Kara Youngentob - Seeing the forest for the trees: Classifying Eucalyptus subgenera for habitat mapping using hyperspectral remote sensing

Seeing the Forest for the

TreesClassifying Eucalyptus subgenera for Classifying Eucalyptus subgenera for

habitat mapping using HyMap

hyperspectral remote sensing

Kara Youngentob, Xiuping Jia, Alex Held, Kara Youngentob, Xiuping Jia, Alex Held,

Luigi Renzullo and David LindenmayerLuigi Renzullo and David Lindenmayer

Where the Wild Things Are

An image from the children's book Where the Wild Things Are by Maurice Sendak

Presentation OutlinePresentation Outline

Background and introduction to the project

Overview of objectivesOverview of objectives

Classifying Eucalypt subgenera

Summing-up

Additional Thanks

Field SiteField Site

IntroductionIntroduction

David Lindenmayer and Ross

Cunningham (et al)

Tumut Fragmentation ExperimentLong-term field site

“Natural Experiment” on

a landscape scale

Possums and GlidersImages from David Lindenmayer’s Tumut website http://cres.anu.edu.au/dbl/tumutstudy.php

Arboreal Marsupials at TumutArboreal Marsupials at Tumut

Our Wild ThingsOur Wild Things

Brushtail Possum

Photo by: Karen Marsh

Ringtail Possum

Photo by: Esther Beaton

Greater Glider

Photo by: Esther Beaton

Heterogeneous Nature of Heterogeneous Nature of

LandscapesLandscapes

Where do the animals live?Where do the animals live?

Eucalypt forest remnants surrounded by pine plantings

near Tumut, NSW.

Photo from previously cited source

Heterogeneous Nature of Heterogeneous Nature of

LandscapesLandscapes

What do animals like?What do animals like?

Eucalypt forest canopy at Tumut, NSW Australia. Photo by Kara Youngentob

Remote Sensing Hyperspectral Remote Sensing Hyperspectral

DataData

How can we map habitat?How can we map habitat?

Vegetation Spectral Reflectance extracted from AVIRIS dataAVIRIS hyperspectral data cube over

Moffett Field, CA

Hyperspectral imagery source -

http://www.csr.utexas.edu/projects/rs/hrs/hyper.html

Mapping Foliage Chemistry with Hyperspectral DataMapping Foliage Chemistry with Hyperspectral Data

Tree canopy image

(image pixel in green)

How can we map habitat?How can we map habitat?

Distribution of Total Polyphenol (plant defensive chemical) across a forest landscape, HyMap imagery from PhD thesis by Jelle G. Ferwerda

Vegetation spectra

from pixel

Map based on variations in reflectance at specific wavelengths (bands) of the spectra

RecapRecapIn short, my research involves trying to identifying physical and

chemical properties of the Tumut landscape which might explain the

observed distribution of some folivorous marsupials using

hyperspectral remote sensing.hyperspectral remote sensing.

Photos from previously cited sources

Study ObjectivesStudy Objectives

Study Objective #1Study Objective #1� Re-establish Tumut Transects and collect presence and abundance data for all arboreal marsupials

1

Animal countsAnimal counts

Canberra

Tumut

Map of Tumut transect sites

� Transects were 200-600m depending on size of fragments (most were 600m)

40 pilot study transects. Full study area transects included 82 (20 repeated from pilot) patch

transects, and 34 continuous forest transects. Followed methods from 1995 Tumut study.


Kara re-establishing transects and rejoicing in blackberries

Approx

65k of

transects

24 spotlighting nights for full survey (controls in duplicate).


Averaged 3-4

sites per nite

A volunteer taking notes on observed animals


I noted the GPS locations of animals as

well as their distance from the edge of

patches and edge characteristics

� The purpose of collecting this data was to investigate the response of arboreal marsupials to landscape change in a plantation environment over time and to record the GPS locations of animals observed in the forests.


Images from left to right: Partially cleared plantation landscape, a baby greater glider, Eucalypt forest meets plantation.

