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Characterization of Coastal Wetland Systems using Multiple Remote Sensing Data Types and Analytical Techniques . Daniel Civco (PI) James Hurd University of Connecticut Natural Resource Management & Engineering, Storrs, CT. Marty Gilmore (PI) Wesleyan University

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slide1

Characterization of Coastal Wetland Systems using Multiple Remote Sensing Data Types and Analytical Techniques

  • Daniel Civco (PI)
  • James Hurd
  • University of Connecticut
  • Natural Resource Management
    • & Engineering, Storrs, CT

Marty Gilmore (PI)

Wesleyan University

Earth and Environmental Sciences Middletown, CT

Sandy Prisloe

Emily Wilson

University of Connecticut

Cooperative Extension System Haddam, CT

outline
Outline
  • Salt Marshes 101
  • Moderate resolution remote sensing of tidal marshes
  • Airborne LIDAR and satellite high resolution remote sensing of salt marsh grasses
  • Classification of tidal wetland communities using multi-temporal, single-season QuickBird imagery
outline1
Outline
  • Salt Marshes 101
  • Moderate resolution remote sensing of tidal marshes
  • Airborne LIDAR and satellite high resolution remote sensing of salt marsh grasses
  • Classification of tidal wetland communities using multi-temporal, single-season QuickBird imagery
the value of marshes
The Value of Marshes
  • Important transitional habitat between the ocean and the land
    • estuaries where fresh and salt water mix
  • Among the most productive ecosystems on earth, rivaling that of an Iowa cornfield
  • Salt marsh plants (halophytes) are salt tolerant and adapted to water levels that fluctuate with the tide
  • Tides carry in nutrients that stimulate plant growth in the marsh and carry out organic material that feeds fish and other coastal organisms
  • Over time, salt marshes accumulate organic material, forming into a dense layer called peat
the value of marshes1
The Value of Marshes
  • Position on the landscape and their productivity makes them important not only as a part of the natural world but also to humans
  • About 15,309 acres of salt marsh in Connecticut, many of which have been damaged by management actions that have had unintentional consequences
    • Restricted tidal flow
    • Filling
    • Ditching
    • Increased freshwater flows
  • Due to degradation, restoration is often necessary to improve the following functions that salt marshes provide, such as
    • Nursery area for fish, crustacea, and insects
    • Resting area for migratory waterfowl
    • Protection against waves and sea level rise
    • Aesthetics
marsh morphology1
Marsh Morphology

Artist:  Stephanie Schanda (Lee, NH)

Project SMART Student, 1998

http://www.smart.unh.edu/smartfmb98/saltmarsh/saltmarsh1.html

slide10

Want more ?

TIDAL MARSHES OF

LONG ISLAND SOUND

ECOLOGY, HISTORY, AND RESTORATION

EDITED BY GLENN D. DREYER

AND WILLIAM A. NIERING

ILLUSTRATIONS BY THOMAS R. OUELLETTE

http://www.conncoll.edu/ccrec/greennet/arbo/publications/34/frame.htm

outline2
Outline
  • Salt Marshes 101
  • Moderate resolution remote sensing of tidal marshes
  • Airborne LIDAR and satellite high resolution remote sensing of salt marsh grasses
  • Classification of tidal wetland communities using multi-temporal, single-season QuickBird imagery
slide12

A basic question …

Can tidal wetlands, such as those found

throughout Long Island Sound (those

being of various shapes and relatively small

in size, and varying distances from open

water) be adequately classified from only a

single date Landsat image (and derived

products) ?

slide13

… approach

Integrated Pixel-based and

Object-based Classification

slide14

Landsat Enhanced Thematic Mapper

Stitch line

Mosaic Path 13, Row 31 & Path 13, Row 32

Landsat ETM Sept. 8, 2002.

