Contract NNS06AA98B; Task Order NNS07AA48T
Submitted by
Gregory A. Carter and Dan M. Holiday
The University of Southern Mississippi
Gulf Coast Geospatial Center
1203 Broad Ave.
Gulfport, MS 39501
Carlos E. Del Castillo
Ocean Remote Sensing Group
The Johns Hopkins University
Applied Physics Laboratory
11100 Johns Hopkins Road
Laurel, MD 20723
Lee Estep
Science Systems and Applications, Inc.
Stennis Space Center, MS 39529
July 20, 2009
1

Introduction
The majority of studies concerning the driving forces behind abundance and diversity of phytoplankton populations have focused on temperate ecosystems in well-studied estuaries such as Chesapeake Bay (Marshall et al., 2006; Harding 1994), San Francisco Bay (Lucas et al.,
1999a, 1999b; Cloern 1996), Neuse Bay (Gallegos et al., 2002; Lohrenz et al. 2002), and the subtropical blackwater river systems of Florida (Quinlan and Phlips 2007; Bledsoe et al., 2004).
These studies have concentrated on estuaries and bays with water residence times in the month to year temporal cycles (Phlips et al., 2002; Harding 1994), systems that are influenced primarily by strong tidal regimes (Lucas et al. 1999a; Jonge et al. 1994), or seasonal upwelling caused by wind and current interactions (Andersen et al., 2008; Lanerolle et al., 2006). Recent work has demonstrated importance of salinity gradients and resultant spatial patterns of available nutrients and their effects on distribution of phytoplankton populations in estuaries (Bledsoe et al., 2007;
Muylaert et al., 2009).
This study focused on Mobile Bay (MB) and the eastern Mississippi Sound (MS) in the northern Gulf of Mexico, systems dominated by freshwater discharge rather than tidal influences
(Schroeder 1977; Wilber 2007) and short water residence times (Noble et al., 1996; Schroeder
1977). Neither the identity of the microalgal populations of these systems nor the factors which drive their abundance, composition or distribution have been well described (Pennock et al.,
2001, 2002). This work concentrated on spatial variability of phytoplankton populations from sampling stations at the north end of MB to the region near Horn Island and Biloxi Bay within the MS. Five functional groupings, or zones of differing population assemblages, have been identified that correspond with statistical analysis of phytoplankton abundance, nutrients, and environmental factors. We propose salinity, temperature, and nutrient gradients of total phosphorous drive the spatial variability of phytoplankton populations within this study area.
Site Description
The MB estuary (30.5 N, 88.0 W) is a drowned river system, approximately 50 km long with a width of up to 31 km (Schroeder 1977). It is a shallow (average depth 3m), highly stratified estuary with a surface area of approximately 1,070 km 2 , a relatively small volume of
3.2 X 10
9
m
3
and a short residence time of days to weeks (NOAA/EPA 1989). A dredged shipping channel runs north to south from the city of Mobile through Main Pass to the east of
Dauphin Island, creating maximum depth waters of 15 m (Lamb 1979). Disposal of dredged materials in open water adjacent to the channel has influenced bottom contours, particularly at the northern bay and west of the channel where spoil banks influence water circulation and stratification of the water column (Schroeder et al., 1998). River discharge and seasonal wind patterns rather than tidal cycles control the hydrology of the estuary, with water quality being heavily influenced by groundwater input, regional weather patterns, dredging and subsequent deposition of tailings and human land use patterns throughout the watersheds of the estuary
(Engle et al. 2007; Turner et al. 1987; Lamb 1979). The mouth of MB is formed by Cedar Point to the west and Ft. Morgan Peninsula to the east. At the north end, the Mobile River is formed by the confluence of the Alabama and Tombigbee Rivers, combining to form the fourth largest watershed in the coterminous United States (Bricker et al. 2007). This system dominates water flow into the bay, contributing 95% of freshwater input (Schroeder et al., 1979). The addition of freshwater from the Dog and Fowl Rivers on the west shore and Fish and Magnolia Rivers flowing into Weeks Bay along the east shore create a short freshwater fill time of approximately
2

20 days (Cowan et al., 1996), and average daily freshwater flow of 1.56 x 10
8 m
3 d
-1
(Engle et al.,
2007). This creates an average monthly discharge rate into the northern GoM of 1,800 m
3
s
-1
(for the period from 1929 to 1983, see Schroeder and Weisman, 1986). The land mass of Dauphin
Island channels discharge flow, with approximately 15% passing to the north and west of the island through the east end of MS by way of Pass Aux Heron and the remainder, including contributions from the shipping channel, flow south and west through the main pass between the island and Ft. Morgan Peninsula (Schroeder, 1977). These various contributions combine to create a highly stratified, rapidly changing environment (Schroeder, 1977, Noble et al., 1996).
The MS (30.2 N, 88.3W) is a shallow coastal lagoon with a mean depth of 3 m, approximately 130 km long and 11-24 km wide, bordered on the south by a series of barrier islands and on the north by the mainland shoreline of Mississippi (Wilber et al., 2007). The combination of shipping channels (Gulf Intracoastal Waterway and the Biloxi Shipping Channel) running east to west and into Biloxi Bay and the coastal littoral current brings MB discharge waters from both Pass Aux Heron and the main pass of MB between Dauphin Island and Fort
Morgan Peninsula into the MS, where they mix with fresh water outputs of the Pascagoula,
Biloxi, and several smaller river systems (Rabalais et al., 2002; Dinnel et al., 1999). Waters from the MS are generally well-mixed with minimal difference between surface and bottom temperature and salinity (Wilber et al., 2007).
Materials and Methods
In Situ Sampling
Surface water samples were collected at 4-6 week intervals from December, 2004, through June, 2006, at 12 sites in Mobile Bay (N= 197) and from July, 2005 through June, 2006 at 5 sites in the Mississippi Sound (N= 50) that encompass the major hydrological regimes in
Mobile Bay and its adjacent waters in the Mississippi Sound (Figure 1). The Mobile Bay sites are situated at principle inflows (stations near mouths of Mobile River, Dog River, Fowl River, and Weeks Bay), outflows (Pass Aux Herons and Main Pass), over oyster beds near Cedar Point and within Bon Secour Bay and near Dauphin Island and Ft. Morgan Peninsula. Sites in the
Mississippi Sound follow a south transect from the mouth of Biloxi Bay through Dog Keys Pass and between Horn and Ship Islands. One exception was off the northeast shore of Horn Island, a site within the MS ship channel and away from the influence of the Biloxi Bay discharge.
Laboratory analyses
In the laboratory, nutrient water samples were filtered through GF/F glass filters (0.7μm pore size) separating water sample into particulate phase collected on the filter and dissolved constituents in the filtrate. Physical parameters (T, S, pH) were measured at each sampling site using a hand held YSI. Unique surface water samples were taken for analysis of nutrient pool sizes and microalgal abundances. For all sampling trips, water samples were kept on ice in the dark until processing in the laboratory. Chlorophyll a was measured fluorometrically on a Turner
Designs fluorometer (TD 700). Inorganic nutrients (NO
3
-
,NO
2
-
, NH
4
+
, and PO
4
-3
) were determined directly by colorimetric techniques modified for the Skalar SAN+ nutrient autoanalyzer. Particulate carbon and nitrogen were measured on a Costech CNS analyzer.
Dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) were determined using a
Shimadzu TOC analyzer. Filter pads with suspended solids were dried at 60°C and muffled at
3

