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Applications of Remote Sensing and Geomaticstechnologies in Watershed Health

44th CENTRAL Canadian Symposium on Water Quality Research

Canadian Association on Water Quality

Session:

R. PONCE-HERNANDEZ

FRANK KENY

Chairs

A remote sensing based approach to monitoring watershed ecosystem health and degradation at source

and the impacts of changes on watershed primary productivity and water quality.

R. PONCE-HERNANDEZ

Environmental and Resources Studies Programand

Department of Geography

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SCOPE :

� Importance of remotely-sensed variables as proxies for ecosystem health and water quality.

� Increased access to multi-scale, multi-temporal, multi-spectral remote sensed data -> reduction of monitoring costs while maintaining accuracy of assessments.

� From global to national, regional and local scales.

� Relationships between remotely-sensed variables (NDVI, NPP, GPP, PSNet, et aliae) and ecosystem health and water quality indicators

� Case Studies with focus in the Kawartha Lakes watersheds at multiple scales

IMPACTS OF CHANGES IN WATERSHED NET PRIMARY PRODUCTIVITY ON WATER QUALITY

� Net Primary Productivity (NPP) is strongly related to ecosystem services and to water quality parameters (particularly at source):� e.g. incresed moisture retention, filtration, runoff,

sediment and chemical transport, suspended solids, turbidity and water chemistry, et alii.

� Changes in NPP over time (usually associated with land cover) may serve as an indicator of water quality and therefore as a co-variable for water quality monitoring purposes at regional and watershed scales. watershed

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REMOTE SENSING TECHNOLOGIES AND WATER

QUALITY MONITORING

� The usefulness of Remote Sensing Platforms in monitoring water quality resides in its ability to detect and map the spatial distribution of “detectable” or sensed co-variables of water quality indicators.

� There is a wide range of sensors and platforms collecting data at multiple scales, with a wide range of spatial, temporal and spectral and radiometric resolutions

MULTI-SCALE AND MULTISPECTRAL FRAMEWORK FOR

MONITORING ECOSYSTEM HEALTH AND WATER QUALITY

� The release of multi-platform, multi-scale, multi-temporal and multi-spectral data sources for free access and download has created a wide range of opportunities for monitoring watershed ecosystems health and water quality.

� A methodological framework that takes advantage of these sources and mitigates monitoring costs is within reach of agencies and conservation authorities.

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Earth observation and global monitoring programs

generating data and becoming sources

� NASA’s :

� MODIS Programs (“Terra” and “Aqua” satellite platforms, operational in 2000).

http://modis.gsfc.nasa.gov/news/group.php?classification=data

� LANDSAT missions (1-7): MSS, Thematic Mapper (TM ) and Enhanced Thematic Mapper (ETM+)

http://glcf.umiacs.umd.edu/data/landsat/

� NOAA’s AVHRR and Global Land Cover Facility’s GIMMS Programs http://glcf.umiacs.umd.edu/data/gimms/

� SPOT Mission and SPOT satellite

http://www.spot.com/web/SICORP/403-sicorp-spot-images.php

� QuickBird and other commercially available satellite data

http://www.digitalglobe.com/index.php/48/Products?product_category_id=9

LOW RESOLUTION MULTISPECTRAL DATA FOR NET PRIMARY PRODUCTIVITY (NPP) ESTIMATION (NATIONAL OR REGIONAL SCALES)

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The Moderate Resolution Spectroradiometer Sensor (MODIS) mounted on the “Terra” satellite (NASA, 2000) provides a dramatic improvement in our ability to accurately monitor global ecological conditions. Large scale climate shifts, deforestation, desertification, pollution damage, crop conditions, glacial retreats, flooding, wildfires and urbanization. These are the types of earth systems monitoring planning that can be achieved with MODIS data.

MODIS 1 Km resolution Image of Gross Gross Primary Productivity GPP (kg C day-1) computed from AbsorvedPhotosynthetically Active Radiation (APAR) and Maximum Radiation Conversion Efficiency (ε) for July 21st 20

MODIS Sinusoidal 1 Km resolution image of Gross Primary Productivity GPP for July 21st 2000 from the NASA MODIS program

HDF to TIF conversion algorithm

(NASA, 2008)

MODIS data used in a regional context for watershed NPP estimation and ecosystem health

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Estimate of daily of Gross Primary Productivity (GPP) for 8-day period in (kg C day-1) to 1 Km resolution derived from GPP = e * APAR where :e = (kg C MJ-1) The maximum radiation conversion efficiency and APAR = IPAR * FPAR. IPAR where IPAR = (Short Wave Radiation * 0.45)