slide15

Sept 8, 2002 Landsat ETM

Water Index to identify water pixels

digitize some smaller creeks

40 pixel buffer from water

pixels

Extract image pixels

Extracting Analysis Area

slide16

Analysis Area

Landsat ETM, Sept 8, 2002

Connecticut

River

Thames

River

Housatonic

River

New

Haven

Bridgeport

Gardiner’s

Bay

Long Island Sound

New York

City

slide17

Pixel-based Classification

(Unsupervised “Cluster-busting”)

slide18

1st pass

150

Clusters:

Pixel-based Classification

Wheeler Marsh Area, Milford, CT

Landsat ETM Sept. 8, 2002

ISODATA Unsupervised Classification

2nd pass

100

3rd pass

50

4th pass

50

Water, Coastal Marsh, Upland, and OTHER

slide19

Pixel-based Classification

Wheeler Marsh Area, Milford, CT

Landsat ETM Sept. 8, 2002

ISODATA Unsupervised Classification

3x3 Majority Filter (coastal marsh & upland)

slide21

Object-based Classification

(eCognition Image Segmentation

and Object Oriented Classification)

slide22

OBJECT FEATURES

CLASS RELATED

FEATURES

Color

Shape/Size

To Neighbor Objects

Pixels do not have any meaningful

shape characteristics other then

their size

Texture

To Sub-objects

Hierarchy

To Super-objects

Object-based Classification

eCognition

  • Image segmentation and image object classifier
  • Does not classify single pixels, but image objects produced through image segmentation
  • Image objects are, simply put, contiguous groups of image pixels with similar characteristics.
slide23

Landsat 15m Panchromatic

Principal Components 1,2,4,6

NDVI

Wetness

Object-based Classification

Input Data

Landsat 30m 7-band multispectral

slide24

Object-based Classification

Image Segmentation LEVEL 2

INPUT LAYERS

Landsat RED

Landsat NEAR-INFRARED

Landsat MID-INFRARED

Landsat MID-INFRARED

NDVI (greenness)

PC1 (brightness)

Wetness (wetness)

Panchromatic

WEIGHTS

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

Scale parameter: 75

Color: 0.9

Shape: 0.1

Smoothness: 0.5

Compactness: 0.5

slide25

Land Like (based on WETNESS)

Water Like (based on WETNESS)

Object-based Classification

Image Segmentation LEVEL 2

slide26

INPUT LAYERS

Landsat RED

Landsat NEAR-INFRARED

Landsat MID-INFRARED

Landsat MID-INFRARED

NDVI (greenness)

PC1 (brightness)

Wetness (wetness)

Panchromatic

WEIGHTS

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

Object-based Classification

Image Segmentation LEVEL 1

Scale parameter: 15

Color: 0.9

Shape: 0.1

Smoothness: 0.5

Compactness: 0.5

slide27

Forest

High Tidal Marsh

Low Tidal Marsh

Water

Developed

Barren

Bright Dev.

Dark Dev.

Dense Dev.

Forest

Green Grass

Yellow Grass

High Marsh

High Marsh Bright

Low Marsh

Sparse Dev.

Water Rounded

Water Linear

Object-based Classification

Sample Training Data

Image objects exported

to ESRI shapefile

slide28

Object-based Classification

See5 Data Mining Tool

  • See5 is used to extract patterns from substantial databases.
  • See5 analyzes the data and develops decision trees or sets of if-then rules.
slide29

Object-based Classification

Image Segmentation LEVEL 1

slide31

Classification Integration

IMAGINE Knowledge-based Engineer

slide32

Classification Integration

IMAGINE Knowledge-based Engineer

ISODATA Classification

1. Water

2. Tidal Wetland

3. Upland

Object-based Classification

1. Water

2. Low Tidal Marsh

3. High Tidal Marsh

4. Upland

Pixels not captured in the

integration were extracted

and classified using ISODATA

slide34

Upland

High Tidal Marsh

Low Tidal Marsh

Water

Final Tidal Wetland Classification

slide35

Photointerpreted Marsh

Classified as High Marsh

Classified as Low Marsh

Classified as Water

Final Tidal Wetland Classification

Comparison with Photointerpreted Marshes

outline3
Outline
  • Salt Marshes 101
  • Moderate resolution remote sensing of tidal marshes
  • Airborne LIDAR and satellite high resolution remote sensing of salt marsh grasses
  • Classification of tidal wetland communities using multi-temporal, single-season QuickBird imagery
phragmites australis
Very aggressive