500°C. Total suspended solids (TSS) were determined as weight of dried particulates per volume filtered, mineral suspended solids (MSS) were determined as weight of muffled particulates per
Figure 1. Map of the northern Gulf of Mexico and the Mississippi Bight. The expanded portion of the map includes the 12 stations within Mobile Bay and 5 stations within the Mississippi
Sound. volume filtered, and organic suspended solids (OSS) we determined as difference between dried and muffled weight per volume filtered. Total nitrogen (TN), total phosphorus (TP), and total carbon (TC) are the sum of dissolved and particulate pools.
Unique samples were collected from the surface waters for both phytoplankton and nutrient sample collection. One liter of whole water was preserved immediately upon collection with Lugol’s solution. Phytoplankton cell counts were determined by the Alabama Department of Public Health (ADPH) using inverted light microscope. Diatoms, dinoflagellates, chlorophytes, prasinophytes, euglenophytes, cryptophytes, dictyophytes, chrysophytes, and raphidophytes were enumerated in a settling Nunc chamber, with diatoms and dinoflagellates identified to species. Colonies rather than individuals of cyanobacteria were counted, and therefore not included in the analysis due to potential statistical anomalies introduced by this disparity in counting method.
4

Statistical analysis
Phytoplankton community composition was analyzed using statistical software Primer-E v6. Abundances were square root transformed to avoid large influence of the bloom species.
Phytoplankton data were averaged by site. Non-parametric analysis of similarities (ANOSIM) and multidimensional scaling (MDS) were used to examine inter-annual, monthly, and spatial patterns of phytoplankton composition. Environmental variables were log(x+1) transformed if necessary and normalized. Similarity of percentage (SIMPER) analysis was used to examine the contribution of each taxa to differences in site assemblages. Environmental variables best explaining phytoplankton composition patterns were determined using BEST analysis (Clarke and Gorley, 2001).
Results
Phytoplankton Populations
Five functional groupings, or zones, of resemblance between phytoplankton populations within the 17 collection sites were identified using the mean of collections and the Bray-Curtis
Similarity resemblance method and shown graphically in a MDS plot (Fig. 2). These groupings are termed zones due to the resemblance of the class and species taxa among collection sites and relationships with hydrological and geographical features. Zone 1 includes sites 3, 4, 5, 6, 7, and
8. Site 3 is located near Cedar Point, the location of the largest oyster beds in Alabama waters.
Figure 2. Multi-dimensional scaling plot (based on Bray-Curtis similarity matrices) of functional groupings (zones) in phytoplankton community composition. Note the high degree of similarity between the sites inside of MB, with exception to the relative isolation of sites 9 and 10 located in Bon Secour Bay. Also note the isolation of sites 17 and 16 from the east-west latitude pattern of sites near Dauphin and Horn Islands, and the dissimilarity of site 11 and all other sites.
5

Sites 4, 5, and 6 are located at the mouths of the Fowl, Dog, and Alabama Rivers, respectively. Site 7 is at the northeast shore, an area of known hypoxic events (Schroeder 1977) and site 8 near Mid-Bay Lighthouse, which is geographically removed from the dominant inflows and outflows of the bay but near the shipping channel (Lamb, 1979). Zone 2 includes sites 9 and 10 within Bon Secour Bay in the southeastern reach of MB. Zone 3 includes sites 17 and 16 near the mouth of Biloxi Bay in the MS. Zone 4 contains sites 1, 2, and 12 near Dauphin
Island and sites 13, 14, and 15, near Horn Island in the MS. Zone 5 consists of site 11, located south of Ft. Morgan Peninsula and east of Dauphin Island and the mouth of Mobile Bay.
SIMPER analysis with zones as factors for contribution of species driving the relationships within and between zones showed a range of 77.3-78.3 of average similarity within the zones (Table 1). Average dissimilarity between zones showed a range of 32.5-52.6, with the highest degree of dissimilarity exhibited between zone 5 and zone 2 on opposite sides of the Ft.
Morgan Peninsula. The lowest average dissimilarity was exhibited by zones 4 and 1. Four taxa were among the leading contributors driving these relationships in all zones (Figure 3), including the classes Chlorophyceae, Cryptophyceae, and Prasinophyceae, and the diatom genus
Skeletonema.
ANOSIM analysis showed significant differences between months, sites and zones. Tests factoring sites showed an R statistic of 0.377 and a significance level of 0.1%. Factoring zones
(using averages of the sites within the zones) showed an R statistic of 0.911 with a significance level of 0.1%. To address seasonal differences in the collections, months were factored resulting in an R statistic of 0.177 with a significance level of 0.1%.
Nutrients
The effect of nutrients on the composition of diatom and dinoflagellate populations was analyzed using BEST analysis with Spearman rank correlation, BIOENV algorithm and
Euclidian distance. Temperature, salinity, pH, TC, TN, TP, TSS and MSS were used as abiotic variables in analysis. As a single variable, TP showed the maximum rank correlation of 0.609.
The addition of salinity (0.727) and temperature (0.751) provided BEST results showing significant explanation of the relationships between nutrients and phytoplankton populations.
Salinity increased with a decrease in latitude, with the highest ranges recorded in zones 4 and 5
(Figure 4), including the stations south of Ft. Morgan Peninsula, Dauphin Island and Horn
Island. TP was highest in zone 2 within Bon Secour Bay, with an average of 2.3 µmol, and lowest in zone 3 with an average of 0.8 µmol. Surface temperatures exhibited the highest average and maximum values within zone 5.
Discussion
In using MDS, BEST, and SIMPER analyses this study has shown there is no significant difference between the phytoplankton populations of MB and MS when considered as separate entities, yet significant differences are found between the 17 collection sites and between functional groupings or zones formed by resemblances within those sites. TP, salinity and
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Zone 1
Zone 2
Zone 3
Zone 4
Zone 5
Similarity Dissimilarity Dissimilarity Dissimilarity Dissimilarity
% % to Zone 1
% to Zone 2
% to Zone 3
% to Zone 4
78.17
77.87
77.26
78.00
NA
34.39
(R = 0.958, p=0.036)
37.25
(R = 0.969,
35.48
(R = 1, p = 0.036) p = 0.333)
32.51
(R = 0.79,
36.57
(R = 1,
35.20
(R = 0.948, p = 0.002) p = 0.036) p = 0.036)
32.12 45.28
(R = 1,
52.63
(R = 1,
52.07
(R = 1,
p = 0.143) p = 0.333) p = 0.333)
(R = 0.978, p = 0.143)
Table 1. Similarity analysis of taxonomic clusters based on average per site for zones 1-5 as identified by Figure 2. Similarity is the within-group similarity, defined by the SIMPER test of
PRIMER-E. Dissimilarity is between-group dissimilarity. Testing for between-group differences vs the groups with the highest replication (i.e., the Chlorophyceae, Cryptophyceae and
Prasinophyceae) was by ANOSIM. The ANOSIM R statistic and p level are reported for each pair-wise comparison. Zone 5 is not represented for within zone similarity due to having one test variable.
7

