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Open Access Theses & Dissertations

2016-01-01

Predicting The Water Quality Of Shallow ArcticPonds Using Remote SensingGabriela TarinUniversity of Texas at El Paso, gcontreras8@utep.edu

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Recommended CitationTarin, Gabriela, "Predicting The Water Quality Of Shallow Arctic Ponds Using Remote Sensing" (2016). Open Access Theses &Dissertations. 761.https://digitalcommons.utep.edu/open_etd/761

PREDICTING THE WATER QUALITY OF SHALLOW ARCTIC

PONDS USING REMOTE SENSING

GABRIELA TARIN

Master’s Program in Environmental Science

APPROVED:

Vanessa L. Lougheed, Ph.D., Chair

Craig E. Tweedie, Ph.D.

Deana D. Pennington, Ph.D.

Charles Ambler, Ph.D.

Dean of the Graduate School

Copyright ©

by

Gabriela Tarin

2016

PREDICTING THE WATER QUALITY OF SHALLOW ARCTIC

PONDS USING REMOTE SENSING

by

GABRIELA TARIN B.S.

THESIS

Presented to the Faculty of the Graduate School of

The University of Texas at El Paso

in Partial Fulfillment

of the Requirements

for the Degree of

MASTER OF SCIENCE

Environmental Science

THE UNIVERSITY OF TEXAS AT EL PASO

December 2016

iv

Abstract

Barrow, Alaska is in a region dominated by Arctic tundra of which a substantial part is

covered by lakes and ponds. Despite their dominance in the landscape, freshwater ecosystems in

the Arctic have been insufficiently studied. It is clear that furthering understanding of how Arctic

water bodies are responding to warming will depend on the analysis of changes in the

concentration of organic and inorganic constituents in the water; however, scientists are faced with

the task of sampling many remote sites in a relatively hostile environment. Thus, the exploration

and incorporation of remote methods for monitoring changes in water quality. However, ponds are

often excluded from remote sensing studies due their shallow depth and their small size, leading

to difficulty in selecting a platform suitable for their small spatial area, and generally shallow

depth. The objective of this study was to examine the utility of established optical remote sensing

indices collected from both ground-based (JAZ spectrometer) and satellite-based (WorldView-2)

measurements of open water reflectance for predicting water quality of arctic tundra ponds.

Multiple strong relationships were found between environmental parameters and ground-

based reflectance from the JAZ spectrometer. Ground based reflectance appeared to be a strong

predictor for measurements of chlorophyll, total suspended solids (TSS) and dissolved carbon

compounds. Some of the most useful indices appeared to be the single wavelengths at 682 nm

(Index 18) and 806 nm (Index 16), as well as the multiple wavelength ratios 710+820/740 (Index

6), 710+820/675+740 (Index 8) and 700/400 (Index 5).

We found that ponds with different characteristics had unique reflectance signatures that

could, at least in part, be associated with the differing water chemistries of these different pond

types. High concentration of dissolved carbon compounds, especially dissolved organic carbon

(DOC) and C440, in thermokarst ponds in particular, led to very unique signatures and should be

examined further to strengthen the utility of established relationships.

We found substantially more significant relationships with environmental parameters using

the ground-based reflectance data than when we used satellite-based data. Only two ratios proved

v

useful for explaining water quality under both platforms. The ratio of 700:400 was strongly

associated with measures of chlorophyll, which are commonly associated with the 700 nm

wavelength, as well as measures of carbon (DOC, C440), which commonly peak at 400 nm. The

ratio 400/480 was also useful on both platforms for estimating TSS and the spectral ratio.

Using a preliminary model for predicting CO2 based solely on remotely-sensed DOC

levels, we were able to predict CO2 with a reasonable degree of accuracy in small tundra ponds.

These types of models do not exist for shallow freshwater ecosystems, and hold promise for

complementing landscape-level estimates of carbon flux following further analyses.

vi

Table of Contents

Abstract .......................................................................................................................................... iv

Table of Contents ........................................................................................................................... vi

List of Tables ................................................................................................................................ vii

List of Figures .............................................................................................................................. viii

Introduction ......................................................................................................................................1

Methods............................................................................................................................................6

Ground-based measurements ..................................................................................................6

Satellite-based measurements .................................................................................................8

Analyses ................................................................................................................................10

Results ............................................................................................................................................12

Ground based measurements ................................................................................................12

Satellite-based measurements ...............................................................................................16

Comparison of ground and satellite-based models ...............................................................24

Estimate of CO2 efflux from remote sensing: a preliminary model .....................................25

Discussion ......................................................................................................................................26

Satellite-based measurements ...............................................................................................29

Ground and satellite reflectance comparison ........................................................................30

Limitations of the study ........................................................................................................31

Estimation of CO2 efflux from satellite data.........................................................................32

Conclusion .....................................................................................................................................34

References ......................................................................................................................................35

Vita ..............................................................................................................................................39

vii

List of Tables

Table 1: Characteristics of multispectral bands on the WorldView-2 satellite (Digital Globe, ..... 8

2009). .............................................................................................................................................. 9

Table 2: Previously established ratios selected for examining water quality. ............................. 11

Table 3. Mean water quality parameters for 4 regions for dates used in JAZ analyses only. ...... 12

Table 4. Parameters of significant (p<0.05) linear regressions of water quality variables predicted

using JAZ ratios where log Water Quality Parameter = a * log Index + b. .................................. 15

Table 5. Comparison of indices (from Table 2) adapted for use with WorldView-2, and the

reason certain indices were excluded from further analysis. ........................................................ 18

Table 6. Summary of significant (p<0.10) correlations between log-transformed environmental

variables (rows) and WV-2 ratios (columns; Index numbers are defined in Table 2). Darker red

are strongest negative and darker green strongest positive. .......................................................... 19

Table 7. Parameters of significant (p<0.05) linear regressions of water quality variables predicted

using WV-2 ratios where log Water Quality Parameter = a * log Index + b. ............................... 20

Table 8. Water quality variables that can be predicted using similar JAZ & WV-2 ratios (ratio 4

& 5) ............................................................................................................................................... 24

viii

List of Figures

Figure 1. The location of the ponds studied in the analysis . .......................................................... 7

Figure 2. The location of our main 7 IBP ponds studied in the analysis . ...................................... 8

Figure 4. Observed ground-based reflectance within each WorldView-2 Band for 7 ponds in the

IBP Ponds, this is an average of 3 days. ....................................................................................... 17

Figures 7: Linear regression between indices and our water variables show a prediction of water

quality from satellites; indices 4 and 5 were the most significant with our variables (P<0.10) ... 21

Figure 8: Non-linear regression between indices and our water variables show a prediction of

DOC from satellites. ..................................................................................................................... 22

Figures 9: Regression between collected water data and water quality predicted at landscape

level (R2<0.10). ............................................................................................................................. 23

Figures 10: Relationships between the indices 4 and 5 measured on 2 different platforms. ........ 24

Figures 11: Regression between collected data during summer 2015 and model PCO2 data from

DOC non-linear regression. .......................................................................................................... 25

1

Introduction

Inland waters, have important functions in the environment. They provide habitat for a

wide range of species and form essential components in hydrological, nutrient and carbon cycles

(Dörnhöfer and Oppelt, 2016). Barrow, Alaska is in a region dominated by Arctic tundra of which

a substantial part is covered by lakes and ponds (Hinkel, 2003). Despite their dominance in the

landscape, freshwater ecosystems in the Arctic have been insufficiently studied and understanding

their behavior, quality and rate of change is of utmost importance. This is increasingly clear with

the recent recognition that the smallest freshwater ecosystems in the landscape, small tundra ponds,

are being lost from the landscape due to factors related to warming, which will only intensify with

time (Andresen and Lougheed, 2015).

