A 269.45 0.05 1.11 0.98 REALGAR S-3A 157.6 0.08 1.18 2.57 JOHANNSENITE IN-12A 91.03 0.08 1.17 1.13 GOETHITE OH-02A 82.81 0.11 1.25 1.64 HEMATITE O-1A 48.9 0.12 1.26 1.96 ATACAMITE H-4A 40.18 -0.08 0.85 0.31 BIOTITE PS-23A 38.77 0.07 1.14 1.05 AUGITE IN-15A 21.85 -0.09 0.83 0.83 CHLORITE,RIPIDOLITE PS-12A 20.03 -0.03 0.94 0.8 MALACHITE C-7A 19.88 -0.21 0.66 0.26 NATROJAROSITE SO-7C 19.35 -0.03 0.95 1.48 GLAUCOPHANE IN-3A 18.14 0.1 1.23 0.96 JAROSITE SO-7A 17.1 0.02 1.03 1.63 AZURITE C-12A 16.95 -0.1 0.83 0.29 PLUMBOJAROSITE SO-7B 14.09 0 1 1.51 TRIPHYLITE P-4A 12.81 -0.22 0.64 1.26 GLAUCONITE PS-19A 11.31 -0.18 0.7 0.68 SIDERITE C-9A 9.59 -0.17 0.71 1.32 CLINOZOISITE SS-4A 5.43 0 0.99 1.65 ENSTATITE IN-10B 3.55 -0.4 0.43 1.1 TOURMALINE,DRAVITE-S CS-1A 2.71 0.06 1.13 1 CORRENSITE PS-10A -3.49 0.08 1.16 1.16 CORDIERITE CS-3A -3.89 -0.09 0.84 1.11 MIMETITE A-1A -4.69 0.04 1.08 1.3 RHODONITE IN-1A -7.32 -0.09 0.83 1.45 FAYALITE NS-1A -9.36 -0.07 0.86 1.04 CHALCOPYRITE S-4A -11.63 0 1.01 1.11 PYRRHOTITE -13.72 0.09 1.2 1.18 ZIRCON NS-09A -14.53 0.15 1.35 1.45 ACTINOLITE IN-4A -14.77 0.02 1.04 0.8 DIOPSIDE IN-9B -15.22 0.06 1.12 1.03 EPIDOTE SS-1C -15.46 0.16 1.37 1.22 MAGNETITE O-4A -16.02 -0.02 0.96 0.99 NONTRONITE PS-6A -16.03 0.03 1.07 1.21 SULFUR E-2A -16.28 0 1 1.01 BERYL CS-2A -16.82 -0.12 0.78 0.92 HYPERSTHENE IN-14A -17.92 -0.04 0.92 1.09 VERMICULITE PS-18B -18.07 0.05 1.11 1.17 ANGLESITE SO-10A -18.65 0.08 1.18 1.14 PYRITE S-02A -18.87 -0.03 0.94 1.05 SCHEELITE T-1A -19.85 0.02 1.03 1.14 ILLITE PS-11A -20.49 0.1 1.22 1.18 ANTHOPHYLLITE IN-8A -20.58 -0.1 0.81 0.99 COLUMBITE O-7A -21.92 0.03 1.06 1 49