Figure 3. Pie graphs illustrating SIMPER analysis of the average abundance of taxa contributing to within site similarity. Data represented are the highest contributors to average abundance within zones. Note the consistently high representation of chlorophytes, crytptophytes, prasinophytes, and the diatom genus Skeletonema .
8

Figure 4. Box and whisker plots illustrating the range, medians, and 25, 50, and 75 % percentiles of the three factors shown by BEST analysis to give the highest rank correlation between abiotic data and phytoplankton populations. These included sea surface temperature, salinity and total phosphorous (TP). All collections data were used. Note the increase in salinity from north to south and the high values of TP in zone 2.
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surface temperatures were found to drive those differences in populations, with chlorophytes, cryptophytes, prasinophytes, and the diatom genus S keletonema being the most abundant and influential taxa. We have also found significant differences between months of collection.
Past studies concerning spatial and temporal nutrient cycles within MB have shown no monthly, seasonal or interannual relationships. MB has been shown to receive high inputs of freshwater flow and corresponding peaks in nutrient input during spring runoff events (Pennock et al., 1995), while at the same time there has been no reported evidence of a well developed spring bloom within MB (Pennock 2000; 2001). Cowan et al. (1997) demonstrated a pulsing of
N and P within MB that varied independently of interannual and seasonal cycles. Frequent vertical mixing events due to pulses of freshwater input and resultant dynamic changes between benthic and surface dissolved oxygen levels created differences in salinity of up to 12 psu between surface and bottom subtidal waters of MB. This study also noted frequent freshwater inputs that created pulses of nutrients in response to frequent mixing events and resuspension of bottom sediments, causing wide ranges of nutrient levels at sometimes hourly time scales. Turner et al. (1987) and Park et al., (2007), also reported changes in bottom and surface dissolved oxygen levels at hourly to daily temporal cycles in response to mixing events, but no temporal patterns.
The primary freshwater inputs to MB also include the euryhaline sub-estuaries of Dog
River, Fowl River and Weeks Bay. Lehrter (2008) has reported they exhibit no seasonal trends in residence and mixing time scales or nutrient loads, but vary in nutrient outputs into MB. This was determined to be caused by the differences in watershed land use patterns. Differences in nutritional inputs may explain variation between zones 1 and 2. Dog River is closest to the city of Mobile, with 44% of its drainage basin being urbanized and reporting the highest median TP concentrations of these systems. Weeks Bay had a 59% agricultural coverage of its watershed, exhibiting the highest median TN. This differed from our results, with zone 5 showing the highest TP levels. Past studies have shown the correlations between P, raw sewage outputs leading to increasing eutrophication and phytoplankton population increase (Anderson et al.,
2008). The higher TP levels in zone 2 may reflect the presence of phosphorous outputs greater than expected by the Weeks Bay estuary.
The observed relationship between salinity and latitude in relation to the spatial variability and resemblances within and between phytoplankton populations, and the groupings of collection sites into zones over the continuum from low to high salinity is affected by the hydrological conditions in the region. The general circulation patterns within MB are consistent with the north-south salinity gradients exhibited between zones, with the 95% contribution of freshwater in MB by the Mobile River being moved by advection through the length of the bay and influenced by the shipping channel (Lamb 1979, Schroeder and Wiesman 1977). Zone 2 is separated from zone 1 by influences of Weeks Bay discharge and the relative isolation of Bon
Secour Bay caused by currents and gravitational flow (Noble et al., 1996). The sites of zone 3 are connected by the combination of shoreline littoral drift currents, the shipping channel within MS,
MB discharge flow from Main Pass and Pass Aux Herons, and groundwater discharge from. mainland shorelines. Zone 4 is isolated from zone 3 due to the influence of discharge water from
Biloxi Bay and the potential effects of the mixing of waters from the MS ship channel, while phytoplankton populations in zone 5 are dissimilar because of its location as the easternmost station and being influenced by subsurface groundwater and Perdido Bay discharge waters more than MB discharge (Liefer et al., 2009).
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Recognizing patterns of similarity and dissimilarity within these 17 collection sites and the 5 zones they represent is essential in understanding phytoplankton populations in the MB and
MS. Future attempts at developing prediction and monitoring networks while increasing efficient use of resources in collection of samples may be served by understanding these patterns. The results of ANOSIM and SIMPER analyses in this study indicate surface water temperature, salinity, and TP are most influential in driving the composition of phytoplankton populations.
The remote sensors MODIS and AVHRR provide direct measurements of sea surface temperature. Development of regional specific algorithms for prediction of salinity would provide a valuable input to HA prediction, shellfish, and finfish regional planning programs.
THE USE OF SEAWIFS DATA IN FORMING DECISION TREE MODELS TO
PREDICT BLOOMS OF PSEUDO-NITZSCHIA IN MOBILE BAY
During the past three decades, continual development has been seen in the area of pattern recognition and classification of remotely sensed data (Pal and Mather 2003). Research into algorithmic aspects of pattern recognition has proceeded alongside the development of both in situ and remote sensor instruments that are capable of producing high volumes of data, including images with increasingly finer spatial and spectral resolution (DeFries and Townshend, 1994; Pal and Mather 2003). In recent years the use of decision tree modeling (DT) in remote sensing studies has increased. DT models are computationally fast, make no statistical assumptions, and are able to manage data represented on different measurement scales, factors which must be addressed when incorporating large, heterogenous data matrices such as those formulated using sensor imagery (Gahegan and West 1998). In comparison to neural networks they may be trained quickly and with minimal data (Borak and Strahler 1999), an important consideration when working with the small spatial scales of phytoplankton blooms in shoreline areas of the northern
GoM.
Since its launch onboard the Orbview-II satellite in August 1997, the Sea-viewing Wide
Field-of-view Sensor (SeaWiFS) has provided ocean color data products for use in global and local studies for the oceanographic community (Franz et al., 2005; Gordon et al., 1980; McClain et al., 1997). Ocean color data are able to provide global or local information based on the spectrum of radiation from the sun and sky that has penetrated the oceans surface and emerged from below after being scattered upward from subsurface depths. This recorded spectrum of upwelling water leaving radiance ( L w
) is a result of the scattering and absorption of light measured at wavelengths in the visible and near infrared regions and is influenced by the concentration and optical properties of the organic and inorganic constituents of seawater (Franz et al., 2005; Gordon et al., 1980). Another commonly used parameter in the field of ocean color is the remote sensing reflectance, Rrs, which is defined as the ratio between the upwelling radiance, L w, just above the water surface to the downwelling irradiance, E d, at the same level.
R rs is a function of wavelength, λ, as well as the viewing angle (IOCCG 2000). In Case I waters variations in a
(λ) and b b(λ) are due to phytoplankton only (McClain et al., 1997). Consequently, these measurements are used to interpret the gradations of ocean color from blue to green as proxies for concentrations of phytoplankton within the area of study (Gordon et al., 1983;
Muller-Karger et al., 2005). These reflectance measurement products are the foundation of remote sensing data, all ocean color algorithm products and data studies are reliant upon their usage. The use of the SeaWiFS sensor for primary production (Wawrick and Paul, 2004) and
11