The region is underlain by a deep permafrost layer, which is expected to thaw with

warming, increasing active layer depth over the next fifty years (Lawrence and Slater 2005).

Permafrost is very sensitive to temperature changes and deeper active layers can release substantial

amounts of both organic (e.g. DOC) and inorganic compounds (e.g. phosphorus, nitrogen) from

formerly frozen organic ground into the ponds (Reyes and Lougheed, 2015). These effects have

been observed in water bodies throughout the Arctic (Hobbie et al. 1999, Frey and Smith 2005,

Schindler and Smol 2006, Frey et al. 2007, Keller et al. 2007, 2010, McClelland et al. 2007,

Lougheed et al. 2011, Lewis et al. 2012) and are likely leading to increased algal production (e.g.

Lougheed et al. 2011) and expansion of wetland plants (Andresen and Lougheed, 2015).

Furthermore, urban development in the Arctic can also lead to enrichment of water bodies,

including small tundra ponds (Lougheed et al. 2015). It is clear that understanding how Arctic

water bodies are responding to warming will depend on the analysis of these organic and inorganic

compounds; however, scientists are faced with the task of sampling many remote sites in a

relatively extreme environment. Thus, the exploration and incorporation of remote methods for

monitoring changes in water quality into existing labor-intensive field monitoring programs is

2

required (Dörnhöfer and Oppelt, 2016; Park et al. 2016).

The different chemicals contained in water play an important role on its quality, its ability

to support aquatic organisms and its capacity for absorbing light and other electromagnetic

radiation. The change of water reflectance signatures can be one of the first indicators of changes

in water quality, with constituents such as total suspended solids (TSS), colored dissolved organic

matter (CDOM) and chlorophyll pigments (CHL-a), among the most commonly examined

parameters in remote sensing studies. TSS is a measure of organic and inorganic solids that are

trapped by a filter. CDOM is a result of dissolved humic substances that color the water, which

can be highly concentrated in organic Arctic soils. CHL-a is commonly used as a surrogate for

algal (phytoplankton) biomass (Wetzel and Likens, 2000). Inherent optical properties of water,

such as absorption and scattering, have been successfully related to the concentrations of these

optically active substances in lakes (Belzil et al., 2004, Arenz et al., 1996, Menken et al. 2009.

McCullough, 2012), reservoirs (Arenz et al, 1996), rivers (Li-Gang Fang et al., 2009, Olmanson

et al. 2011) and oceans (Fichot et al.2013, Matthews 2011). However, remote sensing of small and

shallow water bodies are the least frequently studied using remote sensing methods.

One of the primary difficulties on working with shallow water bodies is separating the

spectral signal from the water column from that of the bottom sediments. For example, the radiance

reflected from the bottom sediments can contribute to albedo at water depths less than 25 m

(Cannizzaro and Carder, 2006), making it more difficult to isolate the reflective properties of water

alone. Light penetrates the deepest in a range of ~450-600nm. Within these wavelengths,

reflectance from shallow bottom sediments can substantially increase reflectance values, thus

leading to an overestimate of the concentration of compoundsof concern. Therefore, ratios of bands

outside this spectral window are often utilized in shallower waters (Cannizaro and Carder, 2006).

Remote sensing studies typically involve the mapping of concentrations of a given variable

in water bodies using radiance or reflectance collected by a sensor placed above the water surface

(Zimba and Gitelson, 2006). In situ concentrations of the components of interest are then related

3

to observed radiance or reflectance values in order to develop empirical models. Spectral values

from 400 to 1100nm have been successfully used to monitor water quality, with the most common

optically active constituents within this range being CHL-a, TSS, and CDOM. Multispectral

satellites have the ability to detect light in the blue band (450-520 nm), green (510-580 nm) and

red band (630-690 nm) to archive depth estimates in water up to 15 meters in depth. Each

component may serve as an indicator for important biochemical processes in aquatic ecosystems,

which are affected by adjacent terrestrial processes.

CHL-a often has a peak reflectance around 700nm and is associated with phytoplankton

and primary production (Gitelson, 2009). Another study showed that the ratio of reflectance at 700

nm to that at 670 nm is the best predictor of CHL-a over a wide range of conditions, including

high turbidity (Menken et al.,2005). Other studies suggested that a peak greater than 700 nm cannot

be applied to chl-a in shallow waters, due to the high variability of hydromorphological

characteristics, algae content and resuspension (Vinciková et al., 2015). In contrast, TSS has been

shown to have a strong signal around 555 nm (Belzile et al., 2004), while DOC is also often

quantified at wavelengths at or below 600 nm (Kutser et al., 2015).

Because DOC can range from visible to non-visible saturation in lakes, the strong

correlation between colored dissolved organic matter (CDOM) and DOC within lakes has been

used to infer aquatic carbon in aquatic systems using remote sensing (Cardille et al., 2013).

Previous studies from Landsat satellites have found a strong relationship of CDOM with the ratio

of Band 2 (450 nm – 510 nm) to Band 3 (520 nm- 580 nm) (Cardille et al., 2013), while different

studies have also shown that CDOM has the strongest relationship with wavelengths less than 500

nm (Menken et al., 2005). Use of existing models is complicated by the fact that fresh water and

salt water can affect a different effect on these compounds, showing different absorptivity at each

water body type (Belzile et al., 2009; Brezonik et al., 2015). It is important to further define these

spectral properties, especially for shallow fresh water bodies, in order to have a better approach

for estimating water quality.

4

Optical and thermal sensors on satellites provide the opportunity to upscale observed

changes in water quality parameters over large spatial and temporal scales, which provide a

baseline for future comparisons and allow the development of management plans to improve water

quality (Ritchie et al., 2003). Documenting the responses of water quality to climate change is

essential not only for estimating the carbon flux and the albedo of the region, but is also significant

for the interaction between people, nature and the different species living in that ecosystem. In

particular, the measurement of primary production in bodies of water using remote sensing is a

very promising technique since remote sensing can provide daily measurements over a broad

spatial scale. Previous applications of remote sensing towards this end have included the

estimation of annual productivity in Lake Michigan (Shuchman, 2013), and mapping CHL-a

concentrations throughout the Great Lakes (Lesht, 2013).