MODIS 1-Km GROSS PRIMARY PRODUCTIVITY

MODIS 1-Km GROSS PRIMARY PRODUCTIVITY

Areas of interest for monitoring

MODIS 1 Km resolution Image of Net Photosynthesis _PSNnet_ (kg C day-1) calculated fromPSNnet = GPP – Leaf_MR -Froot_MR as weekly average for July 21st 2000

PSNnet = GPP – Leaf_MR - Froot_MRPSNnet = GPP – Leaf_MR - Froot_MR

MODIS 1 Km resolution Image ofGross Primary Productivity GPP(kg C day-1) computed fromAbsorved PhotosyntheticallyActive Radiation (APAR) andMaximum Radiation ConversionEfficiency (ε) for July 21st 2000

Conversion of GPP to Net Photosynthesis as indicator of Ecosystem Health

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20002003

20062008

Gross Primary Productivity (GPP) changes over time (2000-2008) for the month of July in South Central Ontario, from the MODIS sensor (1 Km) resolution

(kg C day-1 Km-2)

20002003

2006

Net Primary Productivity (NPP) (g C m-2 yr-1) in the Kawartha Lakes Region 2000 -2006

g of C m-2 yr-1

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Net Primary Productivity Changes (2000-2006) in g C m-2 yr-1

for the Kawartha Lakes Watersheds

NPP estimation from high resolution

multispectral data at the watershed scale

for monitoring ecosystem health and

water quality

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1993 2002

NPP estimation from high resolution multispectral data (Landsat TM and ETM+) at the watershed scale for monitoring

ecosystem health and water quality

Case studies from the Kawartha Lakes watersheds

Landsat TM and ETM+ colour composite images for the Kawartha Lakes watersheds

20021993

Normalized Difference Vegetation Index (NDVI) as a proxi for NPP estimates from high resolution multispectral data

(Landsat TM and ETM+)

[ ]0.5 ( 0.008 1.075)NPP NDVI PAR= − + ∗ ∗

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Forest Cover (%) Point Score Grade

>25.6 5 A

26.6 - 18.8 4 B

18.7 - 11.9 3 C

11.8 - 5.0 2 D

<5.0 1 F

Forest Interior (%) Point Score Grade

>7.7 5 A

5.7 - 7.7 4 B

3.7 - 5.6 3 C

1.7 - 3.6 2 D

<1.7 1 F

Source: http://www.kawarthaconservation.com/reportcard/forestcoverdata.html

Multispectral Satellite-detected variables and their relationship with watershed health and water quality

Subwatershed Area (km2) Forest Cover

(km2)% Forest

Cover Point Score

Grade

Pigeon Lake 79.65 20.87 26.20 5 A

Pigeon River 221.59 67.08 30.27 5 A

Fleetwood Creek 74.81 30.84 40.74 5 A

Nogies Creek/Crystal

Lake 193.43 97.30 50.30 5 A

WATERSHED 569.48 215.73 37.88 5 A

SubwatershedForest

Interior (km2)

%Forest Interior

Point Score

GradeTotal Score

Total Grade

Pigeon Lake 3.30 4.14 3 C 4 BPigeon River 12.92 5.83 4 B 5 A

Fleetwood Creek 8.86 11.84 5 A 5 A

Nogies Creek/Crystal Lake

5.31 2.75 2 D 4 B

WATERSHED 30.39 5.34 5 A 5 A

Watershed health Scores for Pigeon Lake (Kawarthas) related to multispectral satellite-detected variables

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Monitoring Network for a standard set of water quality indicators:

� Chloride, � Nutrients, � Suspended solids, � Trace metals and� Other general chemistry parameters� Disease-causing substances� Pesticides and other contaminants.

These last two categories of variables are monitored in detailed water quality surveys in priority watersheds.

Monitoring information is used to characterize water quality conditions and trends on a watershed basis, to identify water quality issues and to measure the ongoing effectiveness of source protection plans

Watershed scale spatial patterns related to

water quality

� Search for co-variables or proxies strongly correlated to the spatial and temporal variability of water quality indicators

� Co-variables or proxies amenable of detection by means of low and high resolution multispectral satellite and airborne data.

� Enabling the development of predictive models based on sensed multispectral data to reduce monitoring costs and enhance monitoring frameworks.

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∆ NPP, ∆ GPP,∆ PSNnet, etc.

∆ Chloride, ∆ Nutrients, ∆ Suspended solids, ∆Trace metals ∆ Chemistry parameters∆ Disease-causing substances∆ Pesticides

Spatial Pattern s ?

Quantitative Relationships ?