phytoplankton studies (Stumpf 2009; Tomlinson et al. 2009) have shown the utility of Rrs data in carrying out synoptic and rapid coverage of regional waters, improving the recognition and response time to potential HA events.
Anderson et al., (2008) showed that increase in coastal water nutrient loads correlated directly with increased microalgal populations. As human populations increase in the Mobile
Bay and Mississippi Sound regions, anthropogenic nutrient loading will rise correspondingly
(Baya, 1996; NOAA, 2004), demonstrating a need for economical and efficient means of HAB detection and prediction in the northern GOM. The focus of this study was to develop an integrative DT prediction model that can utilize specific products of readily available SeaWiFS reflectance data products to predict the occurrence of phytoplankton populations in the region in and near MB.
Materials and Methods
Phytoplankton population description
Surveys carried out in Alabama waters by Pennock, et al., in 2001 and 2002 and routine surveys done by Dauphin Island Sea Lab (DISL) and the Alabama Department of Public Health
(ADPH) regularly identify species of microalgae, mostly dinoflagellates and diatoms, with the potential to create HAB events (unpublished data, ADPH). To date, 5 known toxin-producing
HAB species have been detected at significant levels (>10
5
cells L
-1
) in coastal waters of the northern GOM. These include the diatoms Pseudo-nitzschia spp. and the dinoflagellates Karenia brevis , Gymnodinium sanguineum , Dinophysis caudata , and Prorocentrum minimum .
Karlodinium veneficum , a producer of karlotoxins, and Heterocapsa triquetra , a dinoflagellate found in high cell concentrations leading to hypoxic conditions, have caused fish kills in MB
(Bill Smith, ADPH). Other potential HAB species, such as the dinoflagellates Karenia mikimotoi , associated with massive fish kills in Japan and Korea, and two members of the genus
Gonyaulax, G. digitale and G. polygramma , associated with non-toxic red tides in Florida
(Landsberg, 2002) have been found at low levels (<2000 cells L
-1
). The members of the genus
Pseudo-nitzschia were chosen as the taxa used during the formation of this DT due to the potential of this genus for production of domoic acid (Bates and Trainer 1997; Maier-Brown et al., 2006) and the relatively high numbers, seasonal aspects of occurrence, and frequency with which they have been collected in this region (Dortch et al., 2001; Liefer et al., 2009).
Phytoplankton collections
Surface water samples were collected at 4-6 week intervals from December, 2004, through June, 2006, at 17 sites (N= 120) that encompass the major hydrographic regimes in southern Mobile Bay and its adjacent waters in the Mississippi Sound (Figure 5). The stations were situated near Dauphin Island and Cedar Point, within Bon Secour Bay, and near Little
Lagoon and the Ft. Morgan Peninsula, (an area of high cell concentrations, see Liefer et al.,
2009).
One liter of surface water was preserved immediately upon collection with Lugol’s solution. Identification and enumeration of phytoplankton species were determined by the
Alabama Department of Public Health (ADPH) using inverted light microscope. Diatoms, dinoflagellates, chlorophytes, prasinophytes, euglenophytes, cryptophytes, dictyophytes, chrysophytes, and raphidophytes were enumerated in a settling Nunc chamber, only diatoms and dinoflagellates identified to species.
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SeaWiFS data
SeaWiFS data was obtained from the Naval Research Laboratory Offices, Code 7330,
Ocean Sciences Branch, at Stennis Space Center, Mississippi (NRL). The 1 km resolution imagery was processed with the Naval Research Laboratory’s Automated Processing System
(APS, Martinolich, 2005 ). APS Version 3.5 utilized atmospheric correction algorithms proscribed by NASA’s fifth SeaWiFS reprocessing, and includes a near-infrared (NIR) correction for coastal waters ( Arnone, et al., 1998 ; Stumpf, et al., 2003 ). The NIR atmospheric correction method used by APS improves estimates of bio-
Figure 5. Map of collection sites in Mobile Bay, AL, and the eastern Mississippi Sound. Pseudonitzschia spp. population data from sites marked in solid gray were used in DT formation and testing. Sites from this area of Mobile Bay and surrounding waters were used due to maximum values of population data and common presence of Pseudo-nitzschia spp. within collections. optical parameters in coastal regions by applying an iterative technique in which water-leaving radiance at 765 and 865nm is estimated from water-leaving radiance at 670 nm. An absorbing aerosol correction was also applied to improve underestimates of satellite-derived water reflectance ( Ransibrahmanakul and Stumpf, 2006 ). Weekly composites were used due to high percentage of cloud coverage or other aerosol interference in the region. Mobile, Alabama, is listed by the Weather Service as the rainiest city in the United States, with an annual average rainfall of 67 inches and 59 rain days (National Weather Service, 2005). Field collections were undertaken 32 days during the 18 month collection period, with same-day SeaWiFS imagery available12 of those days.
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Decision Tree Development and Evaluation
DT is a type of modeling technique that provides qualitative discrete outputs of a database under certain conditions represented by those data. These outputs are represented by parameters used in challenging those data products with a simple statement (Solomatine and
Dulal 2003). It splits products, or outputs, of those statements into sub-domains for which the output of each is determined by the nature of the statement. Each statement is termed a node, each product of the statement a leaf. The DT classifier function of ENVI v4.3 was used to construct the model (See Figure 6). This function performs multistage classifications by using a series of binary decisions (the statement within the nodes) to place pixels into classes (subdomains represented by leaves). The challenge of the statement within each node divides the pixels in an image into two leaves. The products of the leaves are represented by a numerical output of total pixels affected by the statement and the percentage of pixels surviving the challenge of the statement of each node. The final output is visually represented by a raster image mapping the placement of surviving pixels. In this model, the parameters of the challenge are derived from statistical analysis of the Rrs values from the SeaWiFS sensor and their relationship with population counts of the diatom genus Pseudo-nitzschia .
TableCurve 2-D v5 was used to determine variables used in formation of the nodes of the model. Threshold levels of wavelengths (412, 443, 488, 531, 551, and 670 nm) and all quotients derived from ratios of wavelength products were tested using Gaussian algorithms with leastsquares minimization applied to outputs. Only those quotients yielding an R
2
≤0.4 were used in formation of binary decisions decision tree nodes. Ratio quotients of 670/555, 555/443, and
555/490 were used in model formation. Subsets of Pseudo-nitzschia spp. data were used in formulation and validation of the decision tree. Collections from 05/06/2005 and 05/11/2005
(N=21) were used as training sites for development of the decision tree due to high cell counts
(range 6,800-1.5x10
6
cells/L).
Figure 6. Visualization of Decision Tree classifier from ENVI v4.3. Notice titles of nodes indicating factors used in formation of the statement for the node, while percentages within each leaf represent number of pixels affected by the statement. This output is a result of the test performed on figure 3.D., showing 0.91% of pixels (1,409 of 158,400) having the unique set of environmental properties enabling Pseudo-nitzschia spp. to be present in high population numbers.
Results
Thresholds of Pseudo-nitzschia spp. counts when related to Rrs data were noted early in the analysis of collection data (Figure 7). Wavelengths of 670, 555, and 443 nm exhibited
Gaussian relationships in analysis with population data. These relationships improved as
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quotients of Rrs wavelength products were computed and those quotients applied to analysis. No similar relationships were noted in standard SeaWiFS algorithm products such as Chla, attenuation, or TSS, thus these products were not used in formation of the DT.
DT results were based on a per-pixel comparison of image data. Errors of omission were computed by applying regions of interest to pre- and post-testing images. Total number of pixels corresponding to field data collections of Pseudo-nitzschia spp. populations were shown on final classifications of output imagery (Figure 8), and percentage of pixels
Figure 7. SeaWiFS Rrs products at 555 and 670nm, and quotient of these wavelengths at
670/555, with Pseudo-nitzschia spp. (cells/L
-1
) represented on the y-axis, natural log scale applied to data for allowing observation of cell numbers >10
4
cells/L
-1
.
15