Ponds are often excluded from remote sensing studies due their shallow depth, and their

small size, as described previously, which precludes the use of many satellite platforms. While

several authors have examined a standard ratio of green: red to estimate chl-a from MODIS and

MERIS platforms, these sensors have a relatively coarse resolution (>250m)(Hestir et al. 2015),

which is not suitable for smaller waterbodies. Other commonly used multispectral satellites, such

as Landsat, while having a finer resolution (30m), are still not suitable for these relatively small

water bodies. Tundra ponds tend to be less than 50m across, with the open water areas substantially

less than this (Andresen & Lougheed, 2015). The high-resolution Worldview-2 satellites provide

a resolution of <2m for the multispectral sensor bands, which are ideal for remote sensing

applications involving tundra ponds (Andresen et al. 2016). High spectral resolution data also

provides more spectral band needed to unmix the variables, which is a problem in freshwater

ecosystems. (Hestir et al, 2015)

Ponds are important stores of carbon and reservoirs of biodiversity that are vulnerable to

global change. With a warming, arctic permafrost is expected to thaw, releasing carbon from

terrestrial to aquatic systems (Ping et al., 1998; Frey and McLelland, 2009). Increased dissolved

organic carbon (DOC) has been found to impact photosynthesis, heat budgets, mixing, and oxygen

5

concentrations within lake ecosystems (Brezonik et al., 2015). In Arctic freshwater ecosystems,

there is a strong association between pCO2 and DOC (R2=0.75), with highest DOC and carbon

efflux found in waters more closely linked to terrestrial landscapes (i.e., ponds, rivers) (Lougheed,

unpubl. data). While DOC, is not often quantifiable using remote sensing, CDOM can be used as

a surrogate for DOC at regional scales (Brezonik et al., 2015). Because of these links, remote

sensing has the capacity to provide a clear estimate of the role of freshwater bodies in Earth’s

carbon cycle, and eventually create an extensive global database of carbon (Cardille et al., 2013).

The Arctic tundra ponds at the International Biological Program (IBP) site in Barrow,

Alaska, studied for the first time in the 1970s, represent one of the very few locations in the Arctic

where long-term data are available on freshwater ecosystem structure and function. Recently, we

have demonstrated substantial changes in nutrient concentrations and algal biomass within these

ponds over the past 40 years (Lougheed et al. 2011), and have used remote sensing to show that

these ponds are being encroached on by aquatic vegetation (Andresen and Lougheed, 2015), and

release a substantial amount of methane (CH4) to the atmosphere (Andresen et al, 2016). However,

extrapolating water quality and algal trends across the landscape is limited by our ability to access

these sites. The region itself is dotted with thousands of ponds, which are often logistically

challenging to access and sample, and require the use of very expensive equipment. Remote

sensing technologies and approaches may provide the opportunity to assess water quality on a

large spatial scale. Since ponds will be monitored throughout the seasons and in different years,

these models will provide temporal observations of reflectance properties of water for better

understanding of intra-annual and inter-annual variability of water quality. This approach has

never been used before on shallow waters. Understanding the behavior of the ponds and the

increase and decrease of these variables can help us to estimate the CO2 that is being release to the

atmosphere annually from the Arctic Tundra Ponds.

The objective of this study was to examine the utility of established indices based on

ground-based (JAZ spectrometer) and satellite-based (WorldView-2) open water reflectance for

predicting water quality of arctic tundra ponds.

6

Methods

Ground-based measurements

To have a better understanding of the relationship between water quality and open water

reflectance, we collected samples from 37 ponds and 2 lakes over the summer of 2011 and 2012

(Fig 1). These sites encompassed different levels of nutrients and suspended matter: (1) ten pristine

ponds within the Barrow Environmental Observatory (BEO), (2) sixteen historic research ponds

(IBP) near an area of human development, (3) 7 relatively nutrient-rich or impacted ponds within

the town of Barrow, Alaska, and (4) 5 thermokarst ponds (thaw ponds with relatively high

DOC/CDOM). The ponds represented a variety of depths and are contained within different

drained thaw lake basins. Sampling occurred on 10 dates in 2011, and 7 in 2012; not all sites were

sampled on all dates. The ponds sampled the most frequently were a subset of the ponds found in

the IBP (Figure 2).

Open water reflectance was acquired on a 15-day basis throughout the season (June-August

2011-2012) using a single channel Jaz spectrometer (Ocean Optics Inc) operated through a laptop

with the SpectraSuite software (Ocean Optics Inc), where reflectance was calibrated with a white

panel at each site sampled to correct for illumination changes. Reflectance of water was measured

at a height of approximately 1.5m, with a 20° viewing angle probe perpendicular to the water

surface. Reflectance was calculated by the light reflected from surface reflection of the ponds.

Since the solar elevation angle is relatively low (<65°) in the Arctic, there is little concern of

interference from the sun’s specular reflected radiance.

In combination with the reflectance measurements, water was also collected from the open

water at each site and processed in a laboratory for various water quality parameters. Water

samples and reflectance were both collected from similar locations in the open water of each pond.

7

Samples were analyzed for the major factors affecting water optical properties in oligotrophic

environments: dissolved organic carbon (DOC), total suspended solids (TSS) the light extinction

co-efficient, CHL-a concentration as a surrogate of phytoplankton (free-floating algae) and

periphyton (algal attached to the sediment surface) biomass. Analytical methods are described

elsewhere (Lougheed et al. 2015, Reyes and Lougheed 2015). The character of the DOC was also

estimated using SUVA254, an indicator of aromaticity (Weishaar et al. 2003), C440, a measure of

color from light absorbance at 440 nm (Cuthbert and del Giorgio, 1992), and the spectral ratio

(Helms et al. 2008), an indicator of molecular weight. Weather conditions were also recorded

during sampling; only data from sunny, non-overcast days were used in the analysis.

Figure 1. The location of the ponds studied in the analysis .

8

Figure 2. The location of our main 7 IBP ponds studied in the analysis.

Satellite-based measurements

Satellite imagery from WorldView-2 (WV-2) was acquired for dates matching the ground

sampling dates. We selected images no more than 5 days apart from the field sampling date. After

viewing four candidate images, only one was excluded. One image from July 19, 2011 had low

color quality. We used the remaining three images from July 10, 2011, July 11, 2012 and August

13, 2012.

All the images were pre-processed for atmospheric correction, since the wind and the

clouds can affect pixel values (Mueller, 2003). WorldView-2 imagery has a resolution of 1.8 m

for multispectral bands and 0.46m for Panchromatic image data. Pansharpening, the fusion of

multispectral bands and the panchromatic images, resulting in a resolution of ~0.5m. This

9

resolution allows for characterization of the small ponds, which are generally less than 50m in

diameter, with an even smaller area of open water. Pixels from the middle of the ponds, without

the interference of aquatic vegetation, were manually selected and used to create a Region of

Interest (ROI) for extracting spectral measurements for associated wavelengths and the bands from

the image. The ROI created for each site provided consistency in selecting and analyzing the same

area on multiple dates. We used all 8 multispectral bands from World View-2, which span

wavelengths of 400 to 900nm (See Table 1).

Table 1: Characteristics of multispectral bands on the WorldView-2 satellite (Digital Globe,

2009).

Num of band Segmentation Wavelength

1 Coastal 400-450 nm

2 Blue 450-510nm

3 Green 510-580 nm

4 Yellow 585-625 nm

5 Red 630-690 nm

6 Red Edge 705-745 nm

7 Near IR-1 770-895 nm

8 Near IR-2 860-1040 nm

The surface reflectance of water contains not only the reflection information of water but

also information of diffused sunlight reflected by air-water interface (Li-GangFang et al. 2009).

Shallow waters are more impacted by reflection from the air-water interface. The WorldView-2

images were corrected according to the angle of the sun and the day to get the real reflectance of

each band. We used the reflectance formula for radiance and for reflectance on all the images,

provided by USGS Landsat 8 product:

Lλ = MLQcal + AL

where:

Lλ = Top of atmosphere (TOA) spectral radiance (Watts/(m2 * srad * μm))

ML = Band-specific multiplicative rescaling factor from the metadata

AL = Band-specific additive rescaling factor from the metadata

10

Qcal = Quantized and calibrated standard product pixel values (DN)

And, reflectance with a correction for the sun angle is then:

ρλ = ρλ'

= ρλ'

cos(θSZ) sin(θSE)

where:

ρλ = TOA planetary reflectance

θSE = Local sun elevation angle. The scene center sun elevation angle in degrees is provided in

the metadata (SUN_ELEVATION).