NPP Deficit

Case Study 1:

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Study Area� Graham Creek - Newcastle, Ontario

� A 3 km section of the creek was selected due to a varying degree of vegetation coverage and topography.

� Location is within driving distance.

� Ikonos (1 m resolution) multi spectral imagery was available for the area.

Newcastle

Sampling Sites

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Collection of Field DataTo calibrate and assess model results

Methodology� Along a 3 km stretch of Graham Creek 25 sites (5

m * 5 m) will be surveyed to measure soil loss and topography.

� Once the field measurements are digitized, I will begin to search for a correlation between soil loss and other variables.

� If a correlation is found then soil loss can be predicted based on these variables through

Co-regionalization.

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Methodology� Quantify soil loss through the measurement of the

following indicators.

� Rills (volume and weight of soil loss)

� Gullies (volume and weight of soil loss)

� Pedestals

• These field measurement will be used to calibrate and evaluate the USLE model.

• Looking for a high correlation between predicted soil loss and measured soil loss.

Quantifying Soil Loss through indicators : Rills

Width

Cross sectional depths

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Quantifying Soil Loss in Gullies

Cross sectional Depths

Width

Length between measurements

Quantifying Soil Loss through Pedestals

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Variables

Data Layers Available

� Slope

� NDVI

� NDVI Reclass

� False colour image

� Surficial Geology (Soil properties)

� DEM

� Stream Sinuosity

SLOPE

The slope layer was created based on a 5m DEM provided by the OMNR.

Possible Alternative using TIN created from mass points, lines and polygons.

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NDVI

NDVI = NIR – Red / NIR + Red

NDVI RECLASS

Vegetated regions of the NDVI were isolated and using histograms and the range of values were examined. Following this the image was reclassified (threshold analysis) in order to distinguish between vegetation and no vegetation.

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Lake OntarioSandVegetation

No Vegetation Roads

Houses

Ikonos Image Vegetation Threshold

Railways

FALSE COLOUR

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STREAM SINUOSITY

Sinuosity = Straight line distance / Stream Distance

Straight line distance

Stream Distance

Straighter streams have lower sinuosity values and typically have a higher rater of f low. This variable has shown to be a valid tool in erosion risk assessment.

SLOPE AND VEGETATION THRESHOLDS

STEEP SLOPE

AND

NO VEGETATION

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Slope – Derived from DEM

Site A

Slope and Vegetation

NDVI

Site B

Slope - Derived from TIN

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Correlation between Soil Loss and (Slope * NDVI)

Soil LossSlope * NDVI

Soil Loss 1

Slope * NDVI 0.859342355 1

Regression Statistics

Multiple R 0.859342355

R Square 0.738469282

Adjusted R Square 0.718351535

Standard Error 2.484801421

Observations 15

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Case Study 2: Rice Lake

� Watershed area

� 914,125 ha

� Lake Surface: 10,010 ha

� Land Surface: 904,115 ha

� Part of the Trent-Severn waterway

� Major outflow to Lake Ontario from the Kawartha lakes

� Land use types

� 61% Agriculture

� 37% Forest

� 2% Urban

� Nearest large City is Peterborough, located north of Rice Lake

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P is used in most modern agricultural practices for fertilization

High concentrations in sewage treatment plant (STP) effluents

Occurs naturally from weathering of soils and rocks

Freshwater limiting nutrient for Algae Growth

Thus, algae growth is regulated by Phosphorus

Primary cause of eutrophication in temperate fresh waters

Symptoms include:

1. Excessive algal growth (blooms)Block sunlight (shading), out-competes for resources

2. Shifts in dominant algal species

3. Depleted dissolved oxygen concentrationsCan lead to problems with O2 dependent organisms

Possibly toxic species become dominant

Generating a paired Biomass-Band Ratio Data Set

� Using estimated positions and phosphorus concentrations from Hutchinson et al. 1994, the following data set was created:

Plot # Row Col LAT LON [TP] ug L-1 NDVI GVI BR

1 5655 3408 440555.2 781935.9 44 0.2 2.2 1.5

2 5702 3444 440510.3 781851.7 158 0.2 3.9 2.7

3 5184 3649 441202.1 781406.4 29 0.6 1 0.7

4 5057 3870 441452.1 780417.8 26 -0.2 1.4 1.3

5 4929 4146 441640.7 780253.3 30 0.2 1.9 1.8

6 4895 4286 441707.8 780016.9 22 -0.2 0.9 0.7

7 5614 3460 440630.9 781827.2 33 -0.3 0.8 0.7

8 5432 3714 440911.5 781254.7 30 -0.3 1 0.6

9 5161 3993 441312.4 760641.1 29 -0.2 1 0.6

10 5005 4097 441534.4 780424.1 25 -0.2 0.9 0.6

Table 1: Permanent Sampling plot data for Rice Lake, Ontario

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Scatter plot of Band Ratios and Indices for Rice Lake Study with an

exponential function applied.