A B
C D
Figure 8.A. SeaWiFS image from May 6, 2006, Rrs at 670 nm, 1 km resolution. Red dots indicate locations of in situ collections; B. Results from decision tree analysis, using Pseudonitzschia spp. counts from May 6, 2006. White dots indicate location of in situ data collections, spatial coverage is identical to Figure 3A; C. SeaWiFS image from April 4, 2005, Rrs at 670 nm,
1 km resolution. Red dots indicate locations of in situ collections, and D. Results from decision tree analysis , using Pseudo-nitzschia spp.
counts from April 4, 2005. White dots indicate location of in situ data collections, spatial coverage is identical to Figure 3C. misclassified by the model were counted as errors of model output. The model exhibited an average error of omission rate of 21%, and average accuracy rate of 79% in testing of 10 collection periods. The results indicated a trend toward offshore motion of waters corresponding to high ratio quotient products (Figure 8B and 8D).
Discussion
This model represents an elegant and relatively simple method of tracking and predicting
Pseusdo-nitzschia spp. within waters of the MB. It is computationally simple, uses satellite data input products that are provided without cost and obtained, processed, and extracted to usable form for model input with relative ease by users of the NASA Ocean Color website, capable of being automated by computer programming, and may be adjusted for accuracy with changing water conditions if a different region of the collection territory is to be monitored. Potential use by fisheries and state health offices will be reliant upon these factors.
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THE USE OF IN SITU REMOTE REFLECTANCE DATA TO FORM REGIONAL
ALGORITHMS FOR SALINTIY PREDICTION
Estimating phytoplankton biomass and distribution through quantifying and mapping nutrient concentrations in near shore environments has been a major application of remote sensing in coastal waters (Harding et al., 1994; Stumpf et al., 2009). The concentration of chlorophyll-a (Chla) is often used as a proxy of the phytoplankton biomass, and is essential in mapping the biological activity at the ocean surface, including monitoring of algal blooms and estimation of primary production (Antoine, Andre, and Morel, 1996; O’Reilly, et al., 1998;
Stumpf, et al., 2009). Remote sensing studies involving phytoplankton are based on the study of ocean color, which is determined by absorption and scattering of visible light by the pure water component of the ocean as well as by the inorganic and organic, particulate and dissolved, materials present in the water (Behrenfeld and Falkowski, 1997; Franz, et al., 2007; Han, 2005;
McClain, 2009). The influence of salinity gradients in the distribution of these constituents has been emphasized in numerous studies (Quinlan and Phlips 2007; Marshall et al. 2006; Muylaert, et al., 2009), with the contribution of estuarine DOC and CDOM of terriginous origin being of major importance to the formation of salinity gradients (Del Castillo and Miller, 2007; D’Sa,
Miller and Del Castillo, 2006). Formation of regionally specific algorithms for the purpose of predicting CDOM and salinity values are crucial for inputs to HAB prediction models based solely on remote sensing values, and none presently exist for this region. This study involves the use of remote sensing reflectance (Rrs) as measured in situ during two cruises within the
Mississippi Sound to compute regional specific algorithms for use in satellite-derived salinity.
An Explanation of Remote Sensing Reflectance
When dealing with open ocean, or Case I, waters (Morel and Prieur, 1977), pure water and phytoplankton are the dominant components influencing the remote sensing signal. In these waters, the optical contribution from components such as DOC and CDOM are minimal. In contrast, Case II waters are defined to contain numerous components derived from riverine and terriginous inputs that vary independently of chlorophyll, and in such amounts that they significantly influence the optical properties in the water (IOCCG, 2000; McClain, 2009). Case
II waters are generally found in inland and coastal water bodies, carrying CDOM and DOC from terrestrial sources by river runoff.
The measurement of these water properties is reliant upon remote sensing reflectance,
Rrs, which is defined as the ratio between the upwelling radiance, L w, just above the water surface to the downwelling irradiance, E d, at the same level. Rrs is a function of wavelength, λ, as well as the viewing angle. Studies have shown that Rrs can be expressed in terms of the absorption coefficient, a
(λ) and the backscattering coefficient, b b(λ) in the water (IOCCG 2000).
In Case I waters variations in a
(λ) and b b(λ) are due to effects of phytoplankton only (Morel and
Prieur, 1977). Consequently, simple empirical relationships can be established between Chl-a and variations in Rrs, when a large number of concurrent in situ measurements of these two parameters are utilized (Morel and Antoine 2000; O’Reilly 1998, 2000). However, in Case II waters, CDOM, organic and inorganic constituents, and phytoplankton contribute to a
(λ) and b b(λ) and do not conform to their inputs (IOCCG, 2000). However, the contribution to a (λ) and b b(λ) from each of these constituents are additive, and can for each be expressed as the product between the concentration of the constituent and its concentration specific absorption and backscattering coefficient, respectively. These coefficients are collectively termed inherent
17