θSZ = Local solar zenith angle; θSZ = 90° - θSE

Analyses

Several researchers have examined the use of 20 different band-ratios or indices of spectral

properties of natural waters such as rivers and lakes (Table 2), and we examined the utility of these

indices for Arctic tundra ponds. In some cases, we slightly modified the bands used (e.g Belzile et

al. 2004 used 443 nm in a lake and we used 440 nm in the ponds). We also created several indices

based on what we observed as significant peaks in the reflectance dataset (Figure 3). While we

were able to calculate these ratios with our JAZ ground-based spectrometer, because the

WorldView-2 does not record these precise wavelengths, we created ratios modified for use with

this satellite system (see Table 5).

All water quality parameters and indices were log-transformed, as required, to ensure

normalized residuals in the regression models. All possible combinations of indices and

environmental variables were compared to identify the strongest relationships between reflectance

and water quality.

11

Table 2: Previously established ratios selected for examining water quality.

Index No. Indices Author

1 710/820 Gitelson et al. 2009

2 650/675 Belzile et al. 2004

3 710/675 Belzile et al. 2004

4 400/480 Based on Cannizzaro and Carder 2006

5 700/400 Based Menken et al. 2009

6 710+820/740 Based on Menken et al. 2009

7 740((1/670))-(1/710)) Based on Menken et al. 2009

8 710+820/675+740 Based on Figure 3

9 710+820/675-740 Based on Figure 3

10 440 Based on Belzile et al. 2004

11 440/520 Based on Menken et al. 2009

12 700/670 Menken et al. 2009

13 716/670 Menken et al. 2009

14 806/670 Gitelson et al. 2009

15 571 Based on Gitelson et al. 2009

16 806 Based on Menken et al. 2009

17 443/488 Based on Gitelson et al. 2009

18 682 Based on Gitelson et al. 2009

19 510/670 Menken et al. 2009

20 670/550 Gitelson et al. 2009

12

Results

Ground based measurements

Open water reflectance patterns differed among different site types (Figure 3). Thermokarst

ponds, had a large peak at lower wavelengths (400nm), which corresponds to substantially higher

DOC and C440 at these sites (Table 3). Urban ponds have the highest peak at mid-wavelengths

(550-700 nm), which is often associated with higher TSS and algal biomass. Finally, IBP ponds

have distinct peaks at 700 and 800 nm, which have also been used for algal and vegetation

signatures.

Table 3. Mean water quality parameters for 4 regions for dates used in JAZ analyses only.

Regio

n

Depth

(cm)

Correcte

d CHL

(µg/L)

Total

CHL

(µg/L)

Periphyto

n (µg/cm2)

TSS

(mg/L

)

DOC

(mg/L) C440

Conductivit

y (mS/cm)

BEO 19 0.76 1.18 9.1 21 172 0.16

IBP 25 0.83 1.35 3.4 3.8 19 129 0.14

BRW 44 1.05 1.56 24.4 3.8 29 195 0.68

TK 13 10.21 11.49 7.8 17.6 136 1557 0.24

13

Wavelength

400 500 600 700 800 900

% R

efl

ec

tan

ce

0.0

0.1

0.2

0.3

0.4

0.5

IBP-Ponds

Thermokarst- Ponds

Urban- Ponds

Figure 3. Comparison of reflectance signatures measured by the handheld JAZ spectrometer. Each

line represents the average reflectance of 4-5 sites of each site type visited in August 2012,

the only date when all 3 site types were visited.

Simple linear regressions between JAZ data and environmental data revealed multiple

strong relationships (Table 4). The single wavelengths at 682 (Index 18) and 806 (Index 16) tended

to be strongly associated with measures of total phytoplankton CHLa, TSS and carbon

concentration (DOC) and quality (C440). Similarly, the multiple wavelength ratios 710+820/740

(Index 6) and 710+820/675+740 (Index 8) were strongly related to total and/or corrected CHLa,

as well as TSS, DOC and C440. To a lesser extent, 700/400 (Index 5) was moderately related to

total CHLa, TSS, and C440. Other indices were more likely to be related to only one or two

environmental variables; this included moderately strong relationships of: SUVA254 with 510/620

(Index 19) and 670/550 (Index 20); conductivity with 710/820 (Index 1) and 650/675 (Index 2);

TSS with 400/480 (Index 4), and 710+820/675-740 (Index 9); and, the spectral ratio with 710/820

(Index 1), 400/480 (Index 4), and 710+820/675-740 (Index 9); and, finally, periphyton with

710/820 (Index 1), 440 (Index 10), and 440/520 (Index 11). The average r2 was moderately strong

(0.29), for the 44 observed relationships. Regression coefficients for TSS tended to be highest,

14

largely because TSS was not analyzed for all dates, resulting in fewer data points in these graphs.

There were no observed relationships with light extinction coefficients.

15

Table 4. Parameters of significant (p<0.05) linear regressions of water quality variables predicted

using JAZ ratios where log water quality parameter = a * log Index + b.

Water quality

parameter (Y) Index (X)

Index

# a b R2 p

Corrected

CHLa

710+820/740 6 -1.344 2.09 0.191 0.0007

710+820/675+740 8 -1.175 2.048 0.138 0.0047

806 16 -1 1.482 0.119 0.0091

682 18 -1.167 1.647 0.165 0.0019

Total CHLa

710+820/740 6 -0.951 1.759 0.412 <0.0001

710+820/675+740 8 -0.877 1.813 0.331 <0.0001

571 15 -0.538 0.963 0.137 0.0053

806 16 -0.716 1.333 0.256 <0.0001

682 18 -0.67 1.249 0.252 <0.0001

700/400 5 – 1.252 0.876 0.25 <0.0001

TSS

710+820/740 6 -1.348 2.689 0.682 <0.0001

710+820/675+740 8 -1.631 3.454 0.673 <0.0001

710+820/675-740 9 -4.167 8.795 0.362 0.0049

571 15 -0.636 1.513 0.202 0.0467

806 16 -1.152 2.367 0.371 0.0043

682 18 -1.026 2.089 0.458 0.001

400/480 4 2.331 -0.118 0.262 <0.0002

700/400 5 – 2.096 1.792 0.54 <0.0209

C440

710+820/740 6 -0.992 3.868 0.386 <0.0001

710+820/675+740 8 -0.879 3.86 0.287 <0.0001

806 16 -0.731 3.402 0.226 0.0002

682 18 -0.72 3.325 0.229 0.0002

700/400 5 – 1.250 2.912 0.215 <0.0003

SUVA 254 510/670 19 0.156 0.44 0.242 0.0446

490/670 12 0.135 0.441 0.238 0.0446

Spectra Ratio

710+820/740 6 0.206 -0.424 0.379 0.0065

710+820/675+740 8 0.199 -0.455 0.274 0.0256

710+820/675-740 9 0.854 -1.778 0.321 0.0142

682 18 0.145 -0.313 0.227 0.0451

400/480 4 – 0.461 0.041 0.231 <0.0431

16

DOC

710+820/740 6 -0.772 2.675 0.402 <0.0001

710+820/675+740 8 -0.672 2.645 0.288 <0.0001

571 15 -0.403 1.981 0.113 0.011

806 16 -0.603 2.365 0.264 <0.0001

682 18 -0.643 2.377 0.314 <0.0001

700/400 5 -0.67 1.764 0.105 <0.0145

Conductivity

710/820 1 1.013 -0.645 0.314 <0.0001

650/675 2 4.92 -0.671 0.552 <0.0001

440/520 11 -0.983 -0.707 0.213 0.0003

571 15 0.61 -1.504 0.155 0.0024

670/550 20 -1.105 -0.553 0.15 0.0029

Periphyton

710/820 1 1.085 0.709 0.147 0.0078

440 10 0.812 -0.352 0.087 0.0435

440/520 11 -1.229 0.628 0.142 0.0089

Satellite-based measurements

For each of the WorldView-2 bands, ground-based reflectance varied substantially among

sites (Figure 3). The highest variability among sites was seen within wavelength range of bands 1,

2 and 3 (400-580nm), with less amounts seen for bands 4 & 5 (585-690 nm) and the higher

wavelengths.