Forms dense monocultures

Displaces indigenous vegetation

Does not support rich mix of wildlife

Spreads rapidly

Physically changes marshes

Phragmites australis
goals
Goals
  • Primary goal is to use remote sensing data to map Phragmites australis
  • Secondary goal is to map other commonly vegetation types / communities
  • Using a combination of remote sensing data including Landsat, QuickBird, ADS40 and LIDAR.
project location
Project Location

100 km2 area at the mouth of the Connecticut River

slide42

Typha spp.

S. patens

Phragmites

lidar data collection area
LIDAR Data Collection Area

LIDAR data provide through a grant from

the NOAA Coastal Service Center

lidar data specifications
LIDAR Data Specifications
  • Acquired October 8, 2004 using a Leica ALS50 scanner (multiple returns)
  • ~ 300 square kilometer area, 41 Flight Lines
  • Average ground sampling distance 0.9 m
  • Vertical accuracy assessment – at 21 QC test points, the average error = 0.002 m
  • Horizontal accuracy 0.5 m
  • Data delivered as 264 tiles of LAS (~14 GB) and text files (~30 GB)
  • Attribute data includes: x, y, z, intensity, # of returns, return #, classification (non-ground, ground, outliers, water)
slide45

Phragmites australis

Typha angustifolia

Spartina patens

slide47

12’

6’

0’

slide48

Phragmites australis

Typha angustifolia

Spartina patens

ground non ground returns
Ground & Non-ground Returns

Typha spp. S. patens Typha spp. S. patens P. australis S. patens Typha spp. P. australis S. patens P. australis mix

slide53

Phragmites australis

Typha angustifolia

Spartina patens

slide54

Phragmites australis

Typha angustifolia

Spartina patens

slide55

Phragmites australis

Typha angustifolia

Spartina patens

slide56

Phragmites australis

Typha angustifolia

Spartina patens

slide57

P. australis

T. angustifolia

S. patens

outline4
Outline
  • Salt Marshes 101
  • Moderate resolution remote sensing of tidal marshes
  • Airborne LIDAR and satellite high resolution remote sensing of salt marsh grasses
  • Classification of tidal wetland communities using multi-temporal, single-season QuickBird imagery
goals for mapping phragmites with quickbird
Goals for Mapping Phragmites with QuickBird
  • Explore the use of high-resolution satellite imagery to classify and map P. australis
  • Use multi-temporal images and utilize spectral differences due to phenology and develop mapping protocol
  • Develop maps of untreated P. australis and areas recently treated
  • Create baseline classification of species or communities besides Phragmites
the need to map and monitor
The Need to Map and Monitor
  • Where has P. australis been eradicated?
  • How much area has been treated?
  • Where does P. australis still grow?
  • Is P. australis reinvading areas?
  • What other plants can be classified from imagery?
high resolution imagery
High Resolution Imagery

DigitalGlobe QuickBird satellite imagery

project location2
Project Location

Lord Cove

Upper Island

Lower Connecticut River

Drains to Long Island Sound

quickbird image acquisition
QuickBird Image Acquisition
  • 2004
    • IAGT Ordered QuickBird imagery every 2 weeks from May to October
    • Best case would be 13 images
    • Actually acquired 4 (due to weather conditions and competition)
  • 2005
    • Acquisition extended to 2005 summer season
    • Collected 3 more images
  • 2006
    • Acquisition extended to 2006 summer season
quickbird image acquisition1

Big Data Gap

Quickbird Image Acquisition

DAY

MONTH

4 images from 2004

3 images from 2005

Collection continues in 2006

Take home message: Satellite image acquisition can be unreliable

measuring spectral differences
Measuring Spectral Differences
  • Wetland species change throughout the growing season
  • Each species greens up and dies off at different times
  • These differences can be unique and revealing
measuring spectral differences1
Measuring Spectral Differences

A portable spectrometer has been used to measure the energy reflected from a variety of plant species at different times during the growing season.