optical properties (IOP) (Hoge, et al., 2001; Kuchinke, et al., 2009; Mobley, 1994). The IOP are functions of the wavelength, and can vary substantially between different water bodies containing various types of constituents (IOCCG, 2000). Even though expressions relating the
IOP and in-water constituent concentrations to Rrs theoretically can be universally applicable for
Case II waters, the coefficients of IOP, and products relying upon this input, must be determined for each water body in question.
Studies have shown the importance of salinity in understanding the spatial and temporal relationships among phytoplankton populations in this region (see Chapter 1). BEST analysis, which selects environmental variables "best explaining" community pattern, resulted in a .727 rank correlation of salinity, surface temperature, and TP as the primary factors driving these relationships. This emphasizes the need for better estimates of salinity to provide inputs for models of phytoplankton populations. Ocean color remote sensing has been shown to provide reasonable estimates for salinity in the nearby Mississippi River plume (Del Castillo and Miller,
2007; D’sa, Miller, and Del Castillo, 2006) based solely on Rrs ratios and empirical algorithms.
The purpose of this study is to provide a salinity algorithm based on Rrs data from the SeaWiFS ocean color sensor specific to the waters of the MS.
Materials and Methods
Study site description
The MS (30.2 N, 88.3W) is a shallow coastal lagoon with a mean depth of 3 m, approximately 130 km long and 11-24 km wide, bordered on the south by a series of barrier islands and on the north by the mainland shoreline of Mississippi (Wilber et al., 2007). Waters from the MS are generally well-mixed with minimal difference between surface and bottom temperature and salinity (Dinnel and Schroeder, 1989). The MS is influenced by groundwater discharge and inputs from the Pascagoula, Escatabwa, and Biloxi River systems and MB discharge (Loyacano and Smith, 1979). The MB estuary (30.5 N, 88.0 W) is a drowned river system, approximately 50 km long with a width of up to 31 km (Schroeder 1977). It is a shallow
(average depth 3m), highly stratified estuary with a surface area of approximately 1,070 km
2
, a relatively small volume of 3.2 X 10
9
m
3
and a short residence time of days to weeks
(NOAA/EPA 1989). MB is dominated by nutrient laden freshwater inputs of riverine and groundwater discharge origins (Loyacano and Smith, 1979). At the north end of MB, the Mobile
River is formed by the confluence of the Alabama and Tombigbee Rivers, combining to form the fourth largest watershed in the coterminous United States (Bricker et al. 2007). This system dominates water flow into the bay, contributing 95% of freshwater input (Schroeder et al., 1979).
The addition of freshwater from the Dog and Fowl Rivers on the west shore and Fish and
Magnolia Rivers flowing into Weeks Bay along the east shore create an average daily freshwater input of 1.56 x 10
8 m
3 d
-1
(Engle et al., 2007), and short freshwater fill time of approximately 20 days (Cowan et al., 1996). Average monthly discharge rate into the northern GoM is 1,800 m
3
s
-1
(for the period from 1929 to 1983, see Schroeder and Weisman, 1986).
Fieldwork We collected water samples and above water optical measurements during tow short cruises to the Mississippi Sound – October 25 and 26 of 2007 – on board a small boat from the
University of Southern Mississippi. Skies were overcast the first day, but clear the second permitting measurements under satellite overflies.
18

Figure 9. Locations of stations used in collection of in situ Spectroradiometer readings and
SeaWiFS data extractions.
Optical Measurements We recorded above-water remote sensing reflectance at 1-nm intervals between 400 and 825 nm using an Ocean Optics spectroradiometer. We followed the measurement protocol of Mueller and Austin (1995). Above-water remote sensing reflectance,
R rs
(

) , was derived according to Mueller and Austin (1995). Briefly, we recorded radiance spectra from surface waters, L

, sea
, followed by measurements of sky radiance, L

, sky
,and radiance from a 98%-reference Spectralon placard (Labsphere). All measurements were taken, at least, in five times. R rs
(

) was calculated as
R rs
(

)

(( L

, sea
 
(

) L

, sky
) /(

L pl
/
 pl
))

L residual 750
(1) where
ρ
(0.025) is the Fresnel reflectance at the viewing angle
θ
(30
°
),
 pl
is the reflectance of the Spectralon, and the L residual 750
is the R rs
at 750 nm that is subtracted to remove any residual reflected radiance from the sky.
Water Sampling and Analysis - Water samples for CDOM analyses were collected simultaneously with the optical measurements. Our sampling device was a glass bottle enclosed in a weighed rig suspended ~10 cm from a float. This sampling apparatus was thrown overboard away from the boat and recovered after the sampling bottle was full. CDOM samples were filtered using 0.22
 m membrane filters mounted on polycarbonate apparatus. Filters were prerinsed with methanol and nanopure water. The filtration system was flushed with sample
(~20 ml) before collecting the CDOM samples.
Absorption Spectroscopy - Absorption spectra of filtered samples were obtained between 250 and
700 nm at 1-nm intervals using a Perkin Elmer Lamda-18 double-beam spectrophotometer equipped with matching 10-cm quartz cells. Nanopure water was used in the reference cell. The absorption coefficients, a (

) , were calculated using a (

)

2 .
303 A (

) / l , where A is the
19

absorbance ( log
10
( I
0
/ I ) ) and l is the pathlength in meters. Absorption at 412 nm was used as an index of CDOM concentration and will be referred to as a g
( 412 ) .
Results
Field samples Table 2 shows station information and salinity and ag412 values for all field samples.
Table 2. Station information, salinity and CDOM values
Station ID Date Time
1
Latitude Longitude salinity ag 412 nm s11 10/25/2007 10:00 30.326
88.756
18 3.712
s10 s9
10/25/2007
10/25/2007
10:25
11:00
30.308
30.286
88.602
88.157
28
27
0.928
1.193
s5 10/25/2007 11:20 30.243
88.340
31 0.675
s6 s8 s12
10/25/2007
10/25/2007
10/25/2007
11:50
12:35
13:40
30.270
30.338
30.336
88.250
88.343
88.658
32
29
25
0.398
0.861
1.796
O1
O2
O3
10/26/2007
10/26/2007
10/26/2007
10:25
10:40
10:55
30.200
30.156
30.055
88.775
88.775
88.770
33
35
35
0.366
0.235
0.251
O4
O5
O6
10/26/2007
10/26/2007
10/26/2007
11:06
11:25
11:40
30.103
30.100
30.150
88.726
88.667
88.672
35
35
29
0.267
0.214
0.877
S1 s13 s14
10/26/2007
10/26/2007
10/26/2007
12:10
12:40
13:00
30.255
30.300
30.285
88.774
88.767
88.834
30
26
25
0.686
1.271
1.460
1
Local time
CDOM showed strong conservative behavior. Figure 10 shows the collection of CDOM absorption spectra obtained from the water samples. Note that the wide spread of CDOM absorption properties shown indicates that our data set is representative of the water types found in the Sound. This is consistent with previous work in coastal areas of the Gulf of Mexico (Del
Castillo and Miller, 2007 and within). Both a power and linear function can be fitted to the data, with the power function showing better statistics (Figure 11). However, the power function is probably driven by a datum at low salinity (station 11), and we do not have a clear physical process to explain a behavior based on a power law. Since linear mixing explains well most of the data, we use the linear regression equation.
20