17

Figure 4. Observed ground-based reflectance within each WorldView-2 Band averaged over 3

days for 7 IBP ponds.

The first step in the analysis of the WorldView-2 data was to eliminate indices that were

not suitable for use with this platform. These were eliminated for 4 reasons (see also Table 5):

1. When the 2 wavelengths used in the index were part of the same WorldView-2 band (e.g.

Index 2 = Band 5/Band 5).

2. Most of the non-ratios were excluded. Ratios represent relative values, instead of absolute

values, and thus are less influenced by differences among image dates in factors such as

brightness or resolution. The only single band ratio that was included was Index 10 (Band

1), as it appeared to be unaffected by image date.

3. Ratios between bands 5, 6 and 7 were largely excluded, as these bands were highly

correlated with each other (r>= 0.99). Highly correlated bands may represent similar

information, and suffer from the same problems identified for non-ratios. Figure 4

highlights how these bands also provide less distinction among site types.

4. Finally, after conversion to WorldView-2 bands, some indices became duplicates. This

includes indices 4 & 17 (Band 1 / Band 2) and Indices 3, 12 & 13 (Band 6/ Band 5).

-2

-1.8

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2lo

g (G

rou

nd

-bas

ed

ref

lect

ance

)

Wavelength (nm)

17

A

B

C

E

G

J

18

Table 5. Comparison of indices (from Table 2) adapted for use with WorldView-2, and the

reason certain indices were excluded from further analysis.

Index No. Indices

Modified

WorldView-2

Indices

Reason for exclusion

1 710/820 Band 6 / Band 7 Correlation between bands 5, 6, 7

2 650/675 Band 5 / Band 5 Ratio not possible for WV2

3 710/675 Band 6 / Band 5 Correlation between bands 5, 6, 7

4 400/480 Band 1 / Band 2

5 700/400 Band 6/Band 1

6 710+820/740 Band 6+Band

7/Band 6 Correlation between bands 5, 6, 7

7 740((1/670))-

(1/710))

Band 6 (1+ Band

5)- (1/Band 6) Correlation between bands 5, 6, 7

8 710+820/675+740

Band 6+ Band

8/Band 5 + Band

6

Correlation between bands 5, 6, 7

9 710+820/675-740 Band 6 + 8/ Band

5- Band 6 Correlation between bands 5, 6, 7

10 440 Band 1

11 440/520 Band 1/Band 3

12 700/670 Band 6/Band 5 Correlation between bands 5, 6, 7

13 716/670 Band 6/Band 5 Correlation between bands 5, 6, 7

14 806/670 Band 8/Band 5 No significant regression

15 571 Band 4 Not a ratio

16 806 Band 8 Not a ratio

17 443/488 Band 1/Band 2 Duplicate of Index 4

18 682 Band 5 Not a ratio

19 510/670 Band 2/Band 5

20 670/550 Band 5/Band 3

After excluding those indices not suitable for the WorldView-2 platform, we found 30

significant linear correlations between environmental variables and the seven remaining indices

(Table 6). There were no observed relationships with light extinction coefficients or periphyton.

Indices numbers 5 (700/400), 11 (440/520), 19 (510/670) and 20 (670/550) were correlated with

the most environmental variables (>7); all others were largely correlated with 3 or more variables.

DOC, C440 and TSS were among the variables correlated with all of the indices, while SUVA 254

was only correlated with one index.

19

Table 6. Summary of significant (p<0.05) correlations between log-transformed environmental

variables (rows) and WorldView-2 ratios (columns; Index numbers are defined in Table 2).

Darker red are strongest negative and darker green strongest positive.

Index Number

Variable 4 5 10 11 19 20

DOC 0.371 -0.429 0.338 0.401 0.398 -0.451

C440 0.452 -0.409 0.375 0.409 0.364 -0.4

SUVA254 -0.308

Spectral Ratio -0.482 0.359 -0.373 -0.387 -0.301 0.3047

Total CHLa -0.424 0.468 0.432 -0.378

Corrected

CHLa 0.328 -0.469 0.502 0.453 -0.422

Conductivity -0.363 0.363 0.41 -0.344

TSS 0.734 -0.745 0.753 0.751 0.724 -0.727

Simple linear regression between WorldView-2 data and environmental data revealed

multiple strong relationships (Table 6); however, in general, the strength of these relationships was

less than that observed for the JAZ data. An average r2 of 0.23 was observed for the 35

relationships. As with the correlations above, indices numbers 5 (700/400), 11 (440/520), 19

(510/670) and 20 (670/550) were related to all or almost all the variables. The bands these indices

include are; Band 1, 2, 3, 5 and 6 which vary from 400 nm to 700 nm. A selection of these

regressions, in particular for indices 4, 5 and 19, are also shown in Figure 7. Most indices were

well explained by linear relationships with log-transformed environmental variables. However, the

relationship between DOC and WorldView-2 ratio 5 was best described using an exponential

decay curve (Figure 8), which resulted in an r2 of 0.28, as compared to the linear regression r2of

0.18.

20

Table 7. Parameters of significant (p<0.05) linear regressions of water quality variables predicted

using WorldView-2 ratios where log water quality parameter = a * log Index + b.