Table 1. continued... MATERIAL PYC NDVI VR IO MICROCLINE TS-17A -21.92 0.02 1.05 1.17 ANTLERITE SO-11A -22.56 -0.04 0.93 0.51 CUMMINGTONITE IN-6A -23.25 -0.04 0.92 0.99 PYROLUSITE O-6A -23.54 0 0.99 0.95 SMITHSONITE C-11A -23.56 -0.11 0.81 0.98 GRAPHITE E-1A -24.45 0.04 1.09 1 RUTILE O-2A -25.03 0.15 1.36 1.12 ALMANDINE GARNET NS-04A -25.15 0.18 1.43 1.71 SANIDINE TS-14A -25.64 0.02 1.03 1.09 RHODOCHROSITE C-8A -27.09 0.01 1.02 1.19 ANORTHITE TS-5A -27.12 -0.02 0.96 1.02 ARSENOPYRITE S-5A -27.19 0.01 1.03 1.04 ORTHOCLASE TS-12A -27.38 0 1.01 1.02 SPODUMENE IN-13A -27.53 0.01 1.02 1.05 LABRADORITE TS-2A -27.55 -0.01 0.99 1.05 LEPIDOLITE,YELLOW PS-13A -27.74 -0.04 0.93 1.09 APATITE P-1A -27.84 0.05 1.1 0.92 MUSCOVITE PS-16A -28 0.01 1.03 1.14 GROSSULAR GARNET NS-03B -28.12 0 1.01 1.1 ANDESINE TS-4A -28.17 0 1.01 1.02 FLUORITE, PURPLE H-2A -28.39 0.03 1.06 1.13 QUARTZ, SMOKY TS-1B -28.46 0.09 1.2 1.08 BYTOWNITE TS-13A -28.6 0 1.01 1.02 GLAUBERITE SO-8A -28.87 0.02 1.05 1.03 HALITE HO3A -28.9 0 1 1.01 ALBITE TS-6A -29.12 0 1.01 1.01 ALBITE TS-6A -29.12 0 1.01 1.01 WOLLASTONITE IN-2A -29.2 -0.01 0.98 1 FORSTERITE,SYNTHETI NS-2A -29.37 0 1 1 CRISTOBALITE TS-7A -29.43 0 1 1.03 CRYOLITE H-1A -29.56 0 1 1 TITANITE NS-07A -29.62 0.07 1.15 1.14 TALC PS-14A -29.9 -0.04 0.93 0.96 ZINCITE O-13A -30.13 0 1 1 OLIGOCLASE TS-3A -30.16 0 1.01 1.02 BARITE SO-3A -30.31 0.01 1.02 1.01 GALENA S-7A -30.34 -0.02 0.96 0.99 CHALCOCITE S-8A -30.42 0.01 1.01 0.95 APHTHITALITE SO-9A -30.63 0.01 1.01 1.02 ANATASE,SYNTHETIC O-12A -30.8 0 1 1 NEPHELINE TS-16A -31.45 0.01 1.02 1.01 TREMOLITE IN-5A -32.51 0 0.99 1.03 CELESTITE SO-5A -33.1 0.04 1.08 1.01 PERICLASE O-14A -33.12 0 1 1 PALYGORSKITE PS-4A -33.15 0.02 1.03 1.04 SILLIMANITE NS-08A -33.27 0.03 1.06 1.08 ANALCIME TS-18A -33.49 0.01 1.03 1.04 SAPONITE PS-24A -33.52 0.01 1.01 1.03 DICKITE PS-3A -33.58 0.02 1.04 1.09 CERUSSITE C-10A -33.59 0.01 1.02 1.03 PYROPHYLLITE PS-7A -33.65 0.03 1.05 1.13 50

Table 1. Continued... MATERIAL PYC NDVI VR IO GRASS (SENESCENT) -33.66 0.14 1.33 1.34 WITHERITE C-2A -33.83 0.02 1.04 1.04 VESUVIANITE SS-3A -33.99 0.01 1.03 1.1 STRONTIANITE C-1A -34.18 0.01 1.02 1.05 CORUNDUM,SYNTHETIC O-15A -34.44 0 0.99 1 CALCITE C-3E -34.57 0.01 1.01 1.02 ICE -34.63 -0.01 0.98 0.99 KAOLINITE, Well Ordered PS-01A" -35.34 0.01 1.02 1.03 BUDDINGTONITE,FELDS TS-11A -35.7 0.09 1.19 1.21 SERPENTINE PS-20A -35.84 -0.02 0.97 1.01 MONTMORILLONITE PS02B -36.47 0.01 1.01 1.03 COOKEITE PS-9A -36.7 0.01 1.03 1.09 BRUCITE OH-1A -36.96 -0.05 0.91 1.1 DOLOMITE C-5C -37.12 0.02 1.04 1.05 MAGNESITE C-6A -37.17 0.01 1.02 1.04 MAGNESIOCHROMITE O-8A -37.74 0.02 1.03 0.97 CASSITERITE O-3A -37.96 0.03 1.06 1.01 CHABAZITE TS-15A -37.98 0.01 1.01 1.02 ALUNITE SO-4A -38.23 0.03 1.05 1.24 ANHYDRITE SO-1A -39.22 0.08 1.18 1.06 SNOW (COARSE) -39.89 -0.06 0.88 0.97 MONTEBRASITE P-2A -40.09 0 1.01 1.01 TOPAZ NS-06A -40.27 0.01 1.02 0.99 FERROAXINITE CS-4A -40.56 0.07 1.14 1.52 KERNITE B-2A -41.04 -0.01 0.99 1.01 AMBLYGONITE P-3A -41.06 0.01 1.01 1.03 SCORODITE A-2A -41.18 0.09 1.2 1.42 BORNITE S-9A -41.24 0.23 1.59 1.04 GIBBSITE,SYNTHETIC OH-3A -42.32 0 0.99 1 SEPIOLITE PS-5A -42.33 0.01 1.03 1.05 GYPSUM SO-2B -42.38 -0.01 0.98 1 SNOW (FROST) -42.63 -0.01 0.97 0.99 HEMIMORPHITE SS-2A -43.24 0 1 1.01 HOWLITE B-5A -43.47 0 0.99 1.02 PREHNITE PS-21A -43.47 0 1 0.99 NATROLITE TS-8A -44.67 0.01 1.01 1.02 TINCALCONITE B-4A -44.88 0 0.99 1.02 SNOW (FINE) -45.34 -0.02 0.96 0.99 STILBITE TS-9A -45.94 0 1 1.03 HYDROXYAPOPHYLLITE PS-22A -46.37 -0.01 0.99 0.99 BORAX B-6A -46.51 -0.01 0.98 1 ULEXITE B-3A -47.32 -0.01 0.98 1.01 TSCHERMIGITE SO-6A -47.36 -0.01 0.98 1 COLEMANITE B-1A -47.42 0 1 1.02 TRONA C-4A -50.38 0.03 1.06 1.07 SPHALERITE S-1A -63.07 0.2 1.49 1.25 STIBNITE S-6A -71.67 0.55 3.42 0.95 SODALITE TS-10A -80.08 0.33 1.97 1.09 GAHNITE O-11A -80.61 0.14 1.34 1 GRASS (VIGOROUS) -604.89 0.78 8.15 0.82 CONIFER (AVE.) -750.73 0.8 8.95 0.72 51