12
10
8
6
4
2
0
350 400 450 500 550 600
Wavelength (nm)
650 700 750
O2
O3
O4
O5
O6
S 1
S 13
S 14 s11 s10 s9 s5 s6 s8 s12
O1
Figure 10. Absorption spectra of CDOM obtained from samples collected in the Mississippi Sound.
Optical measurements Weather conditions were very favorable for the collection of good above water measurements of radiance and solar irradiance. Day one was completely overcast, and day two had clear skies. Figure 11 shows a collection of all the Rrs spectra generated from the field measurements based on equation 1. The data show the variability in optical conditions found in the study area. We attempted to compute an error budget for the Rrs measurements based on the variability between replicates collected in the field using the RSS method. Laboratory measurements using spectralon placards showed that the instrument variability is negligible, so we assumed that the errors are caused by natural variability and human errors in the field measurements. Figure 12 shows typical example of an error budget for our field measurements.
Figure 13 shows Rrs data from SeaWiFS and MODIS-Aqua over flights. As in this example,
MODIS and SeaWiFS data matched very well is most stations. Most of the variability is caused by measurements of water reflectance. The variability in sky and plackard measurements was negligible for most stations.
Remote sensing measurements of Rrs done with SeaWiFS and MODIS-Aqua compared well with our field measurements (Figure 14). These results are very encouraging considering that only stations O6, S1, S13, and S14 were within two hours of a satellite overpass. Moreover, our field measurements only represent a point within a 1 km
2
pixel in a highly variable coastal environment, whereas the remote sensing measurement is an average of the 1 km
2
pixel.
Nevertheless, a close examination of figure 13 and the literature indicates that our field measurements of Rrs are high by a factor of ~ 1.5. The cause of this problem is an error in the use of the reference placards. The problem is understood and a fix is pending the
21

0.1
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
400 450 500 550 600
Wavelength (nm)
650 700 750
Figure 11. Remote sensing reflectance data collected in the Mississippi
Sound.
O5
O6
S05
S06
S08
S09
S01
O1
O2
O3
O4
S10
S13
S14
7.0
6.0
5.0
4.0
3.0
2.0
1.0
y = -0.1745x + 6.103
r
2
= 0.90
y = 3E+06x
-4.5167
r
2
= 0.93
0.0
15 20 25 30 35 40
Salinity
Figure 12. ag 412 vs. salinity from samples collected in the Mississippi sound. Error bars based on known instrumental variability and NIST standards are included, but they are smaller than the symbols.
22

0.025
0.020
0.015
0.010
MODIS Rrs
SeaWiFS Rrs
S01
0.005
0.000
400 450 500 550
W (nm)
600 650 700
Figure 13. Example of error budget using data from station 01. Gray error bars represent the RSS calculated using the sd from measurements of sky, water, and placard reflectance. Data from MODIS and SeaWiFS are shown for comparison. completion of a careful characterization of the reference placards used. Once these corrections factors are generated, the Rrs will be re-calculated, and an erratum will be submitted as an addendum to this report. Nevertheless, preliminary correction factors that we have generated have a very small spectral dependency. Therefore, our band ratio algorithms are not affected by this error.
Algorithm development Field optical data and laboratory measurements of CDOM (ag412) and salinity were used to develop empirical algorithms for the region. The algorithms are based on the ratio of 510 to 555 nm, and are valid only for SeaWiFS data. The 510/555 was used because it has been successfully used before in coastal waters of the Gulf of Mexico, and SeaWiFS data
23

0.014
0.025
0.020
0.015
Field Rrs
Modis Rrs 0.012
0.010
0.008
0.010
0.005
0.006
0.004
0.002
y = 0.5815x - 0.0005
r
2
= 0.867
0.000
400 450 500 550 600
Wavelength (nm)
650 700 750
0.000
0 0.005
0.01
0.015
Field Rrs (1/sr)
0.02
0.025
0.050
0.045
0.040
0.035
Field Rrs
SeaWiFS Rrs
0.030
0.025
0.030
0.025
0.020
0.015
0.020
0.015
0.010
y = 0.565x - 0.001
r
2
= 0.885
0.010
0.005
0.000
400 450 500 550 600 650 700 750
0.005
0.000
0 0.01
0.02
0.03
0.04
Wavelength (nm)
Field Rrs (1/sr)
Figure 14. Comparison between field and remote sensing measurements of Rrs. Field and remote sensing images were collected on October 25 and 26, 2007.
0.05
are available since 1997. A MODIS based algorithm will be developed for future publications.
The SeaWiFS CDOM algorithm is a power function of the form ag(412) = 0.2953(Rrs510/Rrs555)
-4.1464
, and the salinity algorithm is salinity = 23.975(Rrs510/Rrs555) + 9.967
Figure 15 shows a SeaWiFS image of the Mississippi Sound from October 26, 2007 reprocessed using the ag412 and salinity algorithms developed here.
24

Salinity
Figure 15. Remote sensing retrievals of ag412 and salinity based on empirical algorithms developed in this work.
25