Parameter (Y) Index (X) Index # a b R2 p

DOC

Band 1 / Band 2 4 2.377 1.384 0.137 0.0133

Band 6/Band 1 5 -0.449 1.412 0.185 0.0036

Band 1 10 0.468 2.245 0.113 0.0255

Band 1/Band 3 11 0.762 1.487 0.162 0.0067

Band 2/Band 5 19 0.455 1.412 0.16 0.0071

Band 5/Band 3 20 -1.04 1.3 0.204 0.002

C440

Band 1 / Band 2 4 3.744 -1.164 0.197 0.0025

Band 6/Band 1 5 -0.57 2.215 0.173 0.0049

Band 1 10 0.672 3.407 0.1356 0.0139

Band 1/Band 3 11 1.033 2.314 0.1729 0.005

Band 2/Band 5 19 0.556 2.215 0.138 0.0128

Band 5/Band 3 20 -1.228 2.072 0.166 0.006

SUVA 254 Band 2/Band 5 19 -0.183 0.426 0.173 0.0198

Spectral Ratio Band 1 / Band 2 4 -0.255 0.166 0.125 0.0466

Band 1 10 -2.774 -0.044 0.111 0.062

Total CHLa

Band 6/Band 1 5 -0.765 0.0342 0.229 0.0008

Band 1/Band 3 11 1.491 0.484 0.265 0.0002

Band 2/Band 5 19 0.821 0.34 0.222 0.0009

Band 5/Band 3 20 -1.517 0.17 0.188 0.0026

Corrected

CHLa

Band 6/Band 1 5 -0.936 0.107 0.231 0.0007

Band 1/Band 3 11 1.8 0.277 0.258 0.0003

Band 2/Band 5 19 0.98 0.104 0.213 0.0012

Band 5/Band 3 20 -1.861 -0.106 0.19 0.0024

Conductivity

Band 6/Band 1 5 -0.374 -0.624 0.111 0.0219

Band 1/Band 3 11 0.676 -0.558 0.11 0.0222

Band 2/Band 5 19 0.46 -0.628 0.141 0.0093

Band 5/Band 3 20 -0.783 -0.715 0.101 0.0293

TSS

Band 1 / Band 2 4 4.451 0.703 0.539 0.0065

Band 6/Band 1 5 -1.022 0.629 0.555 0.0054

Band 1 10 0.992 2.547 0.567 0.0047

Band 1/Band 3 11 1.95 0.806 0.563 0.0049

Band 2/Band 5 19 1.236 0.595 0.524 0.0078

Band 5/Band 3 20 -2.063 0.407 0.528 0.0074

21

Figures 7: Linear regressions between indices and our water variables show the prediction of

water quality from satellites.

log (Band 6/Band 1)

-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2

log

TS

S

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

log (Band 6/Band 1)

-0.6 -0.4 -0.2 0.0 0.2 0.4

log

Co

nd

ucti

vit

y

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

log (Band 6/ Band 1)

-0.6 -0.4 -0.2 0.0 0.2 0.4

log

To

tal C

HL

a

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

log (Band 2/Band 5)

-0.4 -0.2 0.0 0.2 0.4 0.6

log

SU

VA

0.1

0.2

0.3

0.4

0.5

0.6

0.7

log (Band 6/Band 1)

-0.6 -0.4 -0.2 0.0 0.2 0.4

log

Co

rre

cte

d C

HL

a

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

log (Band 1/Band 2)

-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 0.12

log

C4

40

1.0

1.5

2.0

2.5

3.0

3.5

4.0

y = .6293-1.023x

R² = 0.5557

P=0.0054

y = -0.624 - 0.3714x

R² = 0.111

P<0.0219

y = -0.426 - .184x

R² = 0.173

P<0.0198

y = 0.3427 - 0.765x

R² = 0.229

P=0.0008

y =.107- .935x

R² = 0.231

P=0.0007

y = -1.164 + 3.744x

R² = 0.197

P=0.0025

22

Figure 8: Non-linear regression between indices and water quality variables show the prediction

of DOC from satellites.

Using these selected models (see Figure 7 and 8), we estimated the concentration of the

relevant parameters based solely on satellite reflectance. Ideally, this type of analysis would

include a training data set and test data set; however, due to limited sample sizes, the same spectral

and environmental data sets are used in both cases. A comparison between actual environmental

data and these model estimates indicated that some variables could be moderately well predicted

using WorldView-2 imagery (Figure 9). Some of the strongest models were TSS (r2=0.63), DOC

(r2=0.32), and Corrected CHLa (r2=0.30), while SUVA254 was the only model with a slope = 1,

indicating that satellite-based estimates were a good approximation of observed values. Most other

models, with the exception of C440, indicate that satellite-based estimates may overestimate the

true value, most notably at higher concentrations.

23

Modeled SUVA 254

1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2

Da

ta S

UV

A 2

54

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Modeled C440

0 100 200 300 400 500 600 700

Da

ta C

440

0

200

400

600

800

1000

Modeled Total CHLa

0 1 2 3 4 5 6

Da

ta T

ota

l C

HL

a

0

5

10

15

20

25

30

35

Modeled Corrected CHL

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Da

ta C

orr

ec

ted

CH

L

0

5

10

15

20

25

30

Modeled DOC

0 10 20 30 40 50 60 70

Da

ta D

OC

0

50

100

150

200

250

Modeled TSS

0 2 4 6 8 10 12 14 16

Da

ta T

SS

0

5

10

15

20

25

30

35

Figures 9: Regression between collected water data and water quality predicted at landscape

level (p <0.05).

y = -1.36 + 2.40x

R² = 0.296

P<.0001

y = -1.67 + 2.06x

R² = 0.244

P<.001

y = --0.126 + 1.069x

R² = 0.127

P=0.0204

y = 81.77 + 0.466x

R² = 0.140

P=0.0030

y = -2.66 + 1.52x

R² = 0.628

P=0.0007

y = -17.01+ 1.81x

R² = 0.319

P<.0001

24

Comparison of Ground and Satellite-based models

We examined which WorldView-2 and JAZ indices gave similar results (Fig. 10). The only

2 indices that were significantly related between the 2 platforms were Indices 4 (Band 1/ Band 2;

400/480) and 5 (Band 6/Band 1; 700/400). Both these indices showed significant relationships

with environmental variable on both platforms (Table 8).

JAZ (400/480)

-0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30

WV

-2 (

Ba

nd

1/B

an

d 2

)

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

JAZ (700/ 400)

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

WV

-2 (

Ba

nd

6/ B

an

d 1

)

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

Figures 10: Relationships between the indices 4 and 5 measured on 2 different platforms.

Table 8. Water quality variables that can be predicted using similar JAZ & WorldView-2 ratios

(ratio 4 & 5)

Water quality parameter

(Y)

Index (X) WV-2 JAZ

Total CHL 700/400 (5) Y=0.343 – 0.765(X);

R2=0.23, p=0.0008

Y=0.876 – 1.252 (X)

R2 = 0.25, p<0.0001

TSS 400/480 (4) Y=0.703 + 4.451(X);

R2=0.539, p=0.0065

Y=-0.118 + 2.331 (X)

R2 = 0.262, p<0.0002

TSS 700/400 (5) Y=0.629 – 1.022(X);

R2=0.555, p=0.0054

Y=1.792 – 2.096 (X)

R2 = 0.54, p<0.0209

C440 700/400 (5) Y=2.215 – 0.570 (X)

R2 = 0.173, p<0.0049

Y=2.912 – 1.250 (X)

R2 = 0.215, p<0.0003

Spectral Ratio 400/480 (4) Y=-0.090 – 0.60 (X)

R2 = 0.115, p<0.0566

Y=0.041 – 0.461 (X)

R2 = 0.231, p<0.0431

DOC 700/400 (5) Y=1.412 – 0.450 (X)

R2 = 0.185, p<0.0036

Y=1.764 - 0.670 (X)

R2 = 0.105, p<0.0145

25

Estimate of CO2 efflux from remote sensing: a preliminary model

Because DOC is strongly related to pCO2 in tundra ponds (pCO2 = 123.1 + 49.9*DOC;

r2=0.63; p<0.0001; Lougheed, unpubl. data) and, because DOC can be moderately well modeled

using WV-2 data (Figures 8 & 9), we created the following formula to estimate pCO2 in the ponds:

pCO2 = 123.1 + 49.97*(Modeled DOC*1.81-17.02)

Where Modeled DOC = 10^(1.3448 + 0.016 * EXP(-6.8652*log5) (From Figure 8)

This accounts for both the pCO2 formula (above), as well as correcting for the overestimation

observed in Figure 9 (Actual DOC = WV-2 Modeled DOC*1.81 – 17.02).