Table 2: Values for each pixel corresponding to the sample location when processed using the PYC, Vegetation (4:3) ratio and the Iron Oxide (3:4) ratio. Yellow indicates Samples and values appropriate to use for the LANDSAT 5 overpass on 03/04/10. Green indicates those taken on the LANDSAT 7 overpass on 03/05/10. The remainder of the values in blue are not closely temporally related to the overpass of a satellite (sampled on 3/6/10), but were used for calculations using the 3/13/10 L7 overpass. PYC values have been converted from ug/L to ug/m2.

Sample PC 3/4/10 3/5/10 3/12/10 3/4/10 3/5/10 3/12/10 3/4/10 3/5/10 3/12/10 ug/m2 PYC PYC PYC R43 R43 R43 R32 R32 R32 1 148.64 396 36 1 0.92 1.62 1.63 2 350.48 414 604 1 0.82 1.6 1.64 3 143.29 297 530 0.99 0.82 1.62 1.68 4 496.91 506 565 1 0.82 1.59 1.69 5 465.62 -56 307 265 0.99 0.83 0.82 1.6 1.62 1.65 6 611.06 -108 320 255 0.68 0.87 0.85 1.52 1.55 1.65 7 605.1 457 218 1 0.83 1.59 1.6 8 512.99 115 223 304 1.02 0.84 0.82 1.59 1.58 1.63 9 143.09 314 273 365 0.99 0.83 0.82 1.63 1.58 1.63 10 447.69 -161 -673 -476 1.23 1.05 1.07 1.5 1.53 1.57 11 467.16 -154 -592 -363 1.2 1.02 1.03 1.51 1.53 1.63 12 468.08 -101 -5 1.09 0.91 1.54 1.61 13 221.89 358 313 389 0.99 0.83 0.84 1.61 1.58 1.66 14 336.76 183 448 1 0.82 1.62 1.68 15 259.14 247 321 291 1 0.83 0.83 1.61 1.57 1.59 16 583.87 -8 -187 -192 1.1 0.91 0.91 1.55 1.56 1.62 52

Table 3: Reported rms error and regression for data collected ±2hr of the satellite overpass. R2 values and regression equations from Figure 13 are reported for the PYC and the three filter algorithms. Algorithm 03/04/10 03/05/10 3/12/10* PYC RMSE on ±2hr of overpass* 202.61 266.1 235.84 RMSE/Range 24% 31% 29% R2 0.77 0.28 0.58 Regression Equation y = 0.44x + y = -0.45x + y = -0.27x + 278.69 550.23 401.28 Vegetation Filtered PYC RMSE on ±2hr of overpass* 396.57 333.32 381.07 RMSE/Range 26% 88% 26% R2 0.78 0.30 0.36 Regression Equation y = 8.77x – y = 4.95x y = 0.55x + 3646.14 -739.06 210.82 Iron Oxide Filtered PYC RMSE on ±2hr of overpass* 160.08 305.06 728.95 RMSE/Range 13.00% 52.00% 68.00% R2 0.83 0.3 0.61 Regression Equation y = 1.6x – y = -0.56x + y = -0.29x + 353.11 583.27 395.26 Veg. And Iron Ox. Filtered PYC RMSE on ±2hr of overpass* 606.47 199.10 478.01 RMSE/Range 50% 55% 40% R2 1.00 0.20 0.10 Regression Equation y = 0.98x - y = -0.39x + y = 0.12x + 41.81 538.77 389.96 *Data used for the 3/12/10 L7 overpass were collected on 3/6 /10