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