We compared this WV-2 modeled pCO2 with actual data collected from ponds and lakes

in 2015, and found a strong, linear relationship, which only slightly underestimated pCO2. This

data, while only preliminary, holds promise for landscape-level estimation of CO2 flux.

Modeled pCO2

0 1000 2000 3000 4000 5000 6000

CO

2 D

ata

0

1000

2000

3000

4000

5000

6000

Figures 11: Regression between collected data during summer 2015 and model PCO2 data from

DOC non-linear regression.

y = -598.60 + 0.946x

R² = 0.873

P<.0001

26

Discussion

Remote sensing studies in shallow waters are not as common as ocean, lakes and rivers.

However, this study shows the feasibility of predicting the water quality of small, shallow ponds

that vary along a gradient of chlorophyll (CHL) and dissolved organic carbon (DOC). Ponds from

several different regions, including reference sites, urban ponds and thermokarst ponds had unique

reflectance signatures, which we propose can be capitalized on to help predict water quality at a

landscape scale. Arctic tundra ponds, in particular, have been poorly studied, and with a warming

Arctic substantial changes in these systems are expected. This study advances our capacity to

monitor landscape level changes in these valuable ecosystems.

Spectral signatures across pond types

The Arctic coastal plain landscape in north Alaska is dominated by ponds (Andresen and

Lougheed 2015). We found that ponds with different characteristics had unique reflectance

signatures that could, at least in part, be associated with the differing chemistries of these different

pond types. For example, thermokarst regions experience ground subsidence due to thawing of

ground ice, as well as accelerated active layer deepening, resulting in release of nutrients and

carbon compounds into the ponds from thawing permafrost. Thermokarst ponds tend to be highly

colored from DOC (Jones et al., 2011). As such, our results showed these ponds had high C440 and

DOC values, corresponding with reflectance signature peaks between 400-500 nm. Other authors

have similarly found peak reflectance in these wavelengths associated with high DOC (Menken et

al., 2009; Belzile et al., 2004).

On the other hand, urban ponds tended to have their first reflectance peak in the 550-600

range, with is often associated with high TSS (Gitelson et al,. 2009). Urban ponds are closer to

27

town, which can make them more vulnerable to the introduction of suspended sediment and

nutrients. A peak at 700 nm occurs for both IBP and Urban ponds. Chlorophyll-a is commonly

found to peak at 700 nm (Gitelson et al,. 2009). Interestingly, while thermokarst ponds have the

highest chlorophyll and TSS levels, this is not apparent in the reflectance signatures, perhaps

because of a masking effect from the extremely high levels of DOC (Arenz et al., 1996). This is

one of the reasons that chlorophyll has been easier to measure in DOC-poor ocean waters (Arenz

et al., 2006; Canizaro and Carder,2006). Urban ponds also tended to have the highest periphyton

(algae attached to sediment); however, very little work has been done to quantify sediment-

attached algae using remote sensing studies. Canizzaro and Carder (2006), in particular, have

mentioned problems in estimating the reflectance of chlorophyll due to the depth of the body of

water.

Finally, all 3 site types have reflectance peaks at 800 nm. Reflectance in the near IR has

been associated with scattering of light in vegetation, as well as absorption by water (Hestir et al.,

2015). Spatial heterogeneity of aquatic vegetation commonly results in mixed pixels, and

inundation of wetland vegetation significantly changes the spectral signal, reducing the utility of

narrow band indexes such as those centering on the red edge (Hestir et al., 2015). In our study the

selection of the pixels was important in order to avoid any vegetation; while we carefully and

manually selected the open water areas of the ponds, it is possible some of the pixels near the edge

of the ROI experienced scattering of light from vegetation.

Ground-based reflectance

Multiple strong relationships were found between environmental parameters and ground-

based reflectance from the JAZ spectrometer. In fact, there were substantially more relationships

using the ground-based reflectance data than when we used satellite-based data. Ground-based

reflectance appeared to be a strong predictor for measurements of chlorophyll, total suspended

28

solids and carbon compounds. Some of the most useful indices appeared to be the single

wavelengths at 682 (Index 18) and 806 (Index 16), as well as the multiple wavelength ratios

710+820/740 (Index 6), 710+820/675+740 (Index 8) and, 700/400 (Index 5). Some of these indices

have also been found to be useful in lakes and rivers. For example, Menken et al (2009) also found

that the wavelength 806 was strongly related to chlorophyll-a, while Gitelson et al. (2009) found

wavelengths in the high 600s (670 nm) to also be useful in predicting chlorophyll-a. Interestingly,

the indices we composed based on the observed peaks in our data (710+820/740 (Index 6);

710+820/675+740 (Index 8)) also performed very well in these ponds, and may form a future

avenue of investigation for other studies.

Remote sensing in freshwater ecosystems in still a developing field; however, in harsh

ecosystems like the Arctic tundra, where sampling can be cumbersome, ground-based reflectance

may be a useful technique to quickly and efficiently collect field data. For example, collecting

ground-based reflectance data at multiple sites in a single pond takes only a few minutes, as

opposed to the hours and cost of collecting and analyzing multiple samples for chlorophyll,

suspended sediments and carbon compounds. The ability of ground-based reflectance to measure

reflectance at different depths within the water column could also be explored in the future as a

substitution or a complement to water sampling. While the JAZ shows promise for monitoring

water quality of these ponds, there is still a requirement for the scientist to visit the relatively

hostile Arctic environment. Therefore, the use of satellite imagery was also explored, which would

also allow the scaling-up of modeled parameters to the landscape level.

29

Satellite-based measurements

WorldView-2 is a satellite platform that has been used increasingly over the last few years.

Introduced as a commercial high resolution satellite in 2010, it will allow detailed estimates of

reflection from this date into the future. The images used for WorldView-2 contain very useful

data such as the angle of the sun, which is used for data correction. To be able to analyze different

ratios is a great tool of WV-2, the various combinations of bands is one of the tools of great interest

as well as the response of the radiance, which makes easier the data collection from the image

without having to be in the place of the site, many points can be taken at the same time and many

sites can be measured. Indices numbers 5 (700/400), 11 (440/520), 19 (510/670) and 20 (670/550)

were correlated with the most environmental variables (>7), with DOC, C440, TSS, and

phytoplankton CHL generally well predicted by the indices. These, or similar, indices have been

previously used in lakes (Belzile et al., 2004; Menken et al. 2009) and shallow waters before

(Canizzaro and Carder, 2006). For example, Menken et al (2009) also found that the ratio 440/520

was strongly related to chlorophyll-a, while the ratio 670/550 was a strong predictor of C440.

Similarly Canizzaro and Carter (2006) found an association between the ratio 510/670 and

chlorophyll-a. The ratio 700/400 was created to reflect various authors’ identification of the

chlorophyll-a peak at 700, and CDOM peak near 400 (e.g. Menken et al. 2009).

Like other studies looking at shallow waters (Vincikova et al. 2015), the strength of the

relationships between environmental variables and reflectance was greater for ground-based

reflectance (JAZ) than for satellite reflectance values. This may be due to the lack of accumulated

data and the resolution/correction of the images being one of the major difficulties in this study.