Leaf collection and analysisLeaf collection and analysis

Foliage chemistryFoliage chemistry

Study Objective #2Study Objective #2

Stuart helping to collect

more leaf samples

Paddock Tree


Leaf collecting from GG patchesLeaf collecting from GG patches

� We also collected leaf samples in patches where GG were radio-tracked to explore relationships between tree choice and foliage chemistry


Found 237 trees that were marked during the GG study (a little less than half), and collected an

additional 191 “matched” trees that were unmarked


Laboratory AnalysisLaboratory Analysis

NIR machine for capturing spectra

of freeze-dried, ground samplesTitration machine for

measuring nitrogen

Laboratory AnalysisLaboratory Analysis


Measuring samples for digestion Digested samples ready for titration

∧New Spectra

Regression

coefficients

Model Estimate

of Nitrogen Conct.

Making a Prediction Equation from a Training SetMaking a Prediction Equation from a Training Set


βXy =∧

Prediction Equation for Nitrogen (or other chemical) Concentration

coefficients

identified in

training

Results from Prediction Equations based on lab values for our training set

(Modified PLS with LOO Cross-validation)

Constituent Math

treatment

“smoothing”

n Mean SD SEC RSQ SECV 1-VR #

Nitrogen 2881 96 1.2092 0.1993 0.0363 0.9668 0.0579 0.9154 251


Nitrogen 2881 96 1.2092 0.1993 0.0363 0.9668 0.0579 0.9154 251

Digestible

Dry Matter

2881 94 0.4343 0.1173 0.0189 0.9742 0.0290 0.9388 251

Available

Nitrogen

2881 68 0.2398 0.2398 0.0479 0.9601 0.0724 0.9096 251

Table derived from ISI NIR-statistical package output

Study Objective #3Study Objective #3� Measure and map the variations in foliage chemistry using HyMap data

Remote SensingRemote Sensing

HyMap flight-plan

HyMap Imagery CollectedHyMap Imagery Collected


HyMap hyperspectral images of the Tumut

site acquired in March, 2007

Orange tarp

Isolated Trees

Paddock

Black Tarp

Ground TruthingGround Truthing


Paddock

TreesPaddock Trees

Blue Tarp

Isolated Trees


Orange Tarp




Alex Held and Paul Daniel assisting with spectral data collection in the field


Collecting spectraCollecting spectra

0.5

0.55

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1

Refl

ecta

nce

swampwhole

stringywhole

applewhole

ashwhole

unknownstringypepwhole

Field SpectraField Spectra


Fresh, whole leaf spectra (after de-stepping) taken with an ASD Fieldspec Pro

Eucalyptus spectra only (Averages)

0

0.05

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1016

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2126

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Wavelength (nm)

Refl

ecta

nce

pauciflorawhole

sallywhole

camwhole

pepwhole

vimwhole

Reflectances and Ratios to Average

1.5

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2.1

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Refl

ecta

nce &

Fra

cti

on

sw ampleaves

stringyleavesw ithoutyoung

appleleaves

ashleaevs

unknow nstringypepleaves

paucifloraleaves

messmateleaves

sallyleaves

camleaves

vimleaves

Field SpectraField Spectra


0

0.1

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Wavelenghth

Refl

ecta

nce &

Fra

cti

on

vimleaves

pepleaves

all averaged

all averaged Ratios

sw ampleaves

stringyleavesw ithoutyoung

appleleaves

ashleaevs

unknow nstringypepleaves

paucifloraleaves

messmateleaves

sallyleaves

camleaves

vimleaves

pepleaves

all averaged

Classifying Eucalyptus Subgenera

What is a subgenera, why do we care?

ClassificationClassification

Broad-leafed peppermint

(E. Dives) Monocalyptus

Manna Gum (E. viminalis )

Symphyomyrtus

Dominant canopy species at TumutDominant canopy species at Tumut

Tree Species Subgenera Characteristic environment

Pinus radiata N/A Plantation species, native to North America

Eucalyptus radiata Monocalyptus Widespread on a range of substrates. Prefers cooler, wetter areas.

Eucalyptus macrorhyncha Monocalyptus Widespread and dominant in Sclerophyll forests and poor, shallow soils.

Eucalyptus dives Monocalyptus Widespread in Dry Sclerophyll or poor shallow, stony soils


Eucalyptus dives Monocalyptus Widespread in Dry Sclerophyll or poor shallow, stony soils

Eucalyptus viminalis Symphyomyrtus Widely distributed forest species. Prefers cooler areas.

Eucalyptus camphora Symphyomyrtus Common along gully lines and marshy areas.

Eucalyptus bridgesdiana Symphyomyrtus Dry Sclerophyll forest species.