Comparison of Indices for Determination of Phosphorus Concentrations in Rice Lake

y = 15.387e0.5409x

R2 = 0.8566

y = 16.506e0.6617x

R2 = 0.6848y = 35.08e0.6404x

R2 = 0.1169

0

20

40

60

80

100

120

140

160

180

-0.5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Band-Ratio Index

NDVI

GVI

BR

Expon. (GVI)

Expon. (BR)

Expon. (NDVI)

TP

C

on

cen

trat

ion

(u

g L

-1)

Choosing band-ratio Indices

� Results from the scatter plot and linear regression indicate that the GVI band ratio provided the strongest correlation.

� y = 15.387e0.5409x R2 = 0.8566

• This equation was applied to the GVI band-

ratio image created to shown the concentrations

of phosphorus in Rice Lake and its surrounding

catchments

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Map of Phosphorus Concentrations in Rice Lake and Catchments

� This imaged

was stretched

using

histogram

equalization,

with intervals

of 329 and

values of the

previous map

ranging from

0 to 328.8.

Creation of Chlorophyll- A image

� Calculation a scaling factor of 0.499288 for the ratio between [TP]

and [Chlorophyll A].

Relationship

between

Phosphorus

and

Calculated

Chlorophyll

A

Relationship Between Phosphorus and Chlorophyll A

y = 13.404Ln(x) - 29.634R2 = 0.8379

0

2

4

6

8

10

12

14

16

18

20

20 22 24 26 28 30 32 34

[TP] ug L-1

[Ch

loro

pyl

l A]

ug

L-1

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Chlorophyll A

Concentration 35ug L-1

Main water body Chlorophyll A

concentration averages 4-18ug L-1

Figure 6: Comparison of Chlorophyll A in different areas of Rice Lake

Chlorophyll A

Concentration 45ug L-1

Chlorophyll A

Concentration 35ug L-1

Main water body Chlorophyll A

concentration averages 4-18ug L-1

Figure 6: Comparison of Chlorophyll A in different areas of Rice Lake

Chlorophyll A

Concentration 45ug L-1

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The Same image created through Supervised Image Classification, Box Classification

Eutrophication

Figure 10: BOX Supervised Image Classification of Rice Lake

Rice Lake Eutrophication Displayed with Maximum

Likelihood Classification

Eutrophication

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Case Study 3: Change Detection of aquatic vegetation in Sturgeon and Pigeon Lakes

Satellite imagery acquired for this project.

Date Obtained ID # WRS Path Row Sensor 2002-Aug-03 042-381 2 017 029 ETM+ 1993-Jul-17 010-547 2 017 029 TM 1979-Jul-07 044-205 1 019 029 MSS 1976-Oct-11 022-804 1 019 029 MSS

Case Study 3: Change Detection of aquatic vegetation in Sturgeon and Pigeon Lakes

Band subtraction from 2 dates

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Visual Change Detection (Grey, 2006).

In this image, the 2002 TM2 band was loaded into the red and green colour guns. The 1993 TM2 band was loaded into the blue colour gun. Vegetation growth appears yellow and vegetation decrease appears blue.

The result is that pixels that appear brighter in the newer imagery (i.e. plant growth) appear yellow and pixels that appear darker in the newer imagery (i.e. plant decrease) appear blue.

Visual Change Detection

Cattail Stand Change between 1976 and 2002;

A standard false-colour composite image for thematic mapper data shows band 4 (near IR) in red, band 3 (red) in green, and band 2 (green) in blue (Aber, 2006).

Cattail stands were identified in the images and digitized on-screen. Cattail stand locations were known from previous ground-truthing knowledge of southern Pigeon Lake. The digitized areas from 1976 and 2002 were overlain in one image to show the change in extent of cattail stands,

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Vegetation Change Detection in Sturgeon and Pigeon Lakes 1993-2002

Overall Pigeon Lake Changes from 1976 to 2002.

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CONCLUSIONS

• Remote Sensing and Geomatics technologies coupled with new and free-access data products from a variety of platforms and sensors provide opportunities for enhancing watershed health and water quality monitoring programs.

• Methodological design and testing within the framework of a relative abundance of remote sensing data products and services can be advanced and made monitoring programs more efficient while mitigating costs exploiting the virtues of co-variables, indicators and proxies.

• The spatial and temporal patterns of NPP and other variables related to ecosystem productivity provide a good indicator of ecosystem health and services, useful for monitoring water quality at source