One of the biggest problems with WV-2 is the atmospheric correction that each image has to go

through before been analyzed. This can be because no single sensor offers the same resolution,

30

and even with atmospheric correction: clouds, wind and atmospheric particles can make the

wavelength differ (Al-wassai and Kelyankar, 2013).

Ground and satellite reflectance comparison

Ground-based reflectance and satellite-reflectance are developing methods designed to

make data collection at the landscape level easier for scientists. Only two ratios proved useful for

explaining water quality under both platforms; in both cases, these were ratios that we created

based on our interpretation of patterns in our data. The ratio of 700:400 was strongly associated

with measures of chlorophyll, which are commonly associated with the 700nm wavelength, as we

all measured of carbon (DOC, C440), which commonly peak at 400 nm. The ratio 400/480 was also

useful in both platforms for estimating TSS and the spectral ratio, ratios in the blue to green ranges

(400-500 nm) have been shown useful by others in estimation chlorophyll-a in shallow coastal

waters (Cannizzaro & Carder 2006), as well as chlorophyll-a and C440 in temperate lakes (Menken

et al. 2009). Given the rapid pace of climate change, it is of great utility having metrics that

transcend platforms and allow us to monitor water quality more frequently in order to understand

chemical fluxes. Our results showed that these 2 indices may have the potential to monitor regional

scale events in shallow Arctic ponds.

When using satellite data it is necessary to evaluate the satellite products, the reliability of

the images, and the spatial variability that could come with the image (Kauer et al., 2013).

Compared to the satellite data, ground-based reflectance data showed a stronger relationship with

different environmental variables, is easier to interpret and to manage, and does not require

corrections.

31

Reflectance in shallow waters.

For optically shallow waters, radiance reflected by the bottom also contributes to the

reflectance, which can vary with both depth and bottom albedo (Cannizzaro and Carder, 2006). In

this preliminary study, no equation for depth correction was used for two reasons: (1) due to the

color of the water in our ponds, which is very dark and (2) because the JAZ spectrometer is not

known to penetrate far into the water (Ocean Optics, Pers. Comm). While some authors working

in shallow, turbid systems have not corrected reflectance for depth-related factors (Vinciková et

al. 2015), several authors have proposed correcting reflectance in shallow waters for the effects of

depth and bottom albedo (Lee et al. 1999). For example, Cannizzaro and Carder (2006), showed

that such corrections improved estimations of chlorophyll concentrations. For shallow tundra

ponds, future determinations of bottom sediment reflectance, and their inclusion as a correction

factor, should be explored to increase the strength and utility of the models.

Limitations of the study

Remote sensing is currently limited by spatial resolution for assessing small aquatic

ecosystems (Ritchie et al 2003), this limitation will likely be overcome in the near future. Our

study had some difficulties, including low quality of at least one image. Having a good quality

image is rather important since it carries all the information needed to get a good reflectance from

the image. In our study, the fact that the WV-2 image nearest a primary sampling date was of low

quality, resulted in many data points not being utilizable in our analyses. This is similar to

Vinciková et al., (2015) who mentioned that some of the weakness of these models is the lack of

data for some of the days.

Our study was also limited in that we had only 5 data points from thermokarst (TK) ponds,

32

which tended to have some of the highest concentrations of many water quality constituents. More

data from these ponds, in particular, are thus needed in order to get stronger relationships between

reflectance and the water variables. The addition of additional theromokarst pond data could also

help elucidate whether linear regressions, or exponential curves (Gitelson et al., 2009; Belzile et

al., 2005; Menken et al.,2005) are more appropriate for describing these relationships.

In summary, access to more high quality images, as well as additional data from

thermokarst ponds or other high DOC and CHL systems, would lead to greater strength and utility

of the relationships presented.

Estimation of CO2 efflux from satellite data

As rapidly as climate is changing, data on carbon efflux must be to be more frequent and

at a higher spatial scale, in order to understand how regional carbon budgets are being affected by

different landscape components. The arctic tundra contains approximately 15% of the global soil

organic carbon (Post et al.,1982). However, there is uncertainty on the current, and potentially

changing, role of Arctic tundra in the global carbon balance. Freshwater ecosystems in general,

and tundra ponds in particular, have the potential to be huge carbon sources (Andresen et al. 2016);

however, their relative role at the landscape level is only now being explored.

Using a preliminary model predicting CO2 based on remotely-sensed DOC levels, we were

able to predict CO2 with a reasonable degree of accuracy in small tundra ponds. These types of

models do not exist for shallow freshwater ecosystems, and hold promise for complementing

currently landscape-level estimates of carbon flux (e.g. Lara et al. 2015). While multiple studies

have attempted to predict DOC/CDOM using remote sensing (e.g. Brezonik et al. 2015, Cardille

et al. 2013), to our knowledge, very few have been able to predict aquatic CO2 efflux from remote

33

sensing platforms. One of the few exceptions is a study by Kutser et al. 2015, where they found

various products from the MERIS satellites could be used to map lake carbon fractions, including

pCO2; however, their study, like ours suffered from a lack of data to confirm any trends.

Thermokarst ponds, in particular are a great source of CO2 to the atmosphere, as indicated by the

reflectance peak at 400 nm and the high concentration of DOC (123 mg/L) due to all the organic

material that it has been released to those ponds, which is obvious to the naked eye in terms of the

brown-ish color of the water.

These data, while only preliminary, hold promise for landscape-level estimation of CO2

flux. Future directions could include collection of more CO2 and DOC data coincident with both

ground-based and satellite-based reflectance data. In particular, more collection of data from the

TK ponds is needed to study these DOC rich ponds, which have been shown to be a source of CO2

to the atmosphere.

34

Conclusion

This study represents a significant advance in the measure of remote sensing in shallow

waters. The results of this study suggest that satellite remote sensing and ground-based

measurements using the JAZ spectrometer can be used to estimate the concentration of optically

active substances and/or optical characteristics in shallow Arctic ponds at the landscape level. We

also provided a proof-of-concept indicating that modeling of CO2 flux from these highly colored

ponds may be possible confirming that these ponds serve as a CO2 source to the atmosphere.

Studies such has Kutser et al. (2016) and Cardille et al. (2013) among others have shown the

importance of monitoring the carbon flux in water, due to the rapidly climate change.

35

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Vita

Gabriela Tarin, completed her Bachelor’s Degree at The University of Texas at El Paso in

2013. During her last two years she worked Research Assistant at UTEP where she collaborated

in several projects related to wetlands and land-cover mapping in the Arctic. In 2010, she was

awarded an honorable mention at the COURU undergraduate research symposium. While working

in the lab Gabriela found her passion for research and decided to enter the Master program in

Environmental Science at UTEP under the direction of Dr. Vanessa L. Lougheed. Her thesis

entitled “Predicting the Water Quality of Shallow Arctic Ponds Using Remote Sensing”

investigates the relation between water reflectance and water quality as well as the connection and

prediction of CO2.

During her studies, Gabriela worked as a teaching assistant position, giving her the skills

to communicate science to other students. Gabriela participated in a program called TIERA,

working as a mentor and coordinator, helping students to do research. She has presented at several

national conferences many of them awarded with travel scholarship. Most recently Gabriela was

selected to represent the College of Science as the Graduate Student Marshall of Students for her

outstanding academic achievement.

Ms. Tarin research interests focus on the effects of climate change on hydrology and

chemistry effects of this in the ecosystems.

Contact Information: gcontreras@utep.edu

This thesis was typed by Gabriela Tarin