May 27, 2004

gps referenced field observations
Guide classification efforts

Train image analysts

Eventual accuracy assessment

S. patens

S. alterniflora

P. australis

T. angustifolia

GPS-referenced Field Observations
image segmentation and classification
Image Segmentation and Classification
  • Create eCognition project with each ratio for each date along with original bands from July 20 image
  • Experiment and decide on segment size
  • Write rules based on a priori knowledge and results from field spectrometry
image segmentation and classification1
Image Segmentation and Classification

Ragged Rock

Creek Marsh

LEVEL 4

LEVEL 1

LEVEL 3

LEVEL 2

development of mapping protocol
Development of Mapping Protocol

For Phragmites

  • Use late summer image
  • High 4/3 ratio
observations
Observations

Moderate resolution remote

sensing of tidal marshes

observations1
Observations

Moderate resolution remote

sensing of tidal marshes

  • In general, the classification does a fair job of capturing tidal wetlands throughout the Long Island Sound region.
observations2
Observations

Moderate resolution remote

sensing of tidal marshes

  • In general, the classification does a fair job of capturing tidal wetlands throughout the Long Island Sound region
  • Larger marsh complexes are identified more readily. Smaller marsh complexes are difficult to detect due to spatial resolution of Landsat.
observations3
Observations

Airborne LIDAR and satellite high resolution remote sensing of

salt marsh grasses

observations4
Observations

Airborne LIDAR and satellite high resolution remote sensing of

salt marsh grasses

  • LIDAR data effectively can be used to help characterize plant heights for the three species of interest
observations5
Observations

Airborne LIDAR and satellite high resolution remote sensing of

salt marsh grasses

  • LIDAR data effectively can be used to help characterize plant heights for the three species of interest
  • Based solely on LIDAR non-ground returns, it is possible to discriminate among areas dominated by Phragmites australis, Typha angustifolia and Spartina patens
observations6
Observations

Classification of tidal wetland communities using multi-temporal, single-season QuickBird imagery

observations7
Observations

Classification of tidal wetland communities using multi-temporal, single-season QuickBird imagery

  • Multitemporal, single-season in situ radiometry and satellite remote sensing can lead to maximum spectral separability of salt marsh species
observations8
Observations

Classification of tidal wetland communities using multi-temporal, single-season QuickBird imagery

  • Multitemporal, single-season in situ radiometry and satellite remote sensing can lead to maximum spectral separability of salt marsh species
  • Rules based on in situ radiometry can be used to drive an object-oriented classification of salt marsh species
    • S. patens, P. australis, T. spp., and Carex spp.
further work
Further Work
  • Extend Landsat-based classification to adjacent scenes
  • Acquire additional multitemporal QuickBird imagery (especially August – September)
    • Also acquiring John Deere (Geovantage) digital MSS
  • Integrate LIDAR data into the classification process to improve vegetation classes
  • Acquire detailed field data for the entire Ragged Rock Creek tidal marsh to aid in the development of an eCognition classification protocol and to serve as validation data
  • Apply and test classification protocols for other areas
thank you

Characterization of Coastal Wetland Systems using Multiple Remote Sensing Data Types and Analytical Techniques

Thank You

  • Daniel Civco (PI)
  • James Hurd
  • University of Connecticut
  • Natural Resource Management
    • & Engineering, Storrs, CT

Marty Gilmore (PI)

Wesleyan University

Earth and Environmental Sciences Middletown, CT

Sandy Prisloe

Emily Wilson

University of Connecticut

Cooperative Extension System

Haddam, CT