Conclusions
We were able to generate a set of empirical algorithms to estimate values of salinity and ag412 in the Mississippi Sound. The algorithm is base on the ratio of Rrs510 to Rrs555 have shown competence elsewhere in coastal waters of the Gulf of Mexico. Unfortunately, MODIS does not have this band combination, so a separate algorithm is under development for this sensor. Comparisons between field and remote sensing measurements of Rrs are very good for both MODIS and SeaWiFS. Moreover, there is a very small spectral bias between field and remote sensing measurements, and between SeaWiFS and MODIS. Therefore, a multi-sensor approach can be used in the area to increase remote sensing coverage for salinity and CDOM.
This study showed that remote sensing does have utility toward the efforts to monitor and predict location of phytoplankton blooms in the Case II waters of the northern GoM. The characterization of phytoplankton populations in the MS and MB and the understanding of the relationship between salinity, surface temperature, and species composition has allowed for the formation of a decision tree model that is driven completely by satellite data. This model provides another tool for potential use by managers to enhance decision making processes for phytoplankton monitoring networks in the region. Owing to relationships between salinity and phytoplankton populations and the use of SeaWiFS derived data products as input values to decision-tree analysis, the development of predictive algorithms for salinity will enhance the prediction of phytoplankton populations through allowing an input parameter not available through present standard satellite data products.
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APPENDICES
MODIS, SeaWiFs, and VIIRS imagery of Horn and Ship Islands were simulated using hyperspatial and hyperspectral imagery acquired by an airborne sensor, HyMap. The simulation involved both spectral and spatial simulations. Spectroradiometer data was processed as well to emulate the bandsets of the listed satellite sensors. This was done in order for end-users to check point measurements against the spaceborne, area-based outcomes provided by MODIS and
SeaWiFs image results.
Methods
Using HyVista data as input, software routines written in IDL were used to perform the spectral simulations. This was done by acquiring the spectral pass bands of MODIS, SeaWiFs, and VIIRS. The VIIRS spectral channel information is sensitive and only 6 band filters were provided for this exercise. These bands were centered at 488, 640, 672, 865, 1610, and 2250 nm.
Hence, for strictly ocean color work, the band set provided is quite limited.
The IDL routines were written for each sensor separately. In essence, the bandpass filters for the satellite sensors were modeled using, principally, sums of Gaussians. These modeled band filter functions were used to both check whether a HyMap band could contribute to a given satellite sensor band and to weight that band appropriately. All HyMap bands that might contribute were collected at their appropriate weights and summed. The sum of the weights was used as a normalizing quantity for the final simulated sensor band value. An analogous approach was used for the spectroradiometer data.
Spatially, the appropriate pixel size for a specific sensor was used. The HyMap image data had a ~ 3m pixel size. To move to larger pixel sizes a simple‘re-size’ of the pixel was done. This approach was taken since the spectral results were considered key. However, this approach does assume that the PSF of the HyVista is similar to simple square wave (in 2-D language).
Similarly, in re-sizing the pixels, the PSF of the target satellite sensors are ignored and treated in the same manner as the PSF of HyVista.
Results
The most noticeable results are that when emulations are done using a small swath, hyperspatial sensor and moving to pixel sizes for more regionally centric spaceborne sensors, only a few pixels are left to analyze. Figure 1 shows the results for Horn Island using a VIIRS simulation.
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Figure 1. VIIRS simulation over Horn Island. Ship Island HyMap imagery provided even fewer pixels when simulated as satellite based sensor data.
Summary
Simulations for MODIS, SeaWiFs, and VIIRS were completed. Additionally, spectroradiometer data collected at field station sites were used to simulate the spectral bands of the aforementioned sensors. After spectral simulations were complete, spatial re-sizing of pixels was performed to complete the simulation task. The files delivered included the spectroradiometer, MODIS, SeaWiFs, and VIIRS simulations, the spectral/spatial simulations, and also just the spectrally simulated imagery without pixel resizing.
Objective
The objective of this portion of the project to simulate satellite based sensors using
HyMap imagery was to perform an abbreviated assessment of spatial variability as a function of spatial resolution (or pixel size).
Background and Methods
Spatial resolution impacts the relative spectral separability of landcover classes by impacting the variability between such classes. Intuitively, it is recognized that statistical variability of an image is related to the particular scene that is recorded and the sensor that has performed the recording. In general, the variogram of the scene provides a measure of the variability present. The sensor characteristic that impacts the variability is primarily codified by the sensor PSF.
The study documented here represents an empirical analysis of a single scene in which a characterization of the spatial variability of a HyVista scene of Ship Island is presented. The spatial methodology employed is the aggregation of blocks of high resolution imagery into larger pixel sizes to emulate those found in spaceborne sensor imagery. In doing this, it is assumed that the HyVista sensor has a wholly flat response to incoming radiance over its input aperture.
Further, the approach taken in this report is to degrade the HyVista imagery into a series of increasingly coarser resolutions by aggregating blocks of HyVista pixels – and, then, to compute the standard deviation of these degraded images. The fact that a simplified PSF is used for the
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HyVista sensor may impact the overall results of the variation computed. The results presented here ought to be considered an upper bound on the actual variability present in the scene.
Results
The figures shown below, express the results of this study. The pixels sizes used were 3,
30, 100, 500, and 1000 meters.
Figure 1. STD for Ship Island image and a blue spectral band
Figure 2. Same as Figure 1 but for a red spectral band
Discussion
The kind of rolloff seen in Figures 1 and 2 is typical. As the spatial pixel size grows, more spatial averaging occurs, which tends to attenuate a scene’s overall variance. One concern that ought to be mentioned is the impact of atmosphere and its non-correction – or attempts at correction (especially in coastal water areas). Obviously, atmosphere effects can moderate the spatial appearance of a scene as well as impact the magnitude of the light stream reaching the sensor. The HyVista data was used as was sent (use was made of the reflectance data).
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The use of the spatial statistical technique of computing variograms was not used in this study. An estimate of a variogram is easily obtained from ENVI or other remote sensing packages for an image. However, if the data contain significant shifting gradients (on Ship Island illumination shifts are strong), violation of the assumption of stationarity can occur. This renders the use of a variogram approach in this specific instance as dubious.
Summary
The HyVista image was degraded in a serial fashion using the aggregation of pixels into larger pixel sizes. The STD of this series of degraded images was then computed and plotted. Figures 1 and 2 summarize the outcome of these calculations.
Conference Presentations
Holiday, D., G. Carter, R. Gould, and H. MacIntyre. 2007. Harmful algal blooms in Mobile Bay and the Mississippi Sound: a one-year comparison of remote sensing and in situ data.
Proceedings, Annual Meeting of the Association of American Geographers, April 17-21,
2007, San Francisco, CA.
Holiday, D., G. Carter, R. Gould, and H. MacIntyre. 2008. Harmful algal blooms in the northern Gulf of
Mexico: using MODIS Aqua and in situ data toward HABs prediction in turbid coastal waters.
Abstracts, 2008 Ocean Sciences Meeting, March 2-7, 2008, Orlando, FL, p. 168.
Holiday, D., G. Carter, R. Gould, and H. MacIntyre. 2008. Using MODIS Aqua and in situ data for HAB prediction in the northern Gulf of Mexico: decision tree analysis and modeling of ecological conditions. Abstract Book, 2008 American Society of Limnology and Oceanography Summer
Meeting, June 8-13, 2008, St. John’s, Newfoundland and Labrador, Canada, p. 38.
Holiday, D., G. Carter, R. Gould, and H. MacIntyre. 2008. Using MODIS Aqua and in situ data for harmful algal bloom prediction in the northern Gulf of Mexico: decision tree analysis and modeling of ecological conditions. Proceedings, 2008 Mississippi-Alabama Bays and Bayous Symposium, October
28-29, 2008, Biloxi, MS, p. 157.
Holiday, D., G. Carter, and H. MacIntyre. 2009. Using SeaWiFS data and decision tree analysis for predicting Pseudo-nitzschia blooms in the northern Gulf of Mexico. Abstracts of the 94th Annual Ecological Society of America Meeting, August 2-7, 2009, Albuquerque,
NM.
Journal Publications
Three manuscripts are currently being written for submission to peer-reviewed journals. Reprints of final publications will be provided.
ROSES Proposals Selected for Award
Grimes, D. J, C. N. Johnson, G. A. Carter, C. E. Del Castillo, and C. M. Hall. 2009-2011.
Remote Sensing to Assess Microbial Water Quality at Beaches and Shellfish Beds. ROSES
A.28, $400,000, 2 years.
37

Budget
Project Budget
A. SALARIES AND WAGES:
USM PERSONNEL
Salaries
Fringe
TOTAL SAL & FRINGE
B. EXPENDABLE SUPPLIES & EQUIPMENT computational supplies & materials
TOTAL SUPPLIES
C. TRAVEL
Meetings
Local travel @ $.485/mi.
TOTAL TRAVEL
D. PUBLICATIONS & DOCUMENTATIONS COSTS
Journal Charges
E. CONTRACTUAL SERVICES
Subcontract to John's Hopkins U.
Subcontract to SSAI
TOTAL CONTRACTUAL
TOTAL DIRECT COSTS
H. FACILITIES & ADMINISTRATIVE COSTS
On-Campus 46%
TOTAL REQUESTED
18 mo. Period
$46,709
$6,850
$53,559
$1,500
$1,500
$1,050
$450
$1,500
$500
$170,285
$39,100
$209,385
$266,445
$49,247
$320,563
38