Eucalyptus pauciflora Symphyomyrtus Characteristic of higher elevations.

Eucalyptus dalrympleana Symphyomyrtus Widespread forest species. Prefers loamy or sandy soils at higher

elevations.

Monocalypt

Darker green

leaves, non-waxy,

brittle, lots of

volatile terpenes

Symphyomyrt

Typically

lighter green

leaves, waxy,

lots of lignin

Step 1. Identify paddock tree subgeneraStep 1. Identify paddock tree subgenera


Paddock Trees Identified in HyMap Imagery

HyMap Tree Pixels

Paddock TreesPaddock Trees


Photograph of trees that correspond to HyMap Pixels

HyMap Tree Pixels

Step 2. Collect HyMap Spectra from Step 2. Collect HyMap Spectra from

Paddock TreesPaddock Trees


Continumm removed HyMap vegetation spectra from tree pixel--top is “good” pixel, bottom is “bad” pixel.

Step 3. Reduce dimensionality of data by selecting bandsStep 3. Reduce dimensionality of data by selecting bands


Freeze-dried leaf absorbance spectra

(red and green lines). Blue line is

ANOVA F-score. Where the blue line

dips below the dotted black line, there is

significant difference between

Monocalypt (n 318) and Symphyomyrt (n

236) spectra. Performed to explore data.

Tree canopy reflectance spectra from HyMap

(red and green lines). Blue lines are the

bands selected by the Bhattacharyya

algorithm function in Multispec to provide the

best separation of the two classes,

Monocalypt (n 56) and Symphyomyrt (n 49).

Used to select data for classification.

Statistic

Applied Feature Selection

Training

Accuracy

Kappa

Statistic

Kappa

Variance

Testing

Accuracy

Kappa

Statistic

Kappa

Variance

MED All 125 channels selected 56.2 0.124 0.00932 55.2 0.104 0.00939

ML

Bhattacharyya

15 Channels (B-dis 3.93) 100 1.00 0.0 96.2 0.923 0.00141

Step 4. Maximum likelihood classification on reduced dataStep 4. Maximum likelihood classification on reduced data


ML 15 Channels (B-dis 3.93)

2, 9, 24, 35, 50, 61, 72, 79,

83, 84, 85, 87, 95, 96, 104

100 1.00 0.0 96.2 0.923 0.00141

ML

Bhattacharyya

14 Channels (B-dis 3.58)

2, 24, 35, 50, 61, 72, 79,

83, 84, 85, 87, 95, 96, 104

99 0.981 0.00036 92.4 0.847 0.00272

ML 10 Channel PCA 89.5 0.788 0.00362 78.1 0.558 0.00659

ML 10 Channel PCA with

channels (3, 5, 9) removed 86.7 0.729 0.00442 82.9 0.653 0.00546

Classification results for Monocalyptus and Symphyomyrtus HyMap spectra based on a training

data set of 56 Symphyomyrts and 49 Monocalyptus and LOO cross-validation for testing data.

SummingSumming--upup

Re-establish original Tumut transects and collect presence and abundance data to investigate the response of arboreal marsupials to landscape change in a plantation environment

Explore relationship between tree choice and foliage chemistry by collecting leaves in patches where GG were radio tracked and paddock tree leaves under HyMap flightlines

Conduct a hyperspectral remote sensing flight over my study area to investigate relationships between animal occurrence, habitat type (dominant canopy subgenera) and foliage chemistry on a landscape scale.

Photos from previously cited sources

Still to come…Still to come…

Percent nitrogen as estimated by reflectance at 1970nm + 2170nmBands selection based on findings relating to the prominence of these two absorption features in relation to nitrogen concentration (Zhi Huang et. Al 2004).

AcknowledgementsAcknowledgements

The Hermon Slade Foundation

WildCountry and The Wilderness Society

Ecological Society of Australia

Many thanks for your support!Many thanks for your support!

Ecological Society of Australia

CSIRO CMAR and Land and Water

David Lindenmayer’s research team

Bill Foley’s Lab, ANU Botany & Zoology

ANU Vice-chancellery

The Fenner School (formerly CRES) staff, students and administration

You! Thank you for listening.

Image from previously cited source

Documents

Kara Youngentob - Seeing the forest for the trees: Classifying Eucalyptus subgenera for habitat mapping using hyperspectral